Optimization of Genotype by Sequencing data for phylogenetic purposes

Area of Science:

• Evolutionary biology
• Genomics
• Bioinformatics

Background:

• Mastiff bats (Molossus) exhibit low genetic variation, complicating evolutionary relationship studies.
• Traditional Sanger sequencing has been insufficient for resolving phylogenetic uncertainties within this genus.
• Closely related species often present challenges in genetic analysis due to conserved morphology and low genetic divergence.

Purpose of the Study:

• To develop and validate a framework for assembling and analyzing Genotyping by Sequencing (GBS) data for closely related species.
• To assess the impact of different bioinformatic pipelines and phylogenetic inference methods on GBS data analysis.
• To investigate the utility of GBS in resolving evolutionary relationships in genetically conservative taxa.

Main Methods:

• Assembly and analysis of Genotyping by Sequencing (GBS) data from mastiff bats.
• Comparison of various SNP (single nucleotide polymorphism) calling, alignment, and phylogenetic inference strategies.
• Evaluation of different assembly assumptions and tree-building methodologies.

Main Results:

• Consistent phylogenetic results were obtained across different alignment and tree inference approaches using GBS data.
• The GBS framework successfully recovered well-supported species boundaries and phylogenetic relationships.
• The methodology proved effective for macroevolutionary studies in species with low genetic distances.

Conclusions:

• Genotyping by Sequencing (GBS) is a robust approach for resolving macroevolutionary questions in morphologically and genetically conservative taxa.
• Exploring diverse assembly assumptions and phylogenetic inference methods is crucial for accurate phylogenetic reconstruction.
• The proposed framework enhances the understanding of evolutionary history in challenging groups like mastiff bats.