2023 ACMG Annual Clinical Genetics Meeting Digital Edition: Poster Gallery-A universal filter enabling high throughput genomic screening

Pdf Summary

The document discusses the need for a universal filter that enables high throughput genomic screening. Screening healthy individuals for potentially reportable findings is expanding, but it can be challenging due to the large number of variants that need to be assessed. The critical step to reduce the number of variants is to filter based on variant attributes and genomic databases, but there is limited experience using publicly available datasets to streamline genomic screening.<br /><br />The study used a cohort of 50 individuals from the Framingham Heart Study and the Jackson Heart Study and applied a baseline filter previously used in the MedSeq project. They compared the outcomes of the baseline filter with an optimized filter, considering the total number of variants and sensitivity. Both filters interrogated a gene list of approximately 4,000 genes associated with human disease and captured pathogenic and likely pathogenic variants in an internal knowledge base. They also captured novel loss of function variants and variants meeting preset thresholds in gnomAD, ClinVar, and Human Gene Mutation Database.<br /><br />The optimized filter was developed by fine-tuning variables such as minor allele frequency in gnomAD and star status in ClinVar. When compared to the baseline filter, the optimized filter reduced the total number of variants to assess by 55% without affecting the sensitivity of the test. Further validation of the optimized filter in the entire PopSeq cohort will allow for better evaluation of its performance, including the number of variants, reduction in time, and cost.<br /><br />The document concludes that better prediction of loss of function variants and utilization of additional data sources like GenCC and OMIM may further improve the filter in the future. Overall, the study demonstrates the potential of using publicly available datasets and sophisticated filters to streamline genomic screening and reduce the burden of variant assessment.

Asset Subtitle

Presenting Author - Kalotina Machini, PhD; Co-Author - Limin Hao, PhD; Co-Author - Hana Zook, PhD; Co-Author - Lisa M. Mahanta, BSc; Co-Author - Heather Mason-Suares, PhD; Co-Author - Anna Nagy, MS, CGC; Co-Author - Heidi L. Rehm, PhD; Co-Author - Sami S. Amr; Co-Author - Matthew S. Lebo, PhD, FACMG;

Meta Tag

Bioinformatics

Databases

Genetic Testing

Genome sequencing

Co-Author Limin Hao, PhD

Co-Author Hana Zook, PhD

Co-Author Lisa M. Mahanta, BSc

Co-Author Heather Mason-Suares, PhD

Co-Author Anna Nagy, MS, CGC

Co-Author Heidi L. Rehm, PhD

Co-Author Sami S. Amr

Co-Author Matthew S. Lebo, PhD, FACMG

Presenting Author Kalotina Machini, PhD

Keywords

universal filter

high throughput genomic screening

variants

variant attributes

genomic databases

publicly available datasets

cohort

baseline filter

optimized filter

gene list