ABSTRACT
Infectious and inflammatory diseases have repeatedly shown strong genetic associations within the major histocompatibility complex (MHC); however, the basis for these associations remains elusive. To define host genetic effects on the outcome of a chronic viral infection, we performed genome-wide association analysis in a multiethnic cohort of HIV-1 controllers and progressors, and we analyzed the effects of individual amino acids within the classical human leukocyte antigen (HLA) proteins. We identified >300 genome-wide significant single-nucleotide polymorphisms (SNPs) within the MHC and none elsewhere. Specific amino acids in the HLA-B peptide binding groove, as well as an independent HLA-C effect, explain the SNP associations and reconcile both protective and risk HLA alleles. These results implicate the nature of the HLA-viral peptide interaction as the major factor modulating durable control of HIV infection.
Subject(s)
Antigen Presentation , Genes, MHC Class I , HIV Infections/genetics , HIV Infections/immunology , HIV-1 , HLA-B Antigens/genetics , Black or African American/genetics , Alleles , Amino Acids/physiology , CD8-Positive T-Lymphocytes/immunology , Cohort Studies , Disease Progression , Genome-Wide Association Study , HIV Antigens/immunology , HIV Infections/ethnology , HIV Infections/virology , HIV Long-Term Survivors , HIV-1/immunology , HLA-A Antigens/chemistry , HLA-A Antigens/genetics , HLA-A Antigens/immunology , HLA-A Antigens/metabolism , HLA-B Antigens/chemistry , HLA-B Antigens/immunology , HLA-B Antigens/metabolism , HLA-C Antigens/chemistry , HLA-C Antigens/genetics , HLA-C Antigens/immunology , HLA-C Antigens/metabolism , Haplotypes , Hispanic or Latino/genetics , Humans , Immunity, Innate , Logistic Models , Models, Molecular , Polymorphism, Single Nucleotide , Protein Conformation , Viral Load , White People/geneticsABSTRACT
PURPOSE: The study purpose was to describe the use of natural language processing (NLP) and online analytic processing (OLAP) for assessing patterns in recommendations in unstructured radiology reports on the basis of patient and imaging characteristics, such as age, gender, referring physicians, radiology subspecialty, modality, indications, diseases, and patient status (inpatient vs outpatient). MATERIALS AND METHODS: A database of 4,279,179 radiology reports from a single tertiary health care center during a 10-year period (1995-2004) was created. The database includes reports of computed tomography, magnetic resonance imaging, fluoroscopy, nuclear medicine, ultrasound, radiography, mammography, angiography, special procedures, and unclassified imaging tests with patient demographics. A clinical data mining and analysis NLP program (Leximer, Nuance Inc, Burlington, Massachusetts) in conjunction with OLAP was used for classifying reports into those with recommendations (I(REC)) and without recommendations (N(REC)) for imaging and determining I(REC) rates for different patient age groups, gender, imaging modalities, indications, diseases, subspecialties, and referring physicians. In addition, temporal trends for I(REC) were also determined. RESULTS: There was a significant difference in the I(REC) rates in different age groups, varying between 4.8% (10-19 years) and 9.5% (>70 years) (P <.0001). Significant variations in I(REC) rates were observed for different imaging modalities, with the highest rates for computed tomography (17.3%, 100,493/581,032). The I(REC) rates varied significantly for different subspecialties and among radiologists within a subspecialty (P < .0001). For most modalities, outpatients had a higher rate of recommendations when compared with inpatients. CONCLUSION: The radiology reports database analyzed with NLP in conjunction with OLAP revealed considerable differences between recommendation trends for different imaging modalities and other patient and imaging characteristics.
