# Bayesian Hierarchical Methods for Localized Analysis of Genic Intolerance to Variation

> **NIH NIH K01** · CHILDREN'S HOSP OF PHILADELPHIA · 2024 · $168,863

## Abstract

Project Summary: The goal of this proposed mentored research is to tie genetic variation to disease by
analyzing regions that are intolerant to variation. Identifying regions that are intolerant to new variation can help
localize regions of potential functional importance and biologic relevance. Large public population consortia are
now accumulating datasets of sufficient size to detect regions subject to evolutionary selective pressures at an
increasingly granular level. However, there remains a shortage of appropriate analytical tools that are built to
specifically address important issues of disease heterogeneity across diverse populations. Despite the fact that
clinical exome sequencing is increasingly used for improved diagnostic evaluation, many genetic disorders
remain uncharacterized and diagnosis rates are still relatively low. In Aim 1, I will develop methodology that
localizes regions intolerant to variation and differential isoform expression associated with disease. Many genes
display tissue dependent transcript isoforms indicating potential functional implications of different isoforms. I will
characterize selective pressure across all isoforms using Bayesian techniques by looking at patterns of genetic
constraint across large standing populations, predominantly leveraging public data sets on the order of hundreds
of thousands of samples. Then I will leverage existing expression data to isolate key isoforms across different
cell and tissue types that are associated with diseases of interest. Then by accounting for regional intolerance
to variation, a joint transcriptomic variation–intolerance approach can be employed to improve disease
association testing. In Aim 2, I will analyze ancestry and cross species patterns of genetic intolerance to
variation. The majority of genetic studies have focused on European populations, which ignores genetic and
phenotypic diversity that can be leveraged to improve both targeted and overall diagnostic and clinical
capabilities. I will test for ancestry and cross species patterns of genetic intolerance to variation and association
with disease. Expanding to more populations will scale up the already large set of parameters being estimated;
so, I will develop new statistical methods and software to improve optimization of parameter estimation for the
Bayesian hierarchical models. I will isolate key ancestral populations with known differences in selective pressure
to validate findings while then leveraging these new methods and population disease patterns further to isolate
novel signals of ancestry-specific selective pressures. I will look for conserved regions across species to isolate
essential exonic regions while also isolating unique regions in the context of human specific genetic variation
and disease, such as neurodevelopmental disorders. During the training time for this proposed study I will focus
on advancing my understanding of biologic mechanisms and clinical genetics to better inform t...

## Key facts

- **NIH application ID:** 10755322
- **Project number:** 5K01HG010498-04
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Tristan Jonathan Hayeck
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $168,863
- **Award type:** 5
- **Project period:** 2021-01-05 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10755322

## Citation

> US National Institutes of Health, RePORTER application 10755322, Bayesian Hierarchical Methods for Localized Analysis of Genic Intolerance to Variation (5K01HG010498-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10755322. Licensed CC0.

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