Biology subjects

Haycock, P.

Publications and source records attributed to Haycock, P..

7 recordsLinked to original sources

bioRxiv2022

Evaluating the potential benefits and pitfalls of combining protein and expression quantitative trait loci in evidencing drug targets

Molecular quantitative trait loci (molQTL), which can provide functional evidence on the mechanisms underlying phenotype-genotype associations, are increasingly used in drug target validation and safety assessment. In particular, protein abundance QTLs (pQTLs) and gene expression QTLs (eQTLs) are the most commonly used for this purpose. However, questions remain on how to best consolidate results from pQTLs and eQTLs for target validation. In this study, we combined blood cell-derived eQTLs and plasma-derived pQTLs to form QTL pairs representing each gene and its product. We performed a series of enrichment analyses to identify features of QTL pairs that provide consistent evidence for drug targets based on the concordance of the direction of effect of the pQTL and eQTL. We repeated these analyses using eQTLs derived in 49 tissues. We found that 25-30% of blood-cell derived QTL pairs have discordant effects. The difference in tissues of origin for molecular markers contributes to, but is not likely a major source of, this observed discordance. Finally, druggable genes were as likely to have discordant QTL pairs as concordant. Our analyses suggest combining and consolidating evidence from pQTLs and eQTLs for drug target validation is crucial and should be done whenever possible, as many potential drug targets show discordance between the two molecular phenotypes that could be misleading if only one is considered. We also encourage investigating QTL tissue-specificity in target validation applications to help identify reasons for discordance and emphasise that concordance and discordance of QTL pairs across tissues are both informative in target validation.

genetics↗

bioRxiv2020

The MRC IEU OpenGWAS data infrastructure

Data generated by genome-wide association studies (GWAS) are growing fast with the linkage of biobank samples to health records, and expanding capture of high-dimensional molecular phenotypes. However the utility of these efforts can only be fully realised if their complete results are collected from their heterogeneous sources and formats, harmonised and made programmatically accessible. Here we present the OpenGWAS database, an open source, open access, scalable and high-performance cloud-based data infrastructure that imports and publishes complete GWAS summary datasets and metadata for the scientific community. Our import pipeline harmonises these datasets against dbSNP and the human genome reference sequence, generates summary reports and standardises the format of results and metadata. Users can access the data via a website, an application programming interface, R and Python packages, and also as downloadable files that can be rapidly queried in high performance computing environments. OpenGWAS currently contains 126 billion genetic associations from 14,582 complete GWAS datasets representing a range of different human phenotypes and disease outcomes across different populations. We developed R and Python packages to serve as conduits between these GWAS data sources and a range of available analytical tools, enabling Mendelian randomization, genetic colocalisation analysis, fine mapping, genetic correlation and locus visualisation. OpenGWAS is freely accessible at https://gwas.mrcieu.ac.uk, and has been designed to facilitate integration with third party analytical tools.

genetics↗

bioRxiv2019

Mendelian randomisation study exploring the associations of serum folate with pan and site-specific cancers

BackgroundEpidemiological studies report evidence for an association between folate and the risk of several common cancers. However, both protective and harmful effects have been reported, and effects may differ by cancer site. Using Mendelian randomisation (MR), we investigated the causal relationships of genetically predicted serum folate with pan-cancer risk (all cancers excluding non-melanoma skin cancers); breast, prostate, ovarian, lung, and colorectal cancers; and malignant melanoma. MethodsWe conducted a two-sample MR analysis, using genetic instruments for serum folate to appraise the possible causal role on risk of pan-cancer and six site-specific cancers using summary statistics available from large consortia and the population-based cohort study UK Biobank (UKBB). ResultsThere was little evidence that genetically elevated serum folate was causally associated with risk of pan-cancer or six site-specific cancers. Meta-analysis showed odds ratios (OR) per SD increase in log serum folate of 0.93 (95% CI 0.78-1.11) for breast cancer, 0.87 (95% CI 0.71-1.06) for prostate cancer, 0.84 (95% CI 0.59-1.20) for ovarian cancer, and 0.87 (95% CI 0.57-1.32) for lung cancer. The OR for colorectal cancer was 1.18 (95% CI 0.64-2.18) in large consortia analysis, while ORs for pan-cancers and malignant melanoma in UKBB were 0.88 (95% CI 0.73-1.06) and 0.56 (95% CI 0.29-1.08) respectively. The results were powered to detect modest effect sizes (>90% power ( 0.05) to detect ORs 1.2 (0.8) for the GWAS consortia) and were consistent between the two statistical approaches used (inverse variance weighted (IVW) and likelihood-based). ConclusionsThere is little evidence that genetically elevated serum folate may affect the risk of pan-cancer and six site-specific cancers. However, we may still be underpowered to detect clinically relevant but smaller magnitude effects. Our results provide some evidence that increasing levels of circulating folate through widespread supplementation or deregulation of fortification of foods with folic acid is unlikely to lead to moderate unintended population-wide increase in cancer risk. Key MessagesO_LIObservational studies have identified associations between folate (both intake and circulating levels) and risk of developing site-specific cancers. However, these studies are liable to biases such as confounding, measurement error, and reverse causation. C_LIO_LIUsing Mendelian randomisation, we appraised the causal relationships between genetically influenced serum folate levels and pan-cancer risk (all cancers excluding non-melanoma skin cancers); breast, prostate, ovarian, lung, and colorectal cancers; and malignant melanoma. C_LIO_LIOverall findings suggest that there is little evidence for the causal associations between genetically influenced serum folate and risk of pan-cancer and six site-specific cancers. C_LIO_LIWe provide some evidence that increasing levels of circulating folate through widespread supplementation or deregulation of fortification of foods with folic acid is unlikely to lead to moderate unintended population-wide increase in cancer risk. C_LI

genomics↗

bioRxiv2019

Appraising causal relationships of dietary, nutritional and physical-activity exposures with overall and aggressive prostate cancer: two-sample Mendelian randomization study based on 79,148 prostate cancer cases and 61,106 controls

BackgroundProstate cancer is the second most common male cancer worldwide, but there is substantial geographical variation suggesting a potential role for modifiable risk factors in prostate carcinogenesis.\n\nMethodsWe identified previously reported prostate cancer risk factors from the World Cancer Research Funds (WCRF) systematic appraisal of the global evidence (2018). We assessed whether each identified risk factor was causally associated with risk of overall (79,148 cases and 61,106 controls) or aggressive (15,167 cases and 58,308 controls) prostate cancer using Mendelian randomization (MR) based on genome wide association study (GWAS) summary statistics from the PRACTICAL and GAME-ON/ELLIPSE consortia. We assessed evidence for replication in UK Biobank (7,844 prostate cancer cases and 204,001 controls).\n\nFindingsWCRF identified 57 potential risk factors, of which 22 could be instrumented for MR analyses using single nucleotide polymorphisms (SNPs). In MR analyses for overall prostate cancer, we identified evidence compatible with causality for the following risk factors (odds ratio [OR] per standard deviation increase; 95% confidence interval): accelerometer-measured physical-activity, OR=0.49 (0.33-0.72; p=0.0003); serum iron, OR=0.92 (0.86-0.98; p=0.007); body mass index (BMI), OR=0.90 (0.84-0.97; p=0.003); and mono-unsaturated fat, OR=1.11 (1.02-1.20; p=0.02). Findings in our replication analyses in UK Biobank were compatible with our main analyses (albeit with wide confidence intervals). In MR analysis, height was positively associated with aggressive prostate cancer risk: OR=1.07 (1.01-1.15; p=0.03).\n\nInterpretationThe results for physical-activity, serum iron, BMI, mono-unsaturated fat and height are compatible with causality for prostate cancer but more research is needed to rule out violations of MR assumptions for some risk factors. The results suggest that interventions aimed at increasing physical activity may reduce prostate cancer risk, but the direction of effects of BMI, and iron are at odds with their effects on other diseases, so the overall public health impact of intervening on these need to be considered.\n\nFundingWorld Cancer Research Fund International (2015/1421), Cancer Research UK program grant (C18281/A19169), National Institute for Health Research, Bristol Biomedical Research Centre, and Victorian Cancer Agency (MCRF18005).

epidemiology↗

bioRxiv2018

The effect of plasma lipids and lipid lowering interventions on bone mineral density: a Mendelian randomization study

Statin treatment increases bone mineral density (BMD) and reduces fracture risk, but the underlying mechanism is unclear. We used Mendelian randomization (MR) to assess whether this relation is explained by a specific effect in response to statin use, or by a general effect of lipid-lowering. We utilized 400 single nucleotide polymorphisms (SNPs) robustly associated with plasma lipid levels and results from a heel BMD GWAS (derived from quantitative ultrasound) in 426,824 individuals from the UK Biobank. We performed univariate and multivariable MR analyses of low-density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and triglyceride levels on BMD. To test whether the effect of statins on BMD was mediated by lowering lipid levels, MR was repeated with and without SNPs in the HMGCR region, the gene targeted by statins. Univariate MR analyses provided evidence for a causal effect of LDL-C on BMD ({beta} = -0.060; -0.084 to -0.036; P = 4x10-6; standard deviation change in BMD per standard deviation change in LDL-C, with 95% CI), but not HDL or triglycerides. Multivariable MR analysis suggested that the effect of LDL-C on BMD was independent of HDL-C and triglycerides, and sensitivity analyses involving MR Egger and weighted median MR approaches suggested that the LDL-C results were robust to pleiotropy. MR analyses of LDL-C restricted to SNPs in the HMGCR region showed similar effects on BMD({beta} = -0.083; -0.132 to -0.034; P = 0.001) to those excluding these SNPs ({beta} = -0.063; -0.090 to -0.036; P = 8x10-6). Bidirectional MR analyses provided some evidence for a causal effect of BMD on plasma LDL-C levels. Our results suggest that effects of statins on BMD are at least partly due to their LDL-C lowering effect. Further studies are required to examine the potential role of modifying plasma lipid levels in treating osteoporosis.

epidemiology↗

bioRxiv2018

Appraising the causal relevance of DNA methylation for risk of lung cancer

DNA methylation changes in peripheral blood have been identified in relation to lung cancer risk. However, the causal nature of these associations remains to be fully elucidated. Meta-analysis of four epigenome-wide association studies (918 cases, 918 controls) revealed differential methylation at 16 CpG sites (FDR < 0.05) in relation to lung cancer risk. A two-sample Mendelian randomization analysis, using genetic instruments for methylation at 14 of the 16 CpG sites, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, was performed to appraise the causal role of methylation at these sites on lung cancer. This approach provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development, including for cg05575921 AHRR, where methylation is strongly associated with lung cancer risk. Further studies are needed to investigate the causal role played by DNA methylation in lung tissue.

epidemiology↗

bioRxiv2017

PhenoSpD: an atlas of phenotypic correlations and a multiple testing correction for the human phenome

BackgroundIdentifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using genome-wide association study (GWAS) summary statistics.\n\nResultsHere, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate phenotypic correlations using GWAS summary statistics; and 2) to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate phenotypic correlation using samples with only a partial overlap, but as overlap decreases correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to estimated 7,503 pair-wise phenotypic correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites.\n\nConclusionPhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits.\n\nAvailabilityR code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).

bioinformatics↗