Testing Genetic Association With Rare Variants in Admixed Populations

Recent studies suggest that rare variants play an important role in the etiology of many traits. Although a number of methods have been developed for genetic association analysis of rare variants, they all assume a relatively homogeneous population under study. Such an assumption may not be valid for samples collected from admixed populations such asAfricanAmericans andHispanicAmericans as there is a great extent of local variation in ancestry in these populations. To ensure valid and more powerful rare variant association tests performed in admixed populations, we have developed a local ancestry‐based weighted dosage test, which is able to take into account local ancestry of rare alleles, uncertainties in rare variant imputation when imputed data are included, and the direction of effect that rare variants exert on phenotypic outcome. We used simulated sequence data to show that our proposed test has controlled typeIerror rates, whereas naïve application of existing rare variants tests and tests that adjust for global ancestry lead to inflated type I error rates. We showed that our test has higher power than tests without proper adjustment of ancestry. We also applied the proposed method to a candidate gene study on low‐density lipoprotein cholesterol. Our results suggest that it is important to appropriately control for potential population stratification induced by local ancestry difference in the analysis of rare variants in admixed populations.     

PAMAM: Power analysis in multiancestry admixture mapping

Admixed populations arise when two or more previously isolated populations interbreed. Admixture mapping (AM) methods are used for tracing the ancestral origin of disease-susceptibility genetic loci in the admixed population such as African American and Latinos. AM is different from genome-wide association studies in that ancestry rather than genotypes are tracked in the association process. The power and sample size of AM primarily depend on proportion of admixture and differences in the risk allele frequencies among the ancestral populations. Ensuring sufficient power to detect the effect of ancestry on disease susceptibility is critical for interpretability and reliability of studies using AM approach. However, there is no power and sample size analysis tool existing for AM studies in admixed population. In this study, we developed power analysis of multiancestry AM (PAMAM) to estimate power and sample size for two-way and three-way population admixtures. PAMAM is the first web-based bioinformatics tool developed to calculate power and sample size in admixed population under a variety of genetic and disease phenotype models. It is a valuable resource for investigators to design a cost-efficient study and develop grant application to pursue AM studies. PAMAM is built on JavaScript back-end with HTML front-end. It is accessible through any modern web browser such as Firefox, Internet Explorer, and Google Chrome regardless of operating system. It is a user-friendly tool containing links for support information including user manual and examples, and freely available at https://research.cchmc.org/mershalab/PAMAM/login.html .     

Power comparison of admixture mapping and direct association analysis in genome-wide association studies

When dense markers are available, one can interrogate almost every common variant across the genome via imputation and single nucleotide polymorphism (SNP) test, which has become a routine in current genome-wide association studies (GWASs). As a complement, admixture mapping exploits the long-range linkage disequilibrium (LD) generated by admixture between genetically distinct ancestral populations. It is then questionable whether admixture mapping analysis is still necessary in detecting the disease associated variants in admixed populations. We argue that admixture mapping is able to reduce the burden of massive comparisons in GWASs; it therefore can be a powerful tool to locate the disease variants with substantial allele frequency differences between ancestral populations. In this report we studied a two-stage approach, where candidate regions are defined by conducting admixture mapping at stage 1, and single SNP association tests are followed at stage 2 within the candidate regions defined at stage 1. We first established the genome-wide significance levels corresponding to the criteria to define the candidate regions at stage 1 by simulations. We next compared the power of the two-stage approach with direct association analysis. Our simulations suggest that the two-stage approach can be more powerful than the standard genome-wide association analysis when the allele frequency difference of a causal variant in ancestral populations, is larger than 0.4. Our conclusion is consistent with a theoretical prediction by Risch and Tang ([2006] Am J Hum Genet 79:S254). Surprisingly, our study also suggests that power can be improved when we use less strict criteria to define the candidate regions at stage 1.     

A robust and powerful two‐step testing procedure for local ancestry adjusted allelic association analysis in admixed populations

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two‐step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry‐specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups’ previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA .     

Properties of global‐ and local‐ancestry adjustments in genetic association tests in admixed populations

Population substructure can lead to confounding in tests for genetic association, and failure to adjust properly can result in spurious findings. Here we address this issue of confounding by considering the impact of global ancestry (average ancestry across the genome) and local ancestry (ancestry at a specific chromosomal location) on regression parameters and relative power in ancestry‐adjusted and ‐unadjusted models. We examine theoretical expectations under different scenarios for population substructure; applying different regression models, verifying and generalizing using simulations, and exploring the findings in real‐world admixed populations. We show that admixture does not lead to confounding when the trait locus is tested directly in a single admixed population. However, if there is more complex population structure or a marker locus in linkage disequilibrium (LD) with the trait locus is tested, both global and local ancestry can be confounders. Additionally, we show the genotype parameters of adjusted and unadjusted models all provide tests for LD between the marker and trait locus, but in different contexts. The local ancestry adjusted model tests for LD in the ancestral populations, while tests using the unadjusted and the global ancestry adjusted models depend on LD in the admixed population(s), which may be enriched due to different ancestral allele frequencies. Practically, this implies that global‐ancestry adjustment should be used for screening, but local‐ancestry adjustment may better inform fine mapping and provide better effect estimates at trait loci.     

Joint testing of genotype and ancestry association in admixed families

Current genome-wide association studies (GWAS) often involve populations that have experienced recent genetic admixture. Genotype data generated from these studies can be used to test for association directly, as in a non-admixed population. As an alternative, these data can be used to infer chromosomal ancestry, and thus allow for admixture mapping. We quantify the contribution of allele-based and ancestry-based association testing under a family-design, and demonstrate that the two tests can provide non-redundant information. We propose a joint testing procedure, which efficiently integrates the two sources information. The efficiencies of the allele, ancestry and combined tests are compared in the context of a GWAS. We discuss the impact of population history and provide guidelines for future design and analysis of GWAS in admixed populations.     

The Role of Local Ancestry Adjustment in Association Studies Using Admixed Populations

Association analysis using admixed populations imposes challenges and opportunities for disease mapping. By developing some explicit results for the variance of an allele of interest conditional on either local or global ancestry and by simulation of recently admixed genomes we evaluate power and false‐positive rates under a variety of scenarios concerning linkage disequilibrium (LD) and the presence of unmeasured variants. Pairwise LD patterns were compared between admixed and nonadmixed populations using the HapMap phase 3 data. Based on the above, we showed that as follows:

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Evaluating the power to discriminate between highly correlated SNPs in genetic association studies

Neighboring common polymorphisms are often correlated (in linkage disequilibrium (LD)) as a result of shared ancestry. An association between a polymorphism and a disease trait may therefore be the indirect result of a correlated functional variant, and identifying the true causal variant(s) from an initial disease association is a major challenge in genetic association studies. Here, we present a method to estimate the sample size needed to discriminate between a functional variant of a given allele frequency and effect size, and other correlated variants. The sample size required to conduct such fine‐scale mapping is typically 1–4 times larger than required to detect the initial association. Association studies in populations with different LD patterns can substantially improve the power to isolate the causal variant. An online tool to perform these calculations is available at http://moya.srl.cam.ac.uk/ocac/FineMappingPowerCalculator.html . Genet. Epidemiol. 34:463–468, 2010. © 2010 Wiley‐Liss, Inc.     

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.     

Genetic admixture and asthma-related phenotypes in Mexican American and Puerto Rican asthmatics

Genetic association studies in admixed populations may be biased if individual ancestry varies within the population and the phenotype of interest is associated with ancestry. However, recently admixed populations also offer potential benefits in association studies since markers informative for ancestry may be in linkage disequilibrium across large distances. In particular, the enhanced LD in admixed populations may be used to identify alleles that underlie a genetically determined difference in a phenotype between two ancestral populations. Asthma is known to have different prevalence and severity among ancestrally distinct populations. We investigated several asthma-related phenotypes in two ancestrally admixed populations: Mexican Americans and Puerto Ricans. We used ancestry informative markers to estimate the individual ancestry of 181 Mexican American asthmatics and 181 Puerto Rican asthmatics and tested whether individual ancestry is associated with any of these phenotypes independently of known environmental factors. We found an association between higher European ancestry and more severe asthma as measured by both forced expiratory volume at 1 second (r=-0.21, p=0.005) and by a clinical assessment of severity among Mexican Americans (OR: 1.55; 95% CI 1.25 to 1.93). We found no significant associations between ancestry and severity or drug responsiveness among Puerto Ricans. These results suggest that asthma severity may be influenced by genetic factors differentiating Europeans and Native Americans in Mexican Americans, although differing results for Puerto Ricans require further investigation.     

An empirical Bayes approach to improving population-specific genetic association estimation by leveraging cross-population data

Populations of non-European ancestry are substantially underrepresented in genome-wide association studies (GWAS). As genetic effects can differ between ancestries due to possibly different causal variants or linkage disequilibrium patterns, a meta-analysis that includes GWAS of all populations yields biased estimation in each of the populations and the bias disproportionately impacts non-European ancestry populations. This is because meta-analysis combines study-specific estimates with inverse variance as the weights, which causes biases towards studies with the largest sample size, typical of the European ancestry population. In this paper, we propose two empirical Bayes (EB) estimators to borrow the strength of information across populations although accounting for between-population heterogeneity. Extensive simulation studies show that the proposed EB estimators are largely unbiased and improve efficiency compared to the population-specific estimator. In contrast, even though the meta-analysis estimator has a much smaller variance, it yields significant bias when the genetic effect is heterogeneous across populations. We apply the proposed EB estimators to a large-scale trans-ancestry GWAS of stroke and demonstrate that the EB estimators reduce the variance of the population-specific estimator substantially, with the effect estimates close to the population-specific estimates.     

A hidden Markov modeling approach for admixture mapping based on case-control data

Admixture mapping is potentially a powerful method for mapping genes for complex human diseases, when the disease frequency due to a particular disease-susceptible gene is different between founding populations of different ethnicity. The method tests for association of the allele ancestry with the disease. Since the markers used to define ancestral populations are not fully informative for the ancestry status, direct test of such association is not possible. In this report, we develop a unified hidden Markov model (HMM) framework for estimating the unobserved ancestry haplotypes across a chromosomal region based on marker haplotype or genotype data. The HMM efficiently utilizes all the marker data to infer the latent ancestry states at the putative disease locus. In this HMM modelling framework, we develop a likelihood test for association of allele ancestry and the disease risk based on case-control data. Existence of such association may imply linkage between the candidate locus and the disease locus. We evaluate by simulations how several factors affect the power of admixture mapping, including sample size, ethnicity relative risk, marker density, and the different admixture dynamics. Our simulation results indicate correct type 1 error rates of the proposed likelihood ratio tests and great impact of marker density on the power. The simulation results also indicate that the methods work well for the admixed populations derived from both hybrid-isolation and continuous gene-flowing models. Finally, we observed that the genotype-based HMM performs very similarly in power as the haplotype-based HMM when the haplotypes are known and the set of markers is highly informative.     

Genetic model testing and statistical power in population-based association studies of quantitative traits

The optimal method for considering different genetic models in association studies is not clear. We compared analytical strategies that use different genetic models to analyze genotype-phenotype information from association studies of quantitative traits in unrelated individuals. We created simulated datasets where the minor alleles are causal with an additive, dominant, or recessive mode of inheritance over a range of allele frequencies. We then computed power to detect these causal alleles using one or a combination of statistical models in a standard regression framework, including corrections for the multiple testing incurred by analyzing multiple models. Our results show that, as expected, maximal power is achieved when we test a single genetic model that matches the actual underlying mode of inheritance of the causal allele. When the inheritance pattern of the causal allele is unknown, the co-dominant model, a single two degrees of freedom test, has good overall performance in any of the three simple modes of inheritance simulated. Alternatively, it is slightly more powerful to analyze all three genetic models together (additive, dominant, and recessive), but only if the significance thresholds used to correct for analyzing multiple models are appropriately determined (such as by permutation). Finally, a commonly employed approach, testing the additive model alone, performs poorly for recessive causal alleles when the minor allele frequency is not close to 50%. Our observations were confirmed by analyzing an existing genetic association dataset in which we detect the effect of a KCNJ11 variant on insulinogenic index in unrelated non-diabetic individuals.     

Rare variants association testing for a binary outcome when pooling individual level data from heterogeneous studies

Whole genome sequencing (WGS) and whole exome sequencing studies are used to test the association of rare genetic variants with health traits. Many existing WGS efforts now aggregate data from heterogeneous groups, for example, combining sets of individuals of European and African ancestries. We here investigate the statistical implications on rare variant association testing with a binary trait when combining together heterogeneous studies, defined as studies with potentially different disease proportion and different frequency of variant carriers. We study and compare in simulations the Type 1 error control and power of the naïve score test, the saddlepoint approximation to the score test, and the BinomiRare test in a range of settings, focusing on low numbers of variant carriers. We show that Type 1 error control and power patterns depend on both the number of carriers of the rare allele and on disease prevalence in each of the studies. We develop recommendations for association analysis of rare genetic variants. (1) The Score test is preferred when the case proportion in the sample is 50%. (2) Do not down‐sample controls to balance case–control ratio, because it reduces power. Rather, use a test that controls the Type 1 error. (3) Conduct stratified analysis in parallel with combined analysis. Aggregated testing may have lower power when the variant effect size differs between strata.     

Examining markers in 8q24 to explain differences in evidence for association with cleft lip with/without cleft palate between Asians and Europeans

In a recent genome-wide association study (GWAS) from an international consortium, evidence of linkage and association in chr8q24 was much stronger among nonsyndromic cleft lip/palate (CL/P) case-parent trios of European ancestry than among trios of Asian ancestry. We examined marker information content and haplotype diversity across 13 recruitment sites (from Europe, United States, and Asia) separately, and conducted principal components analysis (PCA) on parents. As expected, PCA revealed large genetic distances between Europeans and Asians, and a north-south cline from Korea to Singapore in Asia, with Filipino parents forming a somewhat distinct Southeast Asian cluster. Hierarchical clustering of SNP heterozygosity revealed two major clades consistent with PCA results. All genotyped SNPs giving P < 10(-6) in the allelic transmission disequilibrium test (TDT) showed higher heterozygosity in Europeans than Asians. On average, European ancestry parents had higher haplotype diversity than Asians. Imputing additional variants across chr8q24 increased the strength of statistical evidence among Europeans and also revealed a significant signal among Asians (although it did not reach genome-wide significance). Tests for SNP-population interaction were negative, indicating the lack of strong signal for 8q24 in families of Asian ancestry was not due to any distinct genetic effect, but could simply reflect low power due to lower allele frequencies in Asians.     

Extending the MR‐Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR‐Egger (Mendelian randomization‐Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy). In this paper, we extend the MR‐Egger method to a multivariable setting to correct for both measured and unmeasured pleiotropy. We show, through theoretical arguments and a simulation study, that the multivariable MR‐Egger method has advantages over its univariable counterpart in terms of plausibility of the assumption needed for consistent causal estimation and power to detect a causal effect when this assumption is satisfied. The methods are compared in an applied analysis to investigate the causal effect of high‐density lipoprotein cholesterol on coronary heart disease risk. The multivariable MR‐Egger method will be useful to analyse high‐dimensional data in situations where the risk factors are highly related and it is difficult to find genetic variants specifically associated with the risk factor of interest (multivariable by design), and as a sensitivity analysis when the genetic variants are known to have pleiotropic effects on measured risk factors.     

Information on ancestry from genetic markers

It is possible to estimate the proportionate contributions of ancestral populations to admixed individuals or populations using genetic markers, but different loci and alleles vary considerably in the amount of information that they provide. Conventionally, the allele frequency difference between parental populations (delta) has been used as the criterion to select informative markers. However, it is unclear how to use delta for multiallelic loci, or populations formed by the mixture of more than two groups. Moreover, several other factors, including the actual ancestral proportions and the relative genetic diversities of the parental populations, affect the information provided by genetic markers. We demonstrate here that using delta as the sole criterion for marker selection is inadequate, and we propose, instead, to use Fisher's information, which is the inverse of the variance of the estimated ancestral contributions. This measure is superior because it is directly related to the precision of ancestry estimates. Although delta is related to Fisher's information, the relationship is neither linear nor simple, and the information can vary widely for markers with identical deltas. Fortunately, Fisher's information is easily computed and formally extends to the situation of multiple alleles and/or parental populations. We examined the distribution of information for SNP and microsatellite loci available in the public domain for a variety of model admixed populations. The information, on average, is higher for microsatellite loci, but exceptional SNPs exceed the best microsatellites. Despite the large number of genetic markers that have been identified for admixture analysis, it appears that information for estimating admixture proportions is limited, and estimates will typically have wide confidence intervals.     

On the analysis of sequence data: testing for disease susceptibility loci using patterns of linkage disequilibrium

Despite the numerous and successful applications of genome-wide association studies (GWASs), there has been a lot of difficulty in discovering disease susceptibility loci (DSLs). This is due to the fact that the GWAS approach is an indirect mapping technique, often identifying markers. For the identification of DSLs, which is required for the understanding of the genetic pathways for complex diseases, sequencing data that examines every genetic locus directly is necessary. Yet, there is currently a lack of methodology targeted at the identification of the DSLs in sequencing data: existing methods localize the causal variant to a region but not to a single variant, and therefore do not allow one to identify unique loci that cause the phenotype association. Here, we have developed such a method to determine if there is evidence that an individual loci affects case/control status with sequencing data. This methodology differs from other rare variant approaches: rather than testing an entire region comprised of many loci for association with the phenotype, we can identify the individual genetic locus that causes the association between the phenotype and the genetic region. For each variant, the test determines if the pattern of linkage disequilibrium (LD) across the other variants coincides with the pattern expected if that variant were a DSL. Power simulations show that the method successfully detects the causal variant, distinguishing it from other nearby variants (in high LD with the causal variant), and outperforms the standard tests. The efficiency of the method is especially apparent with small samples, which are currently realistic for studies due to sequencing data costs. The practical relevance of the approach is illustrated by an application to a sequencing dataset for nonsyndromic cleft lip with or without cleft palate. The proposed method implicated one variant (P = 0.002, 0.062 after Bonferroni correction), which was not found by standard analyses. Code for implementation is available.     

Multiway Admixture Deconvolution Using Phased or Unphased Ancestral Panels

We describe a novel method for inferring the local ancestry of admixed individuals from dense genome‐wide single nucleotide polymorphism data. The method, called MULTIMIX, allows multiple source populations, models population linkage disequilibrium between markers and is applicable to datasets in which the sample and source populations are either phased or unphased. The model is based upon a hidden Markov model of switches in ancestry between consecutive windows of loci. We model the observed haplotypes within each window using a multivariate normal distribution with parameters estimated from the ancestral panels. We present three methods to fit the model—Markov chain Monte Carlo sampling, the Expectation Maximization algorithm, and a Classification Expectation Maximization algorithm. The performance of our method on individuals simulated to be admixed with European and West African ancestry shows it to be comparable to HAPMIX, the ancestry calls of the two methods agreeing at 99.26% of loci across the three parameter groups. In addition to it being faster than HAPMIX, it is also found to perform well over a range of extent of admixture in a simulation involving three ancestral populations. In an analysis of real data, we estimate the contribution of European, West African and Native American ancestry to each locus in the Mexican samples of HapMap, giving estimates of ancestral proportions that are consistent with those previously reported.     

Genetic heterogeneity of self-reported ancestry groups in an admixed Brazilian population

Background

Population stratification is the main source of spurious results and poor reproducibility in genetic association findings. Population heterogeneity can be controlled for by grouping individuals in ethnic clusters; however, in admixed populations, there is evidence that such proxies do not provide efficient stratification control. The aim of this study was to evaluate the relation of self-reported with genetic ancestry and the statistical risk of grouping an admixed sample based on self-reported ancestry.

Methods

A questionnaire that included an item on self-reported ancestry was completed by 189 female volunteers from an admixed Brazilian population. Individual genetic ancestry was then determined by genotyping ancestry informative markers.

Results

Self-reported ancestry was classified as white, intermediate, and black. The mean difference among self-reported groups was significant for European and African, but not Amerindian, genetic ancestry. Pairwise fixation index analysis revealed a significant difference among groups. However, the increase in the chance of type 1 error was estimated to be 14%.

Conclusions

Self-reporting of ancestry was not an appropriate methodology to cluster groups in a Brazilian population, due to high variance at the individual level. Ancestry informative markers are more useful for quantitative measurement of biological ancestry.Key words: ethnicity, population structure, ancestry, admixture