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Ghat: an R package for identifying adaptive polygenic traits.

Medhat Mahmoud1,2, Mila Tost1,2, Ngoc-Thuy Ha3

  • 1Department of Crop Science, University of Goettingen, Goettingen 37075, Germany.

G3 (Bethesda, Md.)
|December 1, 2022
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Summary
This summary is machine-generated.

Identifying selection on complex traits is crucial for evolution and breeding. A new R package, Ghat, effectively detects selection on polygenic traits by analyzing allele frequency changes and effect sizes, overcoming previous detection limitations.

Keywords:
R packageadaptationevolutionpolygenic adaptationpolygenic selectionquantitative geneticswheat

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Area of Science:

  • Genetics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Identifying selection on polygenic complex traits is vital for understanding evolution and improving crop/livestock breeding.
  • Quantitative trait loci (QTL) with small or infinitesimal effects pose challenges for detecting polygenic trait selection due to low statistical power.
  • Previous methods struggled to detect selection on complex traits influenced by numerous genes with minor effects.

Purpose of the Study:

  • To introduce "Ghat," an R package designed to implement a novel method for identifying selection on polygenic complex traits.
  • To provide a user-friendly tool for researchers to test for polygenic selection in various organisms.
  • To demonstrate the utility of Ghat in analyzing real-world datasets and simulated populations.

Main Methods:

  • Developed a method evaluating the relationship between genome-wide allele frequency changes and estimated effect sizes.
  • Calculated a composite statistic across all markers to capture the selection signal.
  • Implemented a linkage disequilibrium-aware permutation test to distinguish selection from drift and population stratification.
  • Created the "Ghat" R package to operationalize this method.

Main Results:

  • Successfully applied Ghat to identify polygenic selection on 15 diverse European wheat traits (yield, biomass, quality, morphology, disease resistance).
  • Demonstrated Ghat's effectiveness on simulated populations with varying breeding histories and genetic architectures.
  • Confirmed the power of the Ghat package in detecting selection on complex traits.

Conclusions:

  • The Ghat R package provides a powerful and accessible tool for detecting selection on polygenic traits.
  • This method overcomes previous limitations in identifying selection on complex traits with small-effect loci.
  • Ghat facilitates advancements in evolutionary studies and marker-assisted breeding programs by enabling robust selection detection.