# Title: Functional Annotation of Genomes via Phenotypic Convergence within Large Multi-species Alignments

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $551,829

## Abstract

Project Summary
Linking genotype to phenotype is a unifying goal of genetics and is an important strategy for determining
how biological systems operate in health and disease. This project develops new computational tools in
the context of this FOA in computational genomics to link changes in genes and regulatory regions to the
evolution of specific organismal phenotypes. It then applies those tools to multiple biological traits in
mammals, birds, and fungi. The tools are present in our widely used comparative genomics package,
RERconverge. RERconverge uses the power of convergent evolution, in which different species have
repeatedly evolved the same phenotype or trait, to provide statistical repetition to locate genomic regions
whose evolutionary rates responded to a particular convergent phenotype. The first goal of this project
builds upon the RERconverge toolkit to analyze non-coding, potentially regulatory, regions of the
genome. Regulatory regions evolve in a modular way that could be better analyzed with methods that
operate at the scale of specific functional elements, such as transcription factor binding sites, and other
methods that do not require aligned nucleotides between species, such as models derived from machine
learning of regulatory elements. The aim then applies these methods to identify regulatory regions
responsible for the evolution of large body size in mammals and birds. The second aim creates a new
method to identify adaptive changes that led to a convergent phenotype, since adaptive change often
leads to novel organismal traits. Currently, our convergent evolution methods and others in the field do
not cleanly distinguish between regions experiencing adaptive evolution from those under reduced
constraint. This aim will create specific codon model tests to address that deficit and applies them in
primates and rodents to identify reproductive genes under positive selection in the context of sperm
competition. The third aim is to speed up key functions of the RERconverge platform to allow more
statistically robust analyses and to accommodate contemporary datasets of hundreds of species or more.
The project will also benchmark and apply these new methods to locate genes, non-coding regions, and
specific transcription factor sites responsible for convergent phenotypes of biomedical importance, such
as body size, fertilization, eyesight, metabolism, and transposable element activity. The culmination of
this research program will enable the rapid identification of genes and regulatory elements underlying
countless morphological and physiological traits, thereby propelling experimental and medical genetics
research with the power of evolutionary biology.

## Key facts

- **NIH application ID:** 10935953
- **Project number:** 5R01HG009299-07
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Maria D Chikina
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $551,829
- **Award type:** 5
- **Project period:** 2017-05-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10935953

## Citation

> US National Institutes of Health, RePORTER application 10935953, Title: Functional Annotation of Genomes via Phenotypic Convergence within Large Multi-species Alignments (5R01HG009299-07). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10935953. Licensed CC0.

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