Causal models for investigating complex genetic disease: II. What causal models can tell us about penetrance for additive, heterogeneity, and multiplicative two-locus models

摘要

Background/Aims: Statistical geneticists commonly use certain two-locus penetrance models because these models are familiar and mathematically tractable. We investigate whether and under what circumstances these two-locus penetrance models correspond to models of causation . Methods: We describe a sufficient component cause model for a hypothetical disease with two genetic causes. We then use the potential outcomes framework to determine the expected two-locus penetrances from this causal model and contrast them with commonly used two-locus penetrance models (additive, heterogeneity, and multiplicative penetrance models, as formulated by Risch [Am J Hum Genet 1990; 46: 222-228]). Results: Conventional additive and multiplicative models can correspond to any two-locus causal model only when certain very specific algebraic relationships hold. The heterogeneity model corresponds to a twolocus causal model only if the model stipulates that no disease cases are caused by the combined presence of the caus-al genotypes at both loci (i.e. only when there is no causal gene-gene interaction). Hence the heterogeneity model provides a valid test of the null hypothesis of no gene-gene interaction, whereas the additive and multiplicative models do not. Conclusion: We suggest that causal principles should provide the basis for statistical modeling in genetics.