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 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.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans

“… Polimanti et al (2021) reported a strong association of lifetime polysubstance dependence with lifetime suicidality and using StructLMM, identified multivariate G×E interaction loci ( LCAT , p = 1.82 × 10 –7 ; TSNAXIP1 , p = 2.13 × 10 –7 ; CENPT, p = 2.32 × 10 –7 ; PARD6A , p = 5.57 × 10 –7 ) of suicidality that interact with 4 substance dependences such as opioid, cocaine, nicotine, and polysubstance dependences (15,557 American participants in the Yale-Penn cohort) ( Polimanti et al, 2021 ). We previously applied this StructLMM to BMI in 8,155 Korean samples from the Korea Association Resource to evaluate interactions with seven factors including alcohol consumption, education, income, total calorie intake, protein intake, carbohydrate intake, and smoking status ( Jung et al, 2021 ). This previous study using StructLMM identified genome-wide significant interaction loci (rs2391331 in the EFNB2 ; p = 5.03 × 10 –10 ) with BMI, and BF analyses showed that six environmental factors, except for carbohydrate intake, contributed to the interaction with this SNP on BMI ( Jung et al, 2021 ).…”

“…We previously applied this StructLMM to BMI in 8,155 Korean samples from the Korea Association Resource to evaluate interactions with seven factors including alcohol consumption, education, income, total calorie intake, protein intake, carbohydrate intake, and smoking status ( Jung et al, 2021 ). This previous study using StructLMM identified genome-wide significant interaction loci (rs2391331 in the EFNB2 ; p = 5.03 × 10 –10 ) with BMI, and BF analyses showed that six environmental factors, except for carbohydrate intake, contributed to the interaction with this SNP on BMI ( Jung et al, 2021 ).…”