Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways

Verardo, Lucas L., Silva, Fabyano F., Lopes, Marcos S., Madsen, Ole, Bastiaansen, John W. M., Knol, Egbert F., Kelly, Mathew, Varona, Luis, Lopes, Paulo S. and Guimaraes, Simone E. F. (2016) Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways. Genetics Selection Evolution, 48 9: . doi:10.1186/s12711-016-0189-x


Author Verardo, Lucas L.
Silva, Fabyano F.
Lopes, Marcos S.
Madsen, Ole
Bastiaansen, John W. M.
Knol, Egbert F.
Kelly, Mathew
Varona, Luis
Lopes, Paulo S.
Guimaraes, Simone E. F.
Title Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 0999-193X
1297-9686
Publication date 2016-02-01
Year available 2016
Sub-type Article (original research)
DOI 10.1186/s12711-016-0189-x
Open Access Status DOI
Volume 48
Issue 9
Total pages 13
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: Reproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks.

Results: Comparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes.

Conclusions: Our comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length).
Keyword Genome-wide association
Mammary gland
Campomelic dysplasia
Resource population
Joubert syndrome
Kidney disease
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Alliance for Agriculture and Food Innovation
 
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