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Related Experiment Video

Updated: May 20, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Estimating allele age and selection coefficient from time-serial data.

Anna-Sapfo Malaspinas¹, Orestis Malaspinas, Steven N Evans

¹Centre for Geogenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen, Denmark. anna.sapfo.malaspinas@snm.ku.dk

|August 2, 2012

Summary

This summary is machine-generated.

This study introduces a new method to estimate allele age and selection strength using time-series genetic data. The approach accurately dates genetic variations, distinguishing between new mutations and standing variation, as shown in horse coat color evolution.

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Area of Science:

Population Genetics
Ancient Genomics
Evolutionary Biology

Background:

Advances in sequencing yield vast ancient genomic data, enabling time-series analysis of allele frequencies.
Time-series data are crucial for precise inference of population genetic parameters and testing natural selection hypotheses.
Distinguishing selection on new mutations versus standing variation remains a key challenge in evolutionary studies.

Purpose of the Study:

To develop a likelihood-based method for jointly estimating selection coefficient and allele age from time-series data.
To provide a robust tool for analyzing allele frequency changes over time in population genetics.
To differentiate between selection acting on new mutations versus standing genetic variation.

Main Methods:

Developed a likelihood method to jointly estimate selection coefficient and allele age from time-series allele frequency data.
Utilized a one-step process approximation of the Wright-Fisher model's diffusion equation for transition probabilities.
Validated the method's accuracy and unbiasedness through extensive simulations.

Main Results:

The developed method provides unbiased estimates for selection coefficient and allele age.
Simulations confirmed the accuracy and reliability of the estimation method.
Application to horse coat color loci revealed an allele age predating domestication, illustrating the method's utility.

Conclusions:

The new method effectively estimates allele age and selection strength from time-series genomic data.
This approach aids in distinguishing selection on new mutations versus standing variation.
The findings in horse coat color suggest ancient origins for some selectively important alleles.