# Harnessing the power of genetic relatedness for disease gene discovery

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $626,531

## Abstract

ABSTRACT
Despite decades of research, much of the genetic heritability of human disease remains unmapped to
susceptibility loci; and many gene-phenotype effects do not neatly fit the patterns of heterogeneity required for
well-powered analysis by GWAS nor family-based methods. Some genetic factors that contribute to disease
fall on a detectable, shared haplotypic background, yet have an appreciable population frequency due to
modest effects on disease risk. In such cases, analyses that utilize segmental sharing patterns in distant
relatives, such as identity-by-descent (IBD) mapping, are optimal for disease-gene discovery. This approach
has the advantage of allowing for: lower allele frequency of causal factors and higher allelic heterogeneity than
GWAS, and lower penetrance, more modest effect sizes, and higher genetic heterogeneity than linkage.
Additionally, the creation of large shared segment repositories allows for the identification of people who carry
haplotypes known to harbor rare risk variants, enabling efficient uses of targeted sequencing for evaluating the
effects of rare variants. Building on tools that we have developed as well as others', we propose the following
aims to leverage genetic relatedness estimation and shared segments in big data environments: 1) Create a
resource of shared segments in two large DNA biobanks. We will employ efficient and highly scalable
software architecture to automate analyses of relatedness from genetic data, including deep and accurate
relationship estimation and pedigree-aware shared segment detection across heterogeneous genetic data
types. Existing and novel approaches will be employed in BioVU and BioME, two large EHR-linked DNA
databanks to create shared segment repositories for use by the scientific community. Our analytic framework
will improve scalability and support a variety of standard output formats to integrate with downstream analyses.
2) IBD mapping phenome-wide. Shared segments provide an opportunity to recover power to detect a
tranche of disease-causing variants that contribute to the missing heritability of traits. Furthermore, we will
establish the effect of genetic dysregulation of genes in regions significantly enriched with shared segments
phenome-wide. 3) Demonstrate the utility of shared segments for identifying likely carriers of causal
variants in cancer predisposition genes. We will identify individuals in BioVU and BioME likely to harbor
pathogenic variants in known cancer predisposition genes by matching IBD segments shared between
biorepository participants and cancer cases sequenced at MD Anderson (N>10,000) and performing follow-up
genotyping of the loci to directly assess the clinical significance of the variants using the full EHR. Each aim
represents an innovative approach to data utilization in large EHR-linked DNA databanks, and the creation of
shared resources that will fuel future research. Collectively, our aims map a path towards efficient and
affordable n...

## Key facts

- **NIH application ID:** 10456944
- **Project number:** 5R01GM133169-04
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Jennifer Below
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $626,531
- **Award type:** 5
- **Project period:** 2019-09-19 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10456944

## Citation

> US National Institutes of Health, RePORTER application 10456944, Harnessing the power of genetic relatedness for disease gene discovery (5R01GM133169-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10456944. Licensed CC0.

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