library(tidyverse)
library(knitr)| Feature | DNA | RNA |
|---|---|---|
| Sugar | Deoxyribose, no 2′-OH | Ribose, 2′-OH present |
| Bases | A, T, G, C | A, U, G, C |
| Strandedness | Usually double-stranded helix | Usually single-stranded* |
| Stability | More chemically stable | Less stable |
| Location | Stays in nucleus | Moves throughout cell |
| Primary role | Long-term information storage | Multiple roles in expression |
Slides note exceptions such as dsRNA viruses and structured functional RNAs.
flowchart LR
DNA[(DNA, nucleus)] -->|transcription| mRNA[mRNA]
mRNA -->|translation at ribosome| Protein[Protein]
rRNA[rRNA] --- Ribosome
tRNA[tRNA + amino acids] --> RibosomeTLDR (Obj 1) DNA is stable, double-stranded, nuclear information storage. RNA is less stable, mobile, and supports expression as mRNA, rRNA, and tRNA.
| Stage | Input | Key event | Output |
|---|---|---|---|
| Transcription | DNA | RNA polymerase copies gene region | pre-mRNA |
| Processing | pre-mRNA | Splice introns, add cap/tail | mature mRNA |
| Export | mRNA | Nuclear pore transit | cytosolic mRNA |
| Translation | mRNA, tRNA, rRNA | Peptide synthesis at ribosome | polypeptide → protein |
Three nucleotides = one codon = one amino acid.
TLDR (Obj 2) DNA → pre-mRNA → processed mRNA → translated codons → polypeptide → protein.
flowchart TD
A[Chromatin state] --> B(Transcription)
B --> C[mRNA processing]
C --> D[Nuclear export]
D --> E[Translation rate]
E --> F[Protein level]TLDR (Obj 3) Regulation occurs at chromatin access, transcription level, and RNA processing. The goal is correct protein amounts at correct times.
| Term | Definition | Usage |
|---|---|---|
| Nucleotide | Sugar + phosphate + base | Three nucleotides form a codon. |
| Gene | DNA sequence encoding RNA/protein | HBB gene encodes beta-globin. |
| Allele | Alternate gene version | ΔF508 is an allele of CFTR. |
| Locus | Physical chromosomal position | HBB locus is on 11p15.5. |
| Genome | Full DNA content | Human haploid genome ≈ 3.2 Gb. |
| Chromosome | One DNA molecule with proteins | Humans have 23 pairs. |
| Chromatin | DNA + proteins (packaging) | Open chromatin increases access. |
| Nucleosome | DNA wrapped around histone octamer | Controls access. |
| Ploidy | Sets of chromosomes | Somatic cells are diploid. |
| Haploid | One set | Gametes are haploid. |
| Diploid | Two sets | Somatic cells. |
| Karyotype | Chromosome number/appearance | 46, XX or 46, XY. |
| mtDNA | Circular mitochondrial genome | ~16.5 kb, maternal. |
| Operon | Cluster under one promoter | Common in bacteria. |
| Plasmid | Small circular DNA in bacteria | Often carries resistance genes. |
nucleotide → double helix → nucleosome → chromatin fibre → loop/domains → chromosome → genome
flowchart TD
A[Nucleotide] --> B[Double helix]
B --> C[Nucleosome]
C --> D[Chromatin fibre]
D --> E[Loops]
E --> F[Chromosome]
F --> G[Genome]| Feature | Prokaryote | Eukaryote |
|---|---|---|
| Genome | Circular chromosome, plasmids | Multiple linear chromosomes |
| Packaging | Nucleoid proteins | Histones and nucleosomes |
| Compartment | Nucleoid | Nucleus |
| Gene organisation | Operons | Mostly monocistronic |
| mtDNA | No | Present |
TLDR (Obj 4) Human somatic cells are diploid with 46 chromosomes and ~6.4 Gb DNA. DNA is organised from nucleotides → helix → nucleosomes → chromatin → chromosomes → genome. Bacteria have circular DNA, plasmids, operons, and nucleoid structure.
| Genotype | Molecular state | Phenotype |
|---|---|---|
| AA | Dominant protein present | Dominant trait |
| Aa | Dominant protein present | Dominant trait |
| aa | No dominant product | Recessive trait |
Aa × Aa →
flowchart LR
P1[A or a] --> Offspring
P2[A or a] --> OffspringTLDR Dominant alleles show with 1 or 2 copies. Recessive traits require 2 copies. Punnett squares convert parent genotypes into offspring probabilities.
| Feature | Autosomal | Sex-linked |
|---|---|---|
| Chromosomes | 1–22 | X or Y |
| Copies in XY | Two | One X, one Y |
| Examples | Widow’s peak | Haemophilia, red-green colour blindness |
| Pitfall | Misjudging heterozygotes | Forgetting XY is hemizygous |
XAXa × XAY →
flowchart TB
A[XA] --> D1[XAXA]
A2[Xa] --> D2[XAXa]
B[XA] --> S1[XAY]
C[Y] --> S2[XaY]TLDR Autosomal traits act the same in XX and XY. X-linked traits differ because XY has only one X. Male→male transmission excludes X-linked patterns. Affected fathers passing only to daughters suggests X-linked dominant.
| Mode | Hallmarks |
|---|---|
| Autosomal dominant | Present every generation |
| Autosomal recessive | Unaffected parents → affected children |
| X-linked recessive | Mostly males; no father→son |
| X-linked dominant | All daughters of affected father |
| Y-linked | Father → all sons |
| Mitochondrial | Affected mother → all children |
Unaffected sibling of affected: 2/3 carrier.
TLDR Scan gender pattern, male→male lines, father→daughter patterns, skipped generations, and maternal transmission. Match to inheritance mode and compute genotype risks.
| Feature | DNA | RNA | Why it matters |
|---|---|---|---|
| Sugar | Deoxyribose | Ribose | RNA less stable |
| Bases | A,T,G,C | A,U,G,C | U replaces T |
| Pairing | A=T, G≡C | A=U, G≡C | Stability differs |
| Strandedness | Double-stranded | Single-stranded, folds | Functional shapes |
| Direction | 5′→3′ | 5′→3′ | Same chemistry |
| Charge | Negative backbone | Negative backbone | Binds basic proteins |
| Spectrophotometry | A260 = 50 ng/µL dsDNA | A260 = 40 ng/µL RNA | Prac relevance |
TLDR DNA is stable and double-stranded in chromatin. RNA is less stable, single-stranded, and functional in expression.
TLDR DNA → pre-mRNA → capped/spliced/polyadenylated → mRNA → ribosome → protein.
TLDR 64 triplet codons specify 20 amino acids + stops. Wobble explains degeneracy.
TLDR DNA binding uses charge interactions, groove readout, intercalation, or covalent adducts. These changes alter transcription, replication, or structure.
| Class | Definition | Typical consequence |
|---|---|---|
| Base substitution | One base replaced by another | Silent, missense, or nonsense |
| Insertion | One or more bases added | Frameshift if not multiple of 3 |
| Deletion | One or more bases removed | Frameshift if not multiple of 3 |
| Inversion | Segment flipped | Local disruption or altered regulation |
| Duplication | Segment copied | Dosage increase or frame disruption |
| Translocation | Segment moved | Fusion genes, mis-regulation |
| Mobile element insertion | Transposon/retroelement insertion | Disrupts coding or regulatory DNA |
| Class | Definition | Notes |
|---|---|---|
| Silent | Codon change, same amino acid | Can affect mRNA kinetics |
| Missense | Codon change to different amino acid | Severity depends on chemistry and position |
| Nonsense | Codon to premature stop | Truncation or no protein |
| Frameshift | Indel not in multiples of 3 | Alters downstream codons, usually severe |
flowchart TD
A[Normal codons] --> B[+1 insertion]
B --> C[Frameshift → early stop]
A --> D[+3 insertion]
D --> E[Frame preserved]
A --> F[Substitution]
F --> G[Nonsense/Missense]TLDR – Types of mutations DNA-level: substitution, indels, inversion, duplication, translocation, mobile element insertion. Protein-level: silent, missense, nonsense, frameshift. Frameshifts arise from non-triplet indels and usually abolish function. Impact depends on site, context, and residue chemistry.
| DNA change | RNA effect | Protein effect | Functional outcome |
|---|---|---|---|
| Synonymous substitution | Same amino acid, may alter stability or translation speed | None | Usually neutral, subtle kinetic effects |
| Missense substitution | Different codon | One amino acid changed | Neutral to severe; includes leaky, LOF, GOF, dominant-negative |
| Nonsense substitution | Premature stop | Truncated protein or none | Often NMD if stop is >50–55 nt upstream of last junction |
| In-frame indel | Frame preserved | Adds/removes residues | Motif disruption |
| Frameshift indel | New codons downstream | Radical sequence change, early stop | Usually severe loss-of-function |
| Splice-site mutation | Exon skipping, intron retention, cryptic splicing | In-frame or frameshift outcomes | Often NMD or dysfunctional isoforms |
| Promoter/enhancer mutation | Changes transcription | No coding change | Altered expression |
| UTR mutations | mRNA stability/translation changes | No coding change | Up/downregulation |
| CNV (deletion/duplication) | Dose change | Less or more protein | Haploinsufficiency, overexpression |
| Dominant-negative missense | Normal RNA | Mutant subunit poisons complex | Worse than simple LOF |
flowchart TD
DNA --> pre[pre-mRNA]
pre --> spliced[mRNA]
spliced -->|PTC upstream| NMD
spliced -->|no PTC| TranslationTLDR – Effects of mutations DNA changes propagate through RNA processing to protein. Nonsense and frameshift often trigger NMD. Splice-site and UTR changes alter expression. Outcomes: neutral, partial loss, complete loss, gain of function, or dominant-negative.
flowchart LR
Fork --> Mis[Mispair]
Mis -->|proofread| Correct
Mis -->|escape| MMR
MMR -->|missed| NextRound
NextRound --> Sub[Fixed substitution]TLDR – Replication errors Mispaired bases from rare tautomers or damaged DNA cause mismatches. Proofreading and MMR correct most. Unrepaired mismatches → permanent transition or transversion.
| Method | Detects | Size | Readout | Example |
|---|---|---|---|---|
| Karyotyping | Aneuploidy, large rearrangements | >5 Mb | G-banded metaphase spreads | Trisomy 21 |
| FISH | Targeted deletions/translocations | >100–200kb | Fluorescent probe signals | BCR-ABL1 in CML |
| Method | Detects | Principle | Output |
|---|---|---|---|
| RFLP | Changes in restriction sites | Enzyme cutting + gel | Fragment pattern differences |
| PCR | Presence/absence, small indels | Locus amplification | Amplicon detection |
| Sanger sequencing | Base-level variants | ddNTP chain termination | Exact sequence |
flowchart LR
Q[Clinical question] --> S{Variant size?}
S -->|Large| Karyotype
S -->|Sub-Mb| FISH
S -->|1–1000 bp| PCR
S -->|Base-level| SangerTLDR – Detection Karyotype for Mb-scale events. FISH for targeted sub-Mb. PCR/RFLP for small variants. Sanger for exact base calls.
flowchart TD
D[dsDNA] --> S[ssDNA]
S --> P[Primers]
P --> E[Extension]
E --> Copiesflowchart LR
Template --> Mix[dNTP + ddNTP]
Mix --> Term[Terminated fragments]
Term --> CE[Capillary]
CE --> Seq[Sequence trace]| Feature | PCR | Sanger |
|---|---|---|
| Goal | Detect/amplify | Read sequence |
| Output | Amplicon | Base-by-base |
| Sensitivity | Very high | High |
| Use | Presence/absence | Variant identification |
TLDR – PCR vs Sanger PCR amplifies loci from minimal input. Sanger reads exact sequences using ddNTP termination.
flowchart LR
Q[Clinical question] --> Size{Event size}
Size --> Cytogenetics
Size --> PCR
Size --> Sanger
Size --> ProteinTLDR – Clinical use Chromosomal tests detect large events. PCR handles small, known sequence differences. Sanger confirms exact variants. PCR also supports pathogen detection and viral load tracking.
flowchart LR
SameDNA --> Programs
Programs --> Expression
Expression --> CellTypesTLDR Same DNA, different expression profiles → different tissues.
| State | Definition | Effect |
|---|---|---|
| Heterochromatin | Condensed | Repressive |
| Euchromatin | Open | Permissive |
flowchart TD
Packed --> LowExpr
Open --> HighExpr
Signals --> Open
Signals --> PackedTLDR Open chromatin enables transcription. Closed chromatin represses it.
flowchart TD
Signal --> TF
TF --> Coactivators
Coactivators --> PIC
PIC --> Transcription
Insulator -. limits .-> TFTLDR Promoters set basal transcription. Enhancers and silencers tune it via TFs. Chromatin state integrates signals.
flowchart LR
premRNA --> splice
splice --> mRNA
mRNA --> decay
mRNA --> translation
translation --> protein
protein --> PTM
PTM --> fateTLDR RNA-level steps shape isoforms, stability, and translation. PTMs and degradation fine-tune protein abundance and activity.
| Term | Exam-grade definition | One-line usage |
|---|---|---|
| Allele | Alternative DNA sequence at a gene (locus) | “W and w are alleles for flower colour.” |
| Genotype | Allele pair at a locus in a diploid individual | “WW, Ww, ww are genotypes.” |
| Phenotype | Observable trait influenced by genotype and context | “WW and Ww give purple; ww gives white.” |
| Homozygous | Two identical alleles (AA or aa) | “WW and ww are homozygous.” |
| Heterozygous | Two different alleles (Aa) | “Ww is heterozygous.” |
| Allele frequency | Proportion of all gene copies that are a specific allele | “W = 13/18 = 0.72; w = 5/18 = 0.28; p + q = 1.” |
| Gene pool | All copies of a gene in the population | “18 alleles across 9 plants is the gene pool.” |
| Genotype frequency | Proportion of individuals with a genotype | “WW 6/9 = 0.67; Ww 1/9 = 0.11; ww 2/9 = 0.22.” |
TLDR
Population genetics quantifies allele and genotype frequencies in interbreeding populations. Know allele, genotype, phenotype, homozygous, heterozygous, allele frequency, genotype frequency, and p + q = 1. These measures track population structure and evolutionary change.
TLDR
HWE gives expected genotype frequencies (p², 2pq, q²) when no evolutionary forces act. Deviations signal drift, selection, migration, mutation, or non-random mating. The 1000-mouse example shows perfect HWE alignment.
TLDR
Use genotype counts when available. Otherwise infer q from recessive phenotype (q²), then compute p = 1 − q, then genotype and phenotype frequencies.
TLDR
Populations evolve because of mutation, migration, drift, selection, and non-random mating. HWE is a null model; deviations identify which forces act.
TLDR
Genetic variation (SNPs, indels, structural variants) underpins genetic diversity. Humans differ at ~0.1% of bases; SNP frequency ~1/1000. African populations show highest diversity.
| Aspect | Advantage | Disadvantage |
|---|---|---|
| Population survival | Higher adaptability | Harder to find causal variants |
| Human health | Protective variants exist | More opportunities for pathogenic variants |
| Public health | Supports risk assessment | Complexity for association studies |
TLDR
Diversity improves resilience and shapes disease risk. Forces include drift, gene flow, selection, and mutations (~10⁻⁴–10⁻⁶ per gene per generation).
| SNP location | Effect | Outcome |
|---|---|---|
| Synonymous | Structure/codon use/splicing enhancers | Expression changes |
| Missense | Amino acid change | LOF/GOF/stability/activity shifts |
| Nonsense | Premature stop | Truncation; possible NMD |
| Splice sites/branch/ESE/ESS | Exon skipping etc. | Isoform change, frameshift, dosage change |
| Promoter/enhancer/insulator | TF binding, chromatin | Expression up/down |
| UTR/miRNA sites | Stability, translation initiation | Dose changes |
flowchart LR
SNP --> Assoc
Assoc --> Map
Map --> Follow
Follow --> MechanismTLDR SNPs affect proteins, splicing, transcription, or mRNA stability. Most disease-risk SNPs are non-coding. GWAS detects SNP–trait associations because SNPs tag causal variants via LD.
| Application | What you do | Value |
|---|---|---|
| GWAS risk scores | Combine risk SNPs | Stratify prevention/screening |
| Pharmacogenomics | Genotype drug-response SNPs | Guide therapy |
| Carrier/cascade testing | Genotype in families | Identify carriers |
| Molecular diagnosis | Use tag SNPs/founder markers | Speed diagnosis |
| Ancestry/panel use | Genome-wide SNP sets | Population context |
| Target discovery | Map loci to effector genes | Prioritise biological pathways |
TLDR SNPs guide risk prediction, diagnosis, carrier testing, and drug selection. Pharmacogenomics relies heavily on SNPs with strong clinical evidence.
flowchart LR
A[Define + recruit] --> B[Genotype + QC + impute]
B --> C[Association tests]
C --> D[Replication/meta]
D --> E[Interpret loci]
E --> F[Functional follow-up]TLDR GWAS requires large, ancestry-matched cohorts and high-quality array genotypes. Workflow: recruit → genotype → QC/impute → test → replicate → interpret.
TLDR Use Manhattan plots to locate strong signals, then read table entries for effect allele, OR, and P. Follow-up requires mapping regions to genes and functions.
flowchart TD
Block --> Tag
Tag --> Array
Array --> InferTLDR LD lets one SNP proxy a block of linked variants. GWAS arrays genotype tag SNPs to minimise redundancy.
| Limitation | Explanation |
|---|---|
| Sample size/power | Needs huge N; small studies produce false positives. |
| Common-variant focus | Poor at detecting rare variants. |
| Interpretation difficulty | Most hits are non-coding. |
| Missing heritability | Many traits not fully explained by common variants. |
| Ancestry confounding | Must match ancestry to avoid spurious associations. |
TLDR GWAS is limited by power, its focus on common variants, and difficulty interpreting non-coding hits. It captures only part of heritability and must avoid ancestry confounding.
| Replicator | Typical genome form | Size range taught | Coding content and packaging | Notes for exam answers |
|---|---|---|---|---|
| Bacteria | Usually circular DNA chromosome; plus plasmids | Chromosome ~1×105–1×107 bp; plasmids ~5×103–2×106 bp | Chromosome carries essential genes, vertically inherited; plasmids carry non-essential/fitness genes, often horizontally transferred; genomes are mosaic with mobile elements | Know “chromosome vs plasmid” and the mobilome concept (transposons, insertion sequences). |
| Viruses | DNA or RNA; ss or ds; segmented, non-segmented, sometimes multipartite | ~2 kb to >2 Mb | No nucleus; no histone packaging; wide architectural diversity; giant viruses encode thousands | Some giant viruses exceed small bacteria in size. |
| Viroids | Small circular RNA, no proteins | ~300 nt | RNA acts as information and enzyme; no protein coding | Counterexample to the central dogma. |
TLDR Cellular genomes (bacteria) have large, structured DNA chromosomes plus plasmids with vertical and horizontal inheritance. Non-cellular genomes span extremes, from ~2 kb RNA viruses to >2 Mb giant DNA viruses, with viroids at ~300 nt. Differences in packaging, replication, and inheritance explain the range in size and complexity.
| Mechanism | Core steps | Vehicles/DNA forms | Notes |
|---|---|---|---|
| Transformation | Uptake of free DNA; integrates by homologous recombination | Linear fragments or plasmids | Natural competence is species-specific. |
| Conjugation | Donor plasmid forms a pilus; DNA transfer begins at oriT | F plasmids; Hfr strains mobilise chromosomal segments | Major route for antibiotic resistance spread. |
| Transduction | Phage packages host DNA accidentally (generalised) or adjacent DNA on excision (specialised) | Phage particles | Moves gene cassettes between strains and species. |
| Route | Applies to | What occurs | Why it matters |
|---|---|---|---|
| Reassortment | Segmented RNA viruses | Swap genome segments during co-infection | Sudden antigenic shifts; altered host range/virulence. |
| Recombination | Many DNA viruses, some RNA viruses | Polymerase template-switching | Creates hybrid genomes. |
| Integration | Temperate phages, retroviruses | Insert/remove viral DNA in host genome | Stable persistence, occasional host-gene capture. |
| Feature | Bacteria/Archaea | Viruses | Viroids |
|---|---|---|---|
| Vertical inheritance | Chromosome + plasmid replication | Only when integrated | RNA copied in host cells |
| Horizontal acquisition | Transformation, conjugation, transduction | Reassortment, recombination, co-infection | Cell-to-cell RNA movement |
| Vehicles | Free DNA, plasmids, phages | Virions, genome segments | Naked RNA |
| Dependency on host | Moderate | Complete | Complete |
Prokaryotes inherit chromosomes vertically but acquire new genes by transformation, conjugation, and transduction. Viruses exchange genetic material via infection, reassortment, and recombination, and persist vertically only when integrated. Viroids replicate as RNA circles and transmit through cell-to-cell spread.
| Feature | PCR | qPCR |
|---|---|---|
| Readout | End-point gel band | Fluorescence measured every cycle |
| Quantification | Qualitative only | Ct value gives relative quantity |
| Reagents | Primers, polymerase | Primers + fluorescent probe/dye |
| Interpretation | Amplicon present/absent | Lower Ct = more template |
| Vulnerabilities | Mispriming, contamination | All PCR issues + probe mismatch from mutations |
PCR: extract → (RT) → amplification → gel
qPCR: extract → (RT) → amplification → real-time fluorescence → CtPCR is qualitative and uses gels. qPCR adds fluorescence and reports a Ct, a relative measure of template quantity. Multi-target panels guard against target mutations.
Ct reflects relative template quantity. Multi-target panels detect pathogens even when mutations disrupt one target. Negative panels with ongoing suspicion lead to sequencing.
Extract total RNA → build library → sequence → analyse → phylogeneticsTLDR Untargeted sequencing identifies unknown or novel pathogens and recovers full genomes. It is not first-line when targeted qPCR is available.
| Feature | Pedigree | Phylogeny |
|---|---|---|
| Shows | Parent–offspring relationships | Evolutionary relationships among sequences |
| Nodes | Actual individuals | Inferred ancestors |
| Tips | People | Sampled genomes |
| Branch lengths | Generations | Time or mutations |
Pedigrees track parents and offspring. Phylogenies infer evolutionary relationships; internal nodes are ancestral populations and branch lengths encode time or mutation counts.
tips → inferred ancestor → deeper ancestorTLDR Internal nodes correspond to ancestral populations and branch lengths show how far tips diverged from them.
TLDR Mutations act as markers for transmission. Time-scaled phylogenies reconstruct spread and inform public health action.
TLDR Use MZ vs DZ similarity to partition influences: larger MZ–DZ gaps imply genetic effects; small gaps imply environment or chance. Binary traits use concordance, quantitative traits use correlation. Even high MZ similarity leaves space for non-genetic factors.
TLDR Concordance compares twin pair similarity for binary traits; MZ ≫ DZ implies genetic influence but never proves determinism. Heritability (for example Falconer’s h^2) quantifies the genetic share of variance in a population, not for individuals. It does not fix outcomes or identify mechanisms and can be biased if twin assumptions are violated. Use it alongside context and limitations.
Exposure changes outcome for a given genotype.
Second loci change severity or age of onset without being the primary cause.
TLDR Variable expression arises when environmental exposures interact with genotype and when modifier genes tune pathway flux. Report outcome in terms of penetrance and expressivity. Use PKU–diet and exposure-dependent lung disease as standard illustrations, and explain intrafamilial variability through modifiers acting on the same pathway.
TLDR Not all variation is deterministic. Within individuals, random X-inactivation, epigenetic drift, and somatic mutations change expression and severity. Across populations, genetic drift shifts allele frequencies by chance, especially after bottlenecks. Use these to explain residual MZ twin discordance and population frequency changes that lack a selection signal.
Mutations in a single gene can disrupt different steps between DNA and protein. Name the class, define the mechanism, and give a disease example.
| Class | What changes | Molecular effect | Typical outcome | Lecture examples |
|---|---|---|---|---|
| Coding (missense, nonsense, frameshift, silent) | Base(s) within the coding sequence | Missense swaps one amino acid. Nonsense creates a premature stop and often triggers NMD. Frameshift (indel not ×3) rewrites downstream codons. Silent keeps amino acid but can still affect expression. | Usually loss of function; occasionally toxic gain of function | Sickle cell disease, single-aa missense in β-globin; Duchenne muscular dystrophy, frameshift in dystrophin; silent defined in slides. |
| Splicing (non-coding) | Splice donor or acceptor, branch point, enhancers | Exon skipping, intron retention, cryptic splice site use; frameshift and or premature stop common | Reduced or absent functional protein; often NMD | Spinal muscular atrophy (SMN1 splicing defects); β-thalassemia splice-site variants. |
| Regulatory (promoter or enhancer or 5′UTR) | Promoter and other cis-elements | Transcription initiation or efficiency altered; epigenetic silencing in some repeats | Normal protein sequence but too little (or no) mRNA | β-thalassemia promoter variants; Fragile X, 5′UTR CGG expansion silencing FMR1. |
| Expression level (dosage) | Gene copy number or one functional allele | Haploinsufficiency reduces dosage; duplication or amplification increases dosage | Too little or too much protein; dosage-sensitive phenotypes | Williams syndrome (ELN deletion → low elastin); CMT1A (PMP22 duplication → overexpression). |
| Localisation or folding | Protein folding or trafficking signals | Misfolded protein degraded (ERAD) or trapped; mislocalised protein fails to reach compartment | Loss of function at site of action; possible toxic gain from aggregates | CFTR ΔF508 misfolding and ER retention; α1-antitrypsin Z variant accumulates in liver (deficiency in lung and hepatotoxicity); primary ciliary dyskinesia (dynein mislocalisation). |
flowchart TD
DNA[DNA] -->|coding| ProteinSeq[Protein sequence changed]
DNA -->|splicing| RNAProc[RNA processing altered]
DNA -->|regulatory| Transcription[Transcription altered]
GeneDose[Gene dosage] --> Expression[Protein amount altered]
ProteinSeq --> FoldLoc[Fold/traffic to correct compartment?]
FoldLoc --> Function[Function at site]TLDR Distinguish where the defect acts. Coding alters the protein sequence (missense, nonsense, frameshift). Splicing alters exon–intron processing. Regulatory variants alter transcription without changing sequence. Expression level problems change dose (haploinsufficiency or duplication). Localisation or folding defects produce, but misplace or degrade, the protein. Anchor each class with the lecture’s disease examples and state the molecular consequence succinctly.
| Phenotype | Molecular routes in lecture | Consequence pattern |
|---|---|---|
| β-thalassemia | Promoter variants (reduced transcription), splice-site variants (exon skipping or cryptic splice), coding nonsense or frameshift (PTC → NMD) | Reduced β-globin dose; severity tracks residual expression |
| Cystic fibrosis (CFTR) | Missense gating defects (reduced channel opening), folding or trafficking defects (ΔF508 ER retention), splice defects (aberrant mRNA), rarer promoter or 5′UTR effects | Common phenotype with variable sweat chloride, pulmonary and pancreatic involvement |
| Dystrophinopathies | Frameshift (DMD, null), in-frame deletions (BMD, partial function), splice variants creating either outcome | Shared muscle phenotype with severity gradient |
| A1AT deficiency | Missense misfolding (Z allele, polymer accumulation, gain-of-toxicity in liver plus loss in lung), rarer null alleles (severe loss without hepatic polymer) | Same diagnosis via distinct mechanisms (toxic gain plus loss, or pure loss) |
TLDR The same diagnosis can arise from different genes (locus heterogeneity), different variants in one gene (allelic heterogeneity), and different mechanisms within one locus (regulatory, splicing, coding, localisation). Use β-thal, CFTR, dystrophin, and A1AT as worked anchors from the lecture.
| Defect class | Therapeutic idea | Lecture exemplars to cite |
|---|---|---|
| Gating or function defect | Small-molecule potentiator to increase activity | CFTR gating variants treated with potentiators |
| Folding or trafficking defects | Pharmacological chaperone or corrector to improve folding and delivery | CFTR ΔF508 with correctors; A1AT strategies framed as folding or aggregation problem |
| Splicing error | Antisense oligonucleotide to modulate splicing; exon-skipping strategies | SMA splicing rescue; exon-skipping in dystrophin |
| Nonsense or early stop | Readthrough agents or gene addition or editing | Nonsense-focused approaches summarised; gene addition listed for null states |
| Low transcription or regulatory | Transcriptional up-regulation, gene addition, or vector-mediated expression | β-thalassemia or hemoglobinopathies presented as dose restoration |
| Pure loss of enzyme | Enzyme or protein replacement or gene addition | Enzyme deficiency archetypes; augmentation for deficiency states |
TLDR Monogenic diseases are druggable because mechanism is clear and concentrated in a single node. Match defect class to modality: potentiators for gating, correctors for folding or trafficking, antisense oligos for splicing, readthrough or gene therapy for nonsense or null, replacement for enzyme loss, and transcriptional up-regulation or gene addition for regulatory or dose problems. Many therapies are genotype-specific and delivery or durability constraints matter.
A curated list of SNPs associated with the trait from GWAS, each with:
The individual’s genotype for each SNP, coded as the count of effect alleles (G_i ∈ {0,1,2}).
[ = _{i=1}^{m} _i G_i]
TLDR PRS is a weighted sum of effect alleles across GWAS SNPs. Count the effect alleles per SNP (G_i), multiply by the GWAS effect (β_i), then sum. Interpret the score against a reference population to get a percentile.
TLDR PRS ranks genetic predisposition. It improves stratification, not diagnosis. Outcomes still depend on environment, lifestyle, and chance, and transferability depends on ancestry alignment with the GWAS that produced the weights. Use percentiles and relative-risk language.
Coronary artery disease: high PRS plus smoking, inactivity, and poor diet yields the highest risk; lifestyle improvement buffers risk even with high PRS.
Type 2 diabetes: many variants raise susceptibility, but diet and exercise move risk up or down. High heritability does not imply inevitability.
Alcohol use disorder case study:
TLDR G×E means genetic effects depend on exposure. The course matrix shows four cells: high PRS with poor environment is highest risk; high PRS with good environment is buffered; low PRS with poor environment can still lead to disease; low PRS with good environment is lowest. Use this framework to interpret CAD, T2D, and AUD examples. Always phrase PRS as probability, shaped by lifestyle and chance.
| Feature | Sickle cell trait | Sickle cell anaemia |
|---|---|---|
| Genotype | Heterozygous HbAS (one HbS allele, one normal HbA) | Homozygous HbSS (two HbS alleles) |
| Hemoglobin mixture | ~40–50% HbS, remainder HbA | Nearly all HbS |
| Sickling | Limited sickling under severe hypoxia; usually asymptomatic | Marked sickling in deoxygenated blood; repeated cycles cause irreversible sickling |
| Pathophysiology | Minimal polymer formation because HbA disrupts HbS polymer growth | HbS polymerises into rigid fibres in low O₂, distorting RBCs, causing vaso-occlusion, hemolysis, pain crises, organ damage |
| Population pattern | Common in malaria-endemic regions | 300,000–400,000 new cases per year worldwide; distribution tracks malaria maps |
TLDR Trait (HbAS) mixes HbS with HbA, which limits polymer growth and sickling, so people are usually asymptomatic. Disease (HbSS) is near-pure HbS, which polymerises in low oxygen, causing hemolysis and vaso-occlusion. Trait confers protection against severe malaria because infected RBCs rupture more readily, disrupting parasite replication.
TLDR Glu→Val at β6 creates a hydrophobic patch on HbS. In deoxygenated blood, Val6 docks into a hydrophobic pocket on another hemoglobin (Phe85, Leu88), triggering polymerisation. In HbSS, high HbS allows robust fibre formation, producing rigid, hemolysis-prone cells and vaso-occlusion. In HbAS, HbA dilutes and disrupts HbS packing, so polymerisation and sickling remain limited.
| Stage | Tetramer (globin chains) | Where the chains come from | Notes for exams |
|---|---|---|---|
| Embryonic (very early) | Gower I ζ₂ε₂, Gower II α₂ε₂, Portland ζ₂γ₂ | α-like cluster (ζ→α) on chr16; β-like cluster (ε→γ→δ/β) on chr11 | Short-lived; appear before placental HbF dominance |
| Fetal (mid–late gestation → early infancy) | HbF α₂γ₂ | α from chr16, γ from chr11 | High O₂ affinity supports placental transfer; persists for months after birth |
| Adult (post-infancy) | HbA α₂β₂ (major), HbA₂ α₂δ₂ (minor) | β and δ from chr11 | HbA replaces HbF through developmental switching |
The slides group these as a developmental switch from ε/ζ to γ to β/δ across the two loci. You should state that the β-like cluster is ordered 5′→3′ roughly in the order of expression (ε→γ→δ/β).
HbF does not participate in HbS polymer contacts. The γ chain lacks the β-chain surface features that accept Val6 from HbS in deoxygenated blood, so mixed tetramers and HbF-rich cells are less prone to polymerisation and sickling.
Clinical correlations from the lecture:
TLDR Development moves from embryonic ζ or ε or γ tetramers to HbF (α₂γ₂) in the fetus, then to adult HbA (α₂β₂) with minor HbA₂ (α₂δ₂). HbF protects because γ chains do not accept the HbS Val6 interaction that drives polymerisation, so cells with more HbF sickle less. Treatments that raise HbF (for example hydroxyurea or HbF-reactivation strategies) reduce crises and hemolysis.
TLDR CF is autosomal recessive and caused by CFTR mutations. CFTR is an ATP-gated Cl⁻ channel; loss of function dehydrates airway liquid, raises sweat chloride, and thickens pancreatic secretions. Screen with IRT, confirm by genetics and sweat test.
| System | Primary epithelial defect | Dominant consequences | |
|---|---|---|---|
| Respiratory (upper and lower airways) | ↓ CFTR Cl⁻ secretion → ASL dehydration → impaired mucociliary clearance | Chronic mucus obstruction, recurrent infection, persistent inflammation, airway damage | |
| Pancreas / GI | Thick ductal secretions block enzyme flow | Pancreatic insufficiency, maldigestion, malnutrition, risk of insulin insufficiency | |
| Sweat glands | Impaired salt reabsorption in ducts | High sweat chloride (diagnostic) | |
| Male reproductive tract | CFTR in exocrine epithelia of vas or epididymis | Obstructive phenomena contributing to infertility patterns (not detailed mechanistically in this lecturette beyond organ listing) |
TLDR CF is multi-organ because CFTR functions in many epithelia. Airways: dehydrated ASL → thick mucus, infection, inflammation. Pancreas: blocked ducts → enzyme delivery failure and malnutrition. Sweat glands: high sweat chloride. Male tract: epithelial involvement consistent with obstructive phenotypes.
| Mutation (example) | Molecular defect | Structural or functional consequence | Clinical note from slides or transcript |
|---|---|---|---|
| F508del (ΔF508) | Misfolding of CFTR, chiefly in NBD1 | ER retention and degradation, so little channel reaches the apical membrane; reduced Cl⁻ transport | Most common worldwide; archetype of folding or trafficking defect. |
| Gating defects (e.g., G551D) | Channel reaches membrane but fails to open efficiently | Low open probability despite membrane localisation; ATP-gating cycle is impaired | Distinct from folding; important because it is potentiator-responsive. |
| Nonsense / early stop | Premature termination codon → truncated protein | mRNA often degraded by NMD; absent protein | Results in severe loss of function; grouped as “minimal function” in clinic. |
| Splicing variants | Altered exon inclusion | Reduced or abnormal CFTR; may introduce frameshift or PTC | Severity tracks residual correctly spliced mRNA. |
How these produce disease physiology (link back to Obj. 1): Any mechanism that lowers functional CFTR at the apical membrane reduces Cl⁻ secretion, dehydrates airway surface liquid, and thickens secretions in affected organs.
TLDR Common CFTR mutations fall into folding or trafficking (for example F508del, ER retention), gating (for example G551D, low open probability at membrane), nonsense (absent protein via NMD), and splicing (aberrant transcripts). All reduce functional CFTR, lowering Cl⁻ transport and driving CF pathology.
| Defect category | Modulator strategy | Lecture framing |
|---|---|---|
| Gating (e.g., G551D) | Potentiator to increase channel open probability at the membrane | Increases Cl⁻ flux in variants where the channel is present but opens poorly. |
| Folding or trafficking (e.g., F508del) | Corrector(s) to improve folding or processing and deliver CFTR to the surface; often combined with a potentiator | Addresses misfolding plus low Po; combination therapy highlighted. |
| Minimal-function (nonsense or severe splice) | Limited modulator benefit; non-CFTR approaches remain central | Slides flag limited responsiveness; manage complications aggressively. |
| System | Main interventions and why |
|---|---|
| Respiratory | Airway clearance techniques; nebulised therapies to hydrate or loosen mucus; antibiotics for acute infections and chronic suppression; vaccination; regular physiotherapy. Goal: improve mucociliary clearance, reduce bacterial load, blunt inflammation. |
| Pancreas / GI | Pancreatic enzyme replacement therapy (PERT); high-calorie, high-fat diet; fat-soluble vitamin supplementation; monitor for CF-related diabetes. Goal: restore digestion and nutrition. |
| Sweat/fluids & general | Maintain hydration, electrolyte awareness (sweat salt loss). Multidisciplinary care with regular monitoring. |
Diagnostic–treatment linkage referenced in class: Newborn IRT screen → sweat chloride or genotype confirms CF and guides variant-matched modulator consideration, alongside initiation of airway and nutritional care pathways.
TLDR Management combines variant-guided CFTR modulators (potentiators for gating; corrector±potentiator for F508del) with organ-system care: airway clearance and antibiotics for lungs, PERT and nutrition for pancreas or GI, and comprehensive monitoring. Minimal-function variants rely more on supportive care. Screening and genotyping steer therapy selection.
When asked “why sequence isn’t enough,” state:
flowchart LR
DNA[DNA sequence] -->|sets liability| GR[Gene regulation]
Env[Environment & behaviour] -->|alters| Epig[Epigenetic state]
Epig --> GR
GR --> Phys[β-cell function & insulin sensitivity]
Phys --> Phen[T2D phenotype]TLDR Sequence sets baseline risk, but transcriptional regulation and epigenetics translate exposures into expression changes that determine β-cell output and insulin action. Rapid increases in T2D reflect environment and behaviour, not DNA sequence alone.
High genetic liability + adverse environment → earliest onset / greatest risk
High genetic liability + supportive environment → buffered risk
Low genetic liability + adverse environment → still possible T2D (later onset)
Low genetic liability + supportive environment → lowest riskTLDR Environment and behaviour modulate inherited risk. Obesity, diet, and inactivity push insulin resistance; susceptible genotypes fail β-cell compensation sooner. The same environment does not affect everyone equally because genetic and epigenetic context govern response.
| Intervention class | Primary network nodes | System effects that matter for glycaemia |
|---|---|---|
| Lifestyle therapy (dietary pattern, caloric balance, physical activity, sleep) | Energy balance, insulin signalling in muscle and liver, adipose inflammation, gut hormones | ↓ insulin resistance, ↓ hepatic glucose output, improved glucose disposal; durable risk reduction when maintained |
| Metformin | AMPK activation in liver and gut signalling | ↓ hepatic gluconeogenesis, beneficial gut–liver crosstalk; lowers fasting glucose without changing DNA |
| GLP-1 receptor agonists | Incretin signalling (islet–brain–gut axis) | ↑ glucose-dependent insulin, ↓ glucagon, slower gastric emptying, reduced appetite and body mass; β-cell “rest” |
| SGLT2 inhibitors | Renal glucose transport | Glycosuria independent of insulin; lowers glucotoxicity and relieves β-cell stress; cardiorenal benefits |
| Thiazolidinediones (where used) | PPARγ transcriptional control in adipose | Re-partitioning of lipid, ↓ ectopic fat, improved insulin sensitivity |
| Bariatric/metabolic surgery | Gut hormones, bile acids, nutrient flow | Rapid incretin shifts, reduced insulin resistance, remission in subsets without gene editing |
| Epigenetic/behavioural milieu (weight loss, activity, sleep regularity) | Chromatin accessibility, transcriptional programs | Partly reversible methylation or acetylation states; sustained improvements in tissue insulin response |
These act upstream, in parallel, or downstream of risk variants to change pathway flux and phenotypes, illustrating that pathway control is sufficient for clinical benefit.
flowchart LR
Env[Lifestyle / Surgery / Drugs] --> Signals[Hormones & Nutrient Signals]
Signals --> Reg[Transcriptional & Epigenetic Programs]
Reg --> Tissues[Liver • Muscle • Adipose • Islet • Kidney • Gut]
Tissues --> Outcomes[↓ Insulin resistance • ↓ Gluconeogenesis • ↓ Glucotoxicity]
Outcomes --> Glyc[Improved glycaemia & risk]TLDR You do not need to edit genes to treat T2D. Interventions that act on regulatory networks, such as energy balance, AMPK, incretins, renal glucose handling, PPARγ programs, and gut–brain–liver axes, shift pathway flux to reduce insulin resistance, gluconeogenesis, and glucotoxicity. These network shifts deliver clinical benefit even though DNA sequence is unchanged.
| Feature | Heritable | Non-heritable |
|---|---|---|
| Origin | Germline mutation in egg or sperm | Somatic mutation in body cells |
| Cell ploidy | Gametes are haploid (23) | Somatic cells diploid (46) |
| Transmission | Passed via meiosis to offspring | Not transmitted (no gamete involvement) |
| Drivers | Single-gene, chromosomal, mitochondrial, de novo germline | Mutagens (UV, chemicals, viruses), lifestyle, random mitosis errors |
| Examples | CF, SCD, Huntington disease, familial cancers | UV skin cancer, smoking-related lung cancer |
| Family pattern | Often multi-generational | Usually sporadic |
Mutagen classes to name for non-heritable disease: physical (UV, X-rays), chemical (tobacco smoke, pollutants), biological (HPV). Their damage accumulates in somatic cells and can drive cancer.
TLDR Heritable = germline mutation, present in all cells, passed to children. Non-heritable = somatic mutation from environment, lifestyle, or random division, confined to the individual. Name origin, transmission route, and a matching example for full marks.
What meiosis does: two divisions make four genetically unique haploid gametes (23 chromosomes). Diversity arises via independent assortment, crossing-over (recombination), and random fertilisation.
Where mutations and chromosomal errors arise:
Outcomes: variants are present in every cell of the child; can follow autosomal dominant/recessive, X-linked, or mitochondrial patterns.
MITOSIS (somatic) → growth/repair → replication errors ↦ somatic mutations → cancer risk → not inherited
MEIOSIS (germline) → gametes/diversity → assortment + crossing-over ↦ germline mutations/CNVs → inheritedTLDR Mitosis errors create somatic mutations that can cause cancer but do not transmit. Meiosis generates variation for the next generation and, when errors occur, produces germline mutations or chromosomal rearrangements that are heritable. Name a diversity mechanism and a meiotic error with an example.
TLDR Inheritance follows allele segregation with modes AD, AR, X-linked, and mitochondrial, modulated by penetrance, expressivity, and structural variation. Meiosis generates diversity through independent assortment, crossing-over, and random fertilisation, yielding novel haplotypes and enabling recombination-based trait mapping.
Aneuploidy: whole-chromosome number change from meiotic nondisjunction.
Microdeletions and microduplications: sub-microscopic CNVs.
Balanced rearrangements: reciprocal or Robertsonian translocations in a parent. May be phenotypically silent but produce unbalanced gametes and affected offspring.
Unbalanced structural changes: terminal or interstitial deletions/duplications that alter dosage of multiple genes.
Maternal inheritance. Both sons and daughters can be affected. Only affected mothers transmit.
Heteroplasmy and threshold. Mix of normal and mutant mtDNA per cell. Disease manifests when mutant load exceeds a tissue-specific threshold.
Common examples:
Bottleneck in oogenesis drives variable heteroplasmy among siblings and tissues.
TLDR Heritable disease can arise from single-gene variants (AD, AR, X-linked, imprinting, repeat expansions), chromosomal errors (aneuploidy, CNVs, translocations), or mitochondrial mutations with maternal inheritance and heteroplasmy. Name class, mechanism, and one example.
[ = + + + ]
Use this to structure any answer.
The same genotype yields different outcomes under different exposures.
The same exposure yields different outcomes across genotypes.
Examples:
TLDR Mutations define baseline liability, but environment and G×E move people across the disease threshold. Add penetrance, modifier genes, and stochastic processes to explain variable expression, twin discordance, and shifting population risk.
Definition
TLDR Chromosomal abnormalities are numerical or structural changes to chromosomes. They disrupt accurate genome distribution and lead to developmental and health problems across the lifespan.
| Class | What changes | Typical mechanism | Example(s) |
|---|---|---|---|
| Numerical | Whole chromosome count | Nondisjunction in meiosis | Trisomy 21, 18, 13; Monosomy X |
| Structural | A chromosome segment | Breakage, misrepair, unequal recombination | 5p deletion, PMP22 duplication, t(9;22), factor VIII inversion |
TLDR Numerical errors add or remove entire chromosomes (trisomy, monosomy). Structural errors rearrange segments (deletions, duplications, translocations, inversions). Know one clear example for each.
Non-heritable (de novo)
Heritable
Exam sentences
TLDR De novo changes originate in a gamete or early embryo and usually do not recur. Heritable changes come from a parent with a balanced rearrangement and carry recurrence risk due to unbalanced gametes. Cite one example of each in answers.
| Test | What it detects well | When to use | Caveats |
|---|---|---|---|
| Karyotype (G-banding) | Whole-chromosome changes and large structural rearrangements; visualises balanced translocations | First-line when you suspect aneuploidy, translocation, or infertility with possible Robertsonian/inversion carrier | Lower resolution; misses small CNVs |
| FISH (targeted probes) | Rapid confirmation of a suspected locus or aneuploidy; detects mosaicism in targeted cells | Follow-up of a known or strongly suspected region; rapid prenatal or neonatal answers | Only the probed region; not genome-wide |
| Chromosomal microarray (array CGH/SNP array) | Genome-wide copy-number gains/losses and many microdeletions/duplications | Developmental delay, congenital anomalies, autism spectrum, multiple malformations | Does not detect balanced rearrangements |
| QF-PCR/targeted PCR | Fast aneuploidy check for common trisomies | Prenatal rapid screen or confirm with specific markers | Limited to the loci assayed |
| NIPT (cell-free DNA) | Screening for common fetal trisomies from maternal blood | Population screening before invasive testing | Screening only; positives need diagnostic confirmation |
| Invasive prenatal sampling | Chorionic villus sampling (CVS) or amniocentesis to obtain fetal cells | When NIPT/ultrasound suggests an abnormality or there is high a-priori risk | Procedure risks; then run karyotype/CMA/FISH on the sample |
These methods appear in your diagnostic flow: screen when appropriate, then confirm with a diagnostic test matched to the suspected class of abnormality.
TLDR Use karyotype for aneuploidy and balanced rearrangements, CMA for genome-wide CNVs, FISH/QF-PCR for rapid targeted questions, and NIPT as screening before CVS/amnio. Match the test to the suspected abnormality and confirm positives with a diagnostic assay.
Safe reporting language (use in OSCE-style answers)
TLDR Early, accurate detection changes outcomes: it shapes pregnancy decisions, starts targeted surveillance and interventions, shortens the diagnostic journey, and gives families clear recurrence information. Screen first when appropriate, then confirm with the right diagnostic test.
| Dimension | Heritable risk | Non-heritable (polygenic + somatic) risk |
|---|---|---|
| Genetic architecture | High-penetrance single genes, classically BRCA1/BRCA2 | Many low-penetrance variants across the genome, plus somatic mutations acquired over life |
| Pattern | Runs in families; mutation present in every cell from birth | Often no clear family history; risk emerges from the sum of small effects and exposures |
| Share of total cases | ~5–10% of all breast cancers | Majority of cases |
| Typical quantification | Pathogenic-variant status (e.g., BRCA1/2) | Polygenic Risk Score (PRS) plus environment and age |
| Prevention focus | Genetic counselling, enhanced screening, risk-reducing surgery when appropriate | Risk stratification for screening, lifestyle modification, manage exposures |
| Treatment implications | Sensitivity to DNA-damaging agents and PARP inhibitors | Standard of care guided by tumour features; PRS informs population screening rather than therapy choice |
TLDR Heritable breast cancer reflects high-penetrance variants such as BRCA1/2 and accounts for ~5–10% of cases. Non-heritable risk dominates and comes from many small-effect variants plus environmental exposures; PRS summarises the inherited polygenic part and supports screening and prevention planning.
TLDR BRCA1/2 safeguard the genome by HR repair. Pathogenic variants disable this function, causing genomic instability and large lifetime risk increases. Management: test and counsel, intensify screening, consider risk-reducing surgery, and use HR-defect-targeted therapies when cancer is present.
[ =_{i=1}^{m}_i G_i]
| Strength | Limit |
|---|---|
| Captures polygenic liability invisible to single-gene tests | Not diagnostic; gives relative risk only |
| Helps target scarce screening resources | Sensitive to ancestry mismatch |
| Can combine with age, family history, lifestyle | Does not include environment unless explicitly modelled |
TLDR PRS is a weighted sum of many common variants that ranks relative risk and helps stratify screening. It requires an ancestry-matched reference and does not diagnose disease.
TLDR The “Angelina effect” boosted awareness, counselling referrals, and evidence-based prevention for true high-risk carriers, with the caveat to maintain criteria-based testing and robust counselling.
TLDR Ethical screening rests on consent, privacy, and family-aware counselling, delivered equitably with psychosocial support. Use structured consent, clear data policies, and planned cascade communication to balance benefits and harms.
Definition
Core characteristics from the lecture
TLDR Non-coding DNA is the majority of the human genome. It regulates genes and RNAs, preserves chromosome integrity, and organises chromatin. Changes in these regions can shift expression programs and cause disease.
| Class | Where it sits | What it does | Disease links from lecture |
|---|---|---|---|
| Introns | Within genes between exons | Control expression, RNA stability, export, localisation. Provide sequences for non-coding RNAs. Intron retention can trigger NMD and repress genes. | Mutations at splice dinucleotides cause intron retention and gene silencing, including in cancers; muscular dystrophy examples noted. |
| Telomeres | Chromosome ends | Protect ends, prevent end-to-end fusion, maintain stability, relate to replicative lifespan. | Short telomeres raise risk of CVD, dementia, cancer, osteoporosis; excessively long telomeres associate with some cancers. |
| Satellite DNA | Intergenic, pericentromeric, subtelomeric, intronic arrays | Builds centromere/kinetochore, supports spindle assembly, drives heterochromatin and genome organisation. | Repeat changes can disrupt centromeres → missegregation and genomic instability, contribute to cancer; developmental disorders discussed, with Fragile X example on slides. |
| Non-coding RNA genes | Discrete loci | Encode tRNA, rRNA, miRNA and other RNAs that act structurally or regulatory. | Dysregulation contributes to disease mechanisms; set up for later modules in your course. |
| Gene-regulatory sequences | Promoters upstream of genes; enhancers and silencers can be distal | Promoters bind TFs to initiate transcription. Enhancers facilitate promoter interactions. Silencers inhibit them. DNA loops enable distal contacts. | Mutations reduce or increase TF binding or enhancer–promoter communication, linked to cancer, diabetes, neurological and developmental disorders. Parkinson’s enhancer SNP example shown. |
Genome
├─ Coding DNA → proteins
└─ Non-coding DNA
├─ Introns → splicing, RNA fate, NMD
├─ Telomeres → end protection, ageing
├─ Satellite DNA → centromere, heterochromatin
├─ ncRNA genes → tRNA, rRNA, miRNA
└─ Regulatory DNA → promoters, enhancers, silencers (TF binding, looping)TLDR Non-coding DNA comprises introns, telomeres, satellite repeats, non-coding RNA genes, and regulatory elements. Each class has a distinct role in expression control or chromosome biology. Mutations in any class can perturb regulation or chromosome stability and lead to disease.
| Non-coding element | Typical alteration | Molecular consequence | Disease patterns to quote |
|---|---|---|---|
| Promoters / enhancers / silencers | SNP/indel; structural variant disrupting long-range contact | Gains or losses in transcription factor binding; altered enhancer–promoter looping; inappropriate gene dose | Diabetes, cancers, neurodevelopmental disorders; enhancer SNP example shown in slides (Parkinson’s) |
| Splice regulatory sequences (intron splice sites, enhancers/silencers) | Single-base change at GT/AG dinucleotides or nearby motifs | Exon skipping, intron retention, cryptic splice; frameshift or premature stop; NMD | β-thalassaemia splice defects; cancer splice disruption |
| Introns (retained) | Weak splice sites, regulatory shifts, or mutation | Stable intron-retaining transcripts trigger NMD and down-regulate gene output | Tumour suppressor down-regulation; muscle disease examples |
| Telomeres | Excessive shortening or lengthening via sequence or regulation | Genome instability, senescence, or unchecked proliferation | Short telomeres: CVD, dementia, osteoporosis; very long: some cancers |
| Satellite / centromeric repeats | Repeat expansion, contraction, or epigenetic dysregulation | Kinetochore defects, mis-segregation, aneuploidy | Developmental syndromes, cancers |
| Non-coding RNA genes (miRNA, lncRNA) | Promoter or sequence changes | Mis-regulation of post-transcriptional control or chromatin programs | Oncogenic or tumour-suppressive miRNA mis-expression |
TLDR Non-coding mutations change gene regulation, splicing, RNA stability, or chromosome integrity. They shift dose or timing of key genes, producing disease even though coding sequence is intact.
Explains missing heritability: many GWAS hits lie in regulatory DNA. Knowing which enhancer or promoter is causal clarifies biology and risk.
Improves diagnostics: add non-coding variant interpretation when coding tests are negative; evaluate splice-site and promoter variants; report intron-retention mechanisms.
Guides therapy:
Stratifies prevention: regulatory variants in metabolic or neurodegenerative pathways refine risk models.
Safety in gene therapy: avoid creating harmful enhancer contacts or disrupting insulators.
TLDR Studying non-coding changes sharpens diagnosis, reveals mechanisms behind GWAS signals, and opens therapeutic levers that tune expression or splicing rather than protein sequence. Include non-coding analysis when coding tests are unrevealing.
| Genotype pattern | Typical label | Molecular basis | Clinical pattern |
|---|---|---|---|
| aa/aa | Unaffected | All four α present | Normal |
| −a/aa | Silent carrier | Deletion of one α-gene | Asymptomatic |
| −−/aa or −a/−a | α-thalassaemia trait | Two-gene deletion, either cis (−−/aa) or trans (−a/−a) | Mild microcytic anaemia |
| −−/−a | HbH disease | Three-gene deletion | Moderate–severe anaemia; HbH (β₄) present |
| −−/−− | Hydrops fetalis | Four-gene deletion | Usually lethal in utero |
| Genotype examples | Label | Phenotype |
|---|---|---|
| β/β | Unaffected | Normal |
| β⁺/β or β⁰/β | Minor (trait) | Asymptomatic to mild anaemia |
| β⁺/β⁺ or β⁰/β⁺ | Intermedia | Mild–moderate; usually non-transfusion dependent |
| β⁰/β⁰ | Major | Severe anaemia; transfusion dependent |
TLDR α-thal is mainly gene deletions on chr16 (1→4 genes lost; cis/trans matters). β-thal is mainly non-coding point mutations in promoter/introns/splice sites of HBB on chr11, classified β⁰ (none) or β⁺ (reduced). Genotype sets clinical class from silent carrier to trait, HbH, hydrops (α) and from trait to intermedia to major (β).
↓One chain → Excess partner chains → Precipitation in precursors
↓
Membrane injury, apoptosis (ineffective erythropoiesis)
↓
Few, fragile RBCs → hemolysis → anaemia, hypoxiaTLDR Imbalance drives disease: the surplus chain aggregates, injures precursors (ineffective erythropoiesis), and shortens RBC survival (hemolysis). In β-thal, α-excess is toxic; in α-thal, β-excess forms unstable HbH. Co-inheritance of α + β thal balances chain supply, so fewer unpaired chains and milder disease.
| Phenotype | HbA (~97%) | HbF (~1%) | HbA2 (~2%) | HbH |
|---|---|---|---|---|
| Unaffected | ~97 | ~1 | ~2 | – |
| β-thal major (β⁰/β⁰) | 0 | up to 95 | >5 | – |
| β-thal intermedia (β⁰/β⁺) | 10–30 | 70–90 | >4 | – |
| β-thal minor/trait (β⁺/β) | >88 | <5 | >4 | – |
| HbH disease (−−/−α) | 60–90 | ~1 | ~2 | 1–40 |
| α-thal trait (−α/−α) | >95 | ~1 | ~2 | – |
TLDR β-thal: think ↑HbA2 ± ↑HbF. α-thal: electrophoresis often normal, unless HbH is present. Use DNA tests to confirm α-thal trait.
CBC + smear
Iron studies
Haemoglobin electrophoresis
If α-thal suspected with normal electrophoresis
Thalassaemia vs iron deficiency
α vs β thalassaemia
TLDR Start with CBC/smear → microcytic, hypochromic. Check ferritin: normal/high points to thalassaemia. Use electrophoresis: β-thal = ↑HbA2 ± ↑HbF, α-thal = usually normal, HbH if HbH disease. Confirm α-deletions by DNA when electrophoresis is non-diagnostic.
Definition
General characteristics highlighted in the lecture
TLDR ncRNAs are functional RNAs that do not code for protein. They are grouped into small and long classes and control gene expression at multiple levels. miRNAs act via RISC at 3′UTRs to reduce protein output; lncRNAs regulate from chromatin to translation.
Atherosclerosis
Diabetes and complications
TLDR miRNAs repress target mRNAs post-transcriptionally (e.g., miR-125b in Fragile X and β-cell insulin secretion). lncRNAs regulate from chromatin to translation; examples include H19 and ANRIL in atherosclerosis, MALAT1/Reg1cp/RPL13p5 in diabetes, and MIAT/NEAT1 in diabetic retinopathy.
| ncRNA | Change | Primary effect | Disease context (from lecture) |
|---|---|---|---|
| miR-125b | Overexpression | Represses synaptic PSD-95; impairs β-cell insulin release | Fragile X synaptic dysfunction; reduced glucose-stimulated insulin secretion in T2D models |
| H19 (lncRNA) | Overexpression | Pro-proliferative, pro-inflammatory programs | Atherosclerotic plaque growth and instability |
| ANRIL (lncRNA) | Overexpression | Vascular cell cycle activation | Arterial thickening, atherosclerosis |
| MALAT1 (lncRNA) | Up in diabetes | β-cell dysfunction; insulin secretion block | T1D/T2D pathology contributions |
| MIAT / NEAT1 (lncRNAs) | Up in retina | Inflammation, abnormal angiogenesis | Diabetic retinopathy (fragile, leaky vessels) |
TLDR ncRNA alterations cause disease by changing dose, sequence, processing, localization, or sponging. Small shifts at the ncRNA layer reprogram networks and drive pathology in neuro, cardiometabolic, and ocular settings (e.g., miR-125b, H19, ANRIL, MALAT1, MIAT, NEAT1).
Diagnostics: add ncRNA analysis when coding tests are negative. Use expression panels or single-ncRNA assays as biomarkers for risk, diagnosis, and activity.
Prognosis and stratification: ncRNA signatures separate patient subgroups and predict progression (e.g., vascular risk, retinopathy progression).
Therapeutic targets:
Monitoring: circulating ncRNAs enable minimally invasive follow-up.
TLDR Studying ncRNAs improves diagnosis, risk stratification, and therapy design. Use antagomiRs, mimics, ASOs, or CRISPR-based tuning to correct pathogenic ncRNA programs, with careful attention to specificity, delivery, and context.
TLDR Precision medicine individualises therapy using genomics with clinical context. Genetics affects PK through enzymes and transporters and PD through targets, which guides drug and dose to maximise benefit and minimise harm.
Pharmacokinetics (PK) — movement of drug in the body
Pharmacodynamics (PD) — what drug does to the body
Integrative/system concepts
| Pillar | Genetic levers | Consequence for practice |
|---|---|---|
| PK: Metabolism | Phase I enzymes, mainly CYPs: CYP2C9, CYP2C19, CYP2D6, CYP3A4/5; plus FMOs, ADH, etc. | Alters activation/inactivation rate, exposure, and half-life. Dose selection and contraindications. |
| PK: Transport | ABCB1 (P-gp), ABCG2 (BCRP), OATP1B1 (SLCO1B1) | Changes absorption, tissue entry, biliary/renal efflux. Predicts toxicity for substrates like statins or digoxin. |
| PD: Targets | Receptor or enzyme variants, especially GPCRs and other targets | 5-fold potency differences observed with common missense variants; informs selection of class or dose. |
TLDR Precision work uses PK, PD, and systems concepts. In PK, ADME and parameters drive exposure and are gene-sensitive through CYPs and transporters. In PD, target variation shifts binding, potency, and efficacy. Integrate these with variability, interactions, and ADRs to personalise therapy.
ADME and key parameters
Equations you should quote
First- vs capacity-limited elimination
Organ determinants
Where genetics acts in PK
Dose–response
Ligand classes
Where genetics acts in PD
TLDR PK sets exposure (ADME, (CL), (V_d), (t_{1/2}), (F)); PD links concentration to effect (potency, efficacy, therapeutic window). Genetics shifts PK via enzymes/transporters and PD via targets, so equations and curves move for different genotypes.
| Gene / variant (phenotype) | Drug(s) | Mechanism → clinical effect | Action from lecture |
|---|---|---|---|
| CYP2C19 2/3 (loss), *17 (gain) | Clopidogrel | Prodrug activation depends on CYP2C19. LoF → ↓ active metabolite → ↓ antiplatelet effect; *17 → ↑ activation → bleeding risk | Avoid clopidogrel in PM/IM; consider alternatives. Dose/bleeding caution in *17. |
| CYP2D6 gene duplications (UM) or LoF (PM) | Codeine, tramadol | Codeine/tramadol need 2D6 for active opioid formation. UM → toxicity; PM → no analgesia | Prefer non-2D6 opioids in UM/PM. |
| CYP2C9 2/3 and VKORC1 −1639G>A | Warfarin | 2C9 LoF ↓ S-warfarin clearance; VKORC1 A lowers target expression → higher sensitivity | Use genotype-guided lower initial doses; careful INR titration. |
| SLCO1B1 c.521T>C (OATP1B1↓) | Simvastatin (statins) | ↓ Hepatic uptake → ↑ plasma exposure → myopathy risk | Avoid high-dose simvastatin; choose alternative or lower dose. |
| DPYD decreased-function alleles | 5-FU/capecitabine | DPD deficiency → ↓ catabolism → severe toxicity | Reduce/avoid; titrate to tolerance when variants present. |
| UGT1A1 28/6 | Irinotecan | ↓ Glucuronidation of SN-38 → neutropenia/diarrhoea | Start lower dose in high-risk genotypes. |
| TPMT or NUDT15 LoF | Azathioprine/6-MP | ↓ Inactivation or altered nucleotide sanitation → myelosuppression | Use reduced doses or alternative; monitor counts closely. |
| **HLA-B*57:01** | Abacavir | Immune presentation of drug–peptide → hypersensitivity | Screen and avoid if positive. |
| **HLA-B*15:02** (ancestry-specific risk) | Carbamazepine | T-cell–mediated SJS/TEN risk | Screen in high-risk ancestries; avoid if positive. |
Transporter and target notes
How to write genotype-guided dosing in exams
TLDR Know the anchor pairs: CYP2C19–clopidogrel, CYP2D6–codeine, CYP2C9/VKORC1–warfarin, SLCO1B1–simvastatin, DPYD–5-FU, UGT1A1–irinotecan, TPMT/NUDT15–thiopurines, HLA-B57:01–abacavir, HLA-B15:02–carbamazepine. For each, name mechanism and adjust drug or dose accordingly.
| Stage | What genomics adds | Concrete tools/examples |
|---|---|---|
| Disease biology → Target ID | Finds causal genes, pathways, variants; shows which tissues and cell types matter | GWAS to nominate loci; NGS and single-cell sequencing to map expression and cell states; variant–function links to nominate targets |
| Target validation | Tests necessity/sufficiency of genes and variants | CRISPR knock-out/knock-in; rescue assays; pathway rewiring to confirm on-target effects |
| Lead discovery / optimisation | Aligns chemistry with variant-defined binding pockets and allosteric sites; anticipates resistance | In-silico modelling of mutant proteins; SAR guided by variant maps; avoid metabolism-prone motifs based on enzyme knowledge |
| Preclinical (PK/PD, tox) | Predicts metabolism and transport; flags toxicity risks across genotypes | Map substrates to CYPs (2D6, 2C19, 3A4/5) and transporters (OATP1B1, P-gp, BCRP); toxicogenomics panels |
| Clinical trials | Designs genotype-aware trials; improves power and safety | Enrichment/stratification by pharmacogene or target genotype; genotype-guided dosing arms (e.g., CYP2C19–clopidogrel) |
| Post-approval | Explains variable effectiveness and rare AEs; updates labels | Pharmacovigilance signals traced to variants (e.g., **HLA-B*57:01–abacavir**); real-world PGx implementation and label changes |
Genomic discovery → Target validation → Lead/SAR → Preclinical PK/PD → Genotype-aware trials → Post-market PGx + safety
(GWAS, scRNA-seq) (CRISPR) (mutant structures) (CYPs/transporters) (enrich/stratify) (labels, RWE)TLDR Genomics informs every stage: it finds and validates targets, shapes SAR against variant-defined proteins, predicts PK/PD via enzymes and transporters, stratifies trials, and guides post-market safety and dosing. Examples: ER/PR/HER2 therapy selection, VKORC1/CYP2C9–warfarin, **HLA-B*57:01–abacavir**.
| Genomic change in target | Structural/biophysical effect | Design tactic | What to watch |
|---|---|---|---|
| Activating missense in the active site or switch loop | Pocket reshapes, altered H-bond network, constitutive signaling | Fit the new pocket; exploit mutant-specific cavities for selectivity | Keep WT off-target low; avoid steric clash in WT |
| Gatekeeper mutation in kinases | Blocks first-gen inhibitors | Add narrow hinge vectors or covalent warheads to bypass steric block | Balance reactivity with selectivity |
| Allosteric mutations | Shift inactive/active equilibrium | Target allosteric pockets to re-weight conformational ensemble | Maintain activity across resistance variants |
| Loss of repair pathway (e.g., HRD) | New collateral sensitivity | Synthetic lethality drugs (e.g., PARP) | Patient selection by genotype and biomarkers |
| Fusion proteins | Aberrant dimerisation, novel interfaces | Design interface or ATP-site inhibitors with fusion-aware SAR | Monitor resistance clones |
TLDR Start with genotype and translate it into mutant structure and mechanism. Choose orthosteric, allosteric, covalent, or synthetic-lethal strategies that fit the altered pocket or pathway. Plan around resistance and PK early, then run genotype-enriched trials.
Definition
Why response varies
Where genetics acts
Clinical role
TLDR Pharmacogenomics explains part of inter-patient variability in benefit and harm by altering PK and PD. It enables genotype-based drug choice and dosing that improves outcomes and reduces adverse events.
| Gene / locus | Variant pattern (functional effect) | Primary role | Drug examples | Expected impact on patient |
|---|---|---|---|---|
| CYP2C19 | No-function 2, 3; increased-function *17 | Phase I metabolism | Voriconazole, clopidogrel | Poor metabolisers accumulate voriconazole and risk toxicity; increased-function can over-activate prodrugs or reduce exposure to active drugs. |
| CYP2D6 | No-function 4, decreased 10, *41; gene duplications → UM | Phase I metabolism | Codeine, tramadol, many psychotropics | PMs fail to form morphine from codeine; UMs over-produce morphine and risk respiratory depression. |
| CYP2C9 | Reduced-function 2, 3 | Phase I metabolism | Warfarin | Reduced clearance of S-warfarin, higher bleeding risk at standard doses. Dose reductions needed, combined with VKORC1. |
| VKORC1 | −1639G>A regulatory variant | Drug target (vitamin K cycle) | Warfarin | Increased sensitivity to warfarin. Lower starting doses. |
| DPYD | Decreased-function alleles | Catabolism of 5-FU | 5-FU, capecitabine | Severe toxicity with standard doses; reduce or avoid. |
| TPMT | Low- or no-function alleles | Thiopurine inactivation | Azathioprine, 6-MP | Myelosuppression at standard doses; use reduced dose or alternative. |
| UGT1A1 | 28, 6 | Phase II glucuronidation | Irinotecan | Higher SN-38 exposure, neutropenia and diarrhoea risk; start lower. |
| SLCO1B1 (OATP1B1) | c.521T>C reduced function | Hepatic uptake transporter | Statins (e.g., simvastatin) | Higher plasma statin, increased myopathy risk; choose alternative or lower dose. |
| **HLA-B*57:01** | Risk allele | Immune presentation | Abacavir | High risk of hypersensitivity; screen and avoid if positive. |
| HLA-B15:02, HLA-B58:01 | Risk alleles | Immune presentation | Carbamazepine; allopurinol | Elevated SJS/TEN risk; allopurinol hypersensitivity risk ~hundreds-fold with HLA-B*58:01. Screen in susceptible groups. |
From genotype to phenotype to dosing
Population notes used in triage
TLDR Testing focuses on high-yield pharmacogenes: CYP2C19, CYP2D6, CYP2C9, VKORC1, DPYD, TPMT, UGT1A1, SLCO1B1, and HLA. These variants change exposure or trigger immune reactions, so match drug and dose to phenotype to prevent toxicity and treatment failure.
DNA variant
↓
Protein change (amount, structure, localisation, kinetics)
↓
PK (CL, t1/2, exposure) or PD (EC50, Emax, immune risk)
↓
Efficacy and toxicity at a given dose| Protein class | Variant type | Molecular consequence | PK/PD effect | Lecture examples you can cite |
|---|---|---|---|---|
| Drug-metabolising enzymes (e.g., CYP2C19, CYP2D6, CYP2C9, UGT1A1, TPMT, DPYD) | Missense in active site or heme pocket | Changes pocket geometry or stability, alters Vmax and Km | Slower or faster clearance, changed t1/2 and exposure | CYP2D6 missense or deletion → codeine not activated; CYP2C9 2/3 → slow S-warfarin clearance |
| Splice/frameshift/nonsense | Truncated or absent enzyme | Functional knockout | **CYP2C19 *2** splice defect → minimal voriconazole metabolism, high toxicity risk at standard dose | |
| Promoter/UGT repeats | Lower expression | Lower enzyme abundance, decreased Vmax | **UGT1A1*28** lower SN-38 glucuronidation → irinotecan neutropenia risk | |
| Transporters (OATP1B1/SLCO1B1, ABCB1/P-gp, ABCG2/BCRP) | Missense in transmembrane domain or NBD | Reduced substrate translocation, misfolding or mis-trafficking | Lower hepatic uptake or efflux, higher plasma Cmax/AUC | SLCO1B1 c.521T>C lowers hepatic statin uptake → simvastatin myopathy risk |
| Drug targets (receptors, enzymes) | Missense in binding pocket | Affinity or efficacy shift (EC50, Emax) | Different potency at same concentration | GPCR variation on slides with several-fold potency shifts across common missense variants |
| Immune presentation (HLA) | Allelic variant in peptide-binding groove | Presents drug-peptide adducts, triggers T-cell response | Severe idiosyncratic toxicity independent of dose | HLA-B57:01 with abacavir; HLA-B15:02 with carbamazepine SJS/TEN |
PK (exposure shift)
PD (sensitivity shift)
CYP2C19 poor metaboliser + voriconazole Structure/function: splice LoF → minimal enzyme. PK: AUC↑, crosses toxic threshold on graph. Action: lower dose or alternative antifungal.
CYP2D6 poor or ultra-rapid + codeine Structure/function: absent enzyme (PM) or gene duplication (UM). PD surrogate: active metabolite (morphine) formation ↓ in PM (no effect), ↑ in UM (toxicity). Action: avoid codeine in PM and UM, pick non-2D6 opioid.
VKORC1 −1639G>A + CYP2C92/3 + warfarin Structure/function: VKORC1 promoter variant lowers enzyme abundance (higher drug sensitivity). CYP2C9 missense slows clearance. Combined effect: higher sensitivity and slower CL. Action: lower initial dose and tighter INR titration.
SLCO1B1 c.521T>C + simvastatin Structure/function: transporter activity ↓. PK: hepatic uptake ↓, plasma AUC↑, muscle exposure higher. Action: avoid high-dose simvastatin; choose alternative statin or lower dose.
TLDR Variants change protein amount, structure, localisation, or kinetics in enzymes, transporters, targets, or HLA. That shifts PK (exposure) or PD (sensitivity) and explains toxicity and non-response. Use the allele→protein→PK/PD→action chain with the lecture’s anchors: CYP2C19–voriconazole, CYP2D6–codeine, VKORC1/CYP2C9–warfarin, SLCO1B1–statins, and HLA–hypersensitivity.
Gene + domain mapping → HTS (chloride-efflux) → hits (n=185) → med-chem → ivacaftor
→ genotype-specific Phase 3 → approval (G551D) → corrector discovery → triple combo
→ label expansion + PBS access; ongoing cost and equity challengesTLDR Discovery moved from gene mapping to HTS in CFTR-overexpressing cells, to ivacaftor as the first potentiator, then to correctors that rescue folding. Structural data located modulator binding in TMDs, justifying the triple therapy strategy and broader genotype coverage.
| Class | Representative drugs | Primary action | Mechanistic details you can state |
|---|---|---|---|
| Potentiators | Ivacaftor; deutivacaftor (next-gen, longer half-life) | Increase open probability of CFTR at the cell surface | Allosteric. Keeps CFTR open in a largely ATP-independent way, but requires phosphorylated CFTR to work. Improves WT and gating-mutant activity at the membrane. |
| Correctors, Class I | Lumacaftor, tezacaftor | Stabilise TMD1, aid co-translational folding and ER exit | Extend ER residence to allow proper folding of WT and mutant CFTR so more reaches the surface. |
| Correctors, Class II | Elexacaftor, vanzacaftor | Enhance Class I effect by binding distinct TMD sites | Complementary binding sites produce additive rescue. Some Class II show secondary potentiator-like effects in cells. |
TLDR Potentiators open CFTR channels already at the surface. Correctors raise the amount of CFTR that reaches the surface by stabilising folding in the ER. Combining two complementary correctors with a potentiator yields maximal function, especially for ΔF508 backgrounds. Structural studies place all three ligands in TMDs, consistent with their effects.
| CFTR defect (example) | Molecular problem | Effective modulator(s) | Why it works |
|---|---|---|---|
| Class III, “gating” (e.g., G551D) | Channel reaches membrane but won’t open efficiently | Ivacaftor monotherapy | Potentiator raises open probability of surface CFTR → restored Cl⁻ flux. |
| Class II, folding/trafficking (e.g., F508del) | Misfolded CFTR, ER retention, little reaches surface | Tezacaftor/ivacaftor or lumacaftor/ivacaftor; best response with elexacaftor/tezacaftor/ivacaftor | Correctors stabilise folding and trafficking; adding a potentiator maximises activity of rescued channels. Two complementary correctors (elexacaftor + tezacaftor) target distinct TMD sites → additive rescue. |
| Residual function (Class IV/V) (selected splice or conductance variants) | Reduced conductance or reduced transcript, but some CFTR at surface | Ivacaftor ± a corrector depending on baseline function | Potentiation improves opening; some variants gain further benefit when more protein is trafficked. |
| Minimal/no CFTR production (Class I, nonsense) | No full-length protein | Current modulators ineffective (consider non-modulator approaches) | No substrate for potentiation or correction; requires read-through or gene/additive therapies (not the focus of modulators). |
Why triple therapy dominates F508del care Cryo-EM and structure-function data in the slides show elexacaftor and tezacaftor engage distinct TMD sites, stabilising different folding bottlenecks; ivacaftor then improves gating once rescued protein reaches the surface. This three-point rescue (folding A + folding B + gating) yields the largest functional gain in F508del backgrounds.
TLDR Gating mutations respond to ivacaftor alone. Folding/trafficking mutations (F508del) need corrector(s) and do best with elexacaftor/tezacaftor/ivacaftor. Residual-function variants often benefit from ivacaftor ± a corrector. No-protein nonsense variants are not helped by modulators.
| Regimen | Common AEs in slides/transcript | Monitoring / DDI notes |
|---|---|---|
| Ivacaftor | Headache, GI upset, transaminase elevations; paediatric cataract signal noted historically | LFTs at baseline and periodically; check for CYP3A interactions (ivacaftor is a substrate). |
| Lumacaftor/ivacaftor | Dyspnoea/chest tightness, cough; LFT elevations | Lumacaftor is a strong CYP3A inducer → reduced exposure to many drugs including some hormonal contraceptives; review meds carefully; monitor LFTs. |
| Tezacaftor/ivacaftor | Generally better tolerated; LFT elevations possible | LFTs; fewer DDIs than lumacaftor combo but maintain CYP3A awareness. |
| Elexacaftor/tezacaftor/ivacaftor | Rash, LFT elevations, ↑CK in some; GI symptoms; headache | LFTs at baseline and during therapy; manage rash (more frequent in some populations); check for CYP3A substrate/inhibitor co-meds. |
Practice notes the lecturer emphasised
TLDR Modulators improve lung function, sweat chloride, exacerbations, weight, and QoL. Main risks: LFT elevations across regimens; dyspnoea/chest tightness with lumacaftor/ivacaftor; rash with elexacaftor/tezacaftor/ivacaftor; and CYP3A DDIs (notably with lumacaftor). Do baseline and periodic LFTs, screen for DDIs, and counsel on early symptom trajectories.
What EGFR is
Why it matters in NSCLC
Mutations that make EGFR druggable
Clinical take-home
TLDR EGFR drives NSCLC through a kinase domain that controls key growth pathways. Activating mutations (Ex19del, L858R) reshape the ATP pocket and predict strong benefit from EGFR TKIs. Testing EGFR is central to first-line treatment selection.
Two levels of resistance
Canonical on-target sequence
Gatekeeper T790M
Third-generation solution: osimertinib
Next resistance: C797S
Other genetic routes
Resistance timeline and design logic
Quick reference table
| Setting | Mutation pressure | Binding consequence | Clinical move |
|---|---|---|---|
| 1st/2nd gen TKI on Ex19del/L858R | Selects T790M | Steric block of reversible TKIs, ATP still binds | Switch to osimertinib |
| Osimertinib on T790M | Selects C797S | Loss of covalent bond | Consider 4th-gen TKI trials or alternate strategies |
| Drug pressure on pathway | Amplifications / bypass | Downstream reactivation | Combine or switch based on driver detected |
TLDR Resistance is genetic. T790M blocks first/second-generation TKIs. Osimertinib overcomes T790M by covalently binding C797. C797S then removes the covalent handle and drives next-gen design. Bypass activation and histologic shifts are additional routes. Track mutations and adapt therapy accordingly.
Where binding happens and why mutations matter
Activating mutations that create sensitivity
Resistance mutations that reshape design
Design logic across generations
| Generation | Core design feature | Pocket consequence addressed | Representative drugs |
|---|---|---|---|
| 1st | Reversible ATP-mimetics that H-bond in the hinge (near D855 region) | Leverage sensitising pocket of Ex19del/L858R | Gefitinib, erlotinib |
| 2nd | Irreversible pan-ERBB; broader reactivity | Partial workarounds but limited by T790M at clinical exposures | Afatinib; irreversible scaffolds context |
| 3rd | Mutant-selective covalent binders targeting C797 | Bypass T790M steric block, prefer mutant over WT | Osimertinib; WZ4002 (tool) |
| 4th (in development) | Non-covalent or alternate-covalent strategies | Overcome C797S loss of covalent anchor | “4th gen” TKI concept on slides |
TLDR Map the mutation to the pocket change. Ex19del/L858R open and stabilise the active cleft so reversible ATP-mimetics work. T790M blocks access, so C797-covalent design restores binding (osimertinib). C797S then removes that handle, prompting new scaffolds.
High-frequency actionable mutations
| EGFR alteration | Typical effect on pocket | Preferred TKI(s) per lecture | Rationale |
|---|---|---|---|
| Exon 19 deletion (Ex19del) | Activates and stabilises kinase; increases TKI sensitivity | Gefitinib, erlotinib, afatinib, osimertinib | Sensitising mutation; all generations active, 3rd gen widely used first-line. |
| L858R (exon 21) | Activating point mutation near activation loop | Gefitinib, erlotinib, afatinib, osimertinib | Same sensitising logic as Ex19del; strong clinical benefit. |
| T790M (gatekeeper) | Steric hindrance to reversible binders; ATP still fits | Osimertinib | Covalent C797 engagement overcomes T790M at clinical exposure. |
| C797S (post-osimertinib) | Loss of covalent anchor | Investigational 4th-gen TKIs/strategies | C797S prevents covalent adduct; next-gen designs required. |
| Atypical mutations (ex18/ex20 subsets) | Heterogeneous structural changes | Variable; outcomes generally poorer vs classical | Structure-based classes predict variable response; emerging focus. |
Evidence anchors to cite in answers
TLDR Ex19del/L858R → first-/second-generation TKIs work, osimertinib now common first-line. T790M → osimertinib. C797S → no approved TKI on the slides; look to 4th-gen strategies. Atypical mutations are heterogeneous and need structure-informed choices.
Genomics collapses the front end of vaccine R&D into a digital design step.
| Feature | mRNA vaccines | Traditional platforms (inactivated, live-attenuated, protein subunit) |
|---|---|---|
| Design speed | Digital antigen selection from genome; hours–days to design, weeks to clinical lot | Requires pathogen growth, protein purification, or attenuation; months–years |
| Manufacturing | Single IVT + LNP process reused across targets; scale by sequence swap | Process differs per pathogen; bespoke cell culture and purification |
| Update agility | Rapid code edits follow genomic surveillance (variant boosters) | Reformulation and new production cycles |
| No pathogen | No handling of live virus during manufacture | Live/attenuated lines or viral propagation often required |
| Antigen fidelity | Precise sequence control (codons, stabilising mutations) | Biologic variability in antigen prep |
| Cytoplasmic expression | No nuclear entry; transient expression | Variable depending on platform |
| Platform extensibility | Same chassis for infectious disease and oncology neoantigens | Usually bespoke per disease area |
Genome posted → Antigen picked (in silico) → mRNA designed (1–2 h)
→ IVT + cap/modified nucleosides → LNP formulation → QC/fill-finish → Trials
↑ same chassis, swap the sequence for variantsTLDR Genomics turns vaccine design into a software-like step: sequence in, sequence out. mRNA platforms keep process constant while swapping code, enabling hour-scale design, week-scale manufacture, and fast variant updates, without growing virus.
Precision workflow (what changes and what stays constant)
Why this is precision medicine
Schematic you can sketch
Surveillance (global genomes) → Pick variant antigen in silico
→ Edit mRNA code (codon/stability) → IVT + LNP (unchanged process)
→ QC/fill → Variant-matched booster rolloutPatient-specific pipeline the lecture outlined
Why this is precision medicine
| Aspect | Variant-specific infectious vaccine | Personalised cancer vaccine |
|---|---|---|
| Input data | Population viral genomes (surveillance) | Individual tumour + normal genomes (WES/RNA-seq) |
| Antigen choice | Variant spike (or other pathogen protein) | Patient’s neoantigens (somatic mutations, HLA-matched) |
| Construct | Single or bivalent mRNA | Poly-epitope mRNA (CD8/CD4) |
| Platform | Same IVT + LNP | Same IVT + LNP |
| Goal | Restore neutralisation against circulating variants | Elicit tumour-specific T cells, often with checkpoint inhibitors |
| Precision lever | Match to current variant | Match to patient’s tumour mutations |
TLDR mRNA is a platform for precision medicine. For infectious disease, genomic surveillance drives variant-matched boosters by swapping code, not process. For oncology, a patient’s tumour genome drives neoantigen-encoded, poly-epitope mRNA to prime personalised T-cell responses, frequently paired with checkpoint blockade. Both reuse IVT + LNP with only the sequence changing.
Indication → Consent/Order (NATA) → Sample (blood/saliva/buccal)
→ Genotype → Phenotype → Action (avoid/swap/adjust/monitor)TLDR Follow a fixed pathway: indication → accredited test → sample → genotype→phenotype → action. Use RCPA indications to decide who to test, then apply results to avoid, swap, or adjust therapy, while accounting for DDIs and patient factors.
Confirm gene–drug pair and phenotype on the report (e.g., CYP2C19 IM).
Open guideline: CPIC or DPWG for dosing and alternatives; ClinPGx for evidence strength and summaries.
Select action:
Check context: DDIs, renal/hepatic function, comorbidities. Guard against phenoconversion.
Document: genotype, phenotype, source guideline, chosen action, and follow-up plan. Store results accessibly for future use.
Clopidogrel after PCI
Thiopurines in IBD
5-FU/Capecitabine
Abacavir
| Task | Use this | Why |
|---|---|---|
| Dosing and alternatives | CPIC, DPWG | Actionable, phenotype-based recommendations |
| Evidence strength, summaries, visuals | ClinPGx | Curated evidence levels; mechanism diagrams |
| Local “should I test?” triage | RCPA indications | Australian categories: Recommended / Consider / Available |
TLDR Start from the reported phenotype, open CPIC/DPWG for the action, check ClinPGx for evidence strength, adjust for DDIs and organ function, then document the plan. Use RCPA indications to decide who to test and panel testing to future-proof prescribing.
| Approach | Typical methods | Scope | Turnaround | Strengths | Limitations | Best use |
|---|---|---|---|---|---|---|
| Single-gene targeted test | Targeted genotyping or small amplicon NGS | One drug–gene (e.g., CYP2C19, **HLA-B*57:01**) | Fast | Quick, inexpensive, simple consent/report | Narrow scope; can miss rare alleles or CYP2D6 CNVs; repeats testing later | Reactive prescribing when one decision is urgent |
| Focused PGx panel | Array or amplicon NGS with star-allele translation; CNV calling for CYP2D6 | 10–25+ pharmacogenes (CYPs, UGT1A1, SLCO1B1, DPYD, TPMT/NUDT15, HLA) | Moderate | Pre-emptive value across many drugs; fewer repeat tests; better allele coverage | Interpretation complexity; incidental non-actionable variants; still misses some rare/hybrid events | Pre-emptive storage in EHR; multi-drug clinics (cardiology, oncology, psych) |
| WES/WGS | Exome or whole-genome sequencing | Broadest | Slowest | Future-proof, finds rare/novel variants and non-PGx findings if consented | Cost; variable coverage of PGx loci, CYP2D6 structural complexity; star-allele translation non-trivial; secondary findings and data-governance issues | Research, complex cases, systems building; seldom first-line in routine PGx |
TLDR Match the clinical question to the assay. Use single-gene for urgent, one-off decisions, PGx panels for pre-emptive multi-drug value with CYP2D6 CNV support, and WES/WGS rarely, when broad discovery is required. Regardless of assay, adjust for DDIs, organ function, and phenoconversion.
Indication Post-stent patient planned for clopidogrel. Order CYP2C19.
Result Genotype: 2/2 → Poor metaboliser (PM). Report maps to phenotype and provides action statements.
Guideline lookup Open CPIC/DPWG; avoid clopidogrel in PM/IM; choose ticagrelor or prasugrel. Optionally confirm in ClinPGx for evidence strength.
Context checks
Plan note (exam-ready wording) “CYP2C19 2/2, phenotype poor metaboliser. Per CPIC/DPWG, clopidogrel is unlikely to be effective. Substitute ticagrelor. Document genotype, phenotype, source guideline, and counsel on bleeding and dyspnoea risk.”
Utility Prevents treatment failure and stent thrombosis risk; demonstrates actionable change in drug selection.
Indication Starting azathioprine. Order TPMT/NUDT15 (panel preferred if future PGx use anticipated).
Result TPMT 1/3A (IM) or NUDT15 decreased function.
Guideline lookup CPIC/DPWG: start at reduced dose or consider alternative depending on activity score; schedule serial FBC to monitor for myelosuppression.
Context checks Concomitant myelosuppressants, infection risk, liver tests.
Utility Mitigates severe myelosuppression and hospitalisation; shows how genotype changes dose and monitoring.
Indication Adjuvant chemotherapy. Order DPYD before first cycle.
Result Activity score indicates decreased function.
Guideline lookup Reduce or avoid fluoropyrimidines based on activity score; consider alternative regimen; institute enhanced toxicity monitoring.
Utility Prevents life-threatening toxicity; high yield and often reimbursed.
TLDR Clinical utility is proven when genotype changes management and improves outcomes. Use a fixed playbook: order wisely, map to phenotype, apply CPIC/DPWG, check context and phenoconversion, and document so results are reusable. Panels maximise future value; single-gene tests solve urgent questions now.
Indication and consent Identify a drug–gene question. Obtain consent. Choose an accredited lab.
Collection and transport Buccal swab at home or clinic. Use barcoded kit and biohazard bag. Mail to lab. Swab design preserves DNA integrity in transit.
Accessioning and pre-analytics Receive, label, and check for damage or low yield.
DNA extraction and QC Lyse cells, bind DNA to magnetic beads, wash, elute. Quantify and assess purity by spectrophotometer to confirm suitability for downstream assays.
Target amplification (PCR) Amplify PGx loci to create ample template for genotyping. Primer pairs flank variant sites.
Genotyping Use TaqMan OpenArray or Agena MassARRAY to call alleles at predefined variants. Labs may also deploy NGS panels.
Bioinformatics → diplotype → phenotype Call variants and CNVs. Map to star-alleles using PharmVar rules. Translate diplotype to phenotype with guideline logic (e.g., CYP2D6 1/4 → IM; CYP2C19 2/2 → PM).
Report and delivery Secure report to clinician or pharmacist with action categories (e.g., traffic-light). Store results for reuse.
Therapeutic action and counselling Apply guideline-based action: avoid, switch, or adjust dose. Integrate age, organ function, and DDIs. Watch for phenoconversion from inhibitors or inducers.
Indication → Swab/Ship → Accession → Extract + QC → PCR → Genotype
→ Bioinformatics (PharmVar) → Diplotype → Phenotype → Report → ActionTLDR Follow a fixed chain: collect → extract/QC → PCR → genotype → star-allele → phenotype → report → prescribe. Results guide avoid/switch/dose actions, but you still check comorbidities and DDIs before writing the script.
| Step | TaqMan OpenArray | Agena MassARRAY | NGS PGx panels |
|---|---|---|---|
| Chemistry | Allele-specific probes with fluorescent reporters | Single-base extension + MALDI-TOF mass readout | Targeted capture or amplicon sequencing |
| Output | Fluorescence cluster → genotype | Mass peaks → genotype | VCFs → star-alleles → phenotype |
| Scope | Known variants on array | Known variants with multiplexing | Broad, includes rare/novel variants |
| Strength | Speed, throughput, mature assays | Precision, multiplex, low per-variant cost | Comprehensive, future-proof data |
| Caveat | Limited to panel content | Limited to panel content | Longer TAT, complex interpretation in some loci |
TLDR Extract with beads and QC first. PCR enriches targets. Labs then call alleles by fluorescent probes (TaqMan) or by mass (MassARRAY). NGS expands coverage and future-proofs data. All paths flow into star-alleles → phenotype → action.
Variants (incl. CNVs) → Star-alleles (PharmVar rules) → Diplotype → Activity score (if applicable) → Phenotype → Action| Gene | Diplotype examples → AS | Phenotype | Typical action from report |
|---|---|---|---|
| CYP2D6 | 1/1 (AS 2), 1/4 (AS 1), 4/4 (AS 0), 1xN/1 (AS >2) | NM, IM, PM, UM | Codeine/tramadol: avoid in PM/UM; pick non-2D6 opioid. Psychotropics: adjust per guideline. |
| CYP2C19 | 1/1, 1/2, 2/2, 1/17, 17/17 | NM, IM, PM, RM/UM | Clopidogrel: avoid in IM/PM; voriconazole: dose or switch by phenotype. |
| DPYD | Activity score by variant set | Normal, Intermediate, Poor | 5-FU/capecitabine: reduce or avoid by AS. |
| TPMT / NUDT15 | Decreased-function diplotypes | IM, PM | Thiopurines: start low or use alternative; monitor FBC. |
| UGT1A1 | 1/28, 28/28, *6 etc. | Reduced function | Irinotecan: reduce dose in high-risk genotypes. |
| SLCO1B1 | c.521T>C diplotypes | Reduced function | Statins (simvastatin): avoid high dose or switch. |
| HLA-B | 57:01, 15:02 | Positive | Abacavir: contraindicated; Carbamazepine: avoid in at-risk groups. |
TLDR Convert star-alleles to a diplotype, then to activity/phenotype, then to a clear action. Do not forget CNVs (CYP2D6) or phenoconversion from DDIs. Quote the phenotype and the concrete change to the script.
| Scenario | What you order | Typical result | Action you write | Why it matters |
|---|---|---|---|---|
| Post-PCI dual antiplatelet therapy | CYP2C19 | IM/PM | Avoid clopidogrel → ticagrelor/prasugrel | Prevents stent thrombosis from non-response. |
| New start codeine for pain | CYP2D6 (CNV-aware) | PM or UM | Avoid codeine; pick non-2D6 opioid | Avoids no analgesia in PM and toxicity in UM. |
| Initiating thiopurine in IBD | TPMT/NUDT15 | IM or PM | Reduce dose or alternative; monitor FBC | Prevents severe myelosuppression. |
| Planning fluoropyrimidine chemotherapy | DPYD | Decreased function | Reduce/avoid per activity score; monitor toxicity | Reduces life-threatening toxicity. |
| Statin myopathy risk | SLCO1B1 | Reduced function | Avoid high-dose simvastatin; choose alternative | Lowers myopathy risk and improves adherence. |
| Abacavir initiation | **HLA-B*57:01** | Positive | Do not use abacavir | Prevents hypersensitivity. |
TLDR PGx changes drug choice and dose in real clinics. Use reactive single-gene tests for urgent calls and pre-emptive panels for long-term value. Write a short note that states phenotype, cites a guideline, records the action, and sets follow-up.
| Drug | HLA allele | Risked syndrome(s) | Action if positive |
|---|---|---|---|
| Abacavir | HLA-B*57:01 | Multisystem hypersensitivity | Contraindicated; use non-abacavir regimen |
| Allopurinol | HLA-B*58:01 | SJS/TEN, DRESS | Avoid; choose alternative urate-lowering therapy |
| Carbamazepine / Oxcarbazepine | HLA-B15:02; HLA-A31:01 | SJS/TEN; SJS/TEN/DRESS | Avoid; select non-aromatic anticonvulsant where possible |
TLDR HLA molecules present peptides to T cells. Certain alleles bind drug-derived antigens and trigger T-cell–mediated SCARs. The highest-yield pairs in practice are B57:01–abacavir, B58:01–allopurinol, and B15:02/A31:01–carbamazepine. Pre-treatment HLA genotyping prevents life-threatening reactions and is prioritised based on allele frequency in the local population.
| Syndrome | Onset after exposure | Hallmark features | Mortality | Common triggers in slides |
|---|---|---|---|---|
| DRESS | 2–6 weeks | Fever, facial oedema, diffuse rash >50% BSA, lymphadenopathy, hepatitis, nephritis, pneumonitis | ~1–10% | Carbamazepine, lamotrigine, phenytoin, allopurinol, sulfonamides, some antivirals/antibiotics |
| SJS/TEN | 4–28 days | Prodrome then blistering, mucosal erosions, skin detachment; organ involvement | ~10–40% (TEN higher) | Antiepileptics, allopurinol, sulfonamides, NSAIDs, abacavir |
Drug (or metabolite)
↓ binds covalently (hapten) or non-covalently (p-i)
HLA groove / TCR
↓
T-cell activation (CD8/CD4) → cytokines → cytotoxicity
↓
Keratinocyte apoptosis → DRESS / SJS / TENTLDR SCARs are T-cell–mediated. Drugs trigger them by covalently creating neo-antigens or by non-covalently altering HLA/TCR recognition. Activated T cells and cytokines drive keratinocyte apoptosis, producing DRESS at 2–6 weeks and SJS/TEN at 4–28 days with high mortality. Pre-treatment HLA testing interrupts this cascade.
| Result | Drug(s) affected | Risked syndrome(s) | Action you take | Note to document |
|---|---|---|---|---|
| **HLA-B*57:01 positive** | Abacavir | Multisystem hypersensitivity | Contraindicated | “Avoid abacavir. Record as genomic contraindication.” |
| **HLA-B*58:01 positive** | Allopurinol | SJS/TEN, DRESS | Avoid | “Use febuxostat or non-allopurinol strategy. Counsel on SCAR symptoms.” |
| **HLA-B*15:02 positive** | Carbamazepine, oxcarbazepine | SJS/TEN | Avoid | “Select non-aromatic anticonvulsant (e.g., levetiracetam, valproate if appropriate).” |
| **HLA-A*31:01 positive** | Carbamazepine | SJS/TEN/DRESS | Avoid / consider alternative | “Risk extends beyond SJS/TEN, include DRESS. Choose alternative if possible.” |
TLDR Read the allele, ignore zygosity for these pairs, act categorically: B57:01→no abacavir, B58:01→no allopurinol, B15:02/A31:01→avoid carbamazepine. Store results where prescribing systems can see them.
| Task | Best source | How you use it |
|---|---|---|
| Phenotype-to-action dosing/avoidance | CPIC | Clear, graded recommendations (e.g., “Strong: Do not use abacavir if B*57:01 positive”). |
| Local testing indications and funding | RCPA | Decides who you test pre-treatment in Australia; lists “Recommended/Consider/Available.” |
| Evidence snapshots and visuals | ClinPGx | Summaries of mechanism, effect sizes, allele frequencies, and quick action tables. |
TLDR Open CPIC for the action, RCPA to justify testing and align with local practice, and ClinPGx for quick evidence context. Default to substitution, not “test dosing,” for high-risk alleles.
TLDR Match screening breadth to allele frequency and reaction severity. Use universal pre-abacavir testing and targeted pre-CBZ/allopurinol testing in high-frequency groups. When ancestry is unclear, test.
What it is
Where you use it
Real-world
DAPT snapshot (post-PCI)
TLDR Clopidogrel is a P2Y12 inhibitor used for ACS, post-stent care, and secondary prevention. It is a prodrug, so clinical effect depends on metabolic activation. It remains widely prescribed in PCI pathways alongside aspirin.
Clopidogrel (prodrug) → hepatic CYPs → (active thiol metabolite)
↑
CYP2C19 is the key rate-limiting step
Active metabolite → covalent block of platelet P2Y12 → ↓ aggregationCYP2C19 alleles you must name
PK/PD evidence
TLDR Clopidogrel needs CYP2C19 to form its active metabolite. 2/3 reduce activation, *17 increases it. Loss-of-function lowers active-metabolite exposure and blunts platelet inhibition.
Clinical outcomes
Actionable mapping (CPIC logic reflected in the lecture)
| Phenotype | Common diplotypes | Expected effect on clopidogrel | Clinical action |
|---|---|---|---|
| Ultrarapid / Rapid (UM/RM) | 17/17, 1/17 | More active metabolite; no excess bleeding signal in lecture summary | Standard dose clopidogrel acceptable |
| Normal (NM) | 1/1 | Typical response | Standard dose clopidogrel acceptable |
| Intermediate (IM) | 1/2, 1/3, 2/17 | Less active metabolite; higher on-treatment reactivity | Avoid clopidogrel if possible; use ticagrelor or prasugrel |
| Poor (PM) | 2/2, 2/3, 3/3 | Marked lack of activation; high event risk | Avoid clopidogrel; use ticagrelor or prasugrel |
Practice anchors
TLDR IM/PM get less benefit from clopidogrel and carry higher event risk. Guidance is simple: use clopidogrel for UM/RM/NM, switch to ticagrelor or prasugrel for IM/PM. PGx-guided selection improves outcomes and aligns with regulator warnings.
| Phenotype | Common diplotypes | Active metabolite exposure | Platelet inhibition | Clinical outcomes on clopidogrel | Recommended action (ACS/PCI) | Notes |
|---|---|---|---|---|---|---|
| Poor metaboliser (PM) | 2/2, 2/3, 3/3 | Markedly ↓ | Blunted | Highest on-treatment platelet reactivity, higher MACE and stent thrombosis | Avoid clopidogrel. Use ticagrelor or prasugrel if not contraindicated | FDA boxed warning aligns; “dose-up” clopidogrel is not recommended |
| Intermediate metaboliser (IM) | 1/2, 1/3, 2/17 | ↓ | Reduced | Elevated MACE risk vs NM | Prefer ticagrelor/prasugrel over clopidogrel | If clopidogrel must be used, document risk and monitor closely |
| Normal metaboliser (NM) | 1/1 | Baseline | Expected | Reference risk | Clopidogrel acceptable at standard dosing | Choose alternative P2Y12 if other clinical factors favour it |
| Rapid (RM) | 1/17 | Slightly ↑ | Slightly ↑ | Similar or slightly better efficacy vs NM without clear bleeding excess | Clopidogrel acceptable | Consider clinical context rather than genotype alone |
| Ultrarapid (UM) | 17/17 | ↑ | ↑ | No consistent harm signal in lecture material | Clopidogrel acceptable | Monitor for bleeding with concomitant anticoagulants or high bleeding risk |
Key comparisons to state in answers
TLDR IM and PM get less benefit from clopidogrel; switch to ticagrelor or prasugrel for ACS/PCI. NM/RM/UM can use standard clopidogrel. Do not rely on higher clopidogrel dosing to rescue IM/PM.
CYP2C19 phenotype →
PM or IM → avoid clopidogrel → choose ticagrelor or prasugrel*
NM/RM/UM → clopidogrel acceptable → weigh bleeding risk, comorbidities, cost
*Avoid prasugrel if prior stroke/TIA; consider age/weight/bleeding riskTLDR Testing before PCI guides the P2Y12 choice at the point of maximum impact. IM/PM should not receive clopidogrel; NM/RM/UM can. Always check DDIs, bleeding risk, and contraindications, then document genotype, phenotype, action, and follow-up.
What pharmacogenomics is
Key genes in antidepressant therapy
High-yield summary table (from lecture)
| Drug class / examples | Primary gene(s) | PGx consideration (lecture framing) |
|---|---|---|
| SSRIs — citalopram, escitalopram, sertraline | CYP2C19 | PM → ↑ levels and side-effects; consider alternative SSRI or lower dose (QT caution for citalopram). |
| SSRIs — fluoxetine, paroxetine | CYP2D6 | PM → ↑ levels/toxicity; UM → ↓ efficacy at standard dose. |
| SNRIs — venlafaxine, duloxetine | CYP2D6 | PM → ↑ side-effects; NM → standard dosing. |
| TCAs — amitriptyline, nortriptyline | CYP2D6, CYP2C19 | PM → ↑ concentrations/toxicity; adjust dose or choose alternative; TDM helpful. |
| All SSRIs (target) | SLC6A4 | 5-HTTLPR S allele linked to reduced response in some studies; guideline action not established. |
TLDR (Objective 1) Focus on CYP2D6 and CYP2C19: they drive exposure for most SSRIs/SNRIs/TCAs. SLC6A4 and ABCB1 findings exist but are not dosing levers. Use PGx to choose safer agents or adjust dose when CYP function is low or high.
CYP2D6 phenotype effects
CYP2C19 phenotype effects
Clinical consequences and actions (lecture framing)
Case anchor (from lecture)
Evidence of benefit
Practical cautions
TLDR (Objective 2) Lower CYP activity → higher levels/toxicity; higher activity → lower levels/non-response. Use PGx to avoid SSRIs cleared by the impaired pathway (e.g., CYP2C19 PM on escitalopram) or to select agents outside CYP2D6/2C19 (e.g., desvenlafaxine). PGx-guided care improves remission rates versus trial-and-error.
| Phenotype | Avoid / prefer | Dosing notes |
|---|---|---|
| CYP2C19 PM | Avoid citalopram/escitalopram; prefer sertraline, fluvoxamine, or non-CYP2C19 options (e.g., desvenlafaxine) | If use is unavoidable, start ~50% lower and watch QT for citalopram |
| CYP2C19 UM/RM | Consider agents not reliant on 2C19 if non-response history on escitalopram/citalopram | If using 2C19 SSRIs, titrate to effect; consider TDM if available |
| CYP2D6 PM | Avoid paroxetine, fluoxetine high doses, venlafaxine, TCAs; prefer sertraline, escitalopram (if 2C19 OK), desvenlafaxine, mirtazapine | If using a 2D6 substrate, start lower; consider TDM for TCAs |
| CYP2D6 UM | Avoid nortriptyline, paroxetine, venlafaxine monotherapy; prefer drugs outside 2D6 (e.g., desvenlafaxine) | If unavoidable, higher doses may still underperform; TDM helps |
TLDR Read CYP2C19 and CYP2D6 first. Avoid substrates of the impaired pathway. Prefer agents outside that pathway or adjust dose with monitoring. Record phenotype, action, and follow-up.
Benefits (lecture data)
Limitations and cautions
When PGx adds the most value
Clinic checklist
TLDR PGx improves remission and tolerability when you align drugs with CYP2D6/2C19 phenotypes. Limits arise from phenoconversion, variable evidence for non-CYP genes, and access. Use PGx where the decision stakes are high and store results for reuse.
TLDR Cytotoxics kill fast-dividing cells. That includes tumours and normal renewal tissues, so toxicity is common. 5-FU is an anti-metabolite used widely in solid tumours. Capecitabine is its prodrug.
Capecitabine (oral) --(TP, higher in tumours)--> 5-FU --(cell enzymes)--> FdUMP
FdUMP ┤ Thymidylate synthase → ↓ dTMP → DNA synthesis blockedTLDR Activation: 5-FU → FdUMP inhibits thymidylate synthase; capecitabine uses tumour-enriched TP to generate 5-FU locally. Elimination: DPD (DPYD) inactivates 5-FU. Low DPD activity from DPYD variants drives severe toxicity and higher treatment-related mortality; tumour DPD can blunt efficacy.
Toxicity profile you should name
Red flags for early severe toxicity
Bolus vs infusion quick table
| Schedule | More likely toxicities |
|---|---|
| Bolus 5-FU | Myelosuppression |
| Infusional 5-FU / Capecitabine | Mucositis, diarrhoea, hand–foot syndrome, cardiotoxicity events during infusion |
TLDR 5-FU toxicities cluster by schedule. Bolus drives marrow suppression. Infusion/capecitabine drive mucositis, diarrhoea, and hand–foot syndrome, with infusion-time vasospasm risk. Severe early toxicity signals DPD deficiency.
Clinically relevant DPYD variants covered in the lecture
Activity score (AS) and CPIC-style actions shown on slides
| DPYD activity score | Phenotype label | Starting recommendation for 5-FU or capecitabine | Follow-up |
|---|---|---|---|
| 2.0 | Normal | Full dose | Usual monitoring |
| 1.5–1.0 | Intermediate | Reduce starting dose by 50% (range 25–50% per regimen), then titrate using toxicity and, if available, TDM | Escalate cautiously if well tolerated |
| 0.5–0 | Poor | Avoid fluoropyrimidines | Choose non-fluoropyrimidine regimen |
Genotype vs phenotype testing
Australian implementation highlights
How to write the prescribing note
Clinical cautions from the lecture
TLDR Use DPYD activity score to set the first dose. AS 2 gets full dose. AS 1.5–1 starts at 50% with careful titration. AS ≤0.5 avoids 5-FU/capecitabine. Australian labs offer four-variant panels with rebate notes on the slides. Document AS, starting dose, and the titration plan.