Simons Genome Diversity Project
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
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| srt.aln.bam | Bam | |||
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Project description:Ongoing retrotransposition of Alu, LINE-1, and SINE-VNTR-Alu elements generates diversity and variation among human populations. Previous analyses investigating the population genetics of mobile element insertions (MEIs) have been limited by population ascertainment bias or by relatively small numbers of populations and low sequencing coverage. Here, we use 296 individuals representing 142 global populations from the Simons Genome Diversity Project (SGDP) to discover and characterize MEI diversity from deeply sequenced whole-genome data. We report 5,742 MEIs not originally reported by the 1000 Genomes Project and show that high sampling diversity leads to a 4- to 7-fold increase in MEI discovery rates over the original 1000 Genomes Project data. As a result of negative selection, nonreference polymorphic MEIs are underrepresented within genes, and MEIs within genes are often found in the transcriptional orientation opposite that of the gene. Globally, 80% of Alu subfamilies predate the expansion of modern humans from Africa. Polymorphic MEIs show heterozygosity gradients that decrease from Africa to Eurasia to the Americas, and the number of MEIs found uniquely in a single individual are also distributed in this general pattern. The maximum fraction of MEI diversity partitioned among the seven major SGDP population groups (FST) is 7.4%, similar to, but slightly lower than, previous estimates and likely attributable to the diverse sampling strategy of the SGDP. Finally, we utilize these MEIs to extrapolate the primary Native American shared ancestry component to back to Asia and provide new evidence from genome-wide identical-by-descent genetic markers that add additional support for a southeastern Siberian origin for most Native Americans.
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Project description:Here we report the Simons Genome Diversity Project data set: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioural modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.
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Project description:Here we report the Simons Genome Diversity Project data set: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioural modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that in other non- Africans.
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Project description:Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from SSA have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene–disease association signals. A systematic assessment of genetic diversity within SSA would facilitate genomic epidemiological studies in the region.
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The Genome Diversity in Africa Project (GDAP) aims to produce a comprehensive catalogue of human genetic variation in SSA, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in SSA, as well as providing a global resource to help design, implement and interpret genomic studies in SSA populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out high depth whole genome sequencing of up to 2000 individuals across Africa (25 individuals from each ethnolinguistic group).
Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from SSA accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across SSA; 3) develop a cost-efficient, next-generation genotype array for diverse populations across SSA; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformatics
This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
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