Prowse-Wilkins Claire P, Wang Jianghui, Xiang Ruidong, Garner Josie B, Goddard Michael E, Chamberlain Amanda J
Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia.
Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia.
Front Genet. 2021 Jun 23;12:664379. doi: 10.3389/fgene.2021.664379. eCollection 2021.
Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle.
影响复杂性状的基因变异（因果变异）被认为存在于基因组的功能区域。识别因果变异对于预测奶牛的复杂性状表型将很有用，然而，牛基因组中功能区域的注释很差。通过使用一种称为染色质免疫沉淀测序（ChIP-seq）的方法，检测组蛋白的翻译后修饰（组蛋白修饰）以及与基因组相互作用的蛋白质（例如转录因子），可以在全基因组范围内识别功能区域。在本研究中，通过检测2至3头泌乳奶牛的6种组织（心脏、肾脏、肝脏、肺、乳腺和脾脏）中的四种组蛋白修饰（H3K4Me1、H3K4Me3、H3K27ac和H3K27Me3）和一种转录因子（CTCF），进行ChIP-seq以在牛基因组中找到功能区域。本研究产生了86个ChIP-seq样本，在牛基因组中识别出数百万个功能区域。使用ChromHMM发现组蛋白修饰和CTCF的组合，并通过与全基因组中的活跃和非活跃基因进行比较来进行注释。组织之间的功能标记有所不同，突出了可能对组织特异性调控特别重要的区域。支持功能区域的顺式调控作用，一些ChIP峰中的读数计数与附近基因的表达相关。如在其他物种中所见，本研究中鉴定的功能区域富含推定的因果变异。有趣的是，与基因表达相关的区域特别富含潜在的因果变异。这支持了复杂性状由改变基因表达的变异调控的假设。本研究提供了牛中最大的ChIP-seq注释资源之一，首次包括泌乳奶牛的乳腺。通过将调控区域与表达QTL和性状QTL联系起来，我们展示了一种识别牛中因果变异的新策略。
