Marker-assisted selection for complex traits should not rely on static QTL estimates. The “Mapping As You Go” (MAYG) approach continuously re-estimates QTL effects as breeding populations evolve, accounting for epistasis and genotype-by-environment interactions. By updating marker information across breeding cycles, breeders can maintain more accurate and biologically relevant selection decisions.

Discover how QTL-seq combines bulk segregant analysis and sequencing to rapidly identify trait-linked genomic regions with high precision and low cost.

Discover how linkage disequilibrium drives GWAS and why population structure can create misleading associations if not properly controlled.

Discover how linkage disequilibrium is measured and why r²—not D—is the key metric behind GWAS signal detection.

Discover how Meta-QTL analysis transforms scattered QTL results into stable, high-confidence genomic regions, helping breeders identify reliable markers for selection.

Find out how the LOD score helps detect QTLs across the genome and guides the selection of accurate marker regions for breeding decisions.

QTL mapping has evolved significantly over the last 30 years. Each method—from simple single-marker tests to multi-QTL models—represents a step forward in power, resolution, and biological insight. Here is a practical overview for everyone who want to understand how these methods work and what each can (and cannot) do.

Quantitative Trait Locus (QTL) mapping allows breeders to identify genomic regions controlling complex traits. Here’s a short roadmap of the key steps to get reliable results:

Discover the basic logic behind QTL mapping