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

Evaluation of PANAMA and alternative methods on the simulated eQTL dataset.

<p>(<b>a,b</b>) number of recovered <i>cis</i> and <i>trans</i> associations as a function of the chosen false discovery rate cutoff. To circumvent biases due to linkage, at most one association per chromosome and gene is counted. (<b>c</b>) Receiver Operating Characteristics (ROC) for recovering true simulated associations, depicting the true positive rate (TPR) as a function of the permitted false positive rate (FPR). (<b>d</b>) inflation factors, defined as , indicating either inflated p-value distributions () or deflation () of the respective tests statistics. (<b>e</b>) Area under the ROC curve for alternative simulated datasets, subsampling certain fractions of number of simulated <i>trans</i> association. (<b>f</b>) Area under the ROC curve for alternative simulated datasets, subsampling the number of simulated confounding factors.</p

Illustration of the PANAMA model.

<p>(<b>a</b>) Representation of the linear model used by PANAMA to correct for the effect of confounding factors. (<b>b</b>) Alternative settings of confounders in relation to true genetic signals: First, orthogonality between confounders and genetics. The variation in the gene expression levels (green arrow) can be better explained by the SNP (blue arrow). Second, statistical overlap between variation explained by confounders and the genetic variation as often found in <i>trans</i> hotspots. Gene expression variation can be equally well explained as genetic or due to a confounding factor. Previous methods focus in the first setting, while PANAMA is able to handle both situations. (<b>c</b>) PANAMA applied to the yeast eQTL dataset. Pronounced <i>trans</i> regulators that overlap with the learnt confounding factors are highlighted in red.</p

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

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

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

Additional File 3_retina.xlsx

<div>Additional Data for publication:</div><div><div><br></div><div>f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq, Florian Buettner; Naruemon Pratanwanich; Davis McCarthy; John Marioni; Oliver Stegle, Genome Biology, 2017</div><div><br></div></div><div>Retina residual dataset. Residual gene expression levels for 2,145 retina cells considered in Fig. 5c-e. The residual data were obtained by regressing out the most relevant unannotated factor as inferred by f-scLVM (Supp. Fig. 8a) from the pseudo-observations Y ̃. <br></div

Additional file 3: of f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq

Differentially expressed genes between the populations of astrocytes. Differentially expressed genes and factors between the identified astrocyte subpopulations (using f-scLVM residuals, Fig. 4e). (XLSX 91 kb

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Total Energy Expenditure (Bland-Altman plot).

<p>Difference between DLW-measured and heart rate and accelerometry estimated total energy expenditure (TEE) in Megajoules per day for adult women (triangles) and men (squares) plotted against DLW-measured TEE. Broken lines are mean (±2 SD) estimation errors.</p