title: An Object-Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes.
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.
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington, United States of America.
authors.forename: Lue Ping
authors.initials: LP
authors.lastname: Zhao
authors.affiliation: Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Hamid
authors.initials: H
authors.lastname: Bolouri
authors.affiliation: Bellevue High School, Seattle, Washington, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Zhao
authors.affiliation: Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Geraghty
authors.affiliation: Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden.
authors.forename: Åke
authors.initials: Å
authors.lastname: Lernmark
mesh_terms: D000483:Alleles; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics*; D055106:Genome-Wide Association Study; D005838:Genotype; D006680:HLA Antigens / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008957:Models, Genetic; D015203:Reproducibility of Results
chemical_list: D006680:HLA Antigens
keywords: generalized linear model; kernel machine; multiallelic genotypes; penalized regression; prediction; similarity regression; statistical learning