Conlin, L. K. | Search bioRxiv

bioRxiv2021

Improved Characterization of Balancing Selection Genome Wide and a Detailed Look at HLA Genes

Balancing selection occurs when multiple alleles are kept at elevated frequencies in equilibrium due to opposing evolutionary pressures. A new statistical method was developed to test for selection using efficient Bayesian techniques. Selection signals in three different data sets, generated with variable sequencing technologies, were compared: clinical trios, HLA NGS typed samples, and whole-genome long-read samples. Genome-wide, selection was observed across multiple gene families whose biological functions favor diversification, revealing established targets as well as 45 novel genes under selection. Using high-resolution HLA typing and long-read sequencing data, for the characterization of the MHC, revealed strong selection in expected peptide-binding domains as well as previously understudied intronic and intergenic regions of the MHC. Surprisingly, SIRPA, demonstrated dramatic selection signal, second only to the MHC in most settings. In conclusion, employing novel statistical approaches and improved sequencing technologies is critical to properly analyze complex genomic regions.

genetics↗

bioRxiv2019

Systematic and comprehensive benchmarking of an exome sequencing based germline copy-number variant detection pipeline in a clinical setting

PurposeDetecting germline copy-number variants (CNVs) from exome sequencing (ES) is not a standard practice in clinical settings owing to several reasons concerning performance. We comprehensively characterized an ES-based CNV pipeline and developed frameworks for minimizing false-positives and assess the reproducibility.\n\nMethodsWe used a cohort of 387 individuals with both clinical chromosomal microarray (CMA) and ES data available to estimate the initial performance by comparing CNVs from both platforms. A modification of the default workflow was performed to reduce the number of false positives and the reproducibility of the CNVs was assessed using an iterative variant calling process.\n\nResultsThe default pipeline was 93% sensitive with a high false-discovery rate of 44%. The modified workflow had a higher sensitivity of 96% while reducing the total number of CNVs identified and improving the false-discovery rate to 11.4%. With the modified workflow, we demonstrated a 100% validation rate for the CNVs identified in the STRC, a challenging gene to ascertain by short-read NGS. The exome-based pipeline was 100% sensitive for clinically-relevant, rare variants (including single exon deletions), and was reproducible.\n\nConclusionWe demonstrate with our modified workflow and the benchmarking data that an exome-based CNV detection pipeline can be reliably used to detect clinically-relevant CNVs.

bioinformatics↗