An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

Abstract Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analys...

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Autores principales:	Xuan Zhou, S. Hong Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4ee236e5f55d403ba67c37a904b61f9f
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spelling	oai:doaj.org-article:4ee236e5f55d403ba67c37a904b61f9f2021-11-08T10:54:41ZAn integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction10.1038/s41598-021-00427-y2045-2322https://doaj.org/article/4ee236e5f55d403ba67c37a904b61f9f2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00427-yhttps://doaj.org/toc/2045-2322Abstract Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.Xuan ZhouS. Hong LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xuan Zhou S. Hong Lee An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
description	Abstract Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.
format	article
author	Xuan Zhou S. Hong Lee
author_facet	Xuan Zhou S. Hong Lee
author_sort	Xuan Zhou
title	An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
title_short	An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
title_full	An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
title_fullStr	An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
title_full_unstemmed	An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
title_sort	integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4ee236e5f55d403ba67c37a904b61f9f
work_keys_str_mv	AT xuanzhou anintegrativeanalysisofgenomicandexposomicdataforcomplextraitsandphenotypicprediction AT shonglee anintegrativeanalysisofgenomicandexposomicdataforcomplextraitsandphenotypicprediction AT xuanzhou integrativeanalysisofgenomicandexposomicdataforcomplextraitsandphenotypicprediction AT shonglee integrativeanalysisofgenomicandexposomicdataforcomplextraitsandphenotypicprediction
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An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

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