# Statistical Methods for Analyzing Birth Defects Cohorts

> **NIH NIH R03** · YALE UNIVERSITY · 2022 · $167,500

## Abstract

Project Summary
Birth defects cause significant health and economic burdens to families and societies globally. In recent years,
advances in biotechnologies, such as next-generation sequencing, have helped to identify many disease-
causing genes for birth defects and childhood cancers. Although the identified genes only explain a small
proportion of the cases, these advancements demonstrate the promise of identifying more birth defect-causing
genes from the analysis of sequencing data through powerful statistical methods. The NIH Common Fund
established the Gabriella Miller Kids First Pediatric Research Program (Kids First) to “develop a pediatric
research data resource populated by genome sequence and phenotype data that will be of high value for the
communities of investigators who study the genetics of childhood cancers and/or structural birth defects.” The
ultimate goal of this project is to develop, implement, and apply novel statistical methods to improve the power
of identifying genes causing birth defects across a number of conditions using data from the Kids First Data
Resource Center and to make the developed tools available to the scientific community. This will be
accomplished through three specific aims. First, we will develop a statistical framework that can simultaneously
consider different disease models – including both de novo mutations and rare inherited variants – to more
effectively identify disease-causing genes from whole exome sequencing data. Second, we will develop
statistical methods to quantify the degree of shared de novo mutation contributions to different birth defects
and also methods that can leverage this shared genetics to identify disease-causing genes. Third, after
evaluating the performance of our developed methods, we will implement these methods and apply them to the
birth defect cohorts currently available at the Kids First Data Resource Center as well as other data sets that
will be added in the future. We will also disseminate the software to the scientific community. In accomplishing
our aims, we will contribute new statistical tools to analyze birth defects cohorts as well as make new biological
discoveries of genes and pathways for different birth defects.

## Key facts

- **NIH application ID:** 10372041
- **Project number:** 5R03HD100883-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** HONGYU ZHAO
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $167,500
- **Award type:** 5
- **Project period:** 2021-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10372041

## Citation

> US National Institutes of Health, RePORTER application 10372041, Statistical Methods for Analyzing Birth Defects Cohorts (5R03HD100883-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10372041. Licensed CC0.

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