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.
Highlights
In beef cattle, postnatal body weight is often recorded repeatedly at different ages for the same individual and is a typical example of longitudinal data which the trait of interest changes gradually and continually over time
In relation to quantitative trait loci (QTL) affecting growth curves, the theory of functional mapping proposed by Ma C-X et al.[6] is quite general and can be applied to any dynamic complex traits [7], such as beef cattle body weights over time
We evaluated the performance of the multi-trait genome-wide association studies (GWAS) mixed model by using simulated data, which was provided by QTLMAS2009 and fully described in Coster et al [32]
Summary
Postnatal body weight is often recorded repeatedly at different ages for the same individual and is a typical example of longitudinal data which the trait of interest changes gradually and continually over time. The term growth curve is used as a general designation for such data, and reflects the lifetime interrelationships between an individual's inherent potential to grow and mature in all body parts[1].There are different models to describe growth curves in livestock animals[2], and these models allow us to summarize the weight-age gain through a few parameters, such as mature weight (A) and maturity rate (K),which explain the whole growth process under a biological scenario When fitting these different models to a particular dataset, the use of goodness of fit measures are needed to choose the best model to describe the growth curve to the population in question. These studies have been the basis of QTL detection for longitudinal traits, the postulated theory is based on linkage analysis by using highly spaced markers (low density) in specific designed population mapping
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