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

The number of single-cell states in the MCF-7 response to estrogen.

<p>(<b>A</b>) The mean squared error of the model fit to the microarray data decreases as function of the number of states: as expected, when the number of parameters increases, the quality of the fit improves. (<b>B</b>) The condition number is a measure of the similarity of the transcriptional profiles of the states. It increases as function of the number of states, , highlighting that over-fitting also increases with . A good balance between fit quality and over-fitting must be found. (<b>C</b>) The model posterior probability, derived by a Bayesian approach, has a peak at , which shows that a model with six states strikes a good balance between fit-to-data and model parsimony.</p

The single-cell transition rates in the ZR-75.1 system.

<p>Results of the six-state model for time course data in hormone-starved ZR-75.1 cells responding to estrogen stimulation are shown for comparison with the MCF-7 system of <b>Fig. 3</b>. (<b>A</b>) Cell population dynamics. (<b>B</b>) Rates and mean times of transitions. In ZR-75.1 the response to estrogen is initially one order of magnitude faster than in MCF-7.</p

Estrogen responding genes per state.

<p>Among the entire gene set considered in the MCF-7 cell experiment, 1270 also responded in ZR-75.1 cells. These are referred to as common ‘estrogen-regulated genes’ (E2R genes) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Cicatiello1" target="_blank">[4]</a>. ‘Primary genes’ are their subgroup having a ER transcription factor binding site within 10 kb around the TSS. The figures show how E2R and primary genes are responding across the single-cell states of a six-state model. (<b>A</b>) Fraction of up-regulated and down-regulated E2R genes. (<b>B</b>) Fraction of first-responding E2R genes, i.e., of genes that respond for the first time in a given state. (<b>C</b>) and (<b>D</b>) show the analogous pattern of primary genes.</p

Marker genes in the MCF-7 system.

<p>In each state of a six-state model, genes are ranked by their state-expression fold change with respect to the first state. Here, only the top 50 are shown along with their ranking in the other states. For the top genes of state 2 also the rank assigned considering a maximum fold change criterion over the time course is shown for comparison (separated column). The state-based ranking criterion highlights marker genes which would otherwise pass unnoticed.</p

Fits to gene expression time-course data.

<p>The fit to some key genes, comprising the 11 primary transcription factors identified by Cicatiello et<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Cicatiello1" target="_blank">[4]</a> and other important estrogen-responsive genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Zhu1" target="_blank">[1]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Weisz1" target="_blank">[2]</a>, are shown: black circles represent time-course (standardized) data while green lines represents the gene expression predicted by the six-state model.</p

Genetic Variation in the Social Environment Contributes to Health and Disease

<div><p>Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another (social genetic effects, SGE, also called indirect genetic effects). For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects (effects of an individual’s genotypes on its own phenotype). Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of “missing heritability” in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.</p></div

Ignoring SGE leads to biased estimates of DGE (heritability) in the outbred mice dataset.

<p>(A,B) DGE estimates for all organismal phenotypes. The colour of the dots indicates the sign of the covariance between DGE and SGE (blue: negative; red: positive; see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006498#sec006" target="_blank">Methods</a>); the intensity of the colour indicates the magnitude of SGE. (A) Comparison of DGE estimates from model with DGE only (x-axis) and full model with DGE, SGE, (y-axis). (B) Comparison of DGE estimates from model with DGE and cage effects (x-axis) and full model (y-axis). (C) Difference between estimated and simulated DGE. The contribution of SGE to simulated phenotypes varied and is shown on the x-axis. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006498#sec006" target="_blank">Methods</a> for values given to other parameters. Simulations were analyzed with three different models: DGE only (red), DGE + cage effects (green), DGE + SGE + cage effects + social environmental effects (blue).</p