Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel.
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.
Duke Scholars
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Related Subject Headings
- Polymorphism, Single Nucleotide
- Microarray Analysis
- Humans
- Haplotypes
- Genome-Wide Association Study
- Genome, Human
- Gene Frequency
- Alleles
- Algorithms
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Start / End Page
Related Subject Headings
- Polymorphism, Single Nucleotide
- Microarray Analysis
- Humans
- Haplotypes
- Genome-Wide Association Study
- Genome, Human
- Gene Frequency
- Alleles
- Algorithms