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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 identifying the causal variants and revealing underlying mechanisms have proven complicated. In dairy cattle populations, we face a challeng...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948690/ https://www.ncbi.nlm.nih.gov/pubmed/29751743 http://dx.doi.org/10.1186/s12863-018-0620-0 |
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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 identifying the causal variants and revealing 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 variant. 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/s12863-018-0620-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5948690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59486902018-05-17 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 identifying the causal variants and revealing 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 variant. 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/s12863-018-0620-0) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-11 /pmc/articles/PMC5948690/ /pubmed/29751743 http://dx.doi.org/10.1186/s12863-018-0620-0 Text en © The Author(s). 2018 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/PMC5948690/ https://www.ncbi.nlm.nih.gov/pubmed/29751743 http://dx.doi.org/10.1186/s12863-018-0620-0 |
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