Biology subjects

Scott, R. A.

Publications and source records attributed to Scott, R. A..

5 recordsLinked to original sources

bioRxiv2018

Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform the largest genome-wide association study for osteoarthritis to date (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, fine-mapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1, FGF18, CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

genetics↗

bioRxiv2018

Genome-wide scan and fine-mapping of rare nonsynonymous associations implicates intracellular lipolysis genes in fat distribution and cardio-metabolic risk

Difficulties in identifying causal variants and genes underlying genetic associations have limited the translational potential of genetic studies of body fat distribution, an important, partly-heritable risk factor for cardio-metabolic disease. Rare variant associations facilitate fine-mapping of causal alleles, but their contribution to fat distribution is understudied. We performed a genome-wide scan of rare nonsynonymous variants for body mass index-adjusted waist-to-hip-ratio (BMI-adjusted WHR; a widely-used measure of fat distribution) in 450,562 European ancestry individuals, followed by systematic Bayesian fine-mapping at six genome-wide (p<5x10-08; main-analysis) and two subthreshold signals (significant at a Bonferroni-corrected p<1.3x10-06). We found strong statistical evidence of causal association for nonsynonymous alleles in CALCRL (p.L87P, pconditional=5.9x10-12; posterior-probability of association [PPA]=52%), PLIN1 (p.L90P, pconditional=5.5x10-13; PPA>99%), PDE3B (p.R783X, pconditional=6.2x10-15; PPA>99%), ACVR1C (p.I195T; pconditional=5.4x10-12; PPA>99%), and FGF1 (p.G21E, pconditional=1.6x10-07; PPA=98%). Alleles at the four likely-causal main-analysis genes affected fat distribution primarily via larger hip-rather than smaller waist-circumference and six of nine conditionally-independent WHR-lowering index-variants were associated with protection from cardiovascular or metabolic disease. All four genes are expressed in adipose tissue and have been linked with the regulation of intracellular lipolysis, which controls fat retention in mature cells. Targeted follow-up analyses of key intracellular-lipolysis genes revealed associations for a variant in the initiator of intracellular lipolysis PNPLA2 (p.N252K) with higher BMI-adjusted-WHR and higher cardio-metabolic risk. This study provides human genetic evidence of a link between intracellular lipolysis, fat-distribution and its cardio-metabolic complications in the general population.

genetics↗

bioRxiv2018

Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of {small tilde}27M variants ({small tilde}21M with minor allele frequency [MAF]<5%), identified 243 genome-wide significant loci (p<5x10-8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10-5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%[≤]MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

genomics↗

bioRxiv2017

Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design

Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies in the context of the RbG study design. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.

genetics↗

bioRxiv2017

A Comprehensive Reanalysis Of Publicly Available GWAS Datasets Reveals An X Chromosome Rare Regulatory Variant Associated With High Risk For Type 2 Diabetes.

The reanalysis of publicly available GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics and pathophysiology of complex diseases. We demonstrate this by gathering and reanalyzing public type 2 diabetes (T2D) GWAS data for 70,127 subjects, using an innovative imputation and association strategy based on multiple reference panels (1000G and UK10K). This approach led us replicate and fine map 50 known T2D loci, and identify seven novel associated regions: five driven by common variants in or near LYPLAL1, NEUROG3, CAMKK2, ABO and GIP genes; one by a low frequency variant near EHMT2; and one driven by a rare variant in chromosome Xq23, associated with a 2.7-fold increased risk for T2D in males, and located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a known modulator of insulin sensitivity. We further show that the risk T allele reduces binding of a nuclear protein, resulting in increased enhancer activity in muscle cells. Beyond providing novel insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel analytical approaches.

genetics↗