Author
Hilary C. Martin
Other affiliations: Wellcome Trust Centre for Human Genetics, University of Queensland, University of Oxford ...read more
Bio: Hilary C. Martin is an academic researcher from Wellcome Trust Sanger Institute. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 23, co-authored 49 publications receiving 2540 citations. Previous affiliations of Hilary C. Martin include Wellcome Trust Centre for Human Genetics & University of Queensland.
Topics: Medicine, Population, Genome-wide association study, Biology, Exome
Papers
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TL;DR: IsomiRs are found to be biologically relevant and functionally cooperative partners of canonical miRNAs that act coordinately to target pathways of functionally related genes and helps explain a major miRNA paradox.
Abstract: Variants of microRNAs (miRNAs), called isomiRs, are commonly reported in deep-sequencing studies; however, the functional significance of these variants remains controversial. Observational studies show that isomiR patterns are non-random, hinting that these molecules could be regulated and therefore functional, although no conclusive biological role has been demonstrated for these molecules. To assess the biological relevance of isomiRs, we have performed ultra-deep miRNA-seq on ten adult human tissues, and created an analysis pipeline called miRNA-MATE to align, annotate, and analyze miRNAs and their isomiRs. We find that isomiRs share sequence and expression characteristics with canonical miRNAs, and are generally strongly correlated with canonical miRNA expression. A large proportion of isomiRs potentially derive from AGO2 cleavage independent of Dicer. We isolated polyribosome-associated mRNA, captured the mRNA-bound miRNAs, and found that isomiRs and canonical miRNAs are equally associated with translational machinery. Finally, we transfected cells with biotinylated RNA duplexes encoding isomiRs or their canonical counterparts and directly assayed their mRNA targets. These studies allow us to experimentally determine genome-wide mRNA targets, and these experiments showed substantial overlap in functional mRNA networks suppressed by both canonical miRNAs and their isomiRs. Together, these results find isomiRs to be biologically relevant and functionally cooperative partners of canonical miRNAs that act coordinately to target pathways of functionally related genes. This work exposes the complexity of the miRNA-transcriptome, and helps explain a major miRNA paradox: how specific regulation of biological processes can occur when the specificity of miRNA targeting is mediated by only 6 to 11 nucleotides.
326 citations
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Wellcome Trust Centre for Human Genetics1, National Institute for Health Research2, University of Oxford3, Illumina4, University of Coimbra5, Laboratory of Molecular Biology6, University of Ulm7, Cliniques Universitaires Saint-Luc8, University of Zurich9, University Hospital Southampton NHS Foundation Trust10, Northwestern University11, Queen's University Belfast12, Imperial College London13, Copenhagen University Hospital14, Belfast City Hospital15, King's College London16, Shriners Hospitals for Children17
TL;DR: It is found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy.
Abstract: To assess factors influencing the success of whole-genome sequencing for mainstream clinical diagnosis, we sequenced 217 individuals from 156 independent cases or families across a broad spectrum of disorders in whom previous screening had identified no pathogenic variants. We quantified the number of candidate variants identified using different strategies for variant calling, filtering, annotation and prioritization. We found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy. Overall, we identified disease-causing variants in 21% of cases, with the proportion increasing to 34% (23/68) for mendelian disorders and 57% (8/14) in family trios. We also discovered 32 potentially clinically actionable variants in 18 genes unrelated to the referral disorder, although only 4 were ultimately considered reportable. Our results demonstrate the value of genome sequencing for routine clinical diagnosis but also highlight many outstanding challenges.
318 citations
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26 Aug 2021
TL;DR: This Primer provides an introduction to genome-wide association studies (GWAS), techniques for deriving functional inferences from the results and applications of GWAS in understanding disease risk and trait architecture, and discusses important ethical considerations when considering GWAS populations and data.
Abstract: Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants across many genomes to find those statistically associated with a specific trait or disease. This methodology has generated a myriad of robust associations for a range of traits and diseases, and the number of associated variants is expected to grow steadily as GWAS sample sizes increase. GWAS results have a range of applications, such as gaining insight into a phenotype’s underlying biology, estimating its heritability, calculating genetic correlations, making clinical risk predictions, informing drug development programmes and inferring potential causal relationships between risk factors and health outcomes. In this Primer, we provide the reader with an introduction to GWAS, explaining their statistical basis and how they are conducted, describe state-of-the art approaches and discuss limitations and challenges, concluding with an overview of the current and future applications for GWAS results. Uffelmann et al. describe the key considerations and best practices for conducting genome-wide association studies (GWAS), techniques for deriving functional inferences from the results and applications of GWAS in understanding disease risk and trait architecture. The Primer also provides information on the best practices for data sharing and discusses important ethical considerations when considering GWAS populations and data.
299 citations
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TL;DR: To identify novel DD-associated genes, healthcare and research exome sequences are integrated on 31,058 DD parent-offspring trios, and a simulation-based statistical test is developed to identify gene-specific enrichments of DNMs.
Abstract: De novo mutations in protein-coding genes are a well-established cause of developmental disorders1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.
286 citations
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University of North Carolina at Chapel Hill1, Montreal Heart Institute2, Osaka University3, VA Boston Healthcare System4, Icahn School of Medicine at Mount Sinai5, Queen Mary University of London6, University of Cambridge7, National Institute for Health Research8, Wellcome Trust Sanger Institute9, Harvard University10, Vanderbilt University11, University of Wisconsin–Milwaukee12, Université de Montréal13, University of Southern California14, Kyushu University15, University of Washington16, University of Bristol17, University of Copenhagen18, Erasmus University Medical Center19, National Institutes of Health20, Brigham and Women's Hospital21, Kaiser Permanente22, University of Mississippi Medical Center23, International Agency for Research on Cancer24, Wake Forest University25, Imperial College London26, Broad Institute27, University of Pennsylvania28, Greifswald University Hospital29, Fred Hutchinson Cancer Research Center30, Chinese National Human Genome Center31, Technische Universität München32, University of Tampere33, University of Tokyo34, University of Ioannina35, University of Colorado Denver36, Duke University37, University of Virginia38, NHS Blood and Transplant39, University of Minnesota40, Turku University Hospital41, Los Angeles Biomedical Research Institute42, Stanford University43, King's College London44, Mashhad University of Medical Sciences45, Veterans Health Administration46
TL;DR: The clinical significance and predictive value of trans-ethnic variants in multiple populations are explored, genetic architecture and the effect of natural selection on these blood phenotypes between populations are compared and the value of a more global representation of populations in genetic studies is highlighted.
233 citations
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TL;DR: A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.
Abstract: Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.
4,913 citations
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3,213 citations
01 Jan 2011
TL;DR: The sheer volume and scope of data posed by this flood of data pose a significant challenge to the development of efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.
2,187 citations
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Andrew P. Morris1, Benjamin F. Voight2, Benjamin F. Voight3, Tanya M. Teslovich4 +229 more•Institutions (53)
TL;DR: This article conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent, and identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association.
Abstract: To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.
1,899 citations
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TL;DR: A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function, and these findings suggest potential novel therapeutic pathways for cardiovascular disease prevention.
Abstract: Blood pressure is a heritable trait(1) influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (>= 140 mm Hg systolic blood pressure or >= 90 mm Hg diastolic blood pressure)(2). Even small increments in blood pressure are associated with an increased risk of cardiovascular events(3). This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention.
1,829 citations