Decision letter: Genetic and environmental perturbations lead to regulatory decoherence

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter). https://doi.org/10.7554/eLife.40538.001 Introduction A major goal in evolutionary and medical genetics is to identify the coordinated regulatory processes that differ between individuals as a function of their disease state, environment, or genetic background. One powerful approach for doing so is to identify the effect of environmental or genetic perturbations on the degree to which genes are correlated at the mRNA level – a phenomenon known as ‘co-expression’. From a medical perspective, identifying factors associated with changes in co-expression between healthy and sick individuals should point toward key regulatory changes driving phenotypic differences between groups (Mentzen et al., 2009; Hudson et al., 2009; Kostka and Spang, 2004; Choi et al., 2005; de la Fuente, 2010). From an evolutionary perspective, work on decanalization (Gibson, 2009a; Gibson, 2009b; Careau et al., 2014) indicates that gene regulatory networks evolve over many generations of stabilizing selection, and new mutations or novel environments may disrupt these fine-tuned connections. Decanalization has been hypothesized to explain the recent rise of non-communicable diseases in humans (Gibson, 2009b; Hu et al., 2016), with the idea being that recent major shifts in diet and lifestyle have led to dysregulation of regulatory programs that evolved under past environmental conditions. However, testing this hypothesis remains challenging in practice. Our ability to move forward in understanding how environmental and genetic variation affect molecular correlations – in both humans and other organisms where this topic has been extensively discussed (Wang et al., 2013; Sun et al., 2008; Southworth et al., 2009; Fukushima, 2013; Huang et al., 2015) – is limited by the available methodology (reviewed in (de la Fuente, 2010; van Dam et al., 2017; Singh et al., 2018)). In particular, most studies of differential co-expression to date have relied on two types of approaches: (i) building co-expression networks separately within each group of interest (e.g., diseased versus healthy) and contrasting their properties (Kostka and Spang, 2004; Choi et al., 2005; Steiger, 1980; Watson, 2006); or (ii) asking whether predefined sets of genes are differentially co-expressed between groups (Fukushima, 2013; Jen et al., 2006). Generally, these approaches are designed to compare population-level statistics (e.g., correlation coefficients) between two sample groups, and thus cannot test the effect of continuous predictor variables on variation in correlation. Further, these approaches are generally designed for datasets in which no individual-level covariates (e.g., age, sex, or batch) may bias co-expression estimates, which could lead to false conclusions if not accounted for (Parsana, 2017). This limited flexibility has made it difficult to identify individual-specific factors that predict co-expression in humans or other organism that are not amenable to controlled manipulations. To address this gap, we present a new approach for interrogating sources of variance in correlation. Our approach builds on previous work (Steiger, 1980), and is based on the fact that the Pearson correlation coefficient is equal to the average element-wise product of two traits measured across individuals, after each trait is mean centered and scaled; this value reflects the relationship between two traits within a population sample. By extension, to obtain a measure of the degree of correlation between two traits for each individual in a sample, we can simply keep the vector of products (i.e., we do not perform averaging across individuals). This vector of products can be used as the outcome variable in a linear model or linear mixed model, and consequently our approach can accommodate covariates and continuous predictor variables. We call our approach ‘Correlation by Individual Level Product’ (CILP), because for each individual the product between two traits is estimated and modeled as a continuous outcome variable (Figure 1). Figure 1 with 2 supplements see all Download asset Open asset Illustration of decoherence and ‘Correlation by Individual Level Product’ (CILP). (a) Co-expression networks in two different environments. Environment one represent normal (or control/baseline) conditions where the expression levels of gene 1 and gene 2 are highly correlated and co-regulated. (b) Environment 2 represents a stressful or unhealthy condition leading to lower correlation between the expression levels of gene 1 and gene 2. At the network level, this translates into a lower network degree (i.e., lower average correlation across genes or fewer connected nodes). We call this change in the correlation structure 'decoherence'. This is what we could expect if stressful conditions lower transcriptional robustness and lead to dysregulation of gene expression. (c) Differences in correlation between the expression levels of gene 1 and gene 2 in cases versus controls, or between genotypes, which translates into an average difference in product between these groups. https://doi.org/10.7554/eLife.40538.002 Leveraging this approach, we explore the effects of environmental and genetic variation on the degree to which two molecular traits are correlated. First, we test the hypothesis that stressful physiological conditions (here, bacterial infection or metabolic syndrome) lead to a loss of correlation among molecular traits that are correlated under normal conditions. We refer to this loss of correlation in an infected or diseased state, relative to a baseline or healthy state, as ‘decoherence’. We test for decoherence using gene expression data derived from monocytes at baseline or following stimulation with lipopolysaccharide (Fairfax et al., 2014), a component of bacterial cells walls and a potent stimulant of the innate immune response (Fairfax et al., 2014). We also test for decoherence using blood-derived NMR metabolite data from the Young Finns Study (YFS) (Raitakari et al., 2008). Strikingly, we find strong evidence in both datasets. Finally, we use CILP to map ‘correlation QTL’, defined as SNPs that affect the magnitude of the correlation between two mRNA transcripts. Using genotype and whole blood-derived gene expression data from the Netherlands Study of Depression and Anxiety (Penninx et al., 2008), we identity and replicate hundreds of correlation QTLs. Together, our new approach allows us to identify genetic variants and environmental factors that disrupt molecular co-regulation. Further, the flexible, robust approach we propose opens the door to future investigations of the causes and consequences of trait covariation in many contexts. Results Using 'Correlation by Individual Level Product' (CILP) to test for sources of variance in correlation Let y1, y2 be two outcomes measured across individuals in a population sample, with means y1¯,y2¯ and variances σ12,σ22, respectively. We wish to associate the Pearson correlation coefficient between these two variables, E[(y1−y1¯)( y2−y2¯)]/σ12σ22, with some predictor variable x. We propose the following statistical test. First, we calculate the demeaned, element-wise product of the outcomes:[(y1−y1¯)( y2−y2¯)], and then normalize by the square root of the product of variances:[(y1−y1¯)( y2−y2¯)/σ12σ22]. The resulting vector of values represents the estimate of the correlation within each individual in the sample, which can subsequently be modeled using approaches appropriate for continuous outcome variables. In practice, we use linear models or linear mixed effects models to test for associations between a given set of products and a fixed effect predictor variable, while controlling for covariates. We note that for our correlation QTL analyses, described later in the text, we use a modified version of CILP for computational efficiency and scalability. This modified version is described in the section ‘Comparison of approaches for quantifying correlation’ (see also Supplementary file 1). Simulations reveal power to detect sources of variance in correlation across many scenarios To confirm that our approach does not result in biased p-value distributions, and to understand the power of CILP across a range of effect sizes and sample sizes, we first performed extensive simulations. We focused our simulations on the identification of ‘correlation QTL’, defined as SNPs that affect the magnitude of the correlation between two mRNA transcripts; however, we note that the results are generalizable to other predictor variables. For each simulation, we generated 10,000 gene pairs that differed in covariance as a function of genotypic class. We did so using the multivariate normal distribution to simulate pairs of continuous distributions (but see Figure 1—figure supplement 1 for results where count data were simulated from a negative binomial distribution). Following simulation of gene expression data, we used CILP and linear models to detect differences in correlation as a function of genotype. With an effect size of 0.3 and n = 1000, we detected 98.19% of simulated true positive correlation QTLs at a threshold of p=0.05, and 50.91% of true positives after correcting for multiple hypothesis testing (using Bonferroni correction). Under the null, where the effect size was set to zero, we detect 5.19% of correlation QTLs at a threshold of p=0.05 and no correlation QTL after correcting for multiple testing (Table 1, Figure 1—figure supplement 1). To ensure that changes in the mean, variance, or covariance of gene expression levels do not increase our false positive rate, we also assessed power to detect correlation QTLs when: (i) the focal SNP affected mean gene expression levels (e.g., through an eQTL or proportion QTL); (ii) the focal SNP affected the variance of gene expression levels (e.g., through a varQTL); (iii) the mean or variance of gene expression levels were affected by a variable unrelated to genotype (e.g., through batch, cell type, or environmental effects); and (iv) a variable unrelated to genotype affected the correlation structure of the entire transcriptome. None of these scenarios increased the false positive rate, though the presence of strong varQTLs did decrease power to detect correlation QTLs (Table 1, Figure 1—figure supplement 2). We also found that including the expression levels of both genes as covariates in our models did not improve our power to detect correlation QTLs (Figure 1—figure supplement 1). Table 1 Power to detect simulated correlation QTL across a wide range of scenarios (n = 1000 for all simulations). https://doi.org/10.7554/eLife.40538.005 Simulation parameters(1) Correlation QTL effect*xxxxxxxx(2) Genotype predicts the mean of gene one expression levels† (e.g., through an eQTL or proportion QTL)xxx(3) Genotype predicts the mean of gene two expression levels†x(4) Genotype predicts the variance of gene one expression levels‡ (e.g., through a varQTL)xx(5) A variable that is random with respect to genotype predicts the mean of gene one expression levels†(e.g., through technical, environmental, or cell type heterogeneity effects)x(6) A variable that is random with respect to genotype predicts the mean of gene two expression levels†x(7) A variable that is random with respect to genotype predicts the variance of gene one expression levels‡(e.g., through technical, environmental, or cell type heterogeneity effects)xx(8) A variable that is random with respect to genotype predicts the variance of gene two expression levels‡xResultsPower (proportion of true positives detected, Bonferroni-corrected p<0.05)0.50910.49340.50550.07160.06790.49980.08840.0533False positive rate (proportion of true negatives detected, nominal p<0.05)0.05190.05290.04940.05320.05070.05270.05290.0522 Simulated effect sizes were as follows: correlation QTL* = 0.3, mean effects† = 1, variance effects ‡ = 10. When Bonferroni-corrected p-values were used, the false positive rate was 0 across all simulation scenarios. Abbreviations: eQTL = expression QTL and vQTL = variance QTL. Immune challenges results in decreased co-regulation of gene expression Next, we used gene expression data collected from human monocytes (Fairfax et al., 2014), to ask whether patterns of co-expression differed between cells assayed at baseline versus following treatment with LPS for 2 or 24 hr (n = 214 samples were assayed across the three conditions). To limit our search space, we focused on 1460 genes that were differentially expressed at the 2 hr time point (FDR < 5%), and tested for differences in correlation between unexposed and LPS-exposed cells across 1,063,611 possible transcript pairs (equivalent to 1460 chose 2). We found 958 gene pairs with a significant (FDR < 5%) change in correlation between the uninfected/baseline state versus 2 hr post LPS stimulation, 461 of which replicated with similar effect sizes at the 24 hr time point (correlation between effect sizes at the two time points: R2 = 0.12, p<10−16, binomial test for concordance of effect size direction: p<10−16; Figure 2). Figure 2 with 1 supplement see all Download asset Open asset Bacterial infection (i.e., treatment with LPS) leads to decoherence in primary monocyte gene expression. (a) Correlation changes have similar effect sizes after 2 hr or 24 hr of LPS stimulation. (b) Comparison of the Spearman correlation coefficient estimated in unstimulated cells (x-axis) versus those treated with LPS (y-axis). Gene pairs for which we detect significantly stronger correlations in the unstimulated (blue dots) or LPS treated (green dots) condition are highlighted. (c) An example of changes in correlation as a function of condition. Gene expression levels for OAS1 and OAS3 are negatively correlated at baseline, but become positively correlated after 2 hr or 24 hr treatment with LPS. https://doi.org/10.7554/eLife.40538.006 We observed no relationship between the magnitude of the difference in mean gene expression levels between conditions and the magnitude of the difference in correlation between conditions, suggesting our results are not driven by statistical artifacts associated with large mean changes in transcription following LPS stimulation (Figure 2—figure supplement 1). Related to this point, we found minimal overlap (<5% for all comparisons) between genes with significant infection-associated variation in correlation and (i) genes that were differentially expressed after 2 or 24 hr of LPS treatment (FDR < 5%) or (ii) genes with eQTL that were specific to the baseline, 2 hr LPS treatment, or 24 hr LPS treatment samples (as reported in Fairfax et al. (2014)). In total, we identified gene pairs that are more highly correlated in cells assayed post LPS stimulation versus at baseline, as well as gene pairs that lose correlation following a simulated bacterial infection. Overall, however, we found much greater support for the latter pattern: 61% (at the 2 hr time point) and 73% (at the 24 hr time point) of significant transcript pairs were more strongly correlated across individuals in uninfected versus LPS challenged cells. Importantly, this represents a statistically significant bias toward a loss of correlation (i.e. decoherence) following two hours of simulated bacterial infection (p=1.05×10−7, log2 odds = 0.503, Fisher’s exact test), with an even stronger bias toward decoherence after 24 hr of immune challenge (p<10−16, log2 odds = 0.941, Fisher’s exact test). Metabolic syndrome disrupts metabolite co-regulation Identification of disease-associated variation in metabolite correlations We next applied our correlation test to whole blood-derived NMR metabolite data collected from a population-based, longitudinal study of young Finnish individuals (the cardiovascular risk in young Finns study, abbreviated ‘YFS’) (Raitakari et al., 2008; Nuotio et al., 2014). Our dataset included 159 metabolite measures collected across three time points that spanned a 10 year period (2001: n = 1564, 2007: n = 1498, 2011: n = 1501). We note, however, that our analyses did not focus on changes in correlation across time, but rather, changes in correlation between individuals that were healthy versus those that met the criteria for metabolic syndrome (Grundy et al., 2005). To test the hypothesis that disease disrupts homeostasis and leads to decoherence, we asked whether specific pairs of metabolites were correlated in healthy individuals, but no longer correlated in individuals with metabolic syndrome. Across 11491 unique metabolite pairs that met our criteria for analysis (see Materials and methods), healthy individuals and those with metabolic syndrome showed broadly similar correlation structures (Figure 3). However, using CILP, we identified strong effects of health status on the degree of correlation between a subset of metabolite pairs. Specifically, we identified 1528 metabolite pairs that were more correlated in healthy individuals relative to those with metabolic syndrome, and 619 metabolite pairs that showed the opposite pattern (FDR < 5%; linear mixed model controlling for age, sex, year, and individual identity). This represents a 2.20x enrichment (p<10−16, Fisher’s exact test) of metabolite pairs that lose correlation following the onset of disease. Importantly, the set of 1528 metabolite pairs that lose correlation includes metabolites that are both more highly expressed (n = 93 metabolites, FDR < 5%) and more lowly expressed (n = 32) in individuals with metabolic syndrome, as well as metabolites that do not significantly differ between sample groups (n = 4). Figure 3 with 1 supplement see all Download asset Open asset Metabolic syndrome leads to decoherence among particular metabolite pairs. (a) Correlation matrices showing the magnitude of the Spearman correlation coefficient, for healthy individuals and those with metabolic syndrome. (b) Comparison of the Spearman correlation coefficient estimated in healthy individuals (x-axis) versus those with metabolic syndrome (y-axis). Overall, the magnitude of the correlation is similar between groups (R2 = 0.62, p<10−16), though the slope of the line is already less than 1, indicating a tendency toward stronger correlations in healthy individuals (beta = 0.814). For the subset of metabolite pairs that display significantly stronger correlations in the healthy (blue dots) or metabolic syndrome class (green dots), this effect is more pronounced (beta = 0.597). (c) Categorical enrichment of metabolites that exhibit stronger pairwise correlations in the healthy or metabolic syndrome class (x-axis: log2 odds ratio from a Fisher’s exact test; y-axis: 11 functional classes tested (annotations taken from (Nath et al., 2017)). Asterisks indicate significant enrichment. (d) A composite measure of metabolites that are strongly decoherent at the first time point (i.e., that show decreases in correlation in individuals with metabolic syndrome) can predict an individual’s future health status. Y-axis: Principal component 1 of 34 metabolites, measured at the first time point, with the strongest evidence for dysregulation. X-axis: values are stratified by whether an individual was healthy at the first (t0) and the last (t3) time point, had metabolic syndrome at t0 and t3, or developed metabolic syndrome between t0 and t3 (linear model, p=3.03×10−10). https://doi.org/10.7554/eLife.40538.008 Metabolic syndrome affects co-regulation of particular metabolite classes We found overall support for the hypothesis that metabolic syndrome disrupts the correlation structure that exists in healthy individuals. To identify the specific pathways and processes by metabolic syndrome, we each metabolite in our dataset to one of 11 functional classes (as in et al. Supplementary file and asked whether each class was for metabolites that decreases in correlation. we found the strongest enrichment for odds ratio = measures of ratio = and in ratio = metabolite pairs that showed the opposite pattern (i.e., were more strongly correlated in individuals with metabolic we found an enrichment of ratio = as well as ratio = and ratio = (Figure Supplementary file 3). Together, these results that metabolite co-regulation is strongly by and that particular classes of metabolites are more and to decoherence than Finally, because our dataset included metabolite data collected from the individuals across multiple time we asked whether metabolite pairs that (i.e., correlation with disease) at the first time point could be used to identify individuals that develop metabolic syndrome at a later time To do we performed on the 34 metabolites that the strongest evidence for decoherence at the first time point, and used to ask whether individuals that developed metabolic syndrome at the last time point already showed changes (see Materials and Strikingly, we found that using data from the first time point, could between individuals that healthy across all time points and individuals that were healthy at the first time point but developed metabolic syndrome later on (R2 = = Figure 3). This effect was stronger than that observed in analyses using levels at the first time point of metabolic R2 = = or an from the 34 metabolites that the strongest mean differences between healthy and metabolic syndrome individuals at the first time point (R2 = = variation co-expression of genes and of hundreds of correlation QTLs Finally, we used CILP to identify genetic variants that control the degree of correlation between a of gene a pattern we refer to as ‘correlation effects on co-expression could through possible For a SNP that disrupts a transcription in a lead to levels of co-expression between the and the but for individuals the In these individuals, increased expression of the to increase expression of the leading to an between SNP genotype and variation in This is one that has been to variation in co-expression (de la Fuente, 2010; et al., 2014), with some support (Nath et al., 2017). However, the relationship between genetic variation and co-expression is suggesting that may that have to be To map SNPs that affect the magnitude or of pairwise gene expression we applied CILP to genotype and whole blood-derived gene expression data from the Netherlands Study of Depression and Anxiety (Penninx et al., We used a set of SNP and gene expression collected for individuals in our dataset and individuals in our dataset (see Materials and our dataset was not to test all possible pairwise of gene expression all and because we were in understanding co-expression patterns among genes to metabolic we focused on the most strongly associated with In total, we identified associations between a SNP and variation in co-expression in our dataset (at a FDR to linear model controlling for sex, age, major cell year of sample study and the first from a on the genotype call associations unique sets and each of which was in and correlation (Figure Supplementary file 4). Figure with supplements see all Download asset Open asset CILP approach hundreds of correlation QTLs. (a) of a correlation where the SNP the magnitude of the correlation between the mRNA expression levels of and (b) Gene pairs in significant (FDR < correlation QTL. represents a and each line two represents a significant correlation QTL. are by the identity of the SNP controlling the magnitude of the correlation between the gene (c) correlation QTL identified in the set (n = replicate in a set of (n = effect sizes for each correlation estimated in the or sizes are derived from in are to indicate whether a given correlation QTL Bonferroni in the To confirm our and potential raised by of the data, we replicated our correlation QTL in a set of we found that correlation QTLs replicated (at a Bonferroni n = correlation QTLs replicated at a Figure 4). Further, in derived gene expression data from we found that correlation QTLs replicated at a FDR replicated at a Bonferroni but the of the effects across binomial p<10−16; see Materials and of correlation QTLs For the of correlation QTLs we we performed analyses to First, we asked the of correlation QTLs relative to the genes for which correlation structure (e.g., are the correlation QTLs in or as well as the degree to which correlations QTLs also had mean or variance effects on gene expression file Figure supplement 1). we found that of correlation QTL were on the as one of the two correlated were on the as both correlated and were in on both Further, of significant correlation QTL were also eQTL or for one of the correlated but no correlation QTL had mean or variance effects on both genes (Figure supplement 1). Next, we asked whether the set of genes in significant correlation QTLs were for (i) particular biological processes and pathways (using Gene et al., (ii) known or (iii) known of For analysis (ii) and we on a of known gene through by et al., We strongly genes in correlation QTLs to be for or known of given that the that have been to genetic effects on variation in correlation or of