Author
Sebastian Schoenherr
Other affiliations: University of Santiago de Compostela, University of Innsbruck
Bio: Sebastian Schoenherr is an academic researcher from Innsbruck Medical University. The author has contributed to research in topics: Imputation (genetics) & Founder effect. The author has an hindex of 10, co-authored 18 publications receiving 4252 citations. Previous affiliations of Sebastian Schoenherr include University of Santiago de Compostela & University of Innsbruck.
Papers
More filters
••
Wellcome Trust Sanger Institute1, University of Michigan2, University of Oxford3, University of Geneva4, University of Exeter5, Greifswald University Hospital6, National Research Council7, University of Bristol8, University of Colorado Boulder9, Fred Hutchinson Cancer Research Center10, University of Washington11, SUNY Downstate Medical Center12, Erasmus University Rotterdam13, University of Trieste14, VU University Amsterdam15, King's College London16, South London and Maudsley NHS Foundation Trust17, University of Edinburgh18, Harvard University19, National Institutes of Health20, Harokopio University21, Innsbruck Medical University22, Broad Institute23, University of Helsinki24, Lund University25, Norwegian University of Science and Technology26, University of Cambridge27, University of Minnesota28, Technische Universität München29, University of North Carolina at Chapel Hill30, University of Toronto31, McGill University32, Leiden University33, University of Pennsylvania34, University of Groningen35, Utrecht University36, Churchill Hospital37
TL;DR: A reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies.
Abstract: We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.
2,149 citations
01 Jan 2016
TL;DR: In this article, a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry is presented.
Abstract: We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.
1,261 citations
••
TL;DR: A new phasing algorithm, Eagle2, is introduced that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium; HRC) using a new data structure based on the positional Burrows-Wheeler transform.
Abstract: Po-Ru Loh, Alkes Price and colleagues present Eagle2, a reference-based phasing algorithm that allows for highly accurate and efficient phasing of genotypes across a broad range of cohort sizes. They demonstrate an approximately 10% improvement in accuracy and 20% improvement in speed compared to a competing method, SHAPEIT2.
1,246 citations
••
Daniel Taliun1, Daniel N. Harris2, Michael D. Kessler2, Jedidiah Carlson3 +202 more•Institutions (61)
TL;DR: The Trans-Omics for Precision Medicine (TOPMed) project as discussed by the authors aims to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases.
Abstract: The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1 In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals) These rare variants provide insights into mutational processes and recent human evolutionary history The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 001% The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history
801 citations
••
Daniel Taliun1, Daniel N. Harris2, Michael D. Kessler2, Jedidiah Carlson1 +191 more•Institutions (61)
TL;DR: The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation as well as resources and early insights from the sequence data.
Abstract: Summary paragraph The Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency
662 citations
Cited by
More filters
••
TL;DR: Deep phenotype and genome-wide genetic data from 500,000 individuals from the UK Biobank is described, describing population structure and relatedness in the cohort, and imputation to increase the number of testable variants to 96 million.
Abstract: The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
4,489 citations
••
Institute for Systems Biology1, BC Cancer Agency2, University of California, San Francisco3, University of North Carolina at Chapel Hill4, Columbia University5, Discovery Institute6, Massachusetts Institute of Technology7, Arizona State University8, Sage Bionetworks9, Harvard University10, Johns Hopkins University11, Stanford University12, University of Calgary13, Université libre de Bruxelles14, University of Texas MD Anderson Cancer Center15, Medical College of Wisconsin16, Qatar Airways17, Cold Spring Harbor Laboratory18, University of São Paulo19, Henry Ford Hospital20, University of Alabama at Birmingham21, Van Andel Institute22, Stony Brook University23
TL;DR: An extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA identifies six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis.
3,246 citations
••
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
••
TL;DR: Improvements to imputation machinery are described that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools.
Abstract: Christian Fuchsberger, Goncalo Abecasis and colleagues describe a new web-based imputation service that enables rapid imputation of large numbers of samples and allows convenient access to large reference panels of sequenced individuals. Their state space reduction provides a computationally efficient solution for genotype imputation with no loss in imputation accuracy.
2,556 citations
••
TL;DR: Genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment are examined, and it is demonstrated that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition.
Abstract: Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.
1,683 citations