An integrated map of genetic variation from 1,092 human genomes
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Abstract
By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations. This report from the 1000 Genomes Project describes the genomes of 1,092 individuals from 14 human populations, providing a resource for common and low-frequency variant analysis in individuals from diverse populations; hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites, can be found in each individual. This report by the 1000 Genomes Project describes the genomes of 1,092 individuals from 14 human populations, providing a resource for common and low-frequency variant analysis in individuals from diverse populations. Integrative analyses reveal profiles of rare and common variants in different populations. The frequencies of rare variants vary across biological pathways, and hundreds of rare, non-coding variants at conserved sites — such as changes disrupting transcription-factor motifs — can be established for each individual.
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The record
- Venue
- Nature
- Topic
- Genetic Associations and Epidemiology
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- Simon Fraser UniversityCentre Hospitalier Universitaire Sainte-JustineMcGill UniversityMcGill University and Génome Québec Innovation Centre
- Funders
- National Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Cancer InstituteNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational High-tech Research and Development ProgramWellcome TrustBiotechnology and Biological Sciences Research CouncilNational Institute of Environmental Health SciencesGuangdong Province Introduction of Innovative R&D TeamGovernment of Jiangxi ProvinceBundesministerium für Bildung und ForschungMax-Planck-GesellschaftSamsungHoward Hughes Medical InstituteNational Natural Science Foundation of ChinaDirectorate for Biological SciencesNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Human Genome Research InstituteNederlandse Organisatie voor Wetenschappelijk OnderzoekNational Key Research and Development Program of ChinaU.S. National Library of MedicineBritish Heart FoundationNational Institute on Minority Health and Health DisparitiesDeutsche ForschungsgemeinschaftNational Center for Advancing Translational SciencesMedical Research CouncilMinistero dello Sviluppo EconomicoNational Science Foundation
- Keywords
- Variation (astronomy)Human genetic variationHuman genomeGenetic variationGenomeEvolutionary biologyBiologyGeneticsGene
- Has abstract in OpenAlex
- yes