Author
Christopher E. Gillies
Bio: Christopher E. Gillies is an academic researcher from University of Michigan. The author has contributed to research in topics: Exome & Intensive care. The author has an hindex of 13, co-authored 35 publications receiving 3375 citations.
Papers
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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, South London and Maudsley NHS Foundation Trust16, King's College London17, 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: This paper used exome sequencing to explore protein altering variants and their consequences in 454,787 UK Biobank study participants and identified 12 million coding variants, including ~1 million loss-of-function and ~1.8 million deleterious missense variants.
Abstract: A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein altering variants and their consequences in 454,787 UK Biobank study participants2. We identified 12 million coding variants, including ~1 million loss-of-function and ~1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P≤2.18x10-11. Rare variant associations were enriched in GWAS loci, but most (91%) were independent of common variant signals. We discover several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as novel risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). 81% of signals available and powered for replication were confirmed in an independent cohort; furthermore, association signals were generally consistent across European, Asian and African ancestry individuals. We illustrate the ability of exome sequencing to identify novel gene-trait associations, elucidate gene function, and pinpoint effector genes underlying GWAS signals at scale.
217 citations
TL;DR: This study discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles.
Abstract: Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian “Deterministic Approximation of Posteriors” (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR 1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, “nephQTL.”
126 citations
Columbia University1, Duke University2, University of Split3, Yeshiva University4, VU University Amsterdam5, Harvard University6, University of Michigan7, Istituto Giannina Gaslini8, University of Pennsylvania9, Heidelberg University10, Paris Descartes University11, French Institute of Health and Medical Research12, Paris-Sorbonne University13, University of Bari14, University of Milan15, University of Brescia16, University of Washington17, University College Dublin18, Jagiellonian University19, University of Silesia in Katowice20, Medical University of Warsaw21, Our Lady's Children's Hospital22, Children's Mercy Hospital23, Boston Children's Hospital24, University of Parma25, University of New Mexico26, University of Chicago27, Yale University28
TL;DR: A recurrent 370‐kb deletion at the 22q11.2 locus is identified as a driver of kidney defects in the DiGeorge syndrome and in sporadic congenital kidney and urinary tract anomalies.
Abstract: BackgroundThe DiGeorge syndrome, the most common of the microdeletion syndromes, affects multiple organs, including the heart, the nervous system, and the kidney. It is caused by deletions on chromosome 22q11.2; the genetic driver of the kidney defects is unknown. MethodsWe conducted a genomewide search for structural variants in two cohorts: 2080 patients with congenital kidney and urinary tract anomalies and 22,094 controls. We performed exome and targeted resequencing in samples obtained from 586 additional patients with congenital kidney anomalies. We also carried out functional studies using zebrafish and mice. ResultsWe identified heterozygous deletions of 22q11.2 in 1.1% of the patients with congenital kidney anomalies and in 0.01% of population controls (odds ratio, 81.5; P=4.5×10−14). We localized the main drivers of renal disease in the DiGeorge syndrome to a 370-kb region containing nine genes. In zebrafish embryos, an induced loss of function in snap29, aifm3, and crkl resulted in renal defect...
115 citations
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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
University of Minnesota1, University of Colorado Boulder2, VU University Amsterdam3, Harvard University4, University of Southern California5, University of Queensland6, University of Tartu7, Erasmus University Rotterdam8, Hospital for Special Surgery9, Statens Serum Institut10, University of Copenhagen11, Broad Institute12, University of Essex13, University of Edinburgh14, University of Cambridge15, University Hospital of Lausanne16, Geisinger Health System17, Wenzhou Medical College18, Stanford University19, University of North Carolina at Chapel Hill20, University of Wisconsin-Madison21, Hofstra University22, The Feinstein Institute for Medical Research23, University of Dundee24, University of Toronto25, Princeton University26, Queen's University27, New York University Shanghai28, National Bureau of Economic Research29, Karolinska Institutet30, Uppsala University31, University of Lausanne32, New York University33, Stockholm School of Economics34
TL;DR: A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance ineducational attainment and 7–10% ofthe variance in cognitive performance, which substantially increases the utility ofpolygenic scores as tools in research.
Abstract: Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
1,658 citations