# Mechanisms and consequences of sequence context-dependency of human mutation rate

> **NIH NIH R35** · UNIVERSITY OF PENNSYLVANIA · 2022 · $406,250

## Abstract

PROJECT SUMMARY/ABSTRACT
All genetic variation—including that underlying heritable disease, cancer, and human evolution—originate from
mutation. Recent large-scale genome sequencing efforts and innovative statistical analysis have uncovered
substantial variation in mutation rate along the human genome, revealing strong impacts of the mutation type
and flanking sequence. However, the molecular mechanisms of this context-dependency of mutation rate are
poorly understood. In addition, mutation rate variation across genomic sites may bias inferences of selection
signals from genomic data, which in turn hinders the identification and functional study of genes that drive
disease. The goal of our research program is to develop computational methods to draw insights into the
molecular mechanisms as well as functional and evolutionary consequences of context-dependent mutation
rate variation. We will first focus on the hypermutability of CpG sites and take a multifaceted approach to
investigate the lesion formation and repair at methylated cytosines in different regions of the genome, by
utilizing existing genomic and epigenomic data from human populations and other species. Successful
completion of this research will contribute to a mechanistic and quantitative understanding of the mutational
processes at methylated cytosines. Next, we will improve computational methods for inferring selection on
human genes by leveraging the inherent mutation rate variation across genomic sites. We propose to combine
population genetics models and machine learning techniques to integrate the allele frequency, site-specific
mutation rate, and functional information of variants. The application of these newly developed methods to the
ever-growing genomic data will identify genes crucial to human health and reproduction, and improve
estimates of the burden of deleterious variants introduced by new mutations in each generation. Finally, we will
expand our research scope to somatic mutations and leverage the context-dependency of mutation rates to
better understand cancer driver genes. We will evaluate the relative mutability of cancer driver genes under
different mutational processes, and investigate how tissue-specific relative mutability and selective effect
interact during somatic evolution of tumor. By taking an evolutionary perspective of tumorigenesis, this
research promises to shed new light on the tissue-specificity of cancer driver genes. Together, the proposed
research will develop novel computational approaches that translate the rich genomic and epigenomic data
available into insights into mutational mechanisms, as well as selective forces acting on genes and genetic
variants in human populations and somatic cells.

## Key facts

- **NIH application ID:** 10499355
- **Project number:** 1R35GM146810-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Ziyue Gao
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $406,250
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10499355

## Citation

> US National Institutes of Health, RePORTER application 10499355, Mechanisms and consequences of sequence context-dependency of human mutation rate (1R35GM146810-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10499355. Licensed CC0.

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