Heterozygous HNF1A mutations cause pancreatic-islet β-cell dysfunction and monogenic diabetes (MODY3). Hnf1α is known to regulate numerous hepatic genes, yet knowledge of its function in pancreatic islets is more limited. We now show that Hnf1a deficiency in mice leads to highly tissue-specific changes in the expression of genes involved in key functions of both islets and liver. To gain insights into the mechanisms of tissue-specific Hnf1α regulation, we integrated expression studies of Hnf1a-deficient mice with identification of direct Hnf1α targets. We demonstrate that Hnf1α can bind in a tissue-selective manner to genes that are expressed only in liver or islets. We also show that Hnf1α is essential only for the transcription of a minor fraction of its direct-target genes. Even among genes that were expressed in both liver and islets, the subset of targets showing functional dependence on Hnf1α was highly tissue specific. This was partly explained by the compensatory occupancy by the paralog Hnf1β at selected genes in Hnf1a-deficient liver. In keeping with these findings, the biological consequences of Hnf1a deficiency were markedly different in islets and liver. Notably, Hnf1a deficiency led to impaired large-T-antigen-induced growth and oncogenesis in β cells yet enhanced proliferation in hepatocytes. Collectively, these findings show that Hnf1α governs broad, highly tissue-specific genetic programs in pancreatic islets and liver and reveal key consequences of Hnf1a deficiency relevant to the pathophysiology of monogenic diabetes.

OBJECTIVES—Genome-wide association studies have dramatically increased the number of common genetic variants that are robustly associated with type 2 diabetes. A possible clinical use of this information is to identify individuals at high risk of developing the disease, so that preventative measures may be more effectively targeted. Here, we assess the ability of 18 confirmed type 2 diabetes variants to differentiate between type 2 diabetic case and control subjects.

RESEARCH DESIGN AND METHODS—We assessed index single nucleotide polymorphisms (SNPs) for the 18 independent loci in 2,598 control subjects and 2,309 case subjects from the Genetics of Diabetes Audit and Research Tayside Study. The discriminatory ability of the combined SNP information was assessed by grouping individuals based on number of risk alleles carried and determining relative odds of type 2 diabetes and by calculating the area under the receiver-operator characteristic curve (AUC).

RESULTS—Individuals carrying more risk alleles had a higher risk of type 2 diabetes. For example, 1.2% of individuals with >24 risk alleles had an odds ratio of 4.2 (95% CI 2.11–8.56) against the 1.8% with 10–12 risk alleles. The AUC (a measure of discriminative accuracy) for these variants was 0.60. The AUC for age, BMI, and sex was 0.78, and adding the genetic risk variants only marginally increased this to 0.80.

CONCLUSIONS—Currently, common risk variants for type 2 diabetes do not provide strong predictive value at a population level. However, the joint effect of risk variants identified subgroups of the population at substantially different risk of disease. Further studies are needed to assess whether individuals with extreme numbers of risk alleles may benefit from genetic testing.

Genome-wide association studies have successfully identified numerous loci at which common variants influence disease risk or quantitative traits. Despite these successes, the variants identified by these studies have generally explained only a small fraction of the heritable component of disease risk, and have not pinpointed with certainty the causal variant(s) at the associated loci. Furthermore, the mechanisms of action by which associated loci influence disease or quantitative phenotypes are often unclear, because we do not know through which gene(s) the associated variants exert their effects or because these gene(s) are of unknown function or have no clear connection to known disease biology. Thus, the initial set of genome-wide association studies serve as a starting point for future genetic and functional studies. We outline possible next steps that may help accelerate progress from genetic studies to the biological knowledge that can guide the development of predictive, preventive, or therapeutic measures.

Despite numerous candidate gene and linkage studies, the field of type 2 diabetes (T2D) genetics had until recently succeeded in identifying few genuine disease-susceptibility loci. The advent of genome-wide association (GWA) scans has transformed the situation, leading to an expansion in the number of established, robustly replicating T2D loci to almost 20. These novel findings offer unique insights into the pathogenesis of T2D and in the main point towards the etiological importance of disorders of beta-cell development and function. All associated variants have common allele frequencies in the discovery populations, and exert modest to small effects on the risk of disease, characteristics which limit their prognostic and diagnostic potential. However, ongoing studies focussing on the role of copy number variation and targeting low frequency polymorphisms should identify additional T2D-susceptibility loci, some of which may have larger effect sizes and offer better individual prediction of disease risk.

Tumor necrosis factor (TNF) and lymphotoxin-α (LTA) are cytokines with a wide range of inflammatory and immunomodulatory activities. Type 1 diabetes is an autoimmune disease characterized by destruction of insulin-producing pancreatic β cells. The aim of the present study was to evaluate the association of polymorphisms in the TNF/LTA gene region with susceptibility to type 1 diabetes. We investigated 11 TNF/LTA tag polymorphisms, designed to capture the majority of common variation in the region, in 160 trio families from South Croatia. We observed overtransmission of alleles from parents to affected child at five variants: (rs909253, allele C, p = 1.2×10−4; rs1041981, allele A, p = 1.1×10−4; rs1800629 (G-308A), allele A, p = 1.2×10−4; rs361525(G-238A), allele G, p = 8.2×10−3 and rs3093668, allele G, p = 0.014). We also identified overtransmission of the rs 1800629(G-308A)-rs361525(G-238A) A-G haplotype, p = 2.384×10−5. The present study found an association of the TNF/LTA gene region with type 1 diabetes. A careful assessment of TNF/LTA variants adjusted for linkage disequilibrium with HLA loci is needed to further clarify the role of these genes in type 1 diabetes susceptibility in the population of South Croatia.

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

Background

There is considerable interest in the hypothesis that low frequency, intermediate penetrance variants contribute to the proportion of Type 2 Diabetes (T2D) susceptibility not attributable to the common variants uncovered through genome-wide association approaches. Genes previously implicated in monogenic and multifactorial forms of diabetes are obvious candidates in this respect. In this study, we focussed on exons 8–10 of the HNF1A gene since rare, penetrant mutations in these exons (which are only transcribed in selected HNF1A isoforms) are associated with a later age of diagnosis of Maturity onset diabetes of the young (MODY) than mutations in exons 1–7. The age of diagnosis in the subgroup of HNF1A-MODY individuals with exon 8–10 mutations overlaps with that of early multifactorial T2D, and we set out to test the hypothesis that these exons might also harbour low-frequency coding variants of intermediate penetrance that contribute to risk of multifactorial T2D.

Methodology and Principal Findings

We performed targeted capillary resequencing of HNF1A exons 8–10 in 591 European T2D subjects enriched for genetic aetiology on the basis of an early age of diagnosis (≤45 years) and/or family history of T2D (≥1 affected sibling). PCR products were sequenced and compared to the published HNF1A sequence. We identified several variants (rs735396 [IVS9−24T>C], rs1169304 [IVS8+29T>C], c.1768+44C>T [IVS9+44C>T] and rs61953349 [c.1545G>A, p.T515T] but no novel non-synonymous coding variants were detected.

Conclusions and Significance

We conclude that low frequency, nonsynonymous coding variants in the terminal exons of HNF1A are unlikely to contribute to T2D-susceptibility in European samples. Nevertheless, the rationale for seeking low-frequency causal variants in genes known to contain rare, penetrant mutations remains strong and should motivate efforts to screen other genes in a similar fashion.

Background

Variants in FTO (fat mass and obesity associated) gene are associated with obesity and type 2 diabetes (T2D) in white Europeans. These associations are not consistent in Asians and there are few reports in South Asian Indians who develop T2D at a much lower body mass index (BMI) than that in the white Europeans.

Aims and hypothesis

We studied the association of FTO variants with T2D and measures of obesity in South Asian Indians in Pune, India.

Methods

We genotyped by sequencing, two SNPs rs9939609 and rs7191344, in the FTO gene in 1453 type 2 diabetes patients and 1361 controls and a further 961 population based individuals from India .

Results

We observed a strong association of the minor allele A at rs9939609 with T2D (OR per allele =1.26 [95% CI, 1.13-1.40], P=3×10-5). The variant was also associated with BMI but this association appeared to be weaker (0.06SDs; 95%CIs:0.01-0.10, p=0.017) than the previously reported effect in Europeans (0.10SDs 95%CIs:0.09-0.12). Unlike in the Europeans, the association with T2D remained when adjusting for BMI (OR per allele for T2D=1.21 (95% CI, 1.06-1.37); P=4.0 × 10-3). Similar results were obtained when using waist circumference and other anthropometric parameters.

Conclusions

Our study replicates the strong association of FTO variants with type 2 diabetes in South Asian Indians but suggests that the association of FTO with T2D in them might operate through mechanisms other than obesity. This could imply a fundamental difference between Indians and Europeans in the mechanisms linking body size with T2D.

Summary: SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented.

Availability: The source code, documentation and initialization scripts are available at http://simbioms.org.

Contact: support@simbioms.org; mariak@ebi.ac.uk

Activating transcription factor 6 (ATF6) is located within the region of linkage to type 2 diabetes on chromosome 1q21-q23 and is a key activator of the endoplasmic reticulum stress response. We evaluated 78 single nucleotide polymorphisms (SNPs) spanning >213 kb in 95 people, from which we selected 64 SNPs for evaluation in 191 Caucasian case subjects from Utah and between 165 and 188 control subjects. Six SNPs showed nominal associations with type 2 diabetes (P = 0.001-0.04), including the nonsynonymous SNP rs1058405 (M67V) in exon 3 and rs11579627 in the 3′ flanking region. Only rs1159627 remained significant on permutation testing. The associations were not replicated in 353 African-American case subjects and 182 control subjects, nor were ATF6 SNPs associated with altered insulin secretion or insulin sensitivity in nondiabetic Caucasian individuals. No association with type 2 diabetes was found in a subset of 44 SNPs in Caucasian (n = 2,099), Pima Indian (n = 293), and Chinese (n = 287) samples. Allelic expression imbalance was found in transformed lymphocyte cDNA for 3′ untranslated region variants, thus suggesting cis-acting regulatory variants. ATF6 does not appear to play a major role in type 2 diabetes, but further work is required to identify the cause of the allelic expression imbalance.

We studied genes involved in pancreatic β cell function and survival, identifying associations between SNPs in WFS1 and diabetes risk in UK populations that we replicated in an Ashkenazi population and in additional UK studies. In a pooled analysis comprising 9,533 cases and 11,389 controls, SNPs in WFS1 were strongly associated with diabetes risk. Rare mutations in WFS1 cause Wolfram syndrome; using a gene-centric approach, we show that variation in WFS1 also predisposes to common type 2 diabetes.

Recent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell. MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis. We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility. We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescence-activated cell sorting. In total, 366 miRNAs were found to be expressed (i.e. >100 cumulative reads) in islets and 346 in beta-cells; of the total of 384 unique miRNAs, 328 were shared. A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e. >50% of all reads seen across the tissues) in islets. Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g. miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology. As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites. We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values <0.01, q-values <0.1). At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants. In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.

Genetic variants in intron 1 of the fat mass– and obesity-associated (FTO) gene have been consistently associated with body mass index (BMI) in Europeans. However, follow-up studies in African Americans (AA) have shown no support for some of the most consistently BMI–associated FTO index single nucleotide polymorphisms (SNPs). This is most likely explained by different race-specific linkage disequilibrium (LD) patterns and lower correlation overall in AA, which provides the opportunity to fine-map this region and narrow in on the functional variant. To comprehensively explore the 16q12.2/FTO locus and to search for second independent signals in the broader region, we fine-mapped a 646–kb region, encompassing the large FTO gene and the flanking gene RPGRIP1L by investigating a total of 3,756 variants (1,529 genotyped and 2,227 imputed variants) in 20,488 AAs across five studies. We observed associations between BMI and variants in the known FTO intron 1 locus: the SNP with the most significant p-value, rs56137030 (8.3×10−6) had not been highlighted in previous studies. While rs56137030was correlated at r2>0.5 with 103 SNPs in Europeans (including the GWAS index SNPs), this number was reduced to 28 SNPs in AA. Among rs56137030 and the 28 correlated SNPs, six were located within candidate intronic regulatory elements, including rs1421085, for which we predicted allele-specific binding affinity for the transcription factor CUX1, which has recently been implicated in the regulation of FTO. We did not find strong evidence for a second independent signal in the broader region. In summary, this large fine-mapping study in AA has substantially reduced the number of common alleles that are likely to be functional candidates of the known FTO locus. Importantly our study demonstrated that comprehensive fine-mapping in AA provides a powerful approach to narrow in on the functional candidate(s) underlying the initial GWAS findings in European populations.

Author Summary

Genetic variants within the fat mass– and obesity-associated (FTO) gene are associated with increased risk of obesity. To better understand which specific genetic variant(s) in this genetic region is associated with obesity risk, we attempt to genotype or impute all known genetic variants in the region and test for association with body mass index as a measurement of obesity in over 20,000 African Americans. We identified 29 potential candidate variants, of which one variant (rs1421085) is a particularly interesting candidate for future functional follow-up studies. Our example shows the powerful approach of studying a large African American population, substantially reducing the number of possible functional variants compared with European descent populations.

Background

The developmental overnutrition hypothesis suggests that greater maternal obesity during pregnancy results in increased offspring adiposity in later life. If true, this would result in the obesity epidemic progressing across generations irrespective of environmental or genetic changes. It is therefore important to robustly test this hypothesis.

Methods and Findings

We explored this hypothesis by comparing the associations of maternal and paternal pre-pregnancy body mass index (BMI) with offspring dual energy X-ray absorptiometry (DXA)–determined fat mass measured at 9 to 11 y (4,091 parent–offspring trios) and by using maternal FTO genotype, controlling for offspring FTO genotype, as an instrument for maternal adiposity. Both maternal and paternal BMI were positively associated with offspring fat mass, but the maternal association effect size was larger than that in the paternal association in all models: mean difference in offspring sex- and age-standardised fat mass z-score per 1 standard deviation BMI 0.24 (95% confidence interval [CI]: 0.22 to 0.26) for maternal BMI versus 0.13 (95% CI: 0.11, 0.15) for paternal BMI; p-value for difference in effect < 0.001. The stronger maternal association was robust to sensitivity analyses assuming levels of non-paternity up to 20%. When maternal FTO, controlling for offspring FTO, was used as an instrument for the effect of maternal adiposity, the mean difference in offspring fat mass z-score per 1 standard deviation maternal BMI was −0.08 (95% CI: −0.56 to 0.41), with no strong statistical evidence that this differed from the observational ordinary least squares analyses (p = 0.17).

Conclusions

Neither our parental comparisons nor the use of FTO genotype as an instrumental variable, suggest that greater maternal BMI during offspring development has a marked effect on offspring fat mass at age 9–11 y. Developmental overnutrition related to greater maternal BMI is unlikely to have driven the recent obesity epidemic.

Using parental-offspring associations and theFTO gene as an instrumental variable for maternal adiposity, Debbie Lawlor and colleagues found that greater maternal BMI during offspring development does not appear to have a marked effect on offspring fat mass at age 9-11.

Editors' Summary

Background.

Since the 1970s, the proportion of children and adults who are overweight or obese (people who have an unhealthy amount of body fat) has increased sharply in many countries. In the US, 1 in 3 adults is now obese; in the mid-1970s it was only 1 in 7. Similarly, the proportion of overweight children has risen from 1 in 20 to 1 in 5. An adult is considered to be overweight if their body mass index (BMI)—their weight in kilograms divided by their height in meters squared—is between 25 and 30, and obese if it is more than 30. For children, the healthy BMI depends on their age and gender. Compared to people with a healthy weight (a BMI between 18.5 and 25), overweight or obese individuals have an increased lifetime risk of developing diabetes and other adverse health conditions, sometimes becoming ill while they are still young. People become unhealthily fat when they consume food and drink that contains more energy than they need for their daily activities. It should, therefore, be possible to avoid becoming obese by having a healthy diet and exercising regularly.

Why Was This Study Done?

Some researchers think that “developmental overnutrition” may have caused the recent increase in waistline measurements. In other words, if a mother is overweight during pregnancy, high sugar and fat levels in her body might permanently affect her growing baby's appetite control and metabolism, and so her offspring might be at risk of becoming obese in later life. If this hypothesis is true, each generation will tend to be fatter than the previous one and it will be very hard to halt the obesity epidemic simply by encouraging people to eat less and exercise more. In this study, the researchers have used two approaches to test the developmental overnutrition hypothesis. First, they have asked whether offspring fat mass is more strongly related to maternal BMI than to paternal BMI; it should be if the hypothesis is true. Second, they have asked whether a genetic indicator of maternal fatness—the “A” variant of the FTO gene—is related to offspring fat mass. A statistical association between maternal FTO genotype (genetic make-up) and offspring fat mass would support the developmental nutrition hypothesis.

What Did the Researchers Do and Find?

In 1991–1992, the Avon Longitudinal Study of Parents and Children (ALSPAC) enrolled about 14,000 pregnant women and now examines their offspring at regular intervals. The researchers first used statistical methods to look for associations between the self-reported prepregnancy BMI of the parents of about 4,000 children and the children's fat mass at ages 9–11 years measured using a technique called dual energy X-ray absorptiometry. Both maternal and paternal BMI were positively associated with offspring fat mass (that is, fatter parents had fatter children) but the effect of maternal BMI was greater than the effect of paternal BMI. When the researchers examined maternal FTO genotypes and offspring fat mass (after allowing for the offspring's FTO genotype, which would directly affect their fat mass), there was no statistical evidence to suggest that differences in offspring fat mass were related to the maternal FTO genotype.

What Do These Findings Mean?

Although the findings from first approach provide some support for the development overnutrition hypothesis, the effect of maternal BMI on offspring fat mass is too weak to explain the recent obesity epidemic. Developmental overnutrition could, however, be responsible for the much slower increase in obesity that began a century ago. The findings from the second approach provide no support for the developmental overnutrition hypothesis, although these results have wide error margins and need confirming in a larger study. The researchers also note that the effects of developmental overnutrition on offspring fat mass, although weak at age 9–11, might become more important at later ages. Nevertheless, for now, it seems unlikely that developmental overnutrition has been a major driver of the recent obesity epidemic. Interventions that aim to improve people's diet and to increase their physical activity levels could therefore slow or even halt the epidemic.

Additional Information.

Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050033.

See a related PLoS Medicine Perspective article

The MedlinePlus encyclopedia has a page on obesity (in English and Spanish)

The US Centers for Disease Control and Prevention provides information on all aspects of obesity (in English and Spanish)

The UK National Health Service's health Web site (NHS Direct) provides information about obesity

The International Obesity Taskforce provides information about preventing obesity and on childhood obesity

The UK Foods Standards Agency, the United States Department of Agriculture, and Shaping America's Health all provide useful advice about healthy eating for adults and children

The ALSPAC Web site provides information about the Avon Longitudinal Study of Parents and Children and its results so far

Background

A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (~20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed.

Methods and Findings

Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles.

Conclusions

Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases.

Combining information from several known common risk polymorphisms allows the identification of subgroups of the population with markedly differing risks of developing type 2 diabetes.

Editors' Summary

Background.

Diabetes is an important and increasingly common global health problem; the World Health Organization has estimated that about 170 million people currently have diabetes worldwide. One particular form, type 2 diabetes, develops when cells in the body become unable to respond to a hormone called insulin. Insulin is normally released by the pancreas and controls the ability of body cells to take in glucose (sugar). Therefore, when cells become insensitive to insulin as in people with type 2 diabetes, glucose levels in the body are not well controlled and may become dangerously high in the blood. These high levels can have long-term damaging effects on various organs in the body, particularly the eyes, nerves, heart, and kidneys. There are many different factors that affect whether someone is likely to develop type 2 diabetes. These factors can be broadly grouped into two categories: environmental and genetic. Environmental factors such as obesity, a diet high in sugar, and a sedentary lifestyle are all risk factors for developing type 2 diabetes in later life. Genetically, a number of variants in many different genes may affect the risk of developing the disease. Generally, these gene variants are common in human populations but each gene variant only mildly increases the risk that a person possessing it will get type 2 diabetes.

Why Was This Study Done?

The investigators performing this study wanted to understand how different gene variants combine to affect an individual's risk of getting type 2 diabetes. That is, if a person carries many different variants, does their overall risk increase a lot or only a little?

What Did the Researchers Do and Find?

First, the researchers surveyed the published reports to identify those gene variants for which there was strong evidence of an association with type 2 diabetes. They found mutations in three genes that had been shown reproducibly to be associated with type 2 diabetes in different studies: PPARG (whose product is involved in regulation of fat tissue), KCNJ11 (whose product is involved in insulin production), and TCF7L2 (whose product is thought to be involved in controlling sugar levels). Then, they compared two groups of white people in the UK: 2,409 people with type 2 diabetes (“cases”), and 3,668 people from the general population (“controls”). The researchers compared the two groups to see which individuals possessed which gene variants, and did statistical testing to work out to what extent having particular combinations of the gene variants affected an individual's chance of being a “case” versus a “control.” Their results showed that in the groups studied, having an ever-increasing number of gene variants increased the risk of developing diabetes. The risk that someone with none of the gene variants would develop type 2 diabetes was about 2%, while the chance for someone with all gene variants was about10%.

What Do These Findings Mean?

These results show that the risk of developing type 2 diabetes is greater if an individual possesses all of the gene variants that were examined in this study. The analysis also suggests that using information on all three variants, rather than just one, is likely to be more accurate in predicting future risk. How this genetic information should be used alongside other well-known preventative measures such as altered lifestyle requires further study.

Additional Information.

Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030374.

NHS Direct patient information on diabetes

National Diabetes Information Clearinghouse information on type 2 diabetes

World Health Organization Diabetes Programme

Centers for Disease ControlDiabetes Public Health Resource

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptinob/ob mutation (Lepob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Author Summary

Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

A common dilemma arising in linkage studies of complex genetic diseases is the selection of positive signals, their follow-up with association studies and discrimination between true and false positive results. Several strategies for overcoming these issues have been devised. Using the Genetic Analysis Workshop 14 simulated dataset, we aimed to apply different analytical approaches and evaluate their performance in discerning real associations. We considered a) haplotype analyses, b) different methods adjusting for multiple testing, c) replication in a second dataset, and d) exhaustive genotyping of all markers in a sufficiently powered, large sample group. We found that haplotype-based analyses did not substantially improve over single-point analysis, although this may reflect the low levels of linkage disequilibrium simulated in the datasets provided. Multiple testing correction methods were in general found to be over-conservative. Replication of nominally positive results in a second dataset appears to be less stringent, resulting in the follow-up of false positives. Performing a comprehensive assay of all markers in a large, well-powered dataset appears to be the most effective strategy for complex disease gene identification.

Rare genetic variants, identified by in-detail resequencing of loci, may contribute to complex traits. We used the apolipoprotein A-I gene (APOA1), a major high-density lipoprotein (HDL) gene, and population-based resequencing to determine the spectrum of genetic variants, the phenotypic characteristics of these variants, and how these results compared with results based on resequencing only the extremes of the apolipoprotein A-I (apoA-I) distribution. First, we resequenced APOA1 in 10,330 population-based participants in the Copenhagen City Heart Study. The spectrum and distribution of genetic variants was determined as a function of the number of individuals resequenced. Second, apoA-I and HDL cholesterol phenotypes were determined for nonsynonymous (NS) and synonymous (S) variants and were validated in the Copenhagen General Population Study (n = 45,239). Third, observed phenotypes were compared with those predicted using an extreme phenotype approach based on the apoA-I distribution. Our results are as follows: First, population-based resequencing of APOA1 identified 40 variants of which only 7 (18%) had minor allele frequencies >1%, and most were exceedingly rare. Second, 0.27% of individuals in the general population were heterozygous for NS variants which were associated with substantial reductions in apoA-I (up to 39 mg/dL) and/or HDL cholesterol (up to 0.9 mmol/L) and, surprisingly, 0.41% were heterozygous for variants predisposing to amyloidosis. NS variants associated with a hazard ratio of 1.72 (1.09–2.70) for myocardial infarction (MI), largely driven by A164S, a variant not associated with apoA-I or HDL cholesterol levels. Third, using the extreme apoA-I phenotype approach, NS variants correctly predicted the apoA-I phenotype observed in the population-based resequencing. However, using the extreme approach, between 79% (screening 0–1st percentile) and 21% (screening 0–20th percentile) of all variants were not identified; among these were variants previously associated with amyloidosis. Population-based resequencing of APOA1 identified a majority of rare NS variants associated with reduced apoA-1 and HDL cholesterol levels and/or predisposing to amyloidosis. In addition, NS variants associated with increased risk of MI.

Author Summary

Rare genetic variants, identified by in-detail resequencing of loci, may contribute to complex traits. We used the apolipoprotein A-I gene (APOA1), a major high-density lipoprotein (HDL) gene, and population-based resequencing to determine the spectrum of genetic variants, the phenotypic characteristics of these variants, and how these results compared with results based on resequencing only the extremes of the apolipoprotein A-I (apoA-I) distribution. By resequencing APOA1 in >10,000 Danes and genotyping an additional >45,000, we show that population-based resequencing of APOA1 identifies a majority of rare genetic variants that together are relatively frequent: 0.27% of the population are heterozygous for nonsynonymous (NS) variants in APOA1 that associate with substantial reductions in apoA-I and HDL cholesterol, and 0.41% are heterozygous for variants predisposing to amyloidosis. NS variants associated with a hazard ratio of 1.72 (1.09–2.70) for myocardial infarction (MI), largely driven by A164S, a variant not associated with apoA-I or HDL cholesterol levels. Resequencing only the extremes of the apoA-I distribution, between 79% and 21% of all variants are not identified; among these are variants previously associated with amyloidosis. These results provide direct evidence that rare NS variants in APOA1 contribute to low apoA-I and HDL cholesterol levels, to susceptibility to amyloidosis, and to risk of MI in the general population.

Improved techniques for defining disease-gene location and evaluating the biological candidacy of regional transcripts will hasten disease-gene discovery.

Improved techniques for defining disease-gene location and evaluating the biological candidacy of regional transcripts will hasten disease-gene discovery.

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Author Summary

Genome-wide association studies on metabolomics data have demonstrated that genetic variation in metabolic enzymes and transporters leads to concentration changes in the respective metabolite levels. The conventional goal of these studies is the detection of novel interactions between the genome and the metabolic system, providing valuable insights for both basic research as well as clinical applications. In this study, we borrow the metabolomics GWAS concept for a novel, entirely different purpose. Metabolite measurements frequently produce signals where a certain substance can be reliably detected in the sample, but it has not yet been elucidated which specific metabolite this signal actually represents. The concept is comparable to a fingerprint: each one is uniquely identifiable, but as long as it is not registered in a database one cannot tell to whom this fingerprint belongs. Obviously, this issue tremendously reduces the usability of a metabolomics analyses. The genetic associations of such an “unknown,” however, give us concrete evidence of the metabolic pathway this substance is most probably involved in. Moreover, we complement the approach with a specific measure of correlation between metabolites, providing further evidence of the metabolic processes of the unknown. For a number of cases, this even allows for a concrete identity prediction, which we then experimentally validate in the lab.

The ultimate goal of genome-wide association (GWA) studies is to identify genetic variants contributing effects to complex phenotypes in order to improve our understanding of the biological architecture underlying the trait. One approach to allow us to meet this challenge is to consider more refined sub-phenotypes of disease, defined by pattern of symptoms, for example, which may be physiologically distinct, and thus may have different underlying genetic causes. The disadvantage of sub-phenotype analysis is that large disease cohorts are sub-divided into smaller case categories, thus reducing power to detect association. To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes. The modeling framework is extremely flexible, and can be generalized to any number of distinct sub-phenotypes. Simulations demonstrate the power of the multinomial regression-based analysis over existing methods when genetic effects differ between sub-phenotypes, with minimal loss of power when these effects are homogenous for the unified phenotype. Application of the multinomial regression analysis to a genome-wide association study of type 2 diabetes, with cases categorized according to body mass index, highlights previously recognized differential mechanisms underlying obese and non-obese forms of the disease, and provides evidence of a potential novel association that warrants follow-up in independent replication cohorts.

Genome-wide association (GWA) studies have been extremely successful in identifying novel loci contributing effects to a wide range of complex human traits. However, despite this success, the joint marginal effects of these loci account for only a small proportion of the heritability of these traits. Interactions between variants in different loci are not typically modelled in traditional GWA analysis, but may account for some of the missing heritability in humans, as they do in other model organisms. One of the key challenges in performing gene-gene interaction studies is the computational burden of the analysis. We propose a two-stage interaction analysis strategy to address this challenge in the context of both quantitative traits and dichotomous phenotypes. We have performed simulations to demonstrate only a negligible loss in power of this two-stage strategy, while minimizing the computational burden. Application of this interaction strategy to GWA studies of T2D and obesity highlights potential novel signals of association, which warrant follow-up in larger cohorts. Genet. Epidemiol. 2011.© 2011 Wiley Periodicals, Inc.35: 800-808, 2011

It is important to identify patients with Maturity-onset diabetes of the young (MODY) as a molecular diagnosis determines both treatment and prognosis. Genetic testing is currently expensive and many patients are therefore not assessed and are misclassified as having either type 1 or type 2 diabetes. Biomarkers could facilitate the prioritisation of patients for genetic testing. We hypothesised that patients with different underlying genetic aetiologies for their diabetes could have distinct metabolic profiles which may uncover novel biomarkers. The aim of this study was to perform metabolic profiling in urine from patients with MODY due to mutations in the genes encoding glucokinase (GCK) or hepatocyte nuclear factor 1 alpha (HNF1A), type 2 diabetes (T2D) and normoglycaemic control subjects. Urinary metabolic profiling by Nuclear Magnetic Resonance (NMR) and ultra performance liquid chromatography hyphenated to Q-TOF mass spectrometry (UPLC-MS) was performed in a Discovery set of subjects with HNF1A-MODY (n = 14), GCK-MODY (n = 17), T2D (n = 14) and normoglycaemic controls (n = 34). Data were used to build a valid partial least squares discriminate analysis (PLS-DA) model where HNF1A-MODY subjects could be separated from the other diabetes subtypes. No single metabolite contributed significantly to the separation of the patient groups. However, betaine, valine, glycine and glucose were elevated in the urine of HNF1A-MODY subjects compared to the other subgroups. Direct measurements of urinary amino acids and betaine in an extended dataset did not support differences between patients groups. Elevated urinary glucose in HNF1A-MODY is consistent with the previously reported low renal threshold for glucose in this genetic subtype. In conclusion, we report the first metabolic profiling study in monogenic diabetes and show that, despite the distinct biochemical pathways affected, there are unlikely to be robust urinary biomarkers which distinguish monogenic subtypes from T2D. Our results have implications for studies investigating metabolic profiles in complex traits including T2D.

Population structure, including population stratification and cryptic relatedness, can cause spurious associations in genome-wide association studies (GWAS). Usually, the scaled median or mean test statistic for association calculated from multiple single-nucleotide-polymorphisms across the genome is used to assess such effects, and ‘genomic control' can be applied subsequently to adjust test statistics at individual loci by a genomic inflation factor. Published GWAS have clearly shown that there are many loci underlying genetic variation for a wide range of complex diseases and traits, implying that a substantial proportion of the genome should show inflation of the test statistic. Here, we show by theory, simulation and analysis of data that in the absence of population structure and other technical artefacts, but in the presence of polygenic inheritance, substantial genomic inflation is expected. Its magnitude depends on sample size, heritability, linkage disequilibrium structure and the number of causal variants. Our predictions are consistent with empirical observations on height in independent samples of ∼4000 and ∼133 000 individuals.