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      Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population

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          Abstract

          Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a weighted single-step genome-wide association study ( wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight ( CW), eviscerated weight with giblets ( EWG), eviscerated weight ( EW), breast muscle weight ( BMW), drumstick weight ( DW), abdominal fat weight ( AFW), abdominal fat percentage ( AFP), gizzard weight ( GW), and intestine length ( IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development ( CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation ( FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation ( ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2 ). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.

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          Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

          A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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            Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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              Genome-wide panels of SNPs have recently been used in domestic animal species to map and identify genes for many traits and to select genetically desirable livestock. This has led to the discovery of the causal genes and mutations for several single-gene traits but not for complex traits. However, the genetic merit of animals can still be estimated by genomic selection, which uses genome-wide SNP panels as markers and statistical methods that capture the effects of large numbers of SNPs simultaneously. This approach is expected to double the rate of genetic improvement per year in many livestock systems.
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                Author and article information

                Contributors
                Journal
                Poult Sci
                Poult Sci
                Poultry Science
                Elsevier
                0032-5791
                1525-3171
                01 December 2023
                February 2024
                01 December 2023
                : 103
                : 2
                : 103341
                Affiliations
                [* ]State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China
                []Guangdong Xugang Yellow Poultry Seed Industry Group Co., Ltd, Suzhou City, Jiangsu Province, China
                []Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China
                [§ ]Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
                Author notes
                [2 ]Corresponding author: luowen729@ 123456scau.edu.cn
                [1]

                These authors contributed equally to this work.

                Article
                S0032-5791(23)00861-1 103341
                10.1016/j.psj.2023.103341
                10776626
                38134459
                8599d2bc-f3e0-490c-8273-62d2759a3e6f
                © 2023 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 17 September 2023
                : 28 November 2023
                Categories
                GENETICS AND MOLECULAR BIOLOGY

                chinese yellow-feathered chicken,weighted single-step gwas,carcass traits,snp

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