Institution
Wellcome Trust Centre for Human Genetics
Facility•Oxford, United Kingdom•
About: Wellcome Trust Centre for Human Genetics is a facility organization based out in Oxford, United Kingdom. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 2122 authors who have published 4269 publications receiving 433899 citations.
Topics: Population, Genome-wide association study, Single-nucleotide polymorphism, Gene, Locus (genetics)
Papers published on a yearly basis
Papers
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TL;DR: It is found that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method.
Abstract: Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions.
3,902 citations
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Wellcome Trust Centre for Human Genetics1, University of Oxford2, University of Michigan3, Fred Hutchinson Cancer Research Center4, Duke University5, University of Ottawa6, Tufts University7, Foundation for Research & Technology – Hellas8, Harvard University9, Boston Children's Hospital10, Broad Institute11
TL;DR: This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
Abstract: The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
2,908 citations
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TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
Abstract: Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.
2,669 citations
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Wellcome Trust Centre for Human Genetics1, University of Liège2, University of Chicago3, Yale University4, University of Pittsburgh5, Montreal Heart Institute6, Massachusetts Institute of Technology7, Johns Hopkins University8, University of Toronto9, Cedars-Sinai Medical Center10, McGill University11, Harvard University12, Ghent University Hospital13, Katholieke Universiteit Leuven14, Free University of Brussels15, Paris Diderot University16, Western General Hospital17, King's College London18, University of Oxford19, Wellcome Trust Sanger Institute20, University of Cambridge21, Newcastle University22
TL;DR: The results strongly confirm 11 previously reported loci and provide genome-wide significant evidence for 21 additional loci, including the regions containing STAT3, JAK2, ICOSLG, CDKAL1 and ITLN1, which offer promise for informed therapeutic development.
Abstract: Several risk factors for Crohn's disease have been identified in recent genome-wide association studies. To advance gene discovery further, we combined data from three studies on Crohn's disease (a total of 3,230 cases and 4,829 controls) and carried out replication in 3,664 independent cases with a mixture of population-based and family-based controls. The results strongly confirm 11 previously reported loci and provide genome-wide significant evidence for 21 additional loci, including the regions containing STAT3, JAK2, ICOSLG, CDKAL1 and ITLN1. The expanded molecular understanding of the basis of this disease offers promise for informed therapeutic development.
2,584 citations
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TL;DR: A measure of dependence for two-variable relationships: the maximal information coefficient (MIC), which captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination of the data relative to the regression function.
Abstract: Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R2) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.
2,414 citations
Authors
Showing all 2127 results
Name | H-index | Papers | Citations |
---|---|---|---|
Mark I. McCarthy | 200 | 1028 | 187898 |
John P. A. Ioannidis | 185 | 1311 | 193612 |
Gonçalo R. Abecasis | 179 | 595 | 230323 |
Simon I. Hay | 165 | 557 | 153307 |
Robert Plomin | 151 | 1104 | 88588 |
Ashok Kumar | 151 | 5654 | 164086 |
Julian Parkhill | 149 | 759 | 104736 |
James F. Wilson | 146 | 677 | 101883 |
Jeremy K. Nicholson | 141 | 773 | 80275 |
Hugh Watkins | 128 | 524 | 91317 |
Erik Ingelsson | 124 | 538 | 85407 |
Claudia Langenberg | 124 | 452 | 67326 |
Adrian V. S. Hill | 122 | 589 | 64613 |
John A. Todd | 121 | 515 | 67413 |
Elaine Holmes | 119 | 560 | 58975 |