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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University of Regensburg1, Medical Research Council2, University of North Carolina at Chapel Hill3, University of Exeter4, University of Michigan5, University of Tartu6, Wellcome Trust Centre for Human Genetics7, Science for Life Laboratory8, Wellcome Trust Sanger Institute9, Boston Children's Hospital10, Harvard University11, University of Copenhagen12, University of Duisburg-Essen13, University Hospital of Lausanne14, Icahn School of Medicine at Mount Sinai15
TL;DR: In this article, a general protocol for conducting GWAMAs and carrying out QC to minimize errors and to guarantee maximum use of the data is presented. But this protocol is not suitable for large consortia such as the GIANT Consortium.
Abstract: Rigorous organization and quality control (QC) are necessary to facilitate successful genome-wide association meta-analyses (GWAMAs) of statistics aggregated across multiple genome-wide association studies. This protocol provides guidelines for (i) organizational aspects of GWAMAs, and for (ii) QC at the study file level, the meta-level across studies and the meta-analysis output level. Real-world examples highlight issues experienced and solutions developed by the GIANT Consortium that has conducted meta-analyses including data from 125 studies comprising more than 330,000 individuals. We provide a general protocol for conducting GWAMAs and carrying out QC to minimize errors and to guarantee maximum use of the data. We also include details for the use of a powerful and flexible software package called EasyQC. Precise timings will be greatly influenced by consortium size. For consortia of comparable size to the GIANT Consortium, this protocol takes a minimum of about 10 months to complete.
370 citations
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TL;DR: Using a set of validation genotypes at SNP and biallelic indels it is shown that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low frequency variants.
Abstract: A major use of the 1000 Genomes Project (1000 GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000 GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants.
369 citations
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TL;DR: The results indicate that AD is influenced by genes with general effects on dermal inflammation and immunity.
Abstract: We have carried out a genome screen for atopic dermatitis (AD) and have identified linkage to AD on chromosomes 1q21, 17q25 and 20p These regions correspond closely with known psoriasis loci, as does a previously identified AD locus on chromosome 3q21 The results indicate that AD is influenced by genes with general effects on dermal inflammation and immunity
368 citations
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Brigham and Women's Hospital1, Radboud University Nijmegen2, Trinity College, Dublin3, King's College London4, Wellcome Trust Centre for Human Genetics5, Heidelberg University6, University of Valencia7, Ghent University8, University of Göttingen9, VU University Amsterdam10, University of Zurich11, Harvard University12, Broad Institute13, State University of New York Upstate Medical University14
TL;DR: Novel genetic associations at viable ADHD candidate genes are identified and confirmatory evidence for associations at previous candidate genes is provided to confirm the proposed genetic variants for ADHD.
Abstract: Attention deficit hyperactivity disorder (ADHD) is a complex condition with environmental and genetic etiologies. Up to this point, research has identified genetic associations with candidate genes from known biological pathways. In order to identify novel ADHD susceptibility genes, 600,000 SNPs were genotyped in 958 ADHD proband-parent trios. After applying data cleaning procedures we examined 429,981 autosomal SNPs in 909 family trios. We generated six quantitative phenotypes from 18 ADHD symptoms to be used in genome-wide association analyses. With the PBAT screening algorithm, we identified 2 SNPs, rs6565113 and rs552655 that met the criteria for significance within a specified phenotype. These SNPs are located in intronic regions of genes CDH13 and GFOD1, respectively. CDH13 has been implicated previously in substance use disorders. We also evaluated the association of SNPs from a list of 37 ADHD candidate genes that was specified a priori. These findings, along with association P-values with a magnitude less than 10(-5), are discussed in this manuscript. Seventeen of these candidate genes had association P-values lower then 0.01: SLC6A1, SLC9A9, HES1, ADRB2, HTR1E, DDC, ADRA1A, DBH, DRD2, BDNF, TPH2, HTR2A, SLC6A2, PER1, CHRNA4, SNAP25, and COMT. Among the candidate genes, SLC9A9 had the strongest overall associations with 58 association test P-values lower than 0.01 and multiple association P-values at a magnitude of 10(-5) in this gene. In sum, these findings identify novel genetic associations at viable ADHD candidate genes and provide confirmatory evidence for associations at previous candidate genes. Replication of these results is necessary in order to confirm the proposed genetic variants for ADHD.
368 citations
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TL;DR: Both PacBio and ONT sequencing are suitable for full-length single-molecule transcriptome analysis as this first use of ONT reads in a Hybrid-Seq analysis has shown.
Abstract: Background: Given the demonstrated utility of Third Generation Sequencing [Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)] long reads in many studies, a comprehensive analysis and comparison of their data quality and applications is in high demand. Methods: Based on the transcriptome sequencing data from human embryonic stem cells, we analyzed multiple data features of PacBio and ONT, including error pattern, length, mappability and technical improvements over previous platforms. We also evaluated their application to transcriptome analyses, such as isoform identification and quantification and characterization of transcriptome complexity, by comparing the performance of size-selected PacBio, non-size-selected ONT and their corresponding Hybrid-Seq strategies (PacBio+Illumina and ONT+Illumina). Results: PacBio shows overall better data quality, while ONT provides a higher yield. As with data quality, PacBio performs marginally better than ONT in most aspects for both long reads only and Hybrid-Seq strategies in transcriptome analysis. In addition, Hybrid-Seq shows superior performance over long reads only in most transcriptome analyses. Conclusions: Both PacBio and ONT sequencing are suitable for full-length single-molecule transcriptome analysis. As this first use of ONT reads in a Hybrid-Seq analysis has shown, both PacBio and ONT can benefit from a combined Illumina strategy. The tools and analytical methods developed here provide a resource for future applications and evaluations of these rapidly-changing technologies.
368 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 |