Population- and individual-specific regulatory variation in Sardinia

Abstract

Genetic studies of complex traits have mainly identified associations with non-coding variants. To further determine the contribution of regulatory variation, we combined whole genome and transcriptome data for 624 individuals from Sardinia in order to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 novel QTLs. We identified high-frequency QTLs and evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z-score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

Human Immunology through the Lens of Evolutionary Genetics

“…Various studies have mapped eQTLs in contexts relevant to immunity, with the measurement, in particular, of expression levels in the presence of immune (e.g., TLR ligands, IFNb, IFNg) or infectious (e.g., influenza A virus, Mycobacterium tuberculosis, Listeria monocytogenes) stimuli (Alasoo et al, 2018;Barreiro et al, 2012;Ç alıs xkan et al, 2015;Kim-Hellmuth et al, 2017;Lee et al, 2014;Né delec et al, 2016;Piasecka et al, 2018;Quach et al, 2016;Schmiedel et al, 2018;Ye et al, 2018). These studies have identified regulatory variants related to immune activation that act in cis or in trans (e.g., master regulators detected at IFNB1, IRF2, TLR1, or CR1), which are generally cell context dependent, and have revealed major overlaps between such variants and GWAS hits (see, for example, Kim-Hellmuth et al, 2017;Lee et al, 2014;Pala et al, 2017;Piasecka et al, 2018). These findings suggest that regulatory variation may underlie genetic associations with complex organismal phenotypes, such as infectious or autoimmune diseases (Wang et al, 2018).…”

Discovery of novel hepatocyte eQTLs in African Americans

“…Genetic architecture of gene expression varies across populations [17]. eQTL mapping in diverse populations has been shown to implicate novel trait associations reflecting the epidemiological history [18] and adaption of innate immune responses to infection [19]. The catalog of regulatory variants needs to be expanded to include admixed populations such as AAs in disease relevant tissues, as these populations may suffer disproportionally of many chronic disease [20,21].…”

OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data

“…Current standard methods used to control for covariations of RNA-seq data across individuals include PEER 10,34,35 or PCA. 36 Both approaches assume the input data to be log-normal distributed, which is suboptimal for count data.…”

“…More elaborated statistical tests have been designed to leverage family structures in normal population studies. 34,39 These methods are based on the normal distribution and log-transformed counts. Our comparison to PEER and PCA suggests that these methods could be improved by a count distribution such as the NB.…”