# Enabling Precision Genomics Using Adaptive Variation

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $435,089

## Abstract

Project Summary
Genes under natural selection may be related to heritable diseases, and variation in fitness more generally.
For example, genetic variants related to differential mortality rates during pathogenic infections will be under
natural selection when the infectious agents are present in the population. Inferences about selection at the
genomic level in humans, therefore, provide a rich source of new testable hypotheses about functional
relationships. However, while there are many methods for detecting natural selection at the genetic level, it
is often very hard to determine exactly which genetic variants were targeted by selection. The aim of our
study is to provide new computational methods for identifying causal mutations, and to apply these
methods, in order to better understand the map between genotype and phenotype of loci that are, or have
been, targeted by natural selection. We will apply the method to FADS genes, which harbor genetic
variation associated with fatty acid metabolism and which have been under selection in European
populations after the introduction of agriculture. We will test computational predictions experimentally in
human cell lines modified using CRISPR/Cas9 technology. This will lead to a deeper understanding of the
genetic differences among humans in these physiologically very important genes.
In Aim 1 we will develop new computational methods that can infer, from DNA sequence data, which
mutations have been targeted by natural selection. The methods will be able to incorporate the possibility
that more than one mutation has been under selection and will also be able to leverage various forms of
phenotypic and functional data.
In Aim 2, we will test computational predictions regarding selection in the FADS genes using CRISPR/Cas9
in human cell lines. In addition to identifying the functional mutations, we will test hypotheses about
interaction between mutations and between mutations and the environment, as represented by the
distribution of fatty acids available to the cells in the substrate they are growing on.
In Aim 3 we will extend the methods to be able to model selection in complex demographic models. We will
also extend the method to be able to include environmental co-variates and ancient DNA. This will allow us
to test hypotheses informed by the results of Aim 2 regarding the factors causing selection in the FADS
genes.

## Key facts

- **NIH application ID:** 10032497
- **Project number:** 1R01GM138634-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** RASMUS NIELSEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $435,089
- **Award type:** 1
- **Project period:** 2020-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10032497

## Citation

> US National Institutes of Health, RePORTER application 10032497, Enabling Precision Genomics Using Adaptive Variation (1R01GM138634-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10032497. Licensed CC0.

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