Exploring evidence of positive selection reveals genetic basis of meat quality traits in Berkshire pigs through whole genome sequencing

BMC Genetics

Hak-Kyo Lee⁵  Heebal Kim³  Seoae Cho⁶  Dong Kee Jeong¹  EuiSoo Kim²  Jae-don Oh⁵  Woori Kwak⁶  Jaemin Kim⁴  Kelsey Caetano-Anollés⁷  Minseok Seo⁴  Ki-Duk Song⁵  Hyeonsoo Jeong⁴ 

[1] Department of Animal Biotechnology, Faculty of Biotechnology, Jeju National University, Ara-1 Dong, Jeju-Do 690-756, Jeju, Republic of Korea;Department of Animal Science, Iowa State University, Ames 50011, IA, USA;Department of Agricultural Biotechnology, Seoul National University, Seoul 151-742, South Korea;Interdisciplinary Program in Bioinformatics, Seoul National University, Kwan-ak St. 599, Seoul 151-741, Kwan-ak Gu, Republic of Korea;Department of Animal Biotechnology, Chonbuk National University, Jeonju 561-756, Republic of Korea;C&K genomics, Main Bldg. #514, SNU Research Park, Seoul 151-919, Republic of Korea;Department of Animal Sciences, University of Illinois, Urbana 61801, IL, USA

关键词: de novo assembly;    XP-CLR;    XP-EHH;    Meat quality;    Selection signature;    Berkshire pigs;   

Others  :  1224626
DOI  :  10.1186/s12863-015-0265-1

 received in 2015-04-19, accepted in 2015-08-13,  发布年份 2015

【 摘 要 】

Background

Natural and artificial selection following domestication has led to the existence of more than a hundred pig breeds, as well as incredible variation in phenotypic traits. Berkshire pigs are regarded as having superior meat quality compared to other breeds. As the meat production industry seeks selective breeding approaches to improve profitable traits such as meat quality, information about genetic determinants of these traits is in high demand. However, most of the studies have been performed using trained sensory panel analysis without investigating the underlying genetic factors. Here we investigate the relationship between genomic composition and this phenotypic trait by scanning for signatures of positive selection in whole-genome sequencing data.

Results

We generated genomes of 10 Berkshire pigs at a total of 100.6 coverage depth, using the Illumina Hiseq2000 platform. Along with the genomes of 11 Landrace and 13 Yorkshire pigs, we identified genomic variants of 18.9 million SNVs and 3.4 million Indels in the mapped regions. We identified several associated genes related to lipid metabolism, intramuscular fatty acid deposition, and muscle fiber type which attribute to pork quality (TG, FABP1, AKIRIN2, GLP2R, TGFBR3, JPH3, ICAM2, and ERN1) by applying between population statistical tests (XP-EHH and XP-CLR). A statistical enrichment test was also conducted to detect breed specific genetic variation. In addition, de novo short sequence read assembly strategy identified several candidate genes (SLC25A14, IGF1, PI4KA, CACNA1A) as also contributing to lipid metabolism.

Conclusions

Results revealed several candidate genes involved in Berkshire meat quality; most of these genes are involved in lipid metabolism and intramuscular fat deposition. These results can provide a basis for future research on the genomic characteristics of Berkshire pigs.

【 授权许可】

   
2015 Jeong et al.

【 图 表 】

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【 参考文献 】

• [1]Hazel LN. The genetic basis for constructing selection indexes. Genetics. 1943; 28(6):476-90.
• [2]Serenius T, Sevón-Aimonen M-L, Kause A, Mäntysaari E, Mäki-Tanila A. Selection potential of different prolificacy traits in the Finnish Landrace and Large White populations. Acta Agriculturae Scand, Section A-Anim Sci. 2004; 54(1):36-43.
• [3]Wood J, Nute G, Richardson R, Whittington F, Southwood O, Plastow G et al.. Effects of breed, diet and muscle on fat deposition and eating quality in pigs. Meat Sci. 2004; 67(4):651-67.
• [4]Suzuki K, Shibata T, Kadowaki H, Abe H, Toyoshima T. Meat quality comparison of Berkshire, Duroc and crossbred pigs sired by Berkshire and Duroc. Meat Sci. 2003; 64(1):35-42.
• [5]Lee S, Choi Y, Choe J, Kim J, Hong K, Park H et al.. Association between polymorphisms of the heart fatty acid binding protein gene and intramuscular fat content, fatty acid composition, and meat quality in Berkshire breed. Meat Sci. 2010; 86(3):794-800.
• [6]Kang Y, Choi Y, Lee S, Choe J, Hong K, Kim B. Effects of myosin heavy chain isoforms on meat quality, fatty acid composition, and sensory evaluation in Berkshire pigs. Meat Sci. 2011; 89(4):384-9.
• [7]Jeong D, Choi Y, Lee S, Choe J, Hong K, Park H et al.. Correlations of trained panel sensory values of cooked pork with fatty acid composition, muscle fiber type, and pork quality characteristics in Berkshire pigs. Meat Sci. 2010; 86(3):607-15.
• [8]Qanbari S, Pimentel E, Tetens J, Thaller G, Lichtner P, Sharifi A et al.. A genome wide scan for signatures of recent selection in Holstein cattle. Anim Genet. 2010; 41(4):377-89.
• [9]Pollinger JP, Lohmueller KE, Han E, Parker HG, Quignon P, Degenhardt JD et al.. Genome-wide SNP and haplotype analyses reveal a rich history underlying dog domestication. Nature. 2010; 464(7290):898-902.
• [10]Moradi MH, Nejati-Javaremi A, Moradi-Shahrbabak M, Dodds KG, McEwan JC. Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genet. 2012; 13(1):10. BioMed Central Full Text
• [11]Andrews S. Fastqc: a quality control tool for high throughput sequence data. 2010. http://www. bioinformatics.babraham.ac.uk/projects/fastqc webcite
• [12]Bolger AM, Lohse M, Usadel B: Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30(15):2114.
• [13]Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012; 9(4):357-9.
• [14]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al.. The sequence alignment/map format and SAMtools. Bioinformatics. 2009; 25(16):2078-9.
• [15]McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A et al.. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20(9):1297-303.
• [16]Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al.. The variant call format and VCFtools. Bioinformatics. 2011; 27(15):2156-8.
• [17]Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007; 81(5):1084-97.
• [18]Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011; 88(1):76-82.
• [19]Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al.. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81(3):559-75.
• [20]Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C et al.. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007; 449(7164):913-8.
• [21]Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Computational Graphical Statistics. 1996; 5(3):299-314.
• [22]Granka JM, Henn BM, Gignoux CR, Kidd JM, Bustamante CD, Feldman MW. Limited evidence for classic selective sweeps in African populations. Genetics. 2012; 192(3):1049-64.
• [23]Lee H-J, Kim J, Lee T, Son JK, Yoon H-B, Baek K-S et al.. Deciphering the genetic blueprint behind Holstein milk proteins and production. Genome Biol Evol. 2014; 6:1366-74.
• [24]Chen H, Patterson N, Reich D. Population differentiation as a test for selective sweeps. Genome Res. 2010; 20(3):393-402.
• [25]Gnerre S, MacCallum I, Przybylski D, Ribeiro FJ, Burton JN, Walker BJ et al.. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc Natl Acad Sci. 2011; 108(4):1513-8.
• [26]Peng Y, Leung HC, Yiu S-M, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012; 28(11):1420-8.
• [27]Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J et al.. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience. 2012; 1(1):18. BioMed Central Full Text
• [28]Tarailo Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2009; 4(10):11-4.
• [29]Simonson TS, Yang Y, Huff CD, Yun H, Qin G, Witherspoon DJ et al.. Genetic evidence for high-altitude adaptation in Tibet. Science. 2010; 329(5987):72-5.
• [30]Hanenberg E, Knol E, Merks J. Estimates of genetic parameters for reproduction traits at different parities in Dutch Landrace pigs. Livest Production Sci. 2001; 69(2):179-86.
• [31]Johnson R, Omtvedt I. Evaluation of purebreds and two-breed crosses in swine: Reproductive performance. J Anim Sci. 1973; 37(6):1279-88.
• [32]Chmurzyńska A. The multigene family of fatty acid-binding proteins (FABPs): function, structure and polymorphism. J Appl Genet. 2006; 47(1):39-48.
• [33]Atshaves BP, McIntosh AM, Lyuksyutova OI, Zipfel W, Webb WW, Schroeder F. Liver fatty acid-binding protein gene ablation inhibits branched-chain fatty acid metabolism in cultured primary hepatocytes. J Biol Chem. 2004; 279(30):30954-65.
• [34]Y-Z JIANG, X-W LI, G-X YANG. Sequence Characterization, Tissue-specific Expression and Polymorphism of the Porcine (Sus scrofa) Liver-type Fatty Acid Binding Protein Gene. Acta Genetica Sinica. 2006; 33(7):598-606.
• [35]Wang Y, Shu D, Li L, Qu H, Yang C, Zhu Q. Identification of single nucleotide polymorphism of H-FABP gene and its association with fatness traits in chickens. Asian Australas J Anim Sci. 2007; 20(12):1812.
• [36]Rosenfeld M, Mermod J, Amara S, Swanson L, Sawchenko P, Rivier J, Vale W, Evans R: Production of a novel neuropeptide encoded by the calcitonin gene via tissue-specific RNA processing. Nature 1983, 304(5922):129.
• [37]Barendse W, Bunch R, Thomas M, Armitage S, Baud S, Donaldson N. The TG5 thyroglobulin gene test for a marbling quantitative trait loci evaluated in feedlot cattle. Anim Production Sci. 2004; 44(7):669-74.
• [38]Burrell D, Moser G, Hetzel J, Mizoguchi Y, Hirano T, Sugimoto Y et al.. Meta analysis confirms associations of the TG5 thyroglobulin polymorphism with marbling in beef cattle. 29th International conference on animal genetics. ISAG, Tokyo; 2004.
• [39]Fortes MR, Curi RA, Chardulo LAL, Silveira AC, Assumpção ME, Visintin JA et al.. Bovine gene polymorphisms related to fat deposition and meat tenderness. Genet Mol Biol. 2009; 32(1):75-82.
• [40]Smith T, Thomas M, Bidner T, Paschal J, Franke D. Single nucleotide polymorphisms in Brahman steers and their association with carcass and tenderness traits. Gen Mol Res. 2009; 8:39-46.
• [41]Chen X, Huang Z, Wang H, Jia G, Liu G, Guo X et al.. Role of Akirin in skeletal myogenesis. Int J Mol Sci. 2013; 14(2):3817-23.
• [42]Marshall A, Salerno MS, Thomas M, Davies T, Berry C, Dyer K et al.. Mighty is a novel promyogenic factor in skeletal myogenesis. Exp Cell Res. 2008; 314(5):1013-29.
• [43]Sasaki S, Yamada T, Sukegawa S, Miyake T, Fujita T, Morita M et al.. Association of a single nucleotide polymorphism in akirin 2 gene with marbling in Japanese Black beef cattle. BMC Res Notes. 2009; 2(1):131. BioMed Central Full Text
• [44]Luo W, Cheng D, Chen S, Wang L, Li Y, Ma X et al.. Genome-wide association analysis of meat quality traits in a porcine Large White × Minzhu intercross population. Int J Biol Sci. 2012; 8(4):580.
• [45]Park H-B, Jacobsson L, Wahlberg P, Siegel PB, Andersson L. QTL analysis of body composition and metabolic traits in an intercross between chicken lines divergently selected for growth. Physiol Genomics. 2006; 25(2):216-23.
• [46]Cánovas A, Quintanilla R, Amills M, Pena RN. Muscle transcriptomic profiles in pigs with divergent phenotypes for fatness traits. BMC Genomics. 2010; 11(1):372. BioMed Central Full Text
• [47]Chen S, An J, Lian L, Qu L, Zheng J, Xu G et al.. Polymorphisms in AKT3, FIGF, PRKAG3, and TGF-β genes are associated with myofiber characteristics in chickens. Poult Sci. 2013; 92(2):325-30.
• [48]Lebret B, Le Roy P, Monin G, Lefaucheur L, Caritez J, Talmant A et al.. Influence of the three RN genotypes on chemical composition, enzyme activities, and myofiber characteristics of porcine skeletal muscle. J Anim Sci. 1999; 77(6):1482-9.
• [49]Ramos AM, Duijvesteijn N, Knol EF, Merks JW, Bovenhuis H, Crooijmans RP et al.. The distal end of porcine chromosome 6p is involved in the regulation of skatole levels in boars. BMC Genet. 2011; 12(1):35. BioMed Central Full Text
• [50]Hamill RM, McBryan J, McGee C, Mullen AM, Sweeney T, Talbot A et al.. Functional analysis of muscle gene expression profiles associated with tenderness and intramuscular fat content in pork. Meat Sci. 2012; 92(4):440-50.
• [51]Li X, Kim S-W, Do K-T, Ha Y-K, Lee Y-M, Yoon S-H et al.. Analyses of porcine public SNPs in coding-gene regions by re-sequencing and phenotypic association studies. Mol Biol Rep. 2011; 38(6):3805-20.
• [52]Qiu J, Ni Y-h, Chen R-h, Ji C-b, Liu F, Zhang C-m et al.. Gene expression profiles of adipose tissue of obese rats after central administration of neuropeptide Y-Y5 receptor antisense oligodeoxynucleotides by cDNA microarrays. Peptides. 2008; 29(11):2052-60.
• [53]Sen S, Jumaa H, Webster NJ. Splicing factor SRSF3 is crucial for hepatocyte differentiation and metabolic function. Nat Commun. 2013; 4:1336.
• [54]Chang M-L, Yeh C-T, Chen J-C, Huang C-C, Lin S-M, Sheen I-S et al.. Altered expression patterns of lipid metabolism genes in an animal model of HCV core-related, nonobese, modest hepatic steatosis. BMC Genomics. 2008; 9(1):109. BioMed Central Full Text
• [55]Shin S, Chung E. Association of SNP marker in the thyroglobulin gene with carcass and meat quality traits in Korean cattle. Asian Australas J Anim Sci. 2007; 20(2):172.
• [56]Sutherland M, Rodriguez-Zas S, Ellis M, Salak-Johnson J. Breed and age affect baseline immune traits, cortisol, and performance in growing pigs. J Anim Sci. 2005; 83(9):2087-95.
• [57]Hanjie L, Yanhua L, Xingbo Z, Ning L, Changxin W. Structure and nucleotide polymorphisms in pig uncoupling protein 2 and 3 genes. Anim Biotechnol. 2005; 16(2):209-20.
• [58]Li Y, Li H, Zhao X, Li N, Wu C. UCP2 and 3 deletion screening and distribution in 15 pig breeds. Biochem Genet. 2007; 45(1–2):103-11.
• [59]Kopecký J, Rossmeisl M, Flachs P, Brauner P, ŠPONAROVÁ J, MATĚJKOVÁ O et al.. Energy metabolism of adipose tissue–physiological aspects and target in obesity treatment. Physiol Res. 2004; 53 Suppl 1:S225-32.
• [60]Saltiel AR, Kahn CR. Insulin signalling and the regulation of glucose and lipid metabolism. Nature. 2001; 414(6865):799-806.
• [61]Balla A, Tuymetova G, Tsiomenko A, Várnai P, Balla T. A plasma membrane pool of phosphatidylinositol 4-phosphate is generated by phosphatidylinositol 4-kinase type-III alpha: studies with the PH domains of the oxysterol binding protein and FAPP1. Mol Biol Cell. 2005; 16(3):1282-95.
• [62]Taverna E, Saba E, Rowe J, Francolini M, Clementi F, Rosa P. Role of lipid microdomains in P/Q-type calcium channel (Cav2. 1) clustering and function in presynaptic membranes. J Biol Chem. 2004; 279(7):5127-34.