ArticleBMC bioinformatics2019
ICGRM: integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Heritability, phenotypic, and genetic correlations across dimensional and categorical models of bipolar disorder in a family sample.Journal of affective disorders · 2025Article
- Strong Genetic Overlaps Between Dimensional and Categorical Models of Bipolar Disorders in a Family Sample.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundGenomic 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.
resultsIt 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.
conclusionsICGRM is computationally efficient software to build GRM and can be used for big dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.