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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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2
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Hou K, Gogarten S, Kim J, Hua X, Dias JA, Sun Q, Wang Y, Tan T, Atkinson EG, Martin A, Shortt J, Hirbo J, Li Y, Pasaniuc B, Zhang H. Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations. Bioinformatics 2024; 40:btae148. [PMID: 38490256 PMCID: PMC10980565 DOI: 10.1093/bioinformatics/btae148] [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: 09/30/2023] [Revised: 02/08/2024] [Accepted: 03/13/2024] [Indexed: 03/17/2024] Open
Abstract
SUMMARY Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic studies of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations. AVAILABILITY AND IMPLEMENTATION Admix-kit package is open-source and available at https://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available at https://github.com/UW-GAC/admix-kit_workflow.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Stephanie Gogarten
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, United States
| | - Joohyun Kim
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, United States
| | - Xing Hua
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, United States
| | - Julie-Alexia Dias
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02120, United States
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States
| | - Ying Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, United States
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, United States
| | - Alicia Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Jonathan Shortt
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States
| | - Jibril Hirbo
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, United States
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3
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Avadhanam S, Williams AL. Phase-free local ancestry inference mitigates the impact of switch errors on phase-based methods. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.02.569669. [PMID: 38106003 PMCID: PMC10723336 DOI: 10.1101/2023.12.02.569669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Local ancestry inference (LAI) is an indispensable component of a variety of analyses in medical and population genetics, from admixture mapping to characterizing demographic history. However, the accuracy of LAI depends on a number of factors such as phase quality (for phase-based LAI methods), time since admixture of the population under study, and other factors. Here we present an empirical analysis of four LAI methods using simulated individuals of mixed African and European ancestry, examining the impact of variable phase quality and a range of demographic scenarios. We found that regardless of phasing options, calls from LAI methods that operate on unphased genotypes (phase-free LAI) have 2.6-4.6% higher Pearson correlation with the ground truth than methods that operate on phased genotypes (phase-based LAI). Applying the TRACTOR phase-correction algorithm led to modest improvements in phase-based LAI, but despite this, the Pearson correlation of phase-free LAI remained 2.4-3.8% higher than phase-corrected phase-based approaches (considering the best performing methods in each category). Phase-free and phase-based LAI accuracy differences can dramatically impact downstream analyses: estimates of the time since admixture using phase-based LAI tracts are upwardly biased by ≈10 generations using our highest quality phased data but have virtually no bias using phase-free LAI calls. Our study underscores the strong dependence of phase-based LAI accuracy on phase quality and highlights the merits of LAI approaches that analyze unphased genetic data.
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4
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Goovaerts S, Hoskens H, Eller RJ, Herrick N, Musolf AM, Justice CM, Yuan M, Naqvi S, Lee MK, Vandermeulen D, Szabo-Rogers HL, Romitti PA, Boyadjiev SA, Marazita ML, Shaffer JR, Shriver MD, Wysocka J, Walsh S, Weinberg SM, Claes P. Joint multi-ancestry and admixed GWAS reveals the complex genetics behind human cranial vault shape. Nat Commun 2023; 14:7436. [PMID: 37973980 PMCID: PMC10654897 DOI: 10.1038/s41467-023-43237-8] [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: 12/07/2022] [Accepted: 11/01/2023] [Indexed: 11/19/2023] Open
Abstract
The cranial vault in humans is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conduct a joint multi-ancestry and admixed multivariate genome-wide association study on 3D cranial vault shape extracted from magnetic resonance images of 6772 children from the ABCD study cohort yielding 30 genome-wide significant loci. Follow-up analyses indicate that these loci overlap with genomic risk loci for sagittal craniosynostosis, show elevated activity cranial neural crest cells, are enriched for processes related to skeletal development, and are shared with the face and brain. We present supporting evidence of regional localization for several of the identified genes based on expression patterns in the cranial vault bones of E15.5 mice. Overall, our study provides a comprehensive overview of the genetics underlying normal-range cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.
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Affiliation(s)
- Seppe Goovaerts
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
| | - Hanne Hoskens
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Ryan J Eller
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Noah Herrick
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Anthony M Musolf
- Statistical Genetics Section, Computational and Statistical Genomics Branch, NHGRI, NIH, MD, Baltimore, USA
| | - Cristina M Justice
- Genometrics Section, Computational and Statistical Genomics Branch, Division of Intramural Research, NHGRI, NIH, Baltimore, MD, USA
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meng Yuan
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Myoung Keun Lee
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dirk Vandermeulen
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Heather L Szabo-Rogers
- Department of Anatomy, Physiology and Pharmacology, University of Saskatchewan, Saskatchewan, Canada
| | - Paul A Romitti
- Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA, USA
| | - Simeon A Boyadjiev
- Department of Pediatrics, University of California Davis, Sacramento, CA, USA
| | - Mary L Marazita
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - John R Shaffer
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Seth M Weinberg
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.
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5
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Shriner D, Bentley AR, Gouveia MH, Heuston EF, Doumatey AP, Chen G, Zhou J, Adeyemo A, Rotimi CN. Universal genome-wide association studies: Powerful joint ancestry and association testing. HGG ADVANCES 2023; 4:100235. [PMID: 37653728 PMCID: PMC10507155 DOI: 10.1016/j.xhgg.2023.100235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/02/2023] Open
Abstract
The vast majority of human populations and individuals have mixed ancestry. Consequently, adjustment for locus-specific ancestry is essential for genetic association studies. To empower association studies for all populations, it is necessary to integrate effects of locus-specific ancestry and genotype. We developed a joint test of ancestry and association that can be performed with summary statistics, is independent of study design, can take advantage of locus-specific ancestry effects to boost power in association testing, and can utilize association effects to fine map admixture peaks. We illustrate the test using the association between serum triglycerides and LPL. By combining data from African Americans, European Americans, and West Africans, we identify three conditionally independent variants with varying amounts of ancestrally differentiated allele frequencies. Using out-of-sample data, we demonstrate improved prediction achievable by accounting for multiple causal variants and locus-specific ancestry effects at a single locus.
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Affiliation(s)
- Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Elisabeth F Heuston
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA.
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6
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Wang Y, Kanai M, Tan T, Kamariza M, Tsuo K, Yuan K, Zhou W, Okada Y, Huang H, Turley P, Atkinson EG, Martin AR. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology. CELL GENOMICS 2023; 3:100408. [PMID: 37868036 PMCID: PMC10589629 DOI: 10.1016/j.xgen.2023.100408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/21/2023] [Accepted: 08/22/2023] [Indexed: 10/24/2023]
Abstract
Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kai Yuan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - the BioBank Japan Project
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Society of Fellows, Harvard University, Cambridge, MA 02138, USA
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Patrick Turley
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Elizabeth G. Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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7
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Hou K, Gogarten S, Kim J, Hua X, Dias JA, Sun Q, Wang Y, Tan T, Atkinson EG, Martin A, Shortt J, Hirbo J, Li Y, Pasaniuc B, Zhang H. Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.560263. [PMID: 37873338 PMCID: PMC10592849 DOI: 10.1101/2023.09.30.560263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic study of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Joohyun Kim
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xing Hua
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Julie-Alexia Dias
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ying Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Elizabeth G. Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Alicia Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan Shortt
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jibril Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
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8
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Tan T, Atkinson EG. Strategies for the Genomic Analysis of Admixed Populations. Annu Rev Biomed Data Sci 2023; 6:105-127. [PMID: 37127050 PMCID: PMC10871708 DOI: 10.1146/annurev-biodatasci-020722-014310] [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] [Indexed: 05/03/2023]
Abstract
Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.
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Affiliation(s)
- Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
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9
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Mester R, Hou K, Ding Y, Meeks G, Burch KS, Bhattacharya A, Henn BM, Pasaniuc B. Impact of cross-ancestry genetic architecture on GWASs in admixed populations. Am J Hum Genet 2023; 110:927-939. [PMID: 37224807 PMCID: PMC10257009 DOI: 10.1016/j.ajhg.2023.05.001] [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: 01/27/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/26/2023] Open
Abstract
Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.
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Affiliation(s)
- Rachel Mester
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Gillian Meeks
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA 95616, USA
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California, Davis, Davis, CA 95616, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute of Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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10
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Hou K, Ding Y, Xu Z, Wu Y, Bhattacharya A, Mester R, Belbin GM, Buyske S, Conti DV, Darst BF, Fornage M, Gignoux C, Guo X, Haiman C, Kenny EE, Kim M, Kooperberg C, Lange L, Manichaikul A, North KE, Peters U, Rasmussen-Torvik LJ, Rich SS, Rotter JI, Wheeler HE, Wojcik GL, Zhou Y, Sankararaman S, Pasaniuc B. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat Genet 2023; 55:549-558. [PMID: 36941441 PMCID: PMC11120833 DOI: 10.1038/s41588-023-01338-6] [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: 08/10/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023]
Abstract
Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
| | - Yi Ding
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ziqi Xu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Yue Wu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Rachel Mester
- Graduate Program in Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - David V Conti
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Burcu F Darst
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michelle Kim
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Kari E North
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ulrike Peters
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, USA
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ying Zhou
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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11
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Steiner HE, Carrion KC, Giles JB, Lima AR, Yee K, Sun X, Cavallari LH, Perera MA, Duconge J, Karnes JH. Local Ancestry-Informed Candidate Pathway Analysis of Warfarin Stable Dose in Latino Populations. Clin Pharmacol Ther 2023; 113:680-691. [PMID: 36321873 PMCID: PMC9957812 DOI: 10.1002/cpt.2787] [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: 07/25/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
Abstract
Accuracy of warfarin dose prediction algorithms may be improved by including data from diverse populations in genetic studies of dose variability. Here, we surveyed single nucleotide polymorphisms in vitamin K-related genetic pathways for association with warfarin dose requirements in two admixed Latino populations in standard-principal component adjusted and contemporary-local ancestry adjusted regression models. A total of five variants from vitamin K-related genes/pathways were associated with warfarin dose in both cohorts (P < 0.0125) in standard models. Local ancestry-adjusted analysis unveiled 35 associated variants with absolute effects ranging from β = 9.04 ( ±2.23) to 39.18 ( ±10.89) per ancestral allele in the discovery cohort and β = 6.47 (± 2.02) to 17.82 (± 6.83) in the replication cohort. Importantly, we demonstrate the technical validity of the Tractor model in cohorts with admixed ancestry from three founder populations and bring attention to the technical hurdles obstructing the inclusion of diverse, especially admixed, populations in pharmacogenomic research.
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Affiliation(s)
- Heidi E Steiner
- Data Science Institute, University of Arizona, Tucson, Arizona, USA
- Department of Pharmacy Practice and Science, University of Arizona R. Ken Coit College of Pharmacy, Tucson, Arizona, USA
| | - Kelvin Carrasquillo Carrion
- Research Centers in Minority Institutions (RCMI) Program, Center for Collaborative Research in Health Disparities (CCRHD), Academic Affairs Deanship, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, USA
| | - Jason B Giles
- Department of Pharmacy Practice and Science, University of Arizona R. Ken Coit College of Pharmacy, Tucson, Arizona, USA
| | - Abiel Roche Lima
- Research Centers in Minority Institutions (RCMI) Program, Center for Collaborative Research in Health Disparities (CCRHD), Academic Affairs Deanship, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, USA
| | - Kevin Yee
- Banner University Medical Center-Tucson, Tucson, Arizona, USA
| | - Xiaoxiao Sun
- Department of Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Larisa H Cavallari
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Minoli A Perera
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Jorge Duconge
- Department of Pharmaceutical Sciences, University of Puerto Rico School of Pharmacy, Medical Sciences Campus, San Juan, Puerto Rico, USA
| | - Jason H Karnes
- Department of Pharmacy Practice and Science, University of Arizona R. Ken Coit College of Pharmacy, Tucson, Arizona, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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12
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Mester R, Hou K, Ding Y, Meeks G, Burch KS, Bhattacharya A, Henn BM, Pasaniuc B. Impact of cross-ancestry genetic architecture on GWAS in admixed populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.20.524946. [PMID: 36747759 PMCID: PMC9900755 DOI: 10.1101/2023.01.20.524946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.
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Affiliation(s)
- Rachel Mester
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Gillian Meeks
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, 95616 USA
| | - Kathryn S. Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Brenna M. Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California, Davis, Davis, CA, 95616 USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Institute of Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
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13
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Caliebe A, Tekola‐Ayele F, Darst BF, Wang X, Song YE, Gui J, Sebro RA, Balding DJ, Saad M, Dubé M. Including diverse and admixed populations in genetic epidemiology research. Genet Epidemiol 2022; 46:347-371. [PMID: 35842778 PMCID: PMC9452464 DOI: 10.1002/gepi.22492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/25/2022]
Abstract
The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.
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Affiliation(s)
- Amke Caliebe
- Institute of Medical Informatics and StatisticsKiel University and University Hospital Schleswig‐HolsteinKielGermany
| | - Fasil Tekola‐Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMarylandUSA
| | - Burcu F. Darst
- Center for Genetic EpidemiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
| | - Xuexia Wang
- Department of MathematicsUniversity of North TexasDentonTexasUSA
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Jiang Gui
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth CollegeOne Medical Center Dr.LebanonNew HampshireUSA
| | | | - David J. Balding
- Melbourne Integrative Genomics, Schools of BioSciences and of Mathematics & StatisticsUniversity of MelbourneMelbourneAustralia
| | - Mohamad Saad
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
- Neuroscience Research Center, Faculty of Medical SciencesLebanese UniversityBeirutLebanon
| | - Marie‐Pierre Dubé
- Department of Medicine, and Social and Preventive MedicineUniversité de MontréalMontréalQuébecCanada
- Beaulieu‐Saucier Pharmacogenomcis CentreMontreal Heart InstituteMontrealCanada
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14
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Omics approaches to discover pathophysiological pathways contributing to human pain. Pain 2022; 163:S69-S78. [PMID: 35994593 PMCID: PMC9557800 DOI: 10.1097/j.pain.0000000000002726] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/19/2022] [Indexed: 10/26/2022]
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15
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Grishin D, Gusev A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms. Nat Genet 2022; 54:837-849. [PMID: 35697866 PMCID: PMC9886437 DOI: 10.1038/s41588-022-01075-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 04/08/2022] [Indexed: 02/02/2023]
Abstract
While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk.
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Affiliation(s)
- Dennis Grishin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. .,The Eli and Edythe L. Broad Institute, Cambridge, MA, USA. .,Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
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16
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Asiimwe IG, Pirmohamed M. Ethnic Diversity and Warfarin Pharmacogenomics. Front Pharmacol 2022; 13:866058. [PMID: 35444556 PMCID: PMC9014219 DOI: 10.3389/fphar.2022.866058] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/14/2022] [Indexed: 12/23/2022] Open
Abstract
Warfarin has remained the most commonly prescribed vitamin K oral anticoagulant worldwide since its approval in 1954. Dosing challenges including having a narrow therapeutic window and a wide interpatient variability in dosing requirements have contributed to making it the most studied drug in terms of genotype-phenotype relationships. However, most of these studies have been conducted in Whites or Asians which means the current pharmacogenomics evidence-base does not reflect ethnic diversity. Due to differences in minor allele frequencies of key genetic variants, studies conducted in Whites/Asians may not be applicable to underrepresented populations such as Blacks, Hispanics/Latinos, American Indians/Alaska Natives and Native Hawaiians/other Pacific Islanders. This may exacerbate health inequalities when Whites/Asians have better anticoagulation profiles due to the existence of validated pharmacogenomic dosing algorithms which fail to perform similarly in the underrepresented populations. To examine the extent to which individual races/ethnicities are represented in the existing body of pharmacogenomic evidence, we review evidence pertaining to published pharmacogenomic dosing algorithms, including clinical utility studies, cost-effectiveness studies and clinical implementation guidelines that have been published in the warfarin field.
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Affiliation(s)
- Innocent G Asiimwe
- The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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17
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Gopalan S, Smith SP, Korunes K, Hamid I, Ramachandran S, Goldberg A. Human genetic admixture through the lens of population genomics. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200410. [PMID: 35430881 PMCID: PMC9014191 DOI: 10.1098/rstb.2020.0410] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, ‘The Apportionment of Human Diversity’. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.
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Affiliation(s)
- Shyamalika Gopalan
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Samuel Pattillo Smith
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Katharine Korunes
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Iman Hamid
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, RI 02912, USA
- Data Science Initiative, Brown University, Providence, RI 02912, USA
| | - Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
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18
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Galisa SLG, Jacob PL, Farias AAD, Lemes RB, Alves LU, Nóbrega JCL, Zatz M, Santos S, Weller M. Haplotypes of single cancer driver genes and their local ancestry in a highly admixed long-lived population of Northeast Brazil. Genet Mol Biol 2022; 45:e20210172. [PMID: 35112701 PMCID: PMC8811751 DOI: 10.1590/1678-4685-gmb-2021-0172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/17/2021] [Indexed: 12/02/2022] Open
Abstract
Admixed populations have not been examined in detail in cancer genetic studies.
Here, we inferred the local ancestry of cancer-associated single nucleotide
polymorphisms (SNPs) and haplotypes of a highly admixed Brazilian population.
SNP array was used to genotype 73 unrelated individuals aged 80-102 years. Local
ancestry inference was performed by merging genotyped regions with phase three
data from the 1000 Genomes Project Consortium using RFmix. The average ancestry
tract length was 9.12-81.71 megabases. Strong linkage disequilibrium was
detected in 48 haplotypes containing 35 SNPs in 10 cancer driver genes. All
together, 19 risk and eight protective alleles were identified in 23 out of 48
haplotypes. Homozygous individuals were mainly of European ancestry, whereas
heterozygotes had at least one Native American and one African ancestry tract.
Native-American ancestry for homozygous individuals with risk alleles for
HNF1B, CDH1, and BRCA1 was inferred for
the first time. Results indicated that analysis of SNP polymorphism in the
present admixed population has a high potential to identify new
ancestry-associated alleles and haplotypes that modify cancer susceptibility
differentially in distinct human populations. Future case-control studies with
populations with a complex history of admixture could help elucidate
ancestry-associated biological differences in cancer incidence and therapeutic
outcomes.
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Affiliation(s)
- Steffany Larissa Galdino Galisa
- Universidade Estadual da Paraíba (UEPB), Núcleo de Estudos em Genética e Educação, Programa de Pós-Graduação em Saúde Pública, Campina Grande, PB, Brazil
| | - Priscila Lima Jacob
- Universidade Estadual da Paraíba (UEPB), Núcleo de Estudos em Genética e Educação, Programa de Pós-Graduação em Saúde Pública, Campina Grande, PB, Brazil
| | - Allysson Allan de Farias
- Universidade Estadual da Paraíba (UEPB), Núcleo de Estudos em Genética e Educação, Programa de Pós-Graduação em Saúde Pública, Campina Grande, PB, Brazil.,Universidade de São Paulo (USP), Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Renan Barbosa Lemes
- Universidade de São Paulo (USP), Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Leandro Ucela Alves
- Universidade Estadual da Paraíba (UEPB), Núcleo de Estudos em Genética e Educação, Programa de Pós-Graduação em Saúde Pública, Campina Grande, PB, Brazil.,Universidade de São Paulo (USP), Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Júlia Cristina Leite Nóbrega
- Universidade Estadual da Paraíba (UEPB), Núcleo de Estudos em Genética e Educação, Programa de Pós-Graduação em Saúde Pública, Campina Grande, PB, Brazil
| | - Mayana Zatz
- Universidade de São Paulo (USP), Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Silvana Santos
- Universidade Estadual da Paraíba (UEPB), Núcleo de Estudos em Genética e Educação, Programa de Pós-Graduação em Saúde Pública, Campina Grande, PB, Brazil.,Universidade Estadual da Paraíba (UEPB), Departamento de Biologia, Campina Grande, PB, Brazil
| | - Mathias Weller
- Universidade Estadual da Paraíba (UEPB), Núcleo de Estudos em Genética e Educação, Programa de Pós-Graduação em Saúde Pública, Campina Grande, PB, Brazil.,Universidade Estadual da Paraíba (UEPB), Departamento de Biologia, Campina Grande, PB, Brazil
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19
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On powerful GWAS in admixed populations. Nat Genet 2021; 53:1631-1633. [PMID: 34824480 DOI: 10.1038/s41588-021-00953-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 09/14/2021] [Indexed: 12/30/2022]
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20
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Lin M, Park DS, Zaitlen NA, Henn BM, Gignoux CR. Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies. Front Genet 2021; 12:673167. [PMID: 34108994 PMCID: PMC8181458 DOI: 10.3389/fgene.2021.673167] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.
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Affiliation(s)
- Meng Lin
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Danny S Park
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, San Francisco, CA, United States
| | - Noah A Zaitlen
- Department of Neurology and Computational Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California, Davis, Davis, CA, United States
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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21
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Atkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ, Santoro ML, Ulirsch JC, Kamatani Y, Okada Y, Finucane HK, Koenen KC, Nievergelt CM, Daly MJ, Neale BM. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat Genet 2021; 53:195-204. [PMID: 33462486 PMCID: PMC7867648 DOI: 10.1038/s41588-020-00766-y] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 12/15/2020] [Indexed: 12/26/2022]
Abstract
Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.
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Affiliation(s)
- Elizabeth G Atkinson
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Adam X Maihofer
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marcos L Santoro
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departamento de Psiquiatria, Universidade Federal de São Paulo, São Paulo, Brazil
- Departamento de Morfologia e Genética, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Jacob C Ulirsch
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Graduate School of Medicine, Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
| | - Hilary K Finucane
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karestan C Koenen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Mark J Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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22
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Divaris K, Slade GD, Ferreira Zandona AG, Preisser JS, Ginnis J, Simancas-Pallares MA, Agler CS, Shrestha P, Karhade DS, Ribeiro ADA, Cho H, Gu Y, Meyer BD, Joshi AR, Azcarate-Peril MA, Basta PV, Wu D, North KE. Cohort Profile: ZOE 2.0-A Community-Based Genetic Epidemiologic Study of Early Childhood Oral Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E8056. [PMID: 33139633 PMCID: PMC7663650 DOI: 10.3390/ijerph17218056] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023]
Abstract
Early childhood caries (ECC) is an aggressive form of dental caries occurring in the first five years of life. Despite its prevalence and consequences, little progress has been made in its prevention and even less is known about individuals' susceptibility or genomic risk factors. The genome-wide association study (GWAS) of ECC ("ZOE 2.0") is a community-based, multi-ethnic, cross-sectional, genetic epidemiologic study seeking to address this knowledge gap. This paper describes the study's design, the cohort's demographic profile, data domains, and key oral health outcomes. Between 2016 and 2019, the study enrolled 8059 3-5-year-old children attending public preschools in North Carolina, United States. Participants resided in 86 of the state's 100 counties and racial/ethnic minorities predominated-for example, 48% (n = 3872) were African American, 22% white, and 20% (n = 1611) were Hispanic/Latino. Seventy-nine percent (n = 6404) of participants underwent clinical dental examinations yielding ECC outcome measures-ECC (defined at the established caries lesion threshold) prevalence was 54% and the mean number of decayed, missing, filled surfaces due to caries was eight. Nearly all (98%) examined children provided sufficient DNA from saliva for genotyping. The cohort's community-based nature and rich data offer excellent opportunities for addressing important clinical, epidemiologic, and biological questions in early childhood.
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Affiliation(s)
- Kimon Divaris
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA; (G.D.S.); (J.G.); (M.A.S.-P.); (C.S.A.); (P.S.); (D.S.K.)
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (P.V.B.); (K.E.N.)
| | - Gary D. Slade
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA; (G.D.S.); (J.G.); (M.A.S.-P.); (C.S.A.); (P.S.); (D.S.K.)
| | - Andrea G. Ferreira Zandona
- Department of Comprehensive Dentistry, School of Dental Medicine, Tufts University, Boston, MA 02111, USA;
| | - John S. Preisser
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (J.S.P.); (H.C.); (Y.G.); (D.W.)
| | - Jeannie Ginnis
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA; (G.D.S.); (J.G.); (M.A.S.-P.); (C.S.A.); (P.S.); (D.S.K.)
| | - Miguel A. Simancas-Pallares
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA; (G.D.S.); (J.G.); (M.A.S.-P.); (C.S.A.); (P.S.); (D.S.K.)
| | - Cary S. Agler
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA; (G.D.S.); (J.G.); (M.A.S.-P.); (C.S.A.); (P.S.); (D.S.K.)
| | - Poojan Shrestha
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA; (G.D.S.); (J.G.); (M.A.S.-P.); (C.S.A.); (P.S.); (D.S.K.)
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (P.V.B.); (K.E.N.)
| | - Deepti S. Karhade
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA; (G.D.S.); (J.G.); (M.A.S.-P.); (C.S.A.); (P.S.); (D.S.K.)
| | - Apoena de Aguiar Ribeiro
- Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA;
| | - Hunyong Cho
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (J.S.P.); (H.C.); (Y.G.); (D.W.)
| | - Yu Gu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (J.S.P.); (H.C.); (Y.G.); (D.W.)
| | - Beau D. Meyer
- Division of Pediatric Dentistry, College of Dentistry, The Ohio State University, Columbus, OH 43210, USA;
| | - Ashwini R. Joshi
- Division of Surgery, School of Medicine, University of North Carolina-Chapel Hill, NC 27599-7050, USA;
| | - M. Andrea Azcarate-Peril
- Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, and UNC Microbiome Core, Department of Medicine, School of Medicine, University of North Carolina-Chapel Hill, NC 27599-7555, USA;
| | - Patricia V. Basta
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (P.V.B.); (K.E.N.)
| | - Di Wu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (J.S.P.); (H.C.); (Y.G.); (D.W.)
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina-Chapel Hill, NC 27599-7450, USA
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, NC 27599-7400, USA; (P.V.B.); (K.E.N.)
- Carolina Center for Genome Sciences, University of North Carolina-Chapel Hill, NC 27514, USA
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23
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Gay NR, Gloudemans M, Antonio ML, Abell NS, Balliu B, Park Y, Martin AR, Musharoff S, Rao AS, Aguet F, Barbeira AN, Bonazzola R, Hormozdiari F, Ardlie KG, Brown CD, Im HK, Lappalainen T, Wen X, Montgomery SB. Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx. Genome Biol 2020; 21:233. [PMID: 32912333 PMCID: PMC7488497 DOI: 10.1186/s13059-020-02113-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 07/19/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization. RESULTS Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up. CONCLUSIONS We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.
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Affiliation(s)
- Nicole R. Gay
- Department of Genetics, Stanford University, Stanford, CA USA
| | | | | | - Nathan S. Abell
- Department of Genetics, Stanford University, Stanford, CA USA
| | - Brunilda Balliu
- Department of Biomathematics, University of California, Los Angeles, Los Angeles, CA USA
| | - YoSon Park
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA USA
| | | | - Abhiram S. Rao
- Department of Bioengineering, Stanford University, Stanford, CA USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Alvaro N. Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL USA
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL USA
| | - Farhad Hormozdiari
- The Broad Institute of MIT and Harvard, Cambridge, MA USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - GTEx Consortium
- Department of Genetics, Stanford University, Stanford, CA USA
- Biomedical Informatics, Stanford University, Stanford, CA USA
- Department of Biomathematics, University of California, Los Angeles, Los Angeles, CA USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA USA
- Department of Bioengineering, Stanford University, Stanford, CA USA
- The Broad Institute of MIT and Harvard, Cambridge, MA USA
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
- New York Genome Center, New York, NY USA
- Department of Systems Biology, Columbia University, New York, NY USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, Stanford University, Stanford, CA USA
| | | | - Christopher D. Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY USA
- Department of Systems Biology, Columbia University, New York, NY USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI USA
| | - Stephen B. Montgomery
- Department of Genetics, Stanford University, Stanford, CA USA
- Department of Pathology, Stanford University, Stanford, CA USA
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24
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Ziyatdinov A, Parker MM, Vaysse A, Beaty TH, Kraft P, Cho MH, Aschard H. Mixed-model admixture mapping identifies smoking-dependent loci of lung function in African Americans. Eur J Hum Genet 2020; 28:656-668. [PMID: 31836859 PMCID: PMC7171162 DOI: 10.1038/s41431-019-0545-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 10/30/2019] [Accepted: 11/01/2019] [Indexed: 11/08/2022] Open
Abstract
Admixture mapping has led to the discovery of many genes associated with differential disease risk by ancestry, highlighting the importance of ancestry-based approaches to association studies. However, the potential of admixture mapping in deciphering the interplay between genes and environment exposures has been seldom explored. Here we performed a genome-wide screening of local ancestry-smoking interactions for five spirometric lung function phenotypes in 3300 African Americans from the COPDGene study. To account for population structure and outcome heterogeneity across exposure groups, we developed a multi-component linear mixed model for mapping gene-environment interactions and empirically showed its robustness and increased power. When applied to the COPDGene study, our approach identified two 11p15.2-3 and 2q37 loci, exhibiting local ancestry-smoking interactions at genome-wide significant level, which would have been missed by standard single-nucleotide polymorphism analyses. These two loci harbor the PARVA and RAB17 genes previously recognized to be involved in smoking behavior. Overall, our study provides the first evidence for potential synergistic effects between African ancestry and smoking on pulmonary function, and underlines the importance of ethnic diversity in genetic studies.
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Affiliation(s)
- Andrey Ziyatdinov
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Margaret M Parker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Amaury Vaysse
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Terri H Beaty
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Hugues Aschard
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
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25
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Mani A. Local Ancestry Association, Admixture Mapping, and Ongoing Challenges. ACTA ACUST UNITED AC 2019; 10:CIRCGENETICS.117.001747. [PMID: 28408710 DOI: 10.1161/circgenetics.117.001747] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Arya Mani
- From the Department of Medicine and Genetics, Yale University, New Haven, CT.
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26
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Qin H, Zhao J, Zhu X. Identifying Rare Variant Associations in Admixed Populations. Sci Rep 2019; 9:5458. [PMID: 30931973 PMCID: PMC6443736 DOI: 10.1038/s41598-019-41845-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 03/12/2019] [Indexed: 12/27/2022] Open
Abstract
An admixed population and its ancestral populations bear different burdens of a complex disease. The ancestral populations may have different haplotypes of deleterious alleles and thus ancestry-gene interaction can influence disease risk in the admixed population. Among admixed individuals, deleterious haplotypes and their ancestries are dependent and can provide non-redundant association information. Herein we propose a local ancestry boosted sum test (LABST) for identifying chromosomal blocks that harbor rare variants but have no ancestry switches. For such a stable ancestral block, our LABST exploits ancestry-gene interaction and the number of rare alleles therein. Under the null of no genetic association, the test statistic asymptotically follows a chi-square distribution with one degree of freedom (1-df). Our LABST properly controlled type I error rates under extensive simulations, suggesting that the asymptotic approximation was accurate for the null distribution of the test statistic. In terms of power for identifying rare variant associations, our LABST uniformly outperformed several famed methods under four important modes of disease genetics over a large range of relative risks. In conclusion, exploiting ancestry-gene interaction can boost statistical power for rare variant association mapping in admixed populations.
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Affiliation(s)
- Huaizhen Qin
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, 32611, USA
- Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, New Orleans, LA, 70112, USA
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Avenue, Cleveland, Ohio, 44106, USA.
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27
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Skotte L, Jørsboe E, Korneliussen TS, Moltke I, Albrechtsen A. Ancestry‐specific association mapping in admixed populations. Genet Epidemiol 2019; 43:506-521. [DOI: 10.1002/gepi.22200] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 01/10/2019] [Accepted: 02/19/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Line Skotte
- Department of Epidemiology ResearchStatens Serum InstituteCopenhagen Denmark
| | - Emil Jørsboe
- Department of Biology, The Bioinformatics CentreUniversity of CopenhagenCopenhagen Denmark
| | - Thorfinn S. Korneliussen
- Centre for GeoGenetics, Natural History Museum of DenmarkUniversity of CopenhagenCopenhagen Denmark
| | - Ida Moltke
- Department of Biology, The Bioinformatics CentreUniversity of CopenhagenCopenhagen Denmark
| | - Anders Albrechtsen
- Department of Biology, The Bioinformatics CentreUniversity of CopenhagenCopenhagen Denmark
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28
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Duan Q, Xu Z, Raffield L, Chang S, Wu D, Lange EM, Reiner AP, Li Y. A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations. Genet Epidemiol 2018; 42:288-302. [PMID: 29226381 PMCID: PMC5851818 DOI: 10.1002/gepi.22104] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 09/07/2017] [Accepted: 10/20/2017] [Indexed: 12/23/2022]
Abstract
Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.
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Affiliation(s)
- Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Zheng Xu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE
- Initiative of Quantitative Life Sciences, University of Nebraska-Lincoln, Lincoln, NE
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Suhua Chang
- Institute of Psychology, Chinese Academy of Science, Beijing, China
| | - Di Wu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Periodontology, University of North Carolina, Chapel Hill, NC, USA
| | - Ethan M. Lange
- Department of Medicine, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
- Department of Biostatistics and Informatics, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Alex P. Reiner
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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Prasad B, Saxena R, Goel N, Patel SR. Genetic Ancestry for Sleep Research: Leveraging Health Inequalities to Identify Causal Genetic Variants. Chest 2018; 153:1478-1496. [PMID: 29604255 DOI: 10.1016/j.chest.2018.03.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/02/2018] [Accepted: 03/19/2018] [Indexed: 02/08/2023] Open
Abstract
Recent evidence has highlighted the health inequalities in sleep behaviors and sleep disorders that adversely affect outcomes in select populations, including African-American and Hispanic-American subjects. Race-related sleep health inequalities are ascribed to differences in multilevel and interlinked health determinants, such as sociodemographic factors, health behaviors, and biology. African-American and Hispanic-American subjects are admixed populations whose genetic inheritance combines two or more ancestral populations originating from different continents. Racial inequalities in admixed populations can be parsed into relevant groups of mediating factors (environmental vs genetic) with the use of measures of genetic ancestry, including the proportion of an individual's genetic makeup that comes from each of the major ancestral continental populations. This review describes sleep health inequalities in African-American and Hispanic-American subjects and considers the potential utility of ancestry studies to exploit these differences to gain insight into the genetic underpinnings of these phenotypes. The inclusion of genetic approaches in future studies of admixed populations will allow greater understanding of the potential biological basis of race-related sleep health inequalities.
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Affiliation(s)
- Bharati Prasad
- Department of Medicine, University of Illinois at Chicago, and Jesse Brown VA Medical Center, Chicago, IL.
| | - Richa Saxena
- Center for Genomic Medicine and Department of Anesthesia, Pain, and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Namni Goel
- Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Sanjay R Patel
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA
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Abstract
People of recent African ancestry develop kidney disease at much higher rates than most other groups. Two specific coding variants in the Apolipoprotein-L1 gene APOL1 termed G1 and G2 are the causal drivers of much of this difference in risk, following a recessive pattern of inheritance. However, most individuals with a high-risk APOL1 genotype do not develop overt kidney disease, prompting interest in identifying those factors that interact with APOL1 We performed an admixture mapping study to identify genetic modifiers of APOL1-associated kidney disease. Individuals with two APOL1 risk alleles and focal segmental glomerulosclerosis (FSGS) have significantly increased African ancestry at the UBD (also known as FAT10) locus. UBD is a ubiquitin-like protein modifier that targets proteins for proteasomal degradation. African ancestry at the UBD locus correlates with lower levels of UBD expression. In cell-based experiments, the disease-associated APOL1 alleles (known as G1 and G2) lead to increased abundance of UBD mRNA but to decreased levels of UBD protein. UBD gene expression inversely correlates with G1 and G2 APOL1-mediated cell toxicity, as well as with levels of G1 and G2 APOL1 protein in cells. These studies support a model whereby inflammatory stimuli up-regulate both UBD and APOL1, which interact in a functionally important manner. UBD appears to mitigate APOL1-mediated toxicity by targeting it for destruction. Thus, genetically encoded differences in UBD and UBD expression appear to modify the APOL1-associated kidney phenotype.
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Latendresse SJ, Musci R, Maher BS. Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2018; 19:58-67. [PMID: 28409280 PMCID: PMC5640466 DOI: 10.1007/s11121-017-0785-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Human genetic research in the past decade has generated a wealth of data from the genome-wide association scan era, much of which is catalogued and freely available. These data will typically test the relationship between a single nucleotide variant or polymorphism (SNP) and some outcome, disease, or trait. Ongoing investigations will yield a similar wealth of data regarding epigenetic phenomena. These data will typically test the relationship between DNA methylation at a single genomic location/region and some outcome. Most of these findings will be the result of cross-sectional investigations typically using ascertained cases and controls. Consequently, most methodological consideration focuses on methods appropriate for simple case-control comparisons. It is expected that a growing number of investigators with longitudinal experimental prevention or intervention cohorts will also measure genetic and epigenetic indicators as part of their investigations, harvesting the wealth of information generated by the genome-wide association study (GWAS) era to allow for targeted hypothesis testing in the next generation of prevention and intervention trials. Herein, we discuss appropriate quality control and statistical modelling of genetic, polygenic, and epigenetic measures in longitudinal models. We specifically discuss quality control, population stratification, genotype imputation, pathway approaches, and proper modelling of an interaction between a specific genetic variant and an environment variable (GxE interaction).
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Affiliation(s)
- Shawn J Latendresse
- Department of Psychology and Neuroscience, Baylor University, One Bear Place #97334, Waco, TX, 76798, USA.
| | - Rashelle Musci
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway Ave, Baltimore, MD, 21205, USA
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway Ave, Baltimore, MD, 21205, USA.
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Martin ER, Tunc I, Liu Z, Slifer SH, Beecham AH, Beecham GW. Properties of global- and local-ancestry adjustments in genetic association tests in admixed populations. Genet Epidemiol 2017; 42:214-229. [PMID: 29288582 DOI: 10.1002/gepi.22103] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 09/24/2017] [Accepted: 10/29/2017] [Indexed: 12/31/2022]
Abstract
Population substructure can lead to confounding in tests for genetic association, and failure to adjust properly can result in spurious findings. Here we address this issue of confounding by considering the impact of global ancestry (average ancestry across the genome) and local ancestry (ancestry at a specific chromosomal location) on regression parameters and relative power in ancestry-adjusted and -unadjusted models. We examine theoretical expectations under different scenarios for population substructure; applying different regression models, verifying and generalizing using simulations, and exploring the findings in real-world admixed populations. We show that admixture does not lead to confounding when the trait locus is tested directly in a single admixed population. However, if there is more complex population structure or a marker locus in linkage disequilibrium (LD) with the trait locus is tested, both global and local ancestry can be confounders. Additionally, we show the genotype parameters of adjusted and unadjusted models all provide tests for LD between the marker and trait locus, but in different contexts. The local ancestry adjusted model tests for LD in the ancestral populations, while tests using the unadjusted and the global ancestry adjusted models depend on LD in the admixed population(s), which may be enriched due to different ancestral allele frequencies. Practically, this implies that global-ancestry adjustment should be used for screening, but local-ancestry adjustment may better inform fine mapping and provide better effect estimates at trait loci.
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Affiliation(s)
- Eden R Martin
- John P. Hussman Institute for Human Genetics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America.,John T. MacDonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
| | - Ilker Tunc
- Bioinformatics and Systems Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Zhi Liu
- Comparative Genomics Analysis Unit, Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Susan H Slifer
- John P. Hussman Institute for Human Genetics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
| | - Ashley H Beecham
- John T. MacDonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
| | - Gary W Beecham
- John P. Hussman Institute for Human Genetics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America.,John T. MacDonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
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Divers J, Palmer ND, Langefeld CD, Brown WM, Lu L, Hicks PJ, Smith SC, Xu J, Terry JG, Register TC, Wagenknecht LE, Parks JS, Ma L, Chan GC, Buxbaum SG, Correa A, Musani S, Wilson JG, Taylor HA, Bowden DW, Carr JJ, Freedman BI. Genome-wide association study of coronary artery calcified atherosclerotic plaque in African Americans with type 2 diabetes. BMC Genet 2017; 18:105. [PMID: 29221444 PMCID: PMC5723099 DOI: 10.1186/s12863-017-0572-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 11/23/2017] [Indexed: 11/26/2022] Open
Abstract
Background Coronary artery calcified atherosclerotic plaque (CAC) predicts cardiovascular disease (CVD). Despite exposure to more severe conventional CVD risk factors, African Americans (AAs) are less likely to develop CAC, and when they do, have markedly lower levels than European Americans. Genetic factors likely contribute to the observed ethnic differences. To identify genes associated with CAC in AAs with type 2 diabetes (T2D), a genome-wide association study (GWAS) was performed using the Illumina 5 M chip in 691 African American-Diabetes Heart Study participants (AA-DHS), with replication in 205 Jackson Heart Study (JHS) participants with T2D. Genetic association tests were performed on the genotyped and 1000 Genomes-imputed markers separately for each study, and combined in a meta-analysis. Results Single nucleotide polymorphisms (SNPs), rs11353135 (2q22.1), rs16879003 (6p22.3), rs5014012, rs58071836 and rs10244825 (all on chromosome 7), rs10918777 (9q31.2), rs13331874 (16p13.3) and rs4459623 (18q12.1) were associated with presence and/or quantity of CAC in the AA-DHS and JHS, with meta-analysis p-values ≤8.0 × 10−7. The strongest result in AA-DHS alone was rs6491315 in the 13q32.1 region (parameter estimate (SE) = −1.14 (0.20); p-value = 9.1 × 10−9). This GWAS peak replicated a previously reported AA-DHS CAC admixture signal (rs7492028, LOD score 2.8). Conclusions Genetic association between SNPs on chromosomes 2, 6, 7, 9, 16 and 18 and CAC were detected in AAs with T2D from AA-DHS and replicated in the JHS. These data support a role for genetic variation on these chromosomes as contributors to CAC in AAs with T2D, as well as to variation in CAC between populations of African and European ancestry. Electronic supplementary material The online version of this article (10.1186/s12863-017-0572-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jasmin Divers
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157-1053, USA.
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Carl D Langefeld
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157-1053, USA
| | - W Mark Brown
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157-1053, USA
| | - Lingyi Lu
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157-1053, USA
| | - Pamela J Hicks
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - S Carrie Smith
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jianzhao Xu
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - James G Terry
- Department of Radiology and Vanderbilt Center for Translation and Clinical Cardiovascular Research (VTRACC), Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Thomas C Register
- Department of Pathology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lynne E Wagenknecht
- Department of Epidemiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - John S Parks
- Department of Internal Medicine-Section on Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lijun Ma
- Department of Internal Medicine-Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Gary C Chan
- Department of Internal Medicine-Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sarah G Buxbaum
- School of Public Health Initiative, Jackson State University, Jackson, MS, USA
| | | | | | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Herman A Taylor
- Morehouse School of Medicine, Morehouse College, Atlanta, Georgia
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - John Jeffrey Carr
- Department of Radiology and Vanderbilt Center for Translation and Clinical Cardiovascular Research (VTRACC), Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Barry I Freedman
- Department of Internal Medicine-Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Shendre A, Wiener HW, Irvin MR, Aouizerat BE, Overton ET, Lazar J, Liu C, Hodis HN, Limdi NA, Weber KM, Gange SJ, Zhi D, Floris-Moore MA, Ofotokun I, Qi Q, Hanna DB, Kaplan RC, Shrestha S. Genome-wide admixture and association study of subclinical atherosclerosis in the Women's Interagency HIV Study (WIHS). PLoS One 2017; 12:e0188725. [PMID: 29206233 PMCID: PMC5714351 DOI: 10.1371/journal.pone.0188725] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 11/12/2017] [Indexed: 12/20/2022] Open
Abstract
Cardiovascular disease (CVD) is a major comorbidity among HIV-infected individuals. Common carotid artery intima-media thickness (cCIMT) is a valid and reliable subclinical measure of atherosclerosis and is known to predict CVD. We performed genome-wide association (GWA) and admixture analysis among 682 HIV-positive and 288 HIV-negative Black, non-Hispanic women from the Women’s Interagency HIV study (WIHS) cohort using a combined and stratified analysis approach. We found some suggestive associations but none of the SNPs reached genome-wide statistical significance in our GWAS analysis. The top GWAS SNPs were rs2280828 in the region intergenic to mediator complex subunit 30 and exostosin glycosyltransferase 1 (MED30 | EXT1) among all women, rs2907092 in the catenin delta 2 (CTNND2) gene among HIV-positive women, and rs7529733 in the region intergenic to family with sequence similarity 5, member C and regulator of G-protein signaling 18 (FAM5C | RGS18) genes among HIV-negative women. The most significant local European ancestry associations were in the region intergenic to the zinc finger and SCAN domain containing 5D gene and NADH: ubiquinone oxidoreductase complex assembly factor 1 (ZSCAN5D | NDUF1) pseudogene on chromosome 19 among all women, in the region intergenic to vomeronasal 1 receptor 6 pseudogene and zinc finger protein 845 (VN1R6P | ZNF845) gene on chromosome 19 among HIV-positive women, and in the region intergenic to the SEC23-interacting protein and phosphatidic acid phosphatase type 2 domain containing 1A (SEC23IP | PPAPDC1A) genes located on chromosome 10 among HIV-negative women. A number of previously identified SNP associations with cCIMT were also observed and included rs2572204 in the ryanodine receptor 3 (RYR3) and an admixture region in the secretion-regulating guanine nucleotide exchange factor (SERGEF) gene. We report several SNPs and gene regions in the GWAS and admixture analysis, some of which are common across HIV-positive and HIV-negative women as demonstrated using meta-analysis, and also across the two analytic approaches (i.e., GWA and admixture). These findings suggest that local European ancestry plays an important role in genetic associations of cCIMT among black women from WIHS along with other environmental factors that are related to CVD and may also be triggered by HIV. These findings warrant confirmation in independent samples.
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Affiliation(s)
- Aditi Shendre
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Howard W. Wiener
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Bradley E. Aouizerat
- Bluestone Center for Clinical Research, New York University, New York, New York, United States of America
- Department of Oral and Maxillofacial Surgery, New York University, New York, New York, United States of America
| | - Edgar T. Overton
- Division of Infectious Diseases, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Jason Lazar
- Department of Medicine, State University of New York, Downstate Medical Center, Brooklyn, New York, United States of America
| | - Chenglong Liu
- Department of Medicine, Georgetown University Medical Center, Washington, DC, United States of America
| | - Howard N. Hodis
- Atherosclerosis Research Unit, University of Southern California, Los Angeles, California, United States of America
| | - Nita A. Limdi
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Kathleen M. Weber
- Cook County Health and Hospital System/Hektoen Institute of Medicine, Chicago, Illnois, United States of America
| | - Stephen J. Gange
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Degui Zhi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Michelle A. Floris-Moore
- Division of Infectious Diseases, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Ighovwerha Ofotokun
- Department of Medicine/Infectious Diseases, Emory University, and Grady Healthcare System, Atlanta, Georgia, United States of America
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - David B. Hanna
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Sadeep Shrestha
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- * E-mail:
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Hellwege J, Keaton J, Giri A, Gao X, Velez Edwards DR, Edwards TL. Population Stratification in Genetic Association Studies. CURRENT PROTOCOLS IN HUMAN GENETICS 2017; 95:1.22.1-1.22.23. [PMID: 29044472 PMCID: PMC6007879 DOI: 10.1002/cphg.48] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Population stratification (PS) is a primary consideration in studies of genetic determinants of human traits. Failure to control for PS may lead to confounding, causing a study to fail for lack of significant results, or resources to be wasted following false-positive signals. Here, historical and current approaches for addressing PS when performing genetic association studies in human populations are reviewed. Methods for detecting the presence of PS, including global and local ancestry methods, are described. Also described are approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. Many traits are being examined for the first time in minority populations, which may inherently feature PS. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Jacklyn Hellwege
- Vanderbilt Genetics Institute, Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center,
Nashville, TN 37203, USA
| | - Jacob Keaton
- Vanderbilt Genetics Institute, Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center,
Nashville, TN 37203, USA
| | - Ayush Giri
- Vanderbilt Genetics Institute, Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center,
Nashville, TN 37203, USA
| | - Xiaoyi Gao
- Department of Ophthalmology and Preventive Medicine, Keck School of Medicine, University of Southern California, Los
Angeles, CA 90033, USA
| | - Digna R. Velez Edwards
- Vanderbilt Genetics Institute, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center,
Nashville, TN 37203, USA
| | - Todd L. Edwards
- Vanderbilt Genetics Institute, Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center,
Nashville, TN 37203, USA
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36
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Abstract
Admixture mapping is a powerful method of gene mapping for diseases or traits that show differential risk by ancestry. Admixture mapping has been applied most often to Americans who trace ancestry to various combinations of Native Americans, Europeans, and West Africans. Recent developments in admixture mapping include improvements in methods and the reference data needed to make inferences about ancestry, as well as extensions of the mapping approach in the framework of linear mixed models. In this unit, the key concepts of admixture mapping are outlined. Several approaches for inferring local ancestry are described, and strategies for performing admixture mapping depending on the study design are provided. Finally, comparisons and contrasts between linkage analysis, association analysis, and admixture mapping are provided, with an emphasis on integrating admixture mapping and association testing. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland
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37
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Szulc P, Bogdan M, Frommlet F, Tang H. Joint genotype- and ancestry-based genome-wide association studies in admixed populations. Genet Epidemiol 2017; 41:555-566. [PMID: 28657151 DOI: 10.1002/gepi.22056] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 04/01/2017] [Accepted: 04/25/2017] [Indexed: 12/21/2022]
Abstract
In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry). Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here, we extend this approach for population-based GWAS in the direction of multimarker models. A modified version of the Bayesian information criterion is developed for building a multilocus model that accounts for the differential correlation structure due to linkage disequilibrium (LD) and admixture LD. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis or modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. Depending on the signal strength, our procedure automatically chooses whether genotypic or locus-specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/.
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Affiliation(s)
- Piotr Szulc
- Faculty of Mathematics, Wroclaw University of Technology, Wroclaw, Poland
| | - Malgorzata Bogdan
- Faculty of Mathematics and Computer Science, University of Wroclaw, Wroclaw, Poland
| | - Florian Frommlet
- Department of Medical Statistics, CEMSIIS, Medical University of Vienna, Vienna, Austria
| | - Hua Tang
- Departments of Genetics and Statistics, Stanford University, Stanford, California, United States of America
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38
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Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, Daly MJ, Bustamante CD, Kenny EE. Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet 2017. [PMID: 28366442 DOI: 10.1016/j.ajhg] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2023] Open
Abstract
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | | | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | | | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Simon Gravel
- Department of Human Genetics, McGill University, Montreal, QC H3A 0G1, Canada; McGill University and Genome Quebec Innovation Centre, Montreal, QC H3A 0G1, Canada
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | | | - Eimear E Kenny
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Center of Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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39
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Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet 2017; 100:635-649. [PMID: 28366442 DOI: 10.1016/j.ajhg.2017.03.004] [Citation(s) in RCA: 761] [Impact Index Per Article: 108.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 03/10/2017] [Indexed: 01/10/2023] Open
Abstract
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
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40
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Cyr DD, Allen AS, Du GJ, Ruffin F, Adams C, Thaden JT, Maskarinec SA, Souli M, Guo S, Dykxhoorn DM, Scott WK, Fowler VG. Evaluating genetic susceptibility to Staphylococcus aureus bacteremia in African Americans using admixture mapping. Genes Immun 2017; 18:95-99. [PMID: 28332560 PMCID: PMC5435963 DOI: 10.1038/gene.2017.6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 02/14/2017] [Accepted: 02/16/2017] [Indexed: 12/12/2022]
Abstract
The incidence of Staphylococcus aureus bacteremia (SAB) is significantly higher in African American (AA) than in European-descended populations. We used admixture mapping (AM) to test the hypothesis that genomic variations with different frequencies in European and African ancestral genomes influence susceptibility to SAB in AAs. A total of 565 adult AAs (390 cases with SAB; 175 age-matched controls) were genotyped for AM analysis. A case-only admixture score and a mixed χ2(1df) score (MIX) to jointly evaluate both single-nucleotide polymorphism (SNP) and admixture association (P<5.00e-08) were computed using MIXSCORE. In addition, a permutation scheme was implemented to derive multiplicity adjusted P-values (genome-wide 0.05 significance threshold: P<9.46e-05). After empirical multiplicity adjustment, one region on chromosome 6 (52 SNPs, P=4.56e-05) in the HLA class II region was found to exhibit a genome-wide statistically significant increase in European ancestry. This region encodes genes involved in HLA-mediated immune response and these results provide additional evidence for genetic variation influencing HLA-mediated immunity, modulating susceptibility to SAB.
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Affiliation(s)
- D D Cyr
- Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA
| | - A S Allen
- Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - G-J Du
- Duke Center for Genomic and Computational Biology, Durham, NC, USA
| | - F Ruffin
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
| | - C Adams
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
| | - J T Thaden
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
| | - S A Maskarinec
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
| | - M Souli
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA.,School of Medicine, National and Kapodistrian University of Athens, Chaidari, Greece
| | - S Guo
- Dr John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - D M Dykxhoorn
- Dr John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - W K Scott
- Dr John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - V G Fowler
- Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA.,Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA
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Hayeck TJ, Loh PR, Pollack S, Gusev A, Patterson N, Zaitlen NA, Price AL. Mixed Model Association with Family-Biased Case-Control Ascertainment. Am J Hum Genet 2017; 100:31-39. [PMID: 28017371 DOI: 10.1016/j.ajhg.2016.11.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 11/08/2016] [Indexed: 01/06/2023] Open
Abstract
Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ2 = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ2 = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples.
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Rahmani E, Shenhav L, Schweiger R, Yousefi P, Huen K, Eskenazi B, Eng C, Huntsman S, Hu D, Galanter J, Oh SS, Waldenberger M, Strauch K, Grallert H, Meitinger T, Gieger C, Holland N, Burchard EG, Zaitlen N, Halperin E. Genome-wide methylation data mirror ancestry information. Epigenetics Chromatin 2017; 10:1. [PMID: 28149326 PMCID: PMC5267476 DOI: 10.1186/s13072-016-0108-y] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Accepted: 12/14/2016] [Indexed: 11/15/2022] Open
Abstract
Background Genetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data. Results We demonstrate, using three large-cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data. Conclusions EPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io. Electronic supplementary material The online version of this article (doi:10.1186/s13072-016-0108-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Elior Rahmani
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Liat Shenhav
- Department of Statistics, Tel Aviv University, Tel Aviv, Israel
| | - Regev Schweiger
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Paul Yousefi
- Center for Environmental Research and Children's Health (CERCH), School of Public Health, University of California Berkeley, Berkeley, CA USA
| | - Karen Huen
- Center for Environmental Research and Children's Health (CERCH), School of Public Health, University of California Berkeley, Berkeley, CA USA
| | - Brenda Eskenazi
- Center for Environmental Research and Children's Health (CERCH), School of Public Health, University of California Berkeley, Berkeley, CA USA
| | - Celeste Eng
- Department of Medicine, University of California San Francisco, San Francisco, CA USA
| | - Scott Huntsman
- Department of Medicine, University of California San Francisco, San Francisco, CA USA
| | - Donglei Hu
- Department of Medicine, University of California San Francisco, San Francisco, CA USA
| | - Joshua Galanter
- Department of Medicine, University of California San Francisco, San Francisco, CA USA.,Department of Bioengineering and Therapeutic Science, University of California San Francisco, San Francisco, CA USA
| | - Sam S Oh
- Department of Medicine, University of California San Francisco, San Francisco, CA USA
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany.,Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, Munich, Germany.,Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Nina Holland
- Center for Environmental Research and Children's Health (CERCH), School of Public Health, University of California Berkeley, Berkeley, CA USA
| | - Esteban G Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA USA.,Department of Bioengineering and Therapeutic Science, University of California San Francisco, San Francisco, CA USA
| | - Noah Zaitlen
- Department of Medicine, University of California San Francisco, San Francisco, CA USA
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA USA.,Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA USA
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Raj T, Chibnik LB, McCabe C, Wong A, Replogle JM, Yu L, Gao S, Unverzagt FW, Stranger B, Murrell J, Barnes L, Hendrie HC, Foroud T, Krichevsky A, Bennett DA, Hall KS, Evans DA, De Jager PL. Genetic architecture of age-related cognitive decline in African Americans. Neurol Genet 2016; 3:e125. [PMID: 28078323 PMCID: PMC5206965 DOI: 10.1212/nxg.0000000000000125] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 11/09/2016] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To identify genetic risk factors associated with susceptibility to age-related cognitive decline in African Americans (AAs). METHODS We performed a genome-wide association study (GWAS) and an admixture-mapping scan in 3,964 older AAs from 5 longitudinal cohorts; for each participant, we calculated a slope of an individual's global cognitive change from neuropsychological evaluations. We also performed a pathway-based analysis of the age-related cognitive decline GWAS. RESULTS We found no evidence to support the existence of a genomic region which has a strongly different contribution to age-related cognitive decline in African and European genomes. Known Alzheimer disease (AD) susceptibility variants in the ABCA7 and MS4A loci do influence this trait in AAs. Of interest, our pathway-based analyses returned statistically significant results highlighting a shared risk from lipid/metabolism and protein tyrosine signaling pathways between cognitive decline and AD, but the role of inflammatory pathways is polarized, being limited to AD susceptibility. CONCLUSIONS The genetic architecture of aging-related cognitive in AA individuals is largely similar to that of individuals of European descent. In both populations, we note a surprising lack of enrichment for immune pathways in the genetic risk for cognitive decline, despite strong enrichment of these pathways among genetic risk factors for AD.
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Affiliation(s)
- Towfique Raj
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Lori B Chibnik
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Cristin McCabe
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Andus Wong
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Joseph M Replogle
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Lei Yu
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Sujuan Gao
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Frederick W Unverzagt
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Barbara Stranger
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Jill Murrell
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Lisa Barnes
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Hugh C Hendrie
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Tatiana Foroud
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Anna Krichevsky
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - David A Bennett
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Kathleen S Hall
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Denis A Evans
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
| | - Philip L De Jager
- Program in Translational NeuroPsychiatric Genomics (T.R., L.B.C., J.M.R., P.L.D.J.), Institute for the Neurosciences, Departments of Neurology and Psychiatry, Center for Neurologic Disease (T.R., A.W., A.K., P.L.D.J.), Department of Neurology, and Division of Genetics (T.R., L.B.C., P.L.D.J.), Department of Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School (T.R., L.B.C., P.L.D.J.), Boston, MA; Program in Medical and Population genetics (T.R., L.B.C., C.M., J.M.R., P.L.D.J.), The Broad Institute, Cambridge, MA; Section of Genetic Medicine (B.S.), Department of Medicine, and Institute for Genomics and Systems Biology (B.S.), University of Chicago, IL; Indiana University Center for Aging Research (H.C.H.); Department of Psychiatry (F.W.U., H.C.H., K.S.H.), Department of Biostatistics (S.G.), Indiana University School of Medicine; Department of Medical and Molecular Genetics (J.M., T.F.), Indiana University, Indianapolis; Rush Institute for Healthy Aging (D.A.V.), Department of Internal Medicine, Department of Neurology (L.B., D.A.B.), and Rush Alzheimer's Disease Center (L.Y., L.B., D.A.B.), Rush University Medical Center, Chicago, IL. T.R. is currently affiliated with Ronald M. Loeb Center for Alzheimer's Disease, Departments of Neuroscience, and Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York
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Genetic Loci and Novel Discrimination Measures Associated with Blood Pressure Variation in African Americans Living in Tallahassee. PLoS One 2016; 11:e0167700. [PMID: 28002425 PMCID: PMC5176163 DOI: 10.1371/journal.pone.0167700] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Accepted: 11/18/2016] [Indexed: 02/06/2023] Open
Abstract
Sequencing of the human genome and decades of genetic association and linkage studies have dramatically improved our understanding of the etiology of many diseases. However, the multiple causes of complex diseases are still not well understood, in part because genetic and sociocultural risk factors are not typically investigated concurrently. Hypertension is a leading risk factor for cardiovascular disease and afflicts more African Americans than any other racially defined group in the US. Few genetic loci for hypertension have been replicated across populations, which may reflect population-specific differences in genetic variants and/or inattention to relevant sociocultural factors. Discrimination is a salient sociocultural risk factor for poor health and has been associated with hypertension. Here we use a biocultural approach to study blood pressure (BP) variation in African Americans living in Tallahassee, Florida by genotyping over 30,000 single nucleotide polymorphisms (SNPs) and capturing experiences of discrimination using novel measures of unfair treatment of self and others (n = 157). We perform a joint admixture and genetic association analysis for BP that prioritizes regions of the genome with African ancestry. We only report significant SNPs that were confirmed through our simulation analyses, which were performed to determine the false positive rate. We identify eight significant SNPs in five genes that were previously associated with cardiovascular diseases. When we include measures of unfair treatment and test for interactions between SNPs and unfair treatment, we identify a new class of genes involved in multiple phenotypes including psychosocial distress and mood disorders. Our results suggest that inclusion of culturally relevant stress measures, like unfair treatment in African Americans, may reveal new genes and biological pathways relevant to the etiology of hypertension, and may also improve our understanding of the complexity of gene-environment interactions that underlie complex diseases.
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Adhikari K, Mendoza-Revilla J, Chacón-Duque JC, Fuentes-Guajardo M, Ruiz-Linares A. Admixture in Latin America. Curr Opin Genet Dev 2016; 41:106-114. [PMID: 27690355 DOI: 10.1016/j.gde.2016.09.003] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 09/12/2016] [Accepted: 09/13/2016] [Indexed: 12/18/2022]
Abstract
Latin Americans arguably represent the largest recently admixed populations in the world. This reflects a history of massive settlement by immigrants (mostly Europeans and Africans) and their variable admixture with Natives, starting in 1492. This process resulted in the population of Latin America showing an extensive genetic and phenotypic diversity. Here we review how genetic analyses are being applied to examine the demographic history of this population, including patterns of mating, population structure and ancestry. The admixture history of Latin America, and the resulting extensive diversity of the region, represents a natural experiment offering an advantageous setting for genetic association studies. We review how recent analyses in Latin Americans are contributing to elucidating the genetic architecture of human complex traits.
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Affiliation(s)
- Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Javier Mendoza-Revilla
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Juan Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | | | - Andrés Ruiz-Linares
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK.
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Vandenplas J, Calus MPL, Sevillano CA, Windig JJ, Bastiaansen JWM. Assigning breed origin to alleles in crossbred animals. Genet Sel Evol 2016; 48:61. [PMID: 27549177 PMCID: PMC4994281 DOI: 10.1186/s12711-016-0240-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 08/10/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND For some species, animal production systems are based on the use of crossbreeding to take advantage of the increased performance of crossbred compared to purebred animals. Effects of single nucleotide polymorphisms (SNPs) may differ between purebred and crossbred animals for several reasons: (1) differences in linkage disequilibrium between SNP alleles and a quantitative trait locus; (2) differences in genetic backgrounds (e.g., dominance and epistatic interactions); and (3) differences in environmental conditions, which result in genotype-by-environment interactions. Thus, SNP effects may be breed-specific, which has led to the development of genomic evaluations for crossbred performance that take such effects into account. However, to estimate breed-specific effects, it is necessary to know breed origin of alleles in crossbred animals. Therefore, our aim was to develop an approach for assigning breed origin to alleles of crossbred animals (termed BOA) without information on pedigree and to study its accuracy by considering various factors, including distance between breeds. RESULTS The BOA approach consists of: (1) phasing genotypes of purebred and crossbred animals; (2) assigning breed origin to phased haplotypes; and (3) assigning breed origin to alleles of crossbred animals based on a library of assigned haplotypes, the breed composition of crossbred animals, and their SNP genotypes. The accuracy of allele assignments was determined for simulated datasets that include crosses between closely-related, distantly-related and unrelated breeds. Across these scenarios, the percentage of alleles of a crossbred animal that were correctly assigned to their breed origin was greater than 90 %, and increased with increasing distance between breeds, while the percentage of incorrectly assigned alleles was always less than 2 %. For the remaining alleles, i.e. 0 to 10 % of all alleles of a crossbred animal, breed origin could not be assigned. CONCLUSIONS The BOA approach accurately assigns breed origin to alleles of crossbred animals, even if their pedigree is not recorded.
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Affiliation(s)
- Jérémie Vandenplas
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH, Wageningen, The Netherlands.
| | - Mario P L Calus
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH, Wageningen, The Netherlands
| | - Claudia A Sevillano
- Topigs Norsvin Research Center B.V., 6640 AA, Beuningen, The Netherlands.,Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands
| | - Jack J Windig
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH, Wageningen, The Netherlands
| | - John W M Bastiaansen
- Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands
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Genome-wide scans reveal variants at EDAR predominantly affecting hair straightness in Han Chinese and Uyghur populations. Hum Genet 2016; 135:1279-1286. [PMID: 27487801 DOI: 10.1007/s00439-016-1718-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 07/23/2016] [Indexed: 10/21/2022]
Abstract
Hair straightness/curliness is one of the most conspicuous features of human variation and is particularly diverse among populations. A recent genome-wide scan found common variants in the Trichohyalin (TCHH) gene that are associated with hair straightness in Europeans, but different genes might affect this phenotype in other populations. By sampling 2899 Han Chinese, we performed the first genome-wide scan of hair straightness in East Asians, and found EDAR (rs3827760) as the predominant gene (P = 4.67 × 10-16), accounting for 3.66 % of the total variance. The candidate gene approach did not find further significant associations, suggesting that hair straightness may be affected by a large number of genes with subtle effects. Notably, genetic variants associated with hair straightness in Europeans are generally low in frequency in Han Chinese, and vice versa. To evaluate the relative contribution of these variants, we performed a second genome-wide scan in 709 samples from the Uyghur, an admixed population with both eastern and western Eurasian ancestries. In Uyghurs, both EDAR (rs3827760: P = 1.92 × 10-12) and TCHH (rs11803731: P = 1.46 × 10-3) are associated with hair straightness, but EDAR (OR 0.415) has a greater effect than TCHH (OR 0.575). We found no significant interaction between EDAR and TCHH (P = 0.645), suggesting that these two genes affect hair straightness through different mechanisms. Furthermore, haplotype analysis indicates that TCHH is not subject to selection. While EDAR is under strong selection in East Asia, it does not appear to be subject to selection after the admixture in Uyghurs. These suggest that hair straightness is unlikely a trait under selection.
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Rimessi A, Previati M, Nigro F, Wieckowski MR, Pinton P. Mitochondrial reactive oxygen species and inflammation: Molecular mechanisms, diseases and promising therapies. Int J Biochem Cell Biol 2016; 81:281-293. [PMID: 27373679 DOI: 10.1016/j.biocel.2016.06.015] [Citation(s) in RCA: 117] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 06/20/2016] [Accepted: 06/28/2016] [Indexed: 02/06/2023]
Abstract
Over the last few decades, many different groups have been engaged in studies of new roles for mitochondria, particularly the coupling of alterations in the redox pathway with the inflammatory responses involved in different diseases, including Alzheimer's disease, Parkinson's disease, atherosclerosis, cerebral cavernous malformations, cystic fibrosis and cancer. Mitochondrial dysfunction is important in these pathological conditions, suggesting a pivotal role for mitochondria in the coordination of pro-inflammatory signaling from the cytosol and signaling from other subcellular organelles. In this regard, mitochondrial reactive oxygen species are emerging as perfect liaisons that can trigger the assembly and successive activation of large caspase-1- activating complexes known as inflammasomes. This review offers a glimpse into the mechanisms by which inflammasomes are activated by mitochondrial mechanisms, including reactive oxygen species production and mitochondrial Ca2+ uptake, and the roles they can play in several inflammatory pathologies.
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Affiliation(s)
- Alessandro Rimessi
- Dept. of Morphology, Surgery and Experimental Medicine, Section of Pathology, Oncology and Experimental Biology, Laboratory for Technologies of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy
| | - Maurizio Previati
- Dept. of Morphology, Surgery and Experimental Medicine, Section of Human Anatomy and Histology, Laboratory for Technologies of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy
| | - Federica Nigro
- Dept. of Morphology, Surgery and Experimental Medicine, Section of Pathology, Oncology and Experimental Biology, Laboratory for Technologies of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy
| | - Mariusz R Wieckowski
- Dept. of Biochemistry, Nencki Institute of Experimental Biology, Warsaw, Poland.
| | - Paolo Pinton
- Dept. of Morphology, Surgery and Experimental Medicine, Section of Pathology, Oncology and Experimental Biology, Laboratory for Technologies of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy.
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Palmer ND, Divers J, Lu L, Register TC, Carr JJ, Hicks PJ, Smith SC, Xu J, Judd SE, Irvin MR, Gutierrez OM, Bowden DW, Wagenknecht LE, Langefeld CD, Freedman BI. Admixture mapping of serum vitamin D and parathyroid hormone concentrations in the African American-Diabetes Heart Study. Bone 2016; 87:71-7. [PMID: 27032714 PMCID: PMC4862915 DOI: 10.1016/j.bone.2016.01.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 11/17/2015] [Accepted: 01/19/2016] [Indexed: 10/22/2022]
Abstract
Vitamin D and intact parathyroid hormone (iPTH) concentrations differ between individuals of African and European descent and may play a role in observed racial differences in bone mineral density (BMD). These findings suggest that mapping by admixture linkage disequilibrium (MALD) may be informative for identifying genetic variants contributing to these ethnic disparities. Admixture mapping was performed for serum 25-hydroxyvitamin D, 1,25-dihydroxyvitamin D, vitamin D-binding protein (VDBP), bioavailable vitamin D, and iPTH concentrations and computed tomography measured thoracic and lumbar vertebral volumetric BMD in 552 unrelated African Americans with type 2 diabetes from the African American-Diabetes Heart Study. Genotyping was performed using a custom Illumina ancestry informative marker (AIM) panel. For each AIM, the probability of inheriting 0, 1, or 2 copies of a European-derived allele was determined. Non-parametric linkage analysis was performed by testing for association between each AIM using these probabilities among phenotypes, accounting for global ancestry, age, and gender. Fine-mapping of MALD peaks was facilitated by genome-wide association study (GWAS) data. VDBP levels were significantly linked in proximity to the protein coding locus (rs7689609, LOD=11.05). Two loci exhibited significant linkage signals for 1,25-dihydroxyvitamin D on 13q21.2 (rs1622710, LOD=3.20) and 12q13.2 (rs11171526, LOD=3.10). iPTH was significantly linked on 9q31.3 (rs7854368, LOD=3.14). Fine-mapping with GWAS data revealed significant known (rs7041 with VDBP, P=1.38×10(-82)) and novel (rs12741813 and rs10863774 with VDBP, P<6.43×10(-5)) loci with plausible biological roles. Admixture mapping in combination with fine-mapping has focused efforts to identify loci contributing to ethnic differences in vitamin D-related traits.
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Affiliation(s)
- Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Center for Diabetes Research, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Center for Public Health Genomics, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Jasmin Divers
- Center for Public Health Genomics, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Department of Biostatistical Sciences, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Lingyi Lu
- Center for Public Health Genomics, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Department of Biostatistical Sciences, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Thomas C Register
- Department of Pathology, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - J Jeffrey Carr
- Department of Radiology, Vanderbilt University School of Medicine, 2525 West End Ave, Suite 300-B, Nashville, TN 37203, USA.
| | - Pamela J Hicks
- Department of Biochemistry, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - S Carrie Smith
- Department of Biochemistry, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Jianzhao Xu
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Suzanne E Judd
- Department of Biostatistics, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL 35294, USA.
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL 35294, USA.
| | - Orlando M Gutierrez
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL 35294, USA; Department of Medicine, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL, 35294, USA.
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Center for Diabetes Research, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Carl D Langefeld
- Center for Public Health Genomics, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Department of Biostatistical Sciences, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
| | - Barry I Freedman
- Center for Diabetes Research, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Center for Public Health Genomics, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA; Department of Internal Medicine-Section on Nephrology, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA.
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Hejase HA, Liu KJ. Mapping the genomic architecture of adaptive traits with interspecific introgressive origin: a coalescent-based approach. BMC Genomics 2016; 17 Suppl 1:8. [PMID: 26819241 PMCID: PMC4895787 DOI: 10.1186/s12864-015-2298-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Recent studies of eukaryotes including human and Neandertal, mice, and butterflies have highlighted the major role that interspecific introgression has played in adaptive trait evolution. A common question arises in each case: what is the genomic architecture of the introgressed traits? One common approach that can be used to address this question is association mapping, which looks for genotypic markers that have significant statistical association with a trait. It is well understood that sample relatedness can be a confounding factor in association mapping studies if not properly accounted for. Introgression and other evolutionary processes (e.g., incomplete lineage sorting) typically introduce variation among local genealogies, which can also differ from global sample structure measured across all genomic loci. In contrast, state-of-the-art association mapping methods assume fixed sample relatedness across the genome, which can lead to spurious inference. We therefore propose a new association mapping method called Coal-Map, which uses coalescent-based models to capture local genealogical variation alongside global sample structure. Using simulated and empirical data reflecting a range of evolutionary scenarios, we compare the performance of Coal-Map against EIGENSTRAT, a leading association mapping method in terms of its popularity, power, and type I error control. Our empirical data makes use of hundreds of mouse genomes for which adaptive interspecific introgression has recently been described. We found that Coal-Map's performance is comparable or better than EIGENSTRAT in terms of statistical power and false positive rate. Coal-Map's performance advantage was greatest on model conditions that most closely resembled empirically observed scenarios of adaptive introgression. These conditions had: (1) causal SNPs contained in one or a few introgressed genomic loci and (2) varying rates of gene flow - from high rates to very low rates where incomplete lineage sorting dominated as a primary cause of local genealogical variation.
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Affiliation(s)
- Hussein A Hejase
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, MI, USA.
| | - Kevin J Liu
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, MI, USA.
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