Multi-omic data integration and analysis using systems genomics approaches: Methods and applications In animal production, health and welfare

Genetics Selection Evolution

Volumn 48, Issue 1, 2016, Pages

  Suravajhala, Prashanth ^a   Kogelman, Lisette J A ^a   Kadarmideen, Haja N ^a  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL PRODUCT; ANIMAL WELFARE; APPLIED SCIENCE; DISEASE TREATMENT; GENOMICS; INTEGRATED APPROACH; LIVESTOCK FARMING; PROTEOMICS;

ANIMALIA;

ANIMAL; ANIMAL WELFARE; BREEDING; EPIGENETICS; GENE EXPRESSION PROFILING; GENETICS; GENOMICS; HIGH THROUGHPUT SCREENING; LIVESTOCK; METABOLOMICS; PROCEDURES; PROTEOMICS; SYSTEMS BIOLOGY;

ANIMAL WELFARE; ANIMALS; BREEDING; EPIGENOMICS; GENE EXPRESSION PROFILING; GENOMICS; HIGH-THROUGHPUT SCREENING ASSAYS; LIVESTOCK; METABOLOMICS; PROTEOMICS; SYSTEMS BIOLOGY;

EID: 85009912606     PISSN: 0999193X     EISSN: 12979686     Source Type: Journal    
DOI: 10.1186/s12711-016-0217-x     Document Type: Review

References (129)

1. van der Sijde MR, Ng A, Fu J. Systems genetics: from GWAS to disease pathways. Biochim Biophys Acta. 2014;1842:1903-9.
2. Morrison N, Cochrane G, Faruque N, Tatusova T, Tateno Y, Hancock D, et al. Concept of sample in OMICS technology. OMICS. 2006;10:127-37.
3. Kadarmideen HN, von Rohr P, Janss LL. From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding. Mamm Genome. 2006;17:548-64.
4. Kadarmideen HN. Genomics to systems biology in animal and veterinary sciences: progress, lessons and opportunities. Livest Sci. 2014;166:232-48.
5. Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, et al. The Ensembl genome database project. Nucleic Acids Res. 2002;30:38-41.
6. Bernstein BE, Meissner A, Lander ES. The mammalian epigenome. Cell. 2007;128:669-81.
7. Wang Z, Gerstein M, Snyder M. RNA-seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57-63.
8. Haoudi A, Bensmail H. Bioinformatics and data mining in proteomics. Expert Rev Proteomics. 2006;3:333-43.
9. Dunn WB, Bailey NJC, Johnson HE. Measuring the metabolome: current analytical technologies. Analyst. 2005;130:606-25.
10. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009;37:D603-10.
11. Rochfort S. Metabolomics reviewed: a new "omics" platform technology for systems biology and implications for natural products research. J Nat Prod. 2005;68:1813-20.
12. Mueller UG, Sachs JL. Engineering microbiomes to improve plant and animal health. Trends Microbiol. 2015;23:606-17.
13. Thomas T, Gilbert J, Meyer F. Metagenomics- A guide from sampling to data analysis. Microb Inform Exp. 2012;2:3.
14. Freimer N, Sabatti C. The human phenome project. Nat Genet. 2003;34:15-21.
15. Paigen K, Eppig JT. A mouse phenome project. Mamm Genome. 2000;11:715-7.
16. Greenbaum D, Luscombe NM, Jansen R, Qian J, Gerstein M. Interrelating different types of genomic data, from proteome to secretome: 'oming in on function. Genome Res. 2001;11:1463-8.
17. Stenuit B, Agathos SN. Deciphering microbial community robustness through synthetic ecology and molecular systems synecology. Curr Opin Biotechnol. 2015;33:305-17.
18. Li H. Systems genetics in "-omics" era: current and future development. Theory Biosci. 2013;132:1-16.
19. Woelders H, Te Pas MF, Bannink A, Veerkamp RF, Smits MA. Systems biology in animal sciences. Animal. 2011;5:1036-47.
20. Zhu M, Yu M, Zhao S. Understanding quantitative genetics in the systems biology era. Int J Biol Sci. 2009;5:161-70.
21. Cole JB, Lewis RM, Maltecca C, Newman S, Olson KM, Tait RG Jr. Systems biology in animal breeding: identifying relationships among markers, genes, and phenotypes. J Anim Sci. 2013;91:521-2.
22. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26:2069-70.
23. Toyoda T, Wada A. Omic space: coordinate-based integration and analysis of genomic phenomic interactions. Bioinformatics. 2004;20:1759-65.
24. Sharma A, Lee JS, Dang CG, Sudrajad P, Kim HC, Yeon SH, et al. Stories and challenges of genome-wide association studies in livestock- A review. Asian-Australas J Anim Sci. 2015;28:1371-9.
25. Hawken RJ, Zhang YD, Fortes MRS, Collis E, Barris WC, Corbet NJ, et al. Genome-wide association studies of female reproduction in tropically adapted beef cattle. J Anim Sci. 2012;90:1398-410.
26. Do DN, Strathe AB, Ostersen T, Jensen J, Mark T, Kadarmideen HN. Genome-wide association study reveals genetic architecture of eating behavior in pigs and its implications for humans obesity by comparative mapping. PLoS One. 2013;8:e71509.
27. Do DN, Strathe AB, Ostersen T, Pant SD, Kadarmideen HN. Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake. Front Genet. 2014;5:307.
28. Wang H, Misztal I, Aguilar I, Legarra A, Fernando RL, Vitezica Z, et al. Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Front Genet. 2014;5:134.
29. Kogelman LJA, Pant SD, Fredholm M, Kadarmideen HN. Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network and pathway analyses. Front Genet. 2014;5:214.
30. Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods. 2013;9:29.
31. Meuwissen T, Hayes B, Goddard M. Accelerating improvement of livestock with genomic selection. Annu Rev Anim Biosci. 2013;1:221-37.
32. Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information. J Dairy Sci. 2009;92:4656-63.
33. Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Single-step methods for genomic evaluation in pigs. Animal. 2012;6:1565-71.
34. Koivula M, Stranden I, Su G, Mantysaari EA. Different methods to calculate genomic predictions-comparisons of BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP). J Dairy Sci. 2012;95:4065-73.
35. Tribout T, Larzul C, Phocas F. Efficiency of genomic selection in a purebred pig male line. J Anim Sci. 2012;90:4164-76.
36. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci. 2009;92:433-43.
37. Perez-Enciso M, Rincon J, Legarra A. Sequence-vs. chip-assisted genomic selection: accurate biological information is advised. Genet Sel Evol. 2015;47:43.
38. Do DN, Janss LL, Jensen J, Kadarmideen HN. SNP annotation-based whole genomic prediction and selection: an application to feed efficiency and its component traits in pigs. J Anim Sci. 2015;93:2056-63.
39. Zhai Z, Zhao W, He C, Yang K, Tang L, Liu S, et al. SNP discovery and genotyping using restriction-site-associated DNA sequencing in chickens. Anim Genet. 2015;46:216-9.
40. De Donato M, Peters SO, Mitchell SE, Hussain T, Imumorin IG. Genotyping-by-sequencing (GBS): a novel, efficient and cost-effective genotyping method for cattle using next-generation sequencing. PLoS One. 2013;8:e62137.
41. Gorjanc G, Cleveland M, Houston R, Hickey J. Potential of genotypingbysequencing for genomic selection in livestock populations. Genet Sel Evol. 2015;47:12.
42. Jiang J, Wang J, Wang H, Zhang Y, Kang H, Feng X, et al. Global copy number analyses by next generation sequencing provide insight into pig genome variation. BMC Genomics. 2014;15:593.
43. Couldrey C, Cave V. Assessing DNA methylation levels in animals: choosing the right tool for the job. Anim Genet. 2014;45(Suppl 1):15-24.
44. Meissner A, Gnirke A, Bell GW, Ramsahoye B, Lander ES, Jaenisch R. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 2005;33:5868-77.
45. Cotney JL, Noonan JP. Chromatin immunoprecipitation with fixed animal tissues and preparation for high-throughput sequencing. Cold Spring Harb Protoc. 2015;2015:pdb.prot084848.
46. Trepte P, Buntru A, Klockmeier K, Willmore L, Arumughan A, Secker C, et al. DULIP: a dual luminescence-based co-immunoprecipitation assay for interactome mapping in mammalian cells. J Mol Biol. 2015;427:3375-88.
47. Kadarmideen HN, Reverter A. Combined genetic, genomic and transcriptomic methods in the analysis of animal traits. CAB Rev perspect Agric Veterinary Sci Nutr Nat Resour 2007;2:16. doi:10.1079/PAVSNNR20072042.
48. Bourneuf E, Hérault F, Chicault C, Carré W, Assaf S, Monnier A, et al. Microarray analysis of differential gene expression in the liver of lean and fat chickens. Gene. 2006;372:162-70.
49. Lehnert SA, Reverter A, Byrne KA, Wang Y, Nattrass GS, Hudson NJ, et al. Gene expression studies of developing bovine longissimus muscle from two different beef cattle breeds. BMC Dev Biol. 2007;7:95.
50. Huang TH, Zhu MJ, Li XY, Zhao SH. Discovery of porcine microRNAs and profiling from skeletal muscle tissues during development. PLoS One. 2008;3:e3225.
51. Kadarmideen HN, Watson-Haigh NS, Andronicos NM. Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers. Mol BioSyst. 2011;7:235-46.
52. Band MR, Olmstead C, Everts RE, Liu ZL, Lewin HA. A 3800 gene microarray for cattle functional genomics: comparison of gene expression in spleen, placenta, and brain. Anim Biotechnol. 2002;13:163-72.
53. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 2008;9:559.
54. Kogelman LJA, Byrne K, Vuocolo T, Watson-Haigh N, Kadarmideen HN, Kijas J, et al. Genetic architecture of gene expression in ovine skeletal muscle. BMC Genomics. 2011;12:607.
55. Ponsuksili S, Du Y, Hadlich F, Siengdee P, Murani E, Schwerin M, et al. Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties. BMC Genomics. 2013;14:533.
56. Lim D, Lee SH, Kim NK, Cho YM, Chai HH, Seong HH, et al. Gene co-expression analysis to characterize genes related to marbling trait in Hanwoo (Korean) cattle. Asian-Australas J Anim Sci. 2013;26:19-29.
57. Malone JH, Oliver B. Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol. 2011;9:34.
58. Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlen M, et al. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and crosscomparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res. 2012;40:10084-97.
59. McCabe M, Waters S, Morris D, Kenny D, Lynn D, Creevey C. RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance. BMC Genomics. 2012;13:93.
60. Li Y, Carrillo JA, Ding Y, He Y, Zhao C, Liu J, et al. Transcriptomic profiling of spleen in grass-fed and grain-fed Angus cattle. PLoS One. 2015;10:e0135670.
61. McLoughlin KE, Nalpas NC, Rue-Albrecht K, Browne JA, Magee DA, Killick KE, et al. RNA-seq transcriptional profiling of peripheral blood leukocytes from cattle infected with Mycobacterium bovis. Front Immunol. 2014;5:396.
62. Park KD, Park J, Ko J, Kim BC, Kim HS, Ahn K, et al. Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq. BMC Genomics. 2012;13:473.
63. Chen C, Ai H, Ren J, Li W, Li P, Qiao R, Ouyang J, et al. A global view of porcine transcriptome in three tissues from a full-sib pair with extreme phenotypes in growth and fat deposition by paired-end RNA sequencing. BMC Genomics. 2011;12:448.
64. Zhang X, Huang L, Wu T, Feng Y, Ding Y, Ye P, et al. Transcriptomic analysis of ovaries from pigs with high and low litter size. PLoS One. 2015;10:e0139514.
65. Jing L, Hou Y, Wu H, Miao Y, Li X, Cao J, et al. Transcriptome analysis of mRNA and miRNA in skeletal muscle indicates an important network for differential residual feed intake in pigs. Sci Rep. 2015;5:11953.
66. Lee HJ, Park HS, Kim W, Yoon D, Seo S. Comparison of metabolic network between muscle and intramuscular adipose tissues in Hanwoo Beef cattle using a systems biology approach. Int J Genomics. 2014;2014:679437.
67. Tizioto PC, Coutinho LL, Decker JE, Schnabel RD, Rosa KO, Oliveira PS, et al. Global liver gene expression differences in Nelore steers with divergent residual feed intake phenotypes. BMC Genomics. 2015;16:242.
68. Alexandre PA, Kogelman LJ, Santana MH, Passarelli D, Pulz LH, Fantinato-Neto P, et al. Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle. BMC Genomics. 2015;16:1073.
69. Salleh MSB, Höglund J, Løvendahl P, Kadarmideen HN. Systems genetics and transcriptomics of feed efficiency in Nordic Dairy cattle. In: Proceedings of the 66th Annual Meeting of European Association for Animal Production: 31 August-4 September 2015; Warsaw.
70. Sahadevan S, Tholen E, Grosse-Brinkhaus C, Schellander K, Tesfaye D, Hofmann-Apitius M, et al. Identification of gene co-expression clusters in liver tissues from multiple porcine populations with high and low backfat androstenone phenotype. BMC Genet. 2015;16:21.
71. Kommadath A, Bao H, Arantes AS, Plastow GS, Tuggle CK, Bearson SM, et al. Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding. BMC Genomics. 2014;15:452.
72. Kogelman LJA, Cirera S, Zhernakova DV, Fredholm M, Franke L, Kadarmideen HN. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model. BMC Med Genomics. 2014;7:57.
73. Kadarmideen HN, Mazzoni G, Watanabe YF, Strøbech L, Baruselli PS, Meirelles F, et al. Genomic selection on in vitro produced and somatic cell nuclear transfer embryos for rapid genetic improvement in cattle production. Anim Reprod. 2015;12:389-96.
74. Cao S, Han J, Wu J, Li Q, Liu S, Zhang W, Pei Y, et al. Specific gene-regulation networks during the pre-implantation development of the pig embryo as revealed by deep sequencing. BMC Genomics. 2014;15:4.
75. Jiang Z, Sun J, Dong H, Luo O, Zheng X, Obergfell C, et al. Transcriptional profiles of bovine in vivo pre-implantation development. BMC Genomics. 2014;15:756.
76. Strøbech L, Mazzoni G, Pedersen HS, Freude KK, Kadarmideen H, Callesen H, et al. In vitro production of bovine embryos-revisiting oocyte development and application of systems biology. Anim Reprod. 2015;12:465-72.
77. Grindflek E, Meuwissen TH, Aasmundstad T, Hamland H, Hansen MH, Nome T, et al. Revealing genetic relationships between compounds affecting boar taint and reproduction in pigs. J Anim Sci. 2011;89:680-92.
78. Große-Brinkhaus C, Storck LC, Frieden L, Neuhoff C, Schellander K, Looft C, Tholen E. Genome-wide association analyses for boar taint components and testicular traits revealed regions having pleiotropic effects. BMC Genet. 2015;16:36.
79. Rowe SJ, Karacaoren B, de Koning DJ, Lukic B, Hastings-Clark N, Velander I, et al. Analysis of the genetics of boar taint reveals both single SNPs and regional effects. BMC Genomics. 2014;15:424.
80. Strathe AB, Velander IH, Mark T, Kadarmideen HN. Genetic parameters for androstenone and skatole as indicators of boar taint and their relationship to production and litter size traits in Danish Landrace. J Anim Sci. 2013;91:2587-95.
81. Strathe AB, Velander IH, Mark T, Ostersen T, Hansen C, Kadarmideen HN. Genetic parameters for male fertility and its relationship to skatole and androstenone in Danish Landrace boars. J Anim Sci. 2013;91:4659-68.
82. Gunawan A, Sahadevan S, Neuhoff C, Grosse-Brinkhaus C, Gad A, Frieden L, et al. RNA deep sequencing reveals novel candidate genes and polymorphisms in boar testis and liver tissues with divergent androstenone levels. PLoS One. 2013;8:e63259.
83. Drag M, Kogelman LJA, Meinert L, Maribo H, Kadarmideen HN. Genomics and systems biology of boar taint and meat quality in pigs. In: Proceedings of the 6th international symposium on animal functional genomics: 27-29 July 2015; Piacenza; 2015.
84. Tang F, Lao K, Surani MA. Development and applications of single-cell transcriptome analysis. Nat Methods. 2011;8(4 Suppl):S6-11.
85. Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet. 2011;12:87-98.
86. Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A. Metabolomics: available results, current research projects in breast cancer, and future applications. J Clin Oncol. 2007;25:2840-6.
87. Sun HZ, Wang DM, Wang B, Wang JK, Liu HY, le Guan L, et al. Metabolomics of four biofluids from dairy cows: potential biomarkers for milk production and quality. J Proteome Res. 2015;14:1287-98.
88. Shen Y, Shi S, Tong H, Guo Y, Zou J. Metabolomics analysis reveals that bile acids and phospholipids contribute to variable responses to lowtemperatureinduced ascites syndrome. Mol BioSyst. 2014;10:1557-67.
89. Rohart F, Paris A, Laurent B, Canlet C, Molina J, Mercat MJ, et al. Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds. J Anim Sci. 2012;90:4729-40.
90. Wenguang Z, Jianghong W, Jinquan L, Yashizawa M. A subset of skin-expressed microRNAs with possible roles in goat and sheep hair growth based on expression profiling of mammalian microRNAs. OMICS. 2007;11:385-96.
91. Powers R, Mercier KA, Copeland JC. The application of FAST-NMR for the identification of novel drug discovery targets. Drug Discov Today. 2008;13:172-9.
92. Benso A, Di Carlo S, Ur Rehman H, Politano G, Savino A, Suravajhala P. A combined approach for genome wide protein function annotation/prediction. Proteome Sci. 2013;11(Suppl 1): S1.
93. Gutierrez-Gil B, Arranz JJ, Wiener P. An interpretive review of selective sweep studies in Bos taurus cattle populations: identification of unique and shared selection signals across breeds. Front Genet. 2015;6:167.
94. Aslibekyan S, Almeida M, Tintle N. Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18. Genet Epidemiol. 2014;38(Suppl 1): S86-91.
95. Ponzoni I, Nueda M, Tarazona S, Gotz S, Montaner D, Dussaut J, et al. Pathway network inference from gene expression data. BMC Syst Biol. 2014;8(Suppl 2):S7.
96. Deans AR, Lewis SE, Huala E, Anzaldo SS, Ashburner M, Balhoff JP, et al. Finding our way through phenotypes. PLoS Biol. 2015;13:e1002033.
97. Mason CE, Porter SG, Smith TM. Characterizing multi-omic data in systems biology. Adv Exp Med Biol. 2014;799:15-38.
98. Wray NR, Goddard ME. Multi-locus models of genetic risk of disease. Genome Med. 2010;2:10.
99. Huang W, Richards S, Carbone MA, Zhu D, Anholt RR, Ayroles JF, et al. Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc Natl Acad Sci USA. 2012;109:15553-9.
100. Jarvis JP, Cheverud JM. Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics. 2011;187:597-610.
101. Shao H, Burrage LC, Sinasac DS, Hill AE, Ernest SR, O'Brien W, et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc Natl Acad Sci USA. 2008;105:19910-4.
102. Ali AA, Khatkar MS, Kadarmideen HN, Thomson PC. Additive and epistatic genome-wide association for growth and ultrasound scan measures of carcass-related traits in Brahman cattle. J Anim Breed Genet. 2015;132:187-97.
103. Kogelman LJA, Kadarmideen H. Weighted interaction SNP hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data. BMC Syst Biol. 2014;8(Suppl 2):S5.
104. Fortes MRS, Reverter A, Zhang Y, Collis E, Nagaraj SH, Jonsson NN, et al. Association weight matrix for the genetic dissection of puberty in beef cattle. Proc Natl Acad Sci USA. 2010;107:13642-7.
105. Widmann P, Reverter A, Fortes MR, Weikard R, Suhre K, Hammon H, et al. A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle. BMC Genomics. 2013;14:798.
106. Fortes MRS, Reverter A, Nagaraj SH, Zhang Y, Jonsson NN, Barris W, Lehnert S, et al. A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle. J Anim Sci. 2011;89:1669-83.
107. Jalali S, Bhartiya D, Lalwani MK, Sivasubbu S, Scaria V. Systematic transcriptome wide analysis of lncRNA-miRNA interactions. PLoS One. 2013;8:e53823.
108. Suravajhala P, Kogelman LJA, Mazzoni G, Kadarmideen HN. Potential role of lncRNA cyp2c91-protein interactions on diseases of the immune system. Front Genet. 2015;6:255.
109. Civelek M, Lusis AJ. Systems genetics approaches to understand complex traits. Nat Rev Genet. 2014;15:34-48.
110. Canovas A, Reverter A, DeAtley KL, Ashley RL, Colgrave ML, Fortes MR, et al. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle. PLoS One. 2014;9:e102551.
111. Westra HJ, Franke L. From genome to function by studying eQTLs. Biochim Biophys Acta. 2014;1842:1896-902.
112. Ponsuksili S, Murani E, Schwerin M, Schellander K, Wimmers K. Identification of expression QTL (eQTL) of genes expressed in porcine M. longissimus dorsi and associated with meat quality traits. BMC Genomics. 2010;11:572.
113. Steibel JP, Bates RO, Rosa GJ, Tempelman RJ, Rilington VD, Ragavendran A, et al. Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs. PLoS One. 2011;6:e16766.
114. Ponsuksili S, Jonas E, Murani E, Phatsara C, Srikanchai T, Walz C, et al. Trait correlated expression combined with expression QTL analysis reveals biological pathways and candidate genes affecting water holding capacity of muscle. BMC Genomics. 2008;9:367.
115. Kogelman LJ, Zhernakova DV, Westra HJ, Cirera S, Fredholm M, Franke L, et al. An integrative systems genetics approach reveals potential causal genes and pathways related to obesity. Genome Med. 2015;7:105.
116. Tsaih SW, Holl K, Jia S, Kaldunski M, Tschannen M, He H, et al. Identification of a novel gene for diabetic traits in rats, mice, and humans. Genetics. 2014;198:17-29.
117. Buchner DA, Nadeau JH. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res. 2015;25:775-91.
118. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, et al. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339:819-23.
119. Jinwei Z, Qipin X, Jing Y, Shumin Y, Suizhong C. CRISPR/Cas9 genome editing technique and its application in site-directed genome modification of animals. Hereditas. 2015;37:1011-20.
120. Whitelaw CB, Sheets TP, Lillico SG, Telugu BP. Engineering large animal models of human disease. J Pathol. 2015;238:247-56.
121. Peng J, Wang Y, Jiang J, Zhou X, Song L, Wang L, et al. Production of human albumin in pigs through CRISPR/Cas9-mediated knockin of human cDNA into swine albumin locus in the zygotes. Sci Rep. 2015;5:16705.
122. Wang J, Ma MC, Mennie AK, Pettus JM, Xu Y, Lin L, et al. Systems biology with high-throughput sequencing reveals genetic mechanisms underlying the metabolic syndrome in the Lyon hypertensive rat. Circ Cardiovasc Genet. 2015;8:316-26.
123. Kraus WE, Muoio DM, Stevens R, Craig D, Bain JR, Grass E, et al. Metabolomic quantitative trait loci (mQTL) mapping implicates the ubiquitin proteasome system in cardiovascular disease pathogenesis. PLoS Genet. 2015;11:e1005553.
124. Hedjazi L, Gauguier D, Zalloua PA, Nicholson JK, Dumas ME, Cazier JB. mQTL. NMR: an integrated suite for genetic mapping of quantitative variations of 1H NMR-based metabolic profiles. Anal Chem. 2015;87:4377-84.
125. Pant SD, Karlskov-Mortensen P, Jacobsen MJ, Cirera S, Kogelman LJ, Bruun CS, et al. Comparative analyses of QTLs influencing obesity and metabolic phenotypes in pigs and humans. PLoS One. 2015;10:e0137356.
126. Gauguier D. Application of quantitative metabolomics in systems genetics in rodent models of complex phenotypes. Arch Biochem Biophys. 2016;589:158-67.
127. Dumas ME. Metabolome 2.0:quantitative genetics and network biology of metabolic phenotypes. Mol BioSyst. 2012;8:2494-502.
128. Holdt LM, von Delft A, Nicolaou A, Baumann S, Kostrzewa M, Thiery J, et al. Quantitative trait loci mapping of the mouse plasma proteome (pQTL). Genetics. 2013;193:601-8.
129. Stark AL, Hause RJ Jr, Gorsic LK, Antao NN, Wong SS, Chung SH, et al. Protein quantitative trait loci identify novel candidates modulating cellular response to chemotherapy. PLoS Genet. 2014;10:e1004192.