Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

Abstract Multiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following...

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Autores principales:	Hae-Un Jung, Won Jun Lee, Tae-Woong Ha, Ji-One Kang, Jihye Kim, Mi Kyung Kim, Sungho Won, Taesung Park, Ji Eun Lim, Bermseok Oh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5adb486588ea4cfb96d8ebc3b745f174
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spelling	oai:doaj.org-article:5adb486588ea4cfb96d8ebc3b745f1742021-12-02T13:33:51ZIdentification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model10.1038/s41598-021-83684-12045-2322https://doaj.org/article/5adb486588ea4cfb96d8ebc3b745f1742021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83684-1https://doaj.org/toc/2045-2322Abstract Multiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.Hae-Un JungWon Jun LeeTae-Woong HaJi-One KangJihye KimMi Kyung KimSungho WonTaesung ParkJi Eun LimBermseok OhNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hae-Un Jung Won Jun Lee Tae-Woong Ha Ji-One Kang Jihye Kim Mi Kyung Kim Sungho Won Taesung Park Ji Eun Lim Bermseok Oh Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
description	Abstract Multiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.
format	article
author	Hae-Un Jung Won Jun Lee Tae-Woong Ha Ji-One Kang Jihye Kim Mi Kyung Kim Sungho Won Taesung Park Ji Eun Lim Bermseok Oh
author_facet	Hae-Un Jung Won Jun Lee Tae-Woong Ha Ji-One Kang Jihye Kim Mi Kyung Kim Sungho Won Taesung Park Ji Eun Lim Bermseok Oh
author_sort	Hae-Un Jung
title	Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
title_short	Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
title_full	Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
title_fullStr	Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
title_full_unstemmed	Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
title_sort	identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5adb486588ea4cfb96d8ebc3b745f174
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Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

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