Allelic to genome wide perspectives of swine genetic variation to litter size and its component traits

生物 候选基因 遗传力 数量性状位点 遗传学 全基因组关联研究 基因 特质 垃圾箱 基因组 单核苷酸多态性 基因型 生态学 计算机科学 程序设计语言
作者
Sakshi Vaishnav,Anuj Chauhan,Argana Ajay,Babu Lal Saini,Subodh Kumar,Amit Kumar,Bharat Bhushan,Gyanendra Kumar Gaur
出处
期刊:Molecular Biology Reports [Springer Nature]
卷期号:50 (4): 3705-3721 被引量:4
标识
DOI:10.1007/s11033-022-08168-5
摘要

Litter size is a complex and sex limited trait that depends on various biological, managemental and environmental factors. Owing to its low heritability it is inefficaciously selected by traditional methods. However, due to higher heritability of ovulation rate and embryo survival, selection based on component traits of litter size is advocated. QTL analysis and candidate gene approach are among the various supplementary/alternate strategies for selection of litter size. QTL analysis is aimed at identifying genomic regions affecting trait of interest significantly. Candidate gene approach necessitates identification of genes potentially affecting the trait. There are various genes that significantly affect litter size and its component traits viz. ESR, LEP, BF, IGFBP, RBP4, PRLR, CTNNAL1, WNT10B, TCF12, DAZ, and RNF4. These genes affect litter size in a complex interacting manner. Lately, genome wide association study (GWAS) have been utilized to unveil the genetic and biological background of litter traits, and elucidate the genes governing litter size. Favorable SNPs in these genes have been identified and offers a scope for inclusion in selection programs thereby increasing breeding efficiency and profit in pigs. The review provides a comprehensive coverage of investigations carried out globally to unravel the genetic variation in litter size and its component traits in pigs, both at allelic and genome wide level. It offers a current perspective on different strategies including the profiling of candidate genes, QTLs, and genome wide association studies as an aid to efficient selection for litter size and its component traits.

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