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

Timothy Thornton,Steven M Lewis,Charles Yk Cheung,Serge Sverdlov,Christopher G Glazner,Ellen M Wijsman,Elizabeth M Blue,Matthew P Conomos

doi:10.1186/1753-6561-8-s1-s5

Timothy Thornton, Steven M Lewis + Show 6 more

Open Access

https://doi.org/10.1186/1753-6561-8-s1-s5

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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.

Highlights

Principal component analysis (PCA) and model-based estimation of ancestry are two widely used approaches for the detection and correction of population structure in admixed populations
We evaluate the performance of PCA and the model-based individual ancestry estimation method ADMIXTURE in Mexican American pedigrees provided by the Genetic Analysis Workshop 18 (GAW18)
We compare the EMMAX association method [5], which is a linear mixedmodel approach that accounts for pedigree and population structure, with an association analysis using the PLINK software [6], where the top 10 principal components (PCs) from a PCA are included as covariates in a linear regression analysis to account for population structure

Summary

Introduction

Principal component analysis (PCA) and model-based estimation of ancestry are two widely used approaches for the detection and correction of population structure in admixed populations. The top principal components (PCs) from PCA can be used as covariates in a generalized linear model to protect against confounding resulting from population stratification in genetic association studies [1]. Individual ancestry estimates from widely used software programs, such as STRUCTURE [2], FRAPPE [3], and ADMIXTURE [4], can be used for population stratification inference and correction. We evaluate the performance of PCA and the model-based individual ancestry estimation method ADMIXTURE in Mexican American pedigrees provided by the Genetic Analysis Workshop 18 (GAW18). We compare the EMMAX association method [5], which is a linear mixedmodel approach that accounts for pedigree and population structure, with an association analysis using the PLINK software [6], where the top 10 PCs from a PCA are included as covariates in a linear regression analysis to account for population structure

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