Furthermore, GLIMPSE2 outperforms GLIMPSE1, particularly at rare variants (MAF < 0.1%) and for very low-coverage (for 0.1× and 1.0× data at 0.01% MAF, GLIMPSE1 and GLIMPSE2 obtain an r 2 of 0.561 and 0.892 compared to 0.725 and 0.927, respectively) and matches QUILT v.1.0.4 accuracy, designed to condition on the full set of reference haplotypes (for 0.1× and 1.0× data at 0.01% MAF, QUILT v.1.0.4 obtained an r 2 of 0.728 and 0.925, respectively Fig. Compared to other reference panels, the UKB leads to considerable accuracy improvements for British samples across all tested depths of coverage. We used the UKB panel to impute lcWGS samples with GLIMPSE2 and other recently released imputation methods: GLIMPSE1 (ref. 7) and created a UKB reference panel of 280,238 haplotypes and 582,534,516 markers (Supplementary Note 1). To demonstrate the benefits of using sequenced biobanks for lcWGS imputation, we phased the recent release of the UKB WGS data 5, 6 using SHAPEIT5 (ref. To address this issue, we propose GLIMPSE v.2 (GLIMPSE2), a major improvement of GLIMPSE 1, that scales to a reference panel containing millions of reference haplotypes, with high imputation accuracy at rare variants (MAF < 0.1%) and for very low-coverage samples (0.1× to 0.5×). However, current methods struggle to scale to the size of this new generation of reference panels resulting in prohibitive computational costs. Large-scale whole-genome sequencing projects, such as the recent release of 150,119 samples from the UK Biobank 5 (UKB), offer new opportunities to improve lcWGS imputation, potentially improving accuracy at rare variants (minor allele frequency (MAF) < 0.1%). Recent work and method advances 1, 2, 3, 4 highlight the advantages of low-coverage whole-genome sequencing (lcWGS), followed by genotype imputation from a large reference panel, as a cost-effective genotyping technology for statistical and population genetics.
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