bioRxiv preprint doi: https://doi.org/10.1101/797944; this version posted April 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Genotype imputation using the Positional Burrows Wheeler Transform Simone Rubinacci1, Olivier Delaneau2,3, Jonathan Marchini4* 1 Department of Computational Biology, University of Lausanne, Lausanne, Switzerland 2 Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland 3 Regeneron Genetics Center, Tarrytown, New York, USA. *
[email protected] Abstract Genotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. In the last 10 years reference panels have increased in size by more than 100 fold. Increasing reference panel size improves accuracy of markers with low minor allele frequencies but poses ever increasing computational challenges for imputation methods. Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). By using the PBWT data structure at genotyped markers, IMPUTE5 identifies locally best matching haplotypes and long identical by state segments. The method then uses the selected haplotypes as conditioning states within the IMPUTE model.