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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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TL;DR: The modes by which mitochondrial and nuclear genomes may coevolve within natural populations are outlined, and the implications of mitonuclear coadaptation for diverse fields of study in the biological sciences are discussed.
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DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.

TL;DR: Wang et al. as mentioned in this paper used sequence-based deep learning models to accurately predict the TF binding intensities to given DNA sequences, which can be used to identify variants that disrupt TF binding and lead to human diseases.
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Differences in the rare variant spectrum among human populations

TL;DR: It is shown that the spectrum of rare variants across human populations is highly sensitive to recent demography, and should be taken into account when using mutational clocks to make inference about demography.
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