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Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes

We analyzed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factor...

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Autores principales: Lakhani, Chirag M, Tierney, Braden T, Manrai, Arjun K, Yang, Jian, Visscher, Peter M, Patel, Chirag J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358510/
https://www.ncbi.nlm.nih.gov/pubmed/30643253
http://dx.doi.org/10.1038/s41588-018-0313-7
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author Lakhani, Chirag M
Tierney, Braden T
Manrai, Arjun K
Yang, Jian
Visscher, Peter M
Patel, Chirag J
author_facet Lakhani, Chirag M
Tierney, Braden T
Manrai, Arjun K
Yang, Jian
Visscher, Peter M
Patel, Chirag J
author_sort Lakhani, Chirag M
collection PubMed
description We analyzed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status, air pollution, and climate) in each phenotype. Mean heritability (h(2) = 0.311) and shared environmental variance (c(2) = 0.088) were higher than variance attributed to specific environmental factors such as zip code-level socioeconomic status (SES) (var(SES) = 0.002), daily air quality (var(AQI) = 0.0004), and average temperature (var(temp) = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h(2) = 0.433, c(2) = 0.241) and average monthly cost (h(2) = 0.290, c(2) = 0.302). All results are available using our “Claims Analysis of Twin Correlation and Heritability (CaTCH)” web application.
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spelling pubmed-63585102019-07-14 Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes Lakhani, Chirag M Tierney, Braden T Manrai, Arjun K Yang, Jian Visscher, Peter M Patel, Chirag J Nat Genet Article We analyzed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status, air pollution, and climate) in each phenotype. Mean heritability (h(2) = 0.311) and shared environmental variance (c(2) = 0.088) were higher than variance attributed to specific environmental factors such as zip code-level socioeconomic status (SES) (var(SES) = 0.002), daily air quality (var(AQI) = 0.0004), and average temperature (var(temp) = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h(2) = 0.433, c(2) = 0.241) and average monthly cost (h(2) = 0.290, c(2) = 0.302). All results are available using our “Claims Analysis of Twin Correlation and Heritability (CaTCH)” web application. 2019-01-14 2019-02 /pmc/articles/PMC6358510/ /pubmed/30643253 http://dx.doi.org/10.1038/s41588-018-0313-7 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Lakhani, Chirag M
Tierney, Braden T
Manrai, Arjun K
Yang, Jian
Visscher, Peter M
Patel, Chirag J
Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
title Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
title_full Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
title_fullStr Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
title_full_unstemmed Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
title_short Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
title_sort repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358510/
https://www.ncbi.nlm.nih.gov/pubmed/30643253
http://dx.doi.org/10.1038/s41588-018-0313-7
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