Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Friday, March 01, 2019

Testing the assumptions of parametric linear models: the need for biological data mining in disciplines such as human genetics

This editorial is in response to some claims that an observed linear relationship between relative pair trait correlation and IBD genetic sharing is indicative of a simple additive genetic architecture dominated by independent genetic effects. As we show here, you could observe this pattern under a genetic architecture dominated by epistasis.

Moore JH, Mackay TFC, Williams SM. Testing the assumptions of parametric linear models: the need for biological data mining in disciplines such as human genetics. BioData Min. 2019 Feb 11;12:6. [PubMed] [BioData Mining]

Abstract

All data science methods have specific assumptions that are made in order for their inferences to be valid. Some assumptions impact statistical significance testing and some influence the models themselves. For example, a fundamental assumption of linear regression is that the relationship between the independent and dependent variables is additive such that a unit increase in one leads to a unit increase in the other with some error that can be modeled using a normal distribution. The presence of a nonlinear relationship between the variables violates this assumption and can lead to inaccurate inferences. We demonstrate this here using a simple example from human genetics and then end with some thoughts about the role of biological data mining in revealing nonlinear relationships between variables.


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