Cargando…
Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein
BACKGROUND: Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day record...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868076/ https://www.ncbi.nlm.nih.gov/pubmed/29576014 http://dx.doi.org/10.1186/s12711-018-0383-0 |
_version_ | 1783309083408859136 |
---|---|
author | Ning, Chao Wang, Dan Zheng, Xianrui Zhang, Qin Zhang, Shengli Mrode, Raphael Liu, Jian-Feng |
author_facet | Ning, Chao Wang, Dan Zheng, Xianrui Zhang, Qin Zhang, Shengli Mrode, Raphael Liu, Jian-Feng |
author_sort | Ning, Chao |
collection | PubMed |
description | BACKGROUND: Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets. RESULTS: We propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions. CONCLUSIONS: Our rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12711-018-0383-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5868076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58680762018-03-29 Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein Ning, Chao Wang, Dan Zheng, Xianrui Zhang, Qin Zhang, Shengli Mrode, Raphael Liu, Jian-Feng Genet Sel Evol Research Article BACKGROUND: Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets. RESULTS: We propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions. CONCLUSIONS: Our rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12711-018-0383-0) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-26 /pmc/articles/PMC5868076/ /pubmed/29576014 http://dx.doi.org/10.1186/s12711-018-0383-0 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 | Research Article Ning, Chao Wang, Dan Zheng, Xianrui Zhang, Qin Zhang, Shengli Mrode, Raphael Liu, Jian-Feng Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein |
title | Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein |
title_full | Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein |
title_fullStr | Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein |
title_full_unstemmed | Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein |
title_short | Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein |
title_sort | eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in chinese holstein |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868076/ https://www.ncbi.nlm.nih.gov/pubmed/29576014 http://dx.doi.org/10.1186/s12711-018-0383-0 |
work_keys_str_mv | AT ningchao eigendecompositionexpediteslongitudinalgenomewideassociationstudiesformilkproductiontraitsinchineseholstein AT wangdan eigendecompositionexpediteslongitudinalgenomewideassociationstudiesformilkproductiontraitsinchineseholstein AT zhengxianrui eigendecompositionexpediteslongitudinalgenomewideassociationstudiesformilkproductiontraitsinchineseholstein AT zhangqin eigendecompositionexpediteslongitudinalgenomewideassociationstudiesformilkproductiontraitsinchineseholstein AT zhangshengli eigendecompositionexpediteslongitudinalgenomewideassociationstudiesformilkproductiontraitsinchineseholstein AT mroderaphael eigendecompositionexpediteslongitudinalgenomewideassociationstudiesformilkproductiontraitsinchineseholstein AT liujianfeng eigendecompositionexpediteslongitudinalgenomewideassociationstudiesformilkproductiontraitsinchineseholstein |