Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.25.513331v1?rss=1

Authors: Gudbjartsson, H., Isleifsson, H. b., Ragnarsson, B., Guimaraes, R., Wu, H., Olafsdottir, H., Stefasson, S. K.

Abstract: Motivation: Our aim was to simplify and speedup joint-genotyping, from sequence based variation data of individual samples, while maintaining as high sensitivity and specificity as possible. Results: We have leveraged versatile GOR data structures to store biallelic representations of variants and sequence read coverage in a very efficient way, allowing for very fast joint-genotyping that is an order of magnitude faster than any joint-genotyping method published to date. Furthermore, it can be easily extended and executed much faster in an incremental fashion. Concordance analysis based on the Genome In A Bottle (GIAB) samples shows favorable results when compared with the de-facto standard approach, using gVCF files and GATK joint-calling. Additionally, we have developed variant quality classification using XGBoost and variant training sets derived from the GIAB samples. The entire business logic is implemented efficiently and concisely in SparkGOR.

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