Principles of Human Genetics¶
Chapter 479 | Part 16: Genes, the Environment, and Disease · Parts 15-16 – Genetics, Genomics & Precision Medicine · Chapter 479
Key Clinical Points¶
- Genetics focuses on individual genes/inheritance; Genomics covers the entire genome and its interaction with environmental factors.
- The human genome contains ~3 billion base pairs, 23 chromosomes, and ~20,000 protein-coding genes.
- Single nucleotide polymorphisms (SNPs) are the most common variation (>90%) and occur every 100–300 bases.
- Copy number variations (CNVs) involve larger regions (1 kb to several Mb) and can alter gene dosage.
- Epigenetic modifications (DNA methylation, histone acetylation) regulate expression without altering the primary DNA sequence.
- Pharmacogenomics utilizes genetic profiles to optimize drug therapy, predict efficacy, and manage adverse events.
- Next-generation sequencing (NGS) enables high-throughput whole exome (WES) and whole genome (WGS) analysis.
- Genomic imprinting results in monoallelic expression based on parental origin (e.g., Prader-Willi, Angelman).
- Cancer is driven by somatic mutations; genomic profiling often dictates therapy more than the primary organ site.
- Transcription factors are critical regulators; mutations in these can lead to a wide range of clinical disorders.
DEFINITION & CLASSIFICATION¶
• Human Genetics: Study of individual genes, their role/function in disease, and mode of inheritance. • Genomics: Study of the entire genome, including interaction of DNA with environmental or non-genetic factors (e.g., lifestyle). • Precision Medicine: Clinical approach aiming to customize medical decisions to an individual patient's genetic characteristics. • Pharmacogenomics: Use of a patient's genotype to optimize drug therapy, predict efficacy, and determine appropriate dosing/avoidance of adverse events.
ETIOLOGY & PATHOPHYSIOLOGY¶
Structure of the Human Genome¶
• Chromosomes: 23 total (22 autosomes + X/Y sex chromosomes). • Cellular State: Adult cells are diploid; germ cells are haploid. • DNA Composition: Double-stranded helix with four bases (A, T, G, C); A pairs with T, G pairs with C. • Genetic Code: ◦ 64 possible codons (3-base triplets) for 20 amino acids. ◦ Degenerate code: Most amino acids can be specified by multiple codons. • Genome Composition: ◦ Only ~1% of DNA is protein-coding. ◦ Exome constitutes only 1.14% of the genome. ◦ Non-coding DNA includes introns, regulatory elements (promoters, enhancers), and non-coding RNAs (miRNA, lncRNA). • Transcription Factors: ◦ Regulation occurs via DNA-binding proteins that activate/repress transcription. ◦ Most genes have 15–20 discrete regulatory elements within 300 bp of the start site. ◦ Complex interactions between ubiquitous and cell-specific factors create a 'combinatorial code' for expression. • Table 2: Selected Examples of Diseases Caused by Mutations in Transcription Factors ◦ Nuclear receptors (e.g., Androgen receptor) → Androgen insensitivity. ◦ Zinc finger proteins (e.g., WT1) → WAGR syndrome. ◦ Basic helix-loop-helix (e.g., MITF) → Waardenburg’s syndrome type 2A. ◦ Spinobulbar muscular atrophy (CAG repeat expansion). ◦ Homeobox (e.g., IPF1) → Maturity onset of diabetes mellitus type 4. ◦ Leucine zipper (e.g., NRL) → Retinitis pigmentosa. ◦ Forkhead (e.g., HNF1α, HNF1β) → Maturity onset of diabetes mellitus types 1, 3, 5. ◦ T-box (e.g., TBX5) → Holt-Oram syndrome. ◦ Cell cycle control (e.g., P53) → Li-Fraumeni syndrome. ◦ Co-activators (e.g., CREBBP) → Rubinstein-Taybi syndrome. ◦ Transcription elongation factor (e.g., VHL) → von Hippel–Lindau syndrome. ◦ Chimeric proteins (e.g., PML-RAR) → Acute promyelocytic leukemia.
Genetic Variation (SNPs and CNVs)¶
• Single Nucleotide Polymorphisms (SNPs): ◦ Most common type of variation; account for >90% of sequence variation. ◦ Occur every 100–300 bases. ◦ Haplotypes: SNPs in close proximity inherited together. • Copy Number Variations (CNVs): ◦ Large regions (1 kb to several Mb) that are duplicated or deleted. ◦ Account for 5–10% of the genome. ◦ Impact: Alter gene dosage; can lead to detrimental effects if essential genes are affected.
Phenotypic and Locus Heterogeneity¶
• Phenotypic Heterogeneity: Different clinical presentations from mutations in the same gene (e.g., LMNA mutation causing various conditions like Emery-Dreifuss, Progeria, or Dilated cardiomyopathy). ● Table 3: Selected Examples of Phenotypic and Locus Heterogeneity ◦ LMNA (Lamin A/C) → Multiple phenotypes (Emery-Dreifuss, Progeria, etc.). ◦ Myosin heavy chain beta (MYH7) → Familial hypertrophic cardiomyopathy. ◦ Troponin-T2 (TNNT2) → Familial hypertrophic cardiomyopathy. • Locus Heterogeneity: Same phenotype caused by mutations in different genes at different loci (e.g., several genes like MYH7, TNNT2, TPM1 can cause Hypertrophic cardiomyopathy).
DIAGNOSTIC APPROACH¶
- Initial Assessment: Characterization of phenotype → Pedigree analysis.
- Decision Point (Knowledge of Gene):
- If Gene is Unknown:
- Proceed to Deep sequencing (Linkage analysis and sequencing of linked region).
- Perform Mutational analysis.
- Determine functional properties of identified mutations in vitro and in vivo.
- Treatment based on pathophysiology.
- If Gene is Known Candidate:
- Perform Mutational analysis.
- Branching Path:
- Option A: Determine functional properties of identified mutations in vitro and in vivo.
- Option B: Genetic counseling / Testing of other family members.
- Treatment based on pathophysiology.
MANAGEMENT & TREATMENT¶
- Pharmacogenomics: Use genetic characteristics to optimize drug therapy, predict efficacy, and determine appropriate dosing/avoidance of adverse events.
- Molecular Biology Applications:
- Production of large quantities of peptide hormones, growth factors, cytokines, and vaccines (e.g., mRNA vaccines for SARS-CoV-2).
- Use of small interfering RNA (siRNA) to treat hypercholesterolemia.
- Targeted modifications of recombinant peptides (e.g., insulin analogues with favorable kinetics; GLP-1 agonists for type 2 diabetes and weight management).
SPECIAL POPULATIONS¶
Epigenetics and Imprinting¶
• Epigenetic Modifications: ◦ DNA Methylation: Associated with gene silencing (e.g., X-inactivation). ◦ Histone Acetylation: Mediated by HATs; leads to open chromatin and active transcription. ◦ Histone Deacetylation: Mediated by HDACs; results in compact chromatin and silencing. • Genomic Imprinting: ◦ Result of parent-specific methylation (e.g., Prader-Willi, Angelman syndromes).
KEY PEARLS & HIGH-YIELD POINTS¶
• Genetics vs. Genomics: Genetics = individual genes; Genomics = whole genome + environment. • SNPs vs. CNVs: SNPs are common (90% of variation, 100-300bp spacing); CNVs are larger (5-10% of genome) and affect gene dosage. • Clinical Utility of NGS: WES and WGS allow for unbiased identification of mutations in both known and unknown genes. • Allelic Heterogeneity: Different mutations within the same gene (e.g., β-globin) can result in the same clinical phenotype (e.g., thalassemia). • Trinucleotide Repeats: Specific disorders like Huntington's (CAG), Fragile X (CGG), and Friedreich's ataxia (GAA) are caused by expansions of these repeats. • Diabetes Genetics: Monogenic forms (e.g., HNF1β, GCK, KCNJ11) vs. Polygenic/Susceptibility factors (e.g., TCF7L2, PPARG).
Reference Tables¶
TABLE 479-1 Selected Databases Relevant for Genomics and Genetic Disorders SITE National Center for Biotechnology…¶
Harrison's 22e, p.3778
| SITE | URL | COMMENT |
|---|---|---|
| National Center for Biotechnology Information (NCBI) |
http://www.ncbi.nlm.nih.gov/ | Broad access to biomedical and genomic information, literature (PubMed), sequence databases, software for analyses of nucleotides and proteins Extensive links to other databases, genome resources, and tutorials |
| http://www.genome.gov/ | ||
| Catalog of Published Genome-Wide Association Studies |
https://www.ebi.ac.uk/gwas/ | Published high-resolution genome-wide association studies (GWAS) |
| http://www.ensembl.org | ||
| Online Mendelian Inheritance in Man | http://www.ncbi.nlm.nih.gov/omim | Online compendium of Mendelian disorders and human genes causing genetic disorders |
| http://www.acmg.net/ | ||
| American Society of Human Genetics | http://www.ashg.org | Information about advances in genetic research, professional and public education, and social and scientific policies |
| https://cancergenome.nih.gov/ | ||
| COSMIC Catalogue of Somatic Mutations in Cancer |
https://cancer.sanger.ac.uk/cosmic | Comprehensive catalogue of somatic mutations in human cancer |
| https://www.ncbi.nlm.nih.gov/gtr/ | ||
| Genomes Online Database (GOLD) | http://www.genomesonline.org/ | Information on published and unpublished genomes |
| http://www.genenames.org/ | ||
| GENECODE | https://www.gencodegenes.org/ | High-quality reference gene annotation and experimental validation for human and mouse genomes |
| http://www.mitomap.org/ | ||
| The International Genome Sample Resource (IGSR) |
http://www.internationalgenome.org | Public catalogue of human variation and genotype data from numerous ethnic groups |
| https://www.hgvs.org/ | ||
| ENCODE | http://www.genome.gov/10005107 | Encyclopedia of DNA Elements; catalogue of all functional elements in the human genome |
| http://www.dnalc.org/ | ||
| The Online Metabolic and Molecular Bases of Inherited Disease (OMMBID) |
http://ommbid.mhmedical.com | Online version of the comprehensive text on the metabolic and molecular bases of inherited disease |
| https://www.omia.org/home/ | ||
| The Jackson Laboratory | http://www.jax.org/ | Information about murine models and the mouse genome |
| http://www.informatics.jax.org |
TABLE 479-2 Selected Examples of Diseases Caused by Mutations and Rearrangements in Transcription Factors¶
Harrison's 22e, p.3783
| TRANSCRIPTION FACTOR CLASS |
EXAMPLE | ASSOCIATED DISORDER |
|---|---|---|
| Nuclear receptors | Androgen receptor | Complete or partial androgen insensitivity (recessive missense mutations) |
| Spinobulbar muscular atrophy (CAG repeat expansion) |
||
| WT1 | ||
| Basic helix-loop-helix | MITF | Waardenburg’s syndrome type 2A |
| IPF1 | ||
| Leucine zipper | Retina leucine zipper (NRL) |
Autosomal dominant retinitis pigmentosa |
| SRY | ||
| Forkhead | HNF4α, HNF1α, HNF1β |
Maturity onset of diabetes mellitus types 1, 3, 5 |
| PAX3 | ||
| T-box | TBX5 | Holt-Oram syndrome (thumb anomalies, atrial or ventricular septum defects, phocomelia) |
| P53 | ||
| Co-activators | CREB binding protein (CREBBP) |
Rubinstein-Taybi syndrome |
| TATA-binding protein (TBP) |
||
| Transcription elongation factor |
VHL | von Hippel–Lindau syndrome (renal cell carcinoma, pheochromocytoma, pancreatic tumors, hemangioblastomas) Autosomal dominant inheritance, somatic inactivation of second allele (Knudson two-hit model) |
| RUNX1 | ||
| Chimeric proteins due to translocations |
PML-RAR | Acute promyelocytic leukemia t(15;17)(q22;q11.2-q12) translocation |
TABLE 479-3 Selected Examples of Phenotypic Heterogeneity and Locus Heterogeneity Phenotypic Heterogeneity¶
Harrison's 22e, p.3788
| Phenotypic Heterogeneity | |||
|---|---|---|---|
| GENE, PROTEIN | PHENOTYPE | INHERITANCE | OMIM |
| LMNA, Lamin A/C | Emery-Dreifuss muscular dystrophy (AD) |
AD | 181350 |
| Familial partial lipodystrophy Dunnigan | AD | 151660 | |
| Hutchinson-Gilford progeria | AD | 176670 | |
| Atypical Werner’s syndrome | AD | 150330 | |
| Dilated cardiomyopathy 1A | AD | 115200 | |
| Familial atrial fibrillation 3 | AD | 607554 | |
| Charcot-Marie-Tooth type 2B1 | AR | 605588 | |
| Noonan’s syndrome Cardio-facio-cutaneous syndrome 1 |
AD AD |
||
| Locus Heterogeneity | |||
| PHENOTYPE | GENE | CHROMOSOMAL LOCATION | PROTEIN |
| Familial hypertrophic cardiomyopathy | MYH7 | 14q11.2 | Myosin heavy chain beta |
| Genes encoding sarcomeric proteins | TNNT2 | 1q32.1 | Troponin-T2 |
| TPM1 | 15q22.2 | Tropomyosin alpha | |
| MYBPC3 | 11p11q | Myosin-binding protein C | |
| TNNC1 | 19q13.4 | Troponin 1 | |
| MYL2 | 12q24.11 | Myosin light chain 2 | |
| MYL3 | 3p21.31 | Myosin light chain 3 | |
| TTN | 2q31.2 | Cardiac titin | |
| ACTC | 15q14 | Cardiac alpha actin | |
| MYH6 | 14q11.2 | Myosin heavy chain alpha | |
| MYLK2 | 20q11.21 | Myosin light-peptide kinase | |
| CAV3 | 3p25 | Caveolin 3 | |
| Genes encoding nonsarcomeric proteins | MT-T1 | Mitochondrial | tRNA isoleucine |
| MT-TG | Mitochondrial | tRNA glycine | |
| PRKAG2 | 7q36.1 | AMP-activated protein kinase γ2 subunit | |
| DMPK | 19q13.32 | Myotonin protein kinase (myotonic dystrophy) |
|
| FRDA | 9q21.11 | Frataxin (Friedreich’s ataxia) | |
| PKD1 PKD2 PKHD1 |
16p13.3 4q22.1 6p21.1-p12.2 |
||
| Noonan’s syndrome | PTPN11 | 12q24.13 | Protein-tyrosine phosphatase 2c |
| KRAS | 12p12.1 | KRAS |
TABLE 479-4 Indications for Cytogenetic and Cytogenomic Analysis across the Life Span¶
Harrison's 22e, p.3789
| TIMING OF TESTING | INDICATIONS FOR TESTING |
|---|---|
| Prenatal | Advanced maternal age Abnormalities on ultrasound Increased risk for genetic disorder on maternal serum screen |
| Adult | Infertility Recurrent miscarriage Familial cancer |
TABLE 479-5 Selected Trinucleotide Repeat Disorders DISEASE X-chromosomal spinobulbar muscular atrophy (SBMA) Fragile X…¶
Harrison's 22e, p.3793
| DISEASE | LOCUS | REPEAT | TRIPLET LENGTH (NORMAL/DISEASE) |
INHERITANCE | GENE PRODUCT |
|---|---|---|---|---|---|
| X-chromosomal spinobulbar muscular atrophy (SBMA) |
Xq12 | CAG | 11–34/40–62 | XR | Androgen receptor |
| Xq27.3 | CGG | 6–50/200–300 | XR | ||
| Fragile X syndrome (FRAXE) | Xq28 | GCC | 6–25/>200 | XR | FMR-2 protein |
| 19q13.32 | CTG | 5–30/200–1000 | AD, variable penetrance | ||
| Huntington’s disease (HD) | 4p16.3 | CAG | 6–34/37–180 | AD | Huntingtin |
| 6p22.3 | CAG | 6–39/40–88 | AD | ||
| Spinocerebellar ataxia type 2 (SCA2) | 12q24.12 | CAG | 15–31/34–400 | AD | Ataxin 2 |
| 14q32.12 | CAG | 13–36/55–86 | AD | ||
| Spinocerebellar ataxia type 6 (SCA6, CACNAIA) | 19p13 | CAG | 4–16/20–33 | AD | Alpha 1A voltage-dependent L-type calcium channel |
| 3p14.1 | CAG | 4–19/37 to >300 | AD | ||
| Spinocerebellar ataxia type 12 (SCA12) | 5q32 | CAG | 6–26/66–78 | AD | Protein phosphatase 2A |
| 12p13.31 | CAG | 7–23/49–75 | AD | ||
| Friedreich’s ataxia (FRDA1) | 9q21.11 | GAA | 7–22/200–900 | AR | Frataxin |
TABLE 479-6 Examples of Genes and Loci Involved in Mono- and Polygenic Forms of Diabetes DISORDER Monogenic permanent…¶
Harrison's 22e, p.3794
| DISORDER | GENES OR SUSCEPTIBILITY LOCUS | CHROMOSOMAL LOCATION |
OTHER FACTORS |
|---|---|---|---|
| Monogenic permanent neonatal diabetes mellitus |
KCNJ11 (inwardly rectifying potassium channel Kir6.2) | 11p15.1 | AD |
| GCK (glucokinase) | 7p13 | AR | |
| INS (insulin) | 11p15.5 | AR, hyperproinsulinemia | |
| ABCC8 (ATP-binding cassette, subfamily c, member 8; sulfonylurea receptor) | 11p15.1 | AD or AR | |
| GLIS3 (GLIS family zinc finger protein 3) | 9p24.2 | AR, diabetes, congenital hypothyroidism |
|
| HNF4α (hepatocyte nuclear factor 4α) GCK (glucokinase) HNF1α (hepatocyte nuclear factor 1α) IPF1 (insulin receptor substrate) HNF1β (hepatocyte nuclear factor 1β) NeuroD1 (neurogenic differentiation factor 1) KLF1 (Kruppel-like factor 1) CEL (carboxyl ester lipase) PAX4 (paired box transcription factor 4) INS (insulin) BLK (B-lymphocyte-specific tyrosine kinase) ABCC8 (ATP-binding cassette, subfamily c, member 8; sulfonylurea receptor) KCNJ11 (inwardly rectifying potassium channelKir6.2) |
20q13.12 7p13 12q24.31 13q12.2 17q12 2q31.3 19p13.13 9q34.13 7q32.1 11p15.5 8p23.1 11p15.1 11p15.1 |
||
| Diabetes mellitus type 2; loci and genes linked and/or associated with susceptibility for diabetes mellitus type 2 |
Genes and loci identified by linkage/association studies | Heavily influenced by diet, energy expenditure, obesity |
|
| PPARG, KCNJ11/ABCC8, TCF7L2, HNF1B, WFS1, SLC30A8, FTO, HHEX, IGF2BP2, CDKN2A/B, CDKAL1, TSPAN8, ADAMTs9, CDC123/CAMK1D, JAZF1, NOTCH2, THADA, KCNQ1, DUSP8, MTNR1B, IRS1, SPRY2, SRR, ZFAND6, GCK, KLF14, TP53INP1, PROX1, PRC1, BCL11A, ZBED3, RBMS1, HNF1A, DGKB/ TMEM195, CCND2, C2CD4A/C2CD4B, PTPRD, ARAP1/CENTD2, HMGA2, TLE4/ CHCHD9, ADCY5, UBE2E2, DUSP9, GCKR, COBLL1/GRB14, HMG20A, VPS26A, ST6GAL1, AP3S2, HNF4A, BCL2, LAMA1, GIPR, MC4R, TLE1, KCNK16, ANK1, KLHDC5, ZMIZ1, PSMD6, FITM2/R3HDML/HNF4A, CILP2, ANKRD55, GLIS3, PEPD, GCC1/PAX4, ZFAND3, MAEA, BCAR1, RBM43/RND3, MACF1, RASGRP1, GRK5, TMEM163, SGCG, LPP, FAF1, TMEM154, MPHOSPH9, ARL15, POU5F1/ TCF19, SSR1/RREB1, HLA-B, INS-IGF2, GPSM1, LEP, SLC16A13, PAM/PPIP5K2, SLC16A11, CCDC63, C12orf51, CCND2, HNF1A, TBC1D4, CCDC85A, INAFM2, ASB3, FAM60A, ATP8B2, MIR4686, MTMR3, DMRTA1, SLC35D3, GLP2R, GIP, MAP3K11, PLEKHA1, HSD17B12, NRXN3, CMIP, ZZEF1, MNX1, ABO, ACSL1, HLA-DQA1 |
TABLE 479-7 Genetic Approaches for Identifying Disease Genes METHOD Linkage Studies Classical linkage analysis Analysis…¶
Harrison's 22e, p.3797
| METHOD | INDICATIONS AND ADVANTAGES |
LIMITATIONS |
|---|---|---|
| Linkage Studies | ||
| Classical linkage analysis (parametric methods) |
Analysis of monogenic traits |
Difficult to collect large informative pedigrees |
| Suitable for genome scan | Difficult to obtain sufficient statistical power for complex traits |
|
| Control population not required |
||
| Useful for multifactorial disorders in isolated populations |
||
| Suitable for identification of susceptibility genes in polygenic and multifactorial disorders |
||
| Affected sib and relative pair analyses |
Suitable for genome scan | Difficult to obtain sufficient statistical power for complex traits |
| Control population not required if allele frequencies are known Statistical power can be increased by including parents and relatives |
||
| Association Studies | ||
| Case-control studies | Suitable for identification of susceptibility genes in polygenic and multifactorial disorders |
Requires large sample size and matched control population |
| Suitable for testing specific allelic variants of known candidate loci |
||
| Transmission disequilibrium test (TDT) |
Facilitated by comprehensive catalogs of genotypes and variants |
Candidate gene approach does not permit detection of novel genes and pathways |
| Does not necessarily need relatives |
||
| Next-Generations Sequencing Technologies | ||
| Whole exome or genome sequencing |
Unbiased approach, analysis can be performed without reference sequences from parents or siblings |
Requires appropriate bioinformatics, may have low sensitivity if CNV analysis is not included, detects numerous VUS, can lead to the detection of unrelated deleterious alleles |
| Captures multiple candidate genes and loci with hybridization techniques followed by deep sequencing |