Comprehensive and non-invasive prenatal screening of coding variation

NIH RePORTER · NIH · F32 · $87,892 · view on reporter.nih.gov ↗

Abstract

Abstract Non-invasive prenatal screening (NIPS) involves assessment of circulating cell-free fetal DNA (cffDNA) extracted from maternal plasma and the widespread clinical application of this technique to detect chromosomal aneuploidies in pregnancy is now routine and the standard of care. While NIPS technology has advanced to detect some targeted genomic abnormalities, current approaches are low-resolution and able to capture only a small fraction of genetic conditions important to prenatal diagnosis. Our preliminary studies on 6 samples suggest the feasibility of a high resolution non-invasive prenatal screen (hrNIPS), that can capture the vast majority of pathogenic coding variation (SNV, indel, CNV). However, our results suggest that the ability to call all types of variation can be improved through calibration of statistical models and development of new techniques. Integration of phenotype data will allow interpretation and prioritization of identified pathogenic variants. Therefore, we will deploy hrNIPS on 100 samples with paired fetal exome sequencing data to develop and calibrate methods over this large number of samples (Aim 1). We will call variation on these samples using the improved methods and implement an infrastructure to capture systematic phenotypes in these samples using the phenopackets schema (Aim 2). Further, we will use phenopackets and associated HPO terms to leverage phenotype-aware algorithms to prioritize identified variants for further review. We will interpret all variation in a clinical context using ACMG criteria and explore which types of variants might impact prenatal care (Aim 3). Finally, we will investigate the potential added value of hrNIPS as a maternal carrier screen. In parallel with these research aims, an exceptional team of seven mentors and advisors across disciplines, career stages, and institutions will provide didactic training, hands-on research support, and regular opportunities for presentation. Collectively, the ability to comprehensively assess coding variation in pregnancy with hrNIPS would allow early and non-invasive pregnancy assessment for molecular diagnosis and has the potential to transform the standard of care.

Key facts

NIH application ID
10910871
Project number
5F32HD112084-02
Recipient
MASSACHUSETTS GENERAL HOSPITAL
Principal Investigator
Michael H Duyzend
Activity code
F32
Funding institute
NIH
Fiscal year
2024
Award amount
$87,892
Award type
5
Project period
2023-07-01 → 2025-06-30