Data_Sheet_1_Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests.PDF

The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The “adaptive combination of Bayes factors method” (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the “Set-Based gene-EnviRonment InterAction test” (SBERIA), “gene-environment set association test” (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 × 10−7, according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p −5). Regarding the computation time required for a genome-wide G × E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G × E analyses.</p

Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants - Fig 1

Average BMI (A), BFP (B), WC (C) and HC (D) stratified by their respective GRS quartiles and regular exercise. Each plot shows the average of an obesity measure stratified by regular exercise and the quartiles of the 9th GRS, where the marginal-association P-value threshold was set at 0.05. We used this GRS for plots because 0.05 is generally considered as the significance level in statistical analyses. The title on each plot is the GRS-M P-value that can be found from Table 3. “△” represents the difference in average BMI (A), BFP (B), WC (C) or HC (D) between the top GRS quarter and the bottom GRS quarter. From the plots we can see that the effect of GRS was larger in the physically inactive subjects than in the physically active subjects. The plots for WHR are not presented because the WHRGRS-exercise (p = 1) interaction was not significant (Table 3).</p

Interaction between GRS and exercise on each obesity measure (significant results with <i>p</i> < 9.1x10^-5 are highlighted).

Interaction between GRS and exercise on each obesity measure (significant results with p -5 are highlighted).</p

Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants - Fig 2

Average BMI (A), BFP (B), WC (C) and HC (D) stratified by their respective GRS quartiles and jogging. Each plot shows the average of an obesity measure stratified by jogging and the quartiles of the 9th GRS, where the marginal-association P-value threshold was set at 0.05. We used this GRS for plots because 0.05 is generally considered as the significance level in statistical analyses. The title on each plot is the GRS-M P-value that can be found from Table 3. “△” represents the difference in average BMI (A), BFP (B), WC (C) or HC (D) between the top GRS quarter and the bottom GRS quarter. From the plots we can see that the effect of GRS was larger in the nonjoggers than in the joggers. The plots for WHR are not presented because the WHRGRS-jogging (p = 0.01) interaction was not significant (Table 3).</p

Interaction between GRS and exercise frequency per month (significant results with <i>p</i> < 9.1x10^-5 are highlighted) (18 exercise frequencies x 5 obesity measures = 90 tests).

Interaction between GRS and exercise frequency per month (significant results with p -5 are highlighted) (18 exercise frequencies x 5 obesity measures = 90 tests).</p

The regression models for the 5 obesity measures (prior to GRS-exercise interaction analysis).

The regression models for the 5 obesity measures (prior to GRS-exercise interaction analysis).</p

Basic characteristics stratified by the quartiles of the 9^th BMIGRS (marginal-association <i>P</i>-value threshold = 0.05).

Basic characteristics stratified by the quartiles of the 9th BMIGRS (marginal-association P-value threshold = 0.05).</p

Data_Sheet_1_Association and Interaction Effects of Interleukin-12 Related Genes and Physical Activity on Cognitive Aging in Old Adults in the Taiwanese Population.pdf

Evidence suggests that the neuro-inflammation mechanisms associated with interleukin-12 (IL-12) may be linked to Alzheimer's diseases and cognitive aging. In this study, we speculate that single nucleotide polymorphisms (SNPs) in IL-12-associated genes, such as IL12A, IL12B, IL12RB1, and IL12RB2 genes, could be associated with cognitive aging individually and/or via complicated interactions in the elder Taiwanese population. There were totally 3,730 Taiwanese individuals with age ≥60 years from the Taiwan Biobank. Mini-Mental State Examination (MMSE) was analyzed for all participants. We employed MMSE scores to assess cognitive functions. Our analysis revealed that the IL12A gene (including rs116910715, rs78902931, and rs78569420), the IL12B gene (including rs730691), and the IL12RB2 gene (including rs3790558, rs4655538, rs75699623, and rs1874396) were associated with cognitive aging. Among these SNPs, the association with the IL12RB2 rs3790558 SNP remained significant after performing Bonferroni correction (P = 6.87 × 10−4). Additionally, we found that interactions between the IL12A and IL12RB2 genes influenced cognitive aging (P = 0.022). Finally, we pinpointed the effects of interactions between IL12A, IL12B, and IL12RB2 with physical activity (P < 0.001, = 0.002, and < 0.001, respectively). Our study suggests that the IL-12-associated genes may contribute to susceptibility to cognitive aging independently as well as through gene-gene and gene-physical activity interactions.</p

The effect of BMIGRS on BMI.

The regression model (stratified by exercise types) was built as BMI = β0 + βGRS BMIGRS + βCCovariates + ε, where BMIGRS was calculated at the marginal-association P-value threshold of 0.05. We used this BMIGRS for plots because 0.05 is generally considered as the significance level in statistical analyses. The orange bars represent on BMI (stratified by exercise types), and the black segments mark . The text on each bar is the P-value of testing H0: βGRS = 0 vs. H1: βGRS ≠ 0. Covariates adjusted in the regression model included sex, age, educational attainment, drinking status, smoking status, and the first 10 PCs. Consistent with Table 3, the 18 kinds of exercise were sorted according to popularity.</p

Interaction between GRS and exercise duration (in hours) (significant results with <i>p</i> < 9.1x10^-5 are highlighted) (18 exercise durations x 5 obesity measures = 90 tests).

Interaction between GRS and exercise duration (in hours) (significant results with p -5 are highlighted) (18 exercise durations x 5 obesity measures = 90 tests).</p