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1.  Genetic Predisposition to Increased Blood Cholesterol and Triglyceride Lipid Levels and Risk of Alzheimer Disease: A Mendelian Randomization Analysis 
PLoS Medicine  2014;11(9):e1001713.
In this study, Proitsi and colleagues use a Mendelian randomization approach to dissect the causal nature of the association between circulating lipid levels and late onset Alzheimer's Disease (LOAD) and find that genetic predisposition to increased plasma cholesterol and triglyceride lipid levels is not associated with elevated LOAD risk.
Please see later in the article for the Editors' Summary
Background
Although altered lipid metabolism has been extensively implicated in the pathogenesis of Alzheimer disease (AD) through cell biological, epidemiological, and genetic studies, the molecular mechanisms linking cholesterol and AD pathology are still not well understood and contradictory results have been reported. We have used a Mendelian randomization approach to dissect the causal nature of the association between circulating lipid levels and late onset AD (LOAD) and test the hypothesis that genetically raised lipid levels increase the risk of LOAD.
Methods and Findings
We included 3,914 patients with LOAD, 1,675 older individuals without LOAD, and 4,989 individuals from the general population from six genome wide studies drawn from a white population (total n = 10,578). We constructed weighted genotype risk scores (GRSs) for four blood lipid phenotypes (high-density lipoprotein cholesterol [HDL-c], low-density lipoprotein cholesterol [LDL-c], triglycerides, and total cholesterol) using well-established SNPs in 157 loci for blood lipids reported by Willer and colleagues (2013). Both full GRSs using all SNPs associated with each trait at p<5×10−8 and trait specific scores using SNPs associated exclusively with each trait at p<5×10−8 were developed. We used logistic regression to investigate whether the GRSs were associated with LOAD in each study and results were combined together by meta-analysis. We found no association between any of the full GRSs and LOAD (meta-analysis results: odds ratio [OR] = 1.005, 95% CI 0.82–1.24, p = 0.962 per 1 unit increase in HDL-c; OR = 0.901, 95% CI 0.65–1.25, p = 0.530 per 1 unit increase in LDL-c; OR = 1.104, 95% CI 0.89–1.37, p = 0.362 per 1 unit increase in triglycerides; and OR = 0.954, 95% CI 0.76–1.21, p = 0.688 per 1 unit increase in total cholesterol). Results for the trait specific scores were similar; however, the trait specific scores explained much smaller phenotypic variance.
Conclusions
Genetic predisposition to increased blood cholesterol and triglyceride lipid levels is not associated with elevated LOAD risk. The observed epidemiological associations between abnormal lipid levels and LOAD risk could therefore be attributed to the result of biological pleiotropy or could be secondary to LOAD. Limitations of this study include the small proportion of lipid variance explained by the GRS, biases in case-control ascertainment, and the limitations implicit to Mendelian randomization studies. Future studies should focus on larger LOAD datasets with longitudinal sampled peripheral lipid measures and other markers of lipid metabolism, which have been shown to be altered in LOAD.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Currently, about 44 million people worldwide have dementia, a group of brain disorders characterized by an irreversible decline in memory, communication, and other “cognitive” functions. Dementia mainly affects older people and, because people are living longer, experts estimate that more than 135 million people will have dementia by 2050. The commonest form of dementia is Alzheimer disease. In this type of dementia, protein clumps called plaques and neurofibrillary tangles form in the brain and cause its degeneration. The earliest sign of Alzheimer disease is usually increasing forgetfulness. As the disease progresses, affected individuals gradually lose their ability to deal with normal daily activities such as dressing. They may become anxious or aggressive or begin to wander. They may also eventually lose control of their bladder and of other physical functions. At present, there is no cure for Alzheimer disease although some of its symptoms can be managed with drugs. Most people with the disease are initially cared for at home by relatives and other unpaid carers, but many patients end their days in a care home or specialist nursing home.
Why Was This Study Done?
Several lines of evidence suggest that lipid metabolism (how the body handles cholesterol and other fats) is altered in patients whose Alzheimer disease develops after the age of 60 years (late onset Alzheimer disease, LOAD). In particular, epidemiological studies (observational investigations that examine the patterns and causes of disease in populations) have found an association between high amounts of cholesterol in the blood in midlife and the risk of LOAD. However, observational studies cannot prove that abnormal lipid metabolism (dyslipidemia) causes LOAD. People with dyslipidemia may share other characteristics that cause both dyslipidemia and LOAD (confounding) or LOAD might actually cause dyslipidemia (reverse causation). Here, the researchers use “Mendelian randomization” to examine whether lifetime changes in lipid metabolism caused by genes have a causal impact on LOAD risk. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the effect of a modifiable risk factor and the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if dyslipidemia causes LOAD, genetic variants that affect lipid metabolism should be associated with an altered risk of LOAD.
What Did the Researchers Do and Find?
The researchers investigated whether genetic predisposition to raised lipid levels increased the risk of LOAD in 10,578 participants (3,914 patients with LOAD, 1,675 elderly people without LOAD, and 4,989 population controls) using data collected in six genome wide studies looking for gene variants associated with Alzheimer disease. The researchers constructed a genotype risk score (GRS) for each participant using genetic risk markers for four types of blood lipids on the basis of the presence of single nucleotide polymorphisms (SNPs, a type of gene variant) in their DNA. When the researchers used statistical methods to investigate the association between the GRS and LOAD among all the study participants, they found no association between the GRS and LOAD.
What Do These Findings Mean?
These findings suggest that the genetic predisposition to raised blood levels of four types of lipid is not causally associated with LOAD risk. The accuracy of this finding may be affected by several limitations of this study, including the small proportion of lipid variance explained by the GRS and the validity of several assumptions that underlie all Mendelian randomization studies. Moreover, because all the participants in this study were white, these findings may not apply to people of other ethnic backgrounds. Given their findings, the researchers suggest that the observed epidemiological associations between abnormal lipid levels in the blood and variation in lipid levels for reasons other than genetics, or to LOAD risk could be secondary to variation in lipid levels for reasons other than genetics, or to LOAD, a possibility that can be investigated by studying blood lipid levels and other markers of lipid metabolism over time in large groups of patients with LOAD. Importantly, however, these findings provide new information about the role of lipids in LOAD development that may eventually lead to new therapeutic and public-health interventions for Alzheimer disease.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001713.
The UK National Health Service Choices website provides information (including personal stories) about Alzheimer's disease
The UK not-for-profit organization Alzheimer's Society provides information for patients and carers about dementia, including personal experiences of living with Alzheimer's disease
The US not-for-profit organization Alzheimer's Association also provides information for patients and carers about dementia and personal stories about dementia
Alzheimer's Disease International is the international federation of Alzheimer disease associations around the world; it provides links to individual associations, information about dementia, and links to World Alzheimer Reports
MedlinePlus provides links to additional resources about Alzheimer's disease (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001713
PMCID: PMC4165594  PMID: 25226301
2.  Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study 
PLoS Medicine  2015;12(6):e1001841.
Background
Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR).
Methods and Findings
We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP–AD associations from the International Genomics of Alzheimer’s Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62–0.91]; p = 3.4 × 10−3). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10−8). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51–0.89]; p = 6.5 × 10−3), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses.
Conclusions
Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure—or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications—may reduce AD risk.
Robert A. Scott and colleagues use genetic instruments to identify causal associations between known risk factors and Alzheimer's disease.
Editors' Summary
Background
Worldwide, about 44 million people have dementia, a group of brain degeneration disorders characterized by an irreversible decline in memory, communication, and other “cognitive” functions. Dementia mainly affects older people, and because people are living longer, experts estimate that more than 135 million people will have dementia by 2050. The most common form of dementia, which accounts for 60%–70% of cases, is Alzheimer disease (AD). The earliest sign of AD is often increasing forgetfulness. As the disease progresses, affected individuals gradually lose the ability to look after themselves, they may become anxious or aggressive, and they may have difficulty recognizing friends and relatives. People with late stage disease may lose control of their bladder and of other physical functions. At present, there is no cure for AD, although some of its symptoms can be managed with drugs. Most people with AD are initially cared for at home by relatives and other caregivers, but many affected individuals end their days in a care home or specialist nursing home.
Why Was This Study Done?
Researchers are interested in identifying risk factors for AD, particularly modifiable risk factors, because if such risk factors exist, it might be possible to limit the predicted increase in future AD cases. Epidemiological studies (investigations that examine patterns of disease in populations) have identified several potential risk factors for AD, including hypertension (high blood pressure), obesity, smoking, and dyslipidemia (changes in how the body handles fats). However, epidemiological studies cannot prove that a specific risk factor causes AD. For example, people with hypertension might share another characteristic that causes both hypertension and AD (confounding) or AD might cause hypertension (reverse causation). Information on causality is needed to decide which risk factors to target to help prevent AD. Here, the researchers use “Mendelian randomization” to examine whether differences in several epidemiologically identified risk factors for AD have a causal impact on AD risk. In Mendelian randomization, causal associations are inferred from the effects of genetic variants (which predict levels of modifiable risk factors) on the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if hypertension actually causes AD, genetic variants that affect hypertension should be associated with an altered risk of AD.
What Did the Researchers Do and Find?
The researchers identified causal associations between potentially modifiable risk factors and AD risk by analyzing the occurrence of single nucleotide polymorphisms (SNPs, a type of gene variant) known to predict levels of each risk factor, in genetic data from 17,008 individuals with AD and 37,154 cognitively normal elderly controls collected by the International Genomics of Alzheimer’s Project. They report that genetically predicted higher systolic blood pressure (SBP; the pressure exerted on the inside of large blood vessels when the heart is pumping out blood) was associated with lower AD risk (and with a higher probability of taking antihypertensive medication). Predicted smoking quantity was also associated with lower AD risk, but there was no evidence of causal associations between any of the other risk factors investigated and AD risk.
What Do These Findings Mean?
In contrast to some epidemiological studies, these findings suggest that hypertension is associated with lower AD risk. However, because genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication, it could be that exposure to such drugs, rather than having hypertension, reduces AD risk. Like all Mendelian randomization studies, the reliability of these findings depends on the validity of several assumptions made by the researchers and on the ability of the SNPs used in the analyses to explain variations in exposure to the various risk factors. Moreover, because all the participants in the International Genomics of Alzheimer’s Project are of European ancestry, these findings may not be valid for other ethnic groups. Given that hypertension is a risk factor for cardiovascular disease, the researchers do not advocate raising blood pressure as a measure to prevent AD (neither do they advocate that people smoke more cigarettes to lower AD risk). Rather, given the strong association between higher SBP gene scores and the probability of exposure to antihypertensive treatment, they suggest that the possibility that antihypertensive drugs might reduce AD risk independently of their effects on blood pressure should be investigated as a priority.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001841.
The UK National Health Service Choices website provides information (including personal stories) about Alzheimer disease
The UK not-for-profit organization Alzheimer’s Society provides information for patients and carers about dementia, including personal experiences of living with Alzheimer disease
The US not-for-profit organization Alzheimer’s Association also provides information for patients and carers about dementia and personal stories about dementia
Alzheimer’s Disease International is the federation of Alzheimer disease associations around the world; it provides links to individual Alzheimer associations, information about dementia, and links to world Alzheimer reports
MedlinePlus provides links to additional resources about Alzheimer disease (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
A PLOS Medicine Research Article by Proitsi et al. describes a Mendelian randomization study that looked for a causal association between dyslipidemia and Alzheimer disease
doi:10.1371/journal.pmed.1001841
PMCID: PMC4469461  PMID: 26079503
3.  Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability 
PLoS Genetics  2011;7(7):e1002198.
Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ∼10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.
Author Summary
Despite the striking success of genome-wide association studies in identifying genetic loci associated with common complex traits and diseases, much of the heritable risk for these traits and diseases remains unexplained. A higher resolution investigation of the genome through sequencing studies is expected to clarify the sources of this missing heritability. As a preview of what we might learn in these more detailed assessments of genetic variation, we used sequencing to identify potentially interesting variants in seven genes associated with low-density lipoprotein cholesterol (LDL-C) in 256 Sardinian individuals with extreme LDL-C levels, followed by large scale genotyping in 5,524 individuals, to examine newly discovered and previously described variants. We found that a combination of common and rare variants in these loci contributes to variation in LDL-C levels, and also that the initial estimate of the heritability explained by these loci doubled. Importantly, our results include a Sardinian-specific rare variant, highlighting the need for sequencing studies in isolated populations. Our results provide insights about what extensive whole-genome sequencing efforts are likely to reveal for the understanding of the genetic architecture of complex traits.
doi:10.1371/journal.pgen.1002198
PMCID: PMC3145627  PMID: 21829380
4.  Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation 
PLoS Genetics  2016;12(2):e1005804.
Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases. The proportion of phenotypic variance explained by significant associations is, however, limited. Given the same dense SNP panels, mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies. Combining information from pedigree relationships and SNPs, we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20,000 individuals genotyped for ~520,000 common autosomal SNPs. The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment. Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation, shared nuclear family environment, shared couple (partner) environment and shared full-sibling environment. Results demonstrate that trait heritabilities vary widely but, on average across traits, SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance. For most traits the recently-shared environment of couples is also significant, accounting for ~11% of the phenotypic variance on average. On the other hand, the environment shared largely in the past by members of a nuclear family or by full-siblings, has a more limited impact. Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses. Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors.
Author Summary
Unravelling overall trait architecture of complex traits and diseases is important for phenotype prediction and disease prevention and correct modelling of the trait will further aid discovery of causative loci. Here we take advantage of genome-wide data and a large family-based study to examine the role of common genetic variants, pedigree-associated genetic variants, shared family environment, shared couple environment and shared sibling environment on 16 anthropometric and cardiometabolic traits. By analysing up to ~20,000 Scottish individuals, we find that common genetic variants, pedigree-associated genetic variants and recently-shared environment of couples are the most important contributors to variation in these traits, while past family and sibling environment have a limited impact. Further studies on the pedigree-associated genetic variation and the shared couple environment effect are needed, as little research has been devoted to them so far.
doi:10.1371/journal.pgen.1005804
PMCID: PMC4737500  PMID: 26836320
5.  Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model 
PLoS Genetics  2015;11(4):e1004969.
Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.
Author Summary
Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model. We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of seven common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits. These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.
doi:10.1371/journal.pgen.1004969
PMCID: PMC4388571  PMID: 25849665
6.  Assessing the Causal Relationship of Maternal Height on Birth Size and Gestational Age at Birth: A Mendelian Randomization Analysis 
PLoS Medicine  2015;12(8):e1001865.
Background
Observational epidemiological studies indicate that maternal height is associated with gestational age at birth and fetal growth measures (i.e., shorter mothers deliver infants at earlier gestational ages with lower birth weight and birth length). Different mechanisms have been postulated to explain these associations. This study aimed to investigate the casual relationships behind the strong association of maternal height with fetal growth measures (i.e., birth length and birth weight) and gestational age by a Mendelian randomization approach.
Methods and Findings
We conducted a Mendelian randomization analysis using phenotype and genome-wide single nucleotide polymorphism (SNP) data of 3,485 mother/infant pairs from birth cohorts collected from three Nordic countries (Finland, Denmark, and Norway). We constructed a genetic score based on 697 SNPs known to be associated with adult height to index maternal height. To avoid confounding due to genetic sharing between mother and infant, we inferred parental transmission of the height-associated SNPs and utilized the haplotype genetic score derived from nontransmitted alleles as a valid genetic instrument for maternal height. In observational analysis, maternal height was significantly associated with birth length (p = 6.31 × 10−9), birth weight (p = 2.19 × 10−15), and gestational age (p = 1.51 × 10−7). Our parental-specific haplotype score association analysis revealed that birth length and birth weight were significantly associated with the maternal transmitted haplotype score as well as the paternal transmitted haplotype score. Their association with the maternal nontransmitted haplotype score was far less significant, indicating a major fetal genetic influence on these fetal growth measures. In contrast, gestational age was significantly associated with the nontransmitted haplotype score (p = 0.0424) and demonstrated a significant (p = 0.0234) causal effect of every 1 cm increase in maternal height resulting in ~0.4 more gestational d. Limitations of this study include potential influences in causal inference by biological pleiotropy, assortative mating, and the nonrandom sampling of study subjects.
Conclusions
Our results demonstrate that the observed association between maternal height and fetal growth measures (i.e., birth length and birth weight) is mainly defined by fetal genetics. In contrast, the association between maternal height and gestational age is more likely to be causal. In addition, our approach that utilizes the genetic score derived from the nontransmitted maternal haplotype as a genetic instrument is a novel extension to the Mendelian randomization methodology in casual inference between parental phenotype (or exposure) and outcomes in offspring.
Using a Mendelian randomization approach, Ge Zhang and colleagues examine the causal relationship between maternal height, birth size, and gestational age at birth.
Editors' Summary
Background
Soon after the birth of a baby, doting parents send messages to friends and relatives or post information on social media sites to let everyone know when their new baby boy or girl was born. They may also post information about how heavy he/she was at birth and his/her length. These pregnancy outcomes, together with gestational age at birth (the length of time that a baby has spent developing in its mother’s womb), affect the baby’s immediate health and survival. Importantly, however, these pregnancy outcomes are also associated with the risk of long-term adverse health outcomes such as obesity, cardiometabolic disorders (heart disease and conditions such as diabetes that affect how the body makes energy from food), and neuropsychiatric conditions (mental disorders attributable to diseases of the nervous system, such as depression). For example, some studies have shown an association between low birth weight and an increased risk of type 2 diabetes later in life.
Why Was This Study Done?
Identification of the environmental and genetic factors that causally influence gestational age, length, and weight at birth would improve our understanding of why these pregnancy outcomes are associated with disease during adulthood and could help in the design of strategies to prevent these diseases. Epidemiological studies (investigations that examine disease patterns in populations) suggest that, compared to tall mothers, short mothers tend to deliver their babies at earlier gestational ages, with lower birth weights and lengths. Epidemiological studies cannot show, however, whether variations in maternal height cause variations in pregnancy outcomes. Other characteristics shared by tall mothers might actually determine the size and gestational age of their offspring (confounding). Here, the researchers use “Mendelian randomization” to assess the causal effect of maternal height on the size and gestational age at birth of babies. Because gene variants are inherited randomly, they are not prone to confounding. So, if maternal height actually affects gestational age and size at birth, genetic variants (instruments) that affect maternal height should be associated with differences in gestational age and size at birth, provided confounding due to the transmission of parental alleles (variant forms of genes; people have two alleles of every gene, one inherited from each parent) is avoided by adjusting for the baby’s genotype.
What Did the Researchers Do and Find?
The researchers used phenotype data (observable characteristics such as maternal height and birth weight of the baby) and single nucleotide polymorphism (SNP; a type of genetic variant) data obtained from 3,486 Nordic mother/baby pairs. Analysis of the phenotype data indicated that maternal height was significantly associated with length, weight, and gestational age at birth (a significant association is unlikely to have arisen by chance). For their Mendelian randomization analysis, the researchers constructed a genetic score based on 697 SNPs known to be associated with adult height. To avoid confounding due to genetic sharing between the mother and baby, they determined which of the height-associated alleles each baby had inherited from its mother and used the nontransmitted haplotype score as a genetic instrument for maternal height (a haplotype is a set of DNA variations that are inherited together). Birth length and weight were significantly associated with both the maternal and paternal transmitted haplotype scores but not with the maternal nontransmitted haplotype score. However, gestational age was significantly but modestly associated with the maternal nontransmitted haplotype score.
What Do These Findings Mean?
The validity of the assumptions that underlie the Mendelian randomization approach and the design of the studies supplying the data for this analysis may affect the accuracy of the findings reported here. Nevertheless, these findings suggest that the observed association between maternal height and fetal growth measurements is mainly determined by the genetics of the baby. That is, differences in maternal height do not cause differences in birth weight or length. Rather, some of the gene variants that the baby inherits from its mother determine both its size and its mother’s height. These findings also provide weak evidence that the association between maternal height and gestational age is causal. Maternal height might, for example, causally influence gestational age by limiting the space available for the baby’s growth before birth. Finally, these findings introduce an extension to the Mendelian randomization approach that can be used to investigate causal associations between parental characteristics and offspring outcomes.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001865.
The March of Dimes, a not-for-profit organization for pregnancy and baby health, provides information about low birth weight and its consequences
Nemours, a not-for-profit organization for child health, provides information about the weight of newborn babies (in English and Spanish)
Wikipedia has pages on birth weight, gestational age, and Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
MedlinePlus provides information and links to additional resources about birth weight and a brief explanation of gestational age (in English and Spanish)
doi:10.1371/journal.pmed.1001865
PMCID: PMC4540580  PMID: 26284790
7.  Quantifying Missing Heritability at Known GWAS Loci 
PLoS Genetics  2013;9(12):e1003993.
Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.
Author Summary
Heritable diseases have an unknown underlying “genetic architecture” that defines the distribution of effect-sizes for disease-causing mutations. Understanding this genetic architecture is an important first step in designing disease-mapping studies, and many theories have been developed on the nature of this distribution. Here, we evaluate the hypothesis that additional heritable variation lies at previously known associated loci but is not fully explained by the single most associated marker. We develop methods based on variance-components analysis to quantify this type of “local” heritability, demonstrating that standard strategies can be falsely inflated or deflated due to correlation between neighboring markers and propose a robust adjustment. In analysis of nine common diseases we find a significant average increase of local heritability, consistent with multiple common causal variants at an average locus. Intriguingly, for autoimmune diseases we also observe significant local heritability in loci not associated with the specific disease but with other autoimmune diseases, implying a highly correlated underlying disease architecture. These findings have important implications to the design of future studies and our general understanding of common disease.
doi:10.1371/journal.pgen.1003993
PMCID: PMC3873246  PMID: 24385918
8.  Contribution of Large Region Joint Associations to Complex Traits Genetics 
PLoS Genetics  2015;11(4):e1005103.
A polygenic model of inheritance, whereby hundreds or thousands of weakly associated variants contribute to a trait’s heritability, has been proposed to underlie the genetic architecture of complex traits. However, relatively few genetic variants have been positively identified so far and they collectively explain only a small fraction of the predicted heritability. We hypothesized that joint association of multiple weakly associated variants over large chromosomal regions contributes to complex traits variance. Confirmation of such regional associations can help identify new loci and lead to a better understanding of known ones. To test this hypothesis, we first characterized the ability of commonly used genetic association models to identify large region joint associations. Through theoretical derivation and simulation, we showed that multivariate linear models where multiple SNPs are included as independent predictors have the most favorable association profile. Based on these results, we tested for large region association with height in 3,740 European participants from the Health and Retirement Study (HRS) study. Adjusting for SNPs with known association with height, we demonstrated clustering of weak associations (p = 2x10-4) in regions extending up to 433.0 Kb from known height loci. The contribution of regional associations to phenotypic variance was estimated at 0.172 (95% CI 0.063-0.279; p < 0.001), which compared favorably to 0.129 explained by known height variants. Conversely, we showed that suggestively associated regions are enriched for known height loci. To extend our findings to other traits, we also tested BMI, HDLc and CRP for large region associations, with consistent results for CRP. Our results demonstrate the presence of large region joint associations and suggest these can be used to pinpoint weakly associated SNPs.
Author Summary
It is widely accepted that genetics influences a broad range of human traits and diseases, yet only a few genetic variants are known to determine these traits and their impact is modest. In this report, we made the hypothesis that combining information from a large number of genetic variants would help better explain how they together contribute to traits such as height. To do so, we first had to select a proper method to integrate large numbers of genetic variants in a single test, here named “large region joint association”. Next, we tested our method on height in 3,740 European participants from the Health and Retirement Study. We showed that the contribution of regional associations to variation in height was 17.2%, as compared to the 12.9% explained by known genetic determinants of height. In other words, the joint effect of multiple genetic variants integrated together contributed to a substantial fraction of the genetics of height. These results are significant because they can help identify new genes or genetic regions associated with human traits or diseases. Conversely, these results can be used to better understand genes that we already know are associated. Furthermore, our results provide insights on how traits are genetically determined.
doi:10.1371/journal.pgen.1005103
PMCID: PMC4391841  PMID: 25856144
9.  The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease 
PLoS Genetics  2015;11(4):e1005165.
Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.
Author Summary
Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.
doi:10.1371/journal.pgen.1005165
PMCID: PMC4407972  PMID: 25906071
10.  Genome-Wide Association Studies in an Isolated Founder Population from the Pacific Island of Kosrae 
PLoS Genetics  2009;5(2):e1000365.
It has been argued that the limited genetic diversity and reduced allelic heterogeneity observed in isolated founder populations facilitates discovery of loci contributing to both Mendelian and complex disease. A strong founder effect, severe isolation, and substantial inbreeding have dramatically reduced genetic diversity in natives from the island of Kosrae, Federated States of Micronesia, who exhibit a high prevalence of obesity and other metabolic disorders. We hypothesized that genetic drift and possibly natural selection on Kosrae might have increased the frequency of previously rare genetic variants with relatively large effects, making these alleles readily detectable in genome-wide association analysis. However, mapping in large, inbred cohorts introduces analytic challenges, as extensive relatedness between subjects violates the assumptions of independence upon which traditional association test statistics are based. We performed genome-wide association analysis for 15 quantitative traits in 2,906 members of the Kosrae population, using novel approaches to manage the extreme relatedness in the sample. As positive controls, we observe association to known loci for plasma cholesterol, triglycerides, and C-reactive protein and to a compelling candidate loci for thyroid stimulating hormone and fasting plasma glucose. We show that our study is well powered to detect common alleles explaining ≥5% phenotypic variance. However, no such large effects were observed with genome-wide significance, arguing that even in such a severely inbred population, common alleles typically have modest effects. Finally, we show that a majority of common variants discovered in Caucasians have indistinguishable effect sizes on Kosrae, despite the major differences in population genetics and environment.
Author Summary
Isolated populations have contributed to the discovery of loci with simple Mendelian segregation and large effects on disease risk or trait variation. We hypothesized that the use of isolated populations might also facilitate the discovery of common alleles contributing to complex traits with relatively larger effects. However, the use of association analyses to map common loci influencing trait variation in large, inbred cohorts introduces analytic challenges, as extensive relatedness between subjects violates the assumptions of independence upon which traditional association test statistics are based. We developed an analytic strategy to perform genome-wide association studies in an inbred family containing over 2,800 individuals from the island of Kosrae, Federated States of Micronesia. No alleles with large effect were observed with strong statistical support in any of the 15 traits examined, suggesting that the contribution of individual common variants to complex trait variation in Kosraens is typically not much greater than that observed in other populations. We show that the effects of many loci previously identified in Caucasian populations are indistinguishable in Caucasians and Kosraens, despite very different population genetics and environmental influences.
doi:10.1371/journal.pgen.1000365
PMCID: PMC2628735  PMID: 19197348
11.  Genetic interactions affecting human gene expression identified by variance association mapping 
eLife  2014;3:e01381.
Non-additive interaction between genetic variants, or epistasis, is a possible explanation for the gap between heritability of complex traits and the variation explained by identified genetic loci. Interactions give rise to genotype dependent variance, and therefore the identification of variance quantitative trait loci can be an intermediate step to discover both epistasis and gene by environment effects (GxE). Using RNA-sequence data from lymphoblastoid cell lines (LCLs) from the TwinsUK cohort, we identify a candidate set of 508 variance associated SNPs. Exploiting the twin design we show that GxE plays a role in ∼70% of these associations. Further investigation of these loci reveals 57 epistatic interactions that replicated in a smaller dataset, explaining on average 4.3% of phenotypic variance. In 24 cases, more variance is explained by the interaction than their additive contributions. Using molecular phenotypes in this way may provide a route to uncovering genetic interactions underlying more complex traits.
DOI: http://dx.doi.org/10.7554/eLife.01381.001
eLife digest
Every person has two copies of each gene: one is inherited from their mother and the other from their father. These two copies are often not identical because there can be many different variants of the same gene in the human population. Traits (such as height, body mass and risk of disease) vary from one person to the next—and for many traits this variation depends in part on the different gene variants that each person has inherited. Studies seeking to find the differences in DNA that can predict this variation have often assumed that the changes in DNA act on traits independently of the effect of environment and of other genetic variants.
In contrast, studies with animals have shown that some genetic variants can interact to produce a bigger (or smaller) effect than would be expected from simply ‘adding together’ their individual effects—a phenomenon called epistasis. But how much does epistasis contribute to variation in human traits, if at all? This question has been much disputed, and is difficult to test, not least because of the sheer number of interactions to assess: tens of millions of changes in DNA have been observed in the human genome, and so there are many more than billions of possible combinations of these changes to investigate.
Here, Brown et al. have examined the sequences of all the genes that were expressed in cells taken from a cohort of twins and searched for genetic variants that show these epistatic interactions. By studying gene expression, which can be greatly affected by small changes in the DNA code, Brown et al. were able to identify 508 variants that had a bigger than expected effect on the level of gene expression. This may be a sign that these variants act in combinations: if within one genome a variant increased expression and in another it decreased expression, then this would cause greater variation in gene expression. Further investigation of these 508 variants led to the discovery of 256 examples of epistasis, and 57 of these were replicated in samples from another cohort. Brown et al. calculated that these epistatic interactions explained up to 16% of the variation in gene expression. Furthermore, as well as being involved in epistatic interactions, about 70% of the genetic variants that had an effect on the variation in gene expression were also involved in interactions between genes and the environment.
In addition to showing that epistasis contributes to variation in human traits, the work of Brown et al. could help to uncover interactions behind complex traits—beyond the expression level of a gene—that could not previously be investigated.
DOI: http://dx.doi.org/10.7554/eLife.01381.002
doi:10.7554/eLife.01381
PMCID: PMC4017648  PMID: 24771767
gene expression; epistasis; gene-environment interactions; human
12.  Evidence for Community Transmission of Community-Associated but Not Health-Care-Associated Methicillin-Resistant Staphylococcus Aureus Strains Linked to Social and Material Deprivation: Spatial Analysis of Cross-sectional Data 
PLoS Medicine  2016;13(1):e1001944.
Background
Identifying and tackling the social determinants of infectious diseases has become a public health priority following the recognition that individuals with lower socioeconomic status are disproportionately affected by infectious diseases. In many parts of the world, epidemiologically and genotypically defined community-associated (CA) methicillin-resistant Staphylococcus aureus (MRSA) strains have emerged to become frequent causes of hospital infection. The aim of this study was to use spatial models with adjustment for area-level hospital attendance to determine the transmission niche of genotypically defined CA- and health-care-associated (HA)-MRSA strains across a diverse region of South East London and to explore a potential link between MRSA carriage and markers of social and material deprivation.
Methods and Findings
This study involved spatial analysis of cross-sectional data linked with all MRSA isolates identified by three National Health Service (NHS) microbiology laboratories between 1 November 2011 and 29 February 2012. The cohort of hospital-based NHS microbiology diagnostic services serves 867,254 usual residents in the Lambeth, Southwark, and Lewisham boroughs in South East London, United Kingdom (UK). Isolates were classified as HA- or CA-MRSA based on whole genome sequencing. All MRSA cases identified over 4 mo within the three-borough catchment area (n = 471) were mapped to small geographies and linked to area-level aggregated socioeconomic and demographic data. Disease mapping and ecological regression models were used to infer the most likely transmission niches for each MRSA genetic classification and to describe the spatial epidemiology of MRSA in relation to social determinants. Specifically, we aimed to identify demographic and socioeconomic population traits that explain cross-area extra variation in HA- and CA-MRSA relative risks following adjustment for hospital attendance data. We explored the potential for associations with the English Indices of Deprivation 2010 (including the Index of Multiple Deprivation and several deprivation domains and subdomains) and the 2011 England and Wales census demographic and socioeconomic indicators (including numbers of households by deprivation dimension) and indicators of population health. Both CA-and HA-MRSA were associated with household deprivation (CA-MRSA relative risk [RR]: 1.72 [1.03–2.94]; HA-MRSA RR: 1.57 [1.06–2.33]), which was correlated with hospital attendance (Pearson correlation coefficient [PCC] = 0.76). HA-MRSA was also associated with poor health (RR: 1.10 [1.01–1.19]) and residence in communal care homes (RR: 1.24 [1.12–1.37]), whereas CA-MRSA was linked with household overcrowding (RR: 1.58 [1.04–2.41]) and wider barriers, which represent a combined score for household overcrowding, low income, and homelessness (RR: 1.76 [1.16–2.70]). CA-MRSA was also associated with recent immigration to the UK (RR: 1.77 [1.19–2.66]). For the area-level variation in RR for CA-MRSA, 28.67% was attributable to the spatial arrangement of target geographies, compared with only 0.09% for HA-MRSA. An advantage to our study is that it provided a representative sample of usual residents receiving care in the catchment areas. A limitation is that relationships apparent in aggregated data analyses cannot be assumed to operate at the individual level.
Conclusions
There was no evidence of community transmission of HA-MRSA strains, implying that HA-MRSA cases identified in the community originate from the hospital reservoir and are maintained by frequent attendance at health care facilities. In contrast, there was a high risk of CA-MRSA in deprived areas linked with overcrowding, homelessness, low income, and recent immigration to the UK, which was not explainable by health care exposure. Furthermore, areas adjacent to these deprived areas were themselves at greater risk of CA-MRSA, indicating community transmission of CA-MRSA. This ongoing community transmission could lead to CA-MRSA becoming the dominant strain types carried by patients admitted to hospital, particularly if successful hospital-based MRSA infection control programmes are maintained. These results suggest that community infection control programmes targeting transmission of CA-MRSA will be required to control MRSA in both the community and hospital. These epidemiological changes will also have implications for effectiveness of risk-factor-based hospital admission MRSA screening programmes.
Community associated MRSA variants, rather than hospital associated ones, are more readily transmitted and this is where control programs should focus to limit both hospital and community infections.
Editors' Summary
Background
Addressing health inequality requires understanding the social determinants of poor health. Previous studies have suggested a link between deprived living conditions and infections with methicillin-resistant Staphylococcus aureus (MRSA), that is, strains of the common bacterium S. aureus that have acquired antibiotic resistance and are therefore more difficult to treat. MRSA was first identified in the 1960s and for years thought of as a dangerous health-care-associated (HA-) pathogen that infects hospital patients who are predominantly older, sick, or undergoing invasive procedures. In the late 1990s, however, community-associated MRSA (CA-MRSA) emerged as pathogen infecting healthy individuals of all ages and without recent hospital contact. Most CA-MRSA cases are contagious skin infections, and numerous outbreaks have been reported in different communities. The traditional distinction between HA-MRSA and CA-MRSA based on where transmission occurred has become problematic in recent years, because CA-MRSA transmission has also been reported in health care settings. However, as HA- and CA-MRSA strains are genetically distinct, cases can be classified by DNA sequencing regardless of where a patient got infected.
Why Was This Study Done?
With hospitals historically considered the only place of MRSA transmission, prevention efforts remain focused on health care settings. Given the changing patterns of MRSA infections, however, the need to consider HA and CA transmission settings together has been recognized. This study was designed to take a closer look at the relationship between both HA- and CA-MRSA and socioeconomic deprivation, with the ultimate aim to inform prevention efforts. The researchers selected three boroughs in South East London with a highly diverse population of approximately 850,000 residents for whom socioeconomic and demographic data were available at a high level of spatial resolution. They also had data on hospital attendance for the residents and were therefore able to account for this factor in their analysis. The study addressed the following questions: is there a link between socioeconomic deprivation and both HA- and CA-MRSA cases among the residents? What social determinants are associated with HA- and CA-MRSA cases? What are the transmission settings (i.e., community versus health care) for HA- and CA-MRSA?
What Did the Researchers Do and Find?
They analyzed data on all MRSA samples collected over 4 consecutive mo in late 2011 and early 2012 by microbiology laboratories that serve the three boroughs. Of 471 MRSA cases that occurred in residents, 392 could be classified based on genome sequencing. Of these, approximately 72% were HA-MRSA, and 26% were CA-MRSA. Approximately 2% of residents carried both HA- and CA-MRSA. All MRSA cases were mapped to 513 smaller areas (called Lower Layer Super Output Areas, or LSOAs) in the three boroughs for which extensive socioeconomic and demographic data existed. The former included data on income, employment, health, and education, the latter data on number individuals per household, their ages and gender, and length of residence in the UK. MRSA cases were detected in just over half of the LSOAs in the study area. The researchers then used mathematical models to determine the most likely transmission settings for each MRSA genetic classification. They also described the spatial distributions of the two in relation to socioeconomic and demographic determinants. Both CA-and HA-MRSA were associated with household deprivation, which was itself correlated with hospital attendance. HA-MRSA was also associated with poor health and with living in communal care homes, whereas CA-MRSA was linked with household overcrowding and a combination of household overcrowding, low income, and homelessness. CA-MRSA was also associated with recent immigration to the UK. Around 27% of local variation in CA-MRSA could be explained by the spatial arrangement of LSOAs, meaning areas of high risk tended to cluster. No such clustering was observed for HA-MRSA.
What Do these Findings Mean?
The results show that residents in the most deprived areas are at greater risk for MRSA. The absence of spatial clusters of HA-MRSA suggests that transmission of genetically determined HA-MRSA occurs in hospitals, with little or no transmission in the community. The most important risk factor for acquiring HA-MRSA is therefore likely to be hospital attendance as a result of deprivation. In contrast, genetically determined CA-MRSA both affects deprived areas disproportionately, and—as the clusters imply—spreads from such areas in the community. This suggests that living in deprived conditions itself is a risk factor for acquiring CA-MRSA, as is living near deprived neighbors. Some of the CA-MRSA cases are also likely imported by recent immigrants. Whereas transmission of CA-MRSA in health care settings has been reported in a number of other studies, data from this study cannot answer whether or to what extent this is the case here. However, because of ongoing transmission in the community, and because deprived residents are both more likely to have CA-MRSA and to attend a hospital, importation of CA-MRSA strains into hospitals is an obvious concern. While the researchers intentionally located the study in an area with a very diverse population, it is not clear how generalizable the findings are to other communities, either in the UK or in other countries. Nonetheless, the results justify special focus on deprived populations in the control of MRSA and are useful for the design of specific strategies for HA-MRSA and CA-MRSA.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001944.
Online information on MRSA from the UK National Health Service: http://www.nhs.uk/conditions/MRSA/Pages/Introduction.aspx
MRSA webpage from the US Centers of Disease Control and Prevention: http://www.cdc.gov/mrsa/
MRSA page from the San Francisco Department of Public Health: http://www.sfcdcp.org/mrsa.html
MedlinePlus provides links to information about MRSA, including sources in languages other than English: https://www.nlm.nih.gov/medlineplus/mrsa.html
doi:10.1371/journal.pmed.1001944
PMCID: PMC4727805  PMID: 26812054
13.  The Causal Effect of Vitamin D Binding Protein (DBP) Levels on Calcemic and Cardiometabolic Diseases: A Mendelian Randomization Study 
PLoS Medicine  2014;11(10):e1001751.
In this study, Richards and colleagues undertook a Mendelian randomization study to determine whether vitamin D binding protein (DBP) levels have a causal effect on common calcemic and cardiometabolic diseases. They concluded that DBP has no demonstrable causal effect on any of the diseases or traits investigated here, except Vit D levels.
Please see later in the article for the Editors' Summary
Background
Observational studies have shown that vitamin D binding protein (DBP) levels, a key determinant of 25-hydroxy-vitamin D (25OHD) levels, and 25OHD levels themselves both associate with risk of disease. If 25OHD levels have a causal influence on disease, and DBP lies in this causal pathway, then DBP levels should likewise be causally associated with disease. We undertook a Mendelian randomization study to determine whether DBP levels have causal effects on common calcemic and cardiometabolic disease.
Methods and Findings
We measured DBP and 25OHD levels in 2,254 individuals, followed for up to 10 y, in the Canadian Multicentre Osteoporosis Study (CaMos). Using the single nucleotide polymorphism rs2282679 as an instrumental variable, we applied Mendelian randomization methods to determine the causal effect of DBP on calcemic (osteoporosis and hyperparathyroidism) and cardiometabolic diseases (hypertension, type 2 diabetes, coronary artery disease, and stroke) and related traits, first in CaMos and then in large-scale genome-wide association study consortia. The effect allele was associated with an age- and sex-adjusted decrease in DBP level of 27.4 mg/l (95% CI 24.7, 30.0; n = 2,254). DBP had a strong observational and causal association with 25OHD levels (p = 3.2×10−19). While DBP levels were observationally associated with calcium and body mass index (BMI), these associations were not supported by causal analyses. Despite well-powered sample sizes from consortia, there were no associations of rs2282679 with any other traits and diseases: fasting glucose (0.00 mmol/l [95% CI −0.01, 0.01]; p = 1.00; n = 46,186); fasting insulin (0.01 pmol/l [95% CI −0.00, 0.01,]; p = 0.22; n = 46,186); BMI (0.00 kg/m2 [95% CI −0.01, 0.01]; p = 0.80; n = 127,587); bone mineral density (0.01 g/cm2 [95% CI −0.01, 0.03]; p = 0.36; n = 32,961); mean arterial pressure (−0.06 mm Hg [95% CI −0.19, 0.07]); p = 0.36; n = 28,775); ischemic stroke (odds ratio [OR] = 1.00 [95% CI 0.97, 1.04]; p = 0.92; n = 12,389/62,004 cases/controls); coronary artery disease (OR = 1.02 [95% CI 0.99, 1.05]; p = 0.31; n = 22,233/64,762); or type 2 diabetes (OR = 1.01 [95% CI 0.97, 1.05]; p = 0.76; n = 9,580/53,810).
Conclusions
DBP has no demonstrable causal effect on any of the diseases or traits investigated here, except 25OHD levels. It remains to be determined whether 25OHD has a causal effect on these outcomes independent of DBP.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Vitamin D deficiency is an increasingly common public health concern. According to some estimates, more than a billion people worldwide may be vitamin D deficient. Indeed, many people living in the US and Europe (in particular, elderly people, breastfed infants, people with dark skin, and obese individuals) have serum (circulating) 25-hydroxy-vitamin D (25OHD) levels below 50 nmol/l, the threshold for vitamin D deficiency. Vitamin D helps the body absorb calcium, a mineral that is essential for healthy bones. Consequently, vitamin D deficiency can lead to calcemic diseases such as rickets (a condition that affects bone development in children), osteomalacia (soft bones in adults), and osteoporosis (a condition in which the bones weaken and become susceptible to fracture). We get most of our vitamin D needs from our skin, which makes vitamin D after exposure to sunlight. Vitamin D is also found naturally in oily fish and eggs, and is added to some other foods, including cereals and milk, but some people need to take vitamin D supplements to avoid vitamin D deficiency.
Why Was This Study Done?
Observational studies have reported that the low levels of serum 25OHD and serum vitamin D binding protein (DBP, a key determinant of serum 25OHD level) are both associated with the risk of several common diseases and traits. Such studies have implicated vitamin D deficiency in cardiometabolic disease (cardiovascular diseases that affect the heart and/or blood vessels and metabolic diseases that affect the cellular chemical reactions needed to sustain life), in some cancers, and in Alzheimer disease. But observational studies cannot prove that vitamin D deficiency or DBP levels actually cause any of these diseases. So, for example, an observational study might report an association between vitamin D deficiency and type 2 diabetes (a metabolic disease), but the individuals who develop type 2 diabetes might share another unknown characteristic that is actually responsible for disease development (a confounding factor). Alternatively, type 2 diabetes might reduce circulating vitamin D levels (reverse causation). Here, the researchers undertake a Mendelian randomization study to determine whether circulating DBP levels have causal effects on calcemic and cardiometabolic diseases. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the influence of a modifiable environmental exposure and the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if low DBP levels lead to low serum 25OHD levels, and vitamin D levels have a causal effect on common diseases, genetic variants associated with low DBP levels should be associated with the development of common diseases.
What Did the Researchers Do and Find?
The researchers analyzed the association between a genetic variant called single nucleotide polymorphism (SNP) rs2282679, which is known to alter DBP levels, and calcemic and cardiometabolic diseases and related traits in 2,254 participants in the Canadian Multicentre Osteoporosis Study (CaMos). The researchers report that there was a strong association between SNP rs2282679 and both serum DBP and 25OHD levels among the CaMos participants. However, there were no significant associations (associations unlikely to have occurred by chance) between SNP rs2282679 and calcium level, osteoporosis, or several cardiometabolic diseases, including heart attacks and diabetes. Moreover, when the researchers examined publically available genome-wide association study data collected by several international consortia investigating genetic influences on disease, they found no significant associations between rs2282679 and a wide range of calcemic and cardiometabolic diseases.
What Do These Findings Mean?
In this Mendelian randomization study, DBP level had no demonstrable causal effect on any of the calcemic or cardiometabolic diseases or traits investigated, except 25OHD level. Because most of the participants in CaMos and the international consortia were of European descent, these findings are applicable only to people of European ancestry. Moreover, like all Mendelian randomization studies, the reliability of these findings depends on several assumptions made by the researchers. Notably, although this study strongly suggests that DBP level does not have a causal influence on several common diseases, it remains to be determined whether 25OHD has a causal effect on any calcemic or cardiometabolic outcomes independent of DBP level.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001751.
The UK National Health Service Choices website provides information about vitamin D and about how to get vitamin D from sunshine; “Behind the Headlines” articles describe a recent observational study that reported an association between vitamin D deficiency and Alzheimer disease and the media coverage of this study, other health claims made for vitamin D, and a randomized control trial that questioned the role of vitamin D in disease
The US National Institutes of Health Office of Dietary Supplements provides information about vitamin D (in English and Spanish)
The US Centers for Disease Control and Prevention provides information about the vitamin D status of the US population
MedlinePlus has links to further information about vitamin D (in English and Spanish)
Information about the Canadian Multicentre Osteoporosis Study is available
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001751
PMCID: PMC4211663  PMID: 25350643
14.  Hundreds of variants clustered in genomic loci and biological pathways affect human height 
Lango Allen, Hana | Estrada, Karol | Lettre, Guillaume | Berndt, Sonja I. | Weedon, Michael N. | Rivadeneira, Fernando | Willer, Cristen J. | Jackson, Anne U. | Vedantam, Sailaja | Raychaudhuri, Soumya | Ferreira, Teresa | Wood, Andrew R. | Weyant, Robert J. | Segrè, Ayellet V. | Speliotes, Elizabeth K. | Wheeler, Eleanor | Soranzo, Nicole | Park, Ju-Hyun | Yang, Jian | Gudbjartsson, Daniel | Heard-Costa, Nancy L. | Randall, Joshua C. | Qi, Lu | Smith, Albert Vernon | Mägi, Reedik | Pastinen, Tomi | Liang, Liming | Heid, Iris M. | Luan, Jian'an | Thorleifsson, Gudmar | Winkler, Thomas W. | Goddard, Michael E. | Lo, Ken Sin | Palmer, Cameron | Workalemahu, Tsegaselassie | Aulchenko, Yurii S. | Johansson, Åsa | Zillikens, M.Carola | Feitosa, Mary F. | Esko, Tõnu | Johnson, Toby | Ketkar, Shamika | Kraft, Peter | Mangino, Massimo | Prokopenko, Inga | Absher, Devin | Albrecht, Eva | Ernst, Florian | Glazer, Nicole L. | Hayward, Caroline | Hottenga, Jouke-Jan | Jacobs, Kevin B. | Knowles, Joshua W. | Kutalik, Zoltán | Monda, Keri L. | Polasek, Ozren | Preuss, Michael | Rayner, Nigel W. | Robertson, Neil R. | Steinthorsdottir, Valgerdur | Tyrer, Jonathan P. | Voight, Benjamin F. | Wiklund, Fredrik | Xu, Jianfeng | Zhao, Jing Hua | Nyholt, Dale R. | Pellikka, Niina | Perola, Markus | Perry, John R.B. | Surakka, Ida | Tammesoo, Mari-Liis | Altmaier, Elizabeth L. | Amin, Najaf | Aspelund, Thor | Bhangale, Tushar | Boucher, Gabrielle | Chasman, Daniel I. | Chen, Constance | Coin, Lachlan | Cooper, Matthew N. | Dixon, Anna L. | Gibson, Quince | Grundberg, Elin | Hao, Ke | Junttila, M. Juhani | Kaplan, Lee M. | Kettunen, Johannes | König, Inke R. | Kwan, Tony | Lawrence, Robert W. | Levinson, Douglas F. | Lorentzon, Mattias | McKnight, Barbara | Morris, Andrew P. | Müller, Martina | Ngwa, Julius Suh | Purcell, Shaun | Rafelt, Suzanne | Salem, Rany M. | Salvi, Erika | Sanna, Serena | Shi, Jianxin | Sovio, Ulla | Thompson, John R. | Turchin, Michael C. | Vandenput, Liesbeth | Verlaan, Dominique J. | Vitart, Veronique | White, Charles C. | Ziegler, Andreas | Almgren, Peter | Balmforth, Anthony J. | Campbell, Harry | Citterio, Lorena | De Grandi, Alessandro | Dominiczak, Anna | Duan, Jubao | Elliott, Paul | Elosua, Roberto | Eriksson, Johan G. | Freimer, Nelson B. | Geus, Eco J.C. | Glorioso, Nicola | Haiqing, Shen | Hartikainen, Anna-Liisa | Havulinna, Aki S. | Hicks, Andrew A. | Hui, Jennie | Igl, Wilmar | Illig, Thomas | Jula, Antti | Kajantie, Eero | Kilpeläinen, Tuomas O. | Koiranen, Markku | Kolcic, Ivana | Koskinen, Seppo | Kovacs, Peter | Laitinen, Jaana | Liu, Jianjun | Lokki, Marja-Liisa | Marusic, Ana | Maschio, Andrea | Meitinger, Thomas | Mulas, Antonella | Paré, Guillaume | Parker, Alex N. | Peden, John F. | Petersmann, Astrid | Pichler, Irene | Pietiläinen, Kirsi H. | Pouta, Anneli | Ridderstråle, Martin | Rotter, Jerome I. | Sambrook, Jennifer G. | Sanders, Alan R. | Schmidt, Carsten Oliver | Sinisalo, Juha | Smit, Jan H. | Stringham, Heather M. | Walters, G.Bragi | Widen, Elisabeth | Wild, Sarah H. | Willemsen, Gonneke | Zagato, Laura | Zgaga, Lina | Zitting, Paavo | Alavere, Helene | Farrall, Martin | McArdle, Wendy L. | Nelis, Mari | Peters, Marjolein J. | Ripatti, Samuli | van Meurs, Joyce B.J. | Aben, Katja K. | Ardlie, Kristin G | Beckmann, Jacques S. | Beilby, John P. | Bergman, Richard N. | Bergmann, Sven | Collins, Francis S. | Cusi, Daniele | den Heijer, Martin | Eiriksdottir, Gudny | Gejman, Pablo V. | Hall, Alistair S. | Hamsten, Anders | Huikuri, Heikki V. | Iribarren, Carlos | Kähönen, Mika | Kaprio, Jaakko | Kathiresan, Sekar | Kiemeney, Lambertus | Kocher, Thomas | Launer, Lenore J. | Lehtimäki, Terho | Melander, Olle | Mosley, Tom H. | Musk, Arthur W. | Nieminen, Markku S. | O'Donnell, Christopher J. | Ohlsson, Claes | Oostra, Ben | Palmer, Lyle J. | Raitakari, Olli | Ridker, Paul M. | Rioux, John D. | Rissanen, Aila | Rivolta, Carlo | Schunkert, Heribert | Shuldiner, Alan R. | Siscovick, David S. | Stumvoll, Michael | Tönjes, Anke | Tuomilehto, Jaakko | van Ommen, Gert-Jan | Viikari, Jorma | Heath, Andrew C. | Martin, Nicholas G. | Montgomery, Grant W. | Province, Michael A. | Kayser, Manfred | Arnold, Alice M. | Atwood, Larry D. | Boerwinkle, Eric | Chanock, Stephen J. | Deloukas, Panos | Gieger, Christian | Grönberg, Henrik | Hall, Per | Hattersley, Andrew T. | Hengstenberg, Christian | Hoffman, Wolfgang | Lathrop, G.Mark | Salomaa, Veikko | Schreiber, Stefan | Uda, Manuela | Waterworth, Dawn | Wright, Alan F. | Assimes, Themistocles L. | Barroso, Inês | Hofman, Albert | Mohlke, Karen L. | Boomsma, Dorret I. | Caulfield, Mark J. | Cupples, L.Adrienne | Erdmann, Jeanette | Fox, Caroline S. | Gudnason, Vilmundur | Gyllensten, Ulf | Harris, Tamara B. | Hayes, Richard B. | Jarvelin, Marjo-Riitta | Mooser, Vincent | Munroe, Patricia B. | Ouwehand, Willem H. | Penninx, Brenda W. | Pramstaller, Peter P. | Quertermous, Thomas | Rudan, Igor | Samani, Nilesh J. | Spector, Timothy D. | Völzke, Henry | Watkins, Hugh | Wilson, James F. | Groop, Leif C. | Haritunians, Talin | Hu, Frank B. | Kaplan, Robert C. | Metspalu, Andres | North, Kari E. | Schlessinger, David | Wareham, Nicholas J. | Hunter, David J. | O'Connell, Jeffrey R. | Strachan, David P. | Wichmann, H.-Erich | Borecki, Ingrid B. | van Duijn, Cornelia M. | Schadt, Eric E. | Thorsteinsdottir, Unnur | Peltonen, Leena | Uitterlinden, André | Visscher, Peter M. | Chatterjee, Nilanjan | Loos, Ruth J.F. | Boehnke, Michael | McCarthy, Mark I. | Ingelsson, Erik | Lindgren, Cecilia M. | Abecasis, Gonçalo R. | Stefansson, Kari | Frayling, Timothy M. | Hirschhorn, Joel N
Nature  2010;467(7317):832-838.
Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence phenotype. Genome-wide association (GWA) studies have identified >600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the utility of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P=0.016), and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants, and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented amongst variants that alter amino acid structure of proteins and expression levels of nearby genes. Our data explain ∼10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ∼16% of phenotypic variation (∼20% of heritable variation). Although additional approaches are needed to fully dissect the genetic architecture of polygenic human traits, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
doi:10.1038/nature09410
PMCID: PMC2955183  PMID: 20881960
15.  TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies 
PLoS Genetics  2013;9(1):e1003235.
To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.
Author Summary
The genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS methods are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score, which frequently results in a considerable loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. We present a new multivariate method called TATES (Trait-based Association Test that uses Extended Simes procedure). Extensive simulations show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests of composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES uncovers both genetic variants that are common to multiple phenotypes as well as phenotype specific variants. TATES thus provides a more complete view of the genetic architecture of complex traits and constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants.
doi:10.1371/journal.pgen.1003235
PMCID: PMC3554627  PMID: 23359524
16.  Genome-Wide Association Analysis Identifies Variants Associated with Nonalcoholic Fatty Liver Disease That Have Distinct Effects on Metabolic Traits 
PLoS Genetics  2011;7(3):e1001324.
Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%–27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p<5×10−8) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT–assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits.
Author Summary
NAFLD is a spectrum of disease that ranges from steatosis to steatohepatitis (nonalcoholic steatohepatitis or NASH: inflammation around the fat) to fibrosis/cirrhosis. Hepatic steatosis can be measured non-invasively using computed tomography (CT) whereas NASH/fibrosis is assessed histologically. The genetic underpinnings of NAFLD remain to be determined. Here we estimate that 26%–27% of the variation in CT measured hepatic steatosis is heritable or genetic. We identify three variants near PNPLAL3, NCAN, and PPP1R3B that associate with CT hepatic steatosis and show that variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B, associate with histologic lobular inflammation/fibrosis. Variants in or near NCAN, GCKR, and PPP1R3B associate with altered serum lipid levels, whereas those in or near LYPLAL1 and PNPLA3 do not. Variants near GCKR and PPP1R3B also affect glycemic traits. Thus, we show that NAFLD is genetically influenced and expand the number of common genetic variants that associate with this trait. Our findings suggest that development of hepatic steatosis, NASH/fibrosis, or abnormalities in metabolic traits are probably influenced by different metabolic pathways that may represent distinct therapeutic targets.
doi:10.1371/journal.pgen.1001324
PMCID: PMC3053321  PMID: 21423719
17.  A Comprehensive Analysis of Shared Loci between Systemic Lupus Erythematosus (SLE) and Sixteen Autoimmune Diseases Reveals Limited Genetic Overlap 
PLoS Genetics  2011;7(12):e1002406.
In spite of the well-known clustering of multiple autoimmune disorders in families, analyses of specific shared genes and polymorphisms between systemic lupus erythematosus (SLE) and other autoimmune diseases (ADs) have been limited. Therefore, we comprehensively tested autoimmune variants for association with SLE, aiming to identify pleiotropic genetic associations between these diseases. We compiled a list of 446 non–Major Histocompatibility Complex (MHC) variants identified in genome-wide association studies (GWAS) of populations of European ancestry across 17 ADs. We then tested these variants in our combined Caucasian SLE cohorts of 1,500 cases and 5,706 controls. We tested a subset of these polymorphisms in an independent Caucasian replication cohort of 2,085 SLE cases and 2,854 controls, allowing the computation of a meta-analysis between all cohorts. We have uncovered novel shared SLE loci that passed multiple comparisons adjustment, including the VTCN1 (rs12046117, P = 2.02×10−06) region. We observed that the loci shared among the most ADs include IL23R, OLIG3/TNFAIP3, and IL2RA. Given the lack of a universal autoimmune risk locus outside of the MHC and variable specificities for different diseases, our data suggests partial pleiotropy among ADs. Hierarchical clustering of ADs suggested that the most genetically related ADs appear to be type 1 diabetes with rheumatoid arthritis and Crohn's disease with ulcerative colitis. These findings support a relatively distinct genetic susceptibility for SLE. For many of the shared GWAS autoimmune loci, we found no evidence for association with SLE, including IL23R. Also, several established SLE loci are apparently not associated with other ADs, including the ITGAM-ITGAX and TNFSF4 regions. This study represents the most comprehensive evaluation of shared autoimmune loci to date, supports a relatively distinct non–MHC genetic susceptibility for SLE, provides further evidence for previously and newly identified shared genes in SLE, and highlights the value of studies of potentially pleiotropic genes in autoimmune diseases.
Author Summary
It is well known that multiple autoimmune disorders cluster in families. However, all of the genetic variants that explain this clustering have not been discovered, and the specific genetic variants shared between systemic lupus erythematosus (SLE) and other autoimmune diseases (ADs) are not known. In order to better understand the genetic factors that explain this predisposition to autoimmunity, we performed a comprehensive evaluation of shared autoimmune genetic variants. First we considered results from 17 ADs and compiled a list with 446 significant genetic variants from these studies. We identified some genetic variants extensively shared between ADs, as well as the ADs that share the most variants. The genetic overlap between SLE and other ADs was modest. Next we tested how important all the 446 genetic variants were in our collection with a minimum of 1,500 SLE patients. Among the most significant variants in SLE, the majority had already been identified in previous studies, but we also discovered variants in two important immune genes. In summary, our data identified diseases with common genetic risk factors and novel SLE effects, and this supports a relatively distinct genetic susceptibility for SLE. This study helps delineate the genetic architecture of ADs.
doi:10.1371/journal.pgen.1002406
PMCID: PMC3234215  PMID: 22174698
18.  Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture 
Davis, Lea K. | Yu, Dongmei | Keenan, Clare L. | Gamazon, Eric R. | Konkashbaev, Anuar I. | Derks, Eske M. | Neale, Benjamin M. | Yang, Jian | Lee, S. Hong | Evans, Patrick | Barr, Cathy L. | Bellodi, Laura | Benarroch, Fortu | Berrio, Gabriel Bedoya | Bienvenu, Oscar J. | Bloch, Michael H. | Blom, Rianne M. | Bruun, Ruth D. | Budman, Cathy L. | Camarena, Beatriz | Campbell, Desmond | Cappi, Carolina | Cardona Silgado, Julio C. | Cath, Danielle C. | Cavallini, Maria C. | Chavira, Denise A. | Chouinard, Sylvain | Conti, David V. | Cook, Edwin H. | Coric, Vladimir | Cullen, Bernadette A. | Deforce, Dieter | Delorme, Richard | Dion, Yves | Edlund, Christopher K. | Egberts, Karin | Falkai, Peter | Fernandez, Thomas V. | Gallagher, Patience J. | Garrido, Helena | Geller, Daniel | Girard, Simon L. | Grabe, Hans J. | Grados, Marco A. | Greenberg, Benjamin D. | Gross-Tsur, Varda | Haddad, Stephen | Heiman, Gary A. | Hemmings, Sian M. J. | Hounie, Ana G. | Illmann, Cornelia | Jankovic, Joseph | Jenike, Michael A. | Kennedy, James L. | King, Robert A. | Kremeyer, Barbara | Kurlan, Roger | Lanzagorta, Nuria | Leboyer, Marion | Leckman, James F. | Lennertz, Leonhard | Liu, Chunyu | Lochner, Christine | Lowe, Thomas L. | Macciardi, Fabio | McCracken, James T. | McGrath, Lauren M. | Mesa Restrepo, Sandra C. | Moessner, Rainald | Morgan, Jubel | Muller, Heike | Murphy, Dennis L. | Naarden, Allan L. | Ochoa, William Cornejo | Ophoff, Roel A. | Osiecki, Lisa | Pakstis, Andrew J. | Pato, Michele T. | Pato, Carlos N. | Piacentini, John | Pittenger, Christopher | Pollak, Yehuda | Rauch, Scott L. | Renner, Tobias J. | Reus, Victor I. | Richter, Margaret A. | Riddle, Mark A. | Robertson, Mary M. | Romero, Roxana | Rosàrio, Maria C. | Rosenberg, David | Rouleau, Guy A. | Ruhrmann, Stephan | Ruiz-Linares, Andres | Sampaio, Aline S. | Samuels, Jack | Sandor, Paul | Sheppard, Brooke | Singer, Harvey S. | Smit, Jan H. | Stein, Dan J. | Strengman, E. | Tischfield, Jay A. | Valencia Duarte, Ana V. | Vallada, Homero | Van Nieuwerburgh, Filip | Veenstra-VanderWeele, Jeremy | Walitza, Susanne | Wang, Ying | Wendland, Jens R. | Westenberg, Herman G. M. | Shugart, Yin Yao | Miguel, Euripedes C. | McMahon, William | Wagner, Michael | Nicolini, Humberto | Posthuma, Danielle | Hanna, Gregory L. | Heutink, Peter | Denys, Damiaan | Arnold, Paul D. | Oostra, Ben A. | Nestadt, Gerald | Freimer, Nelson B. | Pauls, David L. | Wray, Naomi R. | Stewart, S. Evelyn | Mathews, Carol A. | Knowles, James A. | Cox, Nancy J. | Scharf, Jeremiah M.
PLoS Genetics  2013;9(10):e1003864.
The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.
Author Summary
Family and twin studies have shown that genetic risk factors are important in the development of Tourette Syndrome (TS) and obsessive compulsive disorder (OCD). However, efforts to identify the individual genetic risk factors involved in these two neuropsychiatric disorders have been largely unsuccessful. One possible explanation for this is that many genetic variations scattered throughout the genome each contribute a small amount to the overall risk. For TS and OCD, the genetic architecture (characterized by the number, frequency, and distribution of genetic risk factors) is presently unknown. This study examined the genetic architecture of TS and OCD in a variety of ways. We found that rare genetic changes account for more genetic risk in TS than in OCD; certain chromosomes contribute to OCD risk more than others; and variants that influence the level of genes expressed in two regions of the brain can account for a significant amount of risk for both TS and OCD. Results from this study might help in determining where, and what kind of variants are individual risk factors for TS and OCD and where they might be located in the human genome.
doi:10.1371/journal.pgen.1003864
PMCID: PMC3812053  PMID: 24204291
19.  A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease 
PLoS Genetics  2011;7(2):e1001289.
Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.
Author Summary
Risk to common diseases, such as diabetes, heart disease, etc., is influenced by a complex interaction among genetic and environmental factors. Most of the disease-association studies conducted so far have focused on common variants, widely available on genotyping platforms. However, recent advances in sequencing technologies pave the way for large-scale medical sequencing studies with the goal of elucidating the role rare variants may play in affecting susceptibility to complex traits. The large number of rare variants and their low frequencies pose great challenges for the analysis of these data. We present here a novel testing strategy, based on a weighted-sum statistic, that is less sensitive than existing methods to the presence of both risk and protective variants in the genetic region under investigation. We show applications to simulated data and to a real dataset on Type-1 Diabetes.
doi:10.1371/journal.pgen.1001289
PMCID: PMC3033379  PMID: 21304886
20.  Genetic Markers of Adult Obesity Risk Are Associated with Greater Early Infancy Weight Gain and Growth 
PLoS Medicine  2010;7(5):e1000284.
Ken Ong and colleagues genotyped children from the ALSPAC birth cohort and showed an association between greater early infancy gains in weight and length and genetic markers for adult obesity risk.
Background
Genome-wide studies have identified several common genetic variants that are robustly associated with adult obesity risk. Exploration of these genotype associations in children may provide insights into the timing of weight changes leading to adult obesity.
Methods and Findings
Children from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort were genotyped for ten genetic variants previously associated with adult BMI. Eight variants that showed individual associations with childhood BMI (in/near: FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, and ETV5) were used to derive an “obesity-risk-allele score” comprising the total number of risk alleles (range: 2–15 alleles) in each child with complete genotype data (n = 7,146). Repeated measurements of weight, length/height, and body mass index from birth to age 11 years were expressed as standard deviation scores (SDS). Early infancy was defined as birth to age 6 weeks, and early infancy failure to thrive was defined as weight gain between below the 5th centile, adjusted for birth weight. The obesity-risk-allele score showed little association with birth weight (regression coefficient: 0.01 SDS per allele; 95% CI 0.00–0.02), but had an apparently much larger positive effect on early infancy weight gain (0.119 SDS/allele/year; 0.023–0.216) than on subsequent childhood weight gain (0.004 SDS/allele/year; 0.004–0.005). The obesity-risk-allele score was also positively associated with early infancy length gain (0.158 SDS/allele/year; 0.032–0.284) and with reduced risk of early infancy failure to thrive (odds ratio  = 0.92 per allele; 0.86–0.98; p = 0.009).
Conclusions
The use of robust genetic markers identified greater early infancy gains in weight and length as being on the pathway to adult obesity risk in a contemporary birth cohort.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The proportion of overweight and obese children is increasing across the globe. In the US, the Surgeon General estimates that, compared with 1980, twice as many children and three times the number of adolescents are now overweight. Worldwide, 22 million children under five years old are considered by the World Health Organization to be overweight.
Being overweight or obese in childhood is associated with poor physical and mental health. In addition, childhood obesity is considered a major risk factor for adult obesity, which is itself a major risk factor for cancer, heart disease, diabetes, osteoarthritis, and other chronic conditions.
The most commonly used measure of whether an adult is a healthy weight is body mass index (BMI), defined as weight in kilograms/(height in metres)2. However, adult categories of obese (>30) and overweight (>25) BMI are not directly applicable to children, whose BMI naturally varies as they grow. BMI can be used to screen children for being overweight and or obese but a diagnosis requires further information.
Why Was This Study Done?
As the numbers of obese and overweight children increase, a corresponding rise in future numbers of overweight and obese adults is also expected. This in turn is expected to lead to an increasing incidence of poor health. As a result, there is great interest among health professionals in possible pathways between childhood and adult obesity. It has been proposed that certain periods in childhood may be critical for the development of obesity.
In the last few years, ten genetic variants have been found to be more common in overweight or obese adults. Eight of these have also been linked to childhood BMI and/or obesity. The authors wanted to identify the timing of childhood weight changes that may be associated with adult obesity. Knowledge of obesity risk genetic variants gave them an opportunity to do so now, without following a set of children to adulthood.
What Did the Researchers Do and Find?
The authors analysed data gathered from a subset of 7,146 singleton white European children enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC) study, which is investigating associations between genetics, lifestyle, and health outcomes for a group of children in Bristol whose due date of birth fell between April 1991 and December 1992. They used knowledge of the children's genetic makeup to find associations between an obesity risk allele score—a measure of how many of the obesity risk genetic variants a child possessed—and the children's weight, height, BMI, levels of body fat (at nine years old), and rate of weight gain, up to age 11 years.
They found that, at birth, children with a higher obesity risk allele score were not any heavier, but in the immediate postnatal period they were less likely to be in the bottom 5% of the population for weight gain (adjusted for birthweight), often termed “failure to thrive.” At six weeks of age, children with a higher obesity risk allele score tended to be longer and heavier, even allowing for weight at birth.
After six weeks of age, the obesity risk allele score was not associated with any further increase in length/height, but it was associated with a more rapid weight gain between birth and age 11 years. BMI is derived from height and weight measurements, and the association between the obesity risk allele score and BMI was weak between birth and age three-and-a-half years, but after that age the association with BMI increased rapidly. By age nine, children with a higher obesity risk allele score tended to be heavier and taller, with more fat on their bodies.
What Do These Findings Mean?
The combined obesity allele risk score is associated with higher rates of weight gain and adult obesity, and so the authors conclude that weight gain and growth even in the first few weeks after birth may be the beginning of a pathway of greater adult obesity risk.
A study that tracks a population over time can find associations but it cannot show cause and effect. In addition, only a relatively small proportion (1.7%) of the variation in BMI at nine years of age is explained by the obesity risk allele score.
The authors' method of finding associations between childhood events and adult outcomes via genetic markers of risk of disease as an adult has a significant advantage: the authors did not have to follow the children themselves to adulthood, so their findings are more likely to be relevant to current populations. Despite this, this research does not yield advice for parents how to reduce their children's obesity risk. It does suggest that “failure to thrive” in the first six weeks of life is not simply due to a lack of provision of food by the baby's caregiver but that genetic factors also contribute to early weight gain and growth.
The study looked at the combined obesity risk allele score and the authors did not attempt to identify which individual alleles have greater or weaker associations with weight gain and overweight or obesity. This would require further research based on far larger numbers of babies and children. The findings may also not be relevant to children in other types of setting because of the effects of different nutrition and lifestyles.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000284.
Further information is available on the ALSPAC study
The UK National Health Service and other partners provide guidance on establishing a healthy lifestyle for children and families in their Change4Life programme
The International Obesity Taskforce is a global network of expertise and the advocacy arm of the International Association for the Study of Obesity. It works with the World Health Organization, other NGOs, and stakeholders and provides information on overweight and obesity
The Centers for Disease Control and Prevention (CDC) in the US provide guidance and tips on maintaining a healthy weight, including BMI calculators in both metric and Imperial measurements for both adults and children. They also provide BMI growth charts for boys and girls showing how healthy ranges vary for each sex at with age
The Royal College of Paediatrics and Child Health provides growth charts for weight and length/height from birth to age 4 years that are based on WHO 2006 growth standards and have been adapted for use in the UK
The CDC Web site provides information on overweight and obesity in adults and children, including definitions, causes, and data
The CDC also provide information on the role of genes in causing obesity.
The World Health Organization publishes a fact sheet on obesity, overweight and weight management, including links to childhood overweight and obesity
Wikipedia includes an article on childhood obesity (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1000284
PMCID: PMC2876048  PMID: 20520848
21.  Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change 
PLoS Medicine  2014;11(12):e1001765.
In this study, Wurtz and colleagues investigated to what extent elevated body mass index (BMI) within the normal weight range has causal influences on the detailed systemic metabolite profile in early adulthood using Mendelian randomization analysis.
Please see later in the article for the Editors' Summary
Background
Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood.
Methods and Findings
We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16–39 y; 51% women; mean ± standard deviation BMI 24±4 kg/m2). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%–183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87%±3%; R2 = 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160%±2%; R2 = 0.92).
Conclusions
Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Adiposity—having excessive body fat—is a growing global threat to public health. Body mass index (BMI, calculated by dividing a person's weight in kilograms by their height in meters squared) is a coarse indicator of excess body weight, but the measure is useful in large population studies. Compared to people with a lean body weight (a BMI of 18.5–24.9 kg/m2), individuals with higher BMI have an elevated risk of developing life-shortening cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels (for example, heart failure and stroke) and metabolic diseases that affect the cellular chemical reactions that sustain life (for example, diabetes). People become unhealthily fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So adiposity can be prevented and reversed by eating less and exercising more.
Why Was This Study Done?
Epidemiological studies, which record the patterns of risk factors and disease in populations, suggest that the illness and death associated with excess body weight is partly attributable to abnormalities in how individuals with high adiposity metabolize carbohydrates and fats, leading to higher blood sugar and cholesterol levels. Further, adiposity is also associated with many other deviations in the metabolic profile than these commonly measured risk factors. However, epidemiological studies cannot prove that adiposity causes specific changes in a person's systemic (overall) metabolic profile because individuals with high BMI may share other characteristics (confounding factors) that are the actual causes of both adiposity and metabolic abnormalities. Moreover, having a change in some aspect of metabolism could also lead to adiposity, rather than vice versa (reverse causation). Importantly, if there is a causal effect of adiposity on cardiometabolic risk factor levels, it might be possible to prevent the progression towards cardiometabolic diseases by weight loss. Here, the researchers use “Mendelian randomization” to examine whether increased BMI within the normal and overweight range is causally influencing the metabolic risk factors from many biological pathways during early adulthood. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. Several gene variants are known to lead to modestly increased BMI. Thus, an investigation of the associations between these gene variants and risk factors across the systemic metabolite profile in a population of healthy individuals can indicate whether higher BMI is causally related to known and novel metabolic risk factors and higher cardiometabolic disease risk.
What Did the Researchers Do and Find?
The researchers measured the BMI of 12,664 adolescents and young adults (average BMI 24.7 kg/m2) living in Finland and the blood levels of 82 metabolites in these young individuals at a single time point. Statistical analysis of these data indicated that elevated BMI was adversely associated with numerous cardiometabolic risk factors. For example, elevated BMI was associated with raised levels of low-density lipoprotein, “bad” cholesterol that increases cardiovascular disease risk. Next, the researchers used a gene score for predisposition to increased BMI, composed of 32 gene variants correlated with increased BMI, as an “instrumental variable” to assess whether adiposity causes metabolite abnormalities. The effects on the systemic metabolite profile of a 1-kg/m2 increment in BMI due to genetic predisposition closely matched the effects of an observed 1-kg/m2 increment in adulthood BMI on the metabolic profile. That is, higher levels of adiposity had causal effects on the levels of numerous blood-based metabolic risk factors, including higher levels of low-density lipoprotein cholesterol and triglyceride-carrying lipoproteins, protein markers of chronic inflammation and adverse liver function, impaired insulin sensitivity, and elevated concentrations of several amino acids that have recently been linked with the risk for developing diabetes. Elevated BMI also causally led to lower levels of certain high-density lipoprotein lipids in the blood, a marker for the risk of future cardiovascular disease. Finally, an examination of the metabolic changes associated with changes in BMI in 1,488 young adults after a period of six years showed that those metabolic measures that were most strongly associated with BMI at a single time point likewise displayed the highest responsiveness to weight change over time.
What Do These Findings Mean?
These findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults beyond the effects on cholesterol and blood sugar. Like all Mendelian randomization studies, the reliability of the causal association reported here depends on several assumptions made by the researchers. Nevertheless, these findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults. Importantly, the results of both the causal effect analyses and the longitudinal study suggest that there is no threshold below which a BMI increase does not adversely affect the metabolic profile, and that a systemic metabolic profile linked with high cardiometabolic disease risk that becomes established during early adulthood can be reversed. Overall, these findings therefore highlight the importance of weight reduction as a key target for metabolic risk factor control among young adults.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001765.
The Computational Medicine Research Team of the University of Oulu has a webpage that provides further information on metabolite profiling by high-throughput NMR metabolomics
The World Health Organization provides information on obesity (in several languages)
The Global Burden of Disease Study website provides the latest details about global obesity trends
The UK National Health Service Choices website provides information about obesity, cardiovascular disease, and type 2 diabetes (including some personal stories)
The American Heart Association provides information on all aspects of cardiovascular disease and diabetes and on keeping healthy; its website includes personal stories about heart attacks, stroke, and diabetes
The US Centers for Disease Control and Prevention has information on all aspects of overweight and obesity and information about heart disease, stroke, and diabetes
MedlinePlus provides links to other sources of information on heart disease, vascular disease, and obesity (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001765
PMCID: PMC4260795  PMID: 25490400
22.  Single Nucleotide Variants in Transcription Factors Associate More Tightly with Phenotype than with Gene Expression 
PLoS Genetics  2014;10(5):e1004325.
Mapping the polymorphisms responsible for variation in gene expression, known as Expression Quantitative Trait Loci (eQTL), is a common strategy for investigating the molecular basis of disease. Despite numerous eQTL studies, the relationship between the explanatory power of variants on gene expression versus their power to explain ultimate phenotypes remains to be clarified. We addressed this question using four naturally occurring Quantitative Trait Nucleotides (QTN) in three transcription factors that affect sporulation efficiency in wild strains of the yeast, Saccharomyces cerevisiae. We compared the ability of these QTN to explain the variation in both gene expression and sporulation efficiency. We find that the amount of gene expression variation explained by the sporulation QTN is not predictive of the amount of phenotypic variation explained. The QTN are responsible for 98% of the phenotypic variation in our strains but the median gene expression variation explained is only 49%. The alleles that are responsible for most of the variation in sporulation efficiency do not explain most of the variation in gene expression. The balance between the main effects and gene-gene interactions on gene expression variation is not the same as on sporulation efficiency. Finally, we show that nucleotide variants in the same transcription factor explain the expression variation of different sets of target genes depending on whether the variant alters the level or activity of the transcription factor. Our results suggest that a subset of gene expression changes may be more predictive of ultimate phenotypes than the number of genes affected or the total fraction of variation in gene expression variation explained by causative variants, and that the downstream phenotype is buffered against variation in the gene expression network.
Author Summary
There have been major efforts in the study of human disease to identify genetic polymorphisms that cause changes in gene expression. The assumption underlying these studies is that gene expression changes will be responsible for the disease. However, it is unclear if we can predict how a polymorphism affects the variation in disease based on the extent to which it explains variation in gene expression. We have taken advantage of four genetic polymorphisms that affect the ability of budding yeast cells to form spores. The variants were identified in naturally occurring strains, subject to natural selection pressures in the wild, and not from lab strains. These variants lie in factors that control gene expression, which gives us power to compare how the polymorphisms affect variation in both gene expression and the downstream phenotype. We find that the amount of variation in gene expression explained by the variants does not correlate with the amount of variation observed in spore formation, which has implications for studies that attempt to infer the effect of a polymorphism on phenotypic variation by studying its effect on gene expression variation.
doi:10.1371/journal.pgen.1004325
PMCID: PMC4006743  PMID: 24784239
23.  Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts 
PLoS Medicine  2013;10(2):e1001383.
A mendelian randomization study based on data from multiple cohorts conducted by Karani Santhanakrishnan Vimaleswaran and colleagues re-examines the causal nature of the relationship between vitamin D levels and obesity.
Background
Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis.
Methods and Findings
We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects.
Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m2 higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10−27). The BMI allele score was associated both with BMI (p = 6.30×10−62) and 25(OH)D (−0.06% [95% CI −0.10 to −0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10−57 for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: −4.2 [95% CI −7.1 to −1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores).
Conclusions
On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Obesity—having an unhealthy amount of body fat—is increasing worldwide. In the US, for example, a third of the adult population is now obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30.0 kg/m2. Although there is a genetic contribution to obesity, people generally become obese by consuming food and drink that contains more energy than they need for their daily activities. Thus, obesity can be prevented by having a healthy diet and exercising regularly. Compared to people with a healthy weight, obese individuals have an increased risk of developing diabetes, heart disease and stroke, and tend to die younger. They also have a higher risk of vitamin D deficiency, another increasingly common public health concern. Vitamin D, which is essential for healthy bones as well as other functions, is made in the skin after exposure to sunlight but can also be obtained through the diet and through supplements.
Why Was This Study Done?
Observational studies cannot prove that obesity causes vitamin D deficiency because obese individuals may share other characteristics that reduce their circulating 25-hydroxy vitamin D [25(OH)D] levels (referred to as confounding). Moreover, observational studies cannot indicate whether the larger vitamin D storage capacity of obese individuals (vitamin D is stored in fatty tissues) lowers their 25(OH)D levels or whether 25(OH)D levels influence fat accumulation (reverse causation). If obesity causes vitamin D deficiency, monitoring and treating vitamin D deficiency might alleviate some of the adverse health effects of obesity. Conversely, if low vitamin D levels cause obesity, encouraging people to take vitamin D supplements might help to control the obesity epidemic. Here, the researchers use bi-directional “Mendelian randomization” to examine the direction and causality of the relationship between BMI and 25(OH)D. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the influence of a modifiable environmental exposure and the outcome of interest. Because gene variants do not change over time and are inherited randomly, they are not prone to confounding and are free from reverse causation. Thus, if a lower vitamin D status leads to obesity, genetic variants associated with lower 25(OH)D concentrations should be associated with higher BMI, and if obesity leads to a lower vitamin D status, then genetic variants associated with higher BMI should be associated with lower 25(OH)D concentrations.
What Did the Researchers Do and Find?
The researchers created a “BMI allele score” based on 12 BMI-related gene variants and two “25(OH)D allele scores,” which are based on gene variants that affect either 25(OH)D synthesis or breakdown. Using information on up to 42,024 participants from 21 studies, the researchers showed that the BMI allele score was associated with both BMI and with 25(OH)D levels among the study participants. Based on this information, they calculated that each 10% increase in BMI will lead to a 4.2% decrease in 25(OH)D concentrations. By contrast, although both 25(OH)D allele scores were strongly associated with 25(OH)D levels, neither score was associated with BMI. This lack of an association between 25(OH)D allele scores and obesity was confirmed using data from more than 100,000 individuals involved in 46 studies that has been collected by the GIANT (Genetic Investigation of Anthropometric Traits) consortium.
What Do These Findings Mean?
These findings suggest that a higher BMI leads to a lower vitamin D status whereas any effects of low vitamin D status on BMI are likely to be small. That is, these findings provide evidence for obesity as a causal factor in the development of vitamin D deficiency but not for vitamin D deficiency as a causal factor in the development of obesity. These findings suggest that population-level interventions to reduce obesity should lead to a reduction in the prevalence of vitamin D deficiency and highlight the importance of monitoring and treating vitamin D deficiency as a means of alleviating the adverse influences of obesity on health.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001383.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish); a data brief provides information about the vitamin D status of the US population
The World Health Organization provides information on obesity (in several languages)
The UK National Health Service Choices website provides detailed information about obesity and a link to a personal story about losing weight; it also provides information about vitamin D
The International Obesity Taskforce provides information about the global obesity epidemic
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
The US Office of Dietary Supplements provides information about vitamin D (in English and Spanish)
MedlinePlus has links to further information about obesity and about vitamin D (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Overview and details of the collaborative large-scale genetic association study (D-CarDia) provide information about vitamin D and the risk of cardiovascular disease, diabetes and related traits
doi:10.1371/journal.pmed.1001383
PMCID: PMC3564800  PMID: 23393431
24.  The Role of Abcb5 Alleles in Susceptibility to Haloperidol-Induced Toxicity in Mice and Humans 
PLoS Medicine  2015;12(2):e1001782.
Background
We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study.
Methods and Findings
A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered.
Conclusions
ABCB5 alleles alter susceptibility to HIT in mouse and humans. This discovery leads to a new model that (at least in part) explains inter-individual differences in susceptibility to a drug-induced CNS toxicity.
Gary Peltz and colleagues examine the role of ABCB5 alleles in haloperidol-induced toxicity in a murine genetic model and humans treated with haloperidol.
Editors' Summary
Background
The brain is the control center of the human body. This complex organ controls thoughts, memory, speech, and movement, it is the seat of intelligence, and it regulates the function of many organs. The brain comprises many different parts, all of which work together but all of which have their own special functions. For example, the forebrain is involved in intellectual activities such as thinking whereas the hindbrain controls the body’s vital functions and movements. Messages are passed between the various regions of the brain and to other parts of the body by specialized cells called neurons, which release and receive signal molecules known as neurotransmitters. Like all the organs in the body, blood vessels supply the brain with the oxygen, water, and nutrients it needs to function. Importantly, however, the brain is protected from infectious agents and other potentially dangerous substances circulating in the blood by the “blood-brain barrier,” a highly selective permeability barrier that is formed by the cells lining the fine blood vessels (capillaries) within the brain.
Why Was This Study Done?
Although drugs have been developed to treat various brain disorders, more active and less toxic drugs are needed to improve the treatment of many if not most of these conditions. Unfortunately, relatively little is known about how the blood-brain barrier regulates the entry of drugs into the brain or about the genetic factors that affect the brain’s susceptibility to drug-induced toxicities. It is not known, for example, why about half of patients given haloperidol—a drug used to treat psychotic disorders (conditions that affect how people think, feel, or behave)—develop tremors and other symptoms caused by alterations in the brain region that controls voluntary movements. Here, to improve our understanding of how drugs enter the brain and impact its function, the researchers investigate the genetic factors that affect haloperidol-induced toxicity by genetically analyzing several inbred mouse strains (every individual in an inbred mouse strain is genetically identical) with different susceptibilities to haloperidol-induced toxicity and by undertaking a human genetic association study (a study that looks for non-chance associations between specific traits and genetic variants).
What Did the Researchers Do and Find?
The researchers used a database of genetic variants called single nucleotide polymorphisms (SNPs) and a computational genetic mapping approach to show first that variations within the gene encoding Abcb5 affected susceptibility to haloperidol-induced toxicity (indicated by changes in the length of time taken by mice to move their paws when placed on an inclined wire-mesh screen) among inbred mouse strains. Abcb5 is an ATP-binding cassette transporter, a type of protein that moves molecules across cell membranes. The researchers next showed that Abcb5 is expressed in brain capillaries, which is the location of the blood-brain barrier. Abcb5 was also expressed in cerebellar Purkinje cells, which help to control motor (intentional) movements. They also measured the measured the effect of haloperidol and the haloperidol concentration in brain tissue sections in mice that were genetically engineered to make no Abcb5 (Abcb5 knockout mice). Finally, the researchers investigated whether specific alleles (alternative versions) of ABCB5 are associated with haloperidol-induced toxicity in people. Among a group of 85 patients treated with haloperidol for a psychotic illness, one specific ABCB5 allele was associated with haloperidol-induced toxicity during the first few days of treatment.
What Do These Findings Mean?
These findings indicate that Abcb5 is a component of the blood-brain barrier in mice and suggest that genetic variants in the gene encoding this protein underlie, at least in part, the differences in susceptibility to haloperidol-induced toxicity seen among inbred mice strains. Moreover, the human genetic association study indicates that a specific ABCB5 allele also affects the susceptibility of people to haloperidol-induced toxicity. The researchers note that other ABCB5 alleles or other genetic factors that affect haloperidol-induced toxicity in people might emerge if larger groups of patients were studied. However, based on their findings, the researchers propose a new model for the genetic mechanisms that underlie inter-individual and cell type-specific differences in susceptibility to haloperidol-induced brain toxicity. If confirmed in future studies, this model might facilitate the development of more effective and less toxic drugs to treat a range of brain disorders.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001782.
The US National Institute of Neurological Disorders and Stroke provides information about a wide range of brain diseases (in English and Spanish); its fact sheet “Brain Basics: Know Your Brain” is a simple introduction to the human brain; its “Blueprint Neurotherapeutics Network” was established to develop new drugs for disorders affecting the brain and other parts of the nervous system
MedlinePlus provides links to additional resources about brain diseases and their treatment (in English and Spanish)
Wikipedia provides information about haloperidol, about ATP-binding cassette transporters and about genetic association (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001782
PMCID: PMC4315575  PMID: 25647612
25.  Deep Resequencing of GWAS Loci Identifies Rare Variants in CARD9, IL23R and RNF186 That Are Associated with Ulcerative Colitis 
PLoS Genetics  2013;9(9):e1003723.
Genome-wide association studies and follow-up meta-analyses in Crohn's disease (CD) and ulcerative colitis (UC) have recently identified 163 disease-associated loci that meet genome-wide significance for these two inflammatory bowel diseases (IBD). These discoveries have already had a tremendous impact on our understanding of the genetic architecture of these diseases and have directed functional studies that have revealed some of the biological functions that are important to IBD (e.g. autophagy). Nonetheless, these loci can only explain a small proportion of disease variance (∼14% in CD and 7.5% in UC), suggesting that not only are additional loci to be found but that the known loci may contain high effect rare risk variants that have gone undetected by GWAS. To test this, we have used a targeted sequencing approach in 200 UC cases and 150 healthy controls (HC), all of French Canadian descent, to study 55 genes in regions associated with UC. We performed follow-up genotyping of 42 rare non-synonymous variants in independent case-control cohorts (totaling 14,435 UC cases and 20,204 HC). Our results confirmed significant association to rare non-synonymous coding variants in both IL23R and CARD9, previously identified from sequencing of CD loci, as well as identified a novel association in RNF186. With the exception of CARD9 (OR = 0.39), the rare non-synonymous variants identified were of moderate effect (OR = 1.49 for RNF186 and OR = 0.79 for IL23R). RNF186 encodes a protein with a RING domain having predicted E3 ubiquitin-protein ligase activity and two transmembrane domains. Importantly, the disease-coding variant is located in the ubiquitin ligase domain. Finally, our results suggest that rare variants in genes identified by genome-wide association in UC are unlikely to contribute significantly to the overall variance for the disease. Rather, these are expected to help focus functional studies of the corresponding disease loci.
Author Summary
Genetic studies of common diseases have seen tremendous progress in the last half-decade primarily due to recent technologies that enable a systematic examination of genetic markers across the entire genome in large numbers of patients and healthy controls. The studies, while identifying genomic regions that influence a person's risk for developing disease, often do not pinpoint the actual gene or gene variants that account for this risk (called a causal gene/variant). A prime example of this can be seen with the 163 genetic risk factors that have recently been associated with the chronic inflammatory bowel diseases known as Crohn's disease and ulcerative colitis. For less than a handful of these 163 is the causative change in the genetic code known. The current study used an approach to directly look at the genetic code for a subset of these and identified a causative change in the genetic code for eight risk factors for ulcerative colitis. This finding is particularly important because it directs biological studies to understand the mechanisms that lead to this chronic life-long inflammatory disease.
doi:10.1371/journal.pgen.1003723
PMCID: PMC3772057  PMID: 24068945

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