Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci
Authors: Sonja Neumeyer, Gibran Hemani, Eleftheria Zeggini
Journal: Trends in Molecular Medicine (2019)

GWAS using large sample sizes have allowed the identification of many DNA sequence variants associated with molecular traits such as gene expression, DNA methylation, and protein levels that could be mediators between disease-associated genetic variants and the disease.Quantitative trait loci (QTLs), genetic variants influencing molecular traits, are increasingly used to identify causal features of complex traits.MR, a method using genetic variants as instrumental variables for a modifiable exposure, is employed to evaluate whether a molecular trait has an influence on a complex trait.Many challenges remain, such as linkage disequilibrium between causal variants of different complex traits, pleiotropy, and the non-specificity of molQTLs, or molQTLs being reverse causally influenced by a complex trait; methods to address them are being developed. Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed. Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed. GWAS (Box 1) have identified thousands of sequence variants that contribute to the genetic architecture of complex diseases and medically-relevant quantitative traits. This endeavor has been fuelled by two major ambitions: creating genetic predictors for disease, and identifying the genomic regions responsible for the disease to gain a better understanding of the relevant biological mechanisms [1Nelson M.R. et al.The support of human genetic evidence for approved drug indications.Nat. Genet. 2015; 47: 856-860Crossref PubMed Scopus (697) Google Scholar,2Zheng J. et al.Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.bioRxiv. 2019; (Published online May 5, 2019)https://doi.org/10.1101/627398Crossref Scopus (0) Google Scholar]. The latter objective is the focus of this review.Box 1Genome-wide Association StudiesGWAS compare large numbers of affected with unaffected individuals to identify sequence variants that are associated with the risk of complex diseases, or at the population level to identify associations with quantitative traits. The foundation for GWAS was laid by the sequencing of the human genome [72Lander E.S. Initial impact of the sequencing of the human genome.Nature. 2011; 470: 187-197Crossref PubMed Scopus (793) Google Scholar], the characterization of correlation patterns between pairs of variants genome-wide [73Thorisson G.A. et al.The International HapMap Project Web site.Genome Res. 2005; 15: 1592-1593Crossref PubMed Scopus (464) Google Scholar], the development of high-throughput genotyping platforms, and the availability of large-scale sample sizes. Millions of SNPs have been mapped [74Wang D.G. et al.Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome.Science. 1998; 280: 1077-1082Crossref PubMed Scopus (1714) Google Scholar]. For several reasons it has been difficult to elucidate the underlying mechanism linking an associated genetic variant with a disease trait. One reason is the coinheritance of many genetic variants with the disease-associated variant (linkage disequilibrium, LD) [75Altshuler D. et al.A haplotype map of the human genome.Nature. 2005; 437: 1299-1320Crossref PubMed Scopus (4776) Google Scholar]. Because of the complex correlation structure of the human genome, the most strongly associated GWAS signal (index variant) is often not causal [76Wu Y. et al.Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data.Genome Biol. 2017; 18: 86Crossref PubMed Scopus (58) Google Scholar]. Similarly, compounded by complex regulatory mechanisms, the nearest gene to the top GWAS signal is not necessarily the causal gene [11Zhu Z. et al.Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.Nat. Genet. 2016; 48: 481-487Crossref PubMed Scopus (927) Google Scholar]. GWAS compare large numbers of affected with unaffected individuals to identify sequence variants that are associated with the risk of complex diseases, or at the population level to identify associations with quantitative traits. The foundation for GWAS was laid by the sequencing of the human genome [72Lander E.S. Initial impact of the sequencing of the human genome.Nature. 2011; 470: 187-197Crossref PubMed Scopus (793) Google Scholar], the characterization of correlation patterns between pairs of variants genome-wide [73Thorisson G.A. et al.The International HapMap Project Web site.Genome Res. 2005; 15: 1592-1593Crossref PubMed Scopus (464) Google Scholar], the development of high-throughput genotyping platforms, and the availability of large-scale sample sizes. Millions of SNPs have been mapped [74Wang D.G. et al.Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome.Science. 1998; 280: 1077-1082Crossref PubMed Scopus (1714) Google Scholar]. For several reasons it has been difficult to elucidate the underlying mechanism linking an associated genetic variant with a disease trait. One reason is the coinheritance of many genetic variants with the disease-associated variant (linkage disequilibrium, LD) [75Altshuler D. et al.A haplotype map of the human genome.Nature. 2005; 437: 1299-1320Crossref PubMed Scopus (4776) Google Scholar]. Because of the complex correlation structure of the human genome, the most strongly associated GWAS signal (index variant) is often not causal [76Wu Y. et al.Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data.Genome Biol. 2017; 18: 86Crossref PubMed Scopus (58) Google Scholar]. Similarly, compounded by complex regulatory mechanisms, the nearest gene to the top GWAS signal is not necessarily the causal gene [11Zhu Z. et al.Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.Nat. Genet. 2016; 48: 481-487Crossref PubMed Scopus (927) Google Scholar]. Typically, associated variants individually account for a very small proportion of phenotypic variation. This is common for quantitative or 'complex' traits which are usually influenced by a large number of genes with small effects on the trait [3Boyle E.A. et al.An expanded view of complex traits: from polygenic to omnigenic.Cell. 2017; 169: 1177-1186Abstract Full Text Full Text PDF PubMed Scopus (1386) Google Scholar]. There is no simple Mendelian inheritance pattern, but random sampling of alleles at each associated gene results in a normally distributed phenotype in the population [4Fisher R.A. XV. The correlation between relatives on the supposition of Mendelian inheritance.Trans. R. Soc. Edinburgh. 1919; 52: 399-433Crossref Scopus (2547) Google Scholar]. Functional information on the underlying mechanisms of genetic variants identified by GWAS is often unclear, in other words it is challenging to identify effector genes based only on the observed association summary statistics [3Boyle E.A. et al.An expanded view of complex traits: from polygenic to omnigenic.Cell. 2017; 169: 1177-1186Abstract Full Text Full Text PDF PubMed Scopus (1386) Google Scholar,5Ruiz-Narváez E.A. What is a functional locus? Understanding the genetic basis of complex phenotypic traits.Med. Hypotheses. 2011; 76: 638-642Crossref PubMed Scopus (6) Google Scholar]. The majority of complex trait variants reside in noncoding regions of the genome [6Edwards S.L. et al.Beyond GWASs: illuminating the dark road from association to function.Am. J. Hum. 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In this review we describe how molecular traits are also being assayed and analyzed for genetic associations, and how the understanding of complex disease etiology is improving through combining genetic analysis of both the disease and molecular traits. The presiding manner in which these relationships are constructed is by using a causal inference method, MR, which capitalizes on the abundance of GWAS results now available. In the following we describe MR in terms of both its current implementation and the future developments that will be necessary to address known limitations. The influence of a genetic variant associated with a disease is likely to be mediated via molecular traits (Figure 1), which themselves are often complex. Quantitative molecular traits, such as gene expression or protein abundance, are frequently dysregulated in disease and can act as intermediate phenotypes, affording greater power to detect association compared to the dichotomous definition of a disease endpoint, which is the culmination of multiple biological processes being perturbed [10Civelek M. Lusis A.J. Systems genetics approaches to understand complex traits.Nat. Rev. Genet. 2013; 15: 34-48Crossref PubMed Scopus (412) Google Scholar]. Multiple studies have investigated mRNA levels combined with genome-wide genotype information to identify expression (e)QTLs, in other words genetic variants associated with gene expression levels [11Zhu Z. et al.Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.Nat. Genet. 2016; 48: 481-487Crossref PubMed Scopus (927) Google Scholar]. The first studies to investigate molQTLs started out with small sample sizes. Given the challenges associated with collecting human biospecimens using invasive procedures, analyses initially focused on using the most accessible tissues [12Emilsson V. et al.Genetics of gene expression and its effect on disease.Nature. 2008; 452: 423-428Crossref PubMed Scopus (1008) Google Scholar]. Today, sample sizes used for molQTL investigation in blood have become very large [13Võsa U. et al.Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis.bioRxiv. 2018; (Published online October 19, 2018)https://doi.org/10.1101/447367Crossref Scopus (0) Google Scholar]. MolQTLs are generally classified into cis-acting, which is typically defined as the regulation of genes within 1 Mb, or trans-acting, defined as molQTLs affecting genes further away or on different chromosomes [14Westra H.-J. et al.Systematic identification of trans eQTLs as putative drivers of known disease associations.Nat. Genet. 2013; 45: 1238-1243Crossref PubMed Scopus (1194) Google Scholar]. Whereas the cis effects detected have generally been large and are easily found using small sample sizes, trans effects tend to be much smaller and larger sample sizes are required. Large studies such as the eQTLGen Consortium [13Võsa U. et al.Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis.bioRxiv. 2018; (Published online October 19, 2018)https://doi.org/10.1101/447367Crossref Scopus (0) Google Scholar] or GoDMC (www.godmc.org.uk/) are emerging to identify these small effects that might play central roles in disease etiology. Molecular trait loci seem to be highly tissue-dependent [15Brown C.D. et al.Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.PLoS Genet. 2013; 9: e1003649Crossref PubMed Scopus (102) Google Scholar,16Sun B.B. et al.Genomic atlas of the human plasma proteome.Nature. 2018; 558: 73-79Crossref PubMed Scopus (582) Google Scholar]. However, tissue-sharing of cis-eQTLs seems to be bimodal. Either cis-eQTLs seem to be shared across many tissues or they are very specific to only a small subset of tissues [17The GTEx Consortium et al.Genetic effects on gene expression across human tissues.Nature. 2017; 550: 204-213Crossref PubMed Scopus (1930) Google Scholar]. To provide a resource which enables the systematic study of the effects of genetic variation on gene expression regulation in multiple human tissues, the Genotype-Tissue Expression (GTEx) project was initiated a decade ago [18Lonsdale J. et al.The genotype-tissue expression (GTEx) project.Nat. Genet. 2013; 45: 580-585Crossref PubMed Scopus (4352) Google Scholar]. The current GTEx release provides a total of 11 688 samples and 53 tissues across 714 donors (current release V7, dbGaP accession phs000424.v7.p2). Sample sizes of other studies have also largely increased [19Hannon E. et al.Leveraging DNA-methylation quantitative-trait loci to characterize the relationship between methylomic variation, gene expression, and complex traits.Am. J. Hum. Genet. 2018; 103: 654-665Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar, 20Yao C. et al.Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease.Nat. 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To date, many different molecular traits with a potential influence on gene regulation have been investigated [23Vandiedonck C. Genetic association of molecular traits: a help to identify causative variants in complex diseases.Clin. Genet. 2018; 93: 520-532Crossref PubMed Scopus (29) Google Scholar]. These range from traits that influence the epigenome, such as DNA methylation (meQTL), histone modification (hQTL), and chromatin accessibility (caQTL), to alternative splicing (sQTL), protein levels (pQTL), microRNA expression (mirQTL), and ribosome occupancy (rQTL) [23Vandiedonck C. Genetic association of molecular traits: a help to identify causative variants in complex diseases.Clin. Genet. 2018; 93: 520-532Crossref PubMed Scopus (29) Google Scholar]. In addition, higher-level intermediate phenotypes such as metabolites have been investigated, and QTLs for metabolites such as carbohydrates, amino acids, or fatty acids have been identified [24Kastenmüller G. et al.Genetics of human metabolism: an update.Hum. Mol. Genet. 2015; 24: R93-R101Crossref PubMed Scopus (84) Google Scholar]. In an effort to find the molecular pathways that connect genetic variants to complex traits, overlapping/colocalization methods between GWAS and molQTL signals have been developed. Colocalization of an eQTL with a GWAS signal suggests that the eQTL target gene could be involved in the molecular pathway underlying the complex disease under investigation [25Wen X. et al.Integrating molecular QTL data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization.PLoS Genet. 2017; 13: e1006646Crossref PubMed Scopus (92) Google Scholar]. Several studies have already discovered GWAS signals enriched for molQTLs in a tissue-dependent manner [26Nicolae D.L. et al.Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS.PLoS Genet. 2010; 6: e1000888Crossref PubMed Scopus (913) Google Scholar]. For example, the myocardial infarction and high LDL cholesterol-associated 1p13 locus (see Glossary) had been fine-mapped to the CELSR2 gene. Using eQTL analyses, it was discovered that the expression of SORT1 was influenced by this variant [27Musunuru K. et al.From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus.Nature. 2010; 466: 714-719Crossref PubMed Scopus (811) Google Scholar]. MolQTLs are being used as instrumental variables for molecular traits in a variety of ways: to infer the relative effects of different classes of molecular features on variation in complex traits, to identify the causal gene for a particular complex trait [23Vandiedonck C. Genetic association of molecular traits: a help to identify causative variants in complex diseases.Clin. Genet. 2018; 93: 520-532Crossref PubMed Scopus (29) Google Scholar], to identify the causal tissue for a complex trait [28Ongen H. et al.Estimating the causal tissues for complex traits and diseases.Nat. Genet. 2017; 49: 1676-1683Crossref PubMed Scopus (99) Google Scholar], and to estimate causal relationships between different molecular traits [29Taylor D.L. et al.Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle.Proc. Natl. Acad. Sci. 2019; 116: 10883-10888Crossref PubMed Scopus (60) Google Scholar]. In this review we focus on their use for identifying the causal features of complex traits. MR (Box 2) studies use genetic variants as proxies for modifiable risk factors to test whether the risk factor is causally relevant to an outcome of interest [30Davey Smith G. Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?.Int. J. Epidemiol. 2003; 32: 1-22Crossref PubMed Scopus (2530) Google Scholar,31Ebrahim S. Davey Smith G. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology?.Hum. Genet. 2008; 123: 15-33Crossref PubMed Scopus (223) Google Scholar]. The advantage of such an approach is that unmeasured confounding, an issue of observational studies, and reverse causation can be minimized. It is therefore possible to use genetic information to draw causal inferences.Box 2Mendelian Randomization StudiesThe laws of Mendelian inheritance assign alleles at conception to individuals independently of environmental risk factors and confounders. To obtain valid estimates using MR, three assumptions must be met: first, the genetic variants must be sufficiently associated with the exposure of interest; second, the genetic variants should not be associated with any confounder of the risk factor–outcome relationship; finally, there should not be any other pathway leading from genetic variants to outcome except through the exposure of interest. Except for the first assumption, which can be tested, the other two assumptions can only be addressed by sensitivity analyses [77Burgess S. et al.Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants.Epidemiology. 2017; 28: 30-42Crossref PubMed Scopus (419) Google Scholar]. The laws of Mendelian inheritance assign alleles at conception to individuals independently of environmental risk factors and confounders. To obtain valid estimates using MR, three assumptions must be met: first, the genetic variants must be sufficiently associated with the exposure of interest; second, the genetic variants should not be associated with any confounder of the risk factor–outcome relationship; finally, there should not be any other pathway leading from genetic variants to outcome except through the exposure of interest. Except for the first assumption, which can be tested, the other two assumptions can only be addressed by sensitivity analyses [77Burgess S. et al.Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants.Epidemiology. 2017; 28: 30-42Crossref PubMed Scopus (419) Google Scholar]. Early MR studies mainly used one-sample approaches, where the exposure and outcome phenotypes, as well as the genetic variants that were used to instrument the exposure, were available for all samples in a single dataset. Currently, when many large-scale GWAS are conducted, it is much more powerful to use published single-nucleotide polymorphism (SNP)–trait associations from large consortia. It is therefore common to use two-sample MR approaches where SNP–exposure and SNP–outcome associations are estimated in different studies and are subsequently combined [32Pierce B.L. Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators.Am. J. Epidemiol. 2013; 178: 1177-1184Crossref PubMed Scopus (393) Google Scholar]. When using genome-wide significant SNPs as instrumental variables for an exposure, the first MR assumption should be verified. For two-sample MR methods, only summary statistics are required (per-allele regression coefficients, standard errors, and effect alleles) which are typically obtained from published GWAS of the largest possible datasets [33Hartwig F.P. et al.Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique.Int. J. Epidemiol. 2017; 45: 1717-1726Crossref Scopus (212) Google Scholar]. The causal effect can be estimated using the Wald ratio estimate, which is the ratio of SNP–outcome association and SNP–exposure association. SNP–exposure and SNP–outcome association statistics should ideally be obtained from studies of non-overlapping individuals (two-sample MR). When using summary statistics from only one sample or from partially overlapping samples, results might be biased in the direction of the observational estimate, especially if the genetic effects on the exposure are weak [34Burgess S. et al.Bias due to participant overlap in two-sample Mendelian randomization.Genet. Epidemiol. 2016; 40: 597-608Crossref PubMed Scopus (446) Google Scholar]. When several independent genetic variants are known to be associated with the exposure of interest, these can be combined into a single MR estimate using inverse variance weighted meta-analysis of the single Wald ratio estimates [32Pierce B.L. Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators.Am. J. Epidemiol. 2013; 178: 1177-1184Crossref PubMed Scopus (393) Google Scholar]. In doing so, the MR framework can then be viewed as a meta-analysis problem which itself has a rich set of tools to evaluate and correct for bias [35Bowden J. Holmes M.V. Meta-analysis and Mendelian randomization: a review.Res. Synth. Methods. 2019; (Published online March 12, 2019)https://doi.org/10.1002/jrsm.1346Crossref Scopus (79) Google Scholar]. One issue that has been of particular concern in MR is in proving that violation of the third assumption, in other words that the genetic instrument influences the outcome only through the exposure, does not induce bias [36Hemani G. et al.Evaluating the potential role of pleiotropy in Mendelian randomization studies.Hum. Mol. Genet. 2018; 27: R195-R208Crossref PubMed Scopus (387) Google Scholar]. A suite of sensitivity analyses [37Bowden J. et al.Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.Int. J. Epidemiol. 2015; 44: 512-525Crossref PubMed Scopus (2049) Google Scholar, 38Bowden J. et al.Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator.Genet. 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Whole-genome approaches have indicated that the causal variants influencing complex traits are over-represented in those that are also associated with eQTLs [43Gusev A. et al.Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases.Am. J. Hum. Genet. 2014; 95: 535-552Abstract Full Text Full Text PDF PubMed Scopus (370) Google Scholar,44Banos D.T. et al.Bayesian reassessment of the epigenetic architecture of complex traits.bioRxiv. 2018; (Published online November 14, 2018)https://doi.org/10.1101/450288Crossref Google Scholar]. This supports the notion that disease biology may be unraveled by mapping the causal path from genetic variant through the use of intermediate molQTLs [45Schadt E.E. et al.Genetics of gene expression surveyed in maize, mouse and man.Nature. 2003; 422: 297-302Crossref PubMed Scopus (1184) Google Scholar]. In its most basic implementation, an MR framework for evaluating the causal influence of a molecular trait on a complex trait would be to test if a known molQTL is also associated with the complex trait (Figure 2, Key Figure). The Wald ratio of SNP–complex trait and SNP–molecular trait effects can then be obtained as an estimate of the causal effect. This simple method suffers from several potential pitfalls and is often performed as an initial screen to find, from among many molecular phenotypes (e.g., hundreds of thousands of DNA methylation levels), a few putative causal molecular phenotypes for more detailed follow-up and sensitivity analysis [46Hannon E. et al.Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci.Nat. Neurosci. 2015; 19: 48-54Crossref PubMed Scopus (222) Google Scholar, 47Richardson T.G. et al.Mendelian randomization analysis identifies CpG sites as putative mediators for genetic influences on cardiovascular disease risk.Am. J. 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As a consequence, finding that a chosen SNP happens to show an association with a complex trait might not be surprising because many non-causal common variants are likely to be in linkage disequilibrium (LD) with a causal variant for a complex trait (Figure 3A). Colocalization techniques seek to analyze specific genomic regions, determining whether the pattern of test statistics for one trait is concordant with the pattern from another, often with respect to the underlying LD structure. Evidence for shared causal variants at a locus is determined by the extent to which the test statistic patterns are shared between the two traits. An important recent finding i