Miao J, Wang X, Bao J, Jin S, Chang T, Xia J, Yang L, Zhu B, Xu L, Zhang L, Gao X, Chen Y, Li J, Gao H
Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
College of Animal Sciences, Fujian Agriculture and Forestry University, Fujian, China.
J Anim Breed Genet. 2018 Jun;135(3):159-169. doi: 10.1111/jbg.12326. Epub 2018 Apr 26.
Bone weight, defined as the total weight of the bones in all the forequarter and hindquarter joints, can reflect somebody conformation traits and skeletal diseases. To gain a better understanding of the genetic determinants of bone weight, we used a composite strategy including multimarker and rare-marker association to perform genomewide association studies (GWAS) for that character in Simmental cattle. Our strategy consisted of three models: (i) A traditional linear mixed model (LMM) was applied (Q+K-LMM); (ii) single nucleotide polymorphisms (SNPs) with p-values less than .05 from the LMM were selected to undergo the least absolute shrinkage and selector operator (Lasso) in the second stage (LMM-Lasso); (iii) genes containing two or more rare SNPs were examined by performing the sequence kernel association test (gene-based SKAT). A total of 1,225 cattle were genotyped with an Illumina BovineHD BeadChip containing 770,000 SNPs. After the quality-control procedures, 1,217 individuals with 608,696 common SNPs and 105,787 rare SNPs (with 0.001 < minor allele frequency [MAF] <0.05) remained in the sample for analysis. A traditional LMM successfully mapped three genes associated with bone weight, while LMM-Lasso identified nine genes, which included all genes found by traditional LMM. Only a single gene, EPHB3, surpassed the significance threshold after Bonferroni correction in gene-based SKAT. In conclusion, based on functional annotation and results from previous endeavours, we believe that LCORL, RIMS2, LAP3, PRKAR2B, CHSY1, MAP2K6 and EPHB3 are candidate genes for bone weight. In general, such a comprehensive strategy for GWAS may be useful for researchers seeking to probe the full genetic architecture underlying economic traits in livestock.
骨重定义为所有前肢和后肢关节中骨骼的总重量,它能够反映个体的体型特征和骨骼疾病。为了更好地了解骨重的遗传决定因素,我们采用了一种综合策略,包括多标记和稀有标记关联,对西门塔尔牛的该性状进行全基因组关联研究(GWAS)。我们的策略包括三个模型:(i)应用传统的线性混合模型(LMM)(Q + K - LMM);(ii)从LMM中选择p值小于0.05的单核苷酸多态性(SNP),在第二阶段进行最小绝对收缩和选择算子(Lasso)分析(LMM - Lasso);(iii)通过执行序列核关联检验(基于基因的SKAT)来检测包含两个或更多稀有SNP的基因。共有1225头牛使用包含770,000个SNP的Illumina BovineHD BeadChip进行基因分型。经过质量控制程序后,样本中剩余1217个个体,包含608,696个常见SNP和105,787个稀有SNP(次要等位基因频率[MAF]为0.001 < MAF < 0.05)用于分析。传统的LMM成功定位了三个与骨重相关的基因,而LMM - Lasso鉴定出九个基因,其中包括传统LMM发现的所有基因。在基于基因的SKAT中,只有一个基因EPHB3在Bonferroni校正后超过了显著性阈值。总之,基于功能注释和先前研究的结果,我们认为LCORL、RIMS2、LAP3、PRKAR2B、CHSY1、MAP2K6和EPHB3是骨重的候选基因。一般来说,这种全面的GWAS策略可能对寻求探究家畜经济性状潜在完整遗传结构的研究人员有用。