Estimating and adjusting for ancestry admixture in statistical methods for relatedness inference, heritability estimation, and association testing

Abstract

It is well known that genetic association studies are not robust to population stratification. Two widely used approaches for the detection and correction of population structure are principal component analysis and model-based estimation of ancestry. These methods have been shown to give reliable inference on population structure in unrelated samples. We evaluated these two approaches in Mexican American pedigrees provided by the Genetic Analysis Workshop 18. We also estimated identity-by-descent sharing probabilities and kinship coefficients, with adjustment for ancestry admixture, to confirm documented pedigree relationships as well as to identify cryptic relatedness in the sample. We also estimated the heritability of the first simulated replicate of diastolic blood pressure (DBP). Finally, we performed an association analysis with simulated DBP, comparing the performance of an association method that corrects for population structure but does not account for relatedness to a method that adjusts for both population and pedigree structure. Analyses with simulated DBP were performed with knowledge of the underlying trait model.

A combined association test for rare variants using family and case-control data

“…The sample sizes were similar to the GAW19 data used in our data analysis. We performed admixture analyses using the AXMITURE software [14] for the CEU- and MXL-simulated samples, and observed similar global ancestry proportions for the MXL samples as the proportions in the GAW19 family data reported by Thornton et al [9], where large proportions for the CEU samples were inferred from the same ancestry. We selected 50 and 100 rare variants with MAFs of less than 5 % for the tests.…”

“…The family data include 464 individuals for whom whole genome sequencing data are available, while the sequences for other family members were imputed based on the sequenced individuals. Admixture analysis for the family data suggested that most of the family ancestry is European and Native American, where the proportions of the two ancestries in each individual are different [9]. The data for the unrelated individuals were whole exome sequenced.…”

Local and Global Ancestry Inference and Applications to Genetic Association Analysis for Admixed Populations

“…The individual ancestry results from the unsupervised analysis with reference population samples included are complementary to those provided by the supervised analysis of GAW18 samples with the ADMIXTURE software. Thornton et al [] also found that an unsupervised ADMIXTURE analysis that did not include reference population samples performed poorly because of the relatedness in the sample; proportional ancestry estimates largely reflected membership in the pedigrees that contributed the largest groups of genotyped relatives to the sample.…”

“…The chromosomes of an individual with admixed ancestry represent a mosaic of chromosomal blocks from the ancestral populations, and the overall genetic ancestry, or global ancestry of an individual, has previously been defined as the relative proportion of ancestral blocks from each contributing population across the chromosomes [Tang et al., ]. Thornton et al [] estimated proportional European, African, Native American, and East Asian ancestry for all genotyped individuals from the 20 pedigrees in GAW18 using the ADMIXTURE software program [Alexander et al., ]. The estimation of proportional ancestry was supervised, based on SNP data for odd‐numbered autosomes, where the CEU and YRI samples of release 3, phase III, of the International Haplotype Map Project (HapMap) [International HapMap 3 Consortium, ] were used as surrogates for European and African ancestry and where surrogates for Native American and East Asian ancestry were obtained from the Human Genome Diversity Project (HGDP) [Li et al., ].…”

Estimating relationships between phenotypes and subjects drawn from admixed families

“…We estimated R 2 , the coefficient of determination, from a linear regression model with 10 PCs from a PC analysis (PCA) as predictors and CEU, AMR, and YRI ancestry proportions from a supervised ADMIXTURE [17] analysis as the response. Details on the supervised ADMIXTURE analysis are described elsewhere [18]. We performed a PCA with a GRM, Ψ, with ( i, j ) th entrywhere S is the number of variants, G i s and G i s are the number of minor alleles (0, 1, or 2) that individuals i and j have at marker s , and p s is the MAF at marker s .…”