دورية أكاديمية
أعرض في EDS

Inferring whole-genome histories in large population datasets.

التفاصيل البيبلوغرافية
العنوان: Inferring whole-genome histories in large population datasets.
المؤلفون: Kelleher J; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. jerome.kelleher@bdi.ox.ac.uk., Wong Y; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Wohns AW; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Fadil C; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Albers PK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., McVean G; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
المصدر: Nature genetics [Nat Genet] 2019 Sep; Vol. 51 (9), pp. 1330-1338. Date of Electronic Publication: 2019 Sep 02.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Co Country of Publication: United States NLM ID: 9216904 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-1718 (Electronic) Linking ISSN: 10614036 NLM ISO Abbreviation: Nat Genet Subsets: MEDLINE
أسماء مطبوعة: Original Publication: New York, NY : Nature Pub. Co., c1992-
مواضيع طبية MeSH: Algorithms* , Evolution, Molecular* , Genetics, Population* , Genome, Human* , Pedigree* , Selection, Genetic*, Computer Simulation ; Datasets as Topic ; Haplotypes ; Humans ; Models, Genetic ; Mutation ; Polymorphism, Single Nucleotide ; Population Density
مستخلص: Inferring the full genealogical history of a set of DNA sequences is a core problem in evolutionary biology, because this history encodes information about the events and forces that have influenced a species. However, current methods are limited, and the most accurate techniques are able to process no more than a hundred samples. As datasets that consist of millions of genomes are now being collected, there is a need for scalable and efficient inference methods to fully utilize these resources. Here we introduce an algorithm that is able to not only infer whole-genome histories with comparable accuracy to the state-of-the-art but also process four orders of magnitude more sequences. The approach also provides an 'evolutionary encoding' of the data, enabling efficient calculation of relevant statistics. We apply the method to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the inferred genealogies are rich in biological signal and efficient to process.
التعليقات: Erratum in: Nat Genet. 2019 Nov;51(11):1660. (PMID: 31591513)
Comment in: Nat Methods. 2019 Nov;16(11):1077. (PMID: 31673154)
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معلومات مُعتمدة: United Kingdom WT_ Wellcome Trust; 100956 United Kingdom WT_ Wellcome Trust
تواريخ الأحداث: Date Created: 20190904 Date Completed: 20200123 Latest Revision: 20220420
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC6726478
DOI: 10.1038/s41588-019-0483-y
PMID: 31477934
قاعدة البيانات: MEDLINE
الوصف
تدمد:1546-1718
DOI:10.1038/s41588-019-0483-y