Predicting Sarcoidosis Disease Incidence using Single Nucleotide Polymorphisms and Supervised Machine Learning

Predicting disease incidence based on Single Nucleotide Polymorphisms (SNPs) for a complex multi-factorial disease like sarcoidosis remains a difficult prediction problem. If disease prediction could be improved, genetic screening could be implemented to assist identifying disease early, potentially improving patient outcomes. In this thesis, we examine the predictive performance of several supervised machine learning models to assess if genetic variability can be used to accurately predict disease incidence in an African American patient population (n = 2,915). Further, we consider the use of SNP “functional scores” such as Combined Annotation Dependent Deletion (CADD) scores and FATHMM-XF scores to see if they can improve predictive ability. Here we show that support vector machine (SVM), and random forest (RF) models can significantly outperform the naïve baseline model (p < 0.05) in terms of accuracy and achieve area under the ROC curve (AUC) values of 0.6016 and 0.6019, respectively. A neural network (NN) model had the optimal AUC value of 0.6103 but was slightly non-significant (p = 0.05) when compared to the naïve model in terms of accuracy. The overall impact of adding functional scores was minimal to negative on predictive performance. This work reveals that supervised machine learning based on SNPs can significantly outperform random chance when predicting sarcoidosis incidence and supports the idea that genetic screening and disease modeling prior to disease incidence could improve preventative care

Novel HLA associations with outcomes of Mycobacterium tuberculosis exposure and sarcoidosis in individuals of African ancestry using nearest-neighbor feature selection

Tuberculosis and sarcoidosis are inflammatory diseases characterized by granulomas that may occur in any organ but are often found in the lung. The panoply of classical human leukocyte antigen (HLA) alleles associated with occurrence and/or severity of both diseases varies considerably across studies. This heterogeneity of results, due to variation in factors like ancestry and disease subphenotype, as well as the use of simple modeling strategies to elucidate likely complex relationships, has made conclusions about underlying commonalities difficult. Here we perform HLA association analyses in individuals of African ancestry, using a greater resolution to include subphenotypes of disease and employing more comprehensive analytical techniques. Using a novel application of nearest-neighbor feature selection to score allelic importance, we investigated HLA allele association with Mycobacterium tuberculosis exposure outcomes in the first analysis of both latent Mycobacterium tuberculosis infection and active disease compared with those who, despite long-term exposure to active index cases, have neither positive diagnostic tests nor display clinical symptoms. We also compared persistent to resolved sarcoidosis. This led to the identification of novel HLA associations and evidence of main effects and interaction effects. We found strikingly similar main effects and interaction effects at HLA-DRB1, -DQB1, and -DPB1 in those resistant to tuberculosis (either latent or active) and persistent sarcoidosis