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arXiv 2018年

作者： Yamada, Yutaro Lindenbaum, Ofir Negahban, Sahand Kluger, Yuval Statistics Department Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology and Bioinformatics Yale University New HavenCT United States Department of Pathology Yale University New HavenCT United States

Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in high-dimensional non-linear function estimation problems. The new procedure is based on minimizing the `0 norm of the vector of indicator variables that represent if a feature is selected or not. Our approach relies on the continuous relaxation of Bernoulli distributions, which allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. This general framework simultaneously minimizes a loss function while selecting relevant features. Furthermore, we provide an information-theoretic justification of incorporating Bernoulli distribution into our approach and demonstrate the potential of the approach on synthetic and real-life applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Feature extraction

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Author Correction: Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer

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NPJ breast cancer 2023年第1期9卷 11页

作者： Shivangi Yadav Ranya Virk Carolina H Chung Mariana Bustamante Eduardo David VanDerway Duojiao Chen Kirsten Burdett Hongyu Gao Zexian Zeng Manish Ranjan Gannon Cottone Xiaoling Xuei Sriram Chandrasekaran Vadim Backman Robert Chatterton Seema Ahsan Khan Susan E Clare Department of Surgery Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. Department of Biomedical Engineering Northwestern University Evanston IL 60208-2850 USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI 48109 USA. Center of for Medical Genomics Indiana University School of Medicine Indianapolis IN 46202 USA. Department of Preventive Medicine Northwestern University Chicago IL 60611 USA. Department of Data Sciences Dana Farber Cancer Institute Harvard T.H. Chan School of Public Health Boston MA 02215 USA. Program in Chemical Biology University of Michigan Ann Arbor MI 48109 USA. Rogel Cancer Center University of Michigan Medical School Ann Arbor MI 48109 USA. Center for Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 USA. Department of Obstetrics and Gynecology Northwestern University Feinberg School of Medicine Chicago IL 60611 USA. Department of Surgery Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. s-khan2@northwestern.edu. Department of Surgery Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. susan.clare@northwestern.edu.

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Serotonin transporter inhibits antitumor immunity through regulating the intratumoral serotonin axis

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Cell 2025年

作者： Bo Li James Elsten-Brown Miao Li Enbo Zhu Zhe Li Yuning Chen Elliot Kang Feiyang Ma Jennifer Chiang Yan-Ruide Li Yichen Zhu Jie Huang Audrey Fung Quentin Scarborough Robin Cadd Jin J Zhou Arnold I Chin Matteo Pellegrini Lili Yang Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA. Electronic address: bo.li@ucla.edu. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA. Department of Materials Science and Engineering University of California Los Angeles Los Angeles CA 90095 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA 90095 USA. Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA USA. Department of Urology University of California Los Angeles Los Angeles CA 90095 USA Eli & Edythe Broad Center of Regenerative Medicine and Stem Cell Research University of California Los Angeles Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA 90095 USA Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA 90095 USA Institute for Quantitative and Computational Biosciences-The Collaboratory University of California Los Angeles Los Angeles CA 90095 USA. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Bioengineering University of California Los Angeles Los Angeles CA 90095 USA Goodman-Luskin Microbiome Center University of California Los Angeles Los Angeles CA 90095 USA Molecular Biology Institute University of California Los Angeles Los Angeles CA 90095 USA Parker Institute for Cancer Immunotherapy University of California Los Angeles Los Angel

Identifying additional immune checkpoints hindering antitumor T cell responses is key to the development of next-generation cancer immunotherapies. Here, we report the induction of serotonin transporter (SERT), a regulator of serotonin levels and physiological functions in the brain and peripheral tissues, in tumor-infiltrating CD8 T cells. Inhibition of SERT using selective serotonin reuptake inhibitors (SSRIs), the most widely prescribed antidepressants, significantly suppressed tumor growth and enhanced T cell antitumor immunity in various mouse syngeneic and human xenograft tumor models. Importantly, SSRI treatment exhibited significant therapeutic synergy with programmed cell death protein 1 (PD-1) blockade, and clinical data correlation studies negatively associated intratumoral SERT expression with patient survival in a range of cancers. Mechanistically, SERT functions as a negative-feedback regulator inhibiting CD8 T cell reactivities by depleting intratumoral T cell-autocrine serotonin. These findings highlight the significance of the intratumoral serotonin axis and identify SERT as an immune checkpoint, positioning SSRIs as promising candidates for cancer immunotherapy.

关键词： PD-1 blockade SSRIs T cell antitumor immunity antidepressants cancer immunotherapy immune checkpoint intratumoral serotonin axis serotonin serotonin transporter tumor microenvironment

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Correction to: Authors’ Reply to Letter to the Editor: Continued improvement to genetic diversity indicator for CBD

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Conservation Genetics 2021年第4期22卷 537-539页

作者： Laikre, Linda Hohenlohe, Paul A. Allendorf, Fred W. Bertola, Laura D. Breed, Martin F. Bruford, Michael W. Funk, W. Chris Gajardo, Gonzalo González-Rodríguez, Antonio Grueber, Catherine E. Hedrick, Philip W. Heuertz, Myriam Hunter, Margaret E. Johannesson, Kerstin Liggins, Libby MacDonald, Anna J. Mergeay, Joachim Moharrek, Farideh O’Brien, David Ogden, Rob Orozco-terWengel, Pablo Palma-Silva, Clarisse Pierson, Jennifer Paz-Vinas, Ivan Russo, Isa-Rita M. Ryman, Nils Segelbacher, Gernot Sjögren-Gulve, Per Waits, Lisette P. Vernesi, Cristiano Hoban, Sean Division of Population Genetics Department of Zoology Stockholm University Stockholm Sweden Institute for Bioinformatics and Evolutionary Studies Department of Biological Sciences University of Idaho Moscow USA Division of Biological Sciences University of Montana Missoula USA City College of New York New York USA College of Science and Engineering Flinders University Bedford Park Australia School of Biosciences Cardiff University Cardiff Wales UK Graduate Degree Program in Ecology Department of Biology Colorado State University Fort Collins USA Universidad de Los Lagos Lab Genetics Aquaculture & Biodiversity Osorno Chile Instituto de Investigaciones en Ecosistemas y Sustentabilidad Universidad Nacional Autónoma de México Morelia Mexico School of Life and Environmental Sciences Faculty of Science The University of Sydney Sydney Australia School of Life Sciences Arizona State University Tempe USA INRAE Univ. Bordeaux Biogeco Cestas France U.S. Geological Survey Wetland and Aquatic Research Center Gainesville USA Department of Marine Sciences University of Gothenburg Tjärnö Strömstad Sweden School of Natural and Computational Sciences Massey University Auckland New Zealand The John Curtin School of Medical Research/Research School of Biology The Australian National University Acton Australia Research Institute for Nature and Forest Geraardsbergen Belgium Aquatic Ecology Evolution and Conservation KULeuven Leuven Belgium Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Faculty of Biological Sciences Tarbiat Modares University Tehran Iran Scottish Natural Heritage Inverness UK Royal (Dick) School of Veterinary Studies and the Roslin Institute University of Edinburgh Midlothian UK Department of Plant Science Institute of Biology University of Campinas Campinas Brazil Australian Wildlife Conservancy Subiaco East Australia Laboratoire Evolution & Div

A correction to this paper has been published: https://***/10.1007/s10592-021-01376-9

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TFisher tests: Optimal and adaptive thresholding for combining p-values

arXiv

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arXiv 2018年

作者： Zhang, Hong Tong, Tiejun Landers, John Wu, Zheyang Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong Department of Neurology University of Massachusetts Medical School WorcesterMA01655 United States Department of Mathematical Sciences Program of Bioinformatics and Computational Biology Program of Data Science Worcester Polytechnic Institute WorcesterMA01609 United States

For testing a group of hypotheses, tremendous p-value combination methods have been developed and widely applied since 1930's. Some methods (e.g., the minimal p-value) are optimal for sparse signals, and some others (e.g., Fisher's combination) are optimal for dense signals. To address a wide spectrum of signal patterns, this paper proposes a unifying family of statistics, called TFisher, with general p-value truncation and weighting schemes. Analytical calculations for the p-value and the statistical power of TFisher under general hypotheses are given. Optimal truncation and weighting parameters are studied based on Bahadur Efficiency (BE) and the proposed Asymptotic Power Efficiency (APE), which is superior to BE for studying the signal detection problem. A soft-thresholding scheme is shown to be optimal for signal detection in a large space of signal patterns. When prior information of signal pattern is unavailable, an omnibus test, oTFisher, can adapt to the given data. Simulations evidenced the accuracy of calculations and validated the theoretical properties. The TFisher tests were applied to analyzing a whole exome sequencing data of amyotrophic lateral sclerosis. Relevant tests and calculations have been implemented into an R package TFisher and published on the CRAN. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal detection

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Correction: Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease

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Genome medicine 2023年第1期15卷 38页

作者： Gabriella Assante Sriram Chandrasekaran Stanley Ng Aikaterini Tourna Carolina H Chung Kowsar A Isse Jasmine L Banks Ugo Soffientini Celine Filippi Anil Dhawan Mo Liu Steven G Rozen Matthew Hoare Peter Campbell J William O Ballard Nigel Turner Margaret J Morris Shilpa Chokshi Neil A Youngson Institute of Hepatology Foundation for Liver Research 111 Coldharbour Lane London SE5 9NT UK. Faculty of Life Sciences and Medicine King's College London London UK. Program in Chemical Biology University of Michigan Ann Arbor MI 48109 USA. Center for Bioinformatics and Computational Medicine Ann Arbor MI 48109 USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI 48109 USA. Rogel Cancer Center University of Michigan Medical School Ann Arbor MI 48109 USA. Wellcome Trust Sanger Institute Cambridge UK. UNSW Sydney Sydney Australia. Cellular Bioenergetics Laboratory Victor Chang Cardiac Research Institute Darlinghurst NSW Australia. Institute of Liver Studies King's College Hospital London UK. Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. CRUK Cambridge Institute Cambridge UK. Department of Medicine University of Cambridge Addenbrooke's Hospital Cambridge UK. Department of Ecology Environment and Evolution La Trobe University Bundoora Melbourne VIC 3086 Australia. Institute of Hepatology Foundation for Liver Research 111 Coldharbour Lane London SE5 9NT UK. n.youngson@researchinliver.org.uk. Faculty of Life Sciences and Medicine King's College London London UK. n.youngson@researchinliver.org.uk. UNSW Sydney Sydney Australia. n.youngson@researchinliver.org.uk.

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Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes

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Nature 2022年第7909期605卷 E3页

作者： Jill E Moore Michael J Purcaro Henry E Pratt Charles B Epstein Noam Shoresh Jessika Adrian Trupti Kawli Carrie A Davis Alexander Dobin Rajinder Kaul Jessica Halow Eric L Van Nostrand Peter Freese David U Gorkin Yin Shen Yupeng He Mark Mackiewicz Florencia Pauli-Behn Brian A Williams Ali Mortazavi Cheryl A Keller Xiao-Ou Zhang Shaimae I Elhajjajy Jack Huey Diane E Dickel Valentina Snetkova Xintao Wei Xiaofeng Wang Juan Carlos Rivera-Mulia Joel Rozowsky Jing Zhang Surya B Chhetri Jialing Zhang Alec Victorsen Kevin P White Axel Visel Gene W Yeo Christopher B Burge Eric Lécuyer David M Gilbert Job Dekker John Rinn Eric M Mendenhall Joseph R Ecker Manolis Kellis Robert J Klein William S Noble Anshul Kundaje Roderic Guigó Peggy J Farnham J Michael Cherry Richard M Myers Bing Ren Brenton R Graveley Mark B Gerstein Len A Pennacchio Michael P Snyder Bradley E Bernstein Barbara Wold Ross C Hardison Thomas R Gingeras John A Stamatoyannopoulos Zhiping Weng University of Massachusetts Medical School Program in Bioinformatics and Integrative Biology Worcester MA USA. The Broad Institute of Harvard and MIT Cambridge MA USA. Department of Genetics School of Medicine Stanford University Palo Alto CA USA. Cold Spring Harbor Laboratory Functional Genomics Cold Spring Harbor NY USA. Altius Institute for Biomedical Sciences Seattle WA USA. Department of Medicine University of Washington School of Medicine Seattle WA USA. Department of Cellular and Molecular Medicine Institute for Genomic Medicine Stem Cell Program Sanford Consortium for Regenerative Medicine University of California San Diego La Jolla CA USA. Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA. Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. Ludwig Institute for Cancer Research University of California San Diego La Jolla CA USA. Institute for Human Genetics Department of Neurology University of California San Francisco San Francisco CA USA. Genomics Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. HudsonAlpha Institute for Biotechnology Huntsville AL USA. Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA. Department of Developmental and Cell Biology University of California Irvine Irvine CA USA. Department of Biochemistry and Molecular Biology The Pennsylvania State University University Park PA USA. Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA. Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA. Département de Biochimie et Médecine Moléculaire Université de Montréal Montréal Quebec Canada. Division of Experimental Medicine McGill University Montreal Quebec Canada. Institut de Recherches Cliniques de Montréal (IR

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Inherited mitochondrial genetics as a predictor of immune checkpoint inhibition efficacy in melanoma

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Nature Medicine 2025年 1-12页

作者： Kelsey R. Monson Robert Ferguson Joanna E. Handzlik Leah Morales Jiahan Xiong Vylyny Chat Sasha Dagayev Danny Simpson Esther Kazlow Anabelle Bunis Chaitra Sreenivasaiah Iryna Voloshyna Michelle Krogsgaard Janice Mehnert Iman Osman Tomas Kirchhoff Yuting Lu Yongzhao Shao Milad Ibrahim Alireza Khodadadi-Jamayran Wouter Ouwerkerk Rosalie M. Luiten Mariaelena Capone Gabriele Madonna Paolo A. Ascierto Anna Pavlick Hao Tang Sonia Dolfi Daniel Tenney John B. A. G. Haanen Thomas F. Gajewski F. Stephen Hodi Osama Rahma Keith T. Flaherty Ryan J. Sullivan Kasey Couts William Robinson Igor Puzanov Marc S. Ernstoff Michael Postow Jason J. Luke Perlmutter Cancer Center NYU Langone Health New York NY USA Departments of Population Health and Environmental Medicine NYU Langone Health New York NY USA The Interdisciplinary Melanoma Cooperative Group NYU Langone Health New York NY USA Tri-Institutional Training Program in Computational Biology and Medicine Weill Cornell Medicine New York NY USA New York Genome Center New York NY USA Applied Bioinformatics Laboratories Office of Science and Research New York University School of Medicine New York NY USA Department of Pathology New York University Grossman School of Medicine New York NY USA Department of Medicine NYU Langone Health New York NY USA Ronald O. Perelman Department of Dermatology NYU Langone Health New York NY USA Department of Dermatology and Netherlands Institute for Pigment Disorders Amsterdam UMC University of Amsterdam Amsterdam Institute for Immunology & Infectious Diseases Cancer Center Amsterdam the Netherlands Melanoma Cancer Immunotherapy and Development Therapeutics Unit Istituto Nazionale Tumori IRCCS Fondazione G. Pascale Naples Italy Division of Hematology & Medical Oncology the Cutaneous Oncology Program Weill Cornell New York NY USA Bristol Myers Squibb Corp Princeton NJ USA Medical Oncology Antoni van Leeuwenhoek Nederlands Kanker Instituut Amsterdam the Netherlands Department of Pathology University of Chicago Chicago IL USA Section of Hematology/Oncology Department of Medicine University of Chicago Chicago IL USA Ben May Department for Cancer Research University of Chicago Chicago IL USA Department of Medical Oncology Center for Immuno-Oncology Dana-Farber Cancer Institute Boston MA USA Center for Melanoma Massachusetts General Hospital Cancer Center Harvard Medical School Boston MA USA Department of Medicine Division of Medical Oncology University of Colorado Aurora CO USA Department of Medicine Roswell Park Comprehensive Cancer Center Buffalo NY USA ImmunoOncology Branch (IOB) Developmental

Response to immune checkpoint inhibitors (ICIs) in metastatic melanoma (MM) varies among patients, and current baseline biomarkers predicting treatment outcomes are limited. As mitochondrial (MT) metabolism has emerged as an important regulator of host immune function, we explored the association of host MT genetics (MT haplogroups) with ICI efficacy in 1,225 ICI-treated patients with MM from the clinical trial CheckMate-067 and the International Germline Immuno-Oncology Melanoma Consortium. We discovered and validated significant associations of MT haplogroup T (HG-T) with resistance to anti-programmed cell death protein-1-based ICI (both single-agent and combination) and have shown that HG-T is independent from established tumor predictors. We also found that patients belonging to HG-T exhibit a unique nivolumab-resistant baseline peripheral CD8+T cell repertoire compared to other MT haplogroups, providing, to our knowledge, the first link between MT inheritance, host immunity and ICI resistance. The study proposes a host blood-based biomarker with stand-alone clinical value predicting ICI efficacy and points to an ICI-resistance mechanism associated with MT metabolism, with clinical relevance in immuno-oncology.

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Distributions and statistical power of optimal signal-detection methods in finite cases

arXiv

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arXiv 2017年

作者： Zhang, Hong Jin, Jiashun Wu, Zheyang Worcester Polytechnic Institute Carnegie Mellon University Department of Mathematical Sciences Program of Bioinformatics and Computational Biology Program of Data Science 100 Institute Road Worcester01609 Morocco

In big data analysis for detecting rare and weak signals among n features, some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test (B-J), and φ-divergence test share the similar asymptotical optimality when n → ∞. However, in practical data analysis n is frequently small and moderately large at most. In order to properly apply these optimal tests and wisely choose them for practical studies, it is important to know how to get the p-values and statistical power of them. To address this problem in an even broader context, this paper provides analytical solutions for a general family of goodness-of-fit (GOF) tests, which covers these optimal tests. For any given i.i.d. and continuous distributions of the input test statistics of the n features, both p-value and statistical power of such a GOF test can be calculated. By calculation we compared the finite-sample performances of asymptotically optimal tests under the normal mixture alternative. Results show that HC is the best choice when signals are rare, while B-J is more robust over various signal patterns. In the application to a real genome-wide association study, results illustrate that the p-value calculation works well, and the optimal tests have potentials for detecting novel disease genes with weak genetic effects. The calculations have been implemented in an R package SetTest and published on the CRAN. Copyright © 2017, The Authors. All rights reserved.

关键词： Information analysis

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