Inference and Visualization of Complex Genotype-Phenotype Maps

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Understanding genotype-phenotype maps is crucial in biology but challenging due to high-dimensional sequence space and context-dependent mutation effects.
• Multiplex assays of variant effect (MAVEs) and large natural sequence collections offer new avenues for characterizing these maps.
• Existing tools lack comprehensive statistical and exploratory analysis capabilities for high-dimensional genotype-phenotype data.

Purpose of the Study:

• To develop a computational framework for analyzing complex genotype-phenotype maps.
• To provide tools for inference, imputation, and error estimation from MAVE and natural sequence data.
• To enable visualization and summarization of epistasis patterns in large genotype-phenotype landscapes.

Main Methods:

• Development of the Python library 'gpmap-tools'.
• Integration of models for statistical inference, phenotypic imputation, and error estimation.
• Application of methods for summarizing epistasis and visualizing genotype-phenotype maps with millions of genotypes.

Main Results:

• Successfully inferred genotype-phenotype maps for 262,144 variants of the bacterial Shine-Dalgarno sequence using genomic and MAVE data.
• Visualization revealed high-fitness ridges connecting core motifs, suggesting a new thermodynamic model.
• The study demonstrated the utility of gpmap-tools for analyzing complex genetic interactions.

Conclusions:

• gpmap-tools offers a flexible and interpretable framework for studying complex genotype-phenotype relationships.
• The tool provides new insights into the architecture of genetic interactions and their evolutionary consequences.
• This work facilitates a deeper understanding of how biological sequences determine observable traits.