R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Quantitative trait loci (QTL) mapping is crucial for understanding genetic contributions to complex traits.
• Existing software like R/qtl has limitations with multiparental populations and high-dimensional data.
• There is a need for advanced tools to analyze complex genetic architectures in diverse experimental populations.

Purpose of the Study:

• Introduce R/qtl2, an enhanced software environment for QTL analysis.
• Expand QTL mapping capabilities to multiparent populations and high-density genotype data.
• Facilitate the analysis of high-dimensional molecular phenotypes in genetic studies.

Main Methods:

• Developed R/qtl2 using R and C++ programming languages.
• Implemented genome scans using linear mixed models to control for population structure.
• Integrated SNP imputation from founder strain genomes and association mapping functionalities.

Main Results:

• R/qtl2 supports multiparent populations (e.g., Collaborative Cross, Diversity Outbred, MAGIC).
• The software efficiently processes high-density genotyping data and high-dimensional molecular phenotypes.
• R/qtl2 provides essential QTL mapping features, including graphical displays and summary reports.

Conclusions:

• R/qtl2 significantly advances QTL mapping capabilities for complex experimental populations.
• The software is a versatile and extensible tool for genetic research.
• R/qtl2 is free, open-source, and includes a test framework for reliability.