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

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.

Figure 1. Methods comparison of genotype discordance and imputation accuracy using the CG1 data

Panel (a) shows the discordance at chr20 CG1 SNP genotypes of Beagle (green), Thunder (orange) and SHAPEIT2 without using a scaffold (light blue), using a 1M SNPs haplotype scaffold (medium blue) and using a 2.5M SNPs haplotype scaffold (dark blue). ALT stands for the discordance at genotypes involving at least one non-reference allele, and ALL for the overall discordance. Panel (b) shows the performance of the previous call sets when used as a reference panel to impute 4 CG1 European genotyped on Illumina 1M SNP array. The x-axis shows the non-reference allele frequency of the SNP being imputed. The y-axis shows imputation accuracy measure by aggregate R².

Figure 2. Methods comparison of genotype discordance and imputation accuracy using the CG2 data

Panel (a) shows the whole genome genotype discordance of Beagle (green), Thunder (orange) and SHAPEIT2 using a 2.5M SNPs haplotype scaffold (dark blue) at CG2 SNPs. Panel (b) shows the performance of the 3 call sets to impute SNPs on chromosome 10 in 10 CG2 European samples typed on Illumina 1M and Omni2.5M chips. The x-axis shows the non-reference allele frequency of the SNP being imputed. The y-axis shows imputation accuracy measure by aggregate R². Panels (c) and (d) show similar results than panels (a) and (b), respectively for short bi-allelic indels instead of SNPs.

Figure 3. Imputation accuracy at SNPs and Indels using the CG2 data

The imputation performance at SNPs and indels are shown with the orange and green lines, respectively. Performance at all indels, isolated indels and non-isolated indels are shown using plain, dashed and dotted lines. An indel is isolated when no other indels is in the 50bp flanking regions. The x-axis shows the non-reference allele frequency of the SNP being imputed. The y-axis shows imputation accuracy measure by aggregate R².