To identify whether parent-of-origin effects (POE) of the 15q11.2 BP1-BP2 microdeletion are associated with differences in clinical features in individuals inheriting the deletion, we collected 71 individuals reported with phenotypic data and known inheritance from a clinical cohort, a research cohort, the DECIPHER database, and the primary literature. Chi-squared and Mann-Whitney U tests were used to test for differences in specific and grouped clinical symptoms based on parental inheritance and proband gender. Analyses controlled for sibling sets and individuals with additional variants of uncertain significance (VOUS). Among all probands, maternal deletions were associated with macrocephaly (p = 0.016) and autism spectrum disorder (ASD; p = 0.02), while paternal deletions were associated with congenital heart disease (CHD; p = 0.004). Excluding sibling sets, maternal deletions were associated with epilepsy as well as macrocephaly (p < 0.05), while paternal deletions were associated with CHD and abnormal muscular phenotypes (p < 0.05). Excluding sibling sets and probands with an additional VOUS, maternal deletions were associated with epilepsy (p = 0.019) and paternal deletions associated with muscular phenotypes (p = 0.008). Significant gender-based differences were also observed. Our results supported POEs of this deletion and included macrocephaly, epilepsy and ASD in maternal deletions with CHD and abnormal muscular phenotypes seen in paternal deletions.

Purpose: To assess clinical chromosomal microarray (CMA) genomic testing reports for the following: (a) usage of reporting elements consistent with 2011 ACMG guidelines and other elements identified in the primary literature, (b) information quality, and (c) readability.Methods: We retrospectively analyzed genomic testing reports from 2011 to 2016 provided to, or by our laboratory to aid in clinical detection and interpretation of copy number variants. Analysis was restricted to the following sections: interpretation, recommendations, limitations, and citations. Analysis included descriptive characteristics, reporting elements, reading difficulty using the Simple Measure of Gobbledygook (SMOG), and quality ratings using a subset of questions adapted from the DISCERN‐Genetics questionnaire.Results: The analysis included 44 unique reports from 26 laboratories comprising four groups: specialty laboratories (SL; N = 9), reference laboratories (RL; N = 12), hospital laboratories (HL; N = 10), and university‐based laboratories (UL; N = 13). There were 23 abnormal/pathogenic reports and 21 of uncertain/unknown significance. Nine laboratories did not include one or more pieces of information based on ACMG guidelines; only one of ten laboratories reported condition‐specific management/treatment information when available and relevant. Average quality ratings and readability scores were not significantly different between laboratory types or result classification.Conclusions: Reporting practices for most report elements varied widely; however, readability and quality did not differ significantly between laboratory types. Management and treatment information, even for well‐known conditions, are rarely included. Effectively communicating test results may be improved if certain reporting elements are incorporated. Recommendations to improve laboratory reports are provided.

Objective To evaluate a new tool to aid interpretation of copy number variants (CNVs) in individuals with neurodevelopmental disabilities. Methods Critical exon indexing (CEI) was used to identify genes with critical exons (CEGs) from clinically reported CNVs, which may contribute to neurodevelopmental disorders (NDDs). The 742 pathogenic CNVs and 1,363 variants of unknown significance (VUS) identified by chromosomal microarray analysis in 5,487 individuals with NDDs were subjected to CEI to identify CEGs. CEGs identified in a subsequent random series of VUS were evaluated for relevance to CNV interpretation. Results CEI identified a total of 2,492 unique CEGs in pathogenic CNVs and 953 in VUS compared with 259 CEGs in 6,965 CNVs from 873 controls. These differences are highly significant (p < 0.00001) whether compared as frequency, average, or normalized by CNV size. Twenty-one percent of VUS CEGs were not represented in Online Mendelian Inheritance in Man, highlighting limitations of existing resources for identifying potentially impactful genes within CNVs. CEGs were highly correlated with other indices and known pathways of relevance. Separately, 136 random VUS reports were reevaluated, and 76% of CEGs had not been commented on. In multiple cases, further investigation yielded additional relevant literature aiding interpretation. As one specific example, we discuss GTF2I as a CEG, which likely alters interpretation of several reported duplication VUS in the Williams-Beuren region. Conclusions Application of CEI to CNVs in individuals with NDDs can identify genes of potential clinical relevance, aid laboratories in effectively searching the clinical literature, and support the clinical reporting of poorly annotated VUS.

PURPOSE: Variants of uncertain significance (VUS) are a common result of diagnostic genetic testing and can be difficult to manage with potential misinterpretation and downstream costs, including time investment by clinicians. We investigated the rate of VUS reported on diagnostic testing via multi-gene panels (MGPs) and exome and genome sequencing (ES/GS) to measure the magnitude of uncertain results and explore ways to reduce their potentially detrimental impact. METHODS: Rates of inconclusive results due to VUS were collected from over 1.5 million sequencing test results from 19 clinical laboratories in North America from 2020 - 2021. RESULTS: We found a lower rate of inconclusive test results due to VUSs from ES/GS (22.5%) compared to MGPs (32.6%; p<0.0001). For MGPs, the rate of inconclusive results correlated with panel size. The use of trios reduced inconclusive rates (18.9% vs 27.6%; p<0.001) whereas the use of GS compared to ES had no impact (22.2% vs 22.6%; p=ns). CONCLUSION: The high rate of VUS observed in diagnostic MGP testing warrants examining current variant reporting practices. We propose several approaches to reduce reported VUS rates, while directing clinician resources towards important VUS follow-up.

To identify and prioritize candidate disease genes of the central nervous system (CNS) we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We used five genome-wide metrics highly associated with neurological phenotypes to score 19,598 protein-coding genes. Genes scored one point per metric, resulting in a range of scores from 0-5. Approximately 13,000 genes were then paired with phenotype data from the Online Mendelian Inheritance in Man (OMIM) database. We used logistic regression to determine the odds ratio of each metric and compared genes scoring 1+ to cause a known CNS-related phenotype compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case-control cohorts, mouse model phenotype data, and gene ontology (GO) and pathway analyses. NeuroSCORE identified 8,296 genes scored ≥1, of which 1,580 are “high scoring” genes (scores ≥3). High scoring genes are significantly associated with CNS phenotypes (OR=5.5, p <2×10 ⁻¹⁶ ), enriched in case CNVs, and enriched in mouse ortholog genes associated with behavioral and nervous system abnormalities. GO and pathway analyses showed high scoring genes were enriched in chromatin remodeling, mRNA splicing, dendrite development, and neuron projection. OMIM has no phenotype for 1,062 high scoring genes (67%). Top scoring genes include ANKRD17, CCAR1, CLASP1, DOCK9, EIF4G2, G3BP2, GRIA1, MAP4K4, MARK2, PCBP2, RNF145, SF1, SYNCRIP, TNPO2 , and ZSWIM8 . NeuroSCORE identifies and prioritizes CNS-disease candidate genes, many not yet associated with any phenotype in OMIM. These findings can help direct future research and improve molecular diagnostics for individuals with neurological conditions.