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.

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

“…After performing cis-eQTL mapping in six tissues, we observe that LocalAA has a modest improvement in power, consistent with previous observations (10,44). We also observe that most eQTLs agree between LocalAA and GlobalAA; the majority of eGenes that are called uniquely by one ancestry adjustment method are at the threshold of significance.…”

“…GlobalAA and LocalAA demonstrates that LocalAA has more significant P-values (represented in the highest quantiles) relative to GlobalAA for five of the six tissues, with NSE skin showing more similar P-value distributions between the two methods ( Figure 2a). This corroborates previous findings that LA adjustment results in more significant nominal P-values than GA adjustment in the context of cis-eQTL mapping (10).…”

“…The can be problematic when genes are differentially expressed in ancestral populations of admixed individuals. In contrast, local ancestry (LA), or the number of alleles derived from distinct ancestral populations at a given locus, may be more appropriate for population structure adjustment in admixed populations but typically suffers from much longer compute time and can be prone to errors in estimation at a variant level (5,(8)(9)(10)(11)(12).…”

Discovery of novel hepatocyte eQTLs in African Americans

“…Because AAs are an admixed population, the ancestral makeup of their genomes can differ by locus. Therefore, we applied our recently published method of adjusting for local ancestry (LA) in eQTL mapping (LAMatrix) [28]. This method has demonstrated increased power and control of false positives when gene expression differs by local ancestry [28].…”

“…Here, we performed the largest eQTL mapping to date in hepatocytes derived exclusively from AAs (n = 60) using our local ancestry adjusted method (LAMatrix) [28]. We identified AA-specific eQTLs (found only in our AA dataset) and overlapping eQTLs (found in both datasets) by comparing our results to those from the GTEx liver dataset.…”