Date Published: October 18, 2018
Publisher: Public Library of Science
Author(s): Pablo Augusto de Souza Fonseca, Samir Id-Lahoucine, Antonio Reverter, Juan F. Medrano, Marina S. Fortes, Joaquim Casellas, Filippo Miglior, Luiz Brito, Maria Raquel S. Carvalho, Flávio S. Schenkel, Loan T. Nguyen, Laercio R. Porto-Neto, Milton G. Thomas, Angela Cánovas, Peter J. Hansen.
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.
The regulation of complex traits involves multiple loci, that contribute to a small proportion of the phenotypic expression, and environmental effects [1, 2]. In addition, loci that regulate complex traits are known to be involved in the regulation of several phenotypes. This describes the primary genetic effect that leads to genetic correlation, known as pleiotropy. Currently, the interaction and regulation pattern of these loci are poorly understood, especially in livestock species. However, the use of pleiotropic markers for genomic selection may result in improvement in efficiency of selection. On the other hand, some pleiotropic markers can lead to the indirect selection of undesirable traits. The exclusion of these markers from genetic selection programs may result in the improvement of a specific trait without affecting other traits or multiple traits simultaneously.
A total of 108 genes (S1 File) mapped in pleiotropic regions were shared among all three independent populations of the three beef cattle breeds considered (Fig 2). Among them, 89 genes were mapped in regions with five or six QTL categories annotated (S2 File). It is important to highlight that the approach to evaluate the post-puberty stage was different between Cánovas et al. (2014)  and Nguyen et al. (2017) . The puberty period was defined as the second consecutive day of a circulating progesterone values >1 ng/mL by Cánovas et al. (2014) . On the other hand, Nguyen et al. (2017)  defined the puberty period when the observation of the first corpus luteum occurred. These differences, together with the different genetic backgrounds and environmental effects between these two populations, can result in different expression profiles, even when two similar groups of phenotypes are measured (pre- and post-puberty). However, the same biological processes are expected to underlying the puberty development in both populations. Therefore, it is expected that the key-regulators of these processes maintained a similar DE profile between pre- and post-puberty phenotypes in both populations. Additionally, the consistency of expression among tissues and populations can help to identify these key-regulatory genes. The results obtained from BP and KEGG pathways enrichment analyses indicated that these genes are involved in development and maturation of the body (Table 1). From those 89 genes, 38 were grouped in the main network identified by STRING database (Fig 3 and S1 File). Fig 4 shows the percentage of each type of QTL (exterior appearance, health, reproduction, production, meat and carcass quality and milk traits) present in a 2 Mb interval (1 Mb downstream and 1 Mb upstream) around these 38 genes. The expression pattern across the tissues analyzed by Cánovas et al. (2014)  and Nguyen et al. (2017)  reinforce the functional relevance of these genes due to the DE across specific tissues (Table 2). The detailed mapping of the BP associated with these 38 genes suggested a strong relationship between these genes and the regulation of biological processes involved with cellular metabolism, cellular growth and development (Fig 5 and S3 File). Fig 6 shows the genomic context of the regions where these 38 genes were mapped as a function of the pleiotropic markers harboring these genes. The results from evaluation of the gene list composed of 38 genes suggested that the identification of key-regulatory genes was an important step to elucidate the relationship among these genes and the regulation of BP, as well as their association with economically important traits.
The present study described a multi-breed and multi-OMICs approach to identify key-regulatory candidate genes for pleiotropic effects in beef cattle using the results generated by previous studies. Our findings confirm the feasibility of using a systems biology approach to unravel candidate genes regulating complex traits. Genes identified in this study are mainly involved in two biological processes: energy conservation metabolism and cell proliferation, probably the most theoretically plausible processes to unify the phenotypes investigated in this study: exterior appearance, health, reproduction, production, meat and carcass, and milk traits. This study contributes to the understanding of the cause-consequence relationships between variants mapped on candidate pleiotropic genes affecting complex traits. Additionally, the results obtained here will be useful for better defining statistical models to improve the accuracy of genomic prediction of breeding values and avoid the simultaneous selection for unfavorable genetically correlated traits in beef cattle and other livestock species.