Genes underlying genetic correlation between growth, reproductive and parasite burden traits in beef cattle

• Published in: Livestock science Vol. 244; p. 104332
• Main Authors: Ribeiro, Virgínia Mara Pereira, Gouveia, Gabriela Canabrava, Moraes, Mariana Mamedes de, Araújo, Andresa Eva Melo de, Raidan, Fernanda Santos Silva, Fonseca, Pablo Augusto de Souza, Cardoso, Eduardo Penteado, Silva, Marcos Vinícius Gualberto Barbosa da, Toral, Fabio Luiz Buranelo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-02-2021
• Subjects: Average daily gain; Body weight; Genome-wide association; Nellore; Parasite; Pleiotropy; Parasite; Pleiotropy; Genome-wide association; Body weight; Average daily gain; Nellore
• ISSN: 1871-1413; 1878-0490
• DOI: 10.1016/j.livsci.2020.104332

•The association between different traits can be quantified through correlation analyses;•Two different traits can be control to the same gene;•Candidate genes commons to different traits can contribute to genetic correlation;•The genetic correlation is outcome of both pleiotropy and linked disequilibrium;•There are SNPs mapped in FCG that promoted a profitable improve to different traits.
Genetic correlation is the outcome of linkage disequilibrium and/or pleiotropic genes. As such, identifying which genes take part in the genetic control of genetically correlated traits can help us better understand the relationship between economic traits and promote more efficient breeding programs. We aim to estimate the genetic correlations between growth, reproduction and parasite burden traits and to identify functional candidate genes (FCG) underlying these correlations. Six traits were evaluated, comprising two of growth (body weight - BW and average daily gain - ADG), one reproductive trait (scrotal circumference - SC) and three related to parasite burden (tick count - TICK, gastrointestinal nematode eggs per gram of feaces - GIN, and Eimeria spp. oocysts per gram of faeces - EIM). The genetic correlations were estimated using a multiple-trait model. A total of 21,667 SNP markers were used to perform a single-step GWAS and to identify genomic windows explaining at least 1% of the genetic variance for the studied traits. The posterior means and highest posterior density intervals of the genetic correlations were positive and of moderate magnitudes for the pairs of traits BW-ADG (0.64; 0.52, 0.76), BW-SC (0.38; 0.26, 0.50), BW-TICK (0.39; 0.25, 0.76), ADG-SC (0.27; 0.11, 0.43), and TICK-EIM (0.33; 0.12, 0.53). Only the pair ADG-EIM presented a negative correlation (-0.22; -0.39, -0.05). All the other pairs showed genetic correlations close to zero. Additionally, functional analyses were performed and FCGs were selected based on their roles in biological processes for each of the traits. The effects of the SNPs were calculated as a proportion of the genetic standard deviation. Seven FCGs (SLC16A4, KCNA2, LAMTOR5, DUSP10, MAP3K1, TPMT, and KIF13A) were identified for more than one trait. Regardless of the genetic correlation values (low-moderate), there were FCGs which could influence both correlated traits. There were SNPs mapped in FCGs that might be used to promote genetic improvement in multiple traits. There are common FCGs that might control production, reproduction and parasite burden traits in beef cattle and contribute to genetic correlation values.