Yadav Anupama, Dhole Kaustubh, Sinha Himanshu
Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, India.
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.
Genome Biol Evol. 2016 Dec 1;8(12):3559-3573. doi: 10.1093/gbe/evw258.
Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets.
隐秘遗传变异（CGV）是指那些在大多数情况下其效应被缓冲，但在特定的遗传和环境扰动下表现出表型的遗传变异。尽管CGV在适应性中起着核心作用，但其对数量性状调控的贡献尚不清楚。相反，已知一种相对简单的加性遗传位点结构可调控大多数性状的表型变异。在本文中，我们在全基因组水平上研究了CGV的调控及其对数量性状遗传结构的影响。我们使用先前发表的酿酒酵母双亲重组群体在34种不同环境下表型的数据集，进行单基因座、双基因座和协方差作图。我们鉴定出具有独立加性效应的基因座以及那些调控其他遗传变异表型表现的基因座（方差QTL）。我们发现，虽然加性遗传方差占主导，但一个高阶遗传相互作用网络在某些环境中调控变异。尽管包含多效性基因座，其效应跨越不同环境，但这些遗传网络具有高度的环境特异性。CGV在这些网络的大多数等位基因组合下被缓冲，仅在罕见组合中受到扰动，从而导致高表型方差。这种环境特异性遗传网络的存在是丰富的基因 - 环境相互作用的根本原因。我们证明，将鉴定出的分子网络叠加在这样的遗传网络上可以识别潜在的候选基因和调控表型变异的潜在机制。应用于人类疾病数据集的这种综合方法有潜力提高预测疾病易感性和识别特定治疗靶点的能力。
