# Inferring selection from human population genomic data

> **NIH NIH R00** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $237,600

## Abstract

Project Summary/Abstract
Identifying genomic regions responsible for recent adaptation is a major challenge in population genetics.
Particularly in humans, the task of confidently detecting the action of recent adaptive natural selection (or
positive selection) has proved troublesome. Indeed there is considerable controversy over whether recent
positive selection has a substantial impact on human genetic variation. The work proposed here will address
this problem by creating a more complete map of positive selection across many human populations,
identifying selection on de novo mutations as well as selection on previously standing variation.
 Specifically, the proposed research seeks to construct a scan for positives election that is more robust
and accurate than any currently existing methods (Aim 1). This tool will utilize supervised machine learning
techniques allowing it combine information from a number of existing tests for natural selection, and will be
tested extensively on a large suite of population genetic simulations presenting a wide range of potentially
confounding scenarios. This tool will then be released to the public. Next, it will be applied to 26 human
populations in which a large sample of genomes have been sequenced by the 1000 Genomes Project (Aim 2),
revealing similarities and differences in the tempo, mode, and targets of adaptive evolution across human
populations. Finally, because selection on both beneficial and deleterious mutations skews genetic variation,
our method will be used to identify regions of the genome least affected by natural selection, which will in turn
be used to produce more accurate inferences of human demographic histories (Aim 3).
 The mentored phase of this work will be performed within the Department of Genetics at Rutgers
University. This is an intellectually stimulating environment with numerous journal clubs, an excellent seminar
series, and several other research groups using computational techniques. The project will be performed under
the stewardship of Dr. Andrew Kern, from whom the candidate will also receive training in machine learning
and population genetics. Dr. Schrider will also receive training in population genetics and guidance from Dr.
Jody Hey (Co-mentor) at nearby Temple University. This training will help Dr. Schrider acquire skills that will
aid not only in the completion of the proposed work but also his transition to principle investigator of an
internationally recognized independent research program studying the evolutionary forces driving patterns of
human genetic variation.

## Key facts

- **NIH application ID:** 9868315
- **Project number:** 5R00HG008696-05
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** DANIEL R SCHRIDER
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $237,600
- **Award type:** 5
- **Project period:** 2018-03-13 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9868315

## Citation

> US National Institutes of Health, RePORTER application 9868315, Inferring selection from human population genomic data (5R00HG008696-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9868315. Licensed CC0.

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