Presence Of Population Stratification Research Articles

Fast and efficient correction for population stratification in multi-locus genome-wide association studies

Reducing false discoveries caused by population stratification (PS) has always been a challenge in genome-wide association studies (GWAS). The current literature established several single marker approaches including genomic control (GC), EIGENSTRAT and generalized linear mixed model association test (GMMAT) and multi-marker methods such as LASSO mixed model (LASSOMM). However, the single-marker methods require prespecifying an arbitrary p value threshold in the selection process, likely resulting in suboptimal precision or recall. On the other hand, it appears that LASSOMM is extremely computationally intensive and may not suitable for large-scale GWAS. In this paper, we proposed a simple multi-marker approach (PCA-LASSO) combining principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). We utilize PCA to correct for the confounding effects of PS and LASSO with built-in cross-validation for a data-driven selection. Compared to the current single-marker approaches, the proposed PCA-LASSO provides optimal balance between precision and recall, and consequently superior F1 scores. Similarly, compared to LASSOMM, PCA-LASSO markedly increases the precision while minimizing the loss of recall, and therefore improves the overall F1 score in presence of PS. More importantly, PCA-LASSO drastically reduces the computational time by > 1000 times when compared to LASSOMM. We applied PCA-LASSO to a real dataset of Alzheimer's disease and successfully identified SNP rs429358 (Gene APOE4) which has been widely reported to be associated with the onset and elevated risk of Alzheimer's disease. In conclusion, PCA-LASSO is a simple, fast, but accurate approach for GWAS in presence of latent PS.

Evaluation of population stratification adjustment using genome-wide or exonic variants.

Population stratification may cause an inflated type-I error and spurious association when assessing the association between genetic variations with an outcome. Many genetic association studies are now using exonic variants, which captures only 1% of the genome, however, population stratification adjustments have not been evaluated in the context of exonic variants. We compare the performance of two established approaches: principal components analysis (PCA) and mixed-effects models and assess the utility of genome-wide (GW) and exonic variants, by simulation and using a data set from the Framingham Heart Study. Our results illustrate that although the PCs and genetic relationship matrices computed by GW and exonic markers are different, the type-I error rate of association tests for common variants with additive effect appear to be properly controlled in the presence of population stratification. In addition, by considering single nucleotide variants (SNVs) that have different levels of confounding by population stratification, we also compare the power across multiple association approaches to account for population stratification such as PC-based corrections and mixed-effects models. We find that while these two methods achieve a similar power for SNVs that have a low or medium level of confounding by population stratification, mixed-effects model can reach a higher power for SNVs highly confounded by population stratification.

Imprint of assortative mating on the human genome.

Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1-5 and has evolutionary consequences6-8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes.

Conditioning adaptive combination of P-values method to analyze case-parent trios with or without population controls.

Detection of rare causal variants can help uncover the etiology of complex diseases. Recruiting case-parent trios is a popular study design in family-based studies. If researchers can obtain data from population controls, utilizing them in trio analyses can improve the power of methods. The transmission disequilibrium test (TDT) is a well-known method to analyze case-parent trio data. It has been extended to rare-variant association testing (abbreviated as “rvTDT”), with the flexibility to incorporate population controls. The rvTDT method is robust to population stratification. However, power loss may occur in the conditioning process. Here we propose a “conditioning adaptive combination of P-values method” (abbreviated as “conADA”), to analyze trios with/without unrelated controls. By first truncating the variants with larger P-values, we decrease the vulnerability of conADA to the inclusion of neutral variants. Moreover, because the test statistic is developed by conditioning on parental genotypes, conADA generates valid statistical inference in the presence of population stratification. With regard to statistical methods for next-generation sequencing data analyses, validity may be hampered by population stratification, whereas power may be affected by the inclusion of neutral variants. We recommend conADA for its robustness to these two factors (population stratification and the inclusion of neutral variants).

A Novel Test for Detecting SNP-SNP Interactions in Case-Only Trio Studies.

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple-hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new SNP-SNP interaction test for use in case-only trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard 1 d.f. correlation test of interaction. We show via extensive simulations under a variety of disease models that our test substantially outperforms existing tests of interaction in case-only trio studies. We also demonstrate a bias in a previous case-only trio interaction test and identify its origin. Finally, we show that a previously proposed permutation scheme in trio studies mitigates the known biases of case-only tests in the presence of population stratification. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.

Allowing for population stratification in case-only studies of gene-environment interaction, using genomic control.

Gene-environment interactions (G × E) have attracted considerable research interest in the past owing to their scientific and public health implications, but powerful statistical methods are required to successfully track down G × E, particularly at a genome-wide level. Previously, a case-only (CO) design has been proposed as a means to identify G × E with greater efficiency than traditional case-control or cohort studies. However, as with genotype-phenotype association studies themselves, hidden population stratification (PS) can impact the validity of G × E studies using a CO design. Since this problem has been subject to little research to date, we used comprehensive simulation to systematically assess the type I error rate, power and effect size bias of CO studies of G × E in the presence of PS. Three types of PS were considered, namely genetic-only (PSG), environment-only (PSE), and joint genetic and environmental stratification (PSGE). Our results reveal that the type I error rate of an unadjusted Wald test, appropriate for the CO design, would be close to its nominal level (0.05 in our study) as long as PS involves only one interaction partner (i.e., either PSG or PSE). In contrast, if the study population is stratified with respect to both G and E (i.e., if there is PSGE), then the type I error rate is seriously inflated and estimates of the underlying G × E interaction are biased. Comparison of CO to a family-based case-parents design confirmed that the latter is more robust against PSGE, as expected. However, case-parent trios may be particularly unsuitable for G × E studies in view of the fact that they require genotype data from parents and that many diseases with an environmental component are likely to be of late onset. An alternative approach to adjusting for PS is principal component analysis (PCA), which has been widely used for this very purpose in past genome-wide association studies (GWAS). However, resolving genetic PS properly by PCA requires genetic data at the population level, the availability of which would conflict with the basic idea of the CO design. Therefore, we explored three modified Wald test statistics, inspired by the genomic control (GC) approach to GWAS, as an alternative means to allow for PSGE. The modified statistics were benchmarked against a stratified Wald test assuming known population affiliation, which should provide maximum power under PS. Our results confirm that GC is capable of successfully and efficiently correcting the PS-induced inflation of the type I error rate in CO studies of G × E.

A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis

A major focus of current sequencing studies for human genetics is to identify rare variants associated with complex diseases. Aside from reduced power of detecting associated rare variants, controlling for population stratification is particularly challenging for rare variants. Transmission/disequilibrium tests (TDT) based on family designs are robust to population stratification and admixture, and therefore provide an effective approach to rare variant association studies to eliminate spurious associations. To increase power of rare variant association analysis, gene-based collapsing methods become standard approaches for analyzing rare variants. Existing methods that extend this strategy to rare variants in families usually combine TDT statistics at individual variants and therefore lack the flexibility of incorporating other genetic models. In this study, we describe a haplotype-based framework for group-wise TDT (gTDT) that is flexible to encompass a variety of genetic models such as additive, dominant and compound heterozygous (CH) (i.e. recessive) models as well as other complex interactions. Unlike existing methods, gTDT constructs haplotypes by transmission when possible and inherently takes into account the linkage disequilibrium among variants. Through extensive simulations we showed that type I error was correctly controlled for rare variants under all models investigated, and this remained true in the presence of population stratification. Under a variety of genetic models, gTDT showed increased power compared with the single marker TDT. Application of gTDT to an autism exome sequencing data of 118 trios identified potentially interesting candidate genes with CH rare variants. We implemented gTDT in C++ and the source code and the detailed usage are available on the authors' website (https://medschool.vanderbilt.edu/cgg). bingshan.li@vanderbilt.edu or wei.chen@chp.edu Supplementary data are available at Bioinformatics online.

Improving the power of GWAS and avoiding confounding from population stratification with PC-Select.

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models.

Haplotype association analysis of combining unrelated case-control and triads with consideration of population stratification.

Combining data when data are collected under different study designs, such as family trios and unrelated case-control samples, gains more power and is cost-effective than analyzing each data separately. However, a potential concern is population stratification (PS) among unrelated case-control samples and analyses integrating data should address this confounding effect. In this paper, we develop a simpler method, haplotype generalized linear model (HGLM), that tests and estimates haplotype effects on disease risk and allows for modification against PS for combining data. We proposed to combine information across aggregations of haplotype weighted-counts estimated from population case-control data and trio data separately, and to perform subsequent GLM analysis. Furthermore, we present a framework of analysis of variance based on haplotype weighted-counts for detecting whether it is appropriate to combine two data sources, as well as the modified HGLM with clustering methods for addressing PS. We evaluate the statistical properties in terms of the accuracy, false positive rate (FPR) and empirical power using simulated data with regard to various disease risks, sample sizes, multi-SNP haplotypes and the presence of PS. Our simulation results indicate that HGLM performs comparably well with the likelihood-based haplotype association analysis, particularly when the haplotype effects are moderate, but may not perform well when dealing with lengthy haplotypes for small sample sizes. In the presence of PS, the modified HGLM remains valid and has satisfactory nominal level and small bias. Overall, HGLM appears to be successful in combining data and is simple to implement in standard statistical software.

Genetic Association With Multiple Traits in the Presence of Population Stratification

Testing association between a genetic marker and multiple-dependent traits is a challenging task when both binary and quantitative traits are involved. The inverted regression model is a convenient method, in which the traits are treated as predictors although the genetic marker is an ordinal response. It is known that population stratification (PS) often affects population-based association studies. However, how it would affect the inverted regression for pleiotropic association, especially with the mixed types of traits (binary and quantitative), is not examined and the performance of existing methods to correct for PS using the inverted regression analysis is unknown. In this paper, we focus on the methods based on genomic control and principal component analysis, and investigate type I error of pleiotropic association using the inverted regression model in the presence of PS with allele frequencies and the distributions (or disease prevalences) of multiple traits varying across the subpopulations. We focus on common alleles but simulation results for a rare variant are also reported. An application to the HapMap data is used for illustration.

Family-based association tests for sequence data, and comparisons with population-based association tests

Recent advances in high-throughput sequencing technologies make it increasingly more efficient to sequence large cohorts for many complex traits. We discuss here a class of sequence-based association tests for family-based designs that corresponds naturally to previously proposed population-based tests, including the classical Burden and variance-component tests. This framework allows for a direct comparison between the powers of sequence-based association tests with family- vs population-based designs. We show that for dichotomous traits using family-based controls results in similar power levels as the population-based design (although at an increased sequencing cost for the family-based design), while for continuous traits (in random samples, no ascertainment) the population-based design can be substantially more powerful. A possible disadvantage of population-based designs is that they can lead to increased false-positive rates in the presence of population stratification, while the family-based designs are robust to population stratification. We show also an application to a small exome-sequencing family-based study on autism spectrum disorders. The tests are implemented in publicly available software.

Effect of Population Stratification on False Positive Rates of Population‐Based Association Analyses of Quantitative Traits

It is now well established that population stratification can result in spurious association findings in genetic case-control studies. However, very few studies have addressed similar issues for mapping quantitative traits. Because quantitative phenotypes are often precursors of clinical endpoint traits and carry more information on within-genotype trait variability, it has been argued that studying these quantitative traits may be a more powerful strategy to map genes than the binary clinical endpoints. Thus, it is of interest to evaluate the adverse effects of population stratification on the analyses of quantitative traits. The popular statistical tests of association for quantitative traits using population level data are ANOVA, linear regression with an additive allelic effect and Kruskal-Wallis. We have theoretically studied the marginal effects of genetic heterogeneity and phenotypic heterogeneity as well as their joint effects on the false positive rates of these three tests. We have carried out extensive simulations under different genetic models and probability distributions of quantitative traits to assess the rate of false positives in the presence of population stratification. We find that the rate of false positives increases very quickly with simultaneous increase in differences in the standardized phenotypic means and marker allele frequencies in the subpopulations.

A brief note on the resemblance between relatives in the presence of population stratification

Population stratification occurs when a study population is comprised of several sub-populations, and can result in increased false positive findings in genomewide-association studies. Recently published work shows that sub-population-specific positive assortative mating at the genotypic level results in population stratification. We show that if the allele frequency of a single nucleotide polymorphism responsible for a trait varies between sub-populations and there is no dominance variance, then the heritability of the trait increases, primarily due to an increase in the additive genetic variance of the trait.

Assessing the joint effect of population stratification and sample selection in studies of gene-gene (environment) interactions.

BackgroundIt is well known that the presence of population stratification (PS) may cause the usual test in case-control studies to produce spurious gene-disease associations. However, the impact of the PS and sample selection (SS) is less known. In this paper, we provide a systematic study of the joint effect of PS and SS under a more general risk model containing genetic and environmental factors. We provide simulation results to show the magnitude of the bias and its impact on type I error rate of the usual chi-square test under a wide range of PS level and selection bias.ResultsThe biases to the estimation of main and interaction effect are quantified and then their bounds derived. The estimated bounds can be used to compute conservative p-values for the association test. If the conservative p-value is smaller than the significance level, we can safely claim that the association test is significant regardless of the presence of PS or not, or if there is any selection bias. We also identify conditions for the null bias. The bias depends on the allele frequencies, exposure rates, gene-environment odds ratios and disease risks across subpopulations and the sampling of the cases and controls.ConclusionOur results show that the bias cannot be ignored even the case and control data were matched in ethnicity. A real example is given to illustrate application of the conservative p-value. These results are useful to the genetic association studies of main and interaction effects.

Differentiating Population Stratification from Genotyping Error Using Family Data

Identifying population stratification and genotyping error are important for candidate gene association studies using the Transmission Disequilibrium Test (TDT). Although the TDT retains the prespecified Type I error in the presence of population stratification, the test may have decreased power in the presence of population stratification. Genotyping error can also cause the TDT to have an elevated Type I error. Differentiating population stratification from genotyping error remains a challenge for geneticists. Both genotyping error and population stratification can result in an increase in the observed homozygosity of a sample relative to that expected assuming Hardy-Weinberg Equilibrium (HWE). We show that when family data are available, even if a limited number of markers are genotyped, evaluating the markers that show statistically significant deviation from HWE with the Mating Type Distortion Test (MTDT)--a test based on the mating type distribution--can reliably differentiate genotyping error from population stratification. We simulate data based on several models of genotyping error in previously published literature, and show how this method could be used in practice to assist in differentiating population stratification from systematic genotyping error.

A Novel Method to Detect Gene-Gene Interactions in Structured Populations: MDR-SP

Complex diseases are presumed to be the result of multiple genes and environmental factors, which emphasize the importance of gene - gene and gene - environment interactions. Traditional parametric approaches are limited in their ability to detect high-order interactions and handle sparse data, and standard stepwise procedures may miss interactions with undetectable main effects. To address these limitations, the multifactor dimensionality reduction (MDR) method was developed. MDR is well suited for examining high-order interactions and detecting interactions without main effects. Like most statistical methods in genetic association studies, MDR may also lead to a false positive in the presence of population stratification. Although many statistical methods have been proposed to detect main effects and control for population stratification using genomic markers, not many methods are available to detect interactions and control for population stratification at the same time. In this article, we developed a novel test, MDR in structured populations (MDR-SP), to detect the interactions and control for population stratification. MDR-SP is applicable to both quantitative and qualitative traits and can incorporate covariates. We present simulation studies to demonstrate the validity of the test and to evaluate its power.

Bayes factors in the presence of population stratification

Hidden population stratification (PS) is a main concern in the analysis of case-control genetic association studies. All methods to correct for hidden PS have been focused on classical hypothesis testing, and cannot be directly applied to Bayesian analysis. In this paper, to study the impact and the correction of hidden PS on Bayes factor (BF), we use a simple approximation of BF in terms of the maximum likelihood estimates of the odds ratio (OR) and its asymptotic variance. One advantage is that the commonly used principal components analysis method with a large panel of null markers scanned from existing genome-wide association studies can be directly employed to correct for hidden PS in estimating the OR and its asymptotic variance, through which a correction to BF for hidden PS can be achieved. Using simulations, we examine the impact of ignoring hidden PS on BF and show that the proposed method yields an appropriate correction in Bayesian analysis.

CAPL: a novel association test using case‐control and family data and accounting for population stratification

The recent successes of GWAS based on large sample sizes motivate combining independent datasets to obtain larger sample sizes and thereby increase statistical power. Analysis methods that can accommodate different study designs, such as family-based and case-control designs, are of general interest. However, population stratification can cause spurious association for population-based association analyses. For family-based association analysis that infers missing parental genotypes based on the allele frequencies estimated in the entire sample, the parental mating-type probabilities may not be correctly estimated in the presence of population stratification. Therefore, any approach to combining family and case-control data should also properly account for population stratification. Although several methods have been proposed to accommodate family-based and case-control data, all have restrictions. Most of them require sampling a homogeneous population, which may not be a reasonable assumption for data from a large consortium. One of the methods, FamCC, can account for population stratification and uses nuclear families with arbitrary number of siblings but requires parental genotype data, which are often unavailable for late-onset diseases. We extended the family-based test, Association in the Presence of Linkage (APL), to combine family and case-control data (CAPL). CAPL can accommodate case-control data and families with multiple affected siblings and missing parents in the presence of population stratification. We used simulations to demonstrate that CAPL is a valid test either in a homogeneous population or in the presence of population stratification. We also showed that CAPL can have more power than other methods that combine family and case-control data.

Influence of population stratification on population‐based marker‐disease association analysis

Population-based genetic association analysis may suffer from the failure to control for confounders such as population stratification (PS). There has been extensive study on the influence of PS on candidate gene-disease association analysis, but much less attention has been paid to its influence on marker-disease association analysis. In this paper, we focus on the Pearson chi(2) test and the trend test for marker-disease association analysis. The mean and variance of the test statistics are derived under presence of PS, so that the power and inflated type I error rate can be evaluated. It is shown that the bias and the variance distortion are not zero in the presence of both PS and penetrance heterogeneity (PH). Unlike candidate gene-disease association analysis, when PS is present, the bias is not zero no matter whether PH is present or not. This work generalises the published results, where only the fully recessive penetrance model is considered and only the bias is calculated. It is shown that candidate gene-disease association analysis can be treated as a special case of marker-disease association analysis. Consequently, our results extend previous studies on candidate gene-disease association analysis. A simulation study confirms the theoretical findings.

The power of the Transmission Disequilibrium Test in the presence of population stratification

The Transmission Disequilibrium Test (TDT) is a family-based test for association based on the rate of transmission of alleles from heterozygous parents to affected offspring, and has gained popularity as this test preserves the Type I error rate. Population stratification results in a decreased number of heterozygous parents compared to that expected assuming Hardy-Weinberg Equilibrium (Wahlund Effect). We show that population stratification changes the relative proportion of the informative mating types. The decrease in the number of heterozygous parents and the change in the relative proportion of the informative mating types result in significant changes to the sample sizes required to achieve the power desired. We show examples of the changes in sample sizes, and provide an easy method for estimating TDT sample sizes in the presence of population stratification. This method potentially aids in reducing the number of false-negative association studies.