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Volume 51 Issue 12
Dec.  2024
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Article Navigation >  Journal of Genetics and Genomics  > 2024  >  51(12): 1423-1434

Age-dependent genetic architectures of chicken body weight explored by multidimensional GWAS and molQTL analyses

doi: 10.1016/j.jgg.2024.09.003
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    a. State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100193, China;

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    b. Department of Animal Science, University of California, Davis, CA 95616, USA;

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    c. Jiangsu Institute of Poultry Science, Yangzhou, Jiangsu 225125, China;

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    d. Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus 8000, Denmark

Funds:

This work was supported by the National Key Research and Development Program of China (2022YFF1000204 and 2021YFD1300600), STI 2030—Major Projects (2023ZD04052), the Open Projects of Key Laboratory for Poultry Genetics and Breeding of Jiangsu Province (JQLAB-KF-202301), China Agriculture Research Systems (CARS-40), and the 2115 Talent Development Program of China Agricultural University.

  • Received Date: 2024-04-24
  • Accepted Date: 2024-09-03
  • Rev Recd Date: 2024-09-02
    • Available Online: 2025-06-05
  • Publish Date: 2024-09-19
    Abstract

  • Chicken body weight (BW) is a critical trait in breeding. Although genetic variants associated with BW have been investigated by genome-wide association studies (GWAS), the contributions of causal variants and their molecular mechanisms remain largely unclear in chickens. In this study, we construct a comprehensive genetic atlas of chicken BW by integrative analysis of 30 age points and 5 quantitative trait loci (QTL) across 27 tissues. We find that chicken growth is a cumulative non-linear process, which can be divided into three distinct stages. Our GWAS analysis reveals that BW-related genetic variations show ordered patterns in these three stages. Genetic variations in chromosome 1 may regulate the overall growth process, likely by modulating the hypothalamus-specific expression of SLC25A30 and retina-specific expression of NEK3. Moreover, genetic variations in chromosome 4 and chromosome 27 may play dominant roles in regulating BW during Stage 2 (8-22 weeks) and Stage 3 (23-72 weeks), respectively. In summary, our study presents a comprehensive genetic atlas regulating developmental stage-specific changes in chicken BW, thus providing important resources for genomic selection in breeding programs.

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  • The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K.G., Gabriel, S., Getz, G.A., Graubert, A., Hadley, K., Handsaker, R.E., Huang, K.H., et al., 2020. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318-1330.
    Aulchenko, Y.S., Ripke, S., Isaacs, A., Van Duijn, C.M., 2007. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294-1296.
    Bahry, M.A., Hanlon, C., Ziezold, C.J., Schaus, S., Bedecarrats, G.Y., 2023. Impact of growth trajectory on sexual maturation in layer chickens. Front Physiol. 14, 1174238.
    Barbeira, A.N., Bonazzola, R., Gamazon, E.R., Liang, Y.Y., Park, Y., Kim-Hellmuth, S., Wang, G., Jiang, Z.X., Zhou, D., Hormozdiari, F., et al., 2021. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49.
    Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., Golani, I., 2001. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279-284.
    Beyene, J., Hamid, J.S., 2014. Longitudinal data analysis in genome-wide association studies. Genet. Epidemiol. 38, S68-S73.
    Bolormaa, S., Pryce, J.E., Reverter, A., Zhang, Y., Barendse, W., Kemper, K., Tier, B., Savin, K., Hayes, B.J., Goddard, M.E., 2014. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet. 10, e1004198.
    Browning, B.L., Browning, S.R., 2009. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84, 210-223.
    Buonomo, F.C., Baile, C.A., 1990. The neurophysiological regulation of growth hormone secretion. Domest Anim. Endocrinol. 7, 435-450.
    Cai, W.T., Zhang, Y.P., Chang, T.P., Wang, Z.Z., Zhu, B., Chen, Y., Gao, X., Xu, L.Y., Zhang, L.P., Gao, H.J., et al., 2023. The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in simmental beef cattle. J. Anim. Sci. Biotechnol. 14, 78.
    Chen, J.H., Ren, X.Y., Li, L.M., Lu, S.Y., Chen, T., Tan, L.T., Liu, M.Q., Luo, Q.B., Liang, S.D., Nie, Q.H., et al., 2019. Integrative analyses of mRNA expression profile reveal the involvement of IGF2BP1 in chicken adipogenesis. Int. J. Mol. Sci. 20, 2923.
    Cho, H.I., Kim, M.S., Lee, J., Yoo, B.C., Kim, K.H., Choe, K.M., Jang, Y.K., 2020. BRPF3-HUWE1-mediated regulation of MYST2 is required for differentiation and cell-cycle progression in embryonic stem cells. Cell Death Differ. 27, 3273-3288.
    Crispim, A.C., Kelly, M.J., Guimaraes, S.E., E Silva, F.F., Fortes, M.R., Wenceslau, R.R., Moore, S., 2015. Multi-trait GWAS and new candidate genes annotation for growth curve parameters in brahman cattle. PLoS One 10, e0139906.
    Dantzer, R., 2018. Neuroimmune Interactions: from the brain to the immune system and vice versa. Physiol. Rev. 98, 477-504.
    Das, K., Li, J., Wang, Z., Tong, C., Fu, G., Li, Y., Xu, M., Ahn, K., Mauger, D., Li, R., et al., 2011. A dynamic model for genome-wide association studies. Hum. Genet. 129, 629-639.
    De Mello, F., Oliveira, C.A., Ribeiro, R.P., Resende, E.K., Povh, J.A., Fornari, D.C., Barreto, R.V., McManus, C., Streit, D., 2015. Growth curve by Gompertz nonlinear regression model in female and males in tambaqui (Colossoma macropomum). An Acad. Bras. Cienc. 87, 2309-2315.
    Dou, T.C., Shen, M.M., Ma, M., Qu, L., Li, Y.F., Hu, Y.P., Lu, J., Guo, J., Wang, X.G., Wang, K.H., 2019. Genetic architecture and candidate genes detected for chicken internal organ weight with a 600 K single nucleotide polymorphism array. Asian-Australas. J. Anim. Sci. 32, 341-349.
    Fan, R., Zhang, Y., Albert, P.S., Liu, A., Wang, Y., Xiong, M., 2012. Longitudinal association analysis of quantitative traits. Genet. Epidemiol. 36, 856-869.
    Fang, L.Z., Ben, H., 2022. The cattleGTEx atlas reveals regulatory mechanisms underlying complex traits. Nat. Genet. 54, 1273-1274.
    Finucane, H.K., Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.R., Anttila, V., Xu, H., Zang, C.Z., Farh, K., et al., 2015. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228-1235.
    Furlotte, N.A., Eskin, E., Eyheramendy, S., 2012. Genome-wide association mapping with longitudinal data. Genet. Epidemiol. 36, 463-471.
    Galesloot, T.E., Van Steen, K., Kiemeney, L.A., Janss, L.L., Vermeulen, S.H., 2014. A comparison of multivariate genome-wide association methods. PLoS One 9, e95923.
    Gu, X., Feng, C., Ma, L., Song, C., Wang, Y., Da, Y., Li, H., Chen, K., Ye, S., Ge, C., et al., 2011. Genome-wide association study of body weight in chicken F2 resource population. PLoS One 6, e21872.
    Guan, D., Bai, Z., Zhu, X., Zhong, C., Hou, Y., The Chicken GTEx Consortium, Lan, F., Diao, S., Yao, Y., Zhao, B., et al., 2023. The chickenGTEx pilot analysis: a reference of regulatory variants across 28 chicken tissues. bioRxiv 2023.2006.2027.546670.
    Guo, J., Sun, C.J., Qu, L., Shen, M.M., Dou, T.C., Ma, M., Wang, K.H., Yang, N., 2017. Genetic architecture of bone quality variation in layer chickens revealed by a genome-wide association study. Sci. Rep. 7, 45317.
    Hirschhorn, J.N., Daly, M.J., 2005. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95-108.
    Hoglund, A., Strempfl, K., Fogelholm, J., Wright, D., Henriksen, R., 2020. The genetic regulation of size variation in the transcriptome of the cerebrum in the chicken and its role in domestication and brain size evolution. BMC Genom. 21, 518.
    Hormozdiari, F., Gazal, S., Van De Geijn, B., Finucane, H.K., Ju, C.J.T., Loh, P.R., Schoech, A., Reshef, Y., Liu, X., O’Connor, L., et al., 2018. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041-1047.
    Kaiser, P., Wu, Z., Rothwell, L., Fife, M., Gibson, M., Poh, T.Y., Shini, A., Bryden, W., Shini, S., 2009. Prospects for understanding immune-endocrine interactions in the chicken. Gen. Comp. Endocrinol. 163, 83-91.
    Kirikci, K., Gunlu, A., Cetin, O., Garip, M., 2007. Effect of hen weight on egg production and some egg quality characteristics in the partridge (Alectoris graeca). Poult. Sci. 86, 1380-1383.
    Kopchick, J.J., Cioffi, J.A., 1991. Exogenous and endogenous effects of growth hormone in animals. Livest. Prod. Sci. 27, 61-75.
    Kueh, A.J., Dixon, M.P., Voss, A.K., Thomas, T., 2011. HBO1 is required for H3K14 acetylation and normal transcriptional activity during embryonic development. Mol. Cell. Biol. 31, 845-860.
    Li, C., Tian, D., Tang, B., Liu, X., Teng, X., Zhao, W., Zhang, Z., Song, S., 2021a. Genome Variation Map: a worldwide collection of genome variations across multiple species. Nucleic Acids Res. 49, D1186-D1191.
    Li, Y.D., Liu, X., Li, Z.W., Wang, W.J., Li, Y.M., Cao, Z.P., Luan, P., Xiao, F., Gao, H.H., Guo, H.S., et al., 2021b. A combination of genome-wide association study and selection signature analysis dissects the genetic architecture underlying bone traits in chickens. Animal 15, 100322.
    Li, B., Cai, Y., Chen, C., Li, G., Zhang, M., Lu, Z., Zhang, F., Huang, J., Fan, L., Ning, C., et al., 2023. Genetic variants that impact alternative polyadenylation in cancer represent candidate causal risk loci. Cancer Res. 83, 3650-3666.
    Liu, Y., Liu, X.L., Zheng, Z.W., Ma, T.T., Liu, Y., Long, H., Cheng, H.J., Fang, M., Gong, J., Li, X.Y., et al., 2020. Genome-wide analysis of expression QTL (eQTL) and allele-specific expression (ASE) in pig muscle identifies candidate genes for meat quality traits. Genet. Sel. Evol. 52, 59.
    Liu, S.L., Gao, Y.H., Canela-Xandri, O., Wang, S., Yu, Y., Cai, W.T., Li, B.J., Xiang, R.D., Chamberlain, A.J., Pairo-Castineira, E., et al., 2022. A multi-tissue atlas of regulatory variants in cattle. Nat. Genet. 54, 1438-1447.
    Mariella, E., Marotta, F., Grassi, E., Gilotto, S., Provero, P., 2019. The length of the expressed 3′ UTR is an intermediate molecular phenotype linking genetic variants to complex diseases. Front. Genet. 10, 714.
    Martin, G.R., 2022. Avian vision. Curr. Biol. 32, R1079-R1085.
    Mata-Estrada, A., Gonzalez-Ceron, F., Pro-Martinez, A., Torres-Hernandez, G., Bautista-Ortega, J., Becerril-Perez, C.M., Vargas-Galicia, A.J., Sosa-Montes, E., 2020. Comparison of four nonlinear growth models in Creole chickens of Mexico. Poult Sci 99, 1995-2000.
    Members, C.-N., Partners, 2024. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024. Nucleic Acids Res. 52, D18-D32.
    Moreira, G.C.M., Salvian, M., Boschiero, C., Cesar, A.S.M., Reecy, J.M., Godoy, T.F., Ledur, M.C., Garrick, D., Mourao, G.B., Coutinho, L.L., 2019. Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens. BMC Genom. 20, 669.
    Mott, A.C., Mott, A., Preuss, S., Bennewitz, J., Tetens, J., Falker-Gieske, C., 2022. eQTL analysis of laying hens divergently selected for feather pecking identifies KLF14 as a potential key regulator for this behavioral disorder. Front. Genet. 13, 969752.
    Nahashon, S.N., Aggrey, S.E., Adefope, N.A., Amenyenu, A., Wright, D., 2006. Growth characteristics of pearl gray guinea fowl as predicted by the Richards, Gompertz, and logistic models. Poult. Sci. 85, 359-363.
    Narayana, K.R., 1987. Method of fitting logistic curve. Janasamkhya 5, 69-72.
    Neumeyer, S., Hemani, G., Zeggini, E., 2020. Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci. Trends Mol. Med. 26, 232-241.
    Nicolae, D.L., Gamazon, E., Zhang, W., Duan, S.W., Dolan, M.E., Cox, N.J., 2010. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888.
    Nie, R., Zheng, X., Zhang, W., Zhang, B., Ling, Y., Zhang, H., Wu, C., 2022. Morphological characteristics and transcriptome landscapes of chicken follicles during selective development. Animals 12, 713.
    Pan, J.M., Yang, Y.F., Yang, B., Dai, W.H., Yu, Y.H., 2015. Human-friendly light-emitting diode source stimulates broiler growth. PLoS One 10, e0135330.
    Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., et al., 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575.
    Rana, M.S., Clay, J., Regmi, P., Campbell, D.L.M., 2023. Minimal effects of ultraviolet light supplementation on egg production, egg and bone quality, and health during early lay of laying hens. Peerj 11, e14997.
    Seifert, M., Baden, T., Osorio, D., 2020. The retinal basis of vision in chicken. Semin. Cell Dev. Biol. 106, 106-115.
    Shen, M.M., Qu, L., Ma, M., Dou, T.C., Lu, J., Guo, J., Hu, Y.P., Wang, X.G., Li, Y.F., Wang, K.H., et al., 2017. A genome-wide study to identify genes responsible for oviduct development in chickens. PLoS One 12, e0189955.
    Smith, A.H., Bond, G.H., Ramsey, K.W., Reck, D.G., Spoon, J.E., 1957. Size and rate of involution of the hen’s reproductive organs. Poult. Sci. 36, 346-353.
    Suo, C., Toulopoulou, T., Bramon, E., Walshe, M., Picchioni, M., Murray, R., Ott, J., 2013. Analysis of multiple phenotypes in genome-wide genetic mapping studies. BMC Bioinform. 14, 151.
    Tang, H.H., Ma, Y.C., Li, J.Z., Zhang, Z.Z., Li, W.T., Cai, C.X., Zhang, L.J., Li, Z.J., Tian, Y.D., Zhang, Y.H., et al., 2023. Identification and genetic analysis of major gene related to serum alkaline phosphatase in chicken. Res. Vet. Sci. 155, 115-123.
    The Farm GTEx-PigGTEx Consortium, Teng, J., Gao, Y., Yin, H., Bai, Z., Liu, S., Zeng, H., Bai, L., Cai, Z., Zhao, B., et al., 2022. A compendium of genetic regulatory effects across pig tissues. bioRxiv 2022.2011.2011.516073.
    Tessa, J.P., Chris, P., Orly, M., 2001. Fetal and early life growth and body mass index from birth to early adulthood in 1958 British cohort: longitudinal study. BMJ 323, 1331.
    Uffelmann, E., Huang, Q.Q., Munung, N.S., De Vries, J., Okada, Y., Martin, A.R., Martin, H.C., Lappalainen, T., Posthuma, D., 2021. Genome-wide association studies. Nat. Rev. Methods Primers 1, 59.
    Von Bertalanffy, L., 1957. Quantitative laws in metabolism and growth. Q Rev Biol. 32, 217-231.
    Wang, S., Gao, W., Ngwa, J., Allard, C., Liu, C.T., Cupples, L.A., 2014. Comparing baseline and longitudinal measures in association studies. BMC Proc. 8, S84.
    Wang, K.J., Hu, H.F., Tian, Y.D., Li, J.Y., Scheben, A., Zhang, C.X., Li, Y.Y., Wu, J.F., Yang, L., Fan, X.W., et al., 2021. The chicken pan-genome reveals gene content variation and a promoter region deletion in affecting body size. Mol. Biol. Evol. 38, 5066-5081.
    Xie, L., Luo, C., Zhang, C., Zhang, R., Tang, J., Nie, Q., Ma, L., Hu, X., Li, N., Da, Y., et al., 2012. Genome-wide association study identified a narrow chromosome 1 region associated with chicken growth traits. PLoS One 7, e30910.
    Yang, Q., Chazaro, I., Cui, J., Guo, C.Y., Demissie, S., Larson, M., Atwood, L.D., Cupples, L.A., DeStefano, A.L., 2003. Genetic analyses of longitudinal phenotype data: a comparison of univariate methods and a multivariate approach. BMC Genet. 4, S29.
    Yao, D.W., O’Connor, L.J., Price, A.L., Gusev, A., 2020. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat. Genet. 52, 626-633.
    Yi, G., Shen, M., Yuan, J., Sun, C., Duan, Z., Qu, L., Dou, T., Ma, M., Lu, J., Guo, J., et al., 2015. Genome-wide association study dissects genetic architecture underlying longitudinal egg weights in chickens. BMC Genomics 16, 746.
    Yoo, B.H., Kang, D.S., Park, C.H., Kang, K., Bae, C.D., 2017. CKAP2 phosphorylation by CDK1/cyclinB1 is crucial for maintaining centrosome integrity. Exp. Mol. Med. 49, e354-e354.
    Yuan, J.W., Wang, K.H., Yi, G.Q., Ma, M., Dou, T.C., Sun, C.J., Qu, L.J., Shen, M.M., Qu, L., Yang, N., 2015. Genome-wide association studies for feed intake and efficiency in two laying periods of chickens. Genet. Sel. Evol. 47, 82.
    Zhang, Y., Wang, Y., Li, Y., Wu, J., Wang, X., Bian, C., Tian, Y., Sun, G., Han, R., Liu, X., et al., 2021. Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population. Heredity 126, 293-307.
    Zhou, X., Stephens, M., 2012. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821-824.
    Zhou, X., Stephens, M., 2014. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat. Methods 11, 407-409.
    Zotin, A.A., 2015. The united equation of animal growth. Am. J. Life Sci. 3, 345-351.
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