A powerful and versatile colocalization test

Genetic analyses identify evidence for a causal relationship between Ewing sarcoma and hernias

Knowledge of Ewing sarcoma (EWS) risk factors is exceedingly limited; however, multiple small, independent studies have suggested a possible connection between hernia and EWS. By leveraging hernia summary statistics from the UK Biobank and a recently published genome-wide association study of EWS (733 EWS cases and 1,346 controls), we conducted a genetic investigation of the relationship of 5 hernia types (diaphragmatic, inguinal, umbilical, femoral, and ventral) and EWS. We discovered a positive causal relationship between inguinal hernia and EWS (OR 1.27, 95% confidence interval [CI] 1.01-1.59, and p = 0.041) through Mendelian randomization analysis. Further analyses suggested shared pathways through three genes: HMGA2, LOX, and FBXW7. Diaphragmatic hernia showed a stronger causal relationship with EWS among all of the hernia types (OR 2.26, 95% CI 1.30-3.95, p = 0.004), but no statistically significant local correlation pattern was observed. No evidence of a causal or genetic relationship was observed between EWS and the other three hernia types, including umbilical hernia, despite a previous report indicating an OR as high as 3.3. The finding of our genetic analysis provided additional support to the hypothesis that EWS and hernias may share a common origin.

A Multi-Omic Mosaic Model of Acetaminophen Induced Alanine Aminotransferase Elevation

BACKGROUND

Acetaminophen (APAP) is the most common cause liver injury following alcohol in US patients. Predicting liver injury and subsequent hepatic regeneration in patients taking therapeutic doses of APAP may be possible using new 'omic methods such as metabolomics and genomics. Multi'omic techniques increase our ability to find new mechanisms of injury and regeneration.

METHODS

We used metabolomic and genomic data from a randomized controlled trial of patients administered 4 g of APAP per day for 14 days or longer with blood samples obtained at 0 (baseline), 4, 7, 10, 13 and 16 days. We used the highest ALT as the clinical outcome to be predicted in our integrated analysis. We used penalized regression to model the relationship between genetic variants and day 0 metabolite level, and then performed a metabolite-wide colocalization scan to associate the genetically regulated component of metabolite expression with ALT elevation. Genome-wide association study (GWAS) analyses were conducted for ALT elevation and metabolite level using linear regression, with age, sex, and the first five principal components included as covariates. Colocalization was tested via a weighted sum test.

RESULTS

Out of the 164 metabolites modeled, 120 met the criteria for predictive accuracy and were retained for genetic analyses. After genomic examination, eight metabolites were found to be under genetic control and predictive of ALT elevation due to therapeutic acetaminophen. The metabolites were: 3-oxalomalate, allantoate, diphosphate, L-carnitine, L-proline, maltose, and ornithine. These genes are important in the tricarboxylic acid cycle (TCA), urea breakdown pathway, glutathione production, mitochondrial energy production, and maltose metabolism.

CONCLUSIONS

This multi'omic approach can be used to integrate metabolomic and genomic data allowing identification of genes that control downstream metabolites. These findings confirm prior work that have identified mitochondrial energy production as critical to APAP induced liver injury and have confirmed our prior work that demonstrate the importance of the urea cycle in therapeutic APAP liver injury.

Circulating insulin-like growth factors and risks of overall, aggressive and early-onset prostate cancer: a collaborative analysis of 20 prospective studies and Mendelian randomization analysis

BACKGROUND

Previous studies had limited power to assess the associations of circulating insulin-like growth factors (IGFs) and IGF-binding proteins (IGFBPs) with clinically relevant prostate cancer as a primary endpoint, and the association of genetically predicted IGF-I with aggressive prostate cancer is not known. We aimed to investigate the associations of IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3 concentrations with overall, aggressive and early-onset prostate cancer.

METHODS

Prospective analysis of biomarkers using the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group dataset (up to 20 studies, 17 009 prostate cancer cases, including 2332 aggressive cases). Odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression. For IGF-I, two-sample Mendelian randomization (MR) analysis was undertaken using instruments identified using UK Biobank (158 444 men) and outcome data from PRACTICAL (up to 85 554 cases, including 15 167 aggressive cases). Additionally, we used colocalization to rule out confounding by linkage disequilibrium.

RESULTS

In observational analyses, IGF-I was positively associated with risks of overall (OR per 1 SD = 1.09: 95% CI 1.07, 1.11), aggressive (1.09: 1.03, 1.16) and possibly early-onset disease (1.11: 1.00, 1.24); associations were similar in MR analyses (OR per 1 SD = 1.07: 1.00, 1.15; 1.10: 1.01, 1.20; and 1.13; 0.98, 1.30, respectively). Colocalization also indicated a shared signal for IGF-I and prostate cancer (PP4: 99%). Men with higher IGF-II (1.06: 1.02, 1.11) and IGFBP-3 (1.08: 1.04, 1.11) had higher risks of overall prostate cancer, whereas higher IGFBP-1 was associated with a lower risk (0.95: 0.91, 0.99); these associations were attenuated following adjustment for IGF-I.

CONCLUSIONS

These findings support the role of IGF-I in the development of prostate cancer, including for aggressive disease.

Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

Uncovering the functional impact of genetic variation on gene expression is important in understanding tissue biology and the pathogenesis of complex traits. Despite large efforts to map expression quantitative trait loci (eQTLs) across many human tissues, our ability to translate those findings to understanding human disease has been incomplete, and the majority of disease loci are not explained by association with expression of a target gene. Cell-type specificity and the presence of multiple independent causal variants for many eQTLs are potential confounders contributing to the apparent discrepancy with disease loci. In this study, we investigate the tissue specificity of genetic effects on gene expression and the overlap with disease loci while considering the presence of multiple causal variants within and across tissues. We find evidence of pervasive tissue specificity of eQTLs, often masked by linkage disequilibrium that misleads traditional meta-analytic approaches. We propose CAFEH (colocalization and fine-mapping in the presence of allelic heterogeneity), a Bayesian method that integrates genetic association data across multiple traits, incorporating linkage disequilibrium to identify causal variants. CAFEH outperforms previous approaches in colocalization and fine-mapping. Using CAFEH, we show that genes with highly tissue-specific genetic effects are under greater selection, enriched in differentiation and developmental processes, and more likely to be involved in human disease. Last, we demonstrate that CAFEH can efficiently leverage the widespread allelic heterogeneity in genetic regulation of gene expression to prioritize the target tissue in genome-wide association complex trait loci, thereby improving our ability to interpret complex trait genetics.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

A more accurate method for colocalisation analysis allowing for multiple causal variants

In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.

Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development

Drug development in cardiovascular disease is stagnating, with lack of efficacy and adverse effects being barriers to innovation. Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization, with genetic support for causation increasing the probability of a clinical trial succeeding. Mendelian randomization applied to quantitative traits can identify risk factors for disease that are both causal and amenable to therapeutic modification. However, important differences exist between genetic investigations of a biomarker (such as HDL cholesterol) and a drug target aimed at modifying the same biomarker of interest (such as cholesteryl ester transfer protein), with implications for the methodology, interpretation and application of Mendelian randomization to drug development. Differences include the comparative nature of the genetic architecture - that is, biomarkers are typically polygenic, whereas protein drug targets are influenced by either cis-acting or trans-acting genetic variants - and the potential for drug targets to show disease associations that might differ from those of the biomarker that they are intended to modify (target-mediated pleiotropy). In this Review, we compare and contrast the use of Mendelian randomization to evaluate potential drug targets versus quantitative traits. We explain how genetic epidemiological studies can be used to assess the aetiological roles of biomarkers in disease and to prioritize drug targets, including designing their evaluation in clinical trials.

Shared associations identify causal relationships between gene expression and immune cell phenotypes

Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared; and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with many due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality; direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.