The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data

Area of Science:

• Evolutionary Biology
• Population Genetics
• Genomics

Background:

• Traditional population genetics studies analyze single time-point samples.
• Genomic data over multiple generations are increasingly used to detect adaptation.
• Distinguishing polygenic selection from genetic drift is challenging.

Purpose of the Study:

• To utilize temporal genomic data to measure the genome-wide impact of polygenic selection.
• To develop methods for characterizing allele frequency changes influenced by selection over time.
• To bridge population genetics and quantitative genetics in adaptation studies.

Main Methods:

• Developing expressions for temporal autocovariance in allele frequency changes.
• Analyzing the relationship between autocovariance and genetic variation, recombination, and linkage disequilibrium.
• Using analytic expressions for temporal variances and autocovariances to estimate population genetic parameters.

Main Results:

• Temporal autocovariance in allele frequency changes can be measured from genomic data.
• The magnitude of autocovariance is influenced by additive genetic variation, recombination, and linkage disequilibrium.
• Additive genetic variation for fitness and drift-effective population size can be estimated.
• The proportion of allele frequency variation due to linked selection can be quantified.

Conclusions:

• Temporal genomic data provide a powerful tool for studying genome-wide diversity under selection.
• This approach allows for the detection of linked selection's role over short evolutionary timescales.
• The findings facilitate the integration of population and quantitative genetic approaches to understand adaptation.