Format

Send to

Choose Destination
Nat Commun. 2014 Jun 13;5:3934. doi: 10.1038/ncomms4934.

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel.

Collaborators (382)

McVean GA, Donnelly P, Lunter G, Marchini JL, Myers S, Gupta-Hinch A, Iqbal Z, Mathieson I, Rimmer A, Xifara DK, Kerasidou A, Churchhouse C, Delaneau O, Altshuler DM, Gabriel SB, Lander ES, Gupta N, Daly MJ, DePristo MA, Banks E, Bhatia G, Carneiro MO, Del Angel G, Genovese G, Handsaker RE, Hart C, McCarroll SA, Nemesh JC, Poplin RE, Schaffner SF, Shakir K, Sabeti PC, Grossman SR, Tabrizi S, Tariya R, Li H, Reich D, Durbin RM, Hurles ME, Balasubramaniam S, Burton J, Danecek P, Keane TM, Kolb-Kokocinski A, McCarthy S, Stalker J, Quail M, Ayub Q, Chen Y, Coffey AJ, Colonna V, Huang N, Jostins L, Scally A, Walter K, Xue Y, Zhang Y, Blackburne B, Lindsay SJ, Ning Z, Frankish A, Harrow J, Tyler-Smith C, Abecasis GR, Kang HM, Anderson P, Blackwell T, Busonero F, Fuchsberger C, Jun G, Maschio A, Porcu E, Sidore C, Tan A, Trost MK, Bentley DR, Grocock R, Humphray S, James T, Kingsbury Z, Bauer M, Cheetham RK, Cox T, Eberle M, Murray L, Shaw R, Chakravarti A, Clark AG, Keinan A, Rodriguez-Flores JL, De La Vega FM, Degenhardt J, Eichler EE, Flicek P, Clarke L, Leinonen R, Smith RE, Zheng-Bradley X, Beal K, Cunningham F, Herrero J, McLaren WM, Ritchie GR, Barker J, Kelman G, Kulesha E, Radhakrishnan R, Roa A, Smirnov D, Streeter I, Toneva I, Gibbs RA, Dinh H, Kovar C, Lee S, Lewis L, Muzny D, Reid J, Wang M, Yu F, Bainbridge M, Challis D, Evani US, Lu J, Nagaswamy U, Sabo A, Wang Y, Yu J, Fowler G, Hale W, Kalra D, Green ED, Knoppers BM, Korbel JO, Rausch T, Sttz AM, Lee C, Griffin L, Hsieh CH, Mills RE, von Grotthuss M, Zhang C, Shi X, Lehrach H, Sudbrak R, Amstislavskiy VS, Lienhard M, Mertes F, Sultan M, Timmermann B, Yaspo ML, Herwig R, Mardis ER, Wilson RK, Fulton L, Fulton R, Weinstock GM, Chinwalla A, Ding L, Dooling D, Koboldt DC, McLellan MD, Wallis JW, Wendl MC, Zhang Q, Marth GT, Garrison EP, Kural D, Lee WP, Leong WF, Ward AN, Wu J, Zhang M, Nickerson DA, Alkan C, Hormozdiari F, Ko A, Sudmant PH, Schmidt JP, Davies CJ, Gollub J, Webster T, Wong B, Zhan Y, Sherry ST, Xiao C, Church D, Ananiev V, Belaia Z, Beloslyudtsev D, Bouk N, Chen C, Cohen R, Cook C, Garner J, Hefferon T, Kimelman M, Liu C, Lopez J, Meric P, Ostapchuk Y, Phan L, Ponomarov S, Schneider V, Shekhtman E, Sirotkin K, Slotta D, Zhang H, Wang J, Fang X, Guo X, Jian M, Jiang H, Jin X, Li G, Li J, Li Y, Liu X, Lu Y, Ma X, Tai S, Tang M, Wang B, Wang G, Wu H, Wu R, Yin Y, Zhang W, Zhao J, Zhao M, Zheng X, Coin LJ, Fang L, Li Q, Li Z, Lin H, Liu B, Luo R, Shao H, Wang B, Xie Y, Ye C, Yu C, Zheng H, Zhu H, Cai H, Cao H, Su Y, Tian Z, Yang H, Yang L, Zhu J, Cai Z, Wang J, Albrecht MW, Borodina TA, Auton A, Yoon SC, Lihm J, Makarov V, Jin H, Kim W, Kim KC, Gottipati S, Jones D, Cooper DN, Ball EV, Stenson PD, Barnes B, Kahn S, Ye K, Batzer MA, Konkel MK, Walker JA, MacArthur DG, Lek M, Shriver MD, Bustamante CD, Gravel S, Kenny EE, Kidd JM, Lacroute P, Maples BK, Moreno-Estrada A, Zakharia F, Henn B, Sandoval K, Byrnes JK, Halperin E, Baran Y, Craig DW, Christoforides A, Izatt T, Kurdoglu AA, Sinari SA, Homer N, Squire K, Sebat J, Bafna V, Ye K, Burchard EG, Hernandez RD, Gignoux CR, Haussler D, Katzman SJ, Kent WJ, Howie B, Ruiz-Linares A, Dermitzakis ET, Lappalainen T, Devine SE, Liu X, Maroo A, Tallon LJ, Rosenfeld JA, Michelson LP, Angius A, Cucca F, Sanna S, Bigham A, Jones C, Reinier F, Li Y, Lyons R, Schlessinger D, Awadalla P, Hodgkinson A, Oleksyk TK, Martinez-Cruzado JC, Fu Y, Liu X, Xiong M, Jorde L, Witherspoon D, Xing J, Browning BL, Hajirasouliha I, Chen K, Albers CA, Gerstein MB, Abyzov A, Chen J, Fu Y, Habegger L, Harmanci AO, Mu XJ, Sisu C, Balasubramanian S, Jin M, Khurana E, Clarke D, Michaelson JJ, OSullivan C, Barnes KC, Gharani N, Toji LH, Gerry N, Kaye JS, Kent A, Mathias R, Ossorio PN, Parker M, Rotimi CN, Royal CD, Tishkoff S, Via M, Bodmer W, Bedoya G, Yang G, You CJ, Garcia-Montero A, Orfao A, Dutil J, Brooks LD, Felsenfeld AL, McEwen JE, Clemm NC, Guyer MS, Peterson JL, Duncanson A, Dunn M, Peltonenz L.

Author information

1
Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.
2
1] Department of Statistics, University of Oxford, Oxford OX1 3TG, UK [2] Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.

Abstract

A major use of the 1000 Genomes Project (1000 GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000 GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants.

PMID:
25653097
PMCID:
PMC4338501
DOI:
10.1038/ncomms4934
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center