A Bayesian approach to genetic association studies with family‐based designs

For genome‐wide association studies with family‐based designs, we propose a Bayesian approach. We show that standard transmission disequilibrium test and family‐based association test statistics can naturally be implemented in a Bayesian framework, allowing flexible specification of the likelihood and prior odds. We construct a Bayes factor conditional on the offspring phenotype and parental genotype data and then use the data we conditioned on to inform the prior odds for each marker. In the construction of the prior odds, the evidence for association for each single marker is obtained at the population‐level by estimating its genetic effect size by fitting the conditional mean model. Since such genetic effect size estimates are statistically independent of the effect size estimation within the families, the actual data set can inform the construction of the prior odds without any statistical penalty. In contrast to Bayesian approaches that have recently been proposed for genome‐wide association studies, our approach does not require assumptions about the genetic effect size; this makes the proposed method entirely data‐driven. The power of the approach was assessed through simulation. We then applied the approach to a genome‐wide association scan to search for associations between single nucleotide polymorphisms and body mass index in the Childhood Asthma Management Program data. Genet. Epidemiol. 34:569–574, 2010. © 2010 Wiley‐Liss, Inc.     

Phenotype validation in electronic health records based genetic association studies

The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR‐derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome‐wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case‐control sampling strategy to select subjects for phenotype validation. We develop a closed‐form maximum‐likelihood estimator for the odds ratio parameters and a score statistic for testing genetic association using the combined validated and error‐prone EHR‐derived phenotype data, and assess the extent of power improvement provided by this approach. Compared with case‐control sampling based only on EHR‐derived phenotype data, our genotype stratified strategy maintains nominal type I error rates, and result in higher power for detecting associations. It also corrects the bias in the odds ratio parameter estimates, and reduces the corresponding variance especially when the minor allele frequency is small.     

A System‐Level Pathway‐Phenotype Association Analysis Using Synthetic Feature Random Forest

As the cost of genome‐wide genotyping decreases, the number of genome‐wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system‐level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high‐throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single‐marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single‐nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene‐gene interactions and pathway‐pathway relationships, we propose a system‐level pathway analysis approach, synthetic feature random forest (SF‐RF), which is designed to detect pathway‐phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF‐RF with pathway‐based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway‐phenotype association. We apply SF‐RF to a population‐based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway‐phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations.     

Functional Linear Models for Association Analysis of Quantitative Traits

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F‐distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT‐O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT‐O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.     

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.     

An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics

We study the problem of testing for single marker‐multiple phenotype associations based on genome‐wide association study (GWAS) summary statistics without access to individual‐level genotype and phenotype data. For most published GWASs, because obtaining summary data is substantially easier than accessing individual‐level phenotype and genotype data, while often multiple correlated traits have been collected, the problem studied here has become increasingly important. We propose a powerful adaptive test and compare its performance with some existing tests. We illustrate its applications to analyses of a meta‐analyzed GWAS dataset with three blood lipid traits and another with sex‐stratified anthropometric traits, and further demonstrate its potential power gain over some existing methods through realistic simulation studies. We start from the situation with only one set of (possibly meta‐analyzed) genome‐wide summary statistics, then extend the method to meta‐analysis of multiple sets of genome‐wide summary statistics, each from one GWAS. We expect the proposed test to be useful in practice as more powerful than or complementary to existing methods.     

A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes

Genome‐wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases. In this paper, we develop a novel variable reduction method using hierarchical clustering method (HCM) for joint analysis of multiple phenotypes in association studies. The proposed method involves two steps. The first step applies a dimension reduction technique by using a representative phenotype for each cluster of phenotypes. Then, existing methods are used in the second step to test the association between genetic variants and the representative phenotypes rather than the individual phenotypes. We perform extensive simulation studies to compare the powers of multivariate analysis of variance (MANOVA), joint model of multiple phenotypes (MultiPhen), and trait‐based association test that uses extended simes procedure (TATES) using HCM with those of without using HCM. Our simulation studies show that using HCM is more powerful than without using HCM in most scenarios. We also illustrate the usefulness of using HCM by analyzing a whole‐genome genotyping data from a lung function study.     

Accounting for Population Stratification in DNA Methylation Studies

DNA methylation is an important epigenetic mechanism that has been linked to complex diseases and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome‐wide association studies, issues such as population stratification will need to be addressed. It is well‐documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components (PCs) from different subsets of genome‐wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our PC‐based approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that (1) all of the methods considered are effective at removing inflation due to population stratification, and (2) maximum power can be obtained with single‐nucleotide polymorphism (SNP)‐based PCs, followed by methylation‐based PCs, which outperform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation‐based PCs, we find that PCs based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjust for population stratification in DNA methylation studies when genome‐wide SNP data are unavailable.     

Statistics for X‐chromosome associations

In a genome‐wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X‐chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X‐chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X‐chromosome data are included, they are often analyzed with suboptimal statistics. Several mathematically sensible statistics for X‐chromosome association have been proposed. The optimality of these statistics, however, is based on very specific simple genetic models. In addition, while previous simulation studies of these statistics have been informative, they have focused on single‐marker tests and have not considered the types of error that occur even under the null hypothesis when the entire X chromosome is scanned. In this study, we comprehensively tested several X‐chromosome association statistics using simulation studies that include the entire chromosome. We also considered a wide range of trait models for sex differences and phenotypic effects of X inactivation. We found that models that do not incorporate a sex effect can have large type I error in some cases. We also found that many of the best statistics perform well even when there are modest deviations, such as trait variance differences between the sexes or small sex differences in allele frequencies, from assumptions.     

Using cases to strengthen inference on the association between single nucleotide polymorphisms and a secondary phenotype in genome‐wide association studies

Case‐control genome‐wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AW or CO approaches, are illustrated by data from a case‐control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome‐wide study, compared to CO analysis. Further experience with genome‐wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions. Genet. Epidemiol. 34:427–433, 2010. Published 2010 Wiley‐Liss, Inc.     

Powerful and robust cross‐phenotype association test for case‐parent trios

There has been increasing interest in identifying genes within the human genome that influence multiple diverse phenotypes. In the presence of pleiotropy, joint testing of these phenotypes is not only biologically meaningful but also statistically more powerful than univariate analysis of each separate phenotype accounting for multiple testing. Although many cross‐phenotype association tests exist, the majority of such methods assume samples composed of unrelated subjects and therefore are not applicable to family‐based designs, including the valuable case‐parent trio design. In this paper, we describe a robust gene‐based association test of multiple phenotypes collected in a case‐parent trio study. Our method is based on the kernel distance covariance (KDC) method, where we first construct a similarity matrix for multiple phenotypes and a similarity matrix for genetic variants in a gene; we then test the dependency between the two similarity matrices. The method is applicable to either common variants or rare variants in a gene, and resulting tests from the method are by design robust to confounding due to population stratification. We evaluated our method through simulation studies and observed that the method is substantially more powerful than standard univariate testing of each separate phenotype. We also applied our method to phenotypic and genotypic data collected in case‐parent trios as part of the Genetics of Kidneys in Diabetes (GoKinD) study and identified a genome‐wide significant gene demonstrating cross‐phenotype effects that was not identified using standard univariate approaches.     

A novel association test for multiple secondary phenotypes from a case‐control GWAS

In the past decade, many genome‐wide association studies (GWASs) have been conducted to explore association of single nucleotide polymorphisms (SNPs) with complex diseases using a case‐control design. These GWASs not only collect information on the disease status (primary phenotype, D) and the SNPs (genotypes, X), but also collect extensive data on several risk factors and traits. Recent literature and grant proposals point toward a trend in reusing existing large case‐control data for exploring genetic associations of some additional traits (secondary phenotypes, Y ) collected during the study. These secondary phenotypes may be correlated, and a proper analysis warrants a multivariate approach. Commonly used multivariate methods are not equipped to properly account for the non‐random sampling scheme. Current ad hoc practices include analyses without any adjustment, and analyses with D adjusted as a covariate. Our theoretical and empirical studies suggest that the type I error for testing genetic association of secondary traits can be substantial when X as well as Y are associated with D, even when there is no association between X and Y in the underlying (target) population. Whether using D as a covariate helps maintain type I error depends heavily on the disease mechanism and the underlying causal structure (which is often unknown). To avoid grossly incorrect inference, we have proposed proportional odds model adjusted for propensity score (POM‐PS). It uses a proportional odds logistic regression of X on Y and adjusts estimated conditional probability of being diseased as a covariate. We demonstrate the validity and advantage of POM‐PS, and compare to some existing methods in extensive simulation experiments mimicking plausible scenarios of dependency among Y , X, and D. Finally, we use POM‐PS to jointly analyze four adiposity traits using a type 2 diabetes (T2D) case‐control sample from the population‐based Metabolic Syndrome in Men (METSIM) study. Only POM‐PS analysis of the T2D case‐control sample seems to provide valid association signals.     

Genotype‐based matching to correct for population stratification in large‐scale case‐control genetic association studies

Genome‐wide association studies are helping to dissect the etiology of complex diseases. Although case‐control association tests are generally more powerful than family‐based association tests, population stratification can lead to spurious disease‐marker association or mask a true association. Several methods have been proposed to match cases and controls prior to genotyping, using family information or epidemiological data, or using genotype data for a modest number of genetic markers. Here, we describe a genetic similarity score matching (GSM) method for efficient matched analysis of cases and controls in a genome‐wide or large‐scale candidate gene association study. GSM comprises three steps: (1) calculating similarity scores for pairs of individuals using the genotype data; (2) matching sets of cases and controls based on the similarity scores so that matched cases and controls have similar genetic background; and (3) using conditional logistic regression to perform association tests. Through computer simulation we show that GSM correctly controls false‐positive rates and improves power to detect true disease predisposing variants. We compare GSM to genomic control using computer simulations, and find improved power using GSM. We suggest that initial matching of cases and controls prior to genotyping combined with careful re‐matching after genotyping is a method of choice for genome‐wide association studies. Genet. Epidemiol. 33:508–517, 2009. © 2009 Wiley‐Liss, Inc.     

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.     

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.     

Generalized Functional Linear Models for Gene‐Based Case‐Control Association Studies

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT‐O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT‐O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT‐O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT‐O in the real data analysis. Our methods can be used in either gene‐disease genome‐wide/exome‐wide association studies or candidate gene analyses.     

On the adjustment for covariates in genetic association analysis: a novel,simple principle to infer direct causal effects

In genetic association studies, different complex phenotypes are often associated with the same marker. Such associations can be indicative of pleiotropy (i.e. common genetic causes), of indirect genetic effects via one of these phenotypes, or can be solely attributable to non‐genetic/environmental links between the traits. To identify the phenotypes with the inducing genetic association, statistical methodology is needed that is able to distinguish between the different causes of the genetic associations. Here, we propose a simple, general adjustment principle that can be incorporated into many standard genetic association tests which are then able to infer whether an SNP has a direct biological influence on a given trait other than through the SNP's influence on another correlated phenotype. Using simulation studies, we show that, in the presence of a non‐marker related link between phenotypes, standard association tests without the proposed adjustment can be biased. In contrast to that, the proposed methodology remains unbiased. Its achieved power levels are identical to those of standard adjustment methods, making the adjustment principle universally applicable in genetic association studies. The principle is illustrated by an application to three genome‐wide association analyses. Genet. Epidemiol. 33:394–405, 2009. © 2009 Wiley‐Liss, Inc.     

An efficient and robust method for analyzing population pharmacokinetic data in genome‐wide pharmacogenomic studies: a generalized estimating equation approach

Powerful array‐based single‐nucleotide polymorphism‐typing platforms have recently heralded a new era in which genome‐wide studies are conducted with increasing frequency. A genetic polymorphism associated with population pharmacokinetics (PK) is typically analyzed using nonlinear mixed‐effect models (NLMM). Applying NLMM to large‐scale data, such as those generated by genome‐wide studies, raises several issues related to the assumption of random effects as follows: (i) computation time: it takes a long time to compute the marginal likelihood; (ii) convergence of iterative calculation: an adaptive Gauss–Hermite quadrature is generally used to estimate NLMM; however, iterative calculations may not converge in complex models; and (iii) random‐effects misspecification leads to slightly inflated type‐I error rates. As an alternative effective approach to resolving these issues, in this article, we propose a generalized estimating equation (GEE) approach for analyzing population PK data. In general, GEE analysis does not account for interindividual variability in PK parameters; therefore, the usual GEE estimators cannot be interpreted straightforwardly, and their validities have not been justified. Here, we propose valid inference methods for using GEE even under conditions of interindividual variability and provide theoretical justifications of the proposed GEE estimators for population PK data. In numerical evaluations by simulations, the proposed GEE approach exhibited high computational speed and stability relative to the NLMM approach. Furthermore, the NLMM analysis was sensitive to the misspecification of the random‐effects distribution, and the proposed GEE inference is valid for any distributional form. We provided an illustration by using data from a genome‐wide pharmacogenomic study of an anticancer drug. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

A Powerful Approach to Test an Optimally Weighted Combination of Rare Variants in Admixed Populations

Population stratification has long been recognized as an issue in genetic association studies because unrecognized population stratification can lead to both false‐positive and false‐negative findings and can obscure true association signals if not appropriately corrected. This issue can be even worse in rare variant association analyses because rare variants often demonstrate stronger and potentially different patterns of stratification than common variants. To correct for population stratification in genetic association studies, we proposed a novel method to Test the effect of an Optimally Weighted combination of variants in Admixed populations (TOWA) in which the analytically derived optimal weights can be calculated from existing phenotype and genotype data. TOWA up weights rare variants and those variants that have strong associations with the phenotype. Additionally, it can adjust for the direction of the association, and allows for local ancestry difference among study subjects. Extensive simulations show that the type I error rate of TOWA is under control in the presence of population stratification and it is more powerful than existing methods. We have also applied TOWA to a real sequencing data. Our simulation studies as well as real data analysis results indicate that TOWA is a useful tool for rare variant association analyses in admixed populations.