Biology subjects

Scott, R. A.

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

10 recordsLinked to original sources

bioRxiv2024

Genetic instrumental variable framework for assessing relevance of in-vitro cellular phenotypes to organism-level phenotypes

Gene perturbation screens (e.g. CRISPR-Cas9) assess the impact of gene disruption on in-vitro cellular phenotypes (e.g., proliferation, anti-viral response, transcriptomics). In-vitro experiments can be useful models for understanding the aetiology of in-vivo (organismal) phenotypes. For example, anti-viral response in immune cells and infectious disease outcomes. However, assessing whether an in-vitro cellular model is effectively capturing in-vivo biology is challenging. An in-vitro model is transportable to an in-vivo phenotype of interest if perturbations impacting the in-vitro phenotype also impact the in-vivo phenotype with mechanism-consistent directionality and effect sizes. We propose a framework; Gene Perturbation Analysis for Transportability (GPAT), to assess model transportability using gene perturbation effect estimates from perturbation screens (in-vitro cellular phenotypes) and loss-of-function burden tests (in-vivo phenotypes). Using UK Biobank whole-genome sequence data and data from published genome-wide CRISPR-Cas9 screens, we evaluated transportability of in-vitro cellular models to in-vivo human phenotypes. In hypothesis-driven analyses, we found evidence that higher lysosomal cholesterol accumulation in-vitro is a transportable model for lower LDL-cholesterol measured in human blood plasma (P = 0.0006), consistent with the known role of lysosomes in lipid biosynthesis. In contrast, we found limited evidence for other putative in-vitro models. In hypothesis-free analyses, we found strong evidence for transportability of proliferation in cancer cell lines for in-vivo human plasma cellular phenotypes. For example, higher proliferation in an erythroleukemia cell line and lower plasma lymphocyte percentage. GPAT enables systematic evaluation of the transportability of in-vitro cellular models to in-vivo phenotypes, informing assay prioritization and supporting novel hypothesis generation.

genetics↗

bioRxiv2021

COVID-19 and the abrupt shift to remote learning: Impact on grades and perceived learning for undergraduate biology students

Institutions across the world transitioned abruptly to remote learning in 2020 due to the COVID-19 pandemic. This rapid transition to remote learning has generally been predicted to negatively affect students, particularly those marginalized due to their race, socioeconomic class, or gender identity. In this study, we examined the impact of this transition in the Spring 2020 semester on the grades of students enrolled in the in-person biology program at a large university in Southwestern United States as compared to the grades earned by students in the fully online biology program at the same institution. We also surveyed in-person instructors to understand changes in assessment practices as a result of the transition to remote learning during the pandemic. Finally, we surveyed students in the in-person program to learn about their perceptions of the impacts of this transition. We found that both online and in-person students received a similar small increase in grades in Spring 2020 compared to Spring 2018 and 2019. We also found no evidence of disproportionately negative impacts on grades received by students marginalized due to their race, socioeconomic class, or gender in either modality. Focusing on in-person courses, we documented that instructors made changes to their courses when they transitioned to remote learning, which may have offset some of the potential negative impacts on course grades. However, despite receiving higher grades, in-person students reported negative impacts on their learning, interactions with peers and instructors, feeling part of the campus community, and career preparation. Women reported a more negative impact on their learning and career preparation compared to men. This work provides insights into students perceptions of how they were disadvantaged as a result of the transition to remote instruction and illuminates potential actions that instructors can take to create more inclusive education moving forward.

scientific communication and education↗

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↗

bioRxiv2019

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here, we estimated the effects of 1002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium (LD) is widespread in naive phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes (www.epigraphdb.org/pqtl/). Evaluation of data from historic drug development programmes showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of our approach in identifying and prioritising potential therapeutic targets.

genetics↗

bioRxiv2019

Regional fat depot masses are influenced by protein-coding gene variants

With the identification of a large number of genetic loci associated with human fat distribution and its importance for metabolic health, the question arises as to what the genetic drivers for discrete fat depot expansion might be. To date most studies have focussed on conventional anthropometric measures such as waist-to-hip ratio (WHR) adjusted for body mass index. We searched for genetic loci determining discrete fat depots mass size using an exome-wide approach in 3 large cohorts. Here we report an exome-wide analysis of non-synonymous genetic variants in 17,212 participants in which regional fat masses were quantified using dual-energy X-ray absorptiometry. The missense variant CCDC92S70C, previously associated with WHR, is associated specifically with reduced visceral and increased leg fat masses. Allele-specific expression analysis shows that the deleterious minor allele carrying transcript also has a constitutively higher expression. In addition, we identify two variants associated with the transcriptionally distinct fat depot arm fat (SPATA20K422R and UQCC1R51Q). SPATA20K422R, a rare novel locus with a large effect size specific to arm, and UQCC1R51Q, a common variant exome-wide significant in arm but showing similar trends in other subcutaneous fat depots. In terms of the understanding of human fat distribution, these findings suggest distinct regulation of discrete fat depot expansion. Author summaryHuman fat storing tissues are heterogeneous and comprise functionally and structurally distinct regional fat depots, the relative size of which appear to have significant implications for health. Whilst it is known that inter-individual differences in fat distribution have genetic drivers, studies to date have focussed on crude anthropometric approximations of region fat masses rather than precise measures. Here we describe an exome-wide analysis of a large collection of men and women who have undergone body scanning using dual-energy X-ray absorptiometry (DXA) to better define regional fat masses and identify new genetic drivers for human fat distribution. With this approach we identify three gene regions associated with distinct fat depots which can help to explain the variation in fat distribution between people and may lead to a better understanding of the depot specific fat tissue expansion.

genomics↗

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↗