Additional file 1: of Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression

Supplementary methods and supplementary figures. (PDF 5596 kb

Joint genetic analysis using variant sets reveals polygenic gene-context interactions

<div><p>Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.</p></div

Characterization of genes with significant heterogeneity GxC for stimulus eQTLs in monocytes.

<p><b>(a)</b> Cumulative fraction of probe/stimulus pairs with increasing numbers of distinct univariate eQTLs (average of the naïve and the stimulated state using step-wise selection) for different gene sets (<b>Methods</b>). Shown are cumulative fractions of all probe/stimulus pairs (All), those with significant <i>cis</i> associations (mtSet), pairs with significant GxC (iSet) and instances with significant heterogeneity GxC (iSet-het). <b>(b)</b> Breakdown of 1,281 probe/stimulus pairs with significant heterogeneity GxC into distinct classes defined using the results of a single-variant step-wise LMM (<b>Methods</b>). <b>(c-e)</b> Manhattan plots for representative probes with significant heterogeneity GxC effects. Grey boxes indicate the gene body. <b>(c)</b> Manhattan plot (left) and χ² statistics for variants in both contexts (right) for the gene <i>SLC1A4</i>. Dark circles indicate distinct lead variants in both contexts (r²<0.2). <b>(d)</b> Manhattan plot after conditioning on the lead variant (secondary associations in the stepwise LMM) for the gene <i>PROK2</i>. The star symbol indicates the shared lead variant in both contexts. The conditional analysis reveals a secondary association that is specific to the naïve state. <b>(e)</b> Analogous plot as in <b>c</b> for the gene <i>NSUN2</i>, for which the single-variant model did not provide an interpretation of heterogeneity-GxC. <b>(f)</b> Breakdown of probe / stimulus pairs with shared lead variants, stratified by concordance of the effect direction (opposite-direction versus same-direction eQTLs) and significance of the heterogeneity-GxC test (heter vs No heter; FDR 5%). eQTLs with opposite effects were enriched for significant heterogeneity-GxC (2.2 fold enrichment, P<4e-2).</p

Analysis of stimulus-specific eQTLs in monocytes.

<p><b>(a)</b> Number of probes with at least one significant <i>cis</i> association (Association test) or genotype-stimulus interaction (Interaction test) for alternative methods and stimulus contexts. Considered were the proposed set tests (mtSet, iSet, iSet-het) as well as single-variant multi-trait LMMs (mtLMM, mtLMM-int [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006693#pgen.1006693.ref010" target="_blank">10</a>]), testing for genetic effects in <i>cis</i> (100kb region centered on the transcription start site; FDR < 5%). Additionally, iSet-het was used to test for heterogeneity-GxC effects. Individual rows correspond to different stimulus contexts with “All” denoting the total number of significant effects across all stimulus contexts. (<b>b)</b> Venn diagram of probes and stimuli with significant interactions identified by alternative methods and tests (across all stimuli). <b>(c)</b> Bivariate plot of the variance attributed to persistent genetic effects versus genotype-stimulus interactions for all probes and stimuli. Significant interactions are shown in red. Density plots along the axes indicate the marginal distributions of persistent genetic variance (top) and variance due to interaction effects (right), either considering all (black) or probe/stimulus pairs with significant interactions (iSet in <b>a</b>, dark red). <b>(d)</b> Average proportions of <i>cis</i> genetic variance attributable to persistent effects, rescaling effects and heterogeneity-GxC, considering probe/stimulus pairs with significant <i>cis</i> effects (5% FDR, mtSet), stratified by increasing fractions of the total <i>cis</i> genetic variance. Shown on top of each bar is the number of instances in each variance bin. The top panel shows the density of probes as a function of the total <i>cis</i> genetic variance.</p

Table S2. GCA, SCA, and heritability estimates.

Values for plots in Fig 1

Table S3. Summary of significant SNPs detected in GWA analyses.

Number of significant SNPs detected for each trait for both within-study and across-study Bonferroni correction

Table S5. Summary of SConES SNPs.

Number of associated SNPs detected for each trait

Table S6. Summary of associated SConES SNPs per trait.

IDs (chr_position) of all associated SNPs and their significance status in each GWA study

Table S1. Germplasm information.

The parental genotypes of each hybrid are listed along with whether the line was included in the final data set (yes=1, no=0). Lines were not included if their manually-fertilized parent did not germinate

Table S9. Genomic control (GC) values.

Genomic control values for all GWA analyses. Q-Q plots for GWAs with significant SNPs are shown in SI Appendix, Fig S20