title: Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs.
abstract: The genetic improvement of reproductive traits such as the number of teats is essential to the success of the pig industry. As opposite to most SNP association studies that consider continuous phenotypes under Gaussian assumptions, this trait is characterized as a discrete variable, which could potentially follow other distributions, such as the Poisson. Therefore, in order to access the complexity of a counting random regression considering all SNPs simultaneously as covariate under a GWAS modeling, the Bayesian inference tools become necessary. Currently, another point that deserves to be highlighted in GWAS is the genetic dissection of complex phenotypes through candidate genes network derived from significant SNPs. We present a full Bayesian treatment of SNP association analysis for number of teats assuming alternatively Gaussian and Poisson distributions for this trait. Under this framework, significant SNP effects were identified by hypothesis tests using 95% highest posterior density intervals. These SNPs were used to construct associated candidate genes network aiming to explain the genetic mechanism behind this reproductive trait. The Bayesian model comparisons based on deviance posterior distribution indicated the superiority of Gaussian model. In general, our results suggest the presence of 19 significant SNPs, which mapped 13 genes. Besides, we predicted gene interactions through networks that are consistent with the mammals known breast biology (e.g., development of prolactin receptor signaling, and cell proliferation), captured known regulation binding sites, and provided candidate genes for that trait (e.g., TINAGL1 and ICK).
authors.affiliation: Department of Animal Science, Universidade Federal de Viçosa - UFV, Viçosa, MG, Brazil, lucas_verardo@yahoo.com.br.
authors.forename: L L
authors.initials: LL
authors.lastname: Verardo
authors.forename: F F
authors.initials: FF
authors.lastname: Silva
authors.forename: L
authors.initials: L
authors.lastname: Varona
authors.forename: M D V
authors.initials: MD
authors.lastname: Resende
authors.forename: J W M
authors.initials: JW
authors.lastname: Bastiaansen
authors.forename: P S
authors.initials: PS
authors.lastname: Lopes
authors.forename: S E F
authors.initials: SE
authors.lastname: Guimarães
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D005260:Female; D056726:Genetic Association Studies; D005838:Genotype; D008321:Mammary Glands, Animal / Q000033:anatomy & histology*; D015233:Models, Statistical; D016011:Normal Distribution; D010641:Phenotype; D016012:Poisson Distribution; D020641:Polymorphism, Single Nucleotide; D034421:Sus scrofa / Q000033:anatomy & histology / Q000235:genetics*
references.citation: BMC Proc. 2011 Nov 29;5 Suppl 9:S38
references.pmid: 22373131
references.citation: Eur J Cancer. 2005 Nov;41(16):2438-48
references.pmid: 16209919
references.citation: Anim Genet. 2012 Feb;43(1):18-26
references.pmid: 22221021
references.citation: Genetics. 2006 Dec;174(4):2159-72
references.pmid: 17028335
references.citation: Genet Sel Evol. 2011 Dec 01;43:42
references.pmid: 22132733
references.citation: Genetics. 2009 May;182(1):343-53
references.pmid: 19299339
references.citation: Gene. 2002 May 29;291(1-2):233-9
references.pmid: 12095696
references.citation: Bioessays. 2002 Nov;24(11):1023-9
references.pmid: 12386933
references.citation: J Anim Sci. 2012 Oct;90(10):3353-9
references.pmid: 22585791
references.citation: Mol Endocrinol. 2005 Feb;19(2):340-9
references.pmid: 15471946
references.citation: J Anim Sci. 2013 Feb;91(2):530-6
references.pmid: 23097399
references.citation: PLoS One. 2009 Aug 05;4(8):e6524
references.pmid: 19654876
references.citation: Genetics. 2010 Jan;184(1):277-84
references.pmid: 19901070
references.citation: Anim Genet. 2011 Jun;42(3):280-92
references.pmid: 21198696
references.citation: Bioinformatics. 2012 Dec 15;28(24):3326-8
references.pmid: 23060615
references.citation: J Anim Sci. 2010 Feb;88(2):497-504
references.pmid: 19820058
references.citation: Med Oncol. 2011 Dec;28(4):1225-54
references.pmid: 20458559
references.citation: Anim Genet. 2000 Dec;31(6):376-84
references.pmid: 11167524
references.citation: J Anim Sci. 2001 Sep;79(9):2320-6
references.pmid: 11583418
references.citation: Animal. 2008 Jun;2(6):813-20
references.pmid: 22443659
references.citation: J Anim Breed Genet. 2005 Feb;122(1):30-6
references.pmid: 16130486
references.citation: Reprod Biol Endocrinol. 2006 Sep 29;4:49
references.pmid: 17010207
references.citation: J Neurosci. 1994 May;14(5 Pt 2):3005-18
references.pmid: 8182455
references.citation: FEBS Lett. 2002 Feb 20;513(1):38-44
references.pmid: 11911878
references.citation: Am J Hum Genet. 2009 Feb;84(2):134-47
references.pmid: 19185282
references.citation: BMC Genet. 2009 Feb 18;10:6
references.pmid: 19226448
references.citation: Algorithms Mol Biol. 2007 Dec 11;2:15
references.pmid: 18072973
references.citation: Mol Biosyst. 2012 Sep;8(9):2360-72
references.pmid: 22777171
references.citation: J Anim Sci. 2011 Jun;89(6):1669-83
references.pmid: 21357453
references.citation: Genetics. 2001 Apr;157(4):1819-29
references.pmid: 11290733
references.citation: J Comp Neurol. 1992 Aug 1;322(1):121-35
references.pmid: 1430307
references.citation: Anim Genet. 2008 Oct;39(5):486-95
references.pmid: 18651874
references.citation: PLoS One. 2013 Jul 26;8(7):e69851
references.pmid: 23922822
references.citation: Int J Oncol. 2007 Nov;31(5):1165-75
references.pmid: 17912444
references.citation: J Anim Breed Genet. 2013 Dec;130(6):417-24
references.pmid: 24236604
references.citation: Proc Natl Acad Sci U S A. 2010 Aug 3;107(31):13642-7
references.pmid: 20643938
references.citation: Genet Mol Biol. 2013 Dec;36(4):511-9
references.pmid: 24385854
references.citation: Cell. 1983 Nov;35(1):79-87
references.pmid: 6313230
references.citation: Anim Genet. 2013 Apr;44(2):139-48
references.pmid: 22690698
references.citation: BMC Bioinformatics. 2011;12 Suppl 5:S4
references.pmid: 21989070