# Power analysis of artificial selection experiments using efficient whole genome simulation of quantitative traits

## TL;DR

This paper introduces a new simulation framework to analyze how artificial selection experiments can detect genes influencing complex traits.

## Contribution

The novel contribution is a whole-genome simulation framework that models QTLs and selection to improve detection power in artificial selection experiments.

## Key findings

- Modeling QTLs provides different insights compared to independent loci with constant selection.
- Interference between QTLs can lengthen allele fixation times during artificial selection.
- Founder haplotype information can significantly improve allele frequency estimates.

## Abstract

Evolve and resequence studies combine artificial selection experiments with massively parallel sequencing technology to study the genetic basis for complex traits. In these experiments, individuals are selected for extreme values of a trait, causing alleles at quantitative trait loci (QTLs) to increase or decrease in frequency in the experimental population. We present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci. This analysis uses a simulation framework that explicitly models whole genomes of individuals, quantitative traits, and selection based on individual trait values. We find that explicitly modeling QTL provides produces qualitatively different insights than considering independent loci with constant selection coefficients. Specifically, we observe how interference between QTLs under selection impacts the trajectories and lengthens the fixation times of selected alleles. We also show that a substantial portion of the genetic variance of the trait (50–100%) can be explained by detected QTLs in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, we show that power depends crucially on the opportunity for recombination during the experiment. Finally, we show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates.