@article{99fb5fc4468547b4bedf2d6ddb5c6f58,
title = "On Using Local Ancestry to Characterize the Genetic Architecture of Human Traits: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations",
abstract = "Understanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of the use of local ancestry on high-dimensional omics analyses, including, most prominently, expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored. Here, we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches. Applying our method to National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that the use of local ancestry can improve eQTL mapping in admixed and multiethnic populations, respectively. We estimate the trait variance explained by ancestry by using local admixture relatedness between individuals. By using simulations of diverse genetic architectures and degrees of confounding, we show improved accuracy in estimating heritability when accounting for local ancestry similarity. Furthermore, we characterize the sparse versus polygenic components of gene expression in admixed individuals. Our study has important methodological implications for genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.",
keywords = "admixture, eQTL, heritability, local ancestry, mixed models, omics, population structure, transcriptome",
author = "Yizhen Zhong and Perera, {Minoli A.} and Gamazon, {Eric R.}",
note = "Funding Information: We would like to thank Yuan Li and Yinan Zheng for advice on software development. We thank Tanima De, Zhou Zhang, Yiben Yang, and Jun Xiong for helpful discussion. E.R.G. benefited immensely from a fellowship at Clare Hall , University of Cambridge while holding a visiting post in the Medical Research Council (MRC) Epidemiology Unit and MRC Biostatistics Unit, Cambridge, UK. We would like to thank the Genotype-Tissue Expression (GTEx) Project, an initiative supported by the Common Fund of the Office of the Director of the National Institutes of Health (NIH), and by the National Cancer Institute (NCI), the National Human Genome Research Institute (NHGRI), the National Heart, Lung, and Blood Institute (NHLBI), the National Institute on Drug Abuse (NIDA), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS), for making the data available to the scientific community. This work was supported by National Institutes of Health (NIH)/ National Institute on Minority Health and Health Disparities (NIMHD) grants R01 MD009217 and U54 MD010723 . E.R.G. acknowledges support from R01 MH101820 and R01 MH090937 . Funding Information: We would like to thank Yuan Li and Yinan Zheng for advice on software development. We thank Tanima De, Zhou Zhang, Yiben Yang, and Jun Xiong for helpful discussion. E.R.G. benefited immensely from a fellowship at Clare Hall, University of Cambridge while holding a visiting post in the Medical Research Council (MRC) Epidemiology Unit and MRC Biostatistics Unit, Cambridge, UK. We would like to thank the Genotype-Tissue Expression (GTEx) Project, an initiative supported by the Common Fund of the Office of the Director of the National Institutes of Health (NIH), and by the National Cancer Institute (NCI), the National Human Genome Research Institute (NHGRI), the National Heart, Lung, and Blood Institute (NHLBI), the National Institute on Drug Abuse (NIDA), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS), for making the data available to the scientific community. This work was supported by National Institutes of Health (NIH)/National Institute on Minority Health and Health Disparities (NIMHD) grants R01 MD009217 and U54 MD010723. E.R.G. acknowledges support from R01 MH101820 and R01 MH090937. Publisher Copyright: {\textcopyright} 2019 American Society of Human Genetics",
year = "2019",
month = jun,
day = "6",
doi = "10.1016/j.ajhg.2019.04.009",
language = "English (US)",
volume = "104",
pages = "1097--1115",
journal = "American Journal of Human Genetics",
issn = "0002-9297",
publisher = "Cell Press",
number = "6",
}