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Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle

BACKGROUND: Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due...

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Autores principales: Cai, Zexi, Guldbrandtsen, Bernt, Lund, Mogens Sandø, Sahana, Goutam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350337/
https://www.ncbi.nlm.nih.gov/pubmed/30696404
http://dx.doi.org/10.1186/s12863-019-0717-0
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author Cai, Zexi
Guldbrandtsen, Bernt
Lund, Mogens Sandø
Sahana, Goutam
author_facet Cai, Zexi
Guldbrandtsen, Bernt
Lund, Mogens Sandø
Sahana, Goutam
author_sort Cai, Zexi
collection PubMed
description BACKGROUND: Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants. RESULTS: We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. CONCLUSIONS: Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12863-019-0717-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-63503372019-02-04 Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle Cai, Zexi Guldbrandtsen, Bernt Lund, Mogens Sandø Sahana, Goutam BMC Genet Methodology Article BACKGROUND: Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants. RESULTS: We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. CONCLUSIONS: Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12863-019-0717-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-29 /pmc/articles/PMC6350337/ /pubmed/30696404 http://dx.doi.org/10.1186/s12863-019-0717-0 Text en © The Author(s). 2019 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 Methodology Article
Cai, Zexi
Guldbrandtsen, Bernt
Lund, Mogens Sandø
Sahana, Goutam
Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
title Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
title_full Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
title_fullStr Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
title_full_unstemmed Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
title_short Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
title_sort dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350337/
https://www.ncbi.nlm.nih.gov/pubmed/30696404
http://dx.doi.org/10.1186/s12863-019-0717-0
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