Dataset Information

A reference panel of 64,976 haplotypes for genotype imputation.

ABSTRACT: We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

SUBMITTER: McCarthy S

PROVIDER: S-EPMC5388176 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A reference panel of 64,976 haplotypes for genotype imputation.

McCarthy Shane S Das Sayantan S Kretzschmar Warren W Delaneau Olivier O Wood Andrew R AR Teumer Alexander A Kang Hyun Min HM Fuchsberger Christian C Danecek Petr P Sharp Kevin K Luo Yang Y Sidore Carlo C Kwong Alan A Timpson Nicholas N Koskinen Seppo S Vrieze Scott S Scott Laura J LJ Zhang He H Mahajan Anubha A Veldink Jan J Peters Ulrike U Pato Carlos C van Duijn Cornelia M CM Gillies Christopher E CE Gandin Ilaria I Mezzavilla Massimo M Gilly Arthur A Cocca Massimiliano M Traglia Michela M Angius Andrea A Barrett Jeffrey C JC Boomsma Dorrett D Branham Kari K Breen Gerome G Brummett Chad M CM Busonero Fabio F Campbell Harry H Chan Andrew A Chen Sai S Chew Emily E Collins Francis S FS Corbin Laura J LJ Smith George Davey GD Dedoussis George G Dorr Marcus M Farmaki Aliki-Eleni AE Ferrucci Luigi L Forer Lukas L Fraser Ross M RM Gabriel Stacey S Levy Shawn S Groop Leif L Harrison Tabitha T Hattersley Andrew A Holmen Oddgeir L OL Hveem Kristian K Kretzler Matthias M Lee James C JC McGue Matt M Meitinger Thomas T Melzer David D Min Josine L JL Mohlke Karen L KL Vincent John B JB Nauck Matthias M Nickerson Deborah D Palotie Aarno A Pato Michele M Pirastu Nicola N McInnis Melvin M Richards J Brent JB Sala Cinzia C Salomaa Veikko V Schlessinger David D Schoenherr Sebastian S Slagboom P Eline PE Small Kerrin K Spector Timothy T Stambolian Dwight D Tuke Marcus M Tuomilehto Jaakko J Van den Berg Leonard H LH Van Rheenen Wouter W Volker Uwe U Wijmenga Cisca C Toniolo Daniela D Zeggini Eleftheria E Gasparini Paolo P Sampson Matthew G MG Wilson James F JF Frayling Timothy T de Bakker Paul I W PI Swertz Morris A MA McCarroll Steven S Kooperberg Charles C Dekker Annelot A Altshuler David D Willer Cristen C Iacono William W Ripatti Samuli S Soranzo Nicole N Walter Klaudia K Swaroop Anand A Cucca Francesco F Anderson Carl A CA Myers Richard M RM Boehnke Michael M McCarthy Mark I MI Durbin Richard R

Nature genetics 20160822 10

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consi ...[more]

PMID: 27548312

Similar Datasets

Project description:BackgroundGenotype imputation is a cost-effective method to generate sequence-level genotypes for a large number of animals. Its application can improve the power of genomic studies, provided that the accuracy of imputation is sufficiently high. The purpose of this study was to develop an optimal strategy for genotype imputation from genotyping array data to sequence level in German warmblood horses, and to investigate the effect of different factors on the accuracy of imputation. Publicly available whole-genome sequence data from 317 horses of 46 breeds was used to conduct the analyses.ResultsDepending on the size and composition of the reference panel, the accuracy of imputation from medium marker density (60K) to sequence level using the software Beagle 5.1 ranged from 0.64 to 0.70 for horse chromosome 3. Generally, imputation accuracy increased as the size of the reference panel increased, but if genetically distant individuals were included in the panel, the accuracy dropped. Imputation was most precise when using a reference panel of multiple but related breeds and the software Beagle 5.1, which outperformed the other two tested computer programs, Impute 5 and Minimac 4. Genome-wide imputation for this scenario resulted in a mean accuracy of 0.66. Stepwise imputation from 60K to 670K markers and subsequently to sequence level did not improve the accuracy of imputation. However, imputation from higher density (670K) was considerably more accurate (about 0.90) than from medium density. Likewise, imputation in genomic regions with a low marker coverage resulted in a reduced accuracy of imputation.ConclusionsThe accuracy of imputation in horses was influenced by the size and composition of the reference panel, the marker density of the genotyping array, and the imputation software. Genotype imputation can be used to extend the limited amount of available sequence-level data from horses in order to boost the power of downstream analyses, such as genome-wide association studies, or the detection of embryonic lethal variants.

Dataset Information

A reference panel of 64,976 haplotypes for genotype imputation.

Publications

A reference panel of 64,976 haplotypes for genotype imputation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets