Genomic Analysis - 1

Genomic Analysis

Data simulated for demonstration purposes.

Integrative Genomics Workflow

This interactive dashboard synthesizes key methodologies in modern genetics. The flow begins with raw biological profiling (Expression Data), moves to linking phenotypes with genotypic variations across populations (Association Mapping/GWAS), and culminates in using whole-genome profiles to forecast complex traits (Genome Prediction).

Analytical Pipeline

🩴

Transcriptomics

RNA-Seq & Microarrays. Identifying differentially expressed genes.

→

⚏

Genotyping

SNP arrays & sequencing. Cataloging genetic markers across populations.

→

📈

Statistical Modeling

GWAS & Genomic Selection. Mapping associations and training predictive models.

Key Concept: Genetic Markers

Measurable variations in DNA (like SNPs - Single Nucleotide Polymorphisms) used to identify individuals or species, and track inheritance of closely linked traits.

Key Concept: Breeding Value

The value of an individual as a genetic parent. Genomic prediction models aim to accurately estimate this value based solely on DNA marker profiles.

Design and Analysis of Expression Data

Expression analysis evaluates the transcriptomic activity of genes under specific conditions. The interactive Volcano Plot below visualizes the results of a differential expression analysis. It plots statistical significance (-log10 p-value) against effect size (log2 Fold Change).

Differential Gene Expression (Condition A vs B)

Hover over points to identify specific genes. Thresholds: |log2FC| > 1, p < 0.05.

Interaction Guide

Points in the upper left (green) are significantly downregulated. Points in the upper right (red) are significantly upregulated. Points low on the Y-axis represent non-significant changes regardless of fold magnitude.

Genome Wide Association Studies (GWAS)

GWAS scans markers across the complete sets of DNA of many individuals to find genetic variations associated with a particular phenotype. The Manhattan plot displays the significance of these associations across all chromosomes.

Manhattan Plot: Trait Z

Click on prominent peaks (high Y-axis values) to examine marker details.

Marker Details

🖱

Select a point on the chart to view locus statistics.

SNP ID

--

Chromosome

--

Position (bp)

--

Significance (p-value)

--

Markers passing the genome-wide significance threshold (red line) indicate a region of the genome statistically linked to the trait variance. Further functional validation is typically required.

Genome Selection & Prediction

Unlike GWAS which focuses on finding few significant markers, Genomic Selection uses all markers simultaneously to calculate Genomic Estimated Breeding Values (GEBVs). Different statistical models handle the genetic architecture of traits differently.

Model Predictive Accuracy

Comparing correlation between predicted and observed values (r) across traits.

Select Trait Architecture: Complex Trait (Highly Polygenic) Simple Trait (Few Major QTLs) Abiotic Stress Tolerance

RR-BLUP

Assumes all markers have small, equal variance. Excellent for highly polygenic traits.

BayesA

Allows each marker to have its own variance, drawn from a specific distribution.

BayesB

Assumes many markers have zero effect, identifying major QTLs better.

Random Forest

Machine learning approach capable of capturing non-linear interactions (epistasis).