Recent work highlights the advantages of low-coverage whole genome sequencing (lcWGS), followed by genotype imputation, as a …

In this work, we address the challenge of genotype imputation and haplotype phasing of low-coverage sequencing datasets using a reference panel of haplotypes. To this aim, we propose a novel method, GLIMPSE (Genotype Likelihoods Imputation and PhaSing mEthod), that is designed for large-scale studies and reference panels, typically comprising thousands of genomes. We show the remarkable performance of GLIMPSE using low-coverage whole genome sequencing data for both European and African American populations, and we demonstrate that low-coverage sequencing can be confidently used in downstream analyses. We provide GLIMPSE as a part of an open source software suite that makes imputation for low-coverage sequencing data as convenient as for traditional SNP array platforms.

We developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.

Genome-wide association studies (GWAS) typically use microarray technology to measure genotypes at several hundred thousand positions in the genome. However reference panels of genetic variation consist of haplotype data at >100 fold more positions in the genome. Genotype imputation makes genotype predictions at all the reference panel sites using the GWAS data. Reference panels are continuing to grow in size and this improves accuracy of the predictions, however methods need to be able to scale to increased size. We have developed a new version of the popular IMPUTE software than can handle referenece panels with millions of haplotypes, and has better performance than other published approaches. A notable property of the new method is that it scales sub-linearly with reference panel size. Keeping the number of imputed markers constant, a 100 fold increase in reference panel size requires less than twice the computation time.