Research Article: Gene expression allelic imbalance in ovine brown adipose tissue impacts energy homeostasis

Date Published: June 30, 2017

Publisher: Public Library of Science

Author(s): Shila Ghazanfar, Tony Vuocolo, Janna L. Morrison, Lisa M. Nicholas, Isabella C. McMillen, Jean Y. H. Yang, Michael J. Buckley, Ross L. Tellam, Ramona Natacha PENA i SUBIRÀ.


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.

Partial Text

A major aim of population genetics is the identification of genetic variants and their biological effects that lead to complex trait variation within individuals. Genetic variants are also important for understanding the molecular relationship between gene function and phenotype. In agriculture, natural genetic variants are exploited for predicting and improving desirable production traits through DNA marker assisted selective breeding practices. In human health, genetic variants affecting gene function can be used to predict disease risk in human populations and this potentially can be coupled with targeted preclinical intervention strategies to minimise disease impact.

The current investigation used RNA-Seq data from perirenal adipose tissue taken from 18 late gestation fetal lambs to identify genes showing allelic imbalance in gene expression by making use of informative SNP markers at heterozygous loci. Initially, a total of 7,631,907 potential SNPs was identified. This number is consistent with SNP discovery rates in outbred sheep populations [21]. A filtered list of 24,355 SNPs at heterozygous loci within ENSEMBL genes was tested for evidence of AI. The process addressed a number of inherent analytical issues and statistical biases to identify AI rankings for 5,810 genes, from which a conservative subset of 1,293 genes (25.6%) was identified (genes in the top ranked 1,500 genes after downsizing and filtered for Bonferroni P<0.05). Genetic variation in a population of sheep is responsible for considerable allele specific gene expression imbalance, which may be associated with energy intensive production traits, such as neonatal survival and growth, and traits involving strong postnatal tissue remodelling and cellular hypertrophy e.g. adipose and skeletal muscle deposition. The marker SNPs associated with AI may therefore have value for DNA marker-assisted selective breeding in the sheep industry. The identification of the causal cis-acting SNPs, whilst in the vicinity of genes showing AI, requires further investigation. It is possible that some of the causal SNPs are represented in the SNP markers used to measure AI.   Source:


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