# Distribution of Sequencing Coverage Gaps in Exomes and Genomes: Potential Implications for Diagnostic Accuracy in Neurodevelopmental Disorder Genes

## TL;DR

This study examines how sequencing coverage gaps affect diagnostic accuracy in neurodevelopmental disorders, finding that coverage gaps vary significantly between sequencing batches and may impact clinically relevant genes.

## Contribution

The study reveals that sequencing coverage gaps are batch-specific and can affect diagnostic sensitivity in neurodevelopmental disorder genes.

## Key findings

- LCRs showed high intra-batch consistency but extreme variability between batches.
- LCRs in exome and genome sequencing impacted different genes and were not associated with GC content or genomic location.
- LCRs occasionally overlapped ClinVar pathogenic variants, suggesting potential diagnostic implications.

## Abstract

Background: Exome (ES) and genome sequencing (GS) are powerful tools for diagnosing neurodevelopmental disorders (NDDs), yet sequencing coverage failures can leave clinically relevant variants undetected. Analyzing the distribution of coverage gaps across sequencing approaches and batches is therefore informative for diagnostic accuracy. Methods: We analyzed sequencing data from 43 NDD patients across four ES runs, including 14 individuals sequenced by both ES (Twist Human-Core-Exome-v1.3) and GS. Low-coverage regions (LCRs) were defined as target intervals with mean depth <20 x, and z-scores < −1.96 were used to identify batch-specific systematic LCRs. LCRs were clinically annotated using OMIM and SysNDD databases. Results: LCR patterns were highly consistent within each ES batch but were characterized by extreme variability between batches. Higher global mean coverage increased intra-batch consistency, but batches sequenced at a commonly accepted yield in clinical sequencing (>100 x mean coverage) showed thousands of batch-specific LCRs. LCR patterns substantially diverged between ES and GS, displaying preferential impact on different genes. Although a restricted group of genes accumulates LCRs disproportionately, most LCRs are broadly dispersed throughout the genome. LCRs were not systematically associated with features such as GC content and genomic location (e.g., exon 1). Interestingly, LCRs affected OMIM/SysNDD genes and occasionally overlapped ClinVar pathogenic variants, indicating potential impact on diagnostic sensitivity. Conclusion: The global distribution of coverage gaps appears strongly influenced by batch-specific effects, making the occurrence of LCRs partly unpredictable even within clinically relevant gene sets. These findings support systematic assessment of LCRs as a component of quality evaluation in diagnostic sequencing workflows.