Ghazanfar Shila, Vuocolo Tony, Morrison Janna L, Nicholas Lisa M, McMillen Isabella C, Yang Jean Y H, Buckley Michael J, Tellam Ross L
Data61, CSIRO, North Ryde, NSW, Australia.
School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
PLoS One. 2017 Jun 30;12(6):e0180378. doi: 10.1371/journal.pone.0180378. eCollection 2017.
Heritable trait variation within a population of organisms is largely governed by DNA variations that impact gene transcription and protein function. Identifying genetic variants that affect complex functional traits is a primary aim of population genetics studies, especially in the context of human disease and agricultural production traits. The identification of alleles directly altering mRNA expression and thereby biological function is challenging due to difficulty in isolating direct effects of cis-acting genetic variations from indirect trans-acting genetic effects. Allele specific gene expression or allelic imbalance in gene expression (AI) occurring at heterozygous loci provides an opportunity to identify genes directly impacted by cis-acting genetic variants as indirect trans-acting effects equally impact the expression of both alleles. However, the identification of genes showing AI in the context of the expression of all genes remains a challenge due to a variety of technical and statistical issues. The current study focuses on the discovery of genes showing AI using single nucleotide polymorphisms as allelic reporters. By developing a computational and statistical process that addressed multiple analytical challenges, we ranked 5,809 genes for evidence of AI using RNA-Seq data derived from brown adipose tissue samples from a cohort of late gestation fetal lambs and then identified a conservative subgroup of 1,293 genes. Thus, AI was extensive, representing approximately 25% of the tested genes. Genes associated with AI were enriched for multiple Gene Ontology (GO) terms relating to lipid metabolism, mitochondrial function and the extracellular matrix. These functions suggest that cis-acting genetic variations causing AI in the population are preferentially impacting genes involved in energy homeostasis and tissue remodelling. These functions may contribute to production traits likely to be under genetic selection in the population.
生物群体内的可遗传性状变异很大程度上由影响基因转录和蛋白质功能的DNA变异所控制。识别影响复杂功能性状的基因变异是群体遗传学研究的主要目标，尤其是在人类疾病和农业生产性状的背景下。由于难以将顺式作用基因变异的直接效应与间接反式作用基因效应区分开来，直接鉴定改变mRNA表达从而影响生物学功能的等位基因具有挑战性。在杂合位点发生的等位基因特异性基因表达或基因表达中的等位基因不平衡（AI）为识别直接受顺式作用基因变异影响的基因提供了机会，因为间接反式作用效应会同等影响两个等位基因的表达。然而，由于各种技术和统计问题，在所有基因表达的背景下识别显示AI的基因仍然是一个挑战。当前的研究聚焦于利用单核苷酸多态性作为等位基因报告基因来发现显示AI的基因。通过开发一个解决多种分析挑战的计算和统计流程，我们利用来自一群妊娠晚期胎羊棕色脂肪组织样本的RNA-Seq数据，对5809个基因进行了AI证据的排名，然后确定了一个由1293个基因组成的保守亚组。因此，AI广泛存在，约占测试基因的25%。与AI相关的基因在与脂质代谢、线粒体功能和细胞外基质相关的多个基因本体（GO）术语中富集。这些功能表明，在群体中导致AI的顺式作用基因变异优先影响参与能量稳态和组织重塑的基因。这些功能可能有助于群体中可能受到遗传选择的生产性状。
