Cargando…
Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers
BACKGROUND: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to i...
Autores principales: | , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3098088/ https://www.ncbi.nlm.nih.gov/pubmed/20969788 http://dx.doi.org/10.1186/1471-2105-11-529 |
_version_ | 1782203914699931648 |
---|---|
author | Shepherd, Ross K Meuwissen, Theo HE Woolliams, John A |
author_facet | Shepherd, Ross K Meuwissen, Theo HE Woolliams, John A |
author_sort | Shepherd, Ross K |
collection | PubMed |
description | BACKGROUND: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. RESULTS: This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. CONCLUSIONS: emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. |
format | Text |
id | pubmed-3098088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30980882011-07-08 Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers Shepherd, Ross K Meuwissen, Theo HE Woolliams, John A BMC Bioinformatics Research Article BACKGROUND: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. RESULTS: This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. CONCLUSIONS: emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. BioMed Central 2010-10-22 /pmc/articles/PMC3098088/ /pubmed/20969788 http://dx.doi.org/10.1186/1471-2105-11-529 Text en Copyright ©2010 Shepherd et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Shepherd, Ross K Meuwissen, Theo HE Woolliams, John A Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers |
title | Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers |
title_full | Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers |
title_fullStr | Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers |
title_full_unstemmed | Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers |
title_short | Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers |
title_sort | genomic selection and complex trait prediction using a fast em algorithm applied to genome-wide markers |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3098088/ https://www.ncbi.nlm.nih.gov/pubmed/20969788 http://dx.doi.org/10.1186/1471-2105-11-529 |
work_keys_str_mv | AT shepherdrossk genomicselectionandcomplextraitpredictionusingafastemalgorithmappliedtogenomewidemarkers AT meuwissentheohe genomicselectionandcomplextraitpredictionusingafastemalgorithmappliedtogenomewidemarkers AT woolliamsjohna genomicselectionandcomplextraitpredictionusingafastemalgorithmappliedtogenomewidemarkers |