Genomic Markers and QTL Mapping

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Genomic Marker Analysis Suite

Interactive Companion Application

Molecular Marker-Based Classification

Understanding genetic diversity within populations is fundamental to breeding and conservation. This section explores how molecular markers (like SNPs or SSRs) are used to quantify genetic relationships, group similar individuals, and assess the statistical reliability of these groupings.

1. Similarity Measures

Before clustering, we calculate a pairwise distance or similarity matrix. Different coefficients handle shared band presence/absence differently.

Jaccard Coefficient: Ignores shared absences. Good for dominant markers.
J = a / (a + b + c)

Simple Matching (SM): Considers both shared presence and shared absence as similarities.
SM = (a + d) / (a + b + c + d)

2. Clustering & Bootstrapping

Methods like UPGMA or Ward's method use the similarity matrix to build hierarchical groups.

Bootstrapping (Validation)

How robust are these clusters? Resampling the marker data with replacement many times over (e.g., 1000x) tests grouping accuracy. A high bootstrap value indicates true, reproducible lineages.

Principal Coordinate Analysis (PCoA)

Visualization of clustering based on a similarity matrix.

Simulated Data

Quantitative Trait Loci (QTL) Mapping

QTL mapping identifies specific regions of the genome associated with complex quantitative traits (like yield or height). By linking phenotype with genotype variation at target loci, we can locate major genes.

Select Analytical Method

Trait Variation by Marker Genotype

Comparing phenotypic means across marker classes.

Significance Threshold: LOD = 3.0