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Title: Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel
Authors: Delaneau, O
Marchini, J
McVeanh, GA
Donnelly, P
Lunter, G
Marchini, JL
Myers, S
Gupta-Hinch, A
Iqbal, Z
Mathieson, I
Rimmer, A
Xifara, DK
Kerasidou, A
Churchhouse, C
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
Hartl, C
McCarroll, SA
Nemesh, JC
Poplin, RE
Schaffner, SF
Shakir, K
Sabeti, PC
Grossman, SR
Tabrizi, S
Tariyal, 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
Chris, TS
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
Issue Date: 2014
Citation: Nature Communications, 2014, 5
Abstract: A major use of the 1000 Genomes Project (1000GP) 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 SNP microarray genotypes on the same samples. Firstly the SNP array data are phased in order 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 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and biallelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low frequency variants.
ISSN: 2041-1723
Appears in Collections:Dept of Life Sciences Research Papers

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