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ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset
BACKGROUND: Genomic prediction is an advanced method for estimating genetic values, which has been widely accepted for genetic evaluation in animal and disease-risk prediction in human. It estimates genetic values with genome-wide distributed SNPs instead of pedigree. The key step of it is to constr...
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/PMC6933885/ https://www.ncbi.nlm.nih.gov/pubmed/31878869 http://dx.doi.org/10.1186/s12859-019-3319-y |
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author | Jiang, Dan Xin, Cong Ye, Jinhua Yuan, Yingbo Fang, Ming |
author_facet | Jiang, Dan Xin, Cong Ye, Jinhua Yuan, Yingbo Fang, Ming |
author_sort | Jiang, Dan |
collection | PubMed |
description | BACKGROUND: Genomic prediction is an advanced method for estimating genetic values, which has been widely accepted for genetic evaluation in animal and disease-risk prediction in human. It estimates genetic values with genome-wide distributed SNPs instead of pedigree. The key step of it is to construct genomic relationship matrix (GRM) via genome-wide SNPs; however, usually the calculation of GRM needs huge computer memory especially when the SNP number and sample size are big, so that sometimes it will become computationally prohibitive even for super computer clusters. We herein developed an integrative algorithm to compute GRM. To avoid calculating GRM for the whole genome, ICGRM freely divides the genome-wide SNPs into several segments and computes the summary statistics related to GRM for each segment that requires quite few computer RAM; then it integrates these summary statistics to produce GRM for whole genome. RESULTS: It showed that the computer memory of ICGRM was reduced by 15 times (from 218Gb to 14Gb) after the genome SNPs were split into 5 to 200 parts in terms of the number of SNPs in our simulation dataset, making it computationally feasible for almost all kinds of computer servers. ICGRM is implemented in C/C++ and freely available via https://github.com/mingfang618/CLGRM. CONCLUSIONS: ICGRM is computationally efficient software to build GRM and can be used for big dataset. |
format | Online Article Text |
id | pubmed-6933885 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69338852019-12-30 ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset Jiang, Dan Xin, Cong Ye, Jinhua Yuan, Yingbo Fang, Ming BMC Bioinformatics Software BACKGROUND: Genomic prediction is an advanced method for estimating genetic values, which has been widely accepted for genetic evaluation in animal and disease-risk prediction in human. It estimates genetic values with genome-wide distributed SNPs instead of pedigree. The key step of it is to construct genomic relationship matrix (GRM) via genome-wide SNPs; however, usually the calculation of GRM needs huge computer memory especially when the SNP number and sample size are big, so that sometimes it will become computationally prohibitive even for super computer clusters. We herein developed an integrative algorithm to compute GRM. To avoid calculating GRM for the whole genome, ICGRM freely divides the genome-wide SNPs into several segments and computes the summary statistics related to GRM for each segment that requires quite few computer RAM; then it integrates these summary statistics to produce GRM for whole genome. RESULTS: It showed that the computer memory of ICGRM was reduced by 15 times (from 218Gb to 14Gb) after the genome SNPs were split into 5 to 200 parts in terms of the number of SNPs in our simulation dataset, making it computationally feasible for almost all kinds of computer servers. ICGRM is implemented in C/C++ and freely available via https://github.com/mingfang618/CLGRM. CONCLUSIONS: ICGRM is computationally efficient software to build GRM and can be used for big dataset. BioMed Central 2019-12-26 /pmc/articles/PMC6933885/ /pubmed/31878869 http://dx.doi.org/10.1186/s12859-019-3319-y 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 | Software Jiang, Dan Xin, Cong Ye, Jinhua Yuan, Yingbo Fang, Ming ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset |
title | ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset |
title_full | ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset |
title_fullStr | ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset |
title_full_unstemmed | ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset |
title_short | ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset |
title_sort | icgrm: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933885/ https://www.ncbi.nlm.nih.gov/pubmed/31878869 http://dx.doi.org/10.1186/s12859-019-3319-y |
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