Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
Autor: | Wonjun Lee, Hae Un Jung, Tae Woong Ha, Sungho Won, Jihye Kim, Bermseok Oh, Ji One Kang, Mi Kyung Kim, Taesung Park, Ji Eun Lim |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
0301 basic medicine
Mixed model Science Single-nucleotide polymorphism Disease 030105 genetics & heredity Biology Affect (psychology) Polymorphism Single Nucleotide Metabolic equivalent Article Body Mass Index 03 medical and health sciences Genetics Humans Gene Multidisciplinary Models Genetic Middle Aged Computational biology and bioinformatics 030104 developmental biology Genetic Loci Medicine Smoking status Female Gene-Environment Interaction Body mass index Genome-Wide Association Study |
Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) Scientific Reports |
ISSN: | 2045-2322 |
Popis: | 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 −6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value P value −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. |
Databáze: | OpenAIRE |
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