University of Guelph, Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, Guelph, Ontario, Canada.
Universidade Federal de Minas Gerais, Departamento de Biologia Geral, Belo Horizonte, Minas Gerais, Brazil.
PLoS One. 2018 Oct 18;13(10):e0205295. doi: 10.1371/journal.pone.0205295. eCollection 2018.
The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.
鉴定与复杂性状调控相关的生物过程是一项艰巨的任务。通常，复杂性状是通过多种基因共同调控的，每个基因对总遗传方差的贡献都很小。此外，一些基因座可以同时调控多个复杂性状，这种现象被定义为多效性。由于缺乏对调节这些性状的生物过程的了解，导致选择效率降低，并选择了不理想的 hitchhiking 效应。鉴定多效性关键调控基因可以协助开发研究复杂性状背后的生物过程的重要工具。本研究采用多品种和多组学方法，利用三个独立的肉牛群体评估繁殖性状，研究关键调控基因的多效性影响。使用与生长、饲料效率、胴体和肉质以及繁殖相关的 32 个性状的多效性图谱，鉴定不同群体和品种在多效性区域中共享的基因。此外，还使用 cattleQTLdb 进行了数据挖掘分析，以鉴定在品种间共享基因周围区域注释的 QTL 类别。这种方法允许鉴定一个主要的基因网络（由 38 个基因组成），该网络在品种间共享。该基因网络与甲状腺活性等其他生物过程显著相关，并且具有较高的调控潜力。此外，还可以鉴定与调节与繁殖、生产和健康相关的经济相关性状的关键生物过程有关的多效性效应基因，如 MYC、PPARG、GSK3B、TG 和 IYD 基因。这些基因将进一步研究，以更好地了解参与复杂性状表达的生物过程，并协助鉴定与不良表型相关的功能变体，如降低的繁殖力、较差的饲料效率和负能平衡。
