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Shore N, Gazi M, Pieczonka C, Heron S, Modh R, Cahn D, Belkoff LH, Berger A, Mazzarella B, Veys J, Idom C, Morris D, Jayram G, Engelman A, Bukkapatnam R, Dato P, Bevan-Thomas R, Cornell R, Wise DR, Hardwick MK, Hernandez RD, Rojahn S, Layman P, Hatchell KE, Heald B, Nussbaum RL, Nielsen SM, Esplin ED. Efficacy of National Comprehensive Cancer Network Guidelines in Identifying Pathogenic Germline Variants Among Unselected Patients with Prostate Cancer: The PROCLAIM Trial. Eur Urol Oncol 2023; 6:477-483. [PMID: 37574391 DOI: 10.1016/j.euo.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/07/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023]
Abstract
BACKGROUND Prostate cancer (PCa) patients with pathogenic/likely pathogenic germline variants (PGVs) in cancer predisposition genes may be eligible for U.S. Food and Drug Administration-approved targeted therapies, clinical trials, or enhanced screening. Studies suggest that eligible patients are missing genetics-informed care due to restrictive testing criteria. OBJECTIVE To establish the prevalence of actionable PGVs among prospectively accrued, unselected PCa patients, stratified by their guideline eligibility. DESIGN, SETTING, AND PARTICIPANTS Consecutive, unselected PCa patients were enrolled at 15 sites in the USA from October 2019 to August 2021, and had multigene cancer panel testing. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Correlates between the prevalence of PGVs and clinician-reported demographic and clinical characteristics were examined. RESULTS AND LIMITATIONS Among 958 patients (median [quartiles] age at diagnosis 65 [60, 71] yr), 627 (65%) had low- or intermediate-risk disease (grade group 1, 2, or 3). A total of 77 PGVs in 17 genes were identified in 74 patients (7.7%, 95% confidence interval [CI] 6.2-9.6%). No significant difference was found in the prevalence of PGVs among patients who met the 2019 National Comprehensive Cancer Network Prostate criteria (8.8%, 43/486, 95% CI 6.6-12%) versus those who did not (6.6%, 31/472, 95% CI 4.6-9.2%; odds ratio 1.38, 95% CI 0.85-2.23), indicating that these criteria would miss 42% of patients (31/74, 95% CI 31-53%) with PGVs. The criteria were less effective at predicting PGVs in patients from under-represented populations. Most PGVs (81%, 60/74) were potentially clinically actionable. Limitations include the inability to stratify analyses based on individual ethnicity due to low numbers of non-White patients with PGVs. CONCLUSIONS Our results indicate that almost half of PCa patients with PGVs are missed by current testing guidelines. Comprehensive germline genetic testing should be offered to all patients with PCa. PATIENT SUMMARY One in 13 patients with prostate cancer carries an inherited variant that may be actionable for the patient's current care or prevention of future cancer, and could benefit from expanded testing criteria.
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Affiliation(s)
- Neal Shore
- Carolina Urologic Research Center, Myrtle Beach, SC, USA.
| | - Mukaram Gazi
- University Urology Associates of New Jersey, Hamilton, NJ, USA
| | | | - Sean Heron
- Advanced Urology Institute, St. Petersburg, FL, USA
| | - Rishi Modh
- Advanced Urology Institute, St. Petersburg, FL, USA
| | | | | | - Aaron Berger
- Associated Urological Specialists, Chicago Ridge, IL, USA
| | | | | | | | | | | | | | | | - Paul Dato
- Genesis Healthcare Partners, San Diego, CA, USA
| | | | | | - David R Wise
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | | | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | | | | | | | | | - Robert L Nussbaum
- Invitae Corporation, San Francisco, CA, USA; Volunteer Faculty, University of California San Francisco, San Francisco, CA, USA
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Torgerson D, Guardado M, Steurer M, Chapin C, Hernandez RD, Ballard PL. The hydrocortisone-responsive urinary metabolome of premature infants. Pediatr Res 2023; 94:1317-1326. [PMID: 37138028 PMCID: PMC10589081 DOI: 10.1038/s41390-023-02610-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/21/2023] [Accepted: 04/01/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND Extremely premature infants are at risk for circulatory collapse or respiratory failure that are often treated with hydrocortisone (HC); however, there is no information on the metabolic consequences of this therapy. METHODS Longitudinal urine samples from infants <28 weeks gestation in the Trial of Late Surfactant were analyzed by untargeted UHPLC:MS/MS. Fourteen infants who received a tapering course of HC beginning at 3 mg/kg/day for ≥9 days were compared to 14 matched control infants. A secondary cross-sectional analysis by logistic regression used urines from 314 infants. RESULTS Of 1145 urinary metabolites detected, abundance of 219, representing all the major biochemical pathways, changed at p < 0.05 in the HC-treated group with 90% decreasing; 3 cortisol derivatives increased ~2-fold with HC therapy. Only 11% of regulated metabolites remained responsive at the lowest HC dose. Regulated metabolites included two steroids and thiamin that are associated with lung inflammation in infants. HC responsiveness was confirmed in 57% of metabolites by cross-sectional analysis. CONCLUSIONS HC treatment of premature infants influenced in a dose-dependent manner abundance of 19% of identified urinary metabolites of diverse biochemical systems, primarily reducing concentrations. These findings indicate that exposure to HC reversibly impacts the nutritional status of premature infants. IMPACT Hydrocortisone treatment of premature infants with respiratory failure or circulatory collapse alters levels of a subset of urinary metabolites representing all major biochemical pathways. This is the first description of the scope, magnitude, timing and reversibility of metabolomic changes in infants in response to hydrocortisone, and it confirms corticosteroid regulation of three biochemicals that are associated with lung inflammatory status. The findings indicate a dose-dependency of hydrocortisone for metabolomic and anti-inflammatory effects, that prolonged therapy may lower the supply of many nutrients, and that monitoring concentrations of cortisol and inflammation markers may be a useful clinical approach during corticosteroid therapy.
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Affiliation(s)
- Dara Torgerson
- Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Miguel Guardado
- Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Martina Steurer
- Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Cheryl Chapin
- Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Ryan D Hernandez
- Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Philip L Ballard
- Pediatrics, University of California San Francisco, San Francisco, CA, USA.
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Cunnane BT, Sinha U, Malis V, Hernandez RD, Smitaman E, Sinha S. Effect of different ankle joint positions on medial gastrocnemius muscle fiber strains during isometric plantarflexion. Sci Rep 2023; 13:14986. [PMID: 37696877 PMCID: PMC10495375 DOI: 10.1038/s41598-023-41127-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023] Open
Abstract
Muscle force production is influenced by muscle fiber and aponeurosis architecture. This prospective cohort study utilizes special MR imaging sequences to examine the structure-function in-vivo in the Medial Gastrocnemius (MG) at three-ankle angles (dorsiflexion, plantar flexion-low and high) and two sub-maximal levels of maximum voluntary contraction (25% and 50%MVC). The study was performed on 6 young male participants. Muscle fiber and aponeurosis strain, fiber strain normalized to force, fiber length and pennation angle (at rest and peak contraction) were analyzed for statistical differences between ankle positions and %MVC. A two-way repeated measures ANOVA and post hoc Bonferroni-adjusted tests were conducted for normal data. A related samples test with Friedman's 2-way ANOVA by ranks with corrections for multiple comparisons was conducted for non-normal data. The dorsiflexed ankle position generated significantly higher force with lower fiber strain than the plantarflexed positions. Sarcomere length extracted from muscle fiber length at each ankle angle was used to track the location on the Force-Length curve and showed the MG operates on the curve's ascending limb. Muscle force changes predicted from the F-L curve going from dorsi- to plantarflexion was less than that experimentally observed suggesting other determinants of force changes with ankle position.
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Affiliation(s)
| | - Usha Sinha
- Physics, San Diego State University, San Diego, CA, USA
| | - Vadim Malis
- Muscle Imaging and Modeling Lab, Dept. of Radiology, UC San Diego, 8939 Villa La Jolla, San Diego, CA, 92121, USA
| | | | | | - Shantanu Sinha
- Muscle Imaging and Modeling Lab, Dept. of Radiology, UC San Diego, 8939 Villa La Jolla, San Diego, CA, 92121, USA.
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Guardado M, Steurer M, Chapin C, Hernandez RD, Ballard PL, Torgerson D. The Urinary Metabolomic Fingerprint in Extremely Preterm Infants on Total Parenteral Nutrition vs. Enteral Feeds. Metabolites 2023; 13:971. [PMID: 37755251 PMCID: PMC10537655 DOI: 10.3390/metabo13090971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/28/2023] Open
Abstract
Total Parenteral Nutrition (TPN), which uses intravenous administration of nutrients, minerals and vitamins, is essential for sustaining premature infants until they transition to enteral feeds, but there is limited information on metabolomic differences between infants on TPN and enteral feeds. We performed untargeted global metabolomics on urine samples collected between 23-30 days of life from 314 infants born <29 weeks gestational age from the TOLSURF and PROP cohorts. Principal component analysis across all metabolites showed a separation of infants solely on TPN compared to infants who had transitioned to enteral feeds, indicating global metabolomic differences between infants based on feeding status. Among 913 metabolites that passed quality control filters, 609 varied in abundance between infants on TPN vs. enteral feeds at p < 0.05. Of these, 88% were in the direction of higher abundance in the urine of infants on enteral feeds. In a subset of infants in a longitudinal analysis, both concurrent and delayed changes in metabolite levels were observed with the initiation of enteral feeds. These infants had higher concentrations of essential amino acids, lipids, and vitamins, which are necessary for growth and development, suggesting the nutritional benefit of an enteral feeding regimen.
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Affiliation(s)
- Miguel Guardado
- Biological and Medical Informatics Graduate Program, School of Medicine, Mission Bay Campus, University of California, San Francisco, CA 94134, USA
- Department of Epidemiology and Biostatistics, School of Medicine, Mission Bay Campus, University of California, San Francisco, CA 94158, USA;
- Department of Bioengineering and Therapeutic Sciences, School of Medicine, Mission Bay Campus, University of California, San Francisco, CA 94134, USA;
| | - Martina Steurer
- Department of Pediatrics, School of Medicine, Mission Bay & Parnassus Campuses, University of California, San Francisco, CA 94158, USA; (M.S.); (C.C.); (P.L.B.)
| | - Cheryl Chapin
- Department of Pediatrics, School of Medicine, Mission Bay & Parnassus Campuses, University of California, San Francisco, CA 94158, USA; (M.S.); (C.C.); (P.L.B.)
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, School of Medicine, Mission Bay Campus, University of California, San Francisco, CA 94134, USA;
| | - Philip L. Ballard
- Department of Pediatrics, School of Medicine, Mission Bay & Parnassus Campuses, University of California, San Francisco, CA 94158, USA; (M.S.); (C.C.); (P.L.B.)
| | - Dara Torgerson
- Department of Epidemiology and Biostatistics, School of Medicine, Mission Bay Campus, University of California, San Francisco, CA 94158, USA;
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Laurent SA, Strauli NB, Eggers EL, Wu H, Michel B, Demuth S, Palanichamy A, Wilson MR, Sirota M, Hernandez RD, Cree BAC, Herman AE, von Büdingen HC. Effect of Ocrelizumab on B- and T-Cell Receptor Repertoire Diversity in Patients With Relapsing Multiple Sclerosis From the Randomized Phase III OPERA Trial. Neurol Neuroimmunol Neuroinflamm 2023; 10:10/4/e200118. [PMID: 37094998 PMCID: PMC10136682 DOI: 10.1212/nxi.0000000000200118] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 02/22/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND AND OBJECTIVES The B cell-depleting anti-CD20 antibody ocrelizumab (OCR) effectively reduces MS disease activity and slows disability progression. Given the role of B cells as antigen-presenting cells, the primary goal of this study was to evaluate the effect of OCR on the T-cell receptor repertoire diversity. METHODS To examine whether OCR substantially alters the molecular diversity of the T-cell receptor repertoire, deep immune repertoire sequencing (RepSeq) of CD4+ and CD8+ T-cell receptor β-chain variable regions was performed on longitudinal blood samples. The IgM and IgG heavy chain variable region repertoire was also analyzed to characterize the residual B-cell repertoire under OCR treatment. RESULTS Peripheral blood samples for RepSeq were obtained from 8 patients with relapsing MS enrolled in the OPERA I trial over a period of up to 39 months. Four patients each were treated with OCR or interferon β1-a during the double-blind period of OPERA I. All patients received OCR during the open-label extension. The diversity of the CD4+/CD8+ T-cell repertoires remained unaffected in OCR-treated patients. The expected OCR-associated B-cell depletion was mirrored by reduced B-cell receptor diversity in peripheral blood and a shift in immunoglobulin gene usage. Despite deep B-cell depletion, longitudinal persistence of clonally related B-cells was observed. DISCUSSION Our data illustrate that the diversity of CD4+/CD8+ T-cell receptor repertoires remained unaltered in OCR-treated patients with relapsing MS. Persistence of a highly diverse T-cell repertoire suggests that aspects of adaptive immunity remain intact despite extended anti-CD20 therapy. TRIAL REGISTRATION INFORMATION This is a substudy (BE29353) of the OPERA I (WA21092; NCT01247324) trial. Date of registration, November 23, 2010; first patient enrollment, August 31, 2011.
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Affiliation(s)
- Sarah A Laurent
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Nicolas B Strauli
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Erica L Eggers
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Hao Wu
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Brady Michel
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Stanislas Demuth
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Arumugam Palanichamy
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Michael R Wilson
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Marina Sirota
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Ryan D Hernandez
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Bruce Anthony Campbell Cree
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - Ann E Herman
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA
| | - H-Christian von Büdingen
- From the Department of Neurology (S.A.L., E.L.E., H.W., B.M., S.D., A.P., M.R.W., B.A.C.C., H.-C.B.), Weill Institute for Neurosciences; Biomedical Sciences Graduate Program (N.B.S.); Bakar Computational Health Sciences Institute and Department of Pediatrics (M.S.); Department of Bioengineering and Therapeutic Sciences (R.D.H.), University of California, San Francisco, CA; Department of Human Genetics (R.D.H.), McGill University, Montreal, QC, Canada; and OMNI Biomarker Development (A.E.H.), Genentech, Inc., South San Francisco, CA.
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Wainschtein P, Jain D, Zheng Z, Cupples LA, Shadyab AH, McKnight B, Shoemaker BM, Mitchell BD, Psaty BM, Kooperberg C, Liu CT, Albert CM, Roden D, Chasman DI, Darbar D, Lloyd-Jones DM, Arnett DK, Regan EA, Boerwinkle E, Rotter JI, O'Connell JR, Yanek LR, de Andrade M, Allison MA, McDonald MLN, Chung MK, Fornage M, Chami N, Smith NL, Ellinor PT, Vasan RS, Mathias RA, Loos RJF, Rich SS, Lubitz SA, Heckbert SR, Redline S, Guo X, Chen YDI, Laurie CA, Hernandez RD, McGarvey ST, Goddard ME, Laurie CC, North KE, Lange LA, Weir BS, Yengo L, Yang J, Visscher PM. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat Genet 2022; 54:263-273. [PMID: 35256806 PMCID: PMC9119698 DOI: 10.1038/s41588-021-00997-7] [Citation(s) in RCA: 112] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 12/01/2021] [Indexed: 12/20/2022]
Abstract
Analyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.
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Affiliation(s)
- Pierrick Wainschtein
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Barbara McKnight
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Benjamin M Shoemaker
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Christine M Albert
- Harvard Medical School, Boston, MA, USA
- Division of Cardiovascular, Brigham and Women's Hospital, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dan Roden
- Departments of Medicine, Pharmacology and Bioinformatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dawood Darbar
- Department of Medicine, University of Illinois-Chicago, Chicago, IL, USA
| | | | - Donna K Arnett
- Dean's Office, College of Public Health, University of Kentucky, Lexington, KY, USA
| | | | - Eric Boerwinkle
- Health Science Center, University of Texas, Houston, TX, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Lisa R Yanek
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mariza de Andrade
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Matthew A Allison
- Department of Family Medicine, University of California San Diego, La Jolla, CA, USA
| | - Merry-Lynn N McDonald
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mina K Chung
- Department of Molecular Cardiology, Cleveland Clinic, Cleveland, OH, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nathalie Chami
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Institute for Child Health and Development, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicholas L Smith
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA
| | - Patrick T Ellinor
- Harvard Medical School, Boston, MA, USA
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Sections of Preventive Medicine and Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Divisions of Allergy and Clinical Immunology and General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Institute for Child Health and Development, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Steven A Lubitz
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Susan R Heckbert
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Y -D Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Cecelia A Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Stephen T McGarvey
- International Health Institute, Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Michael E Goddard
- Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kari E North
- Department of Epidemiology and Carolina Center of Genome Sciences, University of North Carolina, Chapel Hill, NC, USA
| | - Leslie A Lange
- Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Bruce S Weir
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
- School of Life Sciences, Westlake University, Hangzhou Zhejiang, China.
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.
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7
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Seplyarskiy VB, Soldatov RA, Koch E, McGinty RJ, Goldmann JM, Hernandez RD, Barnes K, Correa A, Burchard EG, Ellinor PT, McGarvey ST, Mitchell BD, Vasan RS, Redline S, Silverman E, Weiss ST, Arnett DK, Blangero J, Boerwinkle E, He J, Montgomery C, Rao DC, Rotter JI, Taylor KD, Brody JA, Chen YDI, de Las Fuentes L, Hwu CM, Rich SS, Manichaikul AW, Mychaleckyj JC, Palmer ND, Smith JA, Kardia SLR, Peyser PA, Bielak LF, O'Connor TD, Emery LS, Gilissen C, Wong WSW, Kharchenko PV, Sunyaev S. Population sequencing data reveal a compendium of mutational processes in the human germ line. Science 2021; 373:1030-1035. [PMID: 34385354 PMCID: PMC9217108 DOI: 10.1126/science.aba7408] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 07/14/2021] [Indexed: 12/16/2022]
Abstract
Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.
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Affiliation(s)
- Vladimir B Seplyarskiy
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ruslan A Soldatov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Evan Koch
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ryan J McGinty
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jakob M Goldmann
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Ryan D Hernandez
- Quantitative Life Sciences, McGill University, Montreal, QC, Canada
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Kathleen Barnes
- Department of Medicine, University of Colorado Denver, Aurora, CO 80045, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephen T McGarvey
- International Health Institute, Brown University, Providence, RI, USA
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Anthropology, Brown University, Providence, RI, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Ramachandran S Vasan
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Edwin Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott T Weiss
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Donna K Arnett
- Department of Epidemiology, University of Kentucky, Lexington, KY, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Eric Boerwinkle
- University of Texas Health Science Center at Houston, Houston, TX, USA
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
- Tulane University Translational Science Institute, Tulane University, New Orleans, LA , USA
| | - Courtney Montgomery
- Division of Genomics and Data Science, Department of Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Lisa de Las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
| | - Chii-Min Hwu
- National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA
- Survey Research Center, Institute for Social Research, University of Michigan 426 Thompson St, Room Ann Arbor, MI 48104, USA
| | - Sharon L R Kardia
- Survey Research Center, Institute for Social Research, University of Michigan 426 Thompson St, Room Ann Arbor, MI 48104, USA
| | - Patricia A Peyser
- Survey Research Center, Institute for Social Research, University of Michigan 426 Thompson St, Room Ann Arbor, MI 48104, USA
| | - Lawrence F Bielak
- Survey Research Center, Institute for Social Research, University of Michigan 426 Thompson St, Room Ann Arbor, MI 48104, USA
| | - Timothy D O'Connor
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA
| | - Leslie S Emery
- University of Washington Department of Biostatistics, Seattle, WA 98195, USA
| | - Christian Gilissen
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Wendy S W Wong
- Inova Translational Medicine Institute (ITMI), Inova Health Systems, Falls Church, VA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shamil Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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8
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Claesen J, Spagnolo JB, Ramos SF, Kurita KL, Byrd AL, Aksenov AA, Melnik AV, Wong WR, Wang S, Hernandez RD, Donia MS, Dorrestein PC, Kong HH, Segre JA, Linington RG, Fischbach MA, Lemon KP. A Cutibacterium acnes antibiotic modulates human skin microbiota composition in hair follicles. Sci Transl Med 2021; 12:12/570/eaay5445. [PMID: 33208503 DOI: 10.1126/scitranslmed.aay5445] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 07/17/2019] [Accepted: 10/30/2020] [Indexed: 12/11/2022]
Abstract
The composition of the skin microbiota varies widely among individuals when sampled at the same body site. A key question is which molecular factors determine strain-level variability within sub-ecosystems of the skin microbiota. Here, we used a genomics-guided approach to identify an antibacterial biosynthetic gene cluster in Cutibacterium acnes (formerly Propionibacterium acnes), a human skin commensal bacterium that is widely distributed across individuals and skin sites. Experimental characterization of this biosynthetic gene cluster resulted in identification of a new thiopeptide antibiotic, cutimycin. Analysis of individual human skin hair follicles revealed that cutimycin contributed to the ecology of the skin hair follicle microbiota and helped to reduce colonization of skin hair follicles by Staphylococcus species.
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Affiliation(s)
- Jan Claesen
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jennifer B Spagnolo
- Microbiology, Forsyth Institute, Cambridge, MA 02142, USA.,Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA 02115, USA
| | | | - Kenji L Kurita
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Allyson L Byrd
- Microbial Genomics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alexander A Aksenov
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, and Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Alexey V Melnik
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, and Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Weng R Wong
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Shuo Wang
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08540, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA.,Department of Human Genetics, McGill University and Genome Quebec Innovation Center, Montreal, QC H3A 0C7, Canada
| | - Mohamed S Donia
- Department of Molecular Biology, Princeton University, Princeton, NJ 08540, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, and Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Heidi H Kong
- Dermatology Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Julia A Segre
- Microbial Genomics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Michael A Fischbach
- Department of Bioengineering and ChEM-H, Stanford University, Stanford, CA 94305, USA.
| | - Katherine P Lemon
- Microbiology, Forsyth Institute, Cambridge, MA 02142, USA. .,Division of Infectious Diseases, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology & Microbiology, Baylor College of Medicine, Houston, TX 77030, USA.,Section of Infectious Diseases, Department of Pediatrics, Texas Children's Hospital and Baylor College of Medicine, Houston, TX 77030, USA
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9
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Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM, Pitsillides AN, LeFaive J, Lee SB, Tian X, Browning BL, Das S, Emde AK, Clarke WE, Loesch DP, Shetty AC, Blackwell TW, Smith AV, Wong Q, Liu X, Conomos MP, Bobo DM, Aguet F, Albert C, Alonso A, Ardlie KG, Arking DE, Aslibekyan S, Auer PL, Barnard J, Barr RG, Barwick L, Becker LC, Beer RL, Benjamin EJ, Bielak LF, Blangero J, Boehnke M, Bowden DW, Brody JA, Burchard EG, Cade BE, Casella JF, Chalazan B, Chasman DI, Chen YDI, Cho MH, Choi SH, Chung MK, Clish CB, Correa A, Curran JE, Custer B, Darbar D, Daya M, de Andrade M, DeMeo DL, Dutcher SK, Ellinor PT, Emery LS, Eng C, Fatkin D, Fingerlin T, Forer L, Fornage M, Franceschini N, Fuchsberger C, Fullerton SM, Germer S, Gladwin MT, Gottlieb DJ, Guo X, Hall ME, He J, Heard-Costa NL, Heckbert SR, Irvin MR, Johnsen JM, Johnson AD, Kaplan R, Kardia SLR, Kelly T, Kelly S, Kenny EE, Kiel DP, Klemmer R, Konkle BA, Kooperberg C, Köttgen A, Lange LA, Lasky-Su J, Levy D, Lin X, Lin KH, Liu C, Loos RJF, Garman L, Gerszten R, Lubitz SA, Lunetta KL, Mak ACY, Manichaikul A, Manning AK, Mathias RA, McManus DD, McGarvey ST, Meigs JB, Meyers DA, Mikulla JL, Minear MA, Mitchell BD, Mohanty S, Montasser ME, Montgomery C, Morrison AC, Murabito JM, Natale A, Natarajan P, Nelson SC, North KE, O'Connell JR, Palmer ND, Pankratz N, Peloso GM, Peyser PA, Pleiness J, Post WS, Psaty BM, Rao DC, Redline S, Reiner AP, Roden D, Rotter JI, Ruczinski I, Sarnowski C, Schoenherr S, Schwartz DA, Seo JS, Seshadri S, Sheehan VA, Sheu WH, Shoemaker MB, Smith NL, Smith JA, Sotoodehnia N, Stilp AM, Tang W, Taylor KD, Telen M, Thornton TA, Tracy RP, Van Den Berg DJ, Vasan RS, Viaud-Martinez KA, Vrieze S, Weeks DE, Weir BS, Weiss ST, Weng LC, Willer CJ, Zhang Y, Zhao X, Arnett DK, Ashley-Koch AE, Barnes KC, Boerwinkle E, Gabriel S, Gibbs R, Rice KM, Rich SS, Silverman EK, Qasba P, Gan W, Papanicolaou GJ, Nickerson DA, Browning SR, Zody MC, Zöllner S, Wilson JG, Cupples LA, Laurie CC, Jaquish CE, Hernandez RD, O'Connor TD, Abecasis GR. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 2021; 590:290-299. [PMID: 33568819 PMCID: PMC7875770 DOI: 10.1038/s41586-021-03205-y] [Citation(s) in RCA: 801] [Impact Index Per Article: 267.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 01/07/2021] [Indexed: 02/08/2023]
Abstract
The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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Affiliation(s)
- Daniel Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Daniel N Harris
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Michael D Kessler
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jedidiah Carlson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zachary A Szpiech
- Department of Biology, Pennsylvania State University, University Park, PA, USA
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA
| | - Raul Torres
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah A Gagliano Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | | | - Hyun Min Kang
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | - Jonathon LeFaive
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Seung-Been Lee
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Xiaowen Tian
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Brian L Browning
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Sayantan Das
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | | | - Douglas P Loesch
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Amol C Shetty
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiaoming Liu
- USF Genomics, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Dean M Bobo
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Paul L Auer
- Zilber School of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
| | | | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
| | | | | | - Rebecca L Beer
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emelia J Benjamin
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Brian E Cade
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James F Casella
- Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
- Division of Pediatric Hematology, Johns Hopkins University, Baltimore, MD, USA
| | - Brandon Chalazan
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Mina K Chung
- Department of Cardiovascular Medicine, Heart & Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Clary B Clish
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Dawood Darbar
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Michelle Daya
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Susan K Dutcher
- McDonnell Genome Institute, Washington University, St Louis, MO, USA
- Department of Genetics, Washington University, St Louis, MO, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Diane Fatkin
- Molecular Cardiology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- Faculty of Medicine, University of New South Wales, Kensington, New South Wales, Australia
- Cardiology Department, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
| | - Tasha Fingerlin
- National Jewish Health, Center for Genes, Environment and Health, Denver, CO, USA
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Mark T Gladwin
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel J Gottlieb
- VA Boston Healthcare System, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael E Hall
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
- Tulane University Translational Science Institute, Tulane University, New Orleans, LA, USA
| | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jill M Johnsen
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Andrew D Johnson
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Robert Kaplan
- Albert Einstein College of Medicine, New York, NY, USA
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
| | - Shannon Kelly
- Department of Epidemiology, Vitalant Research Institute, San Francisco, CA, USA
- Department of Pediatrics, UCSF Benioff Children's Hospital, Oakland, CA, USA
- Division of Pediatric Hematology, UCSF Benioff Children's Hospital, Oakland, CA, USA
| | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Douglas P Kiel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert Klemmer
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Barbara A Konkle
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Leslie A Lange
- Department of Medicine, University of Colorado at Denver, Aurora, CO, USA
| | - Jessica Lasky-Su
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Daniel Levy
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Xihong Lin
- Biostatistics and Statistics, Harvard University, Boston, MA, USA
| | - Keng-Han Lin
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lori Garman
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | | | | | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Alisa K Manning
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David D McManus
- Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Stephen T McGarvey
- International Health Institute, Brown University, Providence, RI, USA
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Anthropology, Brown University, Providence, RI, USA
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, The Broad Institute of MIT and Harvard, Boston, MA, USA
| | | | - Julie L Mikulla
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mollie A Minear
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Sanghamitra Mohanty
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
- Department of Internal Medicine, Dell Medical School, Austin, TX, USA
| | - May E Montasser
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Courtney Montgomery
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joanne M Murabito
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Andrea Natale
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sarah C Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jacob Pleiness
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - D C Rao
- Division of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Dan Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sebastian Schoenherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Jeong-Sun Seo
- Precision Medicine Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Macrogen Inc, Seoul, Republic of Korea
- Gong Wu Genomic Medicine Institute, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center at San Antonio, San Antonio, TX, USA
| | - Vivien A Sheehan
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Wayne H Sheu
- Taichung Veterans General Hospital Taiwan, Taichung City, Taiwan
| | | | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | | | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - David J Van Den Berg
- Center for Genetic Epidemiology, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ramachandran S Vasan
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Daniel E Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bruce S Weir
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Scott T Weiss
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | | | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine-Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Yingze Zhang
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xutong Zhao
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Donna K Arnett
- Department of Epidemiology, University of Kentucky, Lexington, KY, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Kathleen C Barnes
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Eric Boerwinkle
- University of Texas Health Science Center at Houston, Houston, TX, USA
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Stacey Gabriel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Pankaj Qasba
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Weiniu Gan
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - George J Papanicolaou
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Northwest Genomics Center, Seattle, WA, USA
- Brotman Baty Institute, Seattle, WA, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- Framingham Heart Study, Framingham, MA, USA.
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Cashell E Jaquish
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Timothy D O'Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Gonçalo R Abecasis
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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Spear ML, Diaz-Papkovich A, Ziv E, Yracheta JM, Gravel S, Torgerson DG, Hernandez RD. Recent shifts in the genomic ancestry of Mexican Americans may alter the genetic architecture of biomedical traits. eLife 2020; 9:e56029. [PMID: 33372659 PMCID: PMC7771964 DOI: 10.7554/elife.56029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 12/13/2020] [Indexed: 11/13/2022] Open
Abstract
People in the Americas represent a diverse continuum of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry increased by an average of ~20% spanning 1940s-1990s in Mexican Americans. These patterns result from complex interactions between several population and cultural factors which shaped patterns of genetic variation and influenced the genetic architecture of complex traits in Mexican Americans. We show for height how polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a need to improve our understanding of admixed populations.
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Affiliation(s)
- Melissa L Spear
- Biomedical Sciences Graduate Program, University of California, San FranciscoSan FranciscoUnited States
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
- McGill Genome Centre, McGill UniversityMontrealCanada
- Department of Human Genetics, McGill UniversityMontrealCanada
| | - Alex Diaz-Papkovich
- McGill Genome Centre, McGill UniversityMontrealCanada
- Quantitative Life Sciences Program, McGill UniversityMontrealCanada
| | - Elad Ziv
- Division of General Internal Medicine, University of California, San FranciscoSan FranciscoUnited States
- Department of Medicine, University of California, San FranciscoSan FranciscoUnited States
- Institute of Human Genetics, University of California, San FranciscoSan FranciscoUnited States
- Helen Diller Family Comprehensive Cancer Center, University of California, San FranciscoSan FranciscoUnited States
| | - Joseph M Yracheta
- Native BioData ConsortiumEagle ButteUnited States
- Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Simon Gravel
- McGill Genome Centre, McGill UniversityMontrealCanada
- Department of Human Genetics, McGill UniversityMontrealCanada
| | - Dara G Torgerson
- McGill Genome Centre, McGill UniversityMontrealCanada
- Department of Human Genetics, McGill UniversityMontrealCanada
- Department of Epidemiology and Biostatistics University of California, San FranciscoSan FranciscoUnited States
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
- McGill Genome Centre, McGill UniversityMontrealCanada
- Department of Human Genetics, McGill UniversityMontrealCanada
- Institute of Human Genetics, University of California, San FranciscoSan FranciscoUnited States
- Bakar Computational Health Sciences Institute, University of California, San FranciscoSan FranciscoUnited States
- Quantitative Biosciences Institute, University of California, San FranciscoSan FranciscoUnited States
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Tong DMH, Hernandez RD. Population genetic simulation study of power in association testing across genetic architectures and study designs. Genet Epidemiol 2020; 44:90-103. [PMID: 31587362 PMCID: PMC6980249 DOI: 10.1002/gepi.22264] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 08/26/2019] [Accepted: 09/16/2019] [Indexed: 12/22/2022]
Abstract
While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.
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Affiliation(s)
- Dominic M. H. Tong
- University of California, Berkeley ‐ University of California, San Francisco Graduate Program in BioengineeringSan FranciscoCalifornia
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCalifornia
- Department of Human GeneticsMcGill UniversityMontrealCanada
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12
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Szpiech ZA, Mak ACY, White MJ, Hu D, Eng C, Burchard EG, Hernandez RD. Ancestry-Dependent Enrichment of Deleterious Homozygotes in Runs of Homozygosity. Am J Hum Genet 2019; 105:747-762. [PMID: 31543216 PMCID: PMC6817522 DOI: 10.1016/j.ajhg.2019.08.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 08/27/2019] [Indexed: 12/20/2022] Open
Abstract
Runs of homozygosity (ROH) are important genomic features that manifest when an individual inherits two haplotypes that are identical by descent. Their length distributions are informative about population history, and their genomic locations are useful for mapping recessive loci contributing to both Mendelian and complex disease risk. We have previously shown that ROH, and especially long ROH that are likely the result of recent parental relatedness, are enriched for homozygous deleterious coding variation in a worldwide sample of outbred individuals. However, the distribution of ROH in admixed populations and their relationship to deleterious homozygous genotypes is understudied. Here we analyze whole-genome sequencing data from 1,441 unrelated individuals from self-identified African American, Puerto Rican, and Mexican American populations. These populations are three-way admixed between European, African, and Native American ancestries and provide an opportunity to study the distribution of deleterious alleles partitioned by local ancestry and ROH. We re-capitulate previous findings that long ROH are enriched for deleterious variation genome-wide. We then partition by local ancestry and show that deleterious homozygotes arise at a higher rate when ROH overlap African ancestry segments than when they overlap European or Native American ancestry segments of the genome. These results suggest that, while ROH on any haplotype background are associated with an inflation of deleterious homozygous variation, African haplotype backgrounds may play a particularly important role in the genetic architecture of complex diseases for admixed individuals, highlighting the need for further study of these populations.
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Affiliation(s)
- Zachary A Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA; Department of Biological Sciences, Auburn University, Auburn, AL 36842, USA.
| | - Angel C Y Mak
- Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA
| | - Marquitta J White
- Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA
| | - Donglei Hu
- Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA
| | - Celeste Eng
- Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA
| | - Esteban G Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA; Department of Human Genetics, McGill University, Montreal, QC H3A 0G1, Canada; Genome Quebec Innovation Center, McGill University, Montreal, QC H3A 0G1, Canada.
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13
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Wainschtein P, Jain DP, Yengo L, Zheng Z, Cupples LA, Shadyab AH, McKnight B, Shoemaker BM, Mitchell BD, Psaty BM, Kooperberg C, Roden D, Darbar D, Arnett DK, Regan EA, Boerwinkle E, Rotter JI, Allison MA, McDonald MLN, Chung MK, Smith NL, Ellinor PT, Vasan RS, Mathias RA, Rich SS, Heckbert SR, Redline S, Guo X, Chen YDI, Liu CT, Andrade MD, Yanek LR, Albert CM, Hernandez RD, McGarvey ST, North KE, Lange LA, Weir BS, Laurie CC, Yang J, Visscher PM. Recovery of trait heritability from whole genome sequence data. ACTA ACUST UNITED AC 2019. [DOI: 10.1530/ey.16.14.15] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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14
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Daya M, Rafaels N, Brunetti TM, Chavan S, Levin AM, Shetty A, Gignoux CR, Boorgula MP, Wojcik G, Campbell M, Vergara C, Torgerson DG, Ortega VE, Doumatey A, Johnston HR, Acevedo N, Araujo MI, Avila PC, Belbin G, Bleecker E, Bustamante C, Caraballo L, Cruz A, Dunston GM, Eng C, Faruque MU, Ferguson TS, Figueiredo C, Ford JG, Gan W, Gourraud PA, Hansel NN, Hernandez RD, Herrera-Paz EF, Jiménez S, Kenny EE, Knight-Madden J, Kumar R, Lange LA, Lange EM, Lizee A, Maul P, Maul T, Mayorga A, Meyers D, Nicolae DL, O'Connor TD, Oliveira RR, Olopade CO, Olopade O, Qin ZS, Rotimi C, Vince N, Watson H, Wilks RJ, Wilson JG, Salzberg S, Ober C, Burchard EG, Williams LK, Beaty TH, Taub MA, Ruczinski I, Mathias RA, Barnes KC. Author Correction: Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations. Nat Commun 2019; 10:4082. [PMID: 31484942 PMCID: PMC6726619 DOI: 10.1038/s41467-019-12158-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Michelle Daya
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Nicholas Rafaels
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Tonya M Brunetti
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Sameer Chavan
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Aniket Shetty
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | | | | | - Genevieve Wojcik
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Monica Campbell
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Candelaria Vergara
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21224, USA
| | - Dara G Torgerson
- Department of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Victor E Ortega
- Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, USA
| | - Ayo Doumatey
- Center for Research on Genomics & Global Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | | | - Nathalie Acevedo
- Institute for Immunological Research, Universidad de Cartagena, Cartagena, 130000, Colombia
| | - Maria Ilma Araujo
- Immunology Service, Universidade Federal da Bahia, Salvador, 401110170, Brazil
| | - Pedro C Avila
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Gillian Belbin
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eugene Bleecker
- Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, 85724, USA
| | - Carlos Bustamante
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Luis Caraballo
- Institute for Immunological Research, Universidad de Cartagena, Cartagena, 130000, Colombia
| | - Alvaro Cruz
- Universidade Federal da Bahia, Salvador, 401110170, Brazil
| | - Georgia M Dunston
- Department of Microbiology, Howard University College of Medicine, Washington, DC, 20059, USA
| | - Celeste Eng
- Department of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Mezbah U Faruque
- National Human Genome Center, Howard University College of Medicine, Washington, DC, 20059, USA
| | - Trevor S Ferguson
- Caribbean Institute for Health Research, The University of the West Indies, Kingston, 00007, Jamaica
| | - Camila Figueiredo
- Departamento de Biorregulacao, Universidade Federal da Bahia, Salvador, 401110170, Brazil
| | - Jean G Ford
- Department of Medicine, Einstein Medical Center, Philadelphia, PA, 19141, USA
| | - Weiniu Gan
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Pierre-Antoine Gourraud
- Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064, ATIP-Avenir, Equipe 5, Nantes, France
| | - Nadia N Hansel
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21224, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Edwin Francisco Herrera-Paz
- Facultad de Medicina, Universidad Católica de Honduras, San Pedro Sula, 21102, Honduras.,Universidad Tecnológica Centroamericana (UNITEC), Facultad de Ciencias Médicas, Tegucigalpa, Honduras
| | - Silvia Jiménez
- Institute for Immunological Research, Universidad de Cartagena, Cartagena, 130000, Colombia
| | - Eimear E Kenny
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jennifer Knight-Madden
- Caribbean Institute for Health Research, The University of the West Indies, Kingston, 00007, Jamaica
| | - Rajesh Kumar
- Department of Pediatrics, Northwestern University, Chicago, IL, 60611, USA
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Ethan M Lange
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Antoine Lizee
- Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064, ATIP-Avenir, Equipe 5, Nantes, France
| | - Pissamai Maul
- Genetics and Epidemiology of Asthma in Barbados, The University of the West Indies, Chronic Disease Research Centre, Jemmots Lane, St. Michael, BB11115, Barbados
| | - Trevor Maul
- Genetics and Epidemiology of Asthma in Barbados, The University of the West Indies, Chronic Disease Research Centre, Jemmots Lane, St. Michael, BB11115, Barbados
| | - Alvaro Mayorga
- Centro de Neumologia y Alergias, San Pedro Sula, 21102, Honduras
| | - Deborah Meyers
- Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, 85724, USA
| | - Dan L Nicolae
- Department of Medicine, University of Chicago, Chicago, IL, 60637, USA
| | - Timothy D O'Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ricardo Riccio Oliveira
- Laboratório de Patologia Experimental, Centro de Pesquisas Gonçalo Moniz, Salvador, 40296-710, Brazil
| | - Christopher O Olopade
- Department of Medicine and Center for Global Health, University of Chicago, Chicago, IL, 60637, USA
| | | | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Charles Rotimi
- Center for Research on Genomics & Global Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nicolas Vince
- Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064, ATIP-Avenir, Equipe 5, Nantes, France
| | - Harold Watson
- Faculty of Medical Sciences, The University of the West Indies, Queen Elizabeth Hospital, Bridgetown, St. Michael, BB11000, Barbados
| | - Rainford J Wilks
- Caribbean Institute for Health Research, The University of the West Indies, Kingston, 00007, Jamaica
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Steven Salzberg
- Departments of Biomedical Engineering and Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Terri H Beaty
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, 21205, USA
| | - Margaret A Taub
- Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD, 21205, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD, 21205, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21224, USA
| | - Kathleen C Barnes
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA.
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15
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Hernandez RD, Uricchio LH, Hartman K, Ye C, Dahl A, Zaitlen N. Ultrarare variants drive substantial cis heritability of human gene expression. Nat Genet 2019; 51:1349-1355. [PMID: 31477931 PMCID: PMC6730564 DOI: 10.1038/s41588-019-0487-7] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Accepted: 07/08/2019] [Indexed: 11/09/2022]
Abstract
The vast majority of human mutations have minor allele frequencies under 1%, with the plurality observed only once (that is, 'singletons'). While Mendelian diseases are predominantly caused by rare alleles, their cumulative contribution to complex phenotypes is largely unknown. We develop and rigorously validate an approach to jointly estimate the contribution of all alleles, including singletons, to phenotypic variation. We apply our approach to transcriptional regulation, an intermediate between genetic variation and complex disease. Using whole-genome DNA and lymphoblastoid cell line RNA sequencing data from 360 European individuals, we conservatively estimate that singletons contribute approximately 25% of cis heritability across genes (dwarfing the contributions of other frequencies). The majority (approximately 76%) of singleton heritability derives from ultrarare variants absent from thousands of additional samples. We develop an inference procedure to demonstrate that our results are consistent with pervasive purifying selection shaping the regulatory architecture of most human genes.
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Affiliation(s)
- Ryan D Hernandez
- Bioengineering & Therapeutic Sciences, UCSF, San Francisco, CA, USA.
- Institute for Human Genetics, UCSF, San Francisco, CA, USA.
- Institute for Quantitative Biosciences, UCSF, San Francisco, CA, USA.
- Institute for Computational Health Sciences, UCSF, San Francisco, CA, USA.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- McGill University and the Genome Quebec Innovation Center, Montreal, Quebec, Canada.
| | | | - Kevin Hartman
- Biological and Medical Informatics Graduate Program, UCSF, San Francisco, CA, USA
| | - Chun Ye
- Institute for Human Genetics, UCSF, San Francisco, CA, USA
- Epidemiology & Biostatistics, UCSF, San Francisco, CA, USA
| | - Andrew Dahl
- Institute for Human Genetics, UCSF, San Francisco, CA, USA
- Institute for Quantitative Biosciences, UCSF, San Francisco, CA, USA
| | - Noah Zaitlen
- Institute for Human Genetics, UCSF, San Francisco, CA, USA.
- Institute for Quantitative Biosciences, UCSF, San Francisco, CA, USA.
- Department of Medicine Lung Biology Center, UCSF, San Francisco, CA, USA.
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16
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Gignoux CR, Torgerson DG, Pino-Yanes M, Uricchio LH, Galanter J, Roth LA, Eng C, Hu D, Nguyen EA, Huntsman S, Mathias RA, Kumar R, Rodriguez-Santana J, Thakur N, Oh SS, McGarry M, Moreno-Estrada A, Sandoval K, Winkler CA, Seibold MA, Padhukasahasram B, Conti DV, Farber HJ, Avila P, Brigino-Buenaventura E, Lenoir M, Meade K, Serebrisky D, Borrell LN, Rodriguez-Cintron W, Thyne S, Joubert BR, Romieu I, Levin AM, Sienra-Monge JJ, Del Rio-Navarro BE, Gan W, Raby BA, Weiss ST, Bleecker E, Meyers DA, Martinez FJ, Gauderman WJ, Gilliland F, London SJ, Bustamante CD, Nicolae DL, Ober C, Sen S, Barnes K, Williams LK, Hernandez RD, Burchard EG. An admixture mapping meta-analysis implicates genetic variation at 18q21 with asthma susceptibility in Latinos. J Allergy Clin Immunol 2019; 143:957-969. [PMID: 30201514 PMCID: PMC6927816 DOI: 10.1016/j.jaci.2016.08.057] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Revised: 08/20/2016] [Accepted: 08/29/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND Asthma is a common but complex disease with racial/ethnic differences in prevalence, morbidity, and response to therapies. OBJECTIVE We sought to perform an analysis of genetic ancestry to identify new loci that contribute to asthma susceptibility. METHODS We leveraged the mixed ancestry of 3902 Latinos and performed an admixture mapping meta-analysis for asthma susceptibility. We replicated associations in an independent study of 3774 Latinos, performed targeted sequencing for fine mapping, and tested for disease correlations with gene expression in the whole blood of more than 500 subjects from 3 racial/ethnic groups. RESULTS We identified a genome-wide significant admixture mapping peak at 18q21 in Latinos (P = 6.8 × 10-6), where Native American ancestry was associated with increased risk of asthma (odds ratio [OR], 1.20; 95% CI, 1.07-1.34; P = .002) and European ancestry was associated with protection (OR, 0.86; 95% CI, 0.77-0.96; P = .008). Our findings were replicated in an independent childhood asthma study in Latinos (P = 5.3 × 10-3, combined P = 2.6 × 10-7). Fine mapping of 18q21 in 1978 Latinos identified a significant association with multiple variants 5' of SMAD family member 2 (SMAD2) in Mexicans, whereas a single rare variant in the same window was the top association in Puerto Ricans. Low versus high SMAD2 blood expression was correlated with case status (13.4% lower expression; OR, 3.93; 95% CI, 2.12-7.28; P < .001). In addition, lower expression of SMAD2 was associated with more frequent exacerbations among Puerto Ricans with asthma. CONCLUSION Ancestry at 18q21 was significantly associated with asthma in Latinos and implicated multiple ancestry-informative noncoding variants upstream of SMAD2 with asthma susceptibility. Furthermore, decreased SMAD2 expression in blood was strongly associated with increased asthma risk and increased exacerbations.
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Affiliation(s)
- Christopher R Gignoux
- Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, Calif; Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, Calif.
| | - Dara G Torgerson
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Maria Pino-Yanes
- Department of Medicine, University of California, San Francisco, San Francisco, Calif; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Lawrence H Uricchio
- Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, Calif
| | - Joshua Galanter
- Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, Calif; Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Lindsey A Roth
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Elizabeth A Nguyen
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | | | - Rajesh Kumar
- Ann and Robert H. Lurie Children's Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Ill
| | | | - Neeta Thakur
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Sam S Oh
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Meghan McGarry
- Department of Pediatrics, University of California, San Francisco, San Francisco, Calif
| | | | - Karla Sandoval
- Department of Genetics, Stanford University, Palo Alto, Calif
| | - Cheryl A Winkler
- Molecular Genetics Epidemiology Section, Frederick National Laboratory for Cancer Research, Frederick, Md
| | - Max A Seibold
- Integrated Center for Genes, Environment, and Health, Department of Pediatrics, Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, Colo
| | - Badri Padhukasahasram
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Mich
| | - David V Conti
- Department of Preventative Medicine, University of Southern California, Los Angeles, Calif
| | - Harold J Farber
- Department of Pediatrics, Section of Pulmonology, Baylor College of Medicine and Texas Children's Hospital, Houston, Tex
| | - Pedro Avila
- Division of Allergy-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Ill
| | | | | | - Kelley Meade
- Children's Hospital and Research Center Oakland, Oakland, Calif
| | | | - Luisa N Borrell
- Department of Health Sciences, Graduate Program in Public Health, Lehman College, City University of New York, Bronx, NY
| | | | - Shannon Thyne
- Department of Medicine, University of California, San Francisco, San Francisco, Calif
| | - Bonnie R Joubert
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC
| | - Isabelle Romieu
- Nutritional Epidemiology Group, International Agency for Research on Cancer, Lyon, France
| | - Albert M Levin
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Mich
| | - Juan-Jose Sienra-Monge
- Departmento de Alergia e Inmunologia, Clinica Hospital Infantil de Mexico Federico Gomez, Mexico City, Mexico
| | | | - Weiniu Gan
- Division of Lung Diseases, National Heart, Lung, and Blood Institute, Bethesda, Md
| | - Benjamin A Raby
- Department of Medicine, Harvard Medical School, Boston, Mass
| | - Scott T Weiss
- Department of Medicine, Harvard Medical School, Boston, Mass
| | - Eugene Bleecker
- Center for Genomics & Personalized Medicine Research, Wake Forest University, Winston-Salem, NC
| | - Deborah A Meyers
- Center for Genomics & Personalized Medicine Research, Wake Forest University, Winston-Salem, NC
| | | | - W James Gauderman
- Department of Preventative Medicine, University of Southern California, Los Angeles, Calif
| | - Frank Gilliland
- Department of Preventative Medicine, University of Southern California, Los Angeles, Calif
| | - Stephanie J London
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC
| | | | - Dan L Nicolae
- Physical Sciences Division, Department of Statistics, University of Chicago, Chicago, Ill
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, Ill
| | - Saunak Sen
- Department of Preventive Medicine, University of Tennessee Health Sciences Center, Memphis, Tenn
| | - Kathleen Barnes
- Department of Medicine, Johns Hopkins University, Baltimore, Md
| | - L Keoki Williams
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Mich; Department of Internal Medicine, Henry Ford Health System, Detroit, Mich
| | - Ryan D Hernandez
- Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, Calif; Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, Calif
| | - Esteban G Burchard
- Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, Calif; Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, Calif; Department of Medicine, University of California, San Francisco, San Francisco, Calif
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17
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Torres R, Szpiech ZA, Hernandez RD. Correction: Human demographic history has amplified the effects of background selection across the genome. PLoS Genet 2019; 15:e1007898. [PMID: 30601801 PMCID: PMC6314599 DOI: 10.1371/journal.pgen.1007898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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18
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Spear ML, Hu D, Pino-Yanes M, Huntsman S, Eng C, Levin AM, Ortega VE, White MJ, McGarry ME, Thakur N, Galanter J, Mak ACY, Oh SS, Ampleford E, Peters SP, Davis A, Kumar R, Farber HJ, Meade K, Avila PC, Serebrisky D, Lenoir MA, Brigino-Buenaventura E, Cintron WR, Thyne SM, Rodriguez-Santana JR, Ford JG, Chapela R, Estrada AM, Sandoval K, Seibold MA, Winkler CA, Bleecker ER, Myers DA, Williams LK, Hernandez RD, Torgerson DG, Burchard EG. A genome-wide association and admixture mapping study of bronchodilator drug response in African Americans with asthma. Pharmacogenomics J 2018; 19:249-259. [PMID: 30206298 PMCID: PMC6414286 DOI: 10.1038/s41397-018-0042-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 06/08/2018] [Accepted: 06/19/2018] [Indexed: 01/15/2023]
Abstract
Short-acting β2-adrenergic receptor agonists (SABAs) are the most commonly prescribed asthma medications worldwide. Response to SABAs is measured as bronchodilator drug response (BDR), which varies among racial/ethnic groups in the U.S1, 2. However, the genetic variation that contributes to BDR is largely undefined in African Americans with asthma3. To identify genetic variants that may contribute to differences in BDR in African Americans with asthma, we performed a genome-wide association study (GWAS) of BDR in 949 African American children with asthma, genotyped with the Axiom World Array 4 (Affymetrix, Santa Clara, CA) followed by imputation using 1000 Genomes phase III genotypes. We used linear regression models adjusting for age, sex, body mass index (BMI) and genetic ancestry to test for an association between BDR and genotype at single nucleotide polymorphisms (SNPs). To increase power and distinguish between shared vs. population-specific associations with BDR in children with asthma, we performed a meta-analysis across 949 African Americans and 1,830 Latinos (Total=2,779). Lastly, we performed genome-wide admixture mapping to identify regions whereby local African or European ancestry is associated with BDR in African Americans. We identified a population-specific association with an intergenic SNP on chromosome 9q21 that was significantly associated with BDR (rs73650726, p=7.69×10−9). A trans-ethnic meta-analysis across African Americans and Latinos identified three additional SNPs within the intron of PRKG1 that were significantly associated with BDR (rs7903366, rs7070958, and rs7081864, p≤5×10−8). Our results failed to replicate in three additional populations of 416 Latinos and 1,615 African Americans. Our findings indicate that both population specific and shared genetic variation contributes to differences in BDR in minority children with asthma, and that the genetic underpinnings of BDR may differ between racial/ethnic groups.
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Affiliation(s)
- Melissa L Spear
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Maria Pino-Yanes
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Tenerife, Spain.,CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.,Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Victor E Ortega
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC, USA
| | - Marquitta J White
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Meghan E McGarry
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Neeta Thakur
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Joshua Galanter
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.,Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Sam S Oh
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Elizabeth Ampleford
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC, USA
| | - Stephen P Peters
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC, USA
| | - Adam Davis
- UCSF Benioff Children's Hospital Oakland, Center for Community Health and Engagement, Oakland, CA, USA
| | - Rajesh Kumar
- Ann & Robert H. Lurie Children's Hospital of Chicago, Pediatrics, Chicago, IL, USA
| | - Harold J Farber
- Department of Pediatrics, Section of Pulmonology, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA
| | - Kelley Meade
- UCSF Benioff Children's Hospital Oakland, Oakland, CA, USA
| | - Pedro C Avila
- Division of Allergy-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Denise Serebrisky
- Pediatric Pulmonary Division, Jacobi Medical Center, Bronx, NY, USA.,Albert Einstein College of Medicine, Pediatrics, Bronx, NY, USA
| | | | | | | | - Shannon M Thyne
- Department of Pediatrics, David Geffen School of Medicine at ULCA, Olive View-UCLA Medical Center, Sylmar, CA, USA
| | | | | | - Rocio Chapela
- Instituto Nacional de Enfermedades Respiratorias, Mexico City, Mexico
| | - Andrés Moreno Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Karla Sandoval
- National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Max A Seibold
- Department of Pediatrics, National Jewish Health, Denver, CO, USA
| | - Cheryl A Winkler
- Basic Research Laboratory, National Cancer Institute, Leidos Biomedical Research, Frederick National Laboratory, Frederick, MD, USA
| | | | - Deborah A Myers
- Department of Medicine, The University of Arizona, Tucson, AZ, USA
| | - L Keoki Williams
- Center for Health Policy and Health Services Research,, Henry Ford Health System, Detroit, MI, USA.,Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.,California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA, USA.,Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Dara G Torgerson
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA. .,Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
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19
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Fedewa G, Radoshitzky SR, Chī X, Dǒng L, Zeng X, Spear M, Strauli N, Ng M, Chandran K, Stenglein MD, Hernandez RD, Jahrling PB, Kuhn JH, DeRisi JL. Ebola virus, but not Marburg virus, replicates efficiently and without required adaptation in snake cells. Virus Evol 2018; 4:vey034. [PMID: 30524754 PMCID: PMC6277580 DOI: 10.1093/ve/vey034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Ebola virus (EBOV) disease is a viral hemorrhagic fever with a high case-fatality rate in humans. This disease is caused by four members of the filoviral genus Ebolavirus, including EBOV. The natural hosts reservoirs of ebolaviruses remain to be identified. Glycoprotein 2 of reptarenaviruses, known to infect only boa constrictors and pythons, is similar in sequence and structure to ebolaviral glycoprotein 2, suggesting that EBOV may be able to infect reptilian cells. Therefore, we serially passaged EBOV and a distantly related filovirus, Marburg virus (MARV), in boa constrictor JK cells and characterized viral infection/replication and mutational frequency by confocal imaging and sequencing. We observed that EBOV efficiently infected and replicated in JK cells, but MARV did not. In contrast to most cell lines, EBOV-infected JK cells did not result in an obvious cytopathic effect. Surprisingly, genomic characterization of serial-passaged EBOV in JK cells revealed that genomic adaptation was not required for infection. Deep sequencing coverage (>10,000×) demonstrated the existence of only a single nonsynonymous variant (EBOV glycoprotein precursor pre-GP T544I) of unknown significance within the viral population that exhibited a shift in frequency of at least 10 per cent over six serial passages. In summary, we present the first reptilian cell line that replicates a filovirus at high titers, and for the first time demonstrate a filovirus genus-specific restriction to MARV in a cell line. Our data suggest the possibility that there may be differences between the natural host spectra of ebolaviruses and marburgviruses.
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Affiliation(s)
- Greg Fedewa
- Integrative Program in Quantitative Biology, Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Sheli R Radoshitzky
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Xiǎolì Chī
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Lián Dǒng
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Xiankun Zeng
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Melissa Spear
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Nicolas Strauli
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Melinda Ng
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kartik Chandran
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Mark D Stenglein
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Ryan D Hernandez
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Peter B Jahrling
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, MD, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, MD, USA
| | - Joseph L DeRisi
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
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20
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Torres R, Szpiech ZA, Hernandez RD. Human demographic history has amplified the effects of background selection across the genome. PLoS Genet 2018; 14:e1007387. [PMID: 29912945 PMCID: PMC6056204 DOI: 10.1371/journal.pgen.1007387] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 07/23/2018] [Accepted: 04/30/2018] [Indexed: 01/22/2023] Open
Abstract
Natural populations often grow, shrink, and migrate over time. Such demographic processes can affect genome-wide levels of genetic diversity. Additionally, genetic variation in functional regions of the genome can be altered by natural selection, which drives adaptive mutations to higher frequencies or purges deleterious ones. Such selective processes affect not only the sites directly under selection but also nearby neutral variation through genetic linkage via processes referred to as genetic hitchhiking in the context of positive selection and background selection (BGS) in the context of purifying selection. While there is extensive literature examining the consequences of selection at linked sites at demographic equilibrium, less is known about how non-equilibrium demographic processes influence the effects of hitchhiking and BGS. Utilizing a global sample of human whole-genome sequences from the Thousand Genomes Project and extensive simulations, we investigate how non-equilibrium demographic processes magnify and dampen the consequences of selection at linked sites across the human genome. When binning the genome by inferred strength of BGS, we observe that, compared to Africans, non-African populations have experienced larger proportional decreases in neutral genetic diversity in strong BGS regions. We replicate these findings in admixed populations by showing that non-African ancestral components of the genome have also been affected more severely in these regions. We attribute these differences to the strong, sustained/recurrent population bottlenecks that non-Africans experienced as they migrated out of Africa and throughout the globe. Furthermore, we observe a strong correlation between FST and the inferred strength of BGS, suggesting a stronger rate of genetic drift. Forward simulations of human demographic history with a model of BGS support these observations. Our results show that non-equilibrium demography significantly alters the consequences of selection at linked sites and support the need for more work investigating the dynamic process of multiple evolutionary forces operating in concert.
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Affiliation(s)
- Raul Torres
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA, United States of America
| | - Zachary A. Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, United States of America
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, United States of America
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States of America
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, United States of America
- * E-mail:
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21
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Mak ACY, White MJ, Eckalbar WL, Szpiech ZA, Oh SS, Pino-Yanes M, Hu D, Goddard P, Huntsman S, Galanter J, Wu AC, Himes BE, Germer S, Vogel JM, Bunting KL, Eng C, Salazar S, Keys KL, Liberto J, Nuckton TJ, Nguyen TA, Torgerson DG, Kwok PY, Levin AM, Celedón JC, Forno E, Hakonarson H, Sleiman PM, Dahlin A, Tantisira KG, Weiss ST, Serebrisky D, Brigino-Buenaventura E, Farber HJ, Meade K, Lenoir MA, Avila PC, Sen S, Thyne SM, Rodriguez-Cintron W, Winkler CA, Moreno-Estrada A, Sandoval K, Rodriguez-Santana JR, Kumar R, Williams LK, Ahituv N, Ziv E, Seibold MA, Darnell RB, Zaitlen N, Hernandez RD. Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma. Am J Respir Crit Care Med 2018; 197:1552-1564. [PMID: 29509491 PMCID: PMC6006403 DOI: 10.1164/rccm.201712-2529oc] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/05/2018] [Indexed: 12/25/2022] Open
Abstract
RATIONALE Albuterol, a bronchodilator medication, is the first-line therapy for asthma worldwide. There are significant racial/ethnic differences in albuterol drug response. OBJECTIVES To identify genetic variants important for bronchodilator drug response (BDR) in racially diverse children. METHODS We performed the first whole-genome sequencing pharmacogenetics study from 1,441 children with asthma from the tails of the BDR distribution to identify genetic association with BDR. MEASUREMENTS AND MAIN RESULTS We identified population-specific and shared genetic variants associated with BDR, including genome-wide significant (P < 3.53 × 10-7) and suggestive (P < 7.06 × 10-6) loci near genes previously associated with lung capacity (DNAH5), immunity (NFKB1 and PLCB1), and β-adrenergic signaling (ADAMTS3 and COX18). Functional analyses of the BDR-associated SNP in NFKB1 revealed potential regulatory function in bronchial smooth muscle cells. The SNP is also an expression quantitative trait locus for a neighboring gene, SLC39A8. The lack of other asthma study populations with BDR and whole-genome sequencing data on minority children makes it impossible to perform replication of our rare variant associations. Minority underrepresentation also poses significant challenges to identify age-matched and population-matched cohorts of sufficient sample size for replication of our common variant findings. CONCLUSIONS The lack of minority data, despite a collaboration of eight universities and 13 individual laboratories, highlights the urgent need for a dedicated national effort to prioritize diversity in research. Our study expands the understanding of pharmacogenetic analyses in racially/ethnically diverse populations and advances the foundation for precision medicine in at-risk and understudied minority populations.
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Affiliation(s)
| | | | | | | | | | - Maria Pino-Yanes
- Research Unit, Hospital Universitario N. S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | | | | | | | | | - Ann Chen Wu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Precision Medicine Translational Research (PRoMoTeR) Center, Department of Population Medicine, Harvard Medical School and Pilgrim Health Care Institute, Boston, Massachusetts
| | - Blanca E. Himes
- Department of Biostatistics, Epidemiology and Informatics and
| | | | | | | | | | | | | | | | | | | | | | - Pui-Yan Kwok
- Cardiovascular Research Institute
- Institute for Human Genetics, and
| | | | - Juan C. Celedón
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Erick Forno
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Hakon Hakonarson
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Applied Genomics, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
| | - Patrick M. Sleiman
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Applied Genomics, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
| | - Amber Dahlin
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Kelan G. Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Denise Serebrisky
- Pediatric Pulmonary Division, Jacobi Medical Center, Bronx, New York
| | | | - Harold J. Farber
- Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas
| | - Kelley Meade
- Children’s Hospital and Research Center, Oakland, California
| | | | - Pedro C. Avila
- Department of Medicine, Northwestern University, Chicago, Illinois
| | | | - Shannon M. Thyne
- Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | | | - Cheryl A. Winkler
- Basic Science Laboratory, Center for Cancer Research, National Cancer Institute, Leidos Biomedical Research, Frederick National Laboratory, Frederick, Maryland
| | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (UGA-LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Karla Sandoval
- National Laboratory of Genomics for Biodiversity (UGA-LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
| | | | - Rajesh Kumar
- Division of Allergy and Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
| | - L. Keoki Williams
- Department of Internal Medicine, and
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences
- Institute for Human Genetics, and
| | | | - Max A. Seibold
- Center for Genes, Environment and Health, Department of Pediatrics, National Jewish Health, Denver, Colorado; and
| | - Robert B. Darnell
- New York Genome Center, New York, New York
- Laboratory of Molecular Neuro-Oncology and
- Howard Hughes Medical Institute, The Rockefeller University, New York, New York
| | | | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences
- Cardiovascular Research Institute
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, California
| | - on behalf of the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
- Department of Medicine
- Department of Bioengineering and Therapeutic Sciences
- Department of Pediatrics
- Cardiovascular Research Institute
- Institute for Human Genetics, and
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, California
- Research Unit, Hospital Universitario N. S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Precision Medicine Translational Research (PRoMoTeR) Center, Department of Population Medicine, Harvard Medical School and Pilgrim Health Care Institute, Boston, Massachusetts
- Department of Biostatistics, Epidemiology and Informatics and
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- New York Genome Center, New York, New York
- Department of Public Health Sciences
- Department of Internal Medicine, and
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Center for Applied Genomics, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
- Pediatric Pulmonary Division, Jacobi Medical Center, Bronx, New York
- Department of Allergy and Immunology, Kaiser Permanente Vallejo Medical Center, Vallejo, California
- Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas
- Children’s Hospital and Research Center, Oakland, California
- Bay Area Pediatrics, Oakland, California
- Department of Medicine, Northwestern University, Chicago, Illinois
- Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Veterans Caribbean Health Care System, San Juan, Puerto Rico
- Basic Science Laboratory, Center for Cancer Research, National Cancer Institute, Leidos Biomedical Research, Frederick National Laboratory, Frederick, Maryland
- National Laboratory of Genomics for Biodiversity (UGA-LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
- Centro de Neumologia Pediatrica, San Juan, Puerto Rico
- Division of Allergy and Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
- Center for Genes, Environment and Health, Department of Pediatrics, National Jewish Health, Denver, Colorado; and
- Laboratory of Molecular Neuro-Oncology and
- Howard Hughes Medical Institute, The Rockefeller University, New York, New York
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22
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Shringarpure SS, Mathias RA, Hernandez RD, O'Connor TD, Szpiech ZA, Torres R, De La Vega FM, Bustamante CD, Barnes KC, Taub MA. Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data. Bioinformatics 2018; 33:1147-1153. [PMID: 28035032 PMCID: PMC5408850 DOI: 10.1093/bioinformatics/btw786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Accepted: 12/07/2016] [Indexed: 12/30/2022] Open
Abstract
Motivation Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X). Results We have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illuminas single-sample caller CASAVA, Real Time Genomics multisample variant caller, and the GATK UnifiedGenotyper, respectively. Since NGS sequencing data may be accompanied by genotype data for the same samples, either collected concurrent to sequencing or from a previous study, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g. a different set of criteria to determine quality for rare versus common variants) and thereby provides insight into sequencing characteristics that indicate call quality for variants of different frequencies. Availability and Implementation Code is available on Github at: https://github.com/suyashss/variant_validation. Contacts suyashs@stanford.edu or mtaub@jhsph.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Suyash S Shringarpure
- Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Rasika A Mathias
- 23 and Me Inc, Mountain View, CA, USA.,Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Ryan D Hernandez
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, USA.,Department of Bioengineering and Therapeutic Sciences.,Institute for Human Genetics
| | - Timothy D O'Connor
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.,Institute for Genome Sciences.,Program in Personalized and Genomic Medicine
| | - Zachary A Szpiech
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, USA
| | - Raul Torres
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Francisco M De La Vega
- Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Carlos D Bustamante
- Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Kathleen C Barnes
- 23 and Me Inc, Mountain View, CA, USA.,Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Margaret A Taub
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
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23
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Mangul S, Yang HT, Strauli N, Gruhl F, Porath HT, Hsieh K, Chen L, Daley T, Christenson S, Wesolowska-Andersen A, Spreafico R, Rios C, Eng C, Smith AD, Hernandez RD, Ophoff RA, Santana JR, Levanon EY, Woodruff PG, Burchard E, Seibold MA, Shifman S, Eskin E, Zaitlen N. ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues. Genome Biol 2018; 19:36. [PMID: 29548336 PMCID: PMC5857127 DOI: 10.1186/s13059-018-1403-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 02/02/2018] [Indexed: 11/22/2022] Open
Abstract
High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki.
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Affiliation(s)
- Serghei Mangul
- Department of Computer Science, University of California, Los Angeles, CA, USA. .,Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, USA.
| | - Harry Taegyun Yang
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Nicolas Strauli
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA
| | - Franziska Gruhl
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Hagit T Porath
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Kevin Hsieh
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Linus Chen
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Timothy Daley
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Stephanie Christenson
- Division of Pulmonary, Critical Care, Sleep and Allergy, Department of Medicine, and Cardiovascular Research Institute, University of California, San Francisco, CA, USA
| | | | - Roberto Spreafico
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, USA
| | - Cydney Rios
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Andrew D Smith
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.,Institute for Quantitative Biosciences, University of California, San Francisco, CA, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California, Los Angeles, CA, USA.,Department of Human Genetics, University of California, Los Angeles, CA, USA.,Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Erez Y Levanon
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Prescott G Woodruff
- Division of Pulmonary, Critical Care, Sleep and Allergy, Department of Medicine, and Cardiovascular Research Institute, University of California, San Francisco, CA, USA
| | - Esteban Burchard
- Schools of Pharmacy and Medicine, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Max A Seibold
- Department of Pediatrics, National Jewish Health, Denver, CO, USA.,University of Colorado School of Medicine, Denver, CO, USA
| | - Sagiv Shifman
- Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA.,Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - Noah Zaitlen
- Division of Pulmonary, Critical Care, Sleep and Allergy, Department of Medicine, and Cardiovascular Research Institute, University of California, San Francisco, CA, USA.
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24
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White KA, Ruiz DG, Szpiech ZA, Strauli NB, Hernandez RD, Jacobson MP, Barber DL. Cancer-associated arginine-to-histidine mutations confer a gain in pH sensing to mutant proteins. Sci Signal 2017; 10:10/495/eaam9931. [PMID: 28874603 DOI: 10.1126/scisignal.aam9931] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The intracellular pH (pHi) of most cancers is constitutively higher than that of normal cells and enhances proliferation and cell survival. We found that increased pHi enabled the tumorigenic behaviors caused by somatic arginine-to-histidine mutations, which are frequent in cancer and confer pH sensing not seen with wild-type proteins. Experimentally raising the pHi increased the activity of R776H mutant epidermal growth factor receptor (EGFR-R776H), thereby increasing proliferation and causing transformation in fibroblasts. An Arg-to-Gly mutation did not confer these effects. Molecular dynamics simulations of EGFR suggested that decreased protonation of His776 at high pH causes conformational changes in the αC helix that may stabilize the active form of the kinase. An Arg-to-His, but not Arg-to-Lys, mutation in the transcription factor p53 (p53-R273H) decreased its transcriptional activity and attenuated the DNA damage response in fibroblasts and breast cancer cells with high pHi. Lowering pHi attenuated the tumorigenic effects of both EGFR-R776H and p53-R273H. Our data suggest that some somatic mutations may confer a fitness advantage to the higher pHi of cancer cells.
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Affiliation(s)
- Katharine A White
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Diego Garrido Ruiz
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Zachary A Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nicolas B Strauli
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA.,Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Matthew P Jacobson
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Diane L Barber
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA.
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25
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Szpiech ZA, Strauli NB, White KA, Ruiz DG, Jacobson MP, Barber DL, Hernandez RD. Prominent features of the amino acid mutation landscape in cancer. PLoS One 2017; 12:e0183273. [PMID: 28837668 PMCID: PMC5570307 DOI: 10.1371/journal.pone.0183273] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 08/01/2017] [Indexed: 01/20/2023] Open
Abstract
Cancer can be viewed as a set of different diseases with distinctions based on tissue origin, driver mutations, and genetic signatures. Accordingly, each of these distinctions have been used to classify cancer subtypes and to reveal common features. Here, we present a different analysis of cancer based on amino acid mutation signatures. Non-negative Matrix Factorization and principal component analysis of 29 cancers revealed six amino acid mutation signatures, including four signatures that were dominated by either arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. Sample-level analyses reveal that while some cancers are heterogeneous, others are largely dominated by one type of mutation. Using a non-overlapping set of samples from the COSMIC somatic mutation database, we validate five of six mutation signatures, including signatures with prominent arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. This suggests that our classification of cancers based on amino acid mutation patterns may provide avenues of inquiry pertaining to specific protein mutations that may generate novel insights into cancer biology.
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Affiliation(s)
- Zachary A. Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- * E-mail: (RDH); (ZAS)
| | - Nicolas B. Strauli
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- Biomedical Sciences Graduate Program, University of California, San Francisco, United States of America
| | - Katharine A. White
- Department of Cell and Tissue Biology, University of California, San Francisco, United States of America
| | - Diego Garrido Ruiz
- Department of Pharmaceutical Chemistry, University of California, San Francisco, United States of America
| | - Matthew P. Jacobson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- Department of Pharmaceutical Chemistry, University of California, San Francisco, United States of America
| | - Diane L. Barber
- Department of Cell and Tissue Biology, University of California, San Francisco, United States of America
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- Quantitative Biosciences Institute, University of California, San Francisco, United States of America
- Institute for Human Genetics, University of California, San Francisco, United States of America
- * E-mail: (RDH); (ZAS)
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26
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Fernandes JD, Faust TB, Strauli NB, Smith C, Crosby DC, Nakamura RL, Hernandez RD, Frankel AD. Functional Segregation of Overlapping Genes in HIV. Cell 2017; 167:1762-1773.e12. [PMID: 27984726 DOI: 10.1016/j.cell.2016.11.031] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2016] [Revised: 09/29/2016] [Accepted: 11/15/2016] [Indexed: 11/28/2022]
Abstract
Overlapping genes pose an evolutionary dilemma as one DNA sequence evolves under the selection pressures of multiple proteins. Here, we perform systematic statistical and mutational analyses of the overlapping HIV-1 genes tat and rev and engineer exhaustive libraries of non-overlapped viruses to perform deep mutational scanning of each gene independently. We find a "segregated" organization in which overlapped sites encode functional residues of one gene or the other, but never both. Furthermore, this organization eliminates unfit genotypes, providing a fitness advantage to the population. Our comprehensive analysis reveals the extraordinary manner in which HIV minimizes the constraint of overlapping genes and repurposes that constraint to its own advantage. Thus, overlaps are not just consequences of evolutionary constraints, but rather can provide population fitness advantages.
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Affiliation(s)
- Jason D Fernandes
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94158, USA; Program in Pharmaceutical Sciences and Pharmacogenomics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Tyler B Faust
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94158, USA; Tetrad Program, Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Nicolas B Strauli
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA; Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA 94158, USA
| | - Cynthia Smith
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
| | - David C Crosby
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Robert L Nakamura
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Alan D Frankel
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA 94158, USA.
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Mathias RA, Taub MA, Gignoux CR, Fu W, Musharoff S, O'Connor TD, Vergara C, Torgerson DG, Pino-Yanes M, Shringarpure SS, Huang L, Rafaels N, Boorgula MP, Johnston HR, Ortega VE, Levin AM, Song W, Torres R, Padhukasahasram B, Eng C, Mejia-Mejia DA, Ferguson T, Qin ZS, Scott AF, Yazdanbakhsh M, Wilson JG, Marrugo J, Lange LA, Kumar R, Avila PC, Williams LK, Watson H, Ware LB, Olopade C, Olopade O, Oliveira R, Ober C, Nicolae DL, Meyers D, Mayorga A, Knight-Madden J, Hartert T, Hansel NN, Foreman MG, Ford JG, Faruque MU, Dunston GM, Caraballo L, Burchard EG, Bleecker E, Araujo MI, Herrera-Paz EF, Gietzen K, Grus WE, Bamshad M, Bustamante CD, Kenny EE, Hernandez RD, Beaty TH, Ruczinski I, Akey J, Barnes KC. A continuum of admixture in the Western Hemisphere revealed by the African Diaspora genome. Nat Commun 2016; 7:12522. [PMID: 27725671 PMCID: PMC5062574 DOI: 10.1038/ncomms12522] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 07/12/2016] [Indexed: 01/20/2023] Open
Abstract
The African Diaspora in the Western Hemisphere represents one of the largest forced migrations in history and had a profound impact on genetic diversity in modern populations. To date, the fine-scale population structure of descendants of the African Diaspora remains largely uncharacterized. Here we present genetic variation from deeply sequenced genomes of 642 individuals from North and South American, Caribbean and West African populations, substantially increasing the lexicon of human genomic variation and suggesting much variation remains to be discovered in African-admixed populations in the Americas. We summarize genetic variation in these populations, quantifying the postcolonial sex-biased European gene flow across multiple regions. Moreover, we refine estimates on the burden of deleterious variants carried across populations and how this varies with African ancestry. Our data are an important resource for empowering disease mapping studies in African-admixed individuals and will facilitate gene discovery for diseases disproportionately affecting individuals of African ancestry.
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Affiliation(s)
- Rasika Ann Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21224, USA
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, Maryland 21205, USA
| | - Margaret A. Taub
- Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, Maryland 21205, USA
| | - Christopher R. Gignoux
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Wenqing Fu
- Department of Genomic Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Shaila Musharoff
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Timothy D. O'Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
| | - Candelaria Vergara
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21224, USA
| | - Dara G. Torgerson
- Department of Medicine, University of California, San Francisco, San Francisco, California 94143, USA
| | - Maria Pino-Yanes
- Department of Medicine, University of California, San Francisco, San Francisco, California 94143, USA
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Suyash S. Shringarpure
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Lili Huang
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21224, USA
| | - Nicholas Rafaels
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21224, USA
| | | | - Henry Richard Johnston
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia 30322, USA
| | - Victor E. Ortega
- Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
| | - Albert M. Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan 48202, USA
| | - Wei Song
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
| | - Raul Torres
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, California 94158, USA
| | - Badri Padhukasahasram
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan 48202, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, California 94143, USA
| | - Delmy-Aracely Mejia-Mejia
- Centro de Neumologia y Alergias, San Pedro Sula 21102, Honduras
- Faculty of Medicine, Centro Medico de la Familia, San Pedro Sula 21102, Honduras
| | - Trevor Ferguson
- Tropical Medicine Research Institute, The University of the West Indies, St. Michael BB11115, Barbados
| | - Zhaohui S. Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia 30322, USA
| | - Alan F. Scott
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21224, USA
| | - Maria Yazdanbakhsh
- Department of Parasitology, Leiden University Medical Center, Leiden 2333ZA, The Netherlands
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi 39216, USA
| | - Javier Marrugo
- Instituto de Investigaciones Immunologicas, Universidad de Cartagena, Cartagena 130000, Colombia
| | - Leslie A. Lange
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Rajesh Kumar
- Department of Pediatrics, Northwestern University, Chicago, Illinois 60637, USA
- The Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60637, USA
| | - Pedro C. Avila
- Department of Medicine, Northwestern University, Chicago, Illinois 60637, USA
| | - L. Keoki Williams
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan 48202, USA
- Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan 48202, USA
| | - Harold Watson
- Faculty of Medical Sciences Cave Hill Campus, The University of the West Indies, Bridgetown BB11000, Barbados
- Queen Elizabeth Hospital, The University of the West Indies, St. Michael BB11115, Barbados
| | - Lorraine B. Ware
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Christopher Olopade
- Department of Medicine and Center for Global Health, University of Chicago, Chicago, Illinois 60637, USA
| | | | - Ricardo Oliveira
- Laboratório de Patologia Experimental, Centro de Pesquisas Gonçalo Moniz, Salvador 40296-710, Brazil
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA
| | - Dan L. Nicolae
- Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA
- Department of Statistics, University of Chicago, Chicago, Illinois 60637, USA
| | - Deborah Meyers
- Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
| | - Alvaro Mayorga
- Centro de Neumologia y Alergias, San Pedro Sula 21102, Honduras
| | - Jennifer Knight-Madden
- Tropical Medicine Research Institute, The University of the West Indies, St. Michael BB11115, Barbados
| | - Tina Hartert
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Nadia N. Hansel
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21224, USA
| | - Marilyn G. Foreman
- Pulmonary and Critical Care Medicine, Morehouse School of Medicine, Atlanta, Georgia 30310, USA
| | - Jean G. Ford
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, Maryland 21205, USA
- Department of Medicine, The Brooklyn Hospital Center, Brooklyn, New York 11201, USA
| | - Mezbah U. Faruque
- National Human Genome Center, Howard University College of Medicine, Washington DC 20059, USA
| | - Georgia M. Dunston
- National Human Genome Center, Howard University College of Medicine, Washington DC 20059, USA
- Department of Microbiology, Howard University College of Medicine, Washington DC 20059, USA
| | - Luis Caraballo
- Institute for Immunological Research, Universidad de Cartagena, Cartagena 130000, Colombia
| | - Esteban G. Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, California 94143, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94158, USA
| | - Eugene Bleecker
- Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
| | - Maria Ilma Araujo
- Immunology Service, Universidade Federal da Bahia, Salvador 401110170, Brazil
| | - Edwin Francisco Herrera-Paz
- Centro de Neumologia y Alergias, San Pedro Sula 21102, Honduras
- Faculty of Medicine, Centro Medico de la Familia, San Pedro Sula 21102, Honduras
- Facultad de Medicina, Universidad Catolica de Honduras, San Pedro Sula 21102, Honduras
| | | | | | - Michael Bamshad
- Department of Pediatrics, University of Washington, Seattle, Washington 98195, USA
| | - Carlos D. Bustamante
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Eimear E. Kenny
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA
- California Institute for Quantitative Biosciences, University of California, San Francisco, California 94143, USA
| | - Terri H. Beaty
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, Maryland 21205, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, Maryland 21205, USA
| | - Joshua Akey
- Department of Genomic Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Kathleen C. Barnes
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21224, USA
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, Maryland 21205, USA
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Strauli NB, Hernandez RD. Statistical inference of a convergent antibody repertoire response to influenza vaccine. Genome Med 2016; 8:60. [PMID: 27255379 PMCID: PMC4891843 DOI: 10.1186/s13073-016-0314-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 05/05/2016] [Indexed: 12/31/2022] Open
Abstract
Background Vaccines dramatically affect an individual’s adaptive immune system and thus provide an excellent means to study human immunity. Upon vaccination, the B cells that express antibodies (Abs) that happen to bind the vaccine are stimulated to proliferate and undergo mutagenesis at their Ab locus. This process may alter the composition of B cell lineages within an individual, which are known collectively as the antibody repertoire (AbR). Antibodies are also highly expressed in whole blood, potentially enabling RNA sequencing (RNA-seq) technologies to query this diversity. Less is known about the diversity of AbR responses across individuals to a given vaccine and if individuals tend to yield a similar response to the same antigenic stimulus. Methods Here we implement a bioinformatic pipeline that extracts the AbR information from a time-series RNA-seq dataset of five patients who were administered a seasonal trivalent influenza vaccine (TIV). We harness the detailed time-series nature of this dataset and use methods based in functional data analysis (FDA) to identify the Abs that respond to the vaccine. We then design and implement rigorous statistical tests in order to ask whether or not these patients exhibit a convergent AbR response to the same TIV. Results We find that high-resolution time-series data can be used to help identify the Abs that respond to an antigenic stimulus and that this response can exhibit a convergent nature across patients inoculated with the same vaccine. However, correlations in AbR diversity among individuals prior to inoculation can confound inference of a convergent signal unless it is taken into account. Conclusions We developed a framework to identify the elements of an AbR that respond to an antigen. This information could be used to understand the diversity of different immune responses in different individuals, as well as to gauge the effectiveness of the immune response to a given stimulus within an individual. We also present a framework for testing a convergent hypothesis between AbRs; a hypothesis that is more difficult to test than previously appreciated. Our discovery of a convergent signal suggests that similar epitopes do select for antibodies with similar sequence characteristics. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0314-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nicolas B Strauli
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, Byers Hall, 1700 4th Street, San Francisco, CA, 94158, USA. .,Institute for Human Genetics, University of California, San Francisco, CA, USA. .,Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA, USA.
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Uricchio LH, Zaitlen NA, Ye CJ, Witte JS, Hernandez RD. Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants. Genome Res 2016; 26:863-73. [PMID: 27197206 PMCID: PMC4937562 DOI: 10.1101/gr.202440.115] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 05/16/2016] [Indexed: 12/20/2022]
Abstract
The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.
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Affiliation(s)
- Lawrence H Uricchio
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Graduate Program in Bioinformatics, University of California, San Francisco, San Francisco, California 94143, USA
| | - Noah A Zaitlen
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California 94143, USA
| | - Chun Jimmie Ye
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94143, USA
| | - John S Witte
- Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94143, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California 94143, USA
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30
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Torgerson DG, Giri T, Druley TE, Zheng J, Huntsman S, Seibold MA, Young AL, Schweiger T, Yin-Declue H, Sajol GD, Schechtman KB, Hernandez RD, Randolph AG, Bacharier LB, Castro M. Pooled Sequencing of Candidate Genes Implicates Rare Variants in the Development of Asthma Following Severe RSV Bronchiolitis in Infancy. PLoS One 2015; 10:e0142649. [PMID: 26587832 PMCID: PMC4654486 DOI: 10.1371/journal.pone.0142649] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 02/06/2015] [Indexed: 12/17/2022] Open
Abstract
Severe infection with respiratory syncytial virus (RSV) during infancy is strongly associated with the development of asthma. To identify genetic variation that contributes to asthma following severe RSV bronchiolitis during infancy, we sequenced the coding exons of 131 asthma candidate genes in 182 European and African American children with severe RSV bronchiolitis in infancy using anonymous pools for variant discovery, and then directly genotyped a set of 190 nonsynonymous variants. Association testing was performed for physician-diagnosed asthma before the 7th birthday (asthma) using genotypes from 6,500 individuals from the Exome Sequencing Project (ESP) as controls to gain statistical power. In addition, among patients with severe RSV bronchiolitis during infancy, we examined genetic associations with asthma, active asthma, persistent wheeze, and bronchial hyperreactivity (methacholine PC20) at age 6 years. We identified four rare nonsynonymous variants that were significantly associated with asthma following severe RSV bronchiolitis, including single variants in ADRB2, FLG and NCAM1 in European Americans (p = 4.6x10-4, 1.9x10-13 and 5.0x10-5, respectively), and NOS1 in African Americans (p = 2.3x10-11). One of the variants was a highly functional nonsynonymous variant in ADRB2 (rs1800888), which was also nominally associated with asthma (p = 0.027) and active asthma (p = 0.013) among European Americans with severe RSV bronchiolitis without including the ESP. Our results suggest that rare nonsynonymous variants contribute to the development of asthma following severe RSV bronchiolitis in infancy, notably in ADRB2. Additional studies are required to explore the role of rare variants in the etiology of asthma and asthma-related traits following severe RSV bronchiolitis.
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Affiliation(s)
- Dara G. Torgerson
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Tusar Giri
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Todd E. Druley
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Jie Zheng
- Department of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Scott Huntsman
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Max A. Seibold
- Integrated Center for Genes, Environment and Health, National Jewish Health, Denver, Colorado, United States of America
| | - Andrew L. Young
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Toni Schweiger
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Huiqing Yin-Declue
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Geneline D. Sajol
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Kenneth B Schechtman
- Department of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, Institute of Human Genetics, and California Institute of Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, United States of America
| | - Adrienne G. Randolph
- Department of Anesthesiology, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Leonard B. Bacharier
- Department of Pediatrics, Washington University School of Medicine and St. Louis Children’s Hospital, St. Louis, Missouri, United States of America
| | - Mario Castro
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
- * E-mail:
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Abstract
Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions. CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.
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Affiliation(s)
- M. Cyrus Maher
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, USA
- Institute for Human Genetics, USA
- Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA, USA
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32
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Davis ZH, Verschueren E, Jang GM, Kleffman K, Johnson JR, Park J, Von Dollen J, Maher MC, Johnson T, Newton W, Jäger S, Shales M, Horner J, Hernandez RD, Krogan NJ, Glaunsinger BA. Global mapping of herpesvirus-host protein complexes reveals a transcription strategy for late genes. Mol Cell 2015; 57:349-60. [PMID: 25544563 PMCID: PMC4305015 DOI: 10.1016/j.molcel.2014.11.026] [Citation(s) in RCA: 145] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 08/20/2014] [Accepted: 11/21/2014] [Indexed: 12/19/2022]
Abstract
Mapping host-pathogen interactions has proven instrumental for understanding how viruses manipulate host machinery and how numerous cellular processes are regulated. DNA viruses such as herpesviruses have relatively large coding capacity and thus can target an extensive network of cellular proteins. To identify the host proteins hijacked by this pathogen, we systematically affinity tagged and purified all 89 proteins of Kaposi's sarcoma-associated herpesvirus (KSHV) from human cells. Mass spectrometry of this material identified over 500 virus-host interactions. KSHV causes AIDS-associated cancers, and its interaction network is enriched for proteins linked to cancer and overlaps with proteins that are also targeted by HIV-1. We found that the conserved KSHV protein ORF24 binds to RNA polymerase II and brings it to viral late promoters by mimicking and replacing cellular TATA-box-binding protein (TBP). This is required for herpesviral late gene expression, a complex and poorly understood phase of the viral lifecycle.
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Affiliation(s)
- Zoe H Davis
- Department of Plant & Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Division of Infectious Diseases and Immunity, School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Erik Verschueren
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gwendolyn M Jang
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kevin Kleffman
- Department of Plant & Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jeffrey R Johnson
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jimin Park
- Department of Plant & Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - John Von Dollen
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - M Cyrus Maher
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Tasha Johnson
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - William Newton
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Stefanie Jäger
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Michael Shales
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Julie Horner
- Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, CA 95134, USA
| | - Ryan D Hernandez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nevan J Krogan
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Britt A Glaunsinger
- Department of Plant & Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
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Pino-Yanes M, Gignoux CR, Galanter JM, Levin AM, Campbell CD, Eng C, Huntsman S, Nishimura KK, Gourraud PA, Mohajeri K, O'Roak BJ, Hu D, Mathias RA, Nguyen EA, Roth LA, Padhukasahasram B, Moreno-Estrada A, Sandoval K, Winkler CA, Lurmann F, Davis A, Farber HJ, Meade K, Avila PC, Serebrisky D, Chapela R, Ford JG, Lenoir MA, Thyne SM, Brigino-Buenaventura E, Borrell LN, Rodriguez-Cintron W, Sen S, Kumar R, Rodriguez-Santana JR, Bustamante CD, Martinez FD, Raby BA, Weiss ST, Nicolae DL, Ober C, Meyers DA, Bleecker ER, Mack SJ, Hernandez RD, Eichler EE, Barnes KC, Williams LK, Torgerson DG, Burchard EG. Genome-wide association study and admixture mapping reveal new loci associated with total IgE levels in Latinos. J Allergy Clin Immunol 2014; 135:1502-10. [PMID: 25488688 DOI: 10.1016/j.jaci.2014.10.033] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Revised: 09/06/2014] [Accepted: 10/15/2014] [Indexed: 12/20/2022]
Abstract
BACKGROUND IgE is a key mediator of allergic inflammation, and its levels are frequently increased in patients with allergic disorders. OBJECTIVE We sought to identify genetic variants associated with IgE levels in Latinos. METHODS We performed a genome-wide association study and admixture mapping of total IgE levels in 3334 Latinos from the Genes-environments & Admixture in Latino Americans (GALA II) study. Replication was evaluated in 454 Latinos, 1564 European Americans, and 3187 African Americans from independent studies. RESULTS We confirmed associations of 6 genes identified by means of previous genome-wide association studies and identified a novel genome-wide significant association of a polymorphism in the zinc finger protein 365 gene (ZNF365) with total IgE levels (rs200076616, P = 2.3 × 10(-8)). We next identified 4 admixture mapping peaks (6p21.32-p22.1, 13p22-31, 14q23.2, and 22q13.1) at which local African, European, and/or Native American ancestry was significantly associated with IgE levels. The most significant peak was 6p21.32-p22.1, where Native American ancestry was associated with lower IgE levels (P = 4.95 × 10(-8)). All but 22q13.1 were replicated in an independent sample of Latinos, and 2 of the peaks were replicated in African Americans (6p21.32-p22.1 and 14q23.2). Fine mapping of 6p21.32-p22.1 identified 6 genome-wide significant single nucleotide polymorphisms in Latinos, 2 of which replicated in European Americans. Another single nucleotide polymorphism was peak-wide significant within 14q23.2 in African Americans (rs1741099, P = 3.7 × 10(-6)) and replicated in non-African American samples (P = .011). CONCLUSION We confirmed genetic associations at 6 genes and identified novel associations within ZNF365, HLA-DQA1, and 14q23.2. Our results highlight the importance of studying diverse multiethnic populations to uncover novel loci associated with total IgE levels.
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Affiliation(s)
- Maria Pino-Yanes
- Department of Medicine, University of California, San Francisco, Calif; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.
| | - Christopher R Gignoux
- Department of Medicine, University of California, San Francisco, Calif; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif; Department of Genetics, Stanford University, Palo Alto, Calif
| | - Joshua M Galanter
- Department of Medicine, University of California, San Francisco, Calif; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Mich
| | | | - Celeste Eng
- Department of Medicine, University of California, San Francisco, Calif
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, Calif
| | | | | | - Kiana Mohajeri
- Department of Genome Sciences, University of Washington, Seattle, Wash
| | - Brian J O'Roak
- Department of Genome Sciences, University of Washington, Seattle, Wash; Molecular & Medical Genetics Department, Oregon Health and Science University, Portland, Ore
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, Calif
| | - Rasika A Mathias
- Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, Md
| | | | - Lindsey A Roth
- Department of Medicine, University of California, San Francisco, Calif
| | - Badri Padhukasahasram
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Mich
| | | | - Karla Sandoval
- Department of Genetics, Stanford University, Palo Alto, Calif
| | - Cheryl A Winkler
- Basic Research Laboratory, Center for Cancer Research, National Cancer Institute, Leidos Biomedical, Frederick National Laboratory for Cancer Research, Frederick, Md
| | | | - Adam Davis
- Children's Hospital and Research Center Oakland, Oakland, Calif
| | - Harold J Farber
- Department of Pediatrics, Section of Pulmonology, Baylor College of Medicine and Texas Children's Hospital, Houston, Tex
| | - Kelley Meade
- Children's Hospital and Research Center Oakland, Oakland, Calif
| | - Pedro C Avila
- Department of Medicine, Northwestern University, Chicago, Ill
| | | | - Rocio Chapela
- Instituto Nacional de Enfermedades Respiratorias (INER), Mexico City, Mexico
| | - Jean G Ford
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md
| | | | - Shannon M Thyne
- Department of Pediatrics, University of California San Francisco, San Francisco General Hospital, San Francisco, Calif
| | | | - Luisa N Borrell
- Department of Health Sciences, Graduate Program in Public Health, City University of New York, Bronx, NY
| | | | - Saunak Sen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, Calif
| | - Rajesh Kumar
- Children's Memorial Hospital and the Feinberg School of Medicine, Northwestern University, Chicago, Ill
| | | | | | - Fernando D Martinez
- Arizona Respiratory Center, University of Arizona, Tucson, Ariz; BIO5 Institute, University of Arizona, Tucson, Ariz
| | - Benjamin A Raby
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass
| | - Dan L Nicolae
- Department of Human Genetics, University of Chicago, Chicago, Ill
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, Ill
| | - Deborah A Meyers
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
| | - Eugene R Bleecker
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
| | - Steven J Mack
- Children's Hospital Oakland Research Institute, Oakland, Calif
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington, Seattle, Wash; Howard Hughes Medical Institute, Seattle, Wash
| | - Kathleen C Barnes
- Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, Md
| | - L Keoki Williams
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Mich; Department of Internal Medicine, Henry Ford Health System, Detroit, Mich
| | - Dara G Torgerson
- Department of Medicine, University of California, San Francisco, Calif
| | - Esteban G Burchard
- Department of Medicine, University of California, San Francisco, Calif; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
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Uricchio LH, Torres R, Witte JS, Hernandez RD. Population genetic simulations of complex phenotypes with implications for rare variant association tests. Genet Epidemiol 2014; 39:35-44. [PMID: 25417809 DOI: 10.1002/gepi.21866] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 09/09/2014] [Accepted: 09/26/2014] [Indexed: 12/12/2022]
Abstract
Demographic events and natural selection alter patterns of genetic variation within populations and may play a substantial role in shaping the genetic architecture of complex phenotypes and disease. However, the joint impact of these basic evolutionary forces is often ignored in the assessment of statistical tests of association. Here, we provide a simulation-based framework for generating DNA sequences that incorporates selection and demography with flexible models for simulating phenotypic variation (sfs_coder). This tool also allows the user to perform locus-specific simulations by automatically querying annotated genomic functional elements and genetic maps. We demonstrate the effects of evolutionary forces on patterns of genetic variation by simulating recently inferred models of human selection and demography. We use these simulations to show that the demographic model and locus-specific features, such as the proportion of sites under selection, may have practical implications for estimating the statistical power of sequencing-based rare variant association tests. In particular, for some phenotype models, there may be higher power to detect rare variant associations in African populations compared to non-Africans, but power is considerably reduced in regions of the genome with rampant negative selection. Furthermore, we show that existing methods for simulating large samples based on resampling from a small set of observed haplotypes fail to recapitulate the distribution of rare variants in the presence of rapid population growth (as has been observed in several human populations).
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Affiliation(s)
- Lawrence H Uricchio
- Graduate Program in Bioinformatics, University of California, San Francisco, California, United States of America
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Chen HS, Hutter CM, Mechanic LE, Amos CI, Bafna V, Hauser ER, Hernandez RD, Li C, Liberles DA, McAllister K, Moore JH, Paltoo DN, Papanicolaou GJ, Peng B, Ritchie MD, Rosenfeld G, Witte JS, Gillanders EM, Feuer EJ. Genetic simulation tools for post-genome wide association studies of complex diseases. Genet Epidemiol 2014; 39:11-19. [PMID: 25371374 DOI: 10.1002/gepi.21870] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2014] [Revised: 09/02/2014] [Accepted: 09/26/2014] [Indexed: 01/12/2023]
Abstract
Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.
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Affiliation(s)
- Huann-Sheng Chen
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Bethesda, MD 20892
| | - Carolyn M Hutter
- Division of Genomic Medicine, National Human Genome Research Institute, NIH, Bethesda, MD 20892
| | - Leah E Mechanic
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Bethesda, MD 20892
| | - Christopher I Amos
- Division of Community, Family Medicine, Dartmouth College, Lebanon, NH 03755
| | - Vineet Bafna
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093
| | | | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143
| | - Chun Li
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37235
| | - David A Liberles
- Department of Molecular Biology, University of Wyoming, Laramie, WY 82071
| | - Kimberly McAllister
- Susceptibility and Population Health Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709
| | - Jason H Moore
- Department of Genetics, Dartmouth College, Lebanon, NH 03755
| | - Dina N Paltoo
- Office of Director, National Institutes of Health, Bethesda, MD 20892
| | - George J Papanicolaou
- Division of Cardiovascular Sciences, Prevention and Population Sciences Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892
| | - Bo Peng
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Marylyn D Ritchie
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802
| | - Gabriel Rosenfeld
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Bethesda, MD 20892
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94107
| | - Elizabeth M Gillanders
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Bethesda, MD 20892
| | - Eric J Feuer
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Bethesda, MD 20892
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Abstract
Haplotype-based scans to detect natural selection are useful to identify recent or ongoing positive selection in genomes. As both real and simulated genomic data sets grow larger, spanning thousands of samples and millions of markers, there is a need for a fast and efficient implementation of these scans for general use. Here, we present selscan, an efficient multithreaded application that implements Extended Haplotype Homozygosity (EHH), Integrated Haplotype Score (iHS), and Cross-population EHH (XPEHH). selscan accepts phased genotypes in multiple formats, including TPED, and performs extremely well on both simulated and real data and over an order of magnitude faster than existing available implementations. It calculates iHS on chromosome 22 (22,147 loci) across 204 CEU haplotypes in 353 s on one thread (33 s on 16 threads) and calculates XPEHH for the same data relative to 210 YRI haplotypes in 578 s on one thread (52 s on 16 threads). Source code and binaries (Windows, OSX, and Linux) are available at https://github.com/szpiech/selscan.
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Affiliation(s)
- Zachary A Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco Institute for Human Genetics, University of California, San Francisco Institute for Quantitative Biosciences (QB3), University of California, San Francisco
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Vujkovic-Cvijin I, Dunham RM, Iwai S, Maher MC, Albright RG, Broadhurst MJ, Hernandez RD, Lederman MM, Huang Y, Somsouk M, Deeks SG, Hunt PW, Lynch SV, McCune JM. Dysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism. Sci Transl Med 2014; 5:193ra91. [PMID: 23843452 DOI: 10.1126/scitranslmed.3006438] [Citation(s) in RCA: 488] [Impact Index Per Article: 48.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Progressive HIV infection is characterized by dysregulation of the intestinal immune barrier, translocation of immunostimulatory microbial products, and chronic systemic inflammation that is thought to drive progression of disease to AIDS. Elements of this pathologic process persist despite viral suppression during highly active antiretroviral therapy (HAART), and drivers of these phenomena remain poorly understood. Disrupted intestinal immunity can precipitate dysbiosis that induces chronic inflammation in the mucosa and periphery of mice. However, putative microbial drivers of HIV-associated immunopathology versus recovery have not been identified in humans. Using high-resolution bacterial community profiling, we identified a dysbiotic mucosal-adherent community enriched in Proteobacteria and depleted of Bacteroidia members that was associated with markers of mucosal immune disruption, T cell activation, and chronic inflammation in HIV-infected subjects. Furthermore, this dysbiosis was evident among HIV-infected subjects undergoing HAART, and the extent of dysbiosis correlated with activity of the kynurenine pathway of tryptophan catabolism and plasma concentrations of the inflammatory cytokine interleukin-6 (IL-6), two established markers of disease progression. Gut-resident bacteria with capacity to catabolize tryptophan through the kynurenine pathway were found to be enriched in HIV-infected subjects, strongly correlated with kynurenine levels in HIV-infected subjects, and capable of kynurenine production in vitro. These observations demonstrate a link between mucosal-adherent colonic bacteria and immunopathogenesis during progressive HIV infection that is apparent even in the setting of viral suppression during HAART. This link suggests that gut-resident microbial populations may influence intestinal homeostasis during HIV disease.
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Affiliation(s)
- Ivan Vujkovic-Cvijin
- Division of Experimental Medicine, Department of Medicine, University of California, San Francisco UCSF, San Francisco, CA 94110, USA
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Drake KA, Torgerson DG, Gignoux CR, Galanter JM, Roth LA, Huntsman S, Eng C, Oh SS, Yee SW, Lin L, Bustamante CD, Moreno-Estrada A, Sandoval K, Davis A, Borrell LN, Farber HJ, Kumar R, Avila PC, Brigino-Buenaventura E, Chapela R, Ford JG, Lenoir MA, Lurmann F, Meade K, Serebrisky D, Thyne S, Rodríguez-Cintrón W, Sen S, Rodríguez-Santana JR, Hernandez RD, Giacomini KM, Burchard EG. A genome-wide association study of bronchodilator response in Latinos implicates rare variants. J Allergy Clin Immunol 2014; 133:370-8. [PMID: 23992748 PMCID: PMC3938989 DOI: 10.1016/j.jaci.2013.06.043] [Citation(s) in RCA: 81] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2013] [Revised: 05/09/2013] [Accepted: 06/18/2013] [Indexed: 01/29/2023]
Abstract
BACKGROUND The primary rescue medication to treat acute asthma exacerbation is the short-acting β₂-adrenergic receptor agonist; however, there is variation in how well a patient responds to treatment. Although these differences might be due to environmental factors, there is mounting evidence for a genetic contribution to variability in bronchodilator response (BDR). OBJECTIVE To identify genetic variation associated with bronchodilator drug response in Latino children with asthma. METHODS We performed a genome-wide association study (GWAS) for BDR in 1782 Latino children with asthma using standard linear regression, adjusting for genetic ancestry and ethnicity, and performed replication studies in an additional 531 Latinos. We also performed admixture mapping across the genome by testing for an association between local European, African, and Native American ancestry and BDR, adjusting for genomic ancestry and ethnicity. RESULTS We identified 7 genetic variants associated with BDR at a genome-wide significant threshold (P < 5 × 10(-8)), all of which had frequencies of less than 5%. Furthermore, we observed an excess of small P values driven by rare variants (frequency, <5%) and by variants in the proximity of solute carrier (SLC) genes. Admixture mapping identified 5 significant peaks; fine mapping within these peaks identified 2 rare variants in SLC22A15 as being associated with increased BDR in Mexicans. Quantitative PCR and immunohistochemistry identified SLC22A15 as being expressed in the lung and bronchial epithelial cells. CONCLUSION Our results suggest that rare variation contributes to individual differences in response to albuterol in Latinos, notably in SLC genes that include membrane transport proteins involved in the transport of endogenous metabolites and xenobiotics. Resequencing in larger, multiethnic population samples and additional functional studies are required to further understand the role of rare variation in BDR.
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Affiliation(s)
- Katherine A Drake
- Department of Medicine, University of California, San Francisco, Calif
| | - Dara G Torgerson
- Department of Medicine, University of California, San Francisco, Calif.
| | | | - Joshua M Galanter
- Department of Medicine, University of California, San Francisco, Calif
| | - Lindsey A Roth
- Department of Medicine, University of California, San Francisco, Calif
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, Calif
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, Calif
| | - Sam S Oh
- Department of Medicine, University of California, San Francisco, Calif
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
| | - Lawrence Lin
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
| | | | | | - Karla Sandoval
- Department of Genetics, Stanford University, Stanford, Calif
| | - Adam Davis
- Children's Hospital and Research Center Oakland, Oakland, Calif
| | - Luisa N Borrell
- Department of Health Sciences, Graduate Program in Public Health, Lehman College, City University of New York, Bronx, New York
| | - Harold J Farber
- Department of Pediatrics, Section of Pulmonology, Baylor College of Medicine and Texas Children's Hospital, Houston, Tex
| | - Rajesh Kumar
- Children's Memorial Hospital, and the Feinberg School of Medicine, Northwestern University, Chicago, Ill
| | - Pedro C Avila
- Division of Allergy-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Ill
| | | | - Rocio Chapela
- Instituto Nacional de Enfermedades Respiratorias (INER), Mexico City, Mexico
| | - Jean G Ford
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md
| | | | | | - Kelley Meade
- Children's Hospital and Research Center Oakland, Oakland, Calif
| | - Denise Serebrisky
- Pediatric Pulmonary Division, Jacobi Medical Center, Bronx, New York
| | - Shannon Thyne
- Department of Pediatrics, University of California, San Francisco, Calif
| | | | - Saunak Sen
- Department of Biostatistics, University of California, San Francisco, Calif
| | | | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
| | - Esteban G Burchard
- Department of Medicine, University of California, San Francisco, Calif; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif
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Abstract
OBJECTIVES Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high-throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history. METHODS Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with many human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral. RESULTS Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enable deleterious rare variants to persist at low frequencies just as long as neutral variants, but low-frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function and that potentially contribute to disease susceptibility. CONCLUSIONS The key question for sequencing-based association studies of complex traits is how to distinguish between deleterious and benign genetic variation. We used population genetic simulations to uncover patterns of genetic variation that distinguish these two categories, especially derived allele age, thereby providing inroads into novel methods for characterizing rare genetic variation driving complex diseases.
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Affiliation(s)
- M. Cyrus Maher
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Lawrence H. Uricchio
- UC Berkeley & UCSF Joint Graduate Group in Bioengineering, University of California, San Francisco
| | | | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco
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40
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Torgerson DG, Gignoux CR, Galanter JM, Drake KA, Roth LA, Eng C, Huntsman S, Torres R, Avila PC, Chapela R, Ford JG, Rodríguez-Santana JR, Rodríguez-Cintrón W, Hernandez RD, Burchard EG. Case-control admixture mapping in Latino populations enriches for known asthma-associated genes. J Allergy Clin Immunol 2012; 130:76-82.e12. [PMID: 22502797 DOI: 10.1016/j.jaci.2012.02.040] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2011] [Revised: 12/20/2011] [Accepted: 02/02/2012] [Indexed: 12/22/2022]
Abstract
BACKGROUND Polymorphisms in more than 100 genes have been associated with asthma susceptibility, yet much of the heritability remains to be explained. Asthma disproportionately affects different racial and ethnic groups in the United States, suggesting that admixture mapping is a useful strategy to identify novel asthma-associated loci. OBJECTIVE We sought to identify novel asthma-associated loci in Latino populations using case-control admixture mapping. METHODS We performed genome-wide admixture mapping by comparing levels of local Native American, European, and African ancestry between children with asthma and nonasthmatic control subjects in Puerto Rican and Mexican populations. Within candidate peaks, we performed allelic tests of association, controlling for differences in local ancestry. RESULTS Between the 2 populations, we identified a total of 62 admixture mapping peaks at a P value of less than 10(-3) that were significantly enriched for previously identified asthma-associated genes (P= .0051). One of the peaks was statistically significant based on 100 permutations in the Mexican sample (6q15); however, it was not significant in Puerto Rican subjects. Another peak was identified at nominal significance in both populations (8q12); however, the association was observed with different ancestries. CONCLUSION Case-control admixture mapping is a promising strategy for identifying novel asthma-associated loci in Latino populations and implicates genetic variation at 6q15 and 8q12 regions with asthma susceptibility. This approach might be useful for identifying regions that contribute to both shared and population-specific differences in asthma susceptibility.
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Affiliation(s)
- Dara G Torgerson
- Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA.
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Auton A, Fledel-Alon A, Pfeifer S, Venn O, Ségurel L, Street T, Leffler EM, Bowden R, Aneas I, Broxholme J, Humburg P, Iqbal Z, Lunter G, Maller J, Hernandez RD, Melton C, Venkat A, Nobrega MA, Bontrop R, Myers S, Donnelly P, Przeworski M, McVean G. A fine-scale chimpanzee genetic map from population sequencing. Science 2012; 336:193-8. [PMID: 22422862 PMCID: PMC3532813 DOI: 10.1126/science.1216872] [Citation(s) in RCA: 208] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To study the evolution of recombination rates in apes, we developed methodology to construct a fine-scale genetic map from high-throughput sequence data from 10 Western chimpanzees, Pan troglodytes verus. Compared to the human genetic map, broad-scale recombination rates tend to be conserved, but with exceptions, particularly in regions of chromosomal rearrangements and around the site of ancestral fusion in human chromosome 2. At fine scales, chimpanzee recombination is dominated by hotspots, which show no overlap with those of humans even though rates are similarly elevated around CpG islands and decreased within genes. The hotspot-specifying protein PRDM9 shows extensive variation among Western chimpanzees, and there is little evidence that any sequence motifs are enriched in hotspots. The contrasting locations of hotspots provide a natural experiment, which demonstrates the impact of recombination on base composition.
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Affiliation(s)
- Adam Auton
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
- Department of Genetics, Albert Einstein College of Medicine, New York, New York, USA
| | - Adi Fledel-Alon
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Susanne Pfeifer
- Department of Statistics, 1 South Parks Road, University of Oxford, Oxford, OX1 3TG, UK
| | - Oliver Venn
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Laure Ségurel
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
- Howard Hughes Medical Institute, University of Chicago, Chicago, Illinois, USA
| | - Teresa Street
- Department of Statistics, 1 South Parks Road, University of Oxford, Oxford, OX1 3TG, UK
| | - Ellen M. Leffler
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Rory Bowden
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
- Department of Statistics, 1 South Parks Road, University of Oxford, Oxford, OX1 3TG, UK
- Oxford Biomedical Research Centre, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 9DU, UK
| | - Ivy Aneas
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - John Broxholme
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Peter Humburg
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Zamin Iqbal
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Gerton Lunter
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Julian Maller
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
- Department of Statistics, 1 South Parks Road, University of Oxford, Oxford, OX1 3TG, UK
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143-0912, USA
| | - Cord Melton
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Aarti Venkat
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
- Howard Hughes Medical Institute, University of Chicago, Chicago, Illinois, USA
| | - Marcelo A. Nobrega
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Ronald Bontrop
- Department of Comparative Genetics and Refinement, Biomedical Primate Research Center, Lange Kleiweg 139 2288 GJ, Rijswijk, Netherlands
| | - Simon Myers
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
- Department of Statistics, 1 South Parks Road, University of Oxford, Oxford, OX1 3TG, UK
| | - Peter Donnelly
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
- Department of Statistics, 1 South Parks Road, University of Oxford, Oxford, OX1 3TG, UK
| | - Molly Przeworski
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
- Howard Hughes Medical Institute, University of Chicago, Chicago, Illinois, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA
| | - Gil McVean
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
- Department of Statistics, 1 South Parks Road, University of Oxford, Oxford, OX1 3TG, UK
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42
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Jäger S, Cimermancic P, Gulbahce N, Johnson JR, McGovern KE, Clarke SC, Shales M, Mercenne G, Pache L, Li K, Hernandez H, Jang GM, Roth SL, Akiva E, Marlett J, Stephens M, D'Orso I, Fernandes J, Fahey M, Mahon C, O'Donoghue AJ, Todorovic A, Morris JH, Maltby DA, Alber T, Cagney G, Bushman FD, Young JA, Chanda SK, Sundquist WI, Kortemme T, Hernandez RD, Craik CS, Burlingame A, Sali A, Frankel AD, Krogan NJ. Global landscape of HIV-human protein complexes. Nature 2011; 481:365-70. [PMID: 22190034 DOI: 10.1038/nature10719] [Citation(s) in RCA: 547] [Impact Index Per Article: 42.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2011] [Accepted: 11/18/2011] [Indexed: 12/16/2022]
Abstract
Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host's cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV-human protein-protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.
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Affiliation(s)
- Stefanie Jäger
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, USA
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43
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Wilson DJ, Hernandez RD, Andolfatto P, Przeworski M. A population genetics-phylogenetics approach to inferring natural selection in coding sequences. PLoS Genet 2011; 7:e1002395. [PMID: 22144911 PMCID: PMC3228810 DOI: 10.1371/journal.pgen.1002395] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2010] [Accepted: 10/08/2011] [Indexed: 01/23/2023] Open
Abstract
Through an analysis of polymorphism within and divergence between species, we can hope to learn about the distribution of selective effects of mutations in the genome, changes in the fitness landscape that occur over time, and the location of sites involved in key adaptations that distinguish modern-day species. We introduce a novel method for the analysis of variation in selection pressures within and between species, spatially along the genome and temporally between lineages. We model codon evolution explicitly using a joint population genetics-phylogenetics approach that we developed for the construction of multiallelic models with mutation, selection, and drift. Our approach has the advantage of performing direct inference on coding sequences, inferring ancestral states probabilistically, utilizing allele frequency information, and generalizing to multiple species. We use a Bayesian sliding window model for intragenic variation in selection coefficients that efficiently combines information across sites and captures spatial clustering within the genome. To demonstrate the utility of the method, we infer selective pressures acting in Drosophila melanogaster and D. simulans from polymorphism and divergence data for 100 X-linked coding regions.
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Affiliation(s)
- Daniel J Wilson
- Department of Human Genetics and Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA.
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44
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Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, Himes BE, Levin AM, Mathias RA, Hancock DB, Baurley JW, Eng C, Stern DA, Celedón JC, Rafaels N, Capurso D, Conti DV, Roth LA, Soto-Quiros M, Togias A, Li X, Myers RA, Romieu I, Van Den Berg DJ, Hu D, Hansel NN, Hernandez RD, Israel E, Salam MT, Galanter J, Avila PC, Avila L, Rodriquez-Santana JR, Chapela R, Rodriguez-Cintron W, Diette GB, Adkinson NF, Abel RA, Ross KD, Shi M, Faruque MU, Dunston GM, Watson HR, Mantese VJ, Ezurum SC, Liang L, Ruczinski I, Ford JG, Huntsman S, Chung KF, Vora H, Li X, Calhoun WJ, Castro M, Sienra-Monge JJ, del Rio-Navarro B, Deichmann KA, Heinzmann A, Wenzel SE, Busse WW, Gern JE, Lemanske RF, Beaty TH, Bleecker ER, Raby BA, Meyers DA, London SJ, Gilliland FD, Burchard EG, Martinez FD, Weiss ST, Williams LK, Barnes KC, Ober C, Nicolae DL. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet 2011; 43:887-92. [PMID: 21804549 PMCID: PMC3445408 DOI: 10.1038/ng.888] [Citation(s) in RCA: 637] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2010] [Accepted: 06/16/2011] [Indexed: 11/09/2022]
Abstract
Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.
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Affiliation(s)
- Dara G Torgerson
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
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45
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Sun C, Southard C, Huo D, Hernandez RD, Witonsky DB, Olopade OI, Di Rienzo A. SNP discovery, expression and cis-regulatory variation in the UGT2B genes. Pharmacogenomics J 2011; 12:287-96. [PMID: 21358749 DOI: 10.1038/tpj.2011.2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
UGT2B enzymes metabolize multiple endogenous and exogenous molecules, including steroid hormones and clinical drugs. However, little is known about the inter-individual variation in gene expression and its determinants. We re-sequenced candidate regulatory regions and the partial coding regions (41.1 kb) of UGT2B genes and identified 332 genetic variants. We measured gene expression in normal breast and liver samples and observed different patterns. The expression levels varied greatly across individuals in both tissues and were significantly correlated with each other in liver. Genotyping of tagging single-nucleotide polymorphisms (SNPs) in the same samples and association tests between genotype and transcript levels identified 62 variants that were associated with at least one UGT2B mRNA levels in either tissue. Most of these cis-regulatory SNPs were not shared between tissues, suggesting that this gene family is regulated in a tissue-specific manner. Our results provide insight into studying the role of UGT2B variation in hormone-dependent cancers and drug response.
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Affiliation(s)
- C Sun
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
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46
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Abstract
Efforts to identify the genetic basis of human adaptations from polymorphism data have sought footprints of "classic selective sweeps" (in which a beneficial mutation arises and rapidly fixes in the population).Yet it remains unknown whether this form of natural selection was common in our evolution. We examined the evidence for classic sweeps in resequencing data from 179 human genomes. As expected under a recurrent-sweep model, we found that diversity levels decrease near exons and conserved noncoding regions. In contrast to expectation, however, the trough in diversity around human-specific amino acid substitutions is no more pronounced than around synonymous substitutions. Moreover, relative to the genome background, amino acid and putative regulatory sites are not significantly enriched in alleles that are highly differentiated between populations. These findings indicate that classic sweeps were not a dominant mode of human adaptation over the past ~250,000 years.
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Affiliation(s)
| | | | - Eyal Elyashiv
- Dept. of Ecology, Systematics and Evolution, Hebrew University, Israel
| | | | - Adam Auton
- Wellcome Trust Centre for Human Genetics, University of Oxford, UK
| | - Gil McVean
- Wellcome Trust Centre for Human Genetics, University of Oxford, UK
- Dept. of Statistics, University of Oxford, UK
| | - Guy Sella
- Dept. of Ecology, Systematics and Evolution, Hebrew University, Israel
| | - Molly Przeworski
- Dept. of Human Genetics, University of Chicago, IL, USA
- Dept. of Ecology and Evolution, University of Chicago, IL, USA
- Howard Hughes Medical Institute, University of Chicago, IL, USA
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47
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Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet 2009; 5:e1000695. [PMID: 19851460 PMCID: PMC2760211 DOI: 10.1371/journal.pgen.1000695] [Citation(s) in RCA: 1102] [Impact Index Per Article: 73.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2009] [Accepted: 09/23/2009] [Indexed: 11/18/2022] Open
Abstract
Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40–270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17–43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3–26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU). The demographic history of our species is reflected in patterns of genetic variation within and among populations. We developed an efficient method for calculating the expected distribution of genetic variation, given a demographic model including such events as population size changes, population splits and joins, and migration. We applied our approach to publicly available human sequencing data, searching for models that best reproduce the observed patterns. Our joint analysis of data from African, European, and Asian populations yielded new dates for when these populations diverged. In particular, we found that African and Eurasian populations diverged around 100,000 years ago. This is earlier than other genetic studies suggest, because our model includes the effects of migration, which we found to be important for reproducing observed patterns of variation in the data. We also analyzed data from European, Asian, and Mexican populations to model the peopling of the Americas. Here, we find no evidence for recurrent migration after East Asian and Native American populations diverged. Our methods are not limited to studying humans, and we hope that future sequencing projects will offer more insights into the history of both our own species and others.
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Affiliation(s)
- Ryan N Gutenkunst
- Theoretical Biology and Biophysics and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
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48
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Torgerson DG, Boyko AR, Hernandez RD, Indap A, Hu X, White TJ, Sninsky JJ, Cargill M, Adams MD, Bustamante CD, Clark AG. Evolutionary processes acting on candidate cis-regulatory regions in humans inferred from patterns of polymorphism and divergence. PLoS Genet 2009; 5:e1000592. [PMID: 19662163 PMCID: PMC2714078 DOI: 10.1371/journal.pgen.1000592] [Citation(s) in RCA: 102] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2008] [Accepted: 07/10/2009] [Indexed: 01/30/2023] Open
Abstract
Analysis of polymorphism and divergence in the non-coding portion of the human genome yields crucial information about factors driving the evolution of gene regulation. Candidate cis-regulatory regions spanning more than 15,000 genes in 15 African Americans and 20 European Americans were re-sequenced and aligned to the chimpanzee genome in order to identify potentially functional polymorphism and to characterize and quantify departures from neutral evolution. Distortions of the site frequency spectra suggest a general pattern of selective constraint on conserved non-coding sites in the flanking regions of genes (CNCs). Moreover, there is an excess of fixed differences that cannot be explained by a Gamma model of deleterious fitness effects, suggesting the presence of positive selection on CNCs. Extensions of the McDonald-Kreitman test identified candidate cis-regulatory regions with high probabilities of positive and negative selection near many known human genes, the biological characteristics of which exhibit genome-wide trends that differ from patterns observed in protein-coding regions. Notably, there is a higher probability of positive selection in candidate cis-regulatory regions near genes expressed in the fetal brain, suggesting that a larger portion of adaptive regulatory changes has occurred in genes expressed during brain development. Overall we find that natural selection has played an important role in the evolution of candidate cis-regulatory regions throughout hominid evolution. It has been suggested that changes in gene expression may have played a more important role in the evolution of modern humans than changes in protein-coding sequences. In order to identify signatures of natural selection on candidate cis-regulatory regions, we examined single nucleotide polymorphisms obtained from the complete re-sequencing of conserved non-coding sites (CNCs) in the flanking regions of over 15,000 genes in 35 humans. Patterns of allele frequencies in CNCs indicate the presence of both positive and negative selection acting on standing variation within these candidate cis-regulatory regions, particularly for the 5′ and 3′ UTRs of genes. Gene-specific tests comparing levels of polymorphism and divergence identify several genes with strong signatures of selection on candidate cis-regulatory regions and suggest that the biological characteristics of genes subject to selection are different between coding and candidate cis-regulatory regions with respect to gene expression and function. For example, we find stronger signatures of positive selection in candidate cis-regulatory regions near genes expressed in the fetal brain, which we do not observe in a concurrent analysis on protein-coding regions. Our results suggest that both positive and negative selection have acted on candidate cis-regulatory regions and that the evolution of non-coding DNA has played an important role throughout hominid evolution.
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Affiliation(s)
- Dara G Torgerson
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA.
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49
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Gibbs RA, Taylor JF, Van Tassell CP, Barendse W, Eversole KA, Gill CA, Green RD, Hamernik DL, Kappes SM, Lien S, Matukumalli LK, McEwan JC, Nazareth LV, Schnabel RD, Weinstock GM, Wheeler DA, Ajmone-Marsan P, Boettcher PJ, Caetano AR, Garcia JF, Hanotte O, Mariani P, Skow LC, Sonstegard TS, Williams JL, Diallo B, Hailemariam L, Martinez ML, Morris CA, Silva LOC, Spelman RJ, Mulatu W, Zhao K, Abbey CA, Agaba M, Araujo FR, Bunch RJ, Burton J, Gorni C, Olivier H, Harrison BE, Luff B, Machado MA, Mwakaya J, Plastow G, Sim W, Smith T, Thomas MB, Valentini A, Williams P, Womack J, Woolliams JA, Liu Y, Qin X, Worley KC, Gao C, Jiang H, Moore SS, Ren Y, Song XZ, Bustamante CD, Hernandez RD, Muzny DM, Patil S, San Lucas A, Fu Q, Kent MP, Vega R, Matukumalli A, McWilliam S, Sclep G, Bryc K, Choi J, Gao H, Grefenstette JJ, Murdoch B, Stella A, Villa-Angulo R, Wright M, Aerts J, Jann O, Negrini R, Goddard ME, Hayes BJ, Bradley DG, Barbosa da Silva M, Lau LPL, Liu GE, Lynn DJ, Panzitta F, Dodds KG. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 2009; 324:528-32. [PMID: 19390050 PMCID: PMC2735092 DOI: 10.1126/science.1167936] [Citation(s) in RCA: 561] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.
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50
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Abstract
UNLABELLED This article introduces a new forward population genetic simulation program that can efficiently generate samples from populations with complex demographic histories under various models of natural selection. The program (SFS_CODE) is highly flexible, allowing the user to simulate realistic genomic regions with several loci evolving according to a variety of mutation models (from simple to context-dependent), and allows for insertions and deletions. Each locus can be annotated as either coding or non-coding, sex-linked or autosomal, selected or neutral, and have an arbitrary linkage structure (from completely linked to independent). AVAILABILITY The source code (written in the C programming language) is available at http://sfscode.sourceforge.net, and a web server (http://cbsuapps.tc.cornell.edu/sfscode.aspx) allows the user to perform simulations using the high-performance computing cluster hosted by the Cornell University Computational Biology Service Unit.
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Affiliation(s)
- Ryan D Hernandez
- Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA.
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