Biology subjects

Oliver-Williams, C.

Publications and source records attributed to Oliver-Williams, C..

6 recordsLinked to original sources

bioRxiv2022

An atlas of genetic scores to predict multi-omic traits

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (OmicsPred.org) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

genomics↗

bioRxiv2020

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10-10) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10-30)]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.Competing Interest StatementA.S.B. has received grants from AstraZeneca, Biogen, Bioverativ, Merck, Novartis, and Sanofi. J. D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), the MRC International Advisory Group (ING) member, London (since 2013), the MRC High Throughput Science Omics Panel Member, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the Astra Zeneca Genomics Advisory Board (2018). E.B.F. is an employee and stock holder of Pfizer.View Full Text

genetics↗

bioRxiv2018

The influence of rare variants in circulating metabolic biomarkers

Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). We tested association between rare sequence variants and 226 serum lipoproteins, lipids and amino acids in 7,142 healthy participants. Gene-based association analyses identified novel gene-trait associations with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5 x 10-6), and confirmed established associations. Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene set analyses, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT <2.41 x 10-6). Individuals at the lower tails of the distributions of four out of 49 lipoproteins and lipids had an excess of predicted deleterious variants in lipoprotein disorder and metabolism gene sets (ppermutation<0.00037). These four traits were CVD risk factors (e.g. S-VLDL-C), demonstrating that rare \"protective\" variation is a significant contributor to lipoprotein levels in a healthy population. In conclusion, rare variant analysis of these important metabolic biomarkers reveals novel loci and pathways involved in their regulation.

genetics↗

bioRxiv2018

Lipoprotein Signatures of Cholesteryl Ester Transfer Protein and HMG-CoA Reductase Inhibition

BackgroundCETP inhibition reduces vascular event rates but confusion surrounds its low-density lipoprotein (LDL)-cholesterol effects. We sought to clarify associations of genetic inhibition of CETP on detailed lipoproteins.\n\nMethods and ResultsWe used variants associated with CETP (rs247617) and HMGCR (rs12916) expression in 62,400 Europeans with detailed lipoprotein profiling from nuclear magnetic resonance spectroscopy. Genetic associations were scaled to 10% lower risk of coronary heart disease (CHD). Associations of lipoprotein measures with risk of incident CHD in three population-based cohorts (770 cases) were examined.\n\nCETP and HMGCR had near-identical associations with LDL-cholesterol concentration estimated by Friedewald-equation. HMGCR had a relatively consistent effect on cholesterol concentrations across all apolipoprotein B-containing lipoproteins. CETP had stronger effects on remnant and very-low-density lipoprotein cholesterol but no effect on cholesterol concentrations in LDL defined by particle size (diameter 18-26 nm) (-0.02SD 95%CI: -0.10, 0.05 for CETP versus -0.24SD, 95%CI -0.30, -0.18 for HMGCR). CETP had profound effects on lipid compositions of lipoproteins, with strong reductions in the triglyceride content of all highdensity lipoprotein (HDL) particles. These alterations in triglyceride composition within HDL subclasses were observationally associated with risk of CHD, independently of total cholesterol and triglycerides (strongest HR per 1-SD higher triglyceride composition in very-large HDL 1.35; 95%CI: 1.18, 1.54).\n\nConclusionCETP inhibition does not affect size-specific LDL cholesterol but may lower CHD risk by lowering cholesterol in other apolipoprotein-B containing lipoproteins and lowering triglyceride content of HDL particles. Conventional composite lipid assays may mask heterogeneous effects of lipid-altering therapies.

genetics↗

bioRxiv2018

Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment

BackgroundBoth statins and PCSK9 inhibitors lower blood low-density lipoprotein cholesterol (LDL-C) levels to reduce risk of cardiovascular events. To assess potential differences between metabolic effects of these two lipid-lowering therapies, we performed detailed lipid and metabolite profiling of a large randomized statin trial, and compared the results with the effects of genetic inhibition of PCSK9, acting as a naturally occurring trial.\n\nMethods228 circulating metabolic measures were quantified by nuclear magnetic resonance spectroscopy, including lipoprotein subclass concentrations and their lipid composition, fatty acids, and amino acids, for 5,359 individuals (2,659 on treatment) in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial at 6-months post-randomization. The corresponding metabolic measures were analyzed in eight population cohorts (N=72,185) using PCSK9 rs11591147 as an unconfounded proxy to mimic the therapeutic effects of PCSK9 inhibitors.\n\nResultsScaled to an equivalent lowering of LDL-C, the effects of genetic inhibition of PCSK9 on 228 metabolic markers were generally consistent with those of statin therapy (R2=0.88). Alterations in lipoprotein lipid composition and fatty acid balance were similar. However, discrepancies were observed for very-low-density lipoprotein (VLDL) lipid measures. For instance, genetic inhibition of PCSK9 showed weaker effects on lowering of VLDL-cholesterol compared with statin therapy (54% vs. 77% reduction, relative to the lowering effect on LDL-C; P=2 x 10-7 for heterogeneity). Genetic inhibition of PCSK9 showed no robust effects on amino acids, ketones, and a marker of inflammation (GlycA); in contrast, statin treatment lowered GlycA levels.\n\nConclusionsGenetic inhibition of PCSK9 results in similar metabolic effects as statin therapy across a detailed lipid and metabolite profile. However, for the same lowering of LDL-C, PCSK9 inhibitors are predicted to be less efficacious than statins at lowering VLDL lipids, which could potentially translate into subtle differences in cardiovascular risk reduction.

epidemiology↗

bioRxiv2017

Consequences Of Natural Perturbations In The Human Plasma Proteome

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

genomics↗