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      Genome-wide genetic structure and selection signatures for color in 10 traditional Chinese yellow-feathered chicken breeds

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          Abstract

          Background

          Yellow-feathered chickens (YFCs) have a long history in China. They are well-known for the nutritional and commercial importance attributable to their yellow color phenotype. Currently, there is a huge paucity in knowledge of the genetic determinants responsible for phenotypic and biochemical properties of these iconic chickens. This study aimed to uncover the genetic structure and the molecular underpinnings of the YFCs trademark coloration.

          Results

          The whole-genomes of 100 YFCs from 10 major traditional breeds and 10 Huaibei partridge chickens from China were re-sequenced. Comparative population genomics based on autosomal single nucleotide polymorphisms (SNPs) revealed three geographically based clusters among the YFCs. Compared to other Chinese indigenous chicken genomes incorporated from previous studies, a closer genetic proximity within YFC breeds than between YFC breeds and other chicken populations is evident. Through genome-wide scans for selective sweeps, we identified RALY heterogeneous nuclear ribonucleoprotein ( RALY), leucine rich repeat containing G protein-coupled receptor 4 ( LGR4), solute carrier family 23 member 2 ( SLC23A2), and solute carrier family 2 member 14 ( SLC2A14), besides the classical beta-carotene dioxygenase 2 ( BCDO2), as major candidates pigment determining genes in the YFCs.

          Conclusion

          We provide the first comprehensive genomic data of the YFCs. Our analyses show phylogeographical patterns among the YFCs and potential candidate genes giving rise to the yellow color trait of the YFCs. This study lays the foundation for further research on the genome-phenotype cross-talks that define important poultry traits and for formulating genetic breeding and conservation strategies for the YFCs.

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          Most cited references37

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          Mapping and sequencing of structural variation from eight human genomes.

          Genetic variation among individual humans occurs on many different scales, ranging from gross alterations in the human karyotype to single nucleotide changes. Here we explore variation on an intermediate scale--particularly insertions, deletions and inversions affecting from a few thousand to a few million base pairs. We employed a clone-based method to interrogate this intermediate structural variation in eight individuals of diverse geographic ancestry. Our analysis provides a comprehensive overview of the normal pattern of structural variation present in these genomes, refining the location of 1,695 structural variants. We find that 50% were seen in more than one individual and that nearly half lay outside regions of the genome previously described as structurally variant. We discover 525 new insertion sequences that are not present in the human reference genome and show that many of these are variable in copy number between individuals. Complete sequencing of 261 structural variants reveals considerable locus complexity and provides insights into the different mutational processes that have shaped the human genome. These data provide the first high-resolution sequence map of human structural variation--a standard for genotyping platforms and a prelude to future individual genome sequencing projects.
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            Toward better understanding of artifacts in variant calling from high-coverage samples.

            Heng Li (2014)
            Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods. We made 10 single nucleotide polymorphism and INDEL call sets with two read mappers and five variant callers, both on a haploid human genome and a diploid genome at a similar coverage. By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas. We estimated that the error rate of raw genotype calls is as high as 1 in 10-15 kb, but the error rate of post-filtered calls is reduced to 1 in 100-200 kb without significant compromise on the sensitivity. BWA-MEM alignment and raw variant calls are available at http://bit.ly/1g8XqRt scripts and miscellaneous data at https://github.com/lh3/varcmp. hengli@broadinstitute.org Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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              Genetic structure of the Han Chinese population revealed by genome-wide SNP variation.

              Population stratification is a potential problem for genome-wide association studies (GWAS), confounding results and causing spurious associations. Hence, understanding how allele frequencies vary across geographic regions or among subpopulations is an important prelude to analyzing GWAS data. Using over 350,000 genome-wide autosomal SNPs in over 6000 Han Chinese samples from ten provinces of China, our study revealed a one-dimensional "north-south" population structure and a close correlation between geography and the genetic structure of the Han Chinese. The north-south population structure is consistent with the historical migration pattern of the Han Chinese population. Metropolitan cities in China were, however, more diffused "outliers," probably because of the impact of modern migration of peoples. At a very local scale within the Guangdong province, we observed evidence of population structure among dialect groups, probably on account of endogamy within these dialects. Via simulation, we show that empirical levels of population structure observed across modern China can cause spurious associations in GWAS if not properly handled. In the Han Chinese, geographic matching is a good proxy for genetic matching, particularly in validation and candidate-gene studies in which population stratification cannot be directly accessed and accounted for because of the lack of genome-wide data, with the exception of the metropolitan cities, where geographical location is no longer a good indicator of ancestral origin. Our findings are important for designing GWAS in the Chinese population, an activity that is expected to intensify greatly in the near future.

                Author and article information

                Contributors
                zhangyp@mail.kiz.ac.cn
                xqzhang@scau.edu.cn
                dudu903@163.com
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                20 April 2020
                20 April 2020
                2020
                : 21
                : 316
                Affiliations
                [1 ]GRID grid.443485.a, Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Guangdong Innovation Centre for Science and Technology of Wuhua Yellow Chicken, , School of Life Science of Jiaying University, ; Meizhou, 514015 China
                [2 ]ISNI 0000000119573309, GRID grid.9227.e, State Key Laboratory of Genetic Resources and Evolution and Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, , Chinese Academy of Sciences, ; Kunming, 650223 China
                [3 ]Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204 China
                [4 ]GRID grid.257160.7, College of Animal Science and Technology, , Hunan Agricultural University, ; Changsha, 410128 China
                [5 ]ISNI 0000 0004 1760 4804, GRID grid.411389.6, College of Animal Science and Technology, , Anhui Agricultural University, ; Hefei, 230036 China
                [6 ]ISNI 0000 0000 9546 5767, GRID grid.20561.30, College of Animal Sciences, , South China Agricultural University, ; Guangzhou, 510642 China
                [7 ]ISNI 0000 0001 0526 1937, GRID grid.410727.7, CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, , Chinese Academy of Agricultural Sciences (CAAS), ; Beijing, 100193 China
                [8 ]GRID grid.419369.0, International Livestock Research Institute (ILRI), ; Nairobi, 30709-00100 Kenya
                [9 ]ISNI 0000 0000 9146 7108, GRID grid.411943.a, Animal Biotechnology Group, Institute For Biotechnology Research, , Jomo Kenyatta University of Agriculture and Technology, ; Nairobi, 62000-00200 Kenya
                [10 ]GRID grid.440773.3, State Key Laboratory for Conservation and Utilization of Bio-resources in Yunnan, , Yunnan University, ; Kunming, 650091 China
                [11 ]ISNI 0000000119573309, GRID grid.9227.e, Center for Excellence in Animal Evolution and Genetics, , Chinese Academy of Sciences, ; Kunming, 650223 China
                Article
                6736
                10.1186/s12864-020-6736-4
                7171827
                32312230
                0763df42-d837-4577-8d7f-ab1c82928bc4
                © The Author(s). 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 12 December 2019
                : 15 April 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100012245, Science and Technology Planning Project of Guangdong Province;
                Award ID: 2016A030303068
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003453, Natural Science Foundation of Guangdong Province;
                Award ID: 2014A030307018
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Genetics
                yellow,chicken,genome,bcdo2,breeding,color,genetic diversity
                Genetics
                yellow, chicken, genome, bcdo2, breeding, color, genetic diversity

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