# Advancing evolutionary genetic inference in humans and other taxa

> **NIH NIH R35** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $382,894

## Abstract

Project Summary/Abstract
Background: A major challenge in evolutionary genomics is to characterize the forces shaping present-day
patterns of genetic variation. For instance, the extent and manner in which natural selection affects genetic
diversity remains highly controversial. Researchers have largely addressed this problem by developing
statistical tests or summaries of genome sequence variation that provide insights into the evolutionary forces at
play. However, because such approaches typically rely on a single univariate summary of the data, valuable
discriminatory information present in the original dataset is lost. A more fruitful strategy would thus be to use
multidimensional summaries of genomic data (e.g. a large vector of summary statistics) or even the totality of
the input data (e.g. a matrix-representation of a sequence alignment) to make more accurate inferences. An
even more powerful approach is to utilize data sets in which the same population is sampled at multiple time
points, allowing one to observe evolutionary dynamics in action. Although such genomic time-series data are
becoming more prevalent, the development of appropriate computational methodologies has lagged behind
the proliferation of such data.
Proposal: The Schrider Lab seeks to develop and apply powerful machine learning methods for evolutionary
inference. Our work over the next five years will yield powerful software tools leveraging novel representations
of genomic datasets, including time-series data. These efforts will dramatically improve researchers' ability to
make accurate evolutionary inferences from both population genomic and phylogenetic data. Indeed,
preliminary results demonstrate that our methods vastly outperform current approaches in evolutionary
genetics. More importantly, we will use these tools to answer pressing evolutionary questions. In particular, our
use of time-series data will reveal loci responsible for recent adaptation with much greater confidence than
currently possible. Our efforts will help to resolve the controversy over the role of adaptation in shaping
patterns of diversity across the human genome. This research has important implications for public health
as well, as genes underlying recent adaptations are enriched for disease-associations. Moreover, we are
constructing a time-series dataset in the mosquito vector species Aedes aegypti and Aedes albopictus. We will
interrogate these data for evidence of recent and ongoing adaptation—this work will reveal loci responsible
for the evolution of resistance to insecticides and other control efforts. Encouraging preliminary data also
suggest that our work in phylogenetics will substantially improve inferential power in this important research
area. More broadly, the success of the novel approaches described in this proposal has the potential to
transform the methodological landscape of evolutionary genomic data analysis.

## Key facts

- **NIH application ID:** 10028474
- **Project number:** 1R35GM138286-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** DANIEL R SCHRIDER
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $382,894
- **Award type:** 1
- **Project period:** 2020-07-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10028474

## Citation

> US National Institutes of Health, RePORTER application 10028474, Advancing evolutionary genetic inference in humans and other taxa (1R35GM138286-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10028474. Licensed CC0.

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