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Detecting Positive Selection in Populations Using Genetic Data.

Angelos Koropoulis^1,2, Nikolaos Alachiotis¹, Pavlos Pavlidis³

¹Institute of Computer Science, Foundation for Research and Technology Hellas, Heraklion, Greece.

Methods in Molecular Biology (Clifton, N.J.)

|January 25, 2020

Summary

This summary is machine-generated.

Detecting positive selection in genomes involves identifying signatures of selective sweeps. Linkage disequilibrium-based methods show higher accuracy than site frequency spectrum methods, but gene flow impacts detection accuracy.

Keywords:

Machine learning Positive selection Selective sweep Software tools Summary statistics

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Area of Science:

Population Genetics
Genomics
Evolutionary Biology

Background:

High-throughput sequencing enables the study of evolutionary forces like positive selection.
Positive selection, or Darwinian selection, drives adaptation by favoring beneficial alleles, leading to selective sweeps.
Selective sweeps leave detectable genomic signatures, including reduced variation and altered linkage disequilibrium (LD).

Purpose of the Study:

To review and compare methodologies for detecting positive selection signatures (selective sweeps) in genomic data.
To evaluate the performance of various detection software and machine learning approaches.
To analyze the impact of demographic factors, particularly gene flow, on selective sweep detection accuracy.

Main Methods:

Comparative analysis of five open-source selective sweep detection software (SweeD, SweepFinder, SweepFinder2, OmegaPlus, RAiSD).
Assessment of statistical approaches including site frequency spectrum (SFS) and linkage disequilibrium (LD) based methods.
Testing and performance analysis of machine learning methods for selective sweep detection.
Investigation into the effects of gene flow on the accuracy of selective sweep detection.

Main Results:

Most methods accurately detect selective sweeps in neutral or mild bottleneck models.
LD-based methods generally outperform SFS-based methods in true positive rates but can have higher false positive rates with misspecified demographic models.
Both LD and SFS methods show reduced accuracy in pinpointing selection targets during bottlenecks.
Gene flow significantly impacts selective sweep detection accuracy, an understudied aspect.

Conclusions:

The choice of selective sweep detection method depends on the specific genomic data and evolutionary scenario.
LD-based methods offer advantages but require careful consideration of demographic history.
Further research is needed to understand and mitigate the effects of gene flow on evolutionary inference.