401
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Dolley S. Big Data's Role in Precision Public Health. Front Public Health 2018; 6:68. [PMID: 29594091 PMCID: PMC5859342 DOI: 10.3389/fpubh.2018.00068] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 02/20/2018] [Indexed: 01/01/2023] Open
Abstract
Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.
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402
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Stalder O, Asher A, Liang L, Carroll RJ, Ma Y, Chatterjee N. Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies. Biometrika 2018; 104:801-812. [PMID: 29430038 DOI: 10.1093/biomet/asx045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Indexed: 01/20/2023] Open
Abstract
Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors. We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically. We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.
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Affiliation(s)
- Odile Stalder
- Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, 3012 Bern,
| | - Alex Asher
- Department of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A
| | - Liang Liang
- Department of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A
| | - Raymond J Carroll
- Department of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A
| | - Yanyuan Ma
- Department of Statistics, Penn State University, University Park, Pennsylvania 16802,
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, Maryland 21205,
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403
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Han SS, Chatterjee N. Review of Statistical Methods for Gene-Environment Interaction Analysis. CURR EPIDEMIOL REP 2018. [DOI: 10.1007/s40471-018-0135-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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404
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Balicza P, Terebessy A, Grosz Z, Varga NA, Gal A, Fekete BA, Molnar MJ. Implementation of personalized medicine in Central-Eastern Europe: pitfalls and potentials based on citizen's attitude. EPMA J 2018. [PMID: 29515690 DOI: 10.1007/s13167-017-0125-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Objective Next-generation sequencing is increasingly utilized worldwide as a research and diagnostic tool and is anticipated to be implemented into everyday clinical practice. Since Central-Eastern European attitude toward genetic testing, especially broad genetic testing, is not well known, we performed a survey on this issue among Hungarian participants. Methods A self-administered questionnaire was distributed among patients and patient relatives at our neurogenetic outpatient clinic. Members of the general population were also recruited via public media. We used chi-square testing and binary logistic regression to examine factors influencing attitude. Results We identified a mixed attitude toward genetic testing. Access to physician consultation positively influenced attitude. A higher self-determined genetic familiarity score associated with higher perceived genetic influence score, which in turn associated with greater willingness to participate in genetic testing. Medical professionals constituted a skeptical group. Conclusions We think that given the controversies and complexities of the next-generation sequencing field, the optimal clinical translation of NGS data should be performed in institutions which have the unique capability to provide interprofessional health education, transformative biomedical research, and crucial patient care. With optimization of the clinical translational process, improvement of genetic literacy may increase patient engagement and empowerment. Relevance of the article for predictive preventive and personalized medicine The paper highlights that in countries with relatively low-genetic literacy, a special strategy is needed to enhance the implementation of personalized medicine.
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Affiliation(s)
- Peter Balicza
- 1Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Tomo Street 25-29, Budapest, 1083 Hungary
| | - Andras Terebessy
- 2Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Zoltan Grosz
- 1Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Tomo Street 25-29, Budapest, 1083 Hungary
| | - Noemi Agnes Varga
- 1Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Tomo Street 25-29, Budapest, 1083 Hungary
| | - Aniko Gal
- 1Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Tomo Street 25-29, Budapest, 1083 Hungary
| | - Balint Andras Fekete
- 1Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Tomo Street 25-29, Budapest, 1083 Hungary
| | - Maria Judit Molnar
- 1Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Tomo Street 25-29, Budapest, 1083 Hungary
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405
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Dudbridge F, Pashayan N, Yang J. Predictive accuracy of combined genetic and environmental risk scores. Genet Epidemiol 2018; 42:4-19. [PMID: 29178508 PMCID: PMC5847122 DOI: 10.1002/gepi.22092] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 07/19/2017] [Accepted: 09/27/2017] [Indexed: 01/19/2023]
Abstract
The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.
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Affiliation(s)
- Frank Dudbridge
- Department of Health SciencesUniversity of LeicesterLeicesterUnited Kingdom
- Department of Non‐Communicable Disease EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUnited Kingdom
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUnited Kingdom
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - Nora Pashayan
- Department of Applied Health ResearchUniversity College LondonLondonUnited Kingdom
| | - Jian Yang
- Institute for Molecular BioscienceUniversity of QueenslandBrisbaneQueenslandAustralia
- Queensland Brain InstituteUniversity of QueenslandBrisbaneQueenslandAustralia
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406
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Affiliation(s)
- Christel M Middeldorp
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Services, Brisbane, QLD, Australia
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
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407
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Carini C, Hunter E, Ramadass AS, Green J, Akoulitchev A, McInnes IB, Goodyear CS. Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis. J Transl Med 2018; 16:18. [PMID: 29378619 PMCID: PMC5789697 DOI: 10.1186/s12967-018-1387-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 01/13/2018] [Indexed: 12/15/2022] Open
Abstract
Background There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL). Methods We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL. Results We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL. Conclusions Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease. Electronic supplementary material The online version of this article (10.1186/s12967-018-1387-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Claudio Carini
- Pfizer Inc., Cambridge, USA. .,Department of Asthma, Allergy & Lung Biology, GSTT Campus, King's College School of Medicine, London, UK.
| | | | | | | | | | | | - Iain B McInnes
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK
| | - Carl S Goodyear
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK
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408
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Valberg M, Stensrud MJ, Aalen OO. The surprising implications of familial association in disease risk. BMC Public Health 2018; 18:135. [PMID: 29334951 PMCID: PMC5769446 DOI: 10.1186/s12889-018-5033-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 01/04/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND A wide range of diseases show some degree of clustering in families; family history is therefore an important aspect for clinicians when making risk predictions. Familial aggregation is often quantified in terms of a familial relative risk (FRR), and although at first glance this measure may seem simple and intuitive as an average risk prediction, its implications are not straightforward. METHODS We use two statistical models for the distribution of disease risk in a population: a dichotomous risk model that gives an intuitive understanding of the implication of a given FRR, and a continuous risk model that facilitates a more detailed computation of the inequalities in disease risk. Published estimates of FRRs are used to produce Lorenz curves and Gini indices that quantifies the inequalities in risk for a range of diseases. RESULTS We demonstrate that even a moderate familial association in disease risk implies a very large difference in risk between individuals in the population. We give examples of diseases for which this is likely to be true, and we further demonstrate the relationship between the point estimates of FRRs and the distribution of risk in the population. CONCLUSIONS The variation in risk for several severe diseases may be larger than the variation in income in many countries. The implications of familial risk estimates should be recognized by epidemiologists and clinicians.
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Affiliation(s)
- Morten Valberg
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, POB. 1122, Blindern, Oslo, N-0317 Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Mats Julius Stensrud
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, POB. 1122, Blindern, Oslo, N-0317 Norway
| | - Odd O. Aalen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, POB. 1122, Blindern, Oslo, N-0317 Norway
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409
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Seibert TM, Fan CC, Wang Y, Zuber V, Karunamuni R, Parsons JK, Eeles RA, Easton DF, Kote-Jarai ZS, Al Olama AA, Garcia SB, Muir K, Grönberg H, Wiklund F, Aly M, Schleutker J, Sipeky C, Tammela TL, Nordestgaard BG, Nielsen SF, Weischer M, Bisbjerg R, Røder MA, Iversen P, Key TJ, Travis RC, Neal DE, Donovan JL, Hamdy FC, Pharoah P, Pashayan N, Khaw KT, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright L, Brenner H, Cuk K, Saum KU, Park JY, Sellers TA, Slavov C, Kaneva R, Mitev V, Batra J, Clements JA, Spurdle A, Teixeira MR, Paulo P, Maia S, Pandha H, Michael A, Kierzek A, Karow DS, Mills IG, Andreassen OA, Dale AM. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts. BMJ 2018; 360:j5757. [PMID: 29321194 PMCID: PMC5759091 DOI: 10.1136/bmj.j5757] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/04/2017] [Indexed: 02/02/2023]
Abstract
OBJECTIVES To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. DESIGN Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. SETTING Multiple institutions that were members of international PRACTICAL consortium. PARTICIPANTS All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. MAIN OUTCOME MEASURES Prediction with hazard score of age of onset of aggressive cancer in validation set. RESULTS In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10-16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. CONCLUSIONS Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.
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Affiliation(s)
- Tyler M Seibert
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Yunpeng Wang
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Verena Zuber
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Roshan Karunamuni
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, USA
| | - J Kellogg Parsons
- Department of Surgery, University of California, San Diego, La Jolla, CA, USA
| | - Rosalind A Eeles
- Institute of Cancer Research, London, SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, R3, Box 83, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Sara Benlloch Garcia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Solna, 171 76 Stockholm, Sweden
- Department of Urology, Karolinska University Hospital, Solna, 171 76 Stockholm, Sweden
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics, Institute of Biomedicine, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland
- Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland
- BioMediTech, 30014 University of Tampere, Tampere, Finland
| | - Csilla Sipeky
- Department of Medical Biochemistry and Genetics, Institute of Biomedicine, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland
- Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland
| | - Teuvo Lj Tammela
- Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Finland
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Sune F Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Maren Weischer
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Rasmus Bisbjerg
- Department of Urology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - M Andreas Røder
- Copenhagen Prostate Cancer Centre, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Peter Iversen
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Copenhagen Prostate Cancer Centre, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford, Oxford OX3 7LF, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford, Oxford OX3 7LF, UK
| | - David E Neal
- Nuffield Department of Surgical Sciences, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
- University of Cambridge, Department of Oncology, Box 279, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Nora Pashayan
- University College London, Department of Applied Health Research, London WC1E 7HB, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge UK
| | - Christiane Maier
- Institute of Human Genetics, University Hospital of Ulm, Ulm, Germany
| | - Walther Vogel
- Institute of Human Genetics, University Hospital of Ulm, Ulm, Germany
| | - Manuel Luedeke
- Institute of Human Genetics, University Hospital of Ulm, Ulm, Germany
| | - Kathleen Herkommer
- Department of Urology, Klinikum rechts der Isar der Technischen Universitaet Muenchen, Munich, Germany
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis Street, Boston, MA 02115, USA
| | - Cezary Cybulski
- International Hereditary Cancer Centre, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Dominika Wokolorczyk
- International Hereditary Cancer Centre, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Wojciech Kluzniak
- International Hereditary Cancer Centre, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Lisa Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Katarina Cuk
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kai-Uwe Saum
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Thomas A Sellers
- Office of the Center Director, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University, Sofia, Bulgaria
| | - Radka Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, 2 Zdrave Str, 1431 Sofia, Bulgaria
| | - Vanio Mitev
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, 2 Zdrave Str, 1431 Sofia, Bulgaria
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - Judith A Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - Amanda Spurdle
- Molecular Cancer Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
- Australian Prostate Cancer BioResource, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | | | | | | | - David S Karow
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Ian G Mills
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
- Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, UK
- Nuffield Department of Surgical Sciences, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
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410
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Wang S, Qian F, Zheng Y, Ogundiran T, Ojengbede O, Zheng W, Blot W, Nathanson KL, Hennis A, Nemesure B, Ambs S, Olopade OI, Huo D. Genetic variants demonstrating flip-flop phenomenon and breast cancer risk prediction among women of African ancestry. Breast Cancer Res Treat 2018; 168:703-712. [PMID: 29302764 DOI: 10.1007/s10549-017-4638-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 12/23/2017] [Indexed: 01/19/2023]
Abstract
BACKGROUND Few studies have evaluated the performance of existing breast cancer risk prediction models among women of African ancestry. In replication studies of genetic variants, a change in direction of the risk association is a common phenomenon. Termed flip-flop, it means that a variant is risk factor in one population but protective in another, affecting the performance of risk prediction models. METHODS We used data from the genome-wide association study (GWAS) of breast cancer in the African diaspora (The Root consortium), which included 3686 participants of African ancestry from Nigeria, USA, and Barbados. Polygenic risk scores (PRSs) were constructed from the published odds ratios (ORs) of four sets of susceptibility loci for breast cancer. Discrimination capacity was measured using the area under the receiver operating characteristic curve (AUC). RESULTS Flip-flop phenomenon was observed among 30~40% of variants across studies. Using the 34 variants with consistent directionality among previous studies, we constructed a PRS with AUC of 0.531 (95% confidence interval [CI]: 0.512-0.550), which is similar to the PRS using 93 variants and ORs from European ancestry populations (AUC = 0.525, 95% CI: 0.506-0.544). Additionally, we found the 34-variant PRS has good discriminative accuracy in women with family history of breast cancer (AUC = 0.586, 95% CI: 0.532-0.640). CONCLUSIONS We found that PRS based on variants identified from prior GWASs conducted in women of European and Asian ancestries did not provide a comparable degree of risk stratification for women of African ancestry. Further large-scale fine-mapping studies in African ancestry populations are desirable to discover population-specific genetic risk variants.
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Affiliation(s)
- Shengfeng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.,Center for Clinical Cancer Genetics & Global Health, Department of Medicine, University of Chicago, 5841 S. Maryland Ave., MC 2020, Chicago, IL, 60637, USA
| | - Frank Qian
- Department of Medicine, University of Chicago, Chicago, USA
| | - Yonglan Zheng
- Center for Clinical Cancer Genetics & Global Health, Department of Medicine, University of Chicago, 5841 S. Maryland Ave., MC 2020, Chicago, IL, 60637, USA
| | - Temidayo Ogundiran
- Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oladosu Ojengbede
- Center for Population and Reproductive Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, USA
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, USA
| | | | - Anselm Hennis
- Chronic Disease Research Centre, Tropical Medicine Research Institute, University of the West Indies, Bridgetown, Barbados
| | - Barbara Nemesure
- Department of Preventive Medicine, State University of New York, Stony Brook, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, Rockville, USA
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics & Global Health, Department of Medicine, University of Chicago, 5841 S. Maryland Ave., MC 2020, Chicago, IL, 60637, USA.
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, 5841 S. Maryland Ave, MC 2007, Chicago, IL, 60637, USA.
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411
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Gui H, Li M, Sham PC, Baum L, Kwan P, Cherny SS. Genetic overlap between epilepsy and schizophrenia: Evidence from cross phenotype analysis in Hong Kong Chinese population. Am J Med Genet B Neuropsychiatr Genet 2018; 177:86-92. [PMID: 29150900 DOI: 10.1002/ajmg.b.32607] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 10/06/2017] [Indexed: 01/05/2023]
Abstract
Epilepsy and schizophrenia are common and typical neurological or mental illness respectively, and sometimes they comorbid in the same patients, however the underlying genetic relationship between the two brain diseases is still not fully understood. To investigate the possible genetic contribution to their comorbidity, we performed polygenic risk score (PRS) analyses and genetic correlation estimation so as to identify the overall genetic overlap between the two diseases. The global schizophrenia PRS is strongly associated with schizophrenia phenotype in Hong Kong population (odds ratio = 1.7, p = 2.26E-16), and focal epilepsy PRS is moderately associated with epilepsy phenotype in Hong Kong population (odds ratio = 1.14, p = 0.013). However the disease-specific PRS can only predict its own well-matched phenotype but not the other ones (p > 0.05). This pattern is further supported by non-significant pairwise genetic correlation and insufficient statistical power for PRS association from the cross-phenotype analyses. Our study reveals there's limited shared genetic aetiology between schizophrenia and epilepsy, and thus supports a model of shared environmental factors to explain the comorbidity between the two phenotypes.
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Affiliation(s)
- Hongsheng Gui
- Center for Genomic Sciences, The University of Hong Kong, Hong Kong, SAR, China.,Center for Health Policy and Health Research Service, Henry Ford Health System, Detroit, Michigan
| | - Miaoxin Li
- Department of Psychiatry, The University of Hong Kong, Hong Kong, SAR, China
| | - Pak C Sham
- Center for Genomic Sciences, The University of Hong Kong, Hong Kong, SAR, China.,Department of Psychiatry, The University of Hong Kong, Hong Kong, SAR, China.,The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, SAR, China
| | - Larry Baum
- Department of Psychiatry, The University of Hong Kong, Hong Kong, SAR, China
| | | | - Patrick Kwan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, SAR, China.,Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia
| | - Stacey S Cherny
- Center for Genomic Sciences, The University of Hong Kong, Hong Kong, SAR, China.,Department of Psychiatry, The University of Hong Kong, Hong Kong, SAR, China.,The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, SAR, China
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412
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Ferrari R, Manzoni C, Momeni P. Genetic Risk Factors for Sporadic Frontotemporal Dementia. NEURODEGENER DIS 2018. [DOI: 10.1007/978-3-319-72938-1_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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413
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Genome-Wide Association Studies and Risk Scores for Coronary Artery Disease: Sex Biases. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1065:627-642. [PMID: 30051411 DOI: 10.1007/978-3-319-77932-4_38] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Phenotypic sex differences in coronary artery disease (CAD) and its risk factors have been apparent for many decades in basic and clinical research; however, whether these are also present at the gene level and thus influence genome-wide association and genetic risk prediction studies has often been ignored. From fundamental and medical standpoints, this is critically important to assess in order to fully understand the underlying genetic architecture that predisposes to CAD and better predict disease outcomes based on the interaction between genes, sex effects, and environment. In this chapter we aimed to (1) integrate the history and latest research from genome-wide association studies for CAD and clinical and genetic risk scores for prediction of CAD, (2) highlight sex-specific differences in these areas of research, and (3) discuss reasons why sex differences have often not been considered and, where present, why sex differences exist at genetic and phenotypic levels and how important they are for consideration in future research. While we find interesting examples of sex differences in effects of genetic variants on CAD, genome-wide association and genetic risk studies have typically not tested for sex-specific effects despite mounting evidence from diverse fields that these are likely very important to consider at both the genetic and phenotypic levels. In-depth testing for sex effects in large-scale genome-wide association studies that include autosomal and often excluded sex chromosomes alongside parallel improvements in resolution of sex-specific differences for risk factors and disease outcomes for CAD has the potential to substantially improve clinical and genetic risk prediction studies. Developing sex-tailored genetic risk scores as has been done recently for other disorders might be also warranted for CAD. In the era of precision medicine, this level of accuracy is essential for such a common and costly disease.
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414
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Cope AP, Barnes MR, Belson A, Binks M, Brockbank S, Bonachela-Capdevila F, Carini C, Fisher BA, Goodyear CS, Emery P, Ehrenstein MR, Gozzard N, Harris R, Hollis S, Keidel S, Levesque M, Lindholm C, McDermott MF, McInnes IB, Mela CM, Parker G, Read S, Pedersen AW, Ponchel F, Porter D, Rao R, Rowe A, Schulz-Knappe P, Sleeman MA, Symmons D, Taylor PC, Tom B, Tsuji W, Verbeeck D, Isaacs JD. The RA-MAP Consortium: a working model for academia-industry collaboration. Nat Rev Rheumatol 2017; 14:53-60. [PMID: 29213124 DOI: 10.1038/nrrheum.2017.200] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.
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Affiliation(s)
- Andrew P Cope
- Centre for Inflammation Biology and Cancer Immunology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, Great Maze Pond, London, SE1 1UL, UK
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415
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Márquez-Luna C, Loh PR, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet Epidemiol 2017; 41:811-823. [PMID: 29110330 PMCID: PMC5726434 DOI: 10.1002/gepi.22083] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 08/16/2017] [Accepted: 08/30/2017] [Indexed: 01/04/2023]
Abstract
Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff = 40k) and Latino training data in small sample size (Neff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2 = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff = 40k) and South Asian (Neff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.
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Affiliation(s)
- Carla Márquez-Luna
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Po-Ru Loh
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Alkes L Price
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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416
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Doble B, Schofield DJ, Roscioli T, Mattick JS. Prioritising the application of genomic medicine. NPJ Genom Med 2017; 2:35. [PMID: 29263844 PMCID: PMC5698310 DOI: 10.1038/s41525-017-0037-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 10/20/2017] [Accepted: 10/25/2017] [Indexed: 12/25/2022] Open
Abstract
The clinical translation of genomic sequencing is hampered by the limited information available to guide investment into those areas where genomics is well placed to deliver improved health and economic outcomes. To date, genomic medicine has achieved its greatest successes through applications to diseases that have a high genotype–phenotype correlation and high penetrance, with a near certainty that the individual will develop the condition in the presence of the genotype. It has been anticipated that genomics will play an important role in promoting population health by targeting at-risk individuals and reducing the incidence of highly prevalent, costly, complex diseases, with potential applications across screening, prevention, and treatment decisions. However, where primary or secondary prevention requires behavioural changes, there is currently very little evidence to support reduction in disease incidence. A better understanding of the relationship between genomic variation and complex diseases will be necessary before effective genomic risk identification and management of the risk of complex diseases in healthy individuals can be carried out in clinical practice. Our recommended approach is that priority for genomic testing should focus on diseases where there is strong genotype–phenotype correlation, high or certain penetrance, the effects of the disease are serious and near-term, there is the potential for prevention and/or treatment, and the net costs incurred are acceptable for the health gains achieved.
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Affiliation(s)
- Brett Doble
- Garvan Institute of Medical Research, Sydney, NSW 2010 Australia.,Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK
| | - Deborah J Schofield
- Garvan Institute of Medical Research, Sydney, NSW 2010 Australia.,Faculty of Pharmacy, The University of Sydney, Sydney, NSW 2006 Australia.,Murdoch Childrens Research Institute, Royal Children's Hospital, Melbourne, VIC 3052 Australia
| | - Tony Roscioli
- Department of Medical Genetics, Sydney Children's Hospital, Sydney, NSW 2031 Australia
| | - John S Mattick
- Garvan Institute of Medical Research, Sydney, NSW 2010 Australia.,St. Vincent's Clinical School, UNSW Australia, Sydney, NSW 2052 Australia
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417
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Development of multivariable models to predict change in Body Mass Index within a clinical trial population of psychotic individuals. Sci Rep 2017; 7:14738. [PMID: 29116126 PMCID: PMC5677086 DOI: 10.1038/s41598-017-15137-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 10/18/2017] [Indexed: 12/20/2022] Open
Abstract
Many antipsychotics promote weight gain, which can lead to non-compliance and relapse of psychosis. By developing models that accurately identify individuals at greater risk of weight gain, clinicians can make informed treatment decisions and target intervention measures. We examined clinical, genetic and expression data for 284 individuals with psychosis derived from a previously published randomised controlled trial (IMPACT). These data were used to develop regression and classification models predicting change in Body Mass Index (BMI) over one year. Clinical predictors included demographics, anthropometrics, cardiac and blood measures, diet and exercise, physical and mental health, medication and BMI outcome measures. We included genetic polygenic risk scores (PRS) for schizophrenia, bipolar disorder, BMI, waist-hip-ratio, insulin resistance and height, as well as gene co-expression modules generated by Weighted Gene Co-expression Network Analysis (WGCNA). The best performing predictive models for BMI and BMI gain after one year used clinical data only, which suggests expression and genetic data do not improve prediction in this cohort.
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418
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Wood ME, Lapane KL, van Gelder MM, Rai D, Nordeng HM. Making fair comparisons in pregnancy medication safety studies: An overview of advanced methods for confounding control. Pharmacoepidemiol Drug Saf 2017; 27:140-147. [PMID: 29044735 PMCID: PMC6646901 DOI: 10.1002/pds.4336] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 08/29/2017] [Accepted: 09/18/2017] [Indexed: 12/12/2022]
Abstract
Understanding the safety of medication use during pregnancy relies on observational studies: However, confounding in observational studies poses a threat to the validity of estimates obtained from observational data. Newer methods, such as marginal structural models and propensity calibration, have emerged to deal with complex confounding problems, but these methods have seen limited uptake in the pregnancy medication literature. In this article, we provide an overview of newer advanced methods for confounding control and show how these methods are relevant for pregnancy medication safety studies.
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Affiliation(s)
- Mollie E. Wood
- PharmacoEpidemiology and Drug Safety Research Group, School of PharmacyUniversity of OsloNorway
| | - Kate L. Lapane
- Department of Quantitative Health SciencesUniversity of Massachusetts Medical SchoolWorcesterMAUSA
| | - Marleen M.H.J. van Gelder
- Department for Health Evidence, Radboud Institute for Health SciencesRadboud University Medical CenterNijmegenThe Netherlands
- Radboud REshape Innovation CenterRadboud University Medical CenterNijmegenThe Netherlands
| | - Dheeraj Rai
- School of Social and Community MedicineUniversity of BristolUK
| | - Hedvig M.E. Nordeng
- PharmacoEpidemiology and Drug Safety Research Group, School of PharmacyUniversity of OsloNorway
- Department of Child Mental and Physical HealthNorwegian Institute of Public HealthOsloNorway
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419
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McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, Conti D, Gauderman WJ, Hsu L, Hutter CM, Jankowska MM, Kerr J, Kraft P, Montgomery SB, Mukherjee B, Papanicolaou GJ, Patel CJ, Ritchie MD, Ritz BR, Thomas DC, Wei P, Witte JS. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 2017; 186:753-761. [PMID: 28978193 PMCID: PMC5860428 DOI: 10.1093/aje/kwx227] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 03/14/2017] [Accepted: 03/16/2017] [Indexed: 12/25/2022] Open
Abstract
Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.
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Affiliation(s)
| | - Leah E. Mechanic
- Correspondence to Dr. Leah E. Mechanic, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, Room 4E104, MSC 9763, Bethesda, MD 20892 (e-mail: )
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420
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Abstract
PURPOSE OF REVIEW Genetic factors contribute to the etiology of anorexia nervosa (AN). This review synthesizes the current state of knowledge about the genetic etiology of AN, provides directions for future research, and discusses clinical implications for this research. RECENT FINDINGS Candidate gene meta-analyses indicate serotonin genes may be involved in the genetic etiology of AN. Three genome-wide association studies have been conducted and one genome-wide significant locus was identified. Cross-disorder analyses suggest shared genetic risk between AN and several psychiatric, educational, and medical phenotypes. Much has been learned about the genetic etiology of AN over the past 3 decades. However, to fully understand the genetic architecture, we must consider all aspects including common variation, cross-disorder analysis, rare variation, copy number variation, and gene-environment interplay. Findings have important implications for the development of treatment and prevention approaches and for how AN, and psychiatric and medical diseases in general, are conceptualized.
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Affiliation(s)
- Jessica H. Baker
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Correspondence to: Dr. Baker, Department of Psychiatry, University of North Carolina at Chapel Hill, CB #7160, 101 Manning Drive, Chapel Hill, NC 27599-7160, Voice: 984-974-3794 Fax: 984-974-3780,
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421
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Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nat Commun 2017; 8:456. [PMID: 28878256 PMCID: PMC5587666 DOI: 10.1038/s41467-017-00470-2] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2016] [Accepted: 06/30/2017] [Indexed: 01/03/2023] Open
Abstract
Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.
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422
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Abstract
PURPOSE OF REVIEW This is an era where we have significantly advanced the understanding of the genetic architecture of schizophrenia. In this review, we consider how this knowledge may translate into advances that will improve patient care. RECENT FINDINGS Large-scale genome-wide association studies (GWAS) have identified more than a hundred loci each making a small contribution to illness risk. Meta-analysis of copy number variants (CNVs) in the Psychiatric Genomics Consortium (PGC) dataset has confirmed that some variants have a moderate or large impact on risk, although these are rare in the population. Genome sequencing advances allow a much more comprehensive evaluation of genomic variation. We describe the key findings from whole exome studies to date. These studies are happening against a backdrop of growing understanding of the regulation and expression of genes and better functional tools to investigate molecular mechanisms in model systems. We provide an overview of how recent approaches in schizophrenia genetics are converging and consider how they could impact on diagnostics, the development of personalized medicine, and drug discovery.
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Affiliation(s)
- Claire Foley
- Department of Psychiatry and Neuropsychiatric Genetics Research Group, Trinity College Dublin, Dublin, Ireland
| | - Aiden Corvin
- Department of Psychiatry and Neuropsychiatric Genetics Research Group, Trinity College Dublin, Dublin, Ireland.
| | - Shigeki Nakagome
- Department of Psychiatry and Neuropsychiatric Genetics Research Group, Trinity College Dublin, Dublin, Ireland
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423
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Meagher KM, McGowan ML, Settersten RA, Fishman JR, Juengst ET. Precisely Where Are We Going? Charting the New Terrain of Precision Prevention. Annu Rev Genomics Hum Genet 2017; 18:369-387. [PMID: 28441061 PMCID: PMC6203331 DOI: 10.1146/annurev-genom-091416-035222] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In addition to genetic data, precision medicine research gathers information about three factors that modulate gene expression: lifestyles, environments, and communities. The relevant research tools-epidemiology, environmental assessment, and socioeconomic analysis-are those of public health sciences rather than molecular biology. Because these methods are designed to support inferences and interventions addressing population health, the aspirations of this research are expanding from individualized treatment toward precision prevention in public health. The purpose of this review is to explore the emerging goals and challenges of such a shift to help ensure that the genomics community and public policy makers understand the ethical issues at stake in embracing and pursuing precision prevention. Two emerging goals bear special attention in this regard: (a) public health risk reduction strategies, such as screening, and (b) the application of genomic variation studies to understand and reduce health disparities among population groups.
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Affiliation(s)
- Karen M Meagher
- Center for Genomics and Society, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599;
| | - Michelle L McGowan
- Ethics Center, Division of General and Community Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229;
- Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio 45229
- Department of Women's, Gender, and Sexuality Studies, University of Cincinnati, Cincinnati, Ohio 45221
| | - Richard A Settersten
- Human Development and Family Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon 97331;
| | - Jennifer R Fishman
- Biomedical Ethics Unit, Department of Social Studies of Medicine, McGill University, Montreal, Quebec H3A 1X1, Canada;
| | - Eric T Juengst
- Center for Bioethics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599;
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424
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Marigorta UM, Denson LA, Hyams JS, Mondal K, Prince J, Walters TD, Griffiths A, Noe JD, Crandall WV, Rosh JR, Mack DR, Kellermayer R, Heyman MB, Baker SS, Stephens MC, Baldassano RN, Markowitz JF, Kim MO, Dubinsky MC, Cho J, Aronow BJ, Kugathasan S, Gibson G. Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn's disease. Nat Genet 2017; 49:1517-1521. [PMID: 28805827 PMCID: PMC5745037 DOI: 10.1038/ng.3936] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Accepted: 07/21/2017] [Indexed: 02/07/2023]
Affiliation(s)
- Urko M Marigorta
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Lee A Denson
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Jeffrey S Hyams
- Division of Digestive Diseases, Hepatology and Nutrition, Connecticut Children's Medical Center, Hartford, Connecticut, USA
| | - Kajari Mondal
- Division of Pediatric Gastroenterology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Jarod Prince
- Division of Pediatric Gastroenterology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Thomas D Walters
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Anne Griffiths
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Joshua D Noe
- Department of Pediatric Gastroenterology, Hepatology and Nutrition, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Wallace V Crandall
- Department of Pediatric Gastroenterology, Nationwide Children's Hospital, Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Joel R Rosh
- Department of Pediatrics, Goryeb Children's Hospital, Morristown, New Jersey, USA
| | - David R Mack
- Department of Pediatrics, Children's Hospital of Eastern Ontario IBD Centre and University of Ottawa, Ottawa, Ontario, Canada
| | - Richard Kellermayer
- Section of Pediatric Gastroenterology, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas, USA
| | - Melvin B Heyman
- Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA
| | - Susan S Baker
- Department of Digestive Diseases and Nutrition Center, University at Buffalo, Buffalo, New York, USA
| | - Michael C Stephens
- Department of Pediatric Gastroenterology, Mayo Clinic, Rochester, Minnesota, USA
| | - Robert N Baldassano
- Department of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Mi-Ok Kim
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Marla C Dubinsky
- Department of Pediatrics, Mount Sinai Hospital, New York, New York, USA
| | - Judy Cho
- Department of Pediatrics, Mount Sinai Hospital, New York, New York, USA
| | - Bruce J Aronow
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Subra Kugathasan
- Division of Pediatric Gastroenterology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia, USA
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425
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Cornel MC, van El CG. Barriers and Facilitating Factors for Implementation of Genetic Services: A Public Health Perspective. Front Public Health 2017; 5:195. [PMID: 28824901 PMCID: PMC5543075 DOI: 10.3389/fpubh.2017.00195] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2017] [Accepted: 07/19/2017] [Indexed: 01/07/2023] Open
Abstract
More than 15 years after the publication of the sequence of the human genome, the resulting changes in health care have been modest. At the same time, some promising examples in genetic services become visible, which contribute to the prevention of chronic disease such as cancer. These are discussed to identify barriers and facilitating factors for the implementation of genetic services. Examples from oncogenetics illustrate a high risk of serious disease where prevention is possible, especially in relatives. Some 5% of breast cancers and colorectal cancers are attributable to an inherited predisposition. These cancers occur at a relatively young age. DNA testing of relatives of affected patients may facilitate primary and secondary prevention. Training of non-genetic health care workers and health technology assessment are needed, as is translational research in terms of bringing genomics to health care practice while monitoring and evaluating. Stratified screening programs could include cascade screening and risk assessment based on family history. New roles and responsibilities will emerge. A clear assessment of the values implied is needed allowing to balance the pros and cons of interventions to further the responsible innovation of genetic services.
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Affiliation(s)
- Martina C Cornel
- Department of Clinical Genetics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, Netherlands
| | - Carla G van El
- Department of Clinical Genetics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, Netherlands
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426
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A randomized controlled trial of disclosing genetic risk information for Alzheimer disease via telephone. Genet Med 2017; 20:132-141. [PMID: 28726810 PMCID: PMC5897910 DOI: 10.1038/gim.2017.103] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 05/18/2017] [Indexed: 12/20/2022] Open
Abstract
Purpose Telephone disclosure of genetic test results can improve access to services. To date, studies of its impact have focused on return of Mendelian risk information, principally hereditary cancer syndromes. Methods In a multisite trial of Alzheimer’s disease genetic risk disclosure, asymptomatic adults were randomized to receive test results in-person or via telephone. Primary analyses examined patient outcomes 12 months after disclosure. Results Data from 257 participants showed that telephone disclosure occurred 7.4 days sooner and were 30% shorter, on average, than in-person disclosure (both p<0.001). Anxiety and depression scores were well below cutoffs for clinical concern across protocols. Comparing telephone and in-person disclosure protocols, 99% CIs of mean differences were within non-inferiority margins on scales assessing anxiety, depression, and test-related distress, but inconclusive about positive impact. No differences were observed on measures of recall and subjective impact. Sub-analyses supported non-inferiority on all outcomes among APOE ε4-negative participants. Sub-analyses were inconclusive for APOE ε4-positive participants, although mean anxiety and depression scores were still well below cutoffs for clinical concern. Conclusion Telephone disclosure of APOE results and risk for Alzheimer’s disease is generally safe and helps providers meet demands for services, even when results identify an increased risk for disease.
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427
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Miller DR, Logue MW, Wolf EJ, Maniates H, Robinson ME, Hayes JP, Stone A, Schichman S, McGlinchey RE, Milberg WP, Miller MW. Posttraumatic stress disorder symptom severity is associated with reduced default mode network connectivity in individuals with elevated genetic risk for psychopathology. Depress Anxiety 2017; 34:632-640. [PMID: 28494120 PMCID: PMC5523965 DOI: 10.1002/da.22633] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 03/28/2017] [Accepted: 03/31/2017] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Accumulating evidence suggests that posttraumatic stress disorder (PTSD) is associated with disrupted default mode network (DMN) connectivity, but findings across studies have not been uniform. Individual differences in relevant genes may account for some of the reported variability in the relationship between DMN connectivity and PTSD. In this study, we investigated this possibility using genome-wide association study (GWAS) derived polygenic risk scores (PRSs) for relevant psychiatric traits. We hypothesized that the association between PTSD and DMN connectivity would be moderated by genetic risk for one or more psychiatric traits such that individuals with elevated polygenic risk for psychopathology and severe PTSD would exhibit disrupted DMN connectivity. METHODS Participants were 156 white, non-Hispanic veterans of the wars in Iraq and Afghanistan who were genotyped and underwent resting state functional magnetic resonance imaging and clinical assessment. PRSs for neuroticism, anxiety, major depressive disorder, and cross-disorder risk (based on five psychiatric disorders) were calculated using summary statistics from published large-scale consortia-based GWASs. RESULTS Cross-disorder polygenic risk influenced the relationship between DMN connectivity and PTSD symptom severity such that individuals at greater genetic risk showed a significant negative association between PTSD symptom severity and connectivity between the posterior cingulate cortex and right middle temporal gyrus. Polygenic risk for neuroticism, anxiety, and major depressive disorder did not influence DMN connectivity directly or through an interaction with PTSD. CONCLUSIONS Findings illustrate the potential power of genome-wide PRSs to advance understanding of the relationship between PTSD and DMN connectivity, a putative neural endophenotype of the disorder.
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Affiliation(s)
- Danielle R. Miller
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA,Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Mark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA,Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Erika J. Wolf
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA,Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Hannah Maniates
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
| | - Meghan E. Robinson
- Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA USA,Department of Neurology, Boston University School of Medicine, Boston, MA, USA,Geriatric Research, Educational and Clinical Center and Translational Research Center for TBI and Stress Disorders, VA Boston Healthcare System, Boston, MA, USA
| | - Jasmeet P. Hayes
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA,Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA USA
| | - Annjanette Stone
- Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System
| | - Steven Schichman
- Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System
| | - Regina E. McGlinchey
- Geriatric Research, Educational and Clinical Center and Translational Research Center for TBI and Stress Disorders, VA Boston Healthcare System, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - William P. Milberg
- Geriatric Research, Educational and Clinical Center and Translational Research Center for TBI and Stress Disorders, VA Boston Healthcare System, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Mark W. Miller
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA,Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
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428
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Han SS, Rivera GA, Tammemägi MC, Plevritis SK, Gomez SL, Cheng I, Wakelee HA. Risk Stratification for Second Primary Lung Cancer. J Clin Oncol 2017. [PMID: 28644772 DOI: 10.1200/jco.2017.72.4203] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Purpose This study estimated the 10-year risk of developing second primary lung cancer (SPLC) among survivors of initial primary lung cancer (IPLC) and evaluated the clinical utility of the risk prediction model for selecting eligibility criteria for screening. Methods SEER data were used to identify a population-based cohort of 20,032 participants diagnosed with IPLC between 1988 and 2003 and who survived ≥ 5 years after the initial diagnosis. We used a proportional subdistribution hazards model to estimate the 10-year risk of developing SPLC among survivors of lung cancer LC in the presence of competing risks. Considered predictors included age, sex, race, treatment, histology, stage, and extent of disease. We examined the risk-stratification ability of the prediction model and performed decision curve analysis to evaluate the clinical utility of the model by calculating its net benefit in varied risk thresholds for screening. Results Although the median 10-year risk of SPLC among survivors of LC was 8.36%, the estimated risk varied substantially (range, 0.56% to 14.3%) when stratified by age, histology, and extent of IPLC in the final prediction model. The stratification by deciles of estimated risk showed that the observed incidence of SPLC was significantly higher in the tenth-decile group (12.5%) versus the first-decile group (2.9%; P < 10-10). The decision curve analysis yielded a range of risk thresholds (1% to 11.5%) at which the clinical net benefit of the risk model was larger than those in hypothetical all-screening or no-screening scenarios. Conclusion The risk stratification approach in SPLC can be potentially useful for identifying survivors of LC to be screened by computed tomography. More comprehensive environmental and genetic data may help enhance the predictability and stratification ability of the risk model for SPLC.
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Affiliation(s)
- Summer S Han
- Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada
| | - Gabriel A Rivera
- Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada
| | - Martin C Tammemägi
- Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada
| | - Sylvia K Plevritis
- Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada
| | - Scarlett L Gomez
- Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada
| | - Iona Cheng
- Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada
| | - Heather A Wakelee
- Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada
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429
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Breen G, Li Q, Roth BL, O'Donnell P, Didriksen M, Dolmetsch R, O'Reilly PF, Gaspar HA, Manji H, Huebel C, Kelsoe JR, Malhotra D, Bertolino A, Posthuma D, Sklar P, Kapur S, Sullivan PF, Collier DA, Edenberg HJ. Translating genome-wide association findings into new therapeutics for psychiatry. Nat Neurosci 2017; 19:1392-1396. [PMID: 27786187 DOI: 10.1038/nn.4411] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies, risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC phase 3 findings into new therapeutics.
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Affiliation(s)
- Gerome Breen
- MRC Social, Genetic &Developmental Psychiatry Centre, Institute of Psychiatry, Psychology &Neuroscience, King's College London, London, UK.,UK National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK
| | - Qingqin Li
- Neuroscience Therapeutic Area, Janssen Research &Development, LLC, Titusville, New Jersey, USA
| | - Bryan L Roth
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Michael Didriksen
- H. Lundbeck A/S, Synaptic Transmission, Neuroscience Research DK, Valby, Denmark
| | - Ricardo Dolmetsch
- Department of Neuroscience, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA
| | - Paul F O'Reilly
- MRC Social, Genetic &Developmental Psychiatry Centre, Institute of Psychiatry, Psychology &Neuroscience, King's College London, London, UK
| | - Héléna A Gaspar
- MRC Social, Genetic &Developmental Psychiatry Centre, Institute of Psychiatry, Psychology &Neuroscience, King's College London, London, UK.,UK National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK
| | - Husseini Manji
- Neuroscience Therapeutic Area, Janssen Research &Development, LLC, Titusville, New Jersey, USA
| | - Christopher Huebel
- MRC Social, Genetic &Developmental Psychiatry Centre, Institute of Psychiatry, Psychology &Neuroscience, King's College London, London, UK.,UK National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK
| | - John R Kelsoe
- Department of Psychiatry, University of California San Diego, and Veterans Affairs San Diego Healthcare System, La Jolla, California, USA
| | - Dheeraj Malhotra
- Neuroscience Discovery and Translational Area, Pharma Research &Early Development, F. Hoffmann - La Roche, Basel, Switzerland
| | - Alessandro Bertolino
- Institute of Psychiatry, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research/VU University Amsterdam, Amsterdam, the Netherlands.,Department of Clinical Genetics, VU University Medical Centre Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Pamela Sklar
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shitij Kapur
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - David A Collier
- MRC Social, Genetic &Developmental Psychiatry Centre, Institute of Psychiatry, Psychology &Neuroscience, King's College London, London, UK.,UK National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK.,Discovery Neuroscience Research, Eli Lilly and Company Ltd, Windlesham, Surrey, UK
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.,Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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430
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Wang MH, Chang B, Sun R, Hu I, Xia X, Wu WKK, Chong KC, Zee BCY. Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data. Hum Mutat 2017; 38:1235-1239. [PMID: 28419606 DOI: 10.1002/humu.23229] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 03/13/2017] [Accepted: 04/04/2017] [Indexed: 01/31/2023]
Abstract
Genetic data consists of a wide range of marker types, including common, low-frequency, and rare variants. Multiple genetic markers and their interactions play central roles in the heritability of complex disease. In this study, we propose an algorithm that uses a stratified variable selection design by genetic architectures and interaction effects, achieved by a dataset-adaptive W-test. The polygenic sets in all strata were integrated to form a classification rule. The algorithm was applied to the Critical Assessment of Genome Interpretation 4 bipolar challenge sequencing data. The prediction accuracy was 60% using genetic markers on an independent test set. We found that epistasis among common genetic variants contributed most substantially to prediction precision. However, the sample size was not large enough to draw conclusions for the lack of predictability of low-frequency variants and their epistasis.
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Affiliation(s)
- Maggie Haitian Wang
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Billy Chang
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Rui Sun
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Inchi Hu
- ISOM Department and Biomedical Engineering Division, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Xiaoxuan Xia
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - William Ka Kei Wu
- Department of Anaethesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ka Chun Chong
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Benny Chung-Ying Zee
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.,CUHK Shenzhen Research Institute, Shenzhen, China
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431
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Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genet 2017; 13:e1006836. [PMID: 28598966 PMCID: PMC5482506 DOI: 10.1371/journal.pgen.1006836] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 06/23/2017] [Accepted: 05/23/2017] [Indexed: 12/25/2022] Open
Abstract
Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn’s disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases. Genetic risk prediction plays a significant role in precision medicine. Accurate prediction models could have great impact on disease prevention and treatment strategies. However, prediction accuracies for most complex diseases remain moderate mainly due to the challenges in identifying and quantifying the effects of genetic variants from millions of markers, limited access to individual-level genotype data, and lack of efficient computational methods. Up to now, most methods have been focused on predicting disease risk using data from a single trait. With the discovery of genetic correlations among many complex diseases, incorporating data of genetically correlated diseases could have the potential to increase prediction accuracy. Current statistical methods are not able to fully exploit the richness of these kinds of data to take into account the shared genetic architecture. To make use of commonly available GWAS summary statistics, we propose a novel method to address these challenges by jointly modeling genetically correlated diseases and integrating genomic functional annotations. We demonstrate the substantial improvement in accuracy in both extensive simulation studies and real data analysis of Crohn’s disease, celiac disease and type-II diabetes. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.
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Affiliation(s)
- Yiming Hu
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Qiongshi Lu
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Wei Liu
- Peking University, Beijing, China
| | - Yuhua Zhang
- Shanghai Jiao Tong University, Shanghai, China
| | - Mo Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Clinical Epidemiology Research Center (CERC), Veterans Affairs (VA) Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- * E-mail:
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432
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Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Comput Biol 2017; 13:e1005589. [PMID: 28594818 PMCID: PMC5481142 DOI: 10.1371/journal.pcbi.1005589] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 06/22/2017] [Accepted: 05/19/2017] [Indexed: 12/25/2022] Open
Abstract
Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.
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433
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Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet 2017; 18:331-344. [PMID: 28286336 PMCID: PMC5935119 DOI: 10.1038/nrg.2016.160] [Citation(s) in RCA: 375] [Impact Index Per Article: 53.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Coronary artery disease is the leading global cause of mortality. Long recognized to be heritable, recent advances have started to unravel the genetic architecture of the disease. Common variant association studies have linked approximately 60 genetic loci to coronary risk. Large-scale gene sequencing efforts and functional studies have facilitated a better understanding of causal risk factors, elucidated underlying biology and informed the development of new therapeutics. Moving forwards, genetic testing could enable precision medicine approaches by identifying subgroups of patients at increased risk of coronary artery disease or those with a specific driving pathophysiology in whom a therapeutic or preventive approach would be most useful.
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Affiliation(s)
- Amit V Khera
- Division of Cardiology, Department of Medicine and Center for Genomic Medicine, Massachusetts General Hospital; Cardiovascular Disease Initiative, Broad Institute of Harvard and Massachusetts Institute of Technology, 185 Cambridge Street, CPZN 5.252, Boston, Massachusetts 02114, USA
| | - Sekar Kathiresan
- Division of Cardiology, Department of Medicine and Center for Genomic Medicine, Massachusetts General Hospital; Cardiovascular Disease Initiative, Broad Institute of Harvard and Massachusetts Institute of Technology, 185 Cambridge Street, CPZN 5.252, Boston, Massachusetts 02114, USA
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434
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Zeng P, Zhou X, Huang S. Prediction of gene expression with cis-SNPs using mixed models and regularization methods. BMC Genomics 2017; 18:368. [PMID: 28490319 PMCID: PMC5425981 DOI: 10.1186/s12864-017-3759-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 05/03/2017] [Indexed: 12/25/2022] Open
Abstract
Background It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. Methods We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. Results The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R2 ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R2 ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Conclusions Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, 209 Tongshan Rd, Xuzhou, Jiangsu, 221004, China. .,Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48104, USA.
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48104, USA
| | - Shuiping Huang
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, 209 Tongshan Rd, Xuzhou, Jiangsu, 221004, China.
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435
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Kraft P. Fine Tuning the Risk of Hereditary Cancer Using Genome-Wide Association Studies. J Clin Oncol 2017; 35:2224-2225. [PMID: 28481708 DOI: 10.1200/jco.2017.72.8071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Peter Kraft
- Peter Kraft, Harvard T.H. Chan School of Public Health, Boston, MA
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436
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Collins SC. Precision reproductive medicine: multigene panel testing for infertility risk assessment. J Assist Reprod Genet 2017; 34:967-973. [PMID: 28470451 DOI: 10.1007/s10815-017-0938-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 04/27/2017] [Indexed: 12/11/2022] Open
Abstract
The concept of precision medicine relies on a thorough understanding of the consequences of unique features of individual patients, such as environmental exposures and genetic profiles. A key component of implementing individualized care in this paradigm will be improved assessment of genetic risk. Compared with single gene tests, multigene panel testing-which has recently become commercially available for female infertility-offers the possibility of a more comprehensive and efficient risk evaluation. However, as the use of multigene panel testing for breast cancer risk has shown, this approach must be used judiciously to ensure its usefulness in a clinical setting. Key challenges which have been encountered in oncology include the interpretation of gene variants of questionable clinical effect and a lack of evidence to guide management after variants are identified. In this review, the core concepts of multigene panel testing for risk assessment are discussed, with careful attention to both its shortcomings as well as its potential for benefit in reproductive medicine.
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Affiliation(s)
- Stephen C Collins
- Division of Reproductive Endocrinology and Infertility, Yale School of Medicine, 150 Sargent Drive, Second Floor, New Haven, CT, 06511, USA.
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437
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Ge T, Chen CY, Neale BM, Sabuncu MR, Smoller JW. Phenome-wide heritability analysis of the UK Biobank. PLoS Genet 2017; 13:e1006711. [PMID: 28388634 PMCID: PMC5400281 DOI: 10.1371/journal.pgen.1006711] [Citation(s) in RCA: 144] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 04/21/2017] [Accepted: 03/22/2017] [Indexed: 11/18/2022] Open
Abstract
Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability. Heritability of a trait refers to the proportion of phenotypic variation that is due to genetic variation among individuals. It provides important information about the genetic basis of complex traits and indicates whether a phenotype is an appropriate target for more specific statistical and molecular genetic analyses. Recent studies have leveraged the increasingly ubiquitous genome-wide data and documented the heritability attributable to common genetic variation captured by genotyping microarrays for a wide range of human traits. However, heritability is not a fixed property of a phenotype and can vary with population-specific differences in the genetic background and environmental variation. Here, using a computationally and memory efficient heritability estimation method, we report the heritability for a large number of traits derived from the large-scale, population-based UK Biobank, and, for the first time, demonstrate the moderating effect of three major demographic variables (age, sex and socioeconomic status) on heritability estimates derived from genome-wide common genetic variation. Our study represents the first comprehensive heritability analysis across the phenotypic spectrum in the UK Biobank.
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Affiliation(s)
- Tian Ge
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital / Harvard Medical School, Charlestown, MA, United States of America
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- * E-mail: (TG); (JWS)
| | - Chia-Yen Chen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Benjamin M. Neale
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Mert R. Sabuncu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital / Harvard Medical School, Charlestown, MA, United States of America
- School of Electrical and Computer Engineering and Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States of America
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- * E-mail: (TG); (JWS)
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438
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Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, Daly MJ, Bustamante CD, Kenny EE. Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet 2017. [PMID: 28366442 DOI: 10.1016/j.ajhg] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2023] Open
Abstract
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | | | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | | | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Simon Gravel
- Department of Human Genetics, McGill University, Montreal, QC H3A 0G1, Canada; McGill University and Genome Quebec Innovation Centre, Montreal, QC H3A 0G1, Canada
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | | | - Eimear E Kenny
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Center of Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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439
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Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet 2017; 100:635-649. [PMID: 28366442 DOI: 10.1016/j.ajhg.2017.03.004] [Citation(s) in RCA: 791] [Impact Index Per Article: 113.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 03/10/2017] [Indexed: 01/10/2023] Open
Abstract
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
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440
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Pharmacogenetics of antidepressant response: A polygenic approach. Prog Neuropsychopharmacol Biol Psychiatry 2017; 75:128-134. [PMID: 28159590 DOI: 10.1016/j.pnpbp.2017.01.011] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 12/30/2016] [Accepted: 01/26/2017] [Indexed: 12/12/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) has a high personal and socio-economic burden and >60% of patients fail to achieve remission with the first antidepressant. The biological mechanisms behind antidepressant response are only partially known but genetic factors play a relevant role. A combined predictor across genetic variants may be useful to investigate this complex trait. METHODS Polygenic risk scores (PRS) were used to estimate multi-allelic contribution to: 1) antidepressant efficacy; 2) its overlap with MDD and schizophrenia. We constructed PRS and tested whether these predicted symptom improvement or remission from the GENDEP study (n=736) to the STAR*D study (n=1409) and vice-versa, including the whole sample or only patients treated with escitalopram or citalopram. Using summary statistics from Psychiatric Genomics Consortium for MDD and schizophrenia, we tested whether PRS from these disorders predicted symptom improvement in GENDEP, STAR*D, and five further studies (n=3756). RESULTS No significant prediction of antidepressant efficacy was obtained from PRS in GENDEP/STAR*D but this analysis might have been underpowered. There was no evidence of overlap in the genetics of antidepressant response with either MDD or schizophrenia, either in individual studies or a meta-analysis. Stratifying by antidepressant did not alter the results. DISCUSSION We identified no significant predictive effect using PRS between pharmacogenetic studies. The genetic liability to MDD or schizophrenia did not predict response to antidepressants, suggesting differences between the genetic component of depression and treatment response. Larger or more homogeneous studies will be necessary to obtain a polygenic predictor of antidepressant response.
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441
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Hong EP, Go MJ, Kim HL, Park JW. Risk prediction of pulmonary tuberculosis using genetic and conventional risk factors in adult Korean population. PLoS One 2017; 12:e0174642. [PMID: 28355295 PMCID: PMC5371343 DOI: 10.1371/journal.pone.0174642] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 03/13/2017] [Indexed: 12/16/2022] Open
Abstract
A complex interplay among host, pathogen, and environmental factors is believed to contribute to the risk of developing pulmonary tuberculosis (PTB). The lack of replication of published genome-wide association study (GWAS) findings limits the clinical utility of reported single nucleotide polymorphisms (SNPs). We conducted a GWAS using 467 PTB cases and 1,313 healthy controls obtained from two community-based cohorts in Korea. We evaluated the performance of PTB risk models based on different combinations of genetic and nongenetic factors and validated the results in an independent Korean population comprised of 179 PTB cases and 500 healthy controls. We demonstrated the polygenic nature of PTB and nongenetic factors such as age, sex, and body mass index (BMI) were strongly associated with PTB risk. None of the SNPs achieved genome-wide significance; instead, we were able to replicate the associations between PTB and ten SNPs near or in the genes, CDCA7, GBE1, GADL1, SPATA16, C6orf118, KIAA1432, DMRT2, CTR9, CCDC67, and CDH13, which may play roles in the immune and inflammatory pathways. Among the replicated SNPs, an intergenic SNP, rs9365798, located downstream of the C6orf118 gene showed the most significant association under the dominant model (OR = 1.59, 95% CI 1.32–1.92, P = 2.1×10−6). The performance of a risk model combining the effects of ten replicated SNPs and six nongenetic factors (i.e., age, sex, BMI, cigarette smoking, systolic blood pressure, and hemoglobin) were validated in the replication set (AUC = 0.80, 95% CI 0.76–0.84). The strategy of combining genetic and nongenetic risk factors ultimately resulted in better risk prediction for PTB in the adult Korean population.
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Affiliation(s)
- Eun Pyo Hong
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon-si, Ganwon-do, Republic of Korea
| | - Min Jin Go
- Center for Genome Science, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Hyung-Lae Kim
- Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Ji Wan Park
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon-si, Ganwon-do, Republic of Korea
- * E-mail:
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442
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Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLoS Med 2017; 14:e1002258. [PMID: 28323831 PMCID: PMC5360219 DOI: 10.1371/journal.pmed.1002258] [Citation(s) in RCA: 245] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Accepted: 02/09/2017] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction. METHODS AND FINDINGS Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer's Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10-5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer's Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer's Disease Center [NIA ADC], and Alzheimer's Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62-4.24, p = 1.0 × 10-22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10-26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran-Armitage trend test, p = 1.5 × 10-10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10-6, and Consortium to Establish a Registry for Alzheimer's Disease score for neuritic plaques, p = 6.8 × 10-6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10-6, and hippocampus, p = 7.9 × 10-5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use. CONCLUSIONS We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.
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443
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Ibáñez-Sanz G, Díez-Villanueva A, Alonso MH, Rodríguez-Moranta F, Pérez-Gómez B, Bustamante M, Martin V, Llorca J, Amiano P, Ardanaz E, Tardón A, Jiménez-Moleón JJ, Peiró R, Alguacil J, Navarro C, Guinó E, Binefa G, Fernández-Navarro P, Espinosa A, Dávila-Batista V, Molina AJ, Palazuelos C, Castaño-Vinyals G, Aragonés N, Kogevinas M, Pollán M, Moreno V. Risk Model for Colorectal Cancer in Spanish Population Using Environmental and Genetic Factors: Results from the MCC-Spain study. Sci Rep 2017; 7:43263. [PMID: 28233817 PMCID: PMC5324108 DOI: 10.1038/srep43263] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 01/20/2017] [Indexed: 12/20/2022] Open
Abstract
Colorectal cancer (CRC) screening of the average risk population is only indicated according to age. We aim to elaborate a model to stratify the risk of CRC by incorporating environmental data and single nucleotide polymorphisms (SNP). The MCC-Spain case-control study included 1336 CRC cases and 2744 controls. Subjects were interviewed on lifestyle factors, family and medical history. Twenty-one CRC susceptibility SNPs were genotyped. The environmental risk model, which included alcohol consumption, obesity, physical activity, red meat and vegetable consumption, and nonsteroidal anti-inflammatory drug use, contributed to CRC with an average per factor OR of 1.36 (95% CI 1.27 to 1.45). Family history of CRC contributed an OR of 2.25 (95% CI 1.87 to 2.72), and each additional SNP contributed an OR of 1.07 (95% CI 1.04 to 1.10). The risk of subjects with more than 25 risk alleles (5th quintile) was 82% higher (OR 1.82, 95% CI 1.11 to 2.98) than subjects with less than 19 alleles (1st quintile). This risk model, with an AUROC curve of 0.63 (95% CI 0.60 to 0.66), could be useful to stratify individuals. Environmental factors had more weight than the genetic score, which should be considered to encourage patients to achieve a healthier lifestyle.
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Affiliation(s)
- Gemma Ibáñez-Sanz
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Anna Díez-Villanueva
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Spain
| | - M Henar Alonso
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Francisco Rodríguez-Moranta
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Gastroenterology Department, Bellvitge University Hospital-IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Beatriz Pérez-Gómez
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Environmental and Cancer Epidemiology Department, National Center of Epidemiology - Instituto de Salud Carlos III, Madrid, Spain.,Oncology and Hematology Area, IIS Puerta De Hierro, Cancer Epidemiology Research Group, Madrid, Spain
| | - Mariona Bustamante
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,ISGlobal Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Vicente Martin
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Instituto de Biomedicina (IBIOMED). Grupo de investigación en interacciones gen ambiente y salud. Universidad de León, León, Spain
| | - Javier Llorca
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Universidad de Cantabria - IDIVAL, Santander, Spain
| | - Pilar Amiano
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Public Health Division of Gipuzkoa, Biodonostia Research Institute, San Sebastian, Spain
| | - Eva Ardanaz
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Navarra Public Health Institute, Navarra, Spain
| | - Adonina Tardón
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,University Institute of Oncology of Asturias (IUOPA), Universidad de Oviedo, Oviedo, Spain
| | - Jose J Jiménez-Moleón
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Hospitales Universitarios de Granada, Granada, Spain
| | - Rosana Peiró
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana FISABIO-Salud Pública, Valencia
| | - Juan Alguacil
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Centre for Research in Health and Environment (CYSMA), Universidad de Huelva, Huelva, Spain
| | - Carmen Navarro
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Department of Epidemiology, IMIB-Arrixaca and Department of Health and Social Sciences, Murcia Regional Health Council, Universidad de Murcia, Murcia, Spain
| | - Elisabet Guinó
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Gemma Binefa
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Pablo Fernández-Navarro
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Environmental and Cancer Epidemiology Department, National Center of Epidemiology - Instituto de Salud Carlos III, Madrid, Spain.,Oncology and Hematology Area, IIS Puerta De Hierro, Cancer Epidemiology Research Group, Madrid, Spain
| | - Anna Espinosa
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,ISGlobal Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Verónica Dávila-Batista
- Instituto de Biomedicina (IBIOMED). Grupo de investigación en interacciones gen ambiente y salud. Universidad de León, León, Spain
| | - Antonio José Molina
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Instituto de Biomedicina (IBIOMED). Grupo de investigación en interacciones gen ambiente y salud. Universidad de León, León, Spain
| | | | - Gemma Castaño-Vinyals
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,ISGlobal Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,IMIM (Hospital Del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Nuria Aragonés
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Environmental and Cancer Epidemiology Department, National Center of Epidemiology - Instituto de Salud Carlos III, Madrid, Spain.,Oncology and Hematology Area, IIS Puerta De Hierro, Cancer Epidemiology Research Group, Madrid, Spain
| | - Manolis Kogevinas
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,ISGlobal Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,IMIM (Hospital Del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,School of Public Health, Athens, Greece
| | - Marina Pollán
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,Environmental and Cancer Epidemiology Department, National Center of Epidemiology - Instituto de Salud Carlos III, Madrid, Spain.,Oncology and Hematology Area, IIS Puerta De Hierro, Cancer Epidemiology Research Group, Madrid, Spain
| | - Victor Moreno
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Spain. .,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. .,Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
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444
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Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 2017; 18:117-127. [PMID: 27840428 PMCID: PMC5449190 DOI: 10.1038/nrg.2016.142] [Citation(s) in RCA: 257] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.
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Affiliation(s)
- Bogdan Pasaniuc
- Departments of Human Genetics, and Pathology and Laboratory Medicine, University of California, Los Angeles, California 90095, USA
| | - Alkes L Price
- Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
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445
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Wang MH, Weng H. Genetic Test, Risk Prediction, and Counseling. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1005:21-46. [DOI: 10.1007/978-981-10-5717-5_2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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446
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Shi J, Park JH, Duan J, Berndt ST, Moy W, Yu K, Song L, Wheeler W, Hua X, Silverman D, Garcia-Closas M, Hsiung CA, Figueroa JD, Cortessis VK, Malats N, Karagas MR, Vineis P, Chang IS, Lin D, Zhou B, Seow A, Matsuo K, Hong YC, Caporaso NE, Wolpin B, Jacobs E, Petersen GM, Klein AP, Li D, Risch H, Sanders AR, Hsu L, Schoen RE, Brenner H, Stolzenberg-Solomon R, Gejman P, Lan Q, Rothman N, Amundadottir LT, Landi MT, Levinson DF, Chanock SJ, Chatterjee N. Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data. PLoS Genet 2016; 12:e1006493. [PMID: 28036406 PMCID: PMC5201242 DOI: 10.1371/journal.pgen.1006493] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 11/16/2016] [Indexed: 12/20/2022] Open
Abstract
Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.
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Affiliation(s)
- Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
- * E-mail: (JS); (NC)
| | - Ju-Hyun Park
- Department of Statistics, Dongguk University, Seoul, Korea
| | - Jubao Duan
- Center for Psychiatric Genetics, Department of Psychiatry and Behavioral Sciences, North Shore University Health System Research Institute, University of Chicago Pritzker School of Medicine, Evanston, Illinois, United States of America
| | - Sonja T. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Winton Moy
- Dept. of Statistics, Northern Illinois University, DeKalb, Illinois, United States of America
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - William Wheeler
- Information Management Services, Inc., Rockville, Maryland, United States of America
| | - Xing Hua
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Debra Silverman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Chao Agnes Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Jonine D. Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Medical School, Edinburgh, United Kingdom
| | - Victoria K. Cortessis
- Department of Preventive Medicine and Department of Obstetrics and Gynecology, USC Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Margaret R. Karagas
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Paolo Vineis
- Human Genetics Foundation, Turin, Italy
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Dongxin Lin
- Department of Etiology & Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Baosen Zhou
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China
| | - Adeline Seow
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Keitaro Matsuo
- Division of Molecular Medicine, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Japan
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Brian Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Eric Jacobs
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, United States of America
| | - Gloria M. Petersen
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Alison P. Klein
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Epidemiology, the Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Harvey Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Alan R. Sanders
- Center for Psychiatric Genetics, Department of Psychiatry and Behavioral Sciences, North Shore University Health System Research Institute, University of Chicago Pritzker School of Medicine, Evanston, Illinois, United States of America
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Robert E. Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | | | | | | | | | - Rachael Stolzenberg-Solomon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Pablo Gejman
- Center for Psychiatric Genetics, Department of Psychiatry and Behavioral Sciences, North Shore University Health System Research Institute, University of Chicago Pritzker School of Medicine, Evanston, Illinois, United States of America
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Laufey T. Amundadottir
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Douglas F. Levinson
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, United States of America
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Nilanjan Chatterjee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail: (JS); (NC)
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447
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McPherson R, Hegele RA. The Sum of Its Parts: The Polygenic Basis of Coronary Artery Disease. Can J Cardiol 2016; 32:1372-1374. [PMID: 27884482 DOI: 10.1016/j.cjca.2016.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 07/06/2016] [Indexed: 10/21/2022] Open
Affiliation(s)
- Ruth McPherson
- Division of Cardiology, Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
| | - Robert A Hegele
- Robarts Research Institute and Department of Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
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448
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Zhao SD. Integrative genetic risk prediction using non-parametric empirical Bayes classification. Biometrics 2016; 73:582-592. [PMID: 27792843 DOI: 10.1111/biom.12619] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 09/01/2016] [Accepted: 09/01/2016] [Indexed: 12/27/2022]
Abstract
Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa.
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Affiliation(s)
- Sihai Dave Zhao
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, U.S.A
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449
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Xue Y, Lameijer EW, Ye K, Zhang K, Chang S, Wang X, Wu J, Gao G, Zhao F, Li J, Han C, Xu S, Xiao J, Yang X, Ying X, Zhang X, Chen WH, Liu Y, Zhang Z, Huang K, Yu J. Precision Medicine: What Challenges Are We Facing? GENOMICS PROTEOMICS & BIOINFORMATICS 2016; 14:253-261. [PMID: 27744061 PMCID: PMC5093857 DOI: 10.1016/j.gpb.2016.10.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Revised: 09/30/2016] [Accepted: 10/01/2016] [Indexed: 11/19/2022]
Affiliation(s)
- Yu Xue
- MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Brain Science, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Eric-Wubbo Lameijer
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Kai Ye
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Kunlin Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Suhua Chang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaoyue Wang
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Jianmin Wu
- MOE/Beijing Key Laboratory of Carcinogenesis and Translational Research, Center for Cancer Bioinformatics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ge Gao
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Jian Li
- MOE Key Laboratory of Developmental Genes and Human Disease, Institute of Life Sciences, Southeast University, Nanjing 210096, China
| | - Chunsheng Han
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Shuhua Xu
- Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jingfa Xiao
- CAS Key Laboratory of Genome Sciences and Information, Chinese Academy of Sciences, Beijing 100101, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
| | - Xiaomin Ying
- Computational Omics Laboratory, Center of Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China
| | - Xuegong Zhang
- Bioinformatics Division, TNLIST and MOE Key Laboratory for Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wei-Hua Chen
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Zhang Zhang
- BIG Data Center and CAS Key Laboratory of Genome Sciences & Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Kun Huang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43017, USA
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and Information, Chinese Academy of Sciences, Beijing 100101, China.
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450
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Kerns SL, Dorling L, Fachal L, Bentzen S, Pharoah PDP, Barnes DR, Gómez-Caamaño A, Carballo AM, Dearnaley DP, Peleteiro P, Gulliford SL, Hall E, Michailidou K, Carracedo Á, Sia M, Stock R, Stone NN, Sydes MR, Tyrer JP, Ahmed S, Parliament M, Ostrer H, Rosenstein BS, Vega A, Burnet NG, Dunning AM, Barnett GC, West CML. Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer. EBioMedicine 2016; 10:150-63. [PMID: 27515689 PMCID: PMC5036513 DOI: 10.1016/j.ebiom.2016.07.022] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Revised: 07/08/2016] [Accepted: 07/18/2016] [Indexed: 12/31/2022] Open
Abstract
Nearly 50% of cancer patients undergo radiotherapy. Late radiotherapy toxicity affects quality-of-life in long-term cancer survivors and risk of side-effects in a minority limits doses prescribed to the majority of patients. Development of a test predicting risk of toxicity could benefit many cancer patients. We aimed to meta-analyze individual level data from four genome-wide association studies from prostate cancer radiotherapy cohorts including 1564 men to identify genetic markers of toxicity. Prospectively assessed two-year toxicity endpoints (urinary frequency, decreased urine stream, rectal bleeding, overall toxicity) and single nucleotide polymorphism (SNP) associations were tested using multivariable regression, adjusting for clinical and patient-related risk factors. A fixed-effects meta-analysis identified two SNPs: rs17599026 on 5q31.2 with urinary frequency (odds ratio [OR] 3.12, 95% confidence interval [CI] 2.08-4.69, p-value 4.16×10(-8)) and rs7720298 on 5p15.2 with decreased urine stream (OR 2.71, 95% CI 1.90-3.86, p-value=3.21×10(-8)). These SNPs lie within genes that are expressed in tissues adversely affected by pelvic radiotherapy including bladder, kidney, rectum and small intestine. The results show that heterogeneous radiotherapy cohorts can be combined to identify new moderate-penetrance genetic variants associated with radiotherapy toxicity. The work provides a basis for larger collaborative efforts to identify enough variants for a future test involving polygenic risk profiling.
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Affiliation(s)
- Sarah L Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA; Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leila Dorling
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Laura Fachal
- Department of Oncology, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK; Grupo de Medicina Xenómica, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain
| | - Søren Bentzen
- Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, Baltimore, USA; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Daniel R Barnes
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Antonio Gómez-Caamaño
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Spain
| | - Ana M Carballo
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Spain
| | - David P Dearnaley
- Joint Department of Physics, Institute of Cancer Research, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5NG, UK
| | - Paula Peleteiro
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Spain
| | - Sarah L Gulliford
- Joint Department of Physics, Institute of Cancer Research, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5NG, UK
| | - Emma Hall
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London SM2 5NG, UK
| | - Kyriaki Michailidou
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Ángel Carracedo
- Grupo de Medicina Xenómica, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Fundación Pública Galega de Medicina Xenómica, Servizo Galego de Saúde (SERGAS), 15706 Santiago de Compostela, Spain
| | - Michael Sia
- Department of Radiation Oncology, Tom Baker Cancer Center, University of Calgary, Calgary, Canada
| | - Richard Stock
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nelson N Stone
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew R Sydes
- Cancer and Other Non-Infectious Diseases, MRC Clinical Trials Unit, London WC2B 6NH, UK
| | - Jonathan P Tyrer
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Shahana Ahmed
- Department of Oncology, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Matthew Parliament
- Division of Radiation Oncology, Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
| | - Harry Ostrer
- Department of Pathology, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Barry S Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiation Oncology, New York University School of Medicine, New York, NY, USA
| | - Ana Vega
- Grupo de Medicina Xenómica, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Department of Radiation Oncology, Tom Baker Cancer Center, University of Calgary, Calgary, Canada
| | - Neil G Burnet
- University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - Alison M Dunning
- Department of Oncology, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Gillian C Barnett
- Department of Oncology, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK; Department of Oncology, Box 193, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB0 0QQ, UK
| | - Catharine M L West
- Institute of Cancer Sciences, The University of Manchester, Manchester Academic Health Science Centre, Christie Hospital, Manchester M20 4BX, UK.
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