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The genome-wide study in Japanese quail fortraits related to feed efficiency using asinglestep approach

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran

2 Department of animal science, University of Arak

3 Arak university

Abstract

The aim of the present study was to evaluate the genetic architecture, genomic regions and candidate genes associated with body weight gain, feed intake and feed conversion ratio in Japanese quails. For detection the informative genomic windows, genotyping data on 920 quails was used in a single-step genome-wide association study. The BLUPf90 family software was used to perform related analyses. Theresults was calculated based on the proportion of additive genetic variance (agv) explained by genomic region with an average size of 1.5-Mb of adjacent SNPs. Windows with accounting for more than 1% of the agv were used to identify genomic regions and to search for candidate genes. A total of 13 significant windows over 8 chromosomes were explained 23% of the agv for the body weight gain and including SMYD1, ADGRG6 and CFL2 candidate genes. A peak on CJA2 explained the largest proportion of variance. For feed intake, we identified 20 informative windows across 8 chromosomes and including ACSL, PPA2, FGF2 and RBL2 candidate genes. These regions explained 38% of the agv and a peak on CJA4 explained of agv. Also, for the feed conversion ratio, 12 significant windows were identified on 7 chromosomes and explained 23.7% of agv, contained ATRNL1 and PTPN4 candidate genes. Four genomic regions had a pleiotropic effect. Considering the identification of new genome regions and the key role of the mentioned genes related to feed intake, the single step method can be validated for GWAS in feed efficiency traits.
 

Keywords

References
  1. Aarabi H (2016) Identification of polymorphism in candidate genes and associated with economically traits in Japanese quail. University of Tehran, Karaj, Ph.D. Dissertation. (In persian)
    1. Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S and Lawlor TJ (2010) Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 93: 743-752.
    2. Christensen OF and Lund MS (2010) Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 42(1):42.

     

     

     

     

     

     

     

     


    1. Du SJ, Rotllant J and Tan X (2006) Muscle-specific expression of the smyd1 gene is controlled by its 5.3-kb promoter and 5'-flanking sequence in zebrafish embryos. Developmental Dynamics, (12):3306-15.
    2. Do DN, Strathe AB, Ostersen T, Jensen J, Mark T and Kadarmideen HN (2013) Genome-wide association study reveals genetic architecture of eating behavior in pigs and its implications for human obesity by comparative mapping. PLoS One, 8(8):e71509.
    3. Han Y and Peñagaricano F (2016) Unravelling the genomic architecture of bull fertility in Holsteincattle. BMC genetics, 17(1): 143.
    4. Kang H, Zhao D, Xiang H, Li J, Zhao G and Li H (2021) Large-scale transcriptome sequencing in broiler chickens to identify candidate genes for breast muscle weight and intramuscular fat content. Genetics Selection Evolution, 53(1):66.
    5. Khaldari M, Pakdel A, Mehrabani Yeganeh H, Nejati Javaremi A and Berg P (2010) Response to selection and genetic parameters of body and carcass weights in Japanese quail selected for 4-week body weight. Poultty Science, 89: 1834-1841.
    6. Lu Y, Chen S and Yang N (2013) Expression and methylation of FGF2, TGF-β and their downstream mediators during different developmental stages of leg muscles in chicken. PLoS One, 8(11):e79495.
    7. Mahmoudi Zarandi M, Rokouei M, Vafaye Valleh M and Maghsoudi A (2020) Estimation of genetic parameters for body weight gain and feed efficiency traits in Japanese quail. Animal production journal. 22(1): 9-22. (In persian)
    8. Misztal I, Tsuruta S, Lourenco D, Aguilar I, Legarra A and Vitezica Z (2018). Manual for BLUPF90 Family of Programs, pp. 125. University of Georgia, Athens, GA.
    9. Marchesi JAP, Ono RK, Cantão ME, Ibelli AMG, Peixoto JO, Moreira GCM, Godoy TF, Coutinho LL, Munari DP and Ledur MC (2021) Exploring the genetic architecture of feed efficiency traits in chickens. Scientific Reports, 11(1):4622.
    10. Nayeri S, Sargolzaei M, Abo-Ismail MK, Miller S, Schenkel F, Moore SS and Stothard P (2017) Genome-wide association study for lactation persistency, female fertility, longevity, and lifetime profit index traits in Holstein dairy cattle. Journal of Dairy Science, 100: 1246-1258.
    11. Neijat M, Eck P and House JD (2017) Impact of dietary precursor ALA versus preformed DHA on fatty acid profiles of eggs, liver and adipose tissue and expression of genes associated with hepatic lipid metabolism in laying hens. Prostaglandins Leukot Essent Fatty Acids, 119: 1-17.
    12. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ and Sham PC (2007) PLINK: a toolset for whole-genome association and population-based linkage analysis. The American Journal of Human Genetics, 81: 559-575.
    13. Ran J, Li J, Yin L, Zhang D, Yu C, Du H, Jiang X, Yang C and Liu Y (2021) Comparative Analysis of Skeletal Muscle DNA Methylation and Transcriptome of the Chicken Embryo at Different Developmental Stages. Frontiers in Physiology, 12:697121.
    14. Rescan PY (2001) Regulation and functions of myogenic regulatory factors in lower vertebrates. Comparative Biochemistry and Physiology-Part B: Biochemistry & Molecular Biology, 130: 1-12.
    15. Tsou R and Bence K (2013) Central regulation of metabolism by protein tyrosine phosphatases. Frontiers in Neuroscience, 6: 1-11.
    16. VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of DairyScience,91(11): 4414-4423.
    17. Wang H, Misztal I, Aguilar I, Legarra A and Muir WM (2012) Genome-wide association mapping including phenotypes from relatives without genotypes. Genetics Research, 94(2): 73-83.
    18. Wang H, Misztal I, Aguilar I, Legarra A, Fernando RL, Vitezica Z, Okimoto R, Wing T, Hawken R and Muir WM (2014) Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Frontiers in Genetics, 5: 134. 
    19. Xiao C, Deng J, Zeng L, Sun T, Yang Z and Yang X (2021) Transcriptome Analysis Identifies Candidate Genes and Signaling Pathways Associated With Feed Efficiency in Xiayan Chicken. Frontiers in Genetics, 12:607719.
    20. Xue Q, Zhang G, Li T, Ling J, Zhang X and Wang J (2017) Transcriptomic profile of leg muscle during early growth in chicken. PLoS One, 12(3): e0173824.
    21. Yang X, Sun J, Zhao G, Li W, Tan X, Zheng M, Feng F, Liu D, Wen J and Liu R (2021)Identification of Major Loci and Candidate Genes for Meat Production-Related Traits in Broilers. Frontiers in Genetics, 12:645107.
    22. Zhou C, Li C, Cai W, Liu S, Yin H, Shi S, Zhang Q and Zhang S (2019) Genome-Wide Association Study for Milk Protein Composition Traits in a Chinese Holstein Population Using a Single-Step Approach. Frontiers in Genetics, 10: 72.
Animal Production
Volume 24, Issue 2
July 2022
Pages 117-126
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