Genome wide association study on feed conversion ratio using imputed sequence data in chickens
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초록
Objective: Feed consumption contributes a large percentage for total production costs in the poultry industry. Detecting genes associated with feeding traits will be of benefit to improve our understanding of the molecular determinants for feed efficiency. The objective of this study was to identify candidate genes associated with feed conversion ratio (FCR) via genome-wide association study (GWAS) using sequence data imputed from single nucleotide polymorphism (SNP) panel in a Chinese indigenous chicken population. Methods: A total of 435 Chinese indigenous chickens were phenotyped for FCR and were genotyped using a 600K SNP genotyping array. Twenty-four birds were selected for sequencing, and the 600K SNP panel data were imputed to whole sequence data with the 24 birds as the reference. The GWAS were performed with GEMMA software. Results: After quality control, 8,626,020 SNPs were used for sequence based GWAS, in which ten significant genomic regions were detected to be associated with FCR. Ten candidate genes, ubiquitin specific peptidase 44, leukotriene A4 hydrolase, ETS transcription factor, R-spondin 2, inhibitor of apoptosis protein 3, sosondowah ankyrin repeat domain family member D, calmodulin regulated spectrin associated protein family member 2, zinc finger and BTB domain containing 41, potassium sodium-activated channel subfamily T member 2, and member of RAS oncogene family were annotated. Several of them were within or near the reported FCR quantitative trait loci, and others were newly reported. Conclusion: Results from this study provide valuable prior information on chicken genomic breeding programs, and potentially improve our understanding of the molecular mechanism for feeding traits.
Abstract
Objective: Feed consumption contributes a large percentage for total production costs in the poultry industry. Detecting genes associated with feeding traits will be of benefit to improve our understanding of the molecular determinants for feed efficiency. The objective of this study was to identify candidate genes associated with feed conversion ratio (FCR) via genome-wide association study (GWAS) using sequence data imputed from single nucleotide polymorphism (SNP) panel in a Chinese indigenous chicken population. Methods: A total of 435 Chinese indigenous chickens were phenotyped for FCR and were genotyped using a 600K SNP genotyping array. Twenty-four birds were selected for sequencing, and the 600K SNP panel data were imputed to whole sequence data with the 24 birds as the reference. The GWAS were performed with GEMMA software. Results: After quality control, 8,626,020 SNPs were used for sequence based GWAS, in which ten significant genomic regions were detected to be associated with FCR. Ten candidate genes, ubiquitin specific peptidase 44, leukotriene A4 hydrolase, ETS transcription factor, R-spondin 2, inhibitor of apoptosis protein 3, sosondowah ankyrin repeat domain family member D, calmodulin regulated spectrin associated protein family member 2, zinc finger and BTB domain containing 41, potassium sodium-activated channel subfamily T member 2, and member of RAS oncogene family were annotated. Several of them were within or near the reported FCR quantitative trait loci, and others were newly reported. Conclusion: Results from this study provide valuable prior information on chicken genomic breeding programs, and potentially improve our understanding of the molecular mechanism for feeding traits.
참고문헌 (33)
- Hume DA, "The future of animal production : improving productivity and sustainability", J Agric Sci, 149, pp.9~16 (2011)
- Zhang W, "Genetic variation in feed utilization efficiency of meat-type chickens", World Poult Sci J, 59, pp.328~339 (2003)
- Havenstein GB, "Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets", Poult Sci, 82, pp.1500~1508 (2003)
- Rao Y, "SNP mapping of QTL affecting growth and fatness on chicken GGA1", Genet Sel Evol, 39, pp.569~582 (2007)
- van Kaam JB, "Whole genome scan in chickens for quantitative trait loci affecting growth and feed efficiency", Poult Sci, 78, pp.15~23 (1999)
- Abo-Ismail MK, "Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle", BMC Genet, 15, pp.14~ (2014)
- Onteru SK, "Whole genome association studies of residual feed intake and related traits in the pig", PLoS One, 8, pp.e61756~ (2013)
- Xu Z, "Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens", BMC Genomics, 17, pp.594~ (2016)
- Isotouru T, "Genome-wide association analysis of milk yield traits in Nordic Red Cattle using imputed whole genome sequence variants", BMC Genet, 17, pp.55~ (2016)
- Zhang Z, "Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population", J Anim Sci, 95, pp.72~80 (2017)
- Druet T, "Toward genomic prediction from whole-genome sequence data : impact of sequencing design on genotype imputation and accuracy of predictions", Heredity(Edinb), 112, pp.39~47 (2014)
- Ye S, "Imputation from SNP chip to sequence : a case study in a Chinese indigenous chicken population", J Anim Sci Biotechnol, 9, pp.30~ (2018)
- Li H, "Fast and accurate short read alignment with Burrows-Wheeler transform", Bioinformatics, 25, pp.1754~1760 (2009)
- Mckenna A, "The Genome Analysis Toolkit : a MapReduce framework for analyzing next-generation DNA sequencing data", Genome Res, 20, pp.1297~1303 (2010)
- Sargolzaei M, "A new approach for efficient genotype imputation using information from relatives", BMC Genomics, 15, pp.478~ (2014)
- Purcell S, "PLINK : a tool set for whole-genome association and population-based linkage analyses", Am J Hum Genet, 81, pp.559~575 (2007)
- Zhou X, "Genome-wide efficient mixed-model analysis for association studies", Nat Genet, 44, pp.821~824 (2012)
- Madsen P, "DMU - a package for analyzing multivariate mixed models", Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, pp.27~11 (2014)
- "qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots" (2014)[URL]
- Gao X, "A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms", Genet Epidemiol, 32, pp.361~369 (2008)
- Aggrey SE, "Genetic properties of feed efficiency parameters in meat-type chickens", Genet Sel Evol, 42, pp.25~ (2010)
- Fuchs G, "RNF20 and USP44 regulate stem cell differentiation by modulating H2B monoubiquitylation", Mol Cell, 46, pp.662~673 (2012)
- Low CM, "The development of novel LTA4H modulators to selectively target LTB4 generation", Sci Rep, 7, pp.44449~ (2017)
- Rogers CD, "Elk3 is essential for the progression from progenitor to definitive neural crest cell", Dev Biol, 374, pp.255~263 (2013)
- Ilmer M, "RSPO2 enhances canonical Wnt signaling to confer stemness-associated traits to susceptible pancreatic cancer cells", Pancreatology, 15, pp.S23~ (2015)
- Pachlopnik SJ, "Clinical similarities and differences of patients with X-linked lymphoproliferative syndrome type 1(XLP-1", SAP deficiency)versus type 2(XLP-2, Blood, pp.2011, 117[DOI]
- Maeso I, "An ancient genomic regulatory block conserved across bilaterians and its dismantling in tetrapods by retrogene replacement", Genome Res, 22, pp.642~655 (2012)
- Jiang K, "Microtubule minus-end stabilization by polymerization-driven CAMSAP deposition", Dev Cell, 28, pp.295~309 (2014)
- Hendershott MC, "Regulation of microtubule minus-end dynamics by CAMSAPs and Patronin", Proc Natl Acad Sci USA, 111, pp.5860~5865 (2014)
- Yokoyama S, "A systems approach reveals that the myogenesis genome network is regulated by the transcriptional repressor RP58", Dev Cell, 17, pp.836~848 (2009)
- Santi CM, "Opposite regulation of Slick and Slack K+ channels by neuromodulators", J Neurosci, 26, pp.5059~5068 (2006)
- Sayyah J, "The Ras-related Protein, Rap1A, mediates thrombin-stimulated, integrin-dependent glioblastoma cell proliferation and tumor growth", J Biol Chem, 289, pp.17689~17698 (2014)
- Lautenberger JA, "Accounting for multiple comparisons in a genome-wide association study(GWAS)", BMC Genomics, 11, pp.724~ (2010)
