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Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies
With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060362/ https://www.ncbi.nlm.nih.gov/pubmed/35442943 http://dx.doi.org/10.1371/journal.pgen.1010151 |
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author | Seal, Souvik Datta, Abhirup Basu, Saonli |
author_facet | Seal, Souvik Datta, Abhirup Basu, Saonli |
author_sort | Seal, Souvik |
collection | PubMed |
description | With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort. |
format | Online Article Text |
id | pubmed-9060362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90603622022-05-03 Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies Seal, Souvik Datta, Abhirup Basu, Saonli PLoS Genet Methods With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort. Public Library of Science 2022-04-20 /pmc/articles/PMC9060362/ /pubmed/35442943 http://dx.doi.org/10.1371/journal.pgen.1010151 Text en © 2022 Seal et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Methods Seal, Souvik Datta, Abhirup Basu, Saonli Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies |
title | Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies |
title_full | Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies |
title_fullStr | Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies |
title_full_unstemmed | Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies |
title_short | Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies |
title_sort | efficient estimation of snp heritability using gaussian predictive process in large scale cohort studies |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060362/ https://www.ncbi.nlm.nih.gov/pubmed/35442943 http://dx.doi.org/10.1371/journal.pgen.1010151 |
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