Crispim Aline Camporez, Kelly Matthew John, Guimarães Simone Eliza Facioni, Fonseca e Silva Fabyano, Fortes Marina Rufino Salinas, Wenceslau Raphael Rocha, Moore Stephen
Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
Queensland Alliance for Agriculture & Food Innovation University of Queensland, Brisbane, Queensland, Australia.
PLoS One. 2015 Oct 7;10(10):e0139906. doi: 10.1371/journal.pone.0139906. eCollection 2015.
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.
了解肉牛生长的遗传结构不能仅仅局限于对任何特定年龄体重的全基因组关联研究（GWAS），而应通过使用生长曲线方法考虑整个生长轨迹，将其扩展到更广泛的目标。对于这种方法，用于描述生长曲线的参数在GWAS模型下被视为表型。分析了1255头婆罗门牛在出生时、6、12、15、18和24月龄时的体重数据。来自非线性模型的参数估计值，如成熟体重（A）和成熟率（K），被用作GWAS分析中原始体重的替代值。我们选择了最佳的非线性模型来描述体重-年龄数据，并将估计参数用作多性状GWAS中的表型。我们的目标是识别和表征相关的SNP标记，以指示SNP衍生的候选基因，并注释它们与肉牛生长过程相关的功能。布罗迪模型显示出最佳的拟合优度，成熟体重（A）和成熟率（K）参数估计值的遗传力分别为0.23和0.32，证明当目标是在遗传改良计划中改变生长曲线形状时，这些性状可以是一种可行的替代方案。A和K之间的遗传相关性为-0.84，表明成熟体重较低的动物在较年轻的年龄达到该体重。分别有167个和262个显著的SNP与A和K相关。与A最显著SNP最接近的注释基因具有与肌肉发育（RAB28）、成肌诱导（BTG1）、胎儿生长（IL2）和体重（APEX2）直接相关的生物学功能；K基因在功能上与体重、身高、平均日增重（TMEM18）和骨骼肌发育（SMN1）相关。从该GWAS中出现的候选基因可能有助于寻找可能支持提高生长率的基因组育种的致病突变。
