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      Whole genome sequencing identified genomic diversity and candidated genes associated with economic traits in Northeasern Merino in China

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          Abstract

          Introduction: Northeast Merino (NMS) is a breed developed in Northeast China during the 1960s for wool and meat production. It exhibits excellent traits such as high wool yield, superior meat quality, rapid growth rate, robust disease resistance, and adaptability to cold climates. However, no studies have used whole-genome sequencing data to investigate the superior traits of NMS.

          Methods: In this study, we investigated the population structure, genetic diversity, and selection signals of NMS using whole-genome sequencing data from 20 individuals. Two methods (integrated haplotype score and composite likelihood ratio) were used for selection signal analysis, and the Fixation Index was used to explore the selection signals of NMS and the other two breeds, Mongolian sheep and South African meat Merino.

          Results: The results showed that NMS had low inbreeding levels, high genomic diversity, and a pedigree of both Merino breeds and Chinese local breeds. A total length of 14.09 Mb genomic region containing 287 genes was detected using the two methods. Further exploration of the functions of these genes revealed that they are mainly concentrated in wool production performance ( IRF2BP2, MAP3K7, and WNT3), meat production performance ( NDUFA9, SETBP1, ZBTB38, and FTO), cold resistance ( DNAJC13, LPGAT1, and PRDM16), and immune response ( PRDM2, GALNT8, and HCAR2). The selection signals of NMS and the other two breeds annotated 87 and 23 genes, respectively. These genes were also mainly focused on wool and meat production performance.

          Conclusion: These results provide a basis for further breeding improvement, comprehensive use of this breed, and a reference for research on other breeds.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

            The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2522966/overviewRole: Role:
                Role: Role: Role: Role:
                Role: Role: Role:
                Role: Role:
                Role: Role: Role:
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                URI : https://loop.frontiersin.org/people/2033091/overviewRole: Role: Role:
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                25 January 2024
                2024
                : 15
                : 1302222
                Affiliations
                [1] 1 College of Animal Science , Jilin University , Changchun, China
                [2] 2 College of Agriculture , Yanbian University , Yanji, China
                [3] 3 Institute of Animal Husbandry and Veterinary , Jilin Academy of Agricultural Sciences , Gongzhuling, China
                Author notes

                Edited by: Mario Barbato, University of Messina, Italy

                Reviewed by: Eui-Soo Kim, Recombinetics, United States

                Samanta Mecocci, University of Perugia, Italy

                *Correspondence: Shouqing Yan, yansq@ 123456jlu.edu.cn ; Zhongli Zhao, zhaozhongli954@ 123456sohu.com
                [ † ]

                These authors have contributed equally to this work and share first authorship

                Article
                1302222
                10.3389/fgene.2024.1302222
                10851152
                38333624
                7b47a556-a05d-4ee4-a701-2a72c9346bc0
                Copyright © 2024 Yi, Hu, Shi, Li, Bai, Sun, Ma, Zhao and Yan.

                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
                : 26 September 2023
                : 12 January 2024
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Science and Technology Development Project of Jilin Province, China (no. 20230202069NC).
                Categories
                Genetics
                Original Research
                Custom metadata
                Livestock Genomics

                Genetics
                northeast merino,whole-genome sequencing,genetic diversity,population structure,selection signatures

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