Population structure, genetic diversity and prolificacy in pishan red sheep under an extreme desert environment

Chenglong, Zhang; Zhang, Jihu; Tuersuntuoheti, Mirenisa; Chang, Qianqian; Liu, Shudong

doi:10.3389/fgene.2023.1092066

Cited by 3 publications

(1 citation statement)

References 62 publications

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“…Next, we will further improve VCF2PCACluster by adopting additional input formats, such as PLINK2, and implementing more clustering algorithm for users to choose from. Since its initial release one year ago, four studies have used VCF2PCACluster (previously known as MingPCACluster) [ 19 , 20 ] to conduct their analyses and visualize the results.…”

Section: Discussionmentioning

confidence: 99%

VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs

He,

Xu,

Wang

et al. 2024

BMC Bioinformatics

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Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

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Section: Discussionmentioning

confidence: 99%

VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs

He,

Xu,

Wang

et al. 2024

BMC Bioinformatics

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Genetic analysis and characterization of reproductive control genes in Belgian and Qinchuan cattle population structure

Li,

Zhang,

Wang

et al. 2024

Preprint

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The objective of this study was to explore the genetic structure of Belgian and Qinchuan cattle, with a particular emphasis on identifying genes associated with reproductive functions.A total of 270 Belgian and 286 Qinchuan cattle were genotyped using the Illumina Bovine SNP 50K microarray. Data processing was conducted using PLINK and Beagle 5.1 to calculate linkage disequilibrium (LD) and effective population size (Ne).Candidate SNP loci were identified by selecting the top 5% based on the fixation index (Fst) and nucleotide diversity (PI), followed by gene annotation.The analysis revealed 160 candidate genes in Qinchuan cattle and 98 candidate genes in Belgian Red and Belgian Red and White cattle. Key genes associated with reproductive function, including NFKBIA, PTHLH, UGT2B10, TRPC4, and ALOX5AP, were identified.This study provides valuable molecular markers for the genetic improvement and selective breeding of Belgian cattle, particularly in enhancing their reproductive efficiency.

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Population structure and selective signature of Kirghiz sheep by Illumina Ovine SNP50 BeadChip

Yang,

Han,

Zhou

et al. 2024

PeerJ

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Objective By assessing the genetic diversity and associated selective traits of Kirghiz sheep (KIR), we aim to uncover the mechanisms that contribute to sheep’s adaptability to the Pamir Plateau environment. Methods This study utilized Illumina Ovine SNP50 BeadChip data from KIR residing in the Pamir Plateau, Qira Black sheep (QBS) inhabiting the Taklamakan Desert, and commonly introduced breeds including Dorper sheep (DOR), Suffolk sheep (SUF), and Hu sheep (HU). The data was analyzed using principal component analysis, phylogenetic analysis, population admixture analysis, kinship matrix analysis, linkage disequilibrium analysis, and selective signature analysis. We employed four methods for selective signature analysis: fixation index (Fst), cross-population extended homozygosity (XP-EHH), integrated haplotype score (iHS), and nucleotide diversity (Pi). These methods aim to uncover the genetic mechanisms underlying the germplasm resources of Kirghiz sheep, enhance their production traits, and explore their adaptation to challenging environmental conditions. Results The test results unveiled potential selective signals associated with adaptive traits and growth characteristics in sheep under harsh environmental conditions, and annotated the corresponding genes accordingly. These genes encompass various functionalities such as adaptations associated with plateau, cold, and arid environment (ETAA1, UBE3D, TLE4, NXPH1, MAT2B, PPARGC1A, VEGFA, TBX15 and PLXNA4), wool traits (LMO3, TRPS1, EPHA5), body size traits (PLXNA2, EFNA5), reproductive traits (PPP3CA, PDHA2, NTRK2), and immunity (GATA3). Conclusion Our study identified candidate genes associated with the production traits and adaptation to the harsh environment of the Pamir Plateau in Kirghiz sheep. These findings provide valuable resources for local sheep breeding programs. The objective of this study is to offer valuable insights for the sustainable development of the Kirghiz sheep industry.

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Population structure, genetic diversity and prolificacy in pishan red sheep under an extreme desert environment

Cited by 3 publications

References 62 publications

VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs

VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs

Genetic analysis and characterization of reproductive control genes in Belgian and Qinchuan cattle population structure

Population structure and selective signature of Kirghiz sheep by Illumina Ovine SNP50 BeadChip

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