Estimating Genome‐Wide Significance for Whole‐Genome Sequencing Studies

Although a standard genome‐wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole‐genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence‐identified variants is very different from common variants, and the identified rare genetic variation is usually jointly analyzed in a series of genomic windows or regions. In nearby or overlapping windows, these test statistics will be correlated, and the degree of correlation is likely to depend on the choice of window size, overlap, and the test statistic. Furthermore, multiple analyses may be performed using different windows or test statistics. Here we propose an empirical approach for estimating genome‐wide significance thresholds for data arising from WGS studies, and we demonstrate that the empirical threshold can be efficiently estimated by extrapolating from calculations performed on a small genomic region. Because analysis of WGS may need to be repeated with different choices of test statistics or windows, this prediction approach makes it computationally feasible to estimate genome‐wide significance thresholds for different analysis choices. Based on UK10K whole‐genome sequence data, we derive genome‐wide significance thresholds ranging between 2.5 × 10^?8 and 8 × 10^?8 for our analytic choices in window‐based testing, and thresholds of 0.6 × 10^?8–1.5 × 10^?8 for a combined analytic strategy of testing common variants using single‐SNP tests together with rare variants analyzed with our sliding‐window test strategy.     

Evidence for SNP‐SNP interaction identified through targeted sequencing of cleft case‐parent trios

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is the most common craniofacial birth defect in humans, affecting 1 in 700 live births. This malformation has a complex etiology where multiple genes and several environmental factors influence risk. At least a dozen different genes have been confirmed to be associated with risk of NSCL/P in previous studies. However, all the known genetic risk factors cannot fully explain the observed heritability of NSCL/P, and several authors have suggested gene‐gene (G × G) interaction may be important in the etiology of this complex and heterogeneous malformation. We tested for G × G interactions using common single nucleotide polymorphic (SNP) markers from targeted sequencing in 13 regions identified by previous studies spanning 6.3 Mb of the genome in a study of 1,498 NSCL/P case‐parent trios. We used the R‐package trio to assess interactions between polymorphic markers in different genes, using a 1 degree of freedom (1df) test for screening, and a 4 degree of freedom (4df) test to assess statistical significance of epistatic interactions. To adjust for multiple comparisons, we performed permutation tests. The most significant interaction was observed between rs6029315 in MAFB and rs6681355 in IRF6 (4df P = 3.8 × 10^?8) in case‐parent trios of European ancestry, which remained significant after correcting for multiple comparisons. However, no significant interaction was detected in trios of Asian ancestry.     

Methods for meta‐analysis of multiple traits using GWAS summary statistics

Genome‐wide association studies (GWAS) for complex diseases have focused primarily on single‐trait analyses for disease status and disease‐related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL‐cholesterol, HDL‐cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses. Recently several multivariate methods have been proposed that require individual‐level data. Here, we develop metaUSAT (where USAT is unified score‐based association test), a novel unified association test of a single genetic variant with multiple traits that uses only summary statistics from existing GWAS. Although the existing methods either perform well when most correlated traits are affected by the genetic variant in the same direction or are powerful when only a few of the correlated traits are associated, metaUSAT is designed to be robust to the association structure of correlated traits. metaUSAT does not require individual‐level data and can test genetic associations of categorical and/or continuous traits. One can also use metaUSAT to analyze a single trait over multiple studies, appropriately accounting for overlapping samples, if any. metaUSAT provides an approximate asymptotic P‐value for association and is computationally efficient for implementation at a genome‐wide level. Simulation experiments show that metaUSAT maintains proper type‐I error at low error levels. It has similar and sometimes greater power to detect association across a wide array of scenarios compared to existing methods, which are usually powerful for some specific association scenarios only. When applied to plasma lipids summary data from the METSIM and the T2D‐GENES studies, metaUSAT detected genome‐wide significant loci beyond the ones identified by univariate analyses. Evidence from larger studies suggest that the variants additionally detected by our test are, indeed, associated with lipid levels in humans. In summary, metaUSAT can provide novel insights into the genetic architecture of a common disease or traits.     

A Genome‐Wide Association Study for Serum Bilirubin Levels and Gene‐Environment Interaction in a Chinese Population

Bilirubin is an effective antioxidant and is influenced by both genetic and environmental factors. Recent genome‐wide association studies (GWAS) have identified multiple loci affecting serum total bilirubin levels. However, most of the studies were conducted in European populations and little attention has been devoted either to genetic variants associated with direct and indirect bilirubin levels or to the gene‐environment interactions on bilirubin levels. In this study, a two‐stage GWAS was performed to identify genetic variants associated with all types of bilirubin levels in 10,282 Han Chinese individuals. Gene‐environment interactions were further examined. Briefly, two previously reported loci, UGT1A1 on 2q37 (rs6742078 and rs4148323, combined P = 1.44 × 10^?89 and P = 5.05 × 10^?69, respectively) and SLCO1B3 on 12p12 (rs2417940, combined P = 6.93 × 10^?19) were successfully replicated. The two loci explained 9.2% and 0.9% of the total variations of total bilirubin levels, respectively. Ethnic genetic differences were observed between Chinese and European populations. More importantly, a significant interaction was found between rs2417940 in SLCO1B3 gene and smoking on total bilirubin levels (P = 1.99 × 10^?3). Single nucleotide polymorphism (SNP) rs2417940 had stronger effects on total bilirubin levels in nonsmokers than in smokers, suggesting that the effects of SLCO1B3 genotype on bilirubin levels were partly dependent on smoking status. Consistent associations and interactions were observed for serum direct and indirect bilirubin levels.     

Leveraging cell type specific regulatory regions to detect SNPs associated with tissue factor pathway inhibitor plasma levels

Tissue factor pathway inhibitor (TFPI) regulates the formation of intravascular blood clots, which manifest clinically as ischemic heart disease, ischemic stroke, and venous thromboembolism (VTE). TFPI plasma levels are heritable, but the genetics underlying TFPI plasma level variability are poorly understood. Herein we report the first genome‐wide association scan (GWAS) of TFPI plasma levels, conducted in 251 individuals from five extended French‐Canadian Families ascertained on VTE. To improve discovery, we also applied a hypothesis‐driven (HD) GWAS approach that prioritized single nucleotide polymorphisms (SNPs) in (1) hemostasis pathway genes, and (2) vascular endothelial cell (EC) regulatory regions, which are among the highest expressers of TFPI . Our GWAS identified 131 SNPs with suggestive evidence of association (P‐value < 5 × 10^?8), but no SNPs reached the genome‐wide threshold for statistical significance. Hemostasis pathway genes were not enriched for TFPI plasma level associated SNPs (global hypothesis test P‐value = 0.147), but EC regulatory regions contained more TFPI plasma level associated SNPs than expected by chance (global hypothesis test P‐value = 0.046). We therefore stratified our genome‐wide SNPs, prioritizing those in EC regulatory regions via stratified false discovery rate (sFDR) control, and reranked the SNPs by q‐value. The minimum q‐value was 0.27, and the top‐ranked SNPs did not show association evidence in the MARTHA replication sample of 1,033 unrelated VTE cases. Although this study did not result in new loci for TFPI, our work lays out a strategy to utilize epigenomic data in prioritization schemes for future GWAS studies.     

Informed Genome‐Wide Association Analysis With Family History As a Secondary Phenotype Identifies Novel Loci of Lung Cancer

Lung cancer is the leading cause of cancer death worldwide. Although several genetic variants associated with lung cancer have been identified in the past, stringent selection criteria of genome‐wide association studies (GWAS) can lead to missed variants. The objective of this study was to uncover missed variants by using the known association between lung cancer and first‐degree family history of lung cancer to enrich the variant prioritization for lung cancer susceptibility regions. In this two‐stage GWAS study, we first selected a list of variants associated with both lung cancer and family history of lung cancer in four GWAS (3,953 cases, 4,730 controls), then replicated our findings for 30 variants in a meta‐analysis of four additional studies (7,510 cases, 7,476 controls). The top ranked genetic variant rs12415204 in chr10q23.33 encoding FFAR4 in the Discovery set was validated in the Replication set with an overall OR of 1.09 (95% CI = 1.04, 1.14, P = 1.63 × 10^?4). When combining the two stages of the study, the strongest association was found in rs1158970 at Ch4p15.2 encoding KCNIP4 with an OR of 0.89 (95% CI = 0.85, 0.94, P = 9.64 × 10^?6). We performed a stratified analysis of rs12415204 and rs1158970 across all eight studies by age, gender, smoking status, and histology, and found consistent results across strata. Four of the 30 replicated variants act as expression quantitative trait loci (eQTL) sites in 1,111 nontumor lung tissues and meet the genome‐wide 10% FDR threshold.     

A Fast and Noise‐Resilient Approach to Detect Rare‐Variant Associations With Deep Sequencing Data for Complex Disorders

Next generation sequencing technology has enabled the paradigm shift in genetic association studies from the common disease/common variant to common disease/rare‐variant hypothesis. Analyzing individual rare variants is known to be underpowered; therefore association methods have been developed that aggregate variants across a genetic region, which for exome sequencing is usually a gene. The foreseeable widespread use of whole genome sequencing poses new challenges in statistical analysis. It calls for new rare‐variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of noncausal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease‐associated P‐values of individual variants using a weight that is the inverse of the expected standard deviation of the allele frequencies under the null. This approach, dubbed as Sigma‐P method, is extremely robust to the inclusion of a high proportion of noncausal variants and is also powerful when both detrimental and protective variants are present within a genetic region. The performance of the Sigma‐P method was tested using simulated data based on realistic population demographic and disease models and its power was compared to several previously published methods. The results demonstrate that this method generally outperforms other rare‐variant association methods over a wide range of models. Additionally, sequence data on the ANGPTL family of genes from the Dallas Heart Study were tested for associations with nine metabolic traits and both known and novel putative associations were uncovered using the Sigma‐P method.     

Trees Assembling Mann‐Whitney Approach for Detecting Genome‐Wide Joint Association Among Low‐Marginal‐Effect Loci

Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low marginal effects (LMEs) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and assessing their joint association on high‐dimensional data (e.g., genome‐wide data) remain a great challenge. To facilitate joint association analysis among a large ensemble of LME genetic variants, we proposed a computationally efficient and powerful approach, which we call Trees Assembling Mann‐Whitney (TAMW). Through simulation studies and an empirical data application, we found that TAMW outperformed multifactor dimensionality reduction (MDR) and the likelihood ratio‐based Mann‐Whitney approach (LRMW) when the underlying complex disease involves multiple LME loci and their interactions. For instance, in a simulation with 20 interacting LME loci, TAMW attained a higher power (power = 0.931) than both MDR (power = 0.599) and LRMW (power = 0.704). In an empirical study of 29 known Crohn's disease (CD) loci, TAMW also identified a stronger joint association with CD than those detected by MDR and LRMW. Finally, we applied TAMW to Wellcome Trust CD GWAS to conduct a genome‐wide analysis. The analysis of 459K single nucleotide polymorphisms was completed in 40 hrs using parallel computing, and revealed a joint association predisposing to CD (P‐value = 2.763 × 10^?19). Further analysis of the newly discovered association suggested that 13 genes, such as ATG16L1 and LACC1, may play an important role in CD pathophysiological and etiological processes.     

A novel haplotype‐sharing approach for genome‐wide case‐control association studies implicates the calpastatin gene in Parkinson's disease

The large number of markers considered in a genome‐wide association study (GWAS) has resulted in a simplification of analyses conducted. Most studies are analyzed one marker at a time using simple tests like the trend test. Methods that account for the special features of genetic association studies, yet remain computationally feasible for genome‐wide analysis, are desirable as they may lead to increased power to detect associations. Haplotype sharing attempts to translate between population genetics and genetic epidemiology. Near a recent mutation that increases disease risk, haplotypes of case participants should be more similar to each other than haplotypes of control participants; conversely, the opposite pattern may be found near a recent mutation that lowers disease risk. We give computationally simple association tests based on haplotype sharing that can be easily applied to GWASs while allowing use of fast (but not likelihood‐based) haplotyping algorithms and properly accounting for the uncertainty introduced by using inferred haplotypes. We also give haplotype‐sharing analyses that adjust for population stratification. Applying our methods to a GWAS of Parkinson's disease, we find a genome‐wide significant signal in the CAST gene that is not found by single‐SNP methods. Further, a missing‐data artifact that causes a spurious single‐SNP association on chromosome 9 does not impact our test. Genet. Epidemiol. 33:657–667, 2009. Published 2009 Wiley‐Liss, Inc.     

Avoiding the high Bonferroni penalty in genome‐wide association studies

A major challenge in genome‐wide association studies (GWASs) is to derive the multiple testing threshold when hypothesis tests are conducted using a large number of single nucleotide polymorphisms. Permutation tests are considered the gold standard in multiple testing adjustment in genetic association studies. However, it is computationally intensive, especially for GWASs, and can be impractical if a large number of random shuffles are used to ensure accuracy. Many researchers have developed approximation algorithms to relieve the computing burden imposed by permutation. One particularly attractive alternative to permutation is to calculate the effective number of independent tests, M_eff, which has been shown to be promising in genetic association studies. In this study, we compare recently developed M_eff methods and validate them by the permutation test with 10,000 random shuffles using two real GWAS data sets: an Illumina 1M BeadChip and an Affymetrix GeneChip^® Human Mapping 500K Array Set. Our results show that the simpleM method produces the best approximation of the permutation threshold, and it does so in the shortest amount of time. We also show that M_eff is indeed valid on a genome‐wide scale in these data sets based on statistical theory and significance tests. The significance thresholds derived can provide practical guidelines for other studies using similar population samples and genotyping platforms. Genet. Epidemiol. 34:100–105, 2010. © 2009 Wiley‐Liss, Inc.     

A genome‐wide linkage and association analysis of imputed insertions and deletions with cardiometabolic phenotypes in Mexican Americans: The Insulin Resistance Atherosclerosis Family Study

Insertions and deletions (INDELs) represent a significant fraction of interindividual variation in the human genome yet their contribution to phenotypes is poorly understood. To confirm the quality of imputed INDELs and investigate their roles in mediating cardiometabolic phenotypes, genome‐wide association and linkage analyses were performed for 15 phenotypes with 1,273,952 imputed INDELs in 1,024 Mexican‐origin Americans. Imputation quality was validated using whole exome sequencing with an average kappa of 0.93 in common INDELs (minor allele frequencies [MAFs] ≥ 5%). Association analysis revealed one genome‐wide significant association signal for the cholesterylester transfer protein gene (CETP ) with high‐density lipoprotein levels (rs36229491, P = 3.06 × 10^?12); linkage analysis identified two peaks with logarithm of the odds (LOD) > 5 (rs60560566, LOD = 5.36 with insulin sensitivity (S _I) and rs5825825, LOD = 5.11 with adiponectin levels). Suggestive overlapping signals between linkage and association were observed: rs59849892 in the WSC domain containing 2 gene (WSCD2 ) was associated and nominally linked with S _I (P = 1.17 × 10^?7, LOD = 1.99). This gene has been implicated in glucose metabolism in human islet cell expression studies. In addition, rs201606363 was linked and nominally associated with low‐density lipoprotein (P = 4.73 × 10^?4, LOD = 3.67), apolipoprotein B (P = 1.39 × 10^?3, LOD = 4.64), and total cholesterol (P = 1.35 × 10^?2, LOD = 3.80) levels. rs201606363 is an intronic variant of the UBE2F‐SCLY (where UBE2F is ubiquitin‐conjugating enzyme E2F and SCLY is selenocysteine lyase) fusion gene that may regulate cholesterol through selenium metabolism. In conclusion, these results confirm the feasibility of imputing INDELs from array‐based single nucleotide polymorphism (SNP) genotypes. Analysis of these variants using association and linkage replicated previously identified SNP signals and identified multiple novel INDEL signals. These results support the inclusion of INDELs into genetic studies to more fully interrogate the spectrum of genetic variation.     

Genome‐Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases

Genome‐wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome‐wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome‐wide genotype data up to high‐density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re‐sequencing experiments. By application of this approach to genome‐wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome‐wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome‐wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.     

Efficient Generalized Least Squares Method for Mixed Population and Family‐based Samples in Genome‐wide Association Studies

Genome‐wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed‐sample data, but few methods have been proposed for the analysis of genotype‐by‐environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family‐based designs. We propose a novel method—generalized least squares (GLX)—to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran–Armitage trend test, and the W_QLS and M_QLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome‐wide interaction with insecticide exposure—rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.     

The Impact of Improved Microarray Coverage and Larger Sample Sizes on Future Genome‐Wide Association Studies

Genome‐wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) associated with complex traits. However, the genetic heritability of most of these traits remains unexplained. To help guide future studies, we address the crucial question of whether future GWAS can detect new SNP associations and explain additional heritability given the new availability of larger GWAS SNP arrays, imputation, and reduced genotyping costs. We first describe the pairwise and imputation coverage of all SNPs in the human genome by commercially available GWAS SNP arrays, using the 1000 Genomes Project as a reference. Next, we describe the findings from 6 years of GWAS of 172 chronic diseases, calculating the power to detect each of them while taking array coverage and sample size into account. We then calculate the power to detect these SNP associations under different conditions using improved coverage and/or sample sizes. Finally, we estimate the percentages of SNP associations and heritability previously detected and detectable by future GWAS under each condition. Overall, we estimated that previous GWAS have detected less than one‐fifth of all GWAS‐detectable SNPs underlying chronic disease. Furthermore, increasing sample size has a much larger impact than increasing coverage on the potential of future GWAS to detect additional SNP‐disease associations and heritability.     

A genetic stochastic process model for genome‐wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post‐GWAS (where GWAS is genome‐wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene‐environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow‐up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10^?7). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10^?7). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.     

Genome‐Wide Association Study of a Heart Failure Related Metabolomic Profile Among African Americans in the Atherosclerosis Risk in Communities (ARIC) Study

Both the prevalence and incidence of heart failure (HF) are increasing, especially among African Americans, but no large‐scale, genome‐wide association study (GWAS) of HF‐related metabolites has been reported. We sought to identify novel genetic variants that are associated with metabolites previously reported to relate to HF incidence. GWASs of three metabolites identified previously as risk factors for incident HF (pyroglutamine, dihydroxy docosatrienoic acid, and X‐11787, being either hydroxy‐leucine or hydroxy‐isoleucine) were performed in 1,260 African Americans free of HF at the baseline examination of the Atherosclerosis Risk in Communities (ARIC) study. A significant association on chromosome 5q33 (rs10463316, MAF = 0.358, P‐value = 1.92 × 10^?10) was identified for pyroglutamine. One region on chromosome 2p13 contained a nonsynonymous substitution in N‐acetyltransferase 8 (NAT8) was associated with X‐11787 (rs13538, MAF = 0.481, P‐value = 1.71 × 10^?23). The smallest P‐value for dihydroxy docosatrienoic acid was rs4006531 on chromosome 8q24 (MAF = 0.400, P‐value = 6.98 × 10^?7). None of the above SNPs were individually associated with incident HF, but a genetic risk score (GRS) created by summing the most significant risk alleles from each metabolite detected 11% greater risk of HF per allele. In summary, we identified three loci associated with previously reported HF‐related metabolites. Further use of metabolomics technology will facilitate replication of these findings in independent samples.     

Risk prediction using genome‐wide association studies

Over the last few years, many new genetic associations have been identified by genome‐wide association studies (GWAS). There are potentially many uses of these identified variants: a better understanding of disease etiology, personalized medicine, new leads for studying underlying biology, and risk prediction. Recently, there has been some skepticism regarding the prospects of risk prediction using GWAS, primarily motivated by the fact that individual effect sizes of variants associated with the phenotype are mostly small. However, there have also been arguments that many disease‐associated variants have not yet been identified; hence, prospects for risk prediction may improve if more variants are included. From a risk prediction perspective, it is reasonable to average a larger number of predictors, of which some may have (limited) predictive power, and some actually may be noise. The idea being that when added together, the combined small signals results in a signal that is stronger than the noise from the unrelated predictors. We examine various aspects of the construction of models for the estimation of disease probability. We compare different methods to construct such models, to examine how implementation of cross‐validation may influence results, and to examine which single nucleotide polymorphisms (SNPs) are most useful for prediction. We carry out our investigation on GWAS of the Welcome Trust Case Control Consortium. For Crohn's disease, we confirm our results on another GWAS. Our results suggest that utilizing a larger number of SNPs than those which reach genome‐wide significance, for example using the lasso, improves the construction of risk prediction models. Genet. Epidemiol. 34: 643‐652, 2010. © 2010 Wiley‐Liss, Inc.     

Using Phenotypic Heterogeneity to Increase the Power of Genome‐Wide Association Studies: Application to Age at Onset of Ischaemic Stroke Subphenotypes

Genome‐wide association studies (GWAS) have been successful in identifying common variants related to complex disorders. However, some disorders have proved resistant to this strategy with few associations confirmed, despite evidence from twin and family studies of a genetic component. Sophisticated strategies that account for phenotypic heterogeneity may be required to uncover these genetic contributions. Age at onset is an example of a potential source of this heterogeneity in ischaemic stroke. We explore the contribution of age at onset in the Wellcome Trust Case‐Control Consortium 2 ischaemic stroke study. We first examine four established stroke loci in younger onset cases. We extend this to all single‐nucleotide polymorphisms (SNPs) genome‐wide, testing for stronger association signals in younger subsets of cases. Finally, we estimate the pseudoheritability accounted for by common SNPs present on genome‐wide genotyping arrays for cases stratified by age at onset. We find evidence for stronger associations in younger onset cases for the four established stroke loci. Genome‐wide, in cardioembolic and small vessel stroke subphenotypes, a significant number of SNPs show stronger association P‐values when the oldest cases are removed. Finally, we show that the pseudoheritability estimated by common SNPs in cardioembolic stroke increased from 16.5% for older onset cases to 28.5% for younger onset cases. Our results indicate that age at onset is a valuable measure for case ascertainment and in analysis of GWAS in ischaemic stroke: focussing on younger cases who may have a stronger genetic predisposition increases power to detect associations.     

Case‐only genome‐wide interaction study of disease risk,prognosis and treatment

Case‐control genome‐wide association (GWA) studies have facilitated the identification of susceptibility loci for many complex diseases; however, these studies are often not adequately powered to detect gene‐environment (G×E) and gene‐gene (G×G) interactions. Case‐only studies are more efficient than case‐control studies for detecting interactions and require no data on control subjects. In this article, we discuss the concept and utility of the case‐only genome‐wide interaction (COGWI) study, in which common genetic variants, measured genome‐wide, are screened for association with environmental exposures or genetic variants of interest. An observed G‐E (or G‐G) association, as measured by the case‐only odds ratio (OR), suggests interaction, but only if the interacting factors are unassociated in the population from which the cases were drawn. The case‐only OR is equivalent to the interaction risk ratio. In addition to risk‐related interactions, we discuss how the COGWI design can be used to efficiently detect G×G, G×E and pharmacogenetic interactions related to disease outcomes in the context of observational clinical studies or randomized clinical trials. Such studies can be conducted using only data on individuals experiencing an outcome of interest or individuals not experiencing the outcome of interest. Sharing data among GWA and COGWI studies of disease risk and outcome can further enhance efficiency. Sample size requirements for COGWI studies, as compared to case‐control GWA studies, are provided. In the current era of genome‐wide analyses, the COGWI design is an efficient and straightforward method for detecting G×G, G×E and pharmacogenetic interactions related to disease risk, prognosis and treatment response. Genet. Epidemiol. 34:7–15, 2010. © 2009 Wiley‐Liss, Inc.     

Joint genotype‐ and ancestry‐based genome‐wide association studies in admixed populations

In genome‐wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus‐specific ancestry). Recently it has been proposed to jointly model genotype and locus‐specific ancestry within the framework of single marker tests. Here, we extend this approach for population‐based GWAS in the direction of multimarker models. A modified version of the Bayesian information criterion is developed for building a multilocus model that accounts for the differential correlation structure due to linkage disequilibrium (LD) and admixture LD. Simulation studies and a real data example illustrate the advantages of this new approach compared to single‐marker analysis or modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single‐marker analysis combining genotypic and ancestry information. Depending on the signal strength, our procedure automatically chooses whether genotypic or locus‐specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/ .