# Genomic sequencing to aid diagnosis in pediatric and prenatal practice: Examining clinical utility, ethical implications, payer coverage, and data integration in a diverse population.

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $3,514,433

## Abstract

Congenital abnormalities and developmental disorders affect 3-5% of live born infants and children.
Despite advances in both pre- and post-natal treatment, the utility of genetic testing in diagnosing the etiology
underlying such conditions in order to guide management has been frustratingly limited. Traditional genetic
testing with specific gene tests, or even gene panels, is diagnostic in only a small percentage of cases. Recent
technological advances in next generation sequencing (NGS) have led to the ability to sequence and interpret
the entire exome relatively quickly, allowing a diagnosis in 25-30% or more of cases of developmental disorders
when other genetic tests have not yielded a result.
 Although whole exome sequencing (WES) holds great promise for improved diagnosis leading to better
clinical outcomes, challenges remain in determining how best to apply and utilize sequence data. Fulfilling the
promise of WES also requires investigation of ELSI (ethical, legal, social) concerns, given skepticism in some
communities that research will benefit them; economic considerations that ultimately determine access to and
equitable use of WES; and a need to share clinical genetic results with families and across health care systems
to enable better prognostication and management of rare conditions in community settings.
 We propose a Program in Prenatal and Pediatric Genomic Sequencing (P3EGS) at UCSF to examine the
diagnostic and clinical utility of WES. P3EGS will recruit and study affected individuals and their parents,
including pregnancies in which the fetus has a confirmed structural anomaly and children with previously
undiagnosed developmental disorders that are likely of genetic etiology. Following consent and collection of
standardized phenotypic data, the families will undergo WES as part of clinical care. To achieve diversity,
patient ascertainment and recruitment will occur at four UCSF sites that serve a broad range of under-
represented minorities (target of 75%) and span the full socio-economic spectrum, including the underserved.
 Our specific aims will: 1) examine the clinical utility of WES, including assessment of a variety of
health-related and reproductive outcomes, in 1100 undiagnosed individuals (300 prenatal, 800 children ages
0-17); 2) address ethical, social and economic issues in the delivery of genomic sequencing results to
ancestrally and economically diverse populations through (2.1) a mixed methods, longitudinal empirical study
of clinical interactions and experiences, (2.2) an economic analysis of insurance coverage, price and
reimbursement of multigene tests, and (2.3) creation of an Ethics Advisory Board to respond to emerging
issues and establishment of authentic stakeholder engagement; and 3) pilot a user-friendly web-based
patient/provider application integrating genomic and clinical data as a shared evidence base to support
result communication, interpretation and clinical decision making; the application wi...

## Key facts

- **NIH application ID:** 9949749
- **Project number:** 5U01HG009599-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Pui-Yan KWOK
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $3,514,433
- **Award type:** 5
- **Project period:** 2017-08-04 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9949749

## Citation

> US National Institutes of Health, RePORTER application 9949749, Genomic sequencing to aid diagnosis in pediatric and prenatal practice: Examining clinical utility, ethical implications, payer coverage, and data integration in a diverse population. (5U01HG009599-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9949749. Licensed CC0.

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