Genetic mapping using 1.4M SNP array refined loci for fatty acid composition traits in Chinese Erhualian and Bamaxiang pigs
Genetic mapping using 1.4M SNP array refined loci for fatty acid composition traits in Chinese Erhualian and Bamaxiang pigs
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利用 1.4M SNP 阵列精化位点对中国二花莲和八马香猪脂肪酸组成性状进行遗传作图
DOI:
10.1111/jbg.12297
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发表时间:
2017-09
影响因子:
2.6
通讯作者:
Yang B
中科院分区:
文献类型:
--
作者:
Zhang J;Zhang Y;Gong H;Cui L;Huang T;Ai H;Ren J;Huang L;Yang B
SummaryChinese indigenous pigs display marked genetic and phenotypic differences compared with western commercial pigs. In this study, we tested the association between 660K SNPs and longissimus muscle fatty acid composition traits in Chinese Erhualian (n = 331) and Bamaxiang (n = 315) pigs based on a customized 1.4 million SNP array. We identified a total of 64 significant associations for 20 fatty acid composition traits at the p‐value threshold of 1 × 10−6 among which 42 associations in low linkage disequilibrium (r2 .2) with previously reported loci were considered novel. We substantially improved the strength and precision of the associations at four previously detected loci near FADS2, ELOVL7, ELOVL6 and FASN genes, facilitating follow‐up candidate gene studies. Moreover, we also identified loci near ABCD2, ACSBG1, ELOVL5, HPGDS, DAGT2, ACAD10 and ACSL1 genes with function relevant to metabolism of fatty acids. In this study, valuable genetic variants and candidate genes associated with fatty acid composition traits were identified in Erhualian and Bamaxiang pigs. Some identified loci could be used to improve pork nutrition in pig breeding practice. Using the SNP array with higher marker density and less ascertainment bias improved QTL detection power and precision in Chinese indigenous pigs.
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影响因子:
3.7
作者:
Corominas J;Ramayo-Caldas Y;Puig-Oliveras A;Pérez-Montarelo D;Noguera JL;Folch JM;Ballester M
通讯作者:
Ballester M
影响因子:
30.8
作者:
Zhou, Xiang;Stephens, Matthew
通讯作者:
Stephens, Matthew
影响因子:
3.7
作者:
Ai H;Huang L;Ren J
通讯作者:
Ren J
影响因子:
64.8
作者:
通讯作者:
--
影响因子:
2.9
作者:
Ibeagha-Awemu EM;Akwanji KA;Beaudoin F;Zhao X
通讯作者:
Zhao X