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Open AccessJournal ArticleDOI

A global reference for human genetic variation.

Adam Auton, +517 more
- 01 Oct 2015 - 
- Vol. 526, Iss: 7571, pp 68-74
TLDR
The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Abstract
The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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Citations
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Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation

TL;DR: It is established that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions, and can play an active role in immune gene regulation.
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Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans.

TL;DR: These estimates are more accurate than previously published results based on ancestrally older variants without considering genomic features, and provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.
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Regulatory variants: from detection to predicting impact.

TL;DR: Methods and techniques for discovering disease-associated non-coding variants using sequencing technologies, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches are described.
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