PopPAnTe: population and pedigree association testing for quantitative data

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Family-based genetic studies are crucial for understanding heritable molecular biomarkers.
• Current software lacks user-friendly tools for large-scale quantitative data analysis in related samples, especially from -omics technologies.

Purpose of the Study:

• To develop a user-friendly software tool for association testing of quantitative data in related samples.
• To extend the utility of family-based designs to large -omics datasets.

Main Methods:

• Development of PopPAnTe, a Java program for pairwise association testing.
• Implementation of data pre/post-processing, region-based testing, and empirical association assessment.
• Adaptability to family data of varying complexity and utilization of genetic similarity for unknown genealogies.

Main Results:

• PopPAnTe provides a user-friendly interface for evaluating quantitative data associations in related individuals.
• The software supports large datasets common in -omics research.
• It includes features for data processing and robust association testing.

Conclusions:

• PopPAnTe offers an integrated and flexible framework for genetic association studies in related samples.
• It is particularly valuable for analyzing biobank data from population isolates with incomplete genealogical information.
• The tool facilitates the use of complex family data and genome-wide genetic data for biomarker discovery.