TY  - JOUR
TI  - A global reference for human genetic variation
AU  - Corresponding authors
AU  - Adam Auton
AU  - Gonçalo R. Abecasis
AU  - David M. Altshuler
AU  - Richard Durbin
AU  - David R. Bentley
AU  - Aravinda Chakravarti
AU  - Andrew G. Clark
AU  - Peter Donnelly
AU  - Evan E. Eichler
AU  - Paul Flicek
AU  - Stacey B. Gabriel
AU  - Richard A. Gibbs
AU  - Eric D. Green
AU  - Matthew E. Hurles
AU  - Bartha M. Knoppers
AU  - Jan O. Korbel
AU  - Eric S. Lander
AU  - Charles Lee
AU  - Hans Lehrach
AU  - Elaine R. Mardis
AU  - Gábor Marth
AU  - Gil A. McVean
AU  - Deborah A. Nickerson
AU  - Jeanette P. Schmidt
AU  - Stephen T. Sherry
AU  - Jun Wang
AU  - Richard K. Wilson
AU  - Production group
AU  - Eric Boerwinkle
AU  - HarshaVardhan Doddapaneni
AU  - Yi Han
AU  - Viktoriya Korchina
AU  - Christie Kovar
AU  - Sandra Lee
AU  - Donna M. Muzny
AU  - Jeffrey G. Reid
AU  - Yiming Zhu
AU  - BGI-Shenzhen
AU  - Yuqi Chang
AU  - Qiang Feng
AU  - Xiaodong Fang
AU  - Xiaosen Guo
AU  - Min Jian
AU  - Hui Jiang
AU  - Xin Jin
AU  - Tianming Lan
AU  - Guoqing Li
AU  - Jingxiang Li
AU  - Yingrui Li
AU  - Shengmao Liu
AU  - Xiao Liu
AU  - Yao Lu
AU  - Xuedi Ma
AU  - Meifang Tang
AU  - Bo Wang
AU  - Guangbiao Wang
AU  - Honglong Wu
AU  - Renhua Wu
AU  - Xun Xu
AU  - Ye Yin
AU  - Dandan Zhang
AU  - Wenwei Zhang
AU  - Jiao Zhao
AU  - Meiru Zhao
AU  - Xiaole Zheng
AU  - Stacey Gabriel
AU  - Namrata Gupta
AU  - Neda Gharani
AU  - Lorraine H. Toji
AU  - Norman P. Gerry
AU  - Alissa Resch
AU  - Jonathan Barker
AU  - Laura Clarke
AU  - Laurent Gil
AU  - Sarah Hunt
AU  - Gavin Kelman
AU  - Eugene Kulesha
AU  - Rasko Leinonen
AU  - William McLaren
AU  - Rajesh Radhakrishnan
AU  - Asier Roa
AU  - Dmitriy Smirnov
AU  - Richard E. Smith
AU  - Ian Streeter
AU  - Anja Thormann
AU  - Iliana Toneva
AU  - Brendan Vaughan
AU  - Xiangqun Zheng-Bradley
AU  - Illumina
AU  - Russell Grocock
AU  - Sean Humphray
AU  - Terena James
PY  - 2015
JO  - Nature
DO  - 10.1038/nature15393
UR  - https://doi.org/10.1038/nature15393
AB  - 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. Results for the final phase of the 1000 Genomes Project are presented including whole-genome sequencing, targeted exome sequencing, and genotyping on high-density SNP arrays for 2,504 individuals across 26 populations, providing a global reference data set to support biomedical genetics. The 1000 Genomes Project has sought to comprehensively catalogue human genetic variation across populations, providing a valuable public genomic resource. The data obtained so far have found applications ranging from association studies and fine mapping studies to the filtering of likely neutral variants in rare-disease cohorts. The authors now report on the final phase of the project, phase 3, which covers previously uncharacterized areas of human genetic diversity in terms of the populations sampled and categories of characterized variation. The sample now includes more than 2,500 individuals from 26 global populations, with low coverage whole-genome and deep exome sequencing, as well as dense microarray genotyping. They find that while most common variants are shared across populations, rarer variants are often restricted to closely related populations. The authors also demonstrate the use of the phase 3 dataset as a reference panel for imputation to improve the resolution in genetic association studies.
ER  -