QTLpoly

QTLpoly implements random-effect multiple QTL mapping as an R package for autopolyploid species to detect and quantify QTLs and estimate additive allele effects and QTL-based breeding values from linkage maps and phenotypic data.

Key Features:

• Random-Effect Model Approach: Employs random-effect models to allow simultaneous mapping of multiple QTLs in autopolyploid genomes.
• Score Statistics Utilization: Leverages score statistics to enhance detection accuracy of multiple QTLs.
• Multiple Interval Mapping: Implements multiple interval mapping for QTL detection across linkage maps.
• Genotype Conditional Probabilities: Computes genotype conditional probabilities at every centimorgan (cM) position.
• Integrated Linkage Map: Uses an integrated linkage map comprising 30,684 markers distributed over 15 linkage groups (LGs) to inform genotype probabilities.
• Bi-parental Population Analysis: Supports analysis of bi-parental populations such as the Beauregard×Tanzania sweetpotato cross with 315 full-sibs.
• Multi-environment Trait Analysis: Handles phenotypic data for multiple traits across environments, exemplified by eight yield components measured in six Peruvian environments.
• QTL Identification Summary: Detects and reports multiple QTLs per trait, as demonstrated by identification of 41 QTLs with between one and ten QTLs per trait in the referenced study.
• Trait-Specific Insights: Distinguishes QTL effects for commercial and noncommercial root traits and identifies consistent regions (LGs 3 and 15) associated with root number and yield traits.
• BLUP and Breeding Values: Uses best linear unbiased predictions (BLUP) to characterize additive allele effects and compute QTL-based breeding values.

Scientific Applications:

• Sweetpotato QTL Mapping: Applied to Ipomoea batatas mapping in a Beauregard×Tanzania bi-parental population to map yield component QTLs.
• Multi-environment Genetic Analysis: Used for mapping genotype–phenotype associations across six distinct Peruvian environments for eight yield traits.
• Candidate Gene Localization: Identifies consistent QTL regions (e.g., on LGs 3 and 15) to guide candidate gene searches for root number and yield traits.
• Breeding Value Estimation: Provides QTL-based breeding values and additive allele effect estimates to inform selection in breeding programs for autopolyploids.

Methodology:

Performs multiple interval mapping and score-statistic-based QTL detection, computes genotype conditional probabilities at every cM from an integrated linkage map of 30,684 markers across 15 LGs, and applies best linear unbiased predictions (BLUP) to estimate additive allele effects and QTL-based breeding values.