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

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 discre...

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Autores principales: 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., Guimarães, Simone E. F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736284/
https://www.ncbi.nlm.nih.gov/pubmed/26830357
http://dx.doi.org/10.1186/s12711-016-0189-x
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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.
Guimarães, Simone E. F.
author_facet 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.
Guimarães, Simone E. F.
author_sort Verardo, Lucas L.
collection PubMed
description 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). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-47362842016-02-03 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. Guimarães, Simone E. F. Genet Sel Evol Research Article 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). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users. BioMed Central 2016-02-01 /pmc/articles/PMC4736284/ /pubmed/26830357 http://dx.doi.org/10.1186/s12711-016-0189-x Text en © Verardo et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
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.
Guimarães, Simone E. F.
Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways
title Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways
title_full Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways
title_fullStr Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways
title_full_unstemmed Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways
title_short Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways
title_sort revealing new candidate genes for reproductive traits in pigs: combining bayesian gwas and functional pathways
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736284/
https://www.ncbi.nlm.nih.gov/pubmed/26830357
http://dx.doi.org/10.1186/s12711-016-0189-x
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