Literature DB >> 31364913

Comparative analysis of five different methods to design a breed-specific SNP panel for cattle.

Harshit Kumar1, Manjit Panigrahi1, Supriya Chhotaray1, Subhashree Parida2, Anuj Chauhan1, Bharat Bhushan1, G K Gaur1, B P Mishra3, R K Singh3.   

Abstract

Single nucleotide polymorphisms (SNPs) have now replaced microsatellite markers in several species for various genetic investigations like parentage assignment, genetic breed composition, assessment for individuality and, most popularly, as a useful tool in genomic selection. However, such a resource, which can offer to assist breed identification in a cost-effective manner is still not explored in cattle breeding programs. In our study, we have tried to describe methods for reducing the number of SNPs to develop a breed-specific panel. We have used SNP data from Dryad open public access repository. Starting from a global dataset of 178 animals belonging to 10 different breeds, we selected five panels each comprising of similar number of SNPs using different methods i.e., Delta, Pairwise Wright's FST, informativeness for assignment, frequent item feature selection (FIFS) and minor allele frequency-linkage disequilibrium (MAF-LD) based method. MAF-LD based method has been recently developed by us for construction of breed-specific SNP panels. The STRUCTURE software analysis of MAF-LD based method showed appropriate clustering in comparison to other panels. Later, the panel of 591 breed-specific SNPs was called to their respective breeds using Venny 2.1.0 and UGent web tools software. Breed-specific SNPs were later annotated by using various Bioinformatics softwares.

Entities:  

Keywords:  Informative markers; SNPs; breed-specific; indigenous cattle; linkage disequilibrium; minor allele frequency

Year:  2019        PMID: 31364913     DOI: 10.1080/10495398.2019.1646266

Source DB:  PubMed          Journal:  Anim Biotechnol        ISSN: 1049-5398            Impact factor:   2.282


  1 in total

1.  Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs.

Authors:  Dongwon Seo; Sunghyun Cho; Prabuddha Manjula; Nuri Choi; Young-Kuk Kim; Yeong Jun Koh; Seung Hwan Lee; Hyung-Yong Kim; Jun Heon Lee
Journal:  Animals (Basel)       Date:  2021-01-19       Impact factor: 2.752

  1 in total

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