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1000 Genomes Project Dataset Analysis (U01)

Sector: Government • Location: United States of America

Source: Grants.gov

Project
Archived

Purpose. The international 1000 Genomes Project is designed to produce a new high-resolution resource on human genetic variation to support genome-wide association studies and other studies of human disease and biology. The primary source of data will be light sequencing of the genomes of about 1200-1500 anonymized individuals. These data should be sufficient to find most DNA sequence variants i

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The project “1000 Genomes Project Dataset Analysis (U01)” is an infrastructure initiative in the Government sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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archived

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Description

Description

Purpose. The international 1000 Genomes Project is designed to produce a new high-resolution resource on human genetic variation to support genome-wide association studies and other studies of human disease and biology. The primary source of data will be light sequencing of the genomes of about 1200-1500 anonymized individuals. These data should be sufficient to find most DNA sequence variants in the human population that have a frequency of 1% or more. Several large-scale sequencing centers, including three funded by NHGRI, are involved in this international effort. Currently pilot projects are underway to assess the sequencing platforms and provide data that will be used to design the full-scale project. By the end of 2008 the full-scale project should be underway; it is expected to take about two years and produce about 20 Terabases of sequence data. The Project regularly will release raw sequence data and data derived from the sequence data, such as genotype calls for single-nucleotide polymorphisms (SNPs) and structural variants, and their linkage disequilibrium patterns. This FOA solicits proposals to characterize and analyze the full dataset, such as for allele frequency distribution and signals of natural selection, to produce additional data types, to evaluate the strategies used to develop the dataset, and to develop the tools needed to work with the data and apply them to other studies such as genome-wide association studies.

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High

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100%

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