利用两种统计模型对中国肉用西门塔尔牛屠宰性状的全基因组关联分析

doi:10.11843/j.issn.0366-6964.2018.04.022

Abstract

Abstract:

The objective of this study was to conduct a genome-wide association study (GWAS) for carcass weight and bone weight by using two statistic models in 1 301 Chinese Simmental beef cattle and promote the research of GWAS method by comparing the results of the two models. The samples and phenotype data were obtained by field collection and measurement, and the genotype data were obtained by Illumina Bovine HDBeadChip (770K) after genomic DNA extracted from samples. Two statistic models were used to conduct the GWAS, which including linear mixed model (LMM) and composite interval mapping-linear mixed model (CIM-LMM), and the significant single nucleotide polymorphisms (SNPs) and candidate genes associated with target traits were identified. In addition, the performance of the models was assessed according to GWAS results. The results showed that the more significant SNPs were detected by CIM-LMM (P<0.05), which overlapped the SNPs detected by LMM and performed a higher detection power than LMM.There were 8 and 7 SNPs associated with carcass weight and bone weight, respectively and 2 SNPs associated with both traits. These SNPs were mapped on chromosome 3, 5, 6, 10, 14, 16 and 17, while mainly focused on chromosome 6 and 14. Finally, 11 candidate genes were identified and the functions of GCNT4, ALDH1A2, LCORL and WDFY3 genes were discussed. This research further explore the potential genetic mechanism of carcass traits in Chinese Simmental beef cattle and offer new ideas for GWAS methods.

CLC Number:

S823.2

CHANG Tian-peng, XIA Jiang-wei, BAO Jin-shan, JIN Sheng-yun, ZHU Bo, XU Ling-yang, CHEN Yan, ZHANG Lu-pei, GAO Xue, LI Jun-ya, GAO Hui-jiang. Genome-wide Association Study for Carcass Traits Using Two Statistic Models in Chinese Simmental Beef Cattle[J]. ACTA VETERINARIA ET ZOOTECHNICA SINICA, 2018, 49(4): 833-840.

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Genome-wide Association Study for Carcass Traits Using Two Statistic Models in Chinese Simmental Beef Cattle

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