Statistical Genomics Course

Lesson 1 • Genome Structure and Organization

Covers chromosomes, genes, regulatory elements, and genome architecture. Establishes biological vocabulary needed throughout the course.

Lesson 2 • DNA Variation and Mutation Types

Introduces SNPs, indels, CNVs, and structural variants. Connects variant classes to downstream statistical modelling choices.

Lesson 3 • Population Genetics Fundamentals

Introduces allele frequencies, Hardy-Weinberg equilibrium, and evolutionary forces. Grounds statistical models in population-level thinking.

Lesson 1 • Hypothesis Testing in Genomics

Covers null hypothesis testing, p-values, and power analysis. Prepares students for the multiple-testing challenges unique to genomics.

Lesson 2 • Probability Theory Essentials

Covers probability axioms, conditional probability, and Bayes' theorem. Provides the mathematical foundation for genomic inference.

Lesson 3 • Statistical Inference and Estimation

Teaches maximum likelihood estimation and confidence intervals. Connects estimation theory to parameter inference in genomic models.

Lesson 4 • Key Probability Distributions

Introduces binomial, Poisson, normal, and beta distributions. Links each distribution to specific genomic data-generating processes.

Lesson 1 • Sequencing Technologies Overview

Surveys short-read, long-read, and single-cell sequencing platforms. Connects platform characteristics to data quality and analysis strategy.

Lesson 2 • Standard Genomic File Formats

Introduces FASTQ, BAM, VCF, BED, and related formats. Enables students to navigate and manipulate genomic data pipelines.

Lesson 3 • Sample and Variant Quality Control

Teaches missingness, heterozygosity, relatedness, and batch-effect checks. Prevents confounded results in all subsequent analyses.

Lesson 4 • Read Alignment and Preprocessing

Covers reference genome alignment, duplicate marking, and base quality recalibration. Produces analysis-ready files for downstream statistics.

Lesson 1 • Linkage Disequilibrium Concepts

Defines LD, recombination, and allelic association between loci. Establishes why LD is central to GWAS and imputation strategies.

Lesson 2 • Statistical Phasing Methods

Introduces hidden Markov models and population-based phasing algorithms. Enables accurate haplotype reconstruction from unphased genotype data.

Lesson 3 • Haplotype Structure and Blocks

Covers haplotype block detection and tag SNP selection. Connects haplotype structure to efficient genotyping array design.

Lesson 1 • Principal Component Analysis in Genomics

Applies PCA to genotype matrices to capture ancestry axes. Provides the primary tool for visualising and correcting population stratification.

Lesson 2 • Fixation Index and Population Differentiation

Introduces FST and related statistics for measuring between-population divergence. Connects differentiation metrics to selection and demographic inference.

Lesson 3 • Demographic History Inference

Covers coalescent theory and methods for inferring population size changes. Provides historical context for interpreting modern genomic variation.

Lesson 1 • Replication and Meta-Analysis

Teaches replication strategies and fixed- and random-effects meta-analysis. Enables combining evidence across cohorts for robust discovery.

Lesson 2 • Confounding and Covariate Adjustment

Addresses population stratification, cryptic relatedness, and batch effects. Teaches covariate inclusion and genomic control correction.

Lesson 3 • GWAS Study Design Principles

Covers case-control and quantitative trait designs, sample size, and power. Ensures statistically valid and reproducible study architecture.

Lesson 4 • GWAS Results Interpretation

Covers Manhattan plots, genome-wide significance thresholds, and locus zoom. Translates statistical signals into biologically meaningful findings.

Lesson 1 • Expression Quantitative Trait Loci

Covers cis- and trans-eQTL mapping and tissue-specific expression effects. Links GWAS loci to gene regulation as a functional mechanism.

Lesson 2 • Pathway and Gene Set Enrichment

Applies enrichment tests to GWAS hits for biological pathway discovery. Connects statistical signals to mechanistic biological processes.

Lesson 3 • Statistical Fine-Mapping Methods

Introduces credible set construction and Bayesian fine-mapping algorithms. Narrows association signals to the most probable causal variants.

Lesson 4 • Genomic Annotation Resources

Surveys regulatory databases, conservation scores, and chromatin accessibility data. Provides the annotation toolkit for variant prioritisation.

Lesson 1 • Transferability Across Populations

Addresses PGS performance decay in non-discovery populations. Motivates diverse training data and ancestry-aware scoring strategies.

Lesson 2 • PGS Construction Methods

Covers P+T thresholding, LDpred, and penalised regression approaches. Enables students to select and implement appropriate scoring methods.

Lesson 3 • PGS Validation and Performance Metrics

Teaches R-squared, AUC, and calibration for score evaluation. Ensures rigorous assessment of predictive accuracy in target samples.

Lesson 4 • Clinical and Research Applications of PGS

Explores risk stratification, drug target prioritisation, and phenome-wide studies. Connects PGS methodology to real-world genomic medicine use cases.