A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to...

Area of Science:

• Genomics
• Evolutionary Biology
• Bioinformatics

Background:

• Detecting adaptive loci is crucial for understanding genome evolution under natural selection.
• Existing methods primarily use molecular data, necessitating complementary approaches.

Purpose of the Study:

• To introduce a novel spatial analysis method (SAM) for detecting natural selection signatures.
• To integrate environmental variables with molecular data for a comprehensive analysis.
• To complement traditional population genetics approaches.

Main Methods:

• Developed a spatial analysis method (SAM) incorporating geographical information systems (GIS), environmental variables, and molecular data.
• Employed multiple univariate logistic regressions to associate allelic frequencies with environmental variables.
• Scanned hundreds of markers against hundreds of environmental variables.

Main Results:

• Demonstrated strong correspondence between SAM results and population genetics approaches in studies of pine weevils and sheep breeds.
• Identified statistical signals associating specific loci with environmental parameters.
• Observed atypical behavior of these loci compared to neutral loci.

Conclusions:

• The spatial analysis method (SAM) effectively identifies loci under selection and proposes ecological factors driving selection.
• This approach accelerates the identification of functional genes at the population level.
• SAM offers a valuable tool for evolutionary and ecological research.