Evaluating runs of homozygosity in exome sequencing data - Utility in disease inheritance model selection and variant filtering

Abstract

Runs of homozygosity (ROH) are regions consistently homozygous for genetic markers, which can occur throughout the human genome. Their size is dependent on the degree of shared parental ancestry, being longer in individuals descending from consanguineous marriages, or from inbred/isolated populations. Based on ROH existence, homozygosity mapping (HM) was developed as powerful tool for gene-discovery in human genetics. HM is based on the assumption that, through identity-by-descent, individuals affected by an autosomal recessive (AR) condition, are more likely to have homozygous markers surrounding the disease locus. In this work, we reviewed some of the algorithms and bioinformatics tools available for HM and ROH detection, with special emphasis on those than can be applied to data from whole-exome sequencing (WES) data. Preliminary data is also shown demonstrating the relevance of performing ROH analysis, especially in sporadic cases. In this study, ROH from WES data of twelve unrelated patients was analyzed. Patients with AR diseases (n = 6) were subdivided into two groups: homozygous and compound heterozygous. ROH analysis was performed using the HomozygosityMapper software, varying the block length and collecting several parameters. Statistically significant differences between the two groups were identified for ROH total size and homozygosity score. The k-means clustering algorithm was then applied, where two clusters were identified, with statistically significant differences, corresponding to each predefined test group. Our results suggest that, in some cases, it may be possible to infer the most likely disease inheritance model from WES data alone, constituting a useful starting point for the subsequent variant filtering strategies.