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.

Original publication




Journal article


Nature communications

Publication Date





Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.


1000 Genomes Project Consortium, 1000 Genomes Project Consortium, Humans, Microarray Analysis, Gene Frequency, Haplotypes, Polymorphism, Single Nucleotide, Alleles, Genome, Human, Algorithms, Genome-Wide Association Study