Thinking too positive? Revisiting current methods of population genetic selection inference

Area of Science:

• Genomics
• Evolutionary Biology
• Population Genetics

Background:

• Next-generation sequencing provides vast, high-quality genomic data at lower costs.
• Distinguishing positive selection from other evolutionary forces like demography and background selection (BGS) remains a challenge.

Purpose of the Study:

• To review recent advancements in inferring natural selection from genomic data.
• To propose a roadmap for enhancing the accuracy of selection inference.

Main Methods:

• Review of current methodologies in population genomics and selection inference.
• Discussion of the role of advanced simulation tools.
• Emphasis on integrating multi-timepoint genomic data and experimental evolution results.

Main Results:

• Current genomic data offers unprecedented opportunities to study natural selection.
• Demography and background selection significantly obscure signals of positive selection.
• Advanced simulation tools and multi-timepoint data are crucial for robust inference.

Conclusions:

• Improved selection inference requires the mandatory use of advanced simulation tools.
• Integrating genomic data from multiple time points will increase the power of evolutionary inference.
• Experimental evolution findings should be incorporated into population genomic studies to refine selection detection.