Module 5: Genomics and Functional Genomics

Functional Genomics

  • Definition: Characterizes the function of sequences (mRNA, protein).

Predicting Gene Functions

  • Bioinformatics: Predicts function from sequence alone.

  • Homology Searching: Identifies orthologs in other species to infer function; requires phylogenetic analysis.

  • Protein Domains: Regions within proteins with specific shapes/functions; scanning databases infers function.

  • Experimental Mutagenesis: Random generation of mutations (radiation, chemicals, transposable elements, CRISPR-Cas) to produce observable phenotypes for screening.

Genome-Wide Mutagenesis

  • Relies on random mutation generation to produce observable phenotypes.

  • Offspring screened for mutant phenotypes; dominant mutations in heterozygotes, recessive require further crosses.

  • Positional cloning identifies mutated gene after phenotype detection.

The ENCODE Project

  • Aim: Identify all functional elements (transcription, chromatin, splicing) in human and mouse genomes.

  • Medical Value: Invaluable for inferring function of non-coding regions where most disease mutations occur.

Medical Genomics and GWAS

  • SNPs (Single Nucleotide Polymorphisms): Single base pair differences, inherited, usually benign, c. rac{1}{1000} bp.

  • SNP Haplotypes: Physically linked SNPs inherited together, can mark disease-causing loci.

  • Genome-Wide Association Studies (GWAS): Compares genetic variations (SNPs) between disease and control groups to find associations, pointing to disease-causing genomic regions.

Calculating Odds Ratio (GWAS)

  • Formula: OR = \frac{\text{Odds of exposure in cases}}{\text{Odds of exposure in controls}} = \frac{a \times d}{b \times c}.

    • a: cases with variant; b: controls with variant.

    • c: cases without variant; d: controls without variant.

  • Interpretation:

    • OR = 1: No association between variant and disease.

    • OR > 1: Variant increases disease risk.

    • OR < 1: Variant decreases disease risk.

Genomic Variation Databases

  • ClinVar: Genomic variation and human health.

  • MedGene: Human medical genetics conditions.

  • OMIM: Human genes and genetic phenotypes.

  • GTR: Genetic test information.

  • dbSNP: SNPs and small indels.

  • dbVar: Structural variants.

Personalized Genomics (e.g., 23andMe)

  • Direct-to-consumer genetic testing for health and ancestry.

  • Initially faced FDA restrictions on health reports, later approved for specific disease risk and carrier status tests.

Pharmacogenetics

  • Genetic testing to predict individual drug response.

  • Warfarin Case Study: Variations in VKORC1 and CYP2C9 affect metabolism/sensitivity, but genetic testing has poor predictive value for dosing in large populations; clinical guidelines advise against routine use.

Limitations of Genomics for Complex Diseases

  • Low Predictive Value: GWAS often identifies genes that explain only a small percentage of total genetic variation for complex traits/diseases.

  • Multigenic diseases require more sophisticated approaches, e.g., Machine Learning methods like Random Forest.