Using Support Vector Machines to gain insights into the genetic aetiology of Schizophrenia

Background

Schizophrenia is a debilitating psychiatric disorder which affects approximately 1% of the general population. Results from twin studies suggest that genetic factors account for 80% of the variation of the disorder. However, to date, these have not been fully identified. Traditional methods involve performing a Genome Wide Association Study (GWAS), to find estimates of association that different mutations in DNA have with the disease.

Schizophrenia is a highly polygenic disorder, meaning that it arises from a combination of genetic mutations, all contributing a small level of effect. These studies require very large sample sizes to have the statistical power to identify mutations of interest.

The common approach is to combine this information into a single polygenic risk score for an individual, which can then be assessed for its predictive power to identify cases of the disease using a single feature Logistic Regression model.

The machine learning approach

Instead of creating this single score, different features from the individual association scores were created for use as inputs to machine learning algorithms. These features were either the information from the individual mutations themselves, or collections of mutations in genes which are known to be involved in various functional networks.

The algorithm chosen was the Support Vector Machine (SVM). This was for a number of reasons:

• They can cope with a large number of input features, and can apply penalty procedures such as L1 regression to identify important features.
• Kernel methods can be used to find evidence for interactions between the features - something that is lacking in the traditional methods.

Python libraries used

This work was made possible by the use of two seminal libraries for Python: Pandas and scikit-learn. In addition the Joblib modules were used to carry out some simple parallel processing tasks on an HPC cluster if the desired information was not already provided by the modules in scikit-learn which already have these built in.

Pandas made collecting information from sub-sets of the data very quick and efficient to carry out. Examples of this functionality will be given in the talk.

Results so far

While not improving on predictive power, the models did provide a rich insight into the association that different gene-networks have with the disorder, and identified genes which are targets of the Fragile X Mental Retardation Protein (FMRP) supporting findings from recent research in molecular biology. The information showing this were the coefficients assigned to the features seen in the boxplot provided. This was created by building multiple models, each with different train/test splits of the data to get the distributions of the coefficients. As can be seen, those assigned to the FMRP feature are consistently higher.

The findings also show evidence for pair wise interactions between single point mutations. A series of procedures were carried out to simulate positive cases based on differing levels of contributions from main effects and interactions. This showed that without the contribution of the interactions, the results seen in the real world dataset by the kernel based models would not have been possible. Full details of how this was carried out will be discussed.