Research ArticleHUMAN GENETICS

Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank

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Science Advances  14 Aug 2019:
Vol. 5, no. 8, eaaw3538
DOI: 10.1126/sciadv.aaw3538
  • Fig. 1 Schematic of the differences in mean or variance among genotype groups in the presence of GEI, QTL, and vQTL effects.

    The phenotypes of 1000 individuals were simulated on the basis of a genetic variant (MAF = 0.3) with (A) both QTL and GEI effects, (B) GEI effect only (no QTL effect), (C) QTL effect only (no GEI or vQTL effect), or (D) vQTL only (no QTL effect).

  • Fig. 2 Evaluation of the statistical methods and phenotype processing strategies for vQTL analysis by simulation.

    Phenotypes of 10,000 individuals were simulated on the basis of the different number of causal SNPs (i.e., 4, 40, or 80), two covariates (i.e., sex and age), and one error term in a multiple-SNP model (Methods). The SNP effects were simulated under four scenarios: (i) effect on neither mean nor variance (nei), (ii) effect on mean only (mean), (iii) effect on variance only (var), or (iv) effect on both mean and variance (both). The error term was generated from five different distributions: normal distribution, t-distribution with df = 10 or 3, or χ2 distribution with df = 15 or 1. (A) Four statistical test methods, i.e., the Bartlett’s test (Bart), the Levene’s test (Lev), the Fligner-Killeen test (FK), and the DGLM, were used to detect vQTLs. (B) The Levene’s test was used to analyze phenotypes processed using six strategies, i.e., raw phenotype (raw), raw phenotype adjusted for covariates (adj), RINT after adj (rint), logarithm transformation after adj (log), square transformation after adj (sq), and cube transformation after adj (cub). Positive rate is defined as the number of vQTLs with P < 0.05 divided by the total number of tests across 1000 simulations, which is the FPR under the null (“nei” and “mean”) and power under the alternative (“var” and “both”). The red horizontal line represents an FPR of 0.05.

  • Fig. 3 Manhattan plots of genome-wide vQTL analysis for 13 traits in the UKB.

    For each of the 13 traits (see Table 1 for full names of the traits), test statistics [−log10(PvQTL)] of all common (MAF ≥ 0.05) SNPs from the vQTL analysis are plotted against their physical positions. The dashed line represents the genome-wide significance level 1.0 × 10−8, and the solid line represents the experiment-wise significance level 2.0 × 10−9. For graphical clarity, SNPs with PvQTL < 1 × 10−25 are omitted, SNPs with PvQTL < 2.0 × 10−9 are color-coded in orange, the top vQTL SNP for each locus is represented by a diamond, and the remaining SNPs on odd and even chromosomes are color-coded in gray and blue, respectively.

  • Fig. 4 Manhattan Sunset plot of genome-wide vQTL and QTL analyses for WC in the UKB.

    Test statistics [−log10(P values)] of all common SNPs from vQTL (red bars) and QTL (blue bars) analysis are plotted against their physical positions. The top vQTL SNP is represented by an orange diamond, and the name of the nearest protein-coding gene is indicated for each significant vQTL locus (PvQTL < 2.0 × 10−9).

  • Fig. 5 Enrichment of GEI effects among the 75 vQTLs compared with a random set of QTLs.

    Five environmental factors/covariates, i.e., sex, age, physical activity (PA), sedentary behavior (SB), and smoking, were used in the GEI analysis. (A) Heatmap plot of GEI test statistics [−log10(PGEI)] for the 75 top vQTL SNPs. “*” denotes significant GEI effects after Bonferroni correction [PGEI < 1.33 × 10−4 = 0.05/(75 × 5)]. (B) Distribution of the number of significant GEI effects for the 75 top QTL SNPs randomly selected from all the top QTL SNPs with 1000 repeats (mean, 2.25; SD, 1.49). The red line represents the number of significant GEI effects for the 75 top vQTL SNPs (i.e., 16).

  • Table 1 The number of experiment-wise significant vQTLs or QTLs for the 13 UKB traits.
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Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/8/eaaw3538/DC1

    Note S1. Theoretical derivation of vQTL as a consequence of GEI

    Note S2. The Bartlett’s test, the FK test, and the DGLM test

    Note S3. Rank-based inverse-normal transformation

    Note S4. The effective number of independent traits

    Note S5. Definitions of the three environmental factors—PA, SB, and smoking

    Note S6. Expected inflation in the Levene’s test statistic due to phantom vQTL effect

    Note S7. Acknowledgments

    Fig. S1. Evaluation of statistical methods and phenotype processing strategies for the vQTL analysis by simulation based on a single-SNP model.

    Fig. S2. Phenotypic correlations among 13 quantitative traits, and PA and SB measures in the UKB.

    Fig. S3. Genome-wide vQTL and QTL analyses for 13 traits in the UKB.

    Fig. S4. Quantile-quantile plots of vQTL associations for the 13 UKB traits.

    Fig. S5. Enrichment of GEI effects among the 75 vQTLs compared with a random set of QTLs using the raw phenotypic values.

    Fig. S6. Comparison of the Young et al. method with the Levene’s test by vQTL simulation.

    Fig. S7. Excluding two alternative explanations for vQTL signals: Phantom vQTLs and epistasis.

    Table S1. Descriptive summary of (A) the quantitative traits and (B) the environmental data used in this study from the UKB.

    Table S2. Seventy-five experiment-wise significant vQTLs for nine UKB traits.

    Table S3. GEI examples.

    References (6875)

  • Supplementary Materials

    This PDF file includes:

    • Note S1. Theoretical derivation of vQTL as a consequence of GEI
    • Note S2. The Bartlett’s test, the FK test, and the DGLM test
    • Note S3. Rank-based inverse-normal transformation
    • Note S4. The effective number of independent traits
    • Note S5. Definitions of the three environmental factors—PA, SB, and smoking
    • Note S6. Expected inflation in the Levene’s test statistic due to phantom vQTL effect
    • Note S7. Acknowledgments
    • Fig. S1. Evaluation of statistical methods and phenotype processing strategies for the vQTL analysis by simulation based on a single-SNP model.
    • Fig. S2. Phenotypic correlations among 13 quantitative traits, and PA and SB measures in the UKB.
    • Fig. S3. Genome-wide vQTL and QTL analyses for 13 traits in the UKB.
    • Fig. S4. Quantile-quantile plots of vQTL associations for the 13 UKB traits.
    • Fig. S5. Enrichment of GEI effects among the 75 vQTLs compared with a random set of QTLs using the raw phenotypic values.
    • Fig. S6. Comparison of the Young et al. method with the Levene’s test by vQTL simulation.
    • Fig. S7. Excluding two alternative explanations for vQTL signals: Phantom vQTLs and epistasis.
    • Table S1. Descriptive summary of (A) the quantitative traits and (B) the environmental data used in this study from the UKB.
    • Table S2. Seventy-five experiment-wise significant vQTLs for nine UKB traits.
    • Table S3. GEI examples.
    • References (6875)

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