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Willis TW, Gkrania-Klotsas E, Wareham NJ, McKinney EF, Lyons PA, Smith KGC, Wallace C. Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency. Clin Immunol 2024; 268:110356. [PMID: 39241920 DOI: 10.1016/j.clim.2024.110356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 08/22/2024] [Accepted: 08/31/2024] [Indexed: 09/09/2024]
Abstract
Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.
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Affiliation(s)
- Thomas W Willis
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK.
| | - Effrossyni Gkrania-Klotsas
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK; Department of Infectious Diseases, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | - Nicholas J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eoin F McKinney
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
| | - Paul A Lyons
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
| | - Kenneth G C Smith
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | - Chris Wallace
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
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2
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Wang Q, Antone J, Alsop E, Reiman R, Funk C, Bendl J, Dudley JT, Liang WS, Karr TL, Roussos P, Bennett DA, De Jager PL, Serrano GE, Beach TG, Van Keuren-Jensen K, Mastroeni D, Reiman EM, Readhead BP. Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease. Nat Commun 2024; 15:5815. [PMID: 38987616 PMCID: PMC11237088 DOI: 10.1038/s41467-024-49790-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
The emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer's disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.
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Affiliation(s)
- Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Jerry Antone
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Eric Alsop
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Rebecca Reiman
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Jaroslav Bendl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Joel T Dudley
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Winnie S Liang
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Timothy L Karr
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Geidy E Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Thomas G Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | | | - Diego Mastroeni
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, 85006, USA
| | - Benjamin P Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA.
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Liley J, Newnham M, Bleda M, Bunclark K, Auger W, Barbera JA, Bogaard H, Delcroix M, Fernandes TM, Howard L, Jenkins D, Lang I, Mayer E, Rhodes C, Simpson M, Southgate L, Trembath R, Wharton J, Wilkins MR, Gräf S, Morrell N, Zaba JP, Toshner M. Shared and Distinct Genomics of Chronic Thromboembolic Pulmonary Hypertension and Pulmonary Embolism. Am J Respir Crit Care Med 2024; 209:1477-1485. [PMID: 38470220 PMCID: PMC11208965 DOI: 10.1164/rccm.202307-1236oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 03/11/2024] [Indexed: 03/13/2024] Open
Abstract
Rationale: Chronic thromboembolic pulmonary hypertension involves the formation and nonresolution of thrombus, dysregulated inflammation, angiogenesis, and the development of a small-vessel vasculopathy. Objectives: We aimed to establish the genetic basis of chronic thromboembolic pulmonary hypertension to gain insight into its pathophysiological contributors. Methods: We conducted a genome-wide association study on 1,907 European cases and 10,363 European control subjects. We coanalyzed our results with existing results from genome-wide association studies on deep vein thrombosis, pulmonary embolism, and idiopathic pulmonary arterial hypertension. Measurements and Main Results: Our primary association study revealed genetic associations at the ABO, FGG, F11, MYH7B, and HLA-DRA loci. Through our coanalysis, we demonstrate further associations with chronic thromboembolic pulmonary hypertension at the F2, TSPAN15, SLC44A2, and F5 loci but find no statistically significant associations shared with idiopathic pulmonary arterial hypertension. Conclusions: Chronic thromboembolic pulmonary hypertension is a partially heritable polygenic disease, with related though distinct genetic associations with pulmonary embolism and deep vein thrombosis.
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Affiliation(s)
| | - Michael Newnham
- Institute of Applied Health Research, Birmingham, United Kingdom
| | - Marta Bleda
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | | | - William Auger
- University of California, San Diego, San Diego, California
| | - Joan Albert Barbera
- Hospital Clinic, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain
| | - Harm Bogaard
- Amsterdam University Medical Center, Amsterdam, the Netherlands
| | | | | | - Luke Howard
- Hammersmith Hospital, London, United Kingdom
| | | | - Irene Lang
- Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | | | | | | | | | | | - John Wharton
- St. George’s, University of London, London, United Kingdom
| | | | - Stefan Gräf
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas Morrell
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | | | - Mark Toshner
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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Wang Q, Antone J, Alsop E, Reiman R, Funk C, Bendl J, Dudley JT, Liang WS, Karr TL, Roussos P, Bennett DA, De Jager PL, Serrano GE, Beach TG, Keuren-Jensen KV, Mastroeni D, Reiman EM, Readhead BP. A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563319. [PMID: 37961404 PMCID: PMC10634692 DOI: 10.1101/2023.10.20.563319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The emergence of technologies that can support high-throughput profiling of single cell transcriptomes offers to revolutionize the study of brain tissue from persons with and without Alzheimer's disease (AD). Integration of these data with additional complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link observed cell subpopulations and molecular network features within a broader disease-relevant context. We report here single nucleus RNA sequencing (snRNA-seq) profiles generated from superior frontal gyrus cortical tissue samples from 101 exceptionally well characterized, aged subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in peripheral hematological lab parameters, with these observations replicated in an independent, prospective cohort study of ageing and dementia. We also observed an AD-associated CD83(+) microglial subtype with unique molecular networks that encompass many known regulators of AD-relevant microglial biology, and which are associated with immunoglobulin IgG4 production in the transverse colon. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal novel disease biology. The transcriptomic, genetic, phenotypic, and network data resources described within this study are available for access and utilization by the scientific community.
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Willis TW, Wallace C. Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context. PLoS Genet 2023; 19:e1010852. [PMID: 37585442 PMCID: PMC10461826 DOI: 10.1371/journal.pgen.1010852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 08/28/2023] [Accepted: 06/30/2023] [Indexed: 08/18/2023] Open
Abstract
Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.
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Affiliation(s)
- Thomas W. Willis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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Holen B, Shadrin AA, Icick R, Filiz TT, Hindley G, Rødevand L, O'Connell KS, Hagen E, Frei O, Bahrami S, Cheng W, Parker N, Tesfaye M, Jahołkowski P, Karadag N, Dale AM, Djurovic S, Smeland OB, Andreassen OA. Genome-wide analyses reveal novel opioid use disorder loci and genetic overlap with schizophrenia, bipolar disorder, and major depression. Addict Biol 2023; 28:e13282. [PMID: 37252880 DOI: 10.1111/adb.13282] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 03/20/2023] [Accepted: 04/03/2023] [Indexed: 06/01/2023]
Abstract
Opioid use disorder (OUD) and mental disorders are often comorbid, with increased morbidity and mortality. The causes underlying this relationship are poorly understood. Although these conditions are highly heritable, their shared genetic vulnerabilities remain unaccounted for. We applied the conditional/conjunctional false discovery rate (cond/conjFDR) approach to analyse summary statistics from independent genome wide association studies of OUD, schizophrenia (SCZ), bipolar disorder (BD) and major depression (MD) of European ancestry. Next, we characterized the identified shared loci using biological annotation resources. OUD data were obtained from the Million Veteran Program, Yale-Penn and Study of Addiction: Genetics and Environment (SAGE) (15 756 cases, 99 039 controls). SCZ (53 386 cases, 77 258 controls), BD (41 917 cases, 371 549 controls) and MD (170 756 cases, 329 443 controls) data were provided by the Psychiatric Genomics Consortium. We discovered genetic enrichment for OUD conditional on associations with SCZ, BD, MD and vice versa, indicating polygenic overlap with identification of 14 novel OUD loci at condFDR < 0.05 and 7 unique loci shared between OUD and SCZ (n = 2), BD (n = 2) and MD (n = 7) at conjFDR < 0.05 with concordant effect directions, in line with estimated positive genetic correlations. Two loci were novel for OUD, one for BD and one for MD. Three OUD risk loci were shared with more than one psychiatric disorder, at DRD2 on chromosome 11 (BD and MD), at FURIN on chromosome 15 (SCZ, BD and MD) and at the major histocompatibility complex region (SCZ and MD). Our findings provide new insights into the shared genetic architecture between OUD and SCZ, BD and MD, indicating a complex genetic relationship, suggesting overlapping neurobiological pathways.
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Affiliation(s)
- Børge Holen
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Romain Icick
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- INSERM UMR-S1144, Paris University, Paris, France
| | - Tahir T Filiz
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Guy Hindley
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Linn Rødevand
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Espen Hagen
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Nadine Parker
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Markos Tesfaye
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Piotr Jahołkowski
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Naz Karadag
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Cognitive Science, University of California San Diego, La Jolla, California, USA
- Department of Radiology, University of California San Diego, La Jolla, California, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, California, USA
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Olav B Smeland
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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Hutchinson A, Liley J, Wallace C. fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS. BMC Bioinformatics 2022; 23:310. [PMID: 35907789 PMCID: PMC9338519 DOI: 10.1186/s12859-022-04838-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 07/13/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) are limited in power to detect associations that exceed the stringent genome-wide significance threshold. This limitation can be alleviated by leveraging relevant auxiliary data, such as functional genomic data. Frameworks utilising the conditional false discovery rate have been developed for this purpose, and have been shown to increase power for GWAS discovery whilst controlling the false discovery rate. However, the methods are currently only applicable for continuous auxiliary data and cannot be used to leverage auxiliary data with a binary representation, such as whether SNPs are synonymous or non-synonymous, or whether they reside in regions of the genome with specific activity states. RESULTS We describe an extension to the cFDR framework for binary auxiliary data, called "Binary cFDR". We demonstrate FDR control of our method using detailed simulations, and show that Binary cFDR performs better than a comparator method in terms of sensitivity and FDR control. We introduce an all-encompassing user-oriented CRAN R package ( https://annahutch.github.io/fcfdr/ ; https://cran.r-project.org/web/packages/fcfdr/index.html ) and demonstrate its utility in an application to type 1 diabetes, where we identify additional genetic associations. CONCLUSIONS Our all-encompassing R package, fcfdr, serves as a comprehensive toolkit to unite GWAS and functional genomic data in order to increase statistical power to detect genetic associations.
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Affiliation(s)
- Anna Hutchinson
- grid.5335.00000000121885934MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James Liley
- grid.4305.20000 0004 1936 7988MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK ,grid.499548.d0000 0004 5903 3632The Alan Turing Institute, London, UK
| | - Chris Wallace
- grid.5335.00000000121885934MRC Biostatistics Unit, University of Cambridge, Cambridge, UK ,grid.5335.00000000121885934Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, UK ,grid.5335.00000000121885934Department of Medicine, University of Cambridge, Cambridge, UK
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8
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Hutchinson A, Reales G, Willis T, Wallace C. Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR. PLoS Genet 2021; 17:e1009853. [PMID: 34669738 PMCID: PMC8559959 DOI: 10.1371/journal.pgen.1009853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/01/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.
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Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Thomas Willis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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Cuomo ASE, Alvari G, Azodi CB, McCarthy DJ, Bonder MJ. Optimizing expression quantitative trait locus mapping workflows for single-cell studies. Genome Biol 2021; 22:188. [PMID: 34167583 PMCID: PMC8223300 DOI: 10.1186/s13059-021-02407-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/09/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. RESULTS While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. CONCLUSION We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.
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Affiliation(s)
- Anna S E Cuomo
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
| | - Giordano Alvari
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christina B Azodi
- St. Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia
- University of Melbourne, Parkville, Victoria, Australia
| | - Davis J McCarthy
- St. Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia.
- University of Melbourne, Parkville, Victoria, Australia.
| | - Marc Jan Bonder
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
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