Robette-et-al_Genetica_2022_Heritability-What's-the-point-What-is-it-not-for-A-human-genetics-perspective

Heritability in Human Genetics

Definition and Significance

  • Heritability: Quantifies genetic contribution to phenotypic variation in individuals.

  • Used to analyze various human traits: quantitative (physical, cognitive) and binary (disease presence).

  • Direct analysis of education and socio-economic status in genetic studies, calling into question previous assumptions.

  • Importance in avoiding misinterpretation leading to genetic determinism in traits.

Historical Context

  • Origin traced back to Ronald Fisher (1918) and subsequent developments in quantitative genetics.

  • Fisher introduced variance decomposition into genetic and environmental components, fundamental to heritability.

  • Variance Components:

    • P (phenotypic variance) = G (genotypic variance) + E (environmental variance).

    • Heritability (H) expressed as the ratio between genetic variance and phenotypic variance.

Heritability Models and Methods

  • Polygenic Model: Basis of heritability; traits influenced by many alleles with small effects.

  • Narrow-Sense Heritability: Refers to additive genetic variance, significant for predicting responses to selection.

Issues with Heritability Estimates

  • Often misused in human genetics leading to questionable interpretations.

  • Methods developed to estimate heritability using genetic data from Genome-Wide Association Studies (GWAS).

  • GWAS show that heritability can vary greatly from estimates from twin studies, leading to a dilemma termed "missing heritability".

Advancements in Heritability Estimates

Recent Technological Developments

  • Genome characterization has enabled computation of heritability from large datasets.

  • Use of Single Nucleotide Polymorphisms (SNPs) and genetic markers to predict heritable traits has intensified.

Types of Studies

  • Twin Studies: Used to identify genetic vs. environmental influences by comparing monozygotic (MZ) and dizygotic (DZ) twins.

  • Heritability estimates from twin studies have been foundational yet continue to face contention over their assumptions.

Educational Implications

  • Previous studies suggested heritability of intelligence (IQ) estimates were high, often promoting deterministic views on intelligence across social groups.

  • Research in behavioral genetics is burgeoning, looking at traits such as personality and mental health disorders using similar heritability estimates.

Misinterpretations and Limitations

Conceptual Confusion

  • Heritability should not reflect genetic determinism; it's a population measure, not an individual characteristic.

  • Errors in interpretation arise from conflating heritability with inheritance; they are distinct concepts.

  • High heritability does not imply that genetic factors are the primary causes of traits, especially in environmentally homogeneous conditions.

Assumptions in Heritability Models

  • Models rely on the assumption that many small effects contribute to a trait without major contributors.

  • Assumes no interactions between genetic and environmental factors, often untrue in complex human traits.

  • Requires random environments which do not hold in human social settings, causing biases in estimates.

Polygenic Risk Scores (PRS)

Overview

  • PRS calculated using SNPs aiming to quantify individual risk for diseases based on genetics.

  • However, PRS are also subject to limitations similar to heritability calculations, particularly in their assumptions of genetic variance.

Concerns with PRS

  • Often misconstrued as equivalent to genetic liability, when in fact they measure relative rather than absolute risks for diseases.

  • Validity of PRS highly context-dependent, influenced by age, environment, and socio-economic factors.

Conclusion

  • Given the extensive criticisms laid upon the heritability concept and its misapplications in human genetics, a reframing is essential.

  • Calls for moving beyond traditional heritability estimates to include broader genetic models like omnigenic models that account for connections among various genes and their functions.

Omnigenic Models: These models represent a modern approach in genetics that acknowledges the complexity of human traits influenced by numerous genes across the genome.

  • Definition: Unlike traditional models that focus solely on specific genes, omnigenic models propose that many unannotated and distant gene variants can contribute to trait variation, underscoring the interconnectedness of genes.

  • Significance: They aim to provide a more comprehensive understanding of genetic architecture, particularly for complex traits and common diseases.

  • Mechanism: It suggests that gene interactions can have varying effects across different biological pathways and environmental contexts.

  • Research Implications: This model encourages the exploration of broader genetic influences and helps to improve genetic prediction models and personalized medicine approaches.