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Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle
BACKGROUND: Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production tra...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466667/ https://www.ncbi.nlm.nih.gov/pubmed/30987590 http://dx.doi.org/10.1186/s12864-019-5656-7 |
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author | van den Berg, I. Hayes, B. J. Chamberlain, A. J. Goddard, M. E. |
author_facet | van den Berg, I. Hayes, B. J. Chamberlain, A. J. Goddard, M. E. |
author_sort | van den Berg, I. |
collection | PubMed |
description | BACKGROUND: Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production traits or fertility and with gene expression, in dairy cattle. We used three different approaches to discover QTL that are also expression QTL (eQTL): 1) estimate the correlation between local genomic estimated breeding values (GEBV) and gene expression, 2) investigate whether the 300 intervals explaining most genetic variance for a trait contain more eQTL than 300 randomly selected intervals, and 3) a colocalisation analysis. Phenotypes and genotypes up to sequence level of 35,775 dairy bulls and cows were used for QTL mapping, and gene expression and genotypes of 131 cows were used to identify eQTL. RESULTS: With all three approaches, we identified some overlap between eQTL and QTL, though the majority of QTL in our dataset did not seem to be eQTL. The most significant associations between QTL and eQTL were found for intervals on chromosome 18, where local GEBV for all traits showed a strong association with the expression of the FUK and DDX19B. Intervals whose local GEBV for a trait correlated highly significantly with the expression of a nearby gene explained only a very small part of the genetic variance for that trait. It is likely that part of these correlations were due to linkage disequilibrium (LD) in the interval. While the 300 intervals explaining most genetic variance explained most of the GEBV variance, they contained only slightly more eQTL than 300 randomly selected intervals that explained a minimal portion of the GEBV variance. Furthermore, some variants showed a high colocalisation probability, but this was only the case for few variants. CONCLUSIONS: Several reasons may have contributed to the low level of overlap between QTL and eQTL detected in our study, including a lack of power in the eQTL study and long-range LD making it difficult to separate QTL and eQTL. Furthermore, it may be that eQTL explain only a small fraction of QTL. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5656-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6466667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64666672019-04-22 Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle van den Berg, I. Hayes, B. J. Chamberlain, A. J. Goddard, M. E. BMC Genomics Research Article BACKGROUND: Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production traits or fertility and with gene expression, in dairy cattle. We used three different approaches to discover QTL that are also expression QTL (eQTL): 1) estimate the correlation between local genomic estimated breeding values (GEBV) and gene expression, 2) investigate whether the 300 intervals explaining most genetic variance for a trait contain more eQTL than 300 randomly selected intervals, and 3) a colocalisation analysis. Phenotypes and genotypes up to sequence level of 35,775 dairy bulls and cows were used for QTL mapping, and gene expression and genotypes of 131 cows were used to identify eQTL. RESULTS: With all three approaches, we identified some overlap between eQTL and QTL, though the majority of QTL in our dataset did not seem to be eQTL. The most significant associations between QTL and eQTL were found for intervals on chromosome 18, where local GEBV for all traits showed a strong association with the expression of the FUK and DDX19B. Intervals whose local GEBV for a trait correlated highly significantly with the expression of a nearby gene explained only a very small part of the genetic variance for that trait. It is likely that part of these correlations were due to linkage disequilibrium (LD) in the interval. While the 300 intervals explaining most genetic variance explained most of the GEBV variance, they contained only slightly more eQTL than 300 randomly selected intervals that explained a minimal portion of the GEBV variance. Furthermore, some variants showed a high colocalisation probability, but this was only the case for few variants. CONCLUSIONS: Several reasons may have contributed to the low level of overlap between QTL and eQTL detected in our study, including a lack of power in the eQTL study and long-range LD making it difficult to separate QTL and eQTL. Furthermore, it may be that eQTL explain only a small fraction of QTL. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5656-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-15 /pmc/articles/PMC6466667/ /pubmed/30987590 http://dx.doi.org/10.1186/s12864-019-5656-7 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 | Research Article van den Berg, I. Hayes, B. J. Chamberlain, A. J. Goddard, M. E. Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle |
title | Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle |
title_full | Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle |
title_fullStr | Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle |
title_full_unstemmed | Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle |
title_short | Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle |
title_sort | overlap between eqtl and qtl associated with production traits and fertility in dairy cattle |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466667/ https://www.ncbi.nlm.nih.gov/pubmed/30987590 http://dx.doi.org/10.1186/s12864-019-5656-7 |
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