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      Genome-Wide Scanning for Signatures of Selection Revealed the Putative Genomic Regions and Candidate Genes Controlling Milk Composition and Coat Color Traits in Sahiwal Cattle

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          Abstract

          Background

          In the evolutionary time scale, selection shapes the genetic variation and alters the architecture of genome in the organisms. Selection leaves detectable signatures at the genomic coordinates that provide clues about the protein-coding regions. Sahiwal is a valuable indicine cattle adapted to tropical environments with desirable milk attributes. Insights into the genomic regions under putative selection may reveal the molecular mechanisms affecting the quantitative and other important traits. To understand this, the present investigation was undertaken to explore signatures of selection in the genome of Sahiwal cattle using a medium-density genotyping INDUS chip.

          Result

          De-correlated composite of multiple selection signals (DCMS), which combines five different univariate statistics, was computed in the dataset to detect the signatures of selection in the Sahiwal genome. Gene annotations, Quantitative Trait Loci (QTL) enrichment, and functional analyses were carried out for the identification of significant genomic regions. A total of 117 genes were identified, which affect a number of important economic traits. The QTL enrichment analysis highlighted 14 significant [False Discovery Rate (FDR)-corrected p-value ≤ 0.05] regions on chromosomes BTA 1, 3, 6, 11, 20, and 21. The top three enriched QTLs were found on BTA 6, 20, and 23, which are associated with exterior, health, milk production, and reproduction traits. The present study on selection signatures revealed some key genes related with coat color ( PDGFRA, KIT, and KDR), facial pigmentation ( LEF), milk fat percent ( MAP3K1, HADH, CYP2U1, and SGMS2), sperm membrane integrity ( OSTC), lactation persistency ( MRPS30, NNT, CCL28, HMGCS1, NIM1K, ZNF131, and CCDC152), milk yield ( GHR and ZNF469), reproduction ( NKX2-1 and DENND1A), and bovine tuberculosis susceptibility ( RNF144B and PAPSS1). Further analysis of candidate gene prioritization identified four hub genes, viz., KIT, KDR, MAP3K1, and LEF, which play a role in coat color, facial pigmentation, and milk fat percentage in cattle. Gene enrichment analysis revealed significant Gene ontology (GO) terms related to breed-specific coat color and milk fat percent.

          Conclusion

          The key candidate genes and putative genomic regions associated with economic traits were identified in Sahiwal using single nucleotide polymorphism data and the DCMS method. It revealed selection for milk production, coat color, and adaptability to tropical climate. The knowledge about signatures of selection and candidate genes affecting phenotypes have provided a background information that can be further utilized to understand the underlying mechanism involved in these traits in Sahiwal cattle.

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          Most cited references82

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            PLINK: a tool set for whole-genome association and population-based linkage analyses.

            Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              The variant call format and VCFtools

              Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                09 July 2021
                2021
                : 12
                : 699422
                Affiliations
                [1] 1Division of Animal Genetics and Breeding, Indian Council of Agricultural Research-National Dairy Research Institute , Karnal, India
                [2] 2Artificial Breeding Research Center, Indian Council of Agricultural Research-National Dairy Research Institute , Karnal, India
                Author notes

                Edited by: Sunday O. Peters, Berry College, United States

                Reviewed by: Guillermo Giovambattista, CONICET Institute of Veterinary Genetics (IGEVET), Argentina; Qianjun Zhao, Chinese Academy of Agricultural Sciences (CAAS), China

                *Correspondence: Anupama Mukherjee, writetoanupama@ 123456gmail.com

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2021.699422
                8299338
                34306039
                cd89768e-93ad-4dde-bbb9-d56256d54d8a
                Copyright © 2021 Illa, Mukherjee, Nath and Mukherjee.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 April 2021
                : 14 June 2021
                Page count
                Figures: 2, Tables: 3, Equations: 1, References: 83, Pages: 14, Words: 0
                Categories
                Genetics
                Original Research

                Genetics
                indigenous cattle,snp genotyping,selection signatures,gene identification,dcms,gene
                Genetics
                indigenous cattle, snp genotyping, selection signatures, gene identification, dcms, gene

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