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Integrated Analysis Reveals Genetic Basis of Growth Curve Parameters in an F 2 Designed Pig Population Based on Genome and Transcriptome Data

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  • Zhaoxuan Che

    (Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China)

  • Jiakun Qiao

    (Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China)

  • Fangjun Xu

    (Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China)

  • Xinyun Li

    (Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
    The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China)

  • Yunxia Zhao

    (Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
    The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China)

  • Mengjin Zhu

    (Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
    The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China)

Abstract

Appropriate growth curves can reflect more sophisticated growth patterns of animals than body weight, and thus, the identification of genes and variants related to the growth curve parameter traits contributes to revealing the fine growth and development characteristics of livestock. However, the ability of single genome-wide association analysis (GWAS) and transcriptome analyses to identify valuable genes and variants is limited. In this study, based on genome and transcriptome data, the growth curve parameter traits of hybrid pigs were analyzed, and a set of genes and variants were identified. The Gompertz–Laird growth curve model was optimized to reveal the growth pattern of F 2 individuals of Duroc × Erhualian pigs over four time points. Five growth parameters were estimated, including initial body weight ( W 0 ) , instantaneous growth rate per day ( L ), coefficient of relative growth or maturing index ( k ), body weight at inflection point ( W i ) , and average growth rate ( GR ). These five parameters were subjected to a genome-wide association study, differential gene expression analysis, and weighted gene co-expression network analysis (WGCNA). In the study, 336 pigs were genotyped, and 39,494 SNP markers were used for each pig in the analysis. Thirty of these pigs were also included in the transcriptomics analysis. Based on genome and transcriptome data, the integrated analyses identified five putative SNPs (including INRA0056566 on chromosome X, DRGA0004151 on chromosome 3, INRA0056460 on chromosome X, H3GA0049324 on chromosome 17, and H3GA0037747 on chromosome 13) and 15 candidate genes ( PDGFA , VEGFD , CSPP1 , EFHC1 , PIK3C3 , ZZZ3 , GCC2 , MAPK14 , ZPR1 , ISG15 , ANG , CEBPD , ZHX3 , CTBP2 , and MYNN ). The functional analysis indicated that these candidate genes played important roles in cell division and differentiation, development and aging, and skeletal muscle and fat formation. Our results provide insight into the genetic mechanisms underlying the growth and development of hybrid pigs and offer a theoretical basis for genomic breeding.

Suggested Citation

  • Zhaoxuan Che & Jiakun Qiao & Fangjun Xu & Xinyun Li & Yunxia Zhao & Mengjin Zhu, 2024. "Integrated Analysis Reveals Genetic Basis of Growth Curve Parameters in an F 2 Designed Pig Population Based on Genome and Transcriptome Data," Agriculture, MDPI, vol. 14(10), pages 1-18, September.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1704-:d:1488391
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    References listed on IDEAS

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    1. Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
    2. Rohan Fernando & Ali Toosi & Anna Wolc & Dorian Garrick & Jack Dekkers, 2017. "Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 172-193, June.
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