Using High-Density SNP Array to Reveal Selection Signatures Related to Prolificacy in Chinese and Kazakhstan Sheep Breeds.

Yi Wang,Yugong Ding,Yao Jiang,Xiangdong Ding,Zhengcheng Zeng,Sen Tang,Hongcai Shi,Zhigang Niu,Yifan Jiang

doi:10.3390/ani10091633

Abstract

Simple SummaryGenetic improvement of litter size trait in domestic animals is an appealing way to improve production efficiency. In our study, the selection signatures between multiparous and uniparous sheep populations are identified, so that potential pathways and candidate genes related to litter size were screened out. Our findings help better understand the mechanisms of selection underlying the prolificacy trait in sheep and other mammals.Selection signature provides an efficient tool to explore genes related to traits of interest. In this study, 176 ewes from one Chinese uniparous breed and three Kazakhstan multiparous breeds are genotyped using Affymetrix 600K HD single nucleotide polymorphism (SNP) arrays, F-statistics (Fst), and a Cross Population Extend Haplotype Homozygosity Test (XPEHH). These are conducted to identify genomic regions that might be under selection in three population pairs comprised the one multiparous breed and the uniparous breed. A total of 177 and 3072 common selective signatures were identified by Fst and XPEHH test, respectively. Nearly half of the common signatures detected by Fst were also captured by XPEHH test. In addition, 1337 positive and 1735 common negative signatures were observed by XPEHH in three Kazakhstan multiparous breeds. In total, 242 and 798 genes were identified in selective regions and positive selective regions identified by Fst and XPEHH, respectively. These genes were further clustered in 50 gene ontology (GO) functional terms and 66 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in enrichment analysis. The GO terms and pathways were relevant with reproductive processes, e.g., oxytocin signaling pathway, thyroid hormone synthesis and GnRH signaling pathway, vascular smooth muscle contraction and lipid metabolism (alpha-Linolenic acid metabolism and Linoleic acid metabolism), etc. Based on the findings, six potential candidate genes ESR1, OXTR, MAPK1, RYR1, PDIA4, and CYP19A1, under positive selection related to characteristics of multiparous sheep breeds were revealed. Our results improve our understanding of the mechanisms of selection that underlies the prolificacy trait in sheep, and provide essential references for future sheep breeding.

Highlights

From the point of view of population genetics, when a novel mutation is subjected to the selection pressure over a long time, it will generate “selection signature”, demonstrating some distinguished features on the genome, e.g., unusual linkage disequilibrium (LD) and changed population frequency [1].identifying the selection signatures underlying phenotypic difference can contribute to target causal variants for breeding, as well as explore the mechanisms of evolution
Figure 1component shows theanalysis results demonstrated of populationthe genetics analysis among four sheep populations
As the haplotype-based method, XPEHH detected the core single nucleotide polymorphism (SNP) as the representative of the corresponding fixed regions based on haplotype, it can utilize more information of LD within one region, while the single locus Fst test only calculated the diversity of two loci between populations, even using the slide-window strategy, Fst could not make full use of the SNPs in the window together, and sometimes the division of window is not reasonable, due to the disperse of LD, resulting in the lower efficiency on the detection of selection signatures of Fst

Summary

Introduction

Identifying the selection signatures underlying phenotypic difference can contribute to target causal variants for breeding, as well as explore the mechanisms of evolution. It can help us to reveal the genetic basis of complex traits with phenotypic difference [2,3]. Many methods have been proposed to detect pre-mentioned selection signatures, including Fst test based on population differentiation [6], the integrated Haplotype Homozygosity Score (iHS) [7] and the Cross Population Extend Haplotype. Homozygosity Test (XPEHH) [8] based on linkage disequilibrium, etc. These methods have been widely applied in many studies to identify selection signatures. We employed the Fst and XPEHH test to detect the selection signatures between populations

Methods

Results

Conclusion