# Evolutionary Human Genomics: Demography, Natural Selection, and Transcriptional Regulation

> **NIH NIH R35** · COLD SPRING HARBOR LABORATORY · 2020 · $479,215

## Abstract

To be fully understood, the human genome must be considered in the context of evolution. The activities that have dominated human genomics for three decades — such as genome sequencing and annotation, interrogation with high-throughput biochemical assays, and the identification of associations between genetic variants and diseases — have been enormously informative, but these descriptive studies must eventually be understood within the theoretical framework of evolutionary genetics. We must continue to press forward from the what? to the why? and how? of human genetics.

The goal of my laboratory is to interpret high-throughput genomic data from an evolutionary perspective. Drawing from ideas and techniques in molecular evolution, population genetics, statistics, and computer science, we aim both to understand the evolutionary forces that have shaped human genomes, and to use evolution to shed light on the phenotypic importance of particular sequences. Our recent activities have focused in three major areas:
(1)  reconstruction  of  features  of  human  evolution  based  on  genome  sequences;  (2)  prediction  of  the  fitness consequences  of  human  mutations;  and  (3)  the  study  of  transcriptional  regulation  and  its  evolution  in primates.   We have reported major findings in each of these areas, including the existence of gene flow from early modern humans to Eastern Neandertals, a map of fitness consequences for mutations across the human genome, and an analysis showing that the architecture of transcription initiation is highly similar at enhancers and promoters in the human genome.

Here we propose to extend our research substantially in each of these areas, working together with a broad range  of  experimental  and  theoretical  collaborators.    Our  new  goals  include  the  development  of  improved methods for reconstructing human demography, with a focus on ancient gene flow; extensions of our ancestral recombination graph (ARG) sampling methods to accommodate much larger samples sizes, with applications in association mapping and the detection of natural selection; two complementary machine-learning approaches for  improving  the  prediction  of  fitness  consequences  from  sequence  data;  an  experimental  collaboration  to leverage CRISPR-Cas9 screens in characterizing noncoding mutations; a multi-pronged study of the sequence determinants of RNA stability and their implications for the evolution of transcription units; and development of a new probabilistic model for turnover of regulatory elements.  Together, these projects will address a wide variety of fundamental questions about the function and evolution of sequences in the human genome.

## Key facts

- **NIH application ID:** 9876220
- **Project number:** 5R35GM127070-03
- **Recipient organization:** COLD SPRING HARBOR LABORATORY
- **Principal Investigator:** Adam Charles Siepel
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $479,215
- **Award type:** 5
- **Project period:** 2018-03-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9876220

## Citation

> US National Institutes of Health, RePORTER application 9876220, Evolutionary Human Genomics: Demography, Natural Selection, and Transcriptional Regulation (5R35GM127070-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9876220. Licensed CC0.

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