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1.  Development and Application of Genomic Control Methods for Genome-Wide Association Studies Using Non-Additive Models 
PLoS ONE  2013;8(12):e81431.
Genome-wide association studies (GWAS) comprise a powerful tool for mapping genes of complex traits. However, an inflation of the test statistic can occur because of population substructure or cryptic relatedness, which could cause spurious associations. If information on a large number of genetic markers is available, adjusting the analysis results by using the method of genomic control (GC) is possible. GC was originally proposed to correct the Cochran-Armitage additive trend test. For non-additive models, correction has been shown to depend on allele frequencies. Therefore, usage of GC is limited to situations where allele frequencies of null markers and candidate markers are matched.
In this work, we extended the capabilities of the GC method for non-additive models, which allows us to use null markers with arbitrary allele frequencies for GC. Analytical expressions for the inflation of a test statistic describing its dependency on allele frequency and several population parameters were obtained for recessive, dominant, and over-dominant models of inheritance. We proposed a method to estimate these required population parameters. Furthermore, we suggested a GC method based on approximation of the correction coefficient by a polynomial of allele frequency and described procedures to correct the genotypic (two degrees of freedom) test for cases when the model of inheritance is unknown. Statistical properties of the described methods were investigated using simulated and real data. We demonstrated that all considered methods were effective in controlling type 1 error in the presence of genetic substructure. The proposed GC methods can be applied to statistical tests for GWAS with various models of inheritance. All methods developed and tested in this work were implemented using R language as a part of the GenABEL package.
doi:10.1371/journal.pone.0081431
PMCID: PMC3864791  PMID: 24358113
2.  Fine-Mapping the Genetic Association of the Major Histocompatibility Complex in Multiple Sclerosis: HLA and Non-HLA Effects 
PLoS Genetics  2013;9(11):e1003926.
The major histocompatibility complex (MHC) region is strongly associated with multiple sclerosis (MS) susceptibility. HLA-DRB1*15:01 has the strongest effect, and several other alleles have been reported at different levels of validation. Using SNP data from genome-wide studies, we imputed and tested classical alleles and amino acid polymorphisms in 8 classical human leukocyte antigen (HLA) genes in 5,091 cases and 9,595 controls. We identified 11 statistically independent effects overall: 6 HLA-DRB1 and one DPB1 alleles in class II, one HLA-A and two B alleles in class I, and one signal in a region spanning from MICB to LST1. This genomic segment does not contain any HLA class I or II genes and provides robust evidence for the involvement of a non-HLA risk allele within the MHC. Interestingly, this region contains the TNF gene, the cognate ligand of the well-validated TNFRSF1A MS susceptibility gene. The classical HLA effects can be explained to some extent by polymorphic amino acid positions in the peptide-binding grooves. This study dissects the independent effects in the MHC, a critical region for MS susceptibility that harbors multiple risk alleles.
Author Summary
Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease with a heritable component. Although it has been known for a long time that the strongest MS risk factor maps to the major histocompatibility complex (MHC) on chromosome 6, there are still many unresolved questions as to the identity and the nature of the risk variants within the MHC. Because the MHC has a complex structure, systematic investigation across this region has been challenging. In this study, we used state-of-the-art imputation methods coupled to statistical regression to query variants in the human leukocyte antigen (HLA) class I and II genes for a role in MS risk. Starting from available SNP genotype data, we replicated the strongest risk factor, the HLA-DRB1*15:01 allele, and were able to identify 11 independent effects in total. Functional studies are now needed to understand their mechanism in MS etiology.
doi:10.1371/journal.pgen.1003926
PMCID: PMC3836799  PMID: 24278027
3.  Identification of a Candidate Gene for Astigmatism 
Purpose.
Astigmatism is a common refractive error that reduces vision, where the curvature and refractive power of the cornea in one meridian are less than those of the perpendicular axis. It is a complex trait likely to be influenced by both genetic and environmental factors. Twin studies of astigmatism have found approximately 60% of phenotypic variance is explained by genetic factors. This study aimed to identify susceptibility loci for astigmatism.
Methods.
We performed a meta-analysis of seven genome-wide association studies that included 22,100 individuals of European descent, where astigmatism was defined as the number of diopters of cylinder prescription, using fixed effect inverse variance-weighted methods.
Results.
A susceptibility locus was identified with lead single nucleotide polymorphism rs3771395 on chromosome 2p13.3 (meta-analysis, P = 1.97 × 10−7) in the VAX2 gene. VAX2 plays an important role in the development of the dorsoventral axis of the eye. Animal studies have shown a gradient in astigmatism along the vertical plane, with corresponding changes in refraction, particularly in the ventral field.
Conclusions.
This finding advances the understanding of refractive error, and provides new potential pathways to be evaluated with regard to the development of astigmatism.
We identified a new susceptibility locus in the VAX2 gene, which is involved in the development of the ventral eye. This finding may allow new insights into astigmatism and advance the understanding of refractive error.
doi:10.1167/iovs.12-10463
PMCID: PMC3576051  PMID: 23322567
4.  Genetic influences on plasma CFH and CFHR1 concentrations and their role in susceptibility to age-related macular degeneration 
Human Molecular Genetics  2013;22(23):4857-4869.
It is a longstanding puzzle why non-coding variants in the complement factor H (CFH) gene are more strongly associated with age-related macular degeneration (AMD) than functional coding variants that directly influence the alternative complement pathway. The situation is complicated by tight genetic associations across the region, including the adjacent CFH-related genes CFHR3 and CFHR1, which may themselves influence the alternative complement pathway and are contained within a common deletion (CNP147) which is associated with protection against AMD. It is unclear whether this association is mediated through a protective effect of low plasma CFHR1 concentrations, high plasma CFH or both. We examined the triangular relationships of CFH/CFHR3/CFHR1 genotype, plasma CFH or CFHR1 concentrations and AMD susceptibility in combined case–control (1256 cases, 1020 controls) and cross-sectional population (n = 1004) studies and carried out genome-wide association studies of plasma CFH and CFHR1 concentrations. A non-coding CFH SNP (rs6677604) and the CNP147 deletion were strongly correlated both with each other and with plasma CFH and CFHR1 concentrations. The plasma CFH-raising rs6677604 allele and raised plasma CFH concentration were each associated with AMD protection. In contrast, the protective association of the CNP147 deletion with AMD was not mediated by low plasma CFHR1, since AMD-free controls showed increased plasma CFHR1 compared with cases, but it may be mediated by the association of CNP147 with raised plasma CFH concentration. The results are most consistent with a regulatory locus within a 32 kb region of the CFH gene, with a major effect on plasma CFH concentration and AMD susceptibility.
doi:10.1093/hmg/ddt336
PMCID: PMC3820139  PMID: 23873044
5.  Region-Based Association Analysis of Human Quantitative Traits in Related Individuals 
PLoS ONE  2013;8(6):e65395.
Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.
doi:10.1371/journal.pone.0065395
PMCID: PMC3684601  PMID: 23799013
6.  Deleterious Alleles in the Human Genome Are on Average Younger Than Neutral Alleles of the Same Frequency 
PLoS Genetics  2013;9(2):e1003301.
Large-scale population sequencing studies provide a complete picture of human genetic variation within the studied populations. A key challenge is to identify, among the myriad alleles, those variants that have an effect on molecular function, phenotypes, and reproductive fitness. Most non-neutral variation consists of deleterious alleles segregating at low population frequency due to incessant mutation. To date, studies characterizing selection against deleterious alleles have been based on allele frequency (testing for a relative excess of rare alleles) or ratio of polymorphism to divergence (testing for a relative increase in the number of polymorphic alleles). Here, starting from Maruyama's theoretical prediction (Maruyama T (1974), Am J Hum Genet USA 6:669–673) that a (slightly) deleterious allele is, on average, younger than a neutral allele segregating at the same frequency, we devised an approach to characterize selection based on allelic age. Unlike existing methods, it compares sets of neutral and deleterious sequence variants at the same allele frequency. When applied to human sequence data from the Genome of the Netherlands Project, our approach distinguishes low-frequency coding non-synonymous variants from synonymous and non-coding variants at the same allele frequency and discriminates between sets of variants independently predicted to be benign or damaging for protein structure and function. The results confirm the abundance of slightly deleterious coding variation in humans.
Author Summary
A key challenge in human genetics is to identify, among the multitude of genetic differences between individuals, those that have an effect on traits. Even though new genetic variants arise through mutation in each generation, most are present only in a small proportion of individuals because they have slightly negative effects on fitness. Detecting such slightly deleterious variants is a key challenge in analyzing how genetics influence human characteristics. In this paper, we test a theoretical prediction by Takeo Maruyama from 1974 that a slightly deleterious variant is, on average, younger than a neutral (non affecting fitness) variant present at the same population frequency. Thus our method detects selection by using estimated age of variants. We applied our method to human data from the Genome of the Netherlands Project, and we show that it distinguishes low-frequency protein-modifying variants from silent variants at the same population frequency and discriminates between sets of variants predicted to be benign or damaging for protein structure and function. Our results confirm the abundance of slightly deleterious protein-coding variation in humans.
doi:10.1371/journal.pgen.1003301
PMCID: PMC3585140  PMID: 23468643
7.  Large common deletions associate with mortality at old age 
Human Molecular Genetics  2011;20(21):4290-4296.
Copy-number variants (CNVs) are a source of genetic variation that increasingly are associated with human disease. However, the role of CNVs in human lifespan is to date unknown. To identify CNVs that influence mortality at old age, we analyzed genome-wide CNV data in 5178 participants of Rotterdam Study (RS1) and positive findings were evaluated in 1714 participants of the second cohort of the Rotterdam Study (RS2) and in 4550 participants of Framingham Heart Study (FHS). First, we assessed the total burden of rare (frequency <1%) and common (frequency >1%) CNVs for association with mortality during follow-up. These analyses were repeated by stratifying CNVs by type and size. Secondly, we assessed individual common CNV regions (CNVR) for association with mortality. We observed that the burden of common but not of rare CNVs influences mortality. A higher burden of large (≥500 kb) common deletions associated with 4% higher mortality [hazard ratio (HR) per CNV 1.04, 95% confidence interval (CI) 1.02–1.07, P = 5.82 × 10−5] in the 11 442 participants of RS1, RS2 and FHS. In the analysis of 312 individual common CNVRs, we identified two regions (11p15.5; 14q21.3) that associated with higher mortality in these cohorts. The 11p15.5 region (combined HR 1.59, 95% CI 1.31–1.93, P = 2.87 × 10−6) encompasses 41 genes, of which some have previously been related to longevity, whereas the 14q21.3 region (combined HR 1.57, 95% CI 1.19–2.07, P = 1.53 × 10−3) does not encompass any genes. In conclusion, the burden of large common deletions, as well as common CNVs in 11p15.5 and 14q21.3 region, associate with higher mortality.
doi:10.1093/hmg/ddr340
PMCID: PMC3188993  PMID: 21835882
8.  ASSOCIATION OF HYPERTENSION DRUG TARGET GENES WITH BLOOD PRESSURE AND HYPERTENSION IN 86,588 INDIVIDUALS 
Hypertension  2011;57(5):903-910.
We previously conducted genome-wide association meta-analysis (GWA) of systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension in 29,136 people from six cohort studies in the CHARGE Consortium. Here we examine associations of these traits with 30 gene regions encoding known anti-hypertensive drug targets. We find nominal evidence of association of ADRB1, ADRB2, AGT, CACNA1A, CACNA1C, and SLC12A3 polymorphisms with one or more BP traits in the CHARGE GWA meta-analysis. We attempted replication of the top meta-analysis SNPs for these genes in the Global BPgen Consortium (GBPG, n=34,433) and the Women’s Genome Health Study (WGHS, n=23,019), and found significant results for rs1801253 in ADRB1 (Arg389Gly), with the Gly allele associated with a lower mean SBP (beta −0.57 (mmHg), se 0.09, meta-analysis P=4.7×10−10), DBP (beta −0.36, se 0.06, meta-analysis P=9.5×10−10) and prevalence of hypertension (beta −0.06, se 0.02, meta-analysis P=3.3×10−4). Variation in AGT (rs2004776) was associated with SBP (beta 0.42, se 0.09, meta-analysis P=3.8×10−6), as well as DBP (P=5.0×10−8) and hypertension (P=3.7×10−7). A polymorphism in ACE (rs4305) showed modest replication of association with increased hypertension (beta 0.06, se 0.01, meta-analysis P=3.0×10−5). Two loci, ADRB1 and AGT, contain SNPs that reached a genome-wide significance threshold in meta-analysis for the first time. Our findings suggest that these genes warrant further studies of their genetic effects on BP, including pharmacogenetic interactions.
doi:10.1161/HYPERTENSIONAHA.110.158667
PMCID: PMC3099407  PMID: 21444836
drug target; genome-wide; SNP; hypertension; blood pressure
9.  Heritability of dietary food intake patterns 
Acta Diabetologica  2012;50:721-726.
The quality and quantity of food intake affect body weight, but little is known about the genetics of such human dietary intake patterns in relation to the genetics of BMI. We aimed to estimate the heritability of dietary intake patterns and genetic correlation with BMI in participants of the Erasmus Rucphen Family study. The study included 1,690 individuals (42 % men; age range, 19–92), of whom 41.4 % were overweight and 15.9 % were obese. Self-report questionnaires were used to assess the number of days (0–7) on which participants consumed vegetables, fruit, fruit juice, fish, unhealthy snacks, fastfood, and soft drinks. Principal component analysis was applied to examine the correlations between the questionnaire items and to generate dietary intake pattern scores. Heritability and the shared genetic and shared non-genetic (environmental) correlations were estimated using the family structure of the cohort. Principal component analysis suggested that the questionnaire items could be grouped in a healthy and unhealthy dietary intake pattern, explaining 22 and 18 % of the phenotypic variance, respectively. The dietary intake patterns had a heritability of 0.32 for the healthy and 0.27 for the unhealthy pattern. Genetic correlations between the dietary intake patterns and BMI were not significant, but we found a significant environmental correlation between the unhealthy dietary intake pattern and BMI. Specific dietary intake patterns are associated with the risk of obesity and are heritable traits. The genetic factors that determine specific dietary intake patterns do not significantly overlap with the genetic factors that determine BMI.
Electronic supplementary material
The online version of this article (doi:10.1007/s00592-012-0387-0) contains supplementary material, which is available to authorized users.
doi:10.1007/s00592-012-0387-0
PMCID: PMC3898132  PMID: 22415036
Heritability; BMI; Food intake
10.  Interactions between PPAR-α and inflammation-related cytokine genes on the development of Alzheimer’s disease, observed by the Epistasis Project 
Objective
Neuroinflammation contributes to the pathogenesis of sporadic Alzheimer’s disease (AD). Variations in genes relevant to inflammation may be candidate genes for AD risk. Whole-genome association studies have identified relevant new and known genes. Their combined effects do not explain 100% of the risk, genetic interactions may contribute. We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1β, IL-6, and IL-10 may interact to increase AD risk.
Methods
The Epistasis Project identifies interactions that affect the risk of AD. Genotyping of single nucleotide polymorphisms (SNPs) in PPARA, IL1A, IL1B, IL6 and IL10 was performed. Possible associations were analyzed by fitting logistic regression models with AD as outcome, controlling for centre, age, sex and presence of apolipoprotein ε4 allele (APOEε4). Adjusted synergy factors were derived from interaction terms (p<0.05 two-sided).
Results
We observed four significant interactions between different SNPs in PPARA and in interleukins IL1A, IL1B, IL10 that may affect AD risk. There were no significant interactions between PPARA and IL6.
Conclusions
In addition to an association of the PPARA L162V polymorphism with the AD risk, we observed four significant interactions between SNPs in PPARA and SNPs in IL1A, IL1B and IL10 affecting AD risk. We prove that gene-gene interactions explain part of the heritability of AD and are to be considered when assessing the genetic risk. Necessary replications will require between 1450 and 2950 of both cases and controls, depending on the prevalence of the SNP, to have 80% power to detect the observed synergy factors.
PMCID: PMC3316448  PMID: 22493750
AD; genetics; epistasis; inflammation; interleukin; steroid receptors; PPAR-alpha; sporadic; genetic interaction
11.  Genome-Wide Association Study Identifies Novel Loci Associated with Circulating Phospho- and Sphingolipid Concentrations 
PLoS Genetics  2012;8(2):e1002490.
Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10−204) and 10 loci for sphingolipids (smallest P-value = 3.10×10−57). After a correction for multiple comparisons (P-value<2.2×10−9), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.
Author Summary
Phospho- and sphingolipids are integral to membrane formation and are involved in crucial cellular functions such as signalling, membrane fluidity, membrane protein trafficking, neurotransmission, and receptor trafficking. In addition to severe monogenic diseases resulting from defective phospho- and sphingolipid function and metabolism, the evidence suggests that variations in these lipid levels at the population level are involved in the determination of cardiovascular and neurologic traits and subsequent disease. We took advantage of modern laboratory methods, including microarray-based genotyping and electrospray ionization tandem mass spectrometry, to hunt for genetic variation influencing the levels of more than 350 phospho- and sphingolipid phenotypes. We identified nine novel loci, in addition to confirming a number of previously described loci. Several other genetic regions provided substantial evidence of their involvement in these traits. All of these loci are strong candidates for further research in the field of lipid biology and are likely to yield considerable insights into the complex metabolic pathways underlying circulating phospho- and sphingolipid levels. Understanding these mechanisms might help to illuminate factors leading to the development of common cardiovascular and neurological diseases and might provide molecular targets for the development of new therapies.
doi:10.1371/journal.pgen.1002490
PMCID: PMC3280968  PMID: 22359512
12.  An R package "VariABEL" for genome-wide searching of potentially interacting loci by testing genotypic variance heterogeneity 
BMC Genetics  2012;13:4.
Background
Hundreds of new loci have been discovered by genome-wide association studies of human traits. These studies mostly focused on associations between single locus and a trait. Interactions between genes and between genes and environmental factors are of interest as they can improve our understanding of the genetic background underlying complex traits. Genome-wide testing of complex genetic models is a computationally demanding task. Moreover, testing of such models leads to multiple comparison problems that reduce the probability of new findings. Assuming that the genetic model underlying a complex trait can include hundreds of genes and environmental factors, testing of these models in genome-wide association studies represent substantial difficulties.
We and Pare with colleagues (2010) developed a method allowing to overcome such difficulties. The method is based on the fact that loci which are involved in interactions can show genotypic variance heterogeneity of a trait. Genome-wide testing of such heterogeneity can be a fast scanning approach which can point to the interacting genetic variants.
Results
In this work we present a new method, SVLM, allowing for variance heterogeneity analysis of imputed genetic variation. Type I error and power of this test are investigated and contracted with these of the Levene's test. We also present an R package, VariABEL, implementing existing and newly developed tests.
Conclusions
Variance heterogeneity analysis is a promising method for detection of potentially interacting loci. New method and software package developed in this work will facilitate such analysis in genome-wide context.
doi:10.1186/1471-2156-13-4
PMCID: PMC3398297  PMID: 22272569
single-nucleotide polymorphisms (SNPs); genome-wide association (GWA); gene-environment interactions (GxE); gene-gene interactions (GxG); variance heterogeneity; environmental sensitivity; VariABEL; the GenABEL project
13.  Multicenter cohort association study of SLC2A1 single nucleotide polymorphisms and age-related macular degeneration 
Molecular Vision  2012;18:657-674.
Purpose
Age-related macular degeneration (AMD) is a major cause of blindness in older adults and has a genetically complex background. This study examines the potential association between single nucleotide polymorphisms (SNPs) in the glucose transporter 1 (SLC2A1) gene and AMD. SLC2A1 regulates the bioavailability of glucose in the retinal pigment epithelium (RPE), which might influence oxidative stress–mediated AMD pathology.
Methods
Twenty-two SNPs spanning the SLC2A1 gene were genotyped in 375 cases and 199 controls from an initial discovery cohort (the Amsterdam-Rotterdam-Netherlands study). Replication testing was performed in The Rotterdam Study (the Netherlands) and study populations from Würzburg (Germany), the Age Related Eye Disease Study (AREDS; United States), Columbia University (United States), and Iowa University (United States). Subsequently, a meta-analysis of SNP association was performed.
Results
In the discovery cohort, significant genotypic association between three SNPs (rs3754219, rs4660687, and rs841853) and AMD was found. Replication in five large independent (Caucasian) cohorts (4,860 cases and 4,004 controls) did not yield consistent association results. The genotype frequencies for these SNPs were significantly different for the controls and/or cases among the six individual populations. Meta-analysis revealed significant heterogeneity of effect between the studies.
Conclusions
No overall association between SLC2A1 SNPs and AMD was demonstrated. Since the genotype frequencies for the three SLC2A1 SNPs were significantly different for the controls and/or cases between the six cohorts, this study corroborates previous evidence that population dependent genetic risk heterogeneity in AMD exists.
PMCID: PMC3324365  PMID: 22509097
14.  Perspectives on the Use of Multiple Sclerosis Risk Genes for Prediction 
PLoS ONE  2011;6(12):e26493.
Objective
A recent collaborative genome-wide association study replicated a large number of susceptibility loci and identified novel loci. This increase in known multiple sclerosis (MS) risk genes raises questions about clinical applicability of genotyping. In an empirical set we assessed the predictive power of typing multiple genes. Next, in a modelling study we explored current and potential predictive performance of genetic MS risk models.
Materials and Methods
Genotype data on 6 MS risk genes in 591 MS patients and 600 controls were used to investigate the predictive value of combining risk alleles. Next, the replicated and novel MS risk loci from the recent and largest international genome-wide association study were used to construct genetic risk models simulating a population of 100,000 individuals. Finally, we assessed the required numbers, frequencies, and ORs of risk SNPs for higher discriminative accuracy in the future.
Results
Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 2.76 (95% CI 2.02–3.77)). In the simulation study we showed that the area under the receiver operating characteristic curve (AUC) for a risk score based on the 6 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 1.1 would be needed to increase the AUC to a slightly higher level of 0.70, and at least 50 novel variants with allele frequency 0.30 and ORs 1.4 would be needed to obtain an AUC of 0.85.
Conclusion
Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.
doi:10.1371/journal.pone.0026493
PMCID: PMC3229479  PMID: 22164203
16.  STROBE-ME too! 
European Journal of Epidemiology  2011;26(10):761-762.
doi:10.1007/s10654-011-9628-8
PMCID: PMC3218276  PMID: 22076058
17.  Genes predict village of origin in rural Europe 
European Journal of Human Genetics  2010;18(11):1269-1270.
The genetic structure of human populations is important in population genetics, forensics and medicine. Using genome-wide scans and individuals with all four grandparents born in the same settlement, we here demonstrate remarkable geographical structure across 8–30 km in three different parts of rural Europe. After excluding close kin and inbreeding, village of origin could still be predicted correctly on the basis of genetic data for 89–100% of individuals.
doi:10.1038/ejhg.2010.92
PMCID: PMC2987479  PMID: 20571506
population structure; principal components; genome-wide genotyping
18.  A Methodological Perspective on Genetic Risk Prediction Studies in Type 2 Diabetes: Recommendations for Future Research 
Current Diabetes Reports  2011;11(6):511-518.
Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions.
doi:10.1007/s11892-011-0235-6
PMCID: PMC3207129  PMID: 21947855
Genetic predisposition; Risk prediction; Type 2 diabetes; Public health; Risk factors; Prevention
19.  The Rotterdam Study: 2012 objectives and design update 
European Journal of Epidemiology  2011;26(8):657-686.
The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.
doi:10.1007/s10654-011-9610-5
PMCID: PMC3168750  PMID: 21877163
Biomarkers; Cardiovascular diseases; Cohort study; Dermatological diseases; Endocrine diseases; Epidemiologic methods; Genetic epidemiology; Liver diseases; Neurological diseases; Oncology; Ophthalmic diseases; Pharmacoepidemiology; Renal diseases; Psychiatric diseases; Respiratory diseases
20.  Strengthening the reporting of genetic risk prediction studies: the GRIPS statement 
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies, building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published on the EJHG website.
doi:10.1038/ejhg.2011.25
PMCID: PMC3172920  PMID: 21407265
21.  Runs of Homozygosity Do Not Influence Survival to Old Age 
PLoS ONE  2011;6(7):e22580.
Runs of homozygosity (ROH) are extended tracts of adjacent homozygous single nucleotide polymorphisms (SNPs) that are more common in unrelated individuals than previously thought. It has been proposed that estimating ROH on a genome-wide level, by making use of the genome-wide single nucleotide polymorphism (SNP) data, will enable to indentify recessive variants underlying complex traits. Here, we examined ROH larger than 1.5 Mb individually and in combination for association with survival in 5974 participants of the Rotterdam Study. In addition, we assessed the role of overall homozygosity, expressed as a percentage of the autosomal genome that is in ROH longer than 1.5 Mb, on survival during a mean follow-up period of 12 years. None of these measures of homozygosity was associated with survival to old age.
doi:10.1371/journal.pone.0022580
PMCID: PMC3143169  PMID: 21799906
22.  A polymorphism in the regulatory region of PRNP is associated with increased risk of sporadic Creutzfeldt-Jakob disease 
BMC Medical Genetics  2011;12:73.
Background
Creutzfeldt-Jakob disease (CJD) is a rare transmissible neurodegenerative disorder. An important determinant for CJD risk and phenotype is the M129V polymorphism of the human prion protein gene (PRNP), but there are also other coding and non-coding polymorphisms inside this gene.
Methods
We tested whether three non-coding polymorphism located inside the PRNP regulatory region (C-101G, G310C and T385C) were associated with risk of CJD and with age at onset in a United Kingdom population-based sample of 131 sporadic CJD (sCJD) patients and 194 controls.
Results
We found no disease association for either PRNP C-101G or PRNP T385C. Although the crude analysis did not show a significant association between PRNP G310C and sCJD (OR: 1.5; 95%CI = 0.7 to 2.9), after adjusting by PRNP M129V genotype, it resulted that being a C allele carrier at PRNP G310C was significantly (p = 0.03) associated with a 2.4 fold increased risk of developing sCJD (95%CI = 1.1 to 5.4). Additionally, haplotypes carrying PRNP 310C coupled with PRNP 129M were significantly overrepresented in patients (p = 0.02) compared to controls. Cases of sCJD carrying a PRNP 310C allele presented at a younger age (on average 8.9 years younger than those without this allele), which was of statistical significance (p = 0.05). As expected, methionine and valine homozygosity at PRNP M129V increased significantly the risk of sCJD, alone and adjusted by PRNP G310C (OR MM/MV = 7.3; 95%CI 3.9 to 13.5 and OR VV/MV = 4.0; 95%CI 1.7 to 9.3).
Conclusions
Our findings support the hypothesis that genetic variations in the PRNP promoter may have a role in the pathogenesis of sCJD.
doi:10.1186/1471-2350-12-73
PMCID: PMC3114709  PMID: 21600043
Creutzfeldt-Jakob disease; prion protein gene; molecular subtype; regulatory region; early onset
23.  Genetic Architecture of Plasma Adiponectin Overlaps With the Genetics of Metabolic Syndrome–Related Traits 
Diabetes Care  2010;33(4):908-913.
OBJECTIVE
Adiponectin, a hormone secreted by adipose tissue, is of particular interest in metabolic syndrome, because it is inversely correlated with obesity and insulin sensitivity. However, it is not known to what extent the genetics of plasma adiponectin and the genetics of obesity and insulin sensitivity are interrelated. We aimed to evaluate the heritability of plasma adiponectin and its genetic correlation with the metabolic syndrome and metabolic syndrome–related traits and the association between these traits and 10 ADIPOQ single nucleotide polymorphisms (SNPs).
RESEARCH DESIGN AND METHODS
We made use of a family-based population, the Erasmus Rucphen Family study (1,258 women and 967 men). Heritability analysis was performed using a polygenic model. Genetic correlations were estimated using bivariate heritability analyses. Genetic association analysis was performed using a mixed model.
RESULTS
Plasma adiponectin showed a heritability of 55.1%. Genetic correlations between plasma adiponectin HDL cholesterol and plasma insulin ranged from 15 to 24% but were not significant for fasting glucose, triglycerides, blood pressure, homeostasis model assessment of insulin resistance (HOMA-IR), and C-reactive protein. A significant association with plasma adiponectin was found for ADIPOQ variants rs17300539 and rs182052. A nominally significant association was found with plasma insulin and HOMA-IR and ADIPOQ variant rs17300539 after adjustment for plasma adiponectin.
CONCLUSIONS
The significant genetic correlation between plasma adiponectin and HDL cholesterol and plasma insulin should be taken into account in the interpretation of genome-wide association studies. Association of ADIPOQ SNPs with plasma adiponectin was replicated, and we showed association between one ADIPOQ SNP and plasma insulin and HOMA-IR.
doi:10.2337/dc09-1385
PMCID: PMC2845050  PMID: 20067957
24.  PredictABEL: an R package for the assessment of risk prediction models 
European Journal of Epidemiology  2011;26(4):261-264.
The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL (http://www.genabel.org) and CRAN (http://cran.r-project.org/).
doi:10.1007/s10654-011-9567-4
PMCID: PMC3088798  PMID: 21431839
Risk prediction; Genetic; Assessment; Measures; Software
25.  Strengthening the reporting of genetic risk prediction studies: the GRIPS statement 
European Journal of Epidemiology  2011;26(4):255-259.
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published.
doi:10.1007/s10654-011-9552-y
PMCID: PMC3088799  PMID: 21431409
Genetic; Risk prediction; Methodology; Guidelines; Reporting

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