Quantitative Genetics 1: Fundamentals and History

Study Notes

Pre-history: Agriculture, animal breeding, early understanding of heredity
Ancient civilizations: Basic understanding of inheritance and bloodlines.
1866: Gregor Mendel's experiments with pea plants, foundational work in genetics, known as the father of modern genetics
1859: Charles Darwin's "On the Origin of Species", theory of evolution, natural selection
1875: Francis Galton, first behavioural geneticist, significant work on the statistics of regression and correlation for understanding inheritance, studied quantitative traits in peas
1918: Ronald Fisher, combined Mendelian principles with biometrics, fundamental work on multiple-gene models of complex traits
1920s: Sewall Wright, work on genetic drift and adaptation
1953: Watson, Crick, and Franklin discovered the structure of DNA
1980s: Linkage studies, identifying genes linked to single-gene disorders
1990s & 2000s: Human genome project, genomic studies, genome-wide association studies (GWAS), polygenic scores
2010s and Beyond: Increased power and sophistication in GWAS; challenges of interpreting complex trait data

Mendel experimented with pea plants to understand inheritance patterns
Pea plants have a rapid life cycle, easily cross-bred, and can self-fertilize
Mendel studied seven qualitative traits, showing they are controlled by single genes
Law of segregation: Each gene has two alleles; one allele is passed from each parent to offspring
Law of dominance: Alleles can be dominant or recessive, with the dominant allele masking the recessive one in heterozygotes
Law of independent assortment: Different traits are inherited independently of each other, unless the genes are located on the same chromosome

Twin studies compare MZ (identical) and DZ (fraternal) twins to estimate heritability.
Heredity and the proportion of variance explained by genetic variation within populations.
MZ twins share 100% genes, and DZ twins share 50% genes
Falconer's formula: Used to estimate the relative importance of genetic and environmental effects on a trait
rMZ = A + C and rDZ = .5A + C
A: Additive genetic effects
C: Common/shared environment effects
E: Non-shared environmental effects

Hypothesis-free approach relating genetic variation across the genome with a trait
Focus on identifying common Single Nucleotide Polymorphisms (SNPs)
Millions of SNPs to be assessed, require big dataset analysis
Millions of tests need significant p-value (<5x10⁻⁸) correction
Key in identifying genes that influence complex traits (diseases), but with a modest effect size on traits, often with limited power

Calculated using GWAS summary statistics
Combine thousands of genetic variants, weighted for their effect sizes
Used for individualised risk scores of traits or diseases
Addressing traditional questions like gene-environment interactions, developmental aspects

Gap between variance explained by GWAS and twin estimates of heritability
Includes contributions of rare variants, complex gene-gene interactions, environment factors (eg GxG)
Further research needed to pinpoint the 'missing' genetic components contributing to complex traits and diseases.

Estimating heritability from GCTA (Genome-Wide Complex Trait Analysis)
Addresses limitations of GWAS approaches, by analysing variants across the whole genome
GCTA is useful for estimating and partition of complex trait variation with large GWAS data sets