An Object‐Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes. Issue 4 (May 2016)

• Record Type: Journal Article
• Authors: Zhao, Lue Ping; Bolouri, Hamid; Zhao, Michael; Geraghty, Daniel E.; Lernmark, Åke
• Is Part Of: Genetic epidemiology. Volume 40:Issue 4(2016)
• Page Start: 315
• Page End: 332
• Publication Date: 2016-05
• Subjects: generalized linear model -- kernel machine -- multiallelic genotypes -- penalized regression -- prediction -- similarity regression -- statistical learning; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21968
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: Recent genome‐wide association studies confirm that human leukocyte antigen (HLA) genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA‐based disease predictive model. However, conventional model‐building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as "objects" or "exemplars, " one focuses on systemic associations of disease phenotype with "objects" via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease‐associated genotypes or alleles. Effectively, it transforms large, discrete, and sparse HLA genotypes into a matrix of similarity‐based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease‐associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA‐DRB1, HLA‐DRB3, HLA‐DRB4, HLA‐DRB5, HLA‐DQA1, HLA‐DQB1, HLA‐DPA1, and HLA‐DPB1) to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set, indicating that this methodology is useful to build predictive models with complex HLA genotypes.