AUDACITY: A comprehensive approach for the detection and classification of Runs of Homozygosity in medical and population genomics

Alberto Magi; Tania Giangregorio; Roberto Semeraro; Giulia Carangelo; Flavia Palombo; Giovanni Romeo; Marco Seri; Tommaso Pippucci

doi:10.1016/j.csbj.2020.07.003

AUDACITY: A comprehensive approach for the detection and classification of Runs of Homozygosity in medical and population genomics

Comput Struct Biotechnol J. 2020 Jul 14:18:1956-1967. doi: 10.1016/j.csbj.2020.07.003. eCollection 2020.

Authors

Alberto Magi¹, Tania Giangregorio², Roberto Semeraro³, Giulia Carangelo³, Flavia Palombo², Giovanni Romeo², Marco Seri^{2

4}, Tommaso Pippucci⁴

Affiliations

¹ Department of Information Engineering, University of Florence, Florence, Italy.
² Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
³ Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
⁴ Medical Genetics Unit, Sant'Orsola-Malpighi University Hospital, Bologna, Italy.

Abstract

Runs of Homozygosity (RoHs) are popular among geneticists as the footprint of demographic processes, evolutionary forces and inbreeding in shaping our genome, and are known to confer risk of Mendelian and complex diseases. Notwithstanding growing interest in their study, there is unmet need for reliable and rapid methods for genomic analyses in large data sets. AUDACITY is a tool integrating novel RoH detection algorithm and autozygosity prediction score for prioritization of mutation-surrounding regions. It processes data in VCF file format, and outperforms existing methods in identifying RoHs of any size. Simulations and analysis of real exomes/genomes show its potential to foster future RoH studies in medical and population genomics.