1. Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model
- Author
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Wonjun Lee, Hae Un Jung, Tae Woong Ha, Sungho Won, Jihye Kim, Bermseok Oh, Ji One Kang, Mi Kyung Kim, Taesung Park, and Ji Eun Lim
- Subjects
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 - 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 −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.
- Published
- 2021