Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle

Abstract

Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to growth processes in beef cattle. The Brody model presented the best goodness of fit, and the heritability values for the parameter estimates for mature weight (A) and maturity rate (K) were 0.23 and 0.32, respectively, proving that these traits can be a feasible alternative when the objective is to change the shape of growth curves within genetic improvement programs. The genetic correlation between A and K was -0.84, indicating that animals with lower mature body weights reached that weight at younger ages. One hundred and sixty seven (167) and two hundred and sixty two (262) significant SNPs were associated with A and K, respectively. The annotated genes closest to the most significant SNPs for A had direct biological functions related to muscle development (RAB28), myogenic induction (BTG1), fetal growth (IL2), and body weights (APEX2); K genes were functionally associated with body weight, body height, average daily gain (TMEM18), and skeletal muscle development (SMN1). Candidate genes emerging from this GWAS may inform the search for causative mutations that could underpin genomic breeding for improved growth rates.

Rab28 is a TBC1D1/TBC1D4 substrate involved in GLUT4 trafficking

“…Rab28 was also implicated in nuclear transport of NF‐κB in endothelial cells , as well as in retromer‐dependent trafficking and ESCRT‐mediated degradative pathways . Recently, genetic variants in the Rab28 gene were associated with body weight and muscle development in cattle, consistent with a possible role of Rab28 in metabolism . Consistent with the complex trafficking pathways of GLUT4, many Rab‐GTPases including Rab3b, Rab4, Rab5, Rab8, Rab10, Rab11, Rab13, Rab14, and Rab31, have been described to be associated to GLUT4‐containing vesicles .…”

Genome-Wide Association Study Reveals the PLAG1 Gene for Knuckle, Biceps and Shank Weight in Simmental Beef Cattle

“…Actually, several QTNs have been found to be located in the well-known PLAG1-CHCHD7 region [5, 6, 13, 16, 17] on BAT14. In human, PLAG1 is related to the pleiomorphic adenoma gene [32–34] and regulates human height [35].…”

“…The huge number of markers genotyped by the BovineSNP50K Bead Chip [3] provide an ample opportunity for us to perform GWAS in beef cattle. In the past few decades, numerous studies were carried out to map quantitative trait loci (QTLs) responsible for milk production, disease resistance [3, 4], reproduction [5], growth [5–8], meat quality [9–12] and carcass traits [12–15] in cattle. For carcass traits, previous studies had mapped several QTLs for carcass weights on BTA 6, 8 and 14, where one QTN was detected on BTA 6 and related to the encoding of NCAPG Ile442Met and the others were identified within gene PLAG1-CHCHD7 in the region on BTA14 [16].…”

Regularized quantile regression applied to genome-enabled prediction of quantitative traits

“…However, owing to the growing number of markers and interest in longitudinal (Crispim et al, 2015) and non-normal (Filardi de Campos et al, 2015) traits, almost 15 years after this seminal publication, statistical issues such as multicollinearity, high dimensionality, and non-normal phenotypic distributions still need to be overcome in GS.…”