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2 results on '"Statistical genomics"'

1. OpenMendel: a cooperative programming project for statistical genetics

Author

Zhou, Hua, Sinsheimer, Janet S, Bates, Douglas M, Chu, Benjamin B, German, Christopher A, Ji, Sarah S, Keys, Kevin L, Kim, Juhyun, Ko, Seyoon, Mosher, Gordon D, Papp, Jeanette C, Sobel, Eric M, Zhai, Jing, Zhou, Jin J, and Lange, Kenneth

Subjects
Biological Sciences, Genetics, Human Genome, Networking and Information Technology R&D (NITRD), Algorithms, Computational Biology, Genome, Human, Genome-Wide Association Study, Humans, Models, Statistical, Polymorphism, Single Nucleotide, Programming Languages, Software, Statistical genomics, GWAS, Computational statistics, Open source, Collaborative programming, stat.AP, q-bio.GN, Complementary and Alternative Medicine, Paediatrics and Reproductive Medicine, Genetics & Heredity, Reproductive medicine
Abstract

Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.

Published
2020

2. OPENMENDEL: a cooperative programming project for statistical genetics

Author

Jing Zhai, Benjamin B. Chu, Janet S. Sinsheimer, Gordon D. Mosher, Christopher A. German, Kenneth Lange, Jin Zhou, Juhyun Kim, Eric M. Sobel, Douglas M. Bates, Hua Zhou, Sarah S. Ji, Jeanette C. Papp, Kevin L. Keys, and Seyoon Ko

Subjects
FOS: Computer and information sciences, Computer science, Big data, Cloud computing, Genome-wide association study, Software, Models, GWAS, Genetics (clinical), Genetics & Heredity, 0303 health sciences, Genome, Data manipulation language, 030305 genetics & heredity, Single Nucleotide, Statistical, Open source, Variety (cybernetics), Networking and Information Technology R&D, Networking and Information Technology R&D (NITRD), Statistical genetics, q-bio.GN, Algorithms, Human, Collaborative programming, Context (language use), Statistics - Applications, Polymorphism, Single Nucleotide, Article, Paediatrics and Reproductive Medicine, 03 medical and health sciences, Complementary and Alternative Medicine, Genetics, Humans, Statistical genomics, Applications (stat.AP), Quantitative Biology - Genomics, Polymorphism, stat.AP, 030304 developmental biology, Genomics (q-bio.GN), Models, Statistical, business.industry, Genome, Human, Human Genome, Computational Biology, Data science, Genetic epidemiology, FOS: Biological sciences, Computational statistics, Programming Languages, business, Genome-Wide Association Study
Abstract

Statistical methods for genomewide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDELproject (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project., 16 pages, 2 figures, 2 tables

Published
2019

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