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Journal Article

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

MPS-Authors
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Lehrach,  H.
The 1000 Genomes Project Consortium;
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Lienhard,  M.
The 1000 Genomes Project Consortium;
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Mertes,  F.
The 1000 Genomes Project Consortium;
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Timmermann,  B.
The 1000 Genomes Project Consortium;
Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Yaspo,  M. L.
The 1000 Genomes Project Consortium;
Gene Regulation and Systems Biology of Cancer (Marie-Laure Yaspo), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Herwig,  R.
The 1000 Genomes Project Consortium;
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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http://www.ncbi.nlm.nih.gov/pubmed/25653097
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Citation

Delaneau, O., Marchini, J., 1000 Genomes Project, C., Lehrach, H., Sudbrak, R., Amstislavskiy, V. S., et al. (2014). Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel. Nature Communications, 5: 5:3934. doi:10.1038/ncomms4934.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0025-B4D5-F
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.