# Characterizing disease-causing variants using personal genomes with large recurrent deletions

> **NIH NIH R35** · BAYLOR COLLEGE OF MEDICINE · 2021 · $480,000

## Abstract

Abstract
A major challenge in the field of genomic precision medicine is the observation that genotype does not always
predict phenotype in Mendelian disorders. This phenotype variation is thought to be caused in part by common
variants and variants with subtle effects, but the potential deterministic roles of such modifier alleles have not
been rigorously or systematically studied in clinical settings. Toward the long-term goal of deciphering the
genetic basis of incomplete penetrance and variable expressivity in human monogenic diseases, this proposal
aims to study patients with recurrent genomic disorders in whom identical genomic rearrangements manifest
disease phenotypes in an incompletely penetrant manner. The overall objective of this proposed project is to
formulate a generalizable approach using cohorts of patients with recurrent large genomic deletions that can
identify and characterize clinically significant disease-modifying variants. The central hypothesis is that patients
with recurrent large genomic deletions offer an effective genomic background to identify disease-modifying
alleles that serve as reliable predictors of disease outcome in individual patients. This central hypothesis will be
tested by defining the genetic determinants of specific phenotype presentations at two genomic loci: early
onset diabetes at 17q12 and abnormal head size at 1q21.1. Large-scale patient resources will be gathered
based on molecular information made available by clinical diagnostics. Recruited patients will be analyzed at
the molecular level by whole genome sequencing. Phenotype–genotype correlation analysis will be performed
to identify candidate modifier alleles under different disease-modifying models based on preliminary data from
each disease locus. Functional validation of selected disease-modifying alleles will be performed using patient-
derived induced pluripotent stem cells. The innovation of this study lies in the assembly of rare personal
genomes with identical copy number variants from clinical diagnostic databases to enable a human subject
“enhancer screen.” The proposed research is significant because it is expected to identify clinically important
alleles in recurrent deletion loci whose presence or absence can be used for precise diagnosis, counseling and
management for patients with genetic disorders. The research strategy utilized herein could be generalized to
other phenotypes or genomic loci, and the general mechanisms discovered will be directly applicable to
understanding incomplete penetrance and variable expressivity in human diseases.

## Key facts

- **NIH application ID:** 10047813
- **Project number:** 1R35HG011311-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Pengfei Liu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $480,000
- **Award type:** 1
- **Project period:** 2021-07-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10047813

## Citation

> US National Institutes of Health, RePORTER application 10047813, Characterizing disease-causing variants using personal genomes with large recurrent deletions (1R35HG011311-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10047813. Licensed CC0.

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