Article

Modeling activation of inflammatory response system: a molecular-genetic neural network analysis.

Psychiatric University Hospital, P,O, Box 1931, CH-8032 Zurich, Switzerland.
BMC proceedings 02/2007; 1 Suppl 1:S61. DOI: 10.1186/1753-6561-1-s1-s61
Source: PubMed

ABSTRACT Significant alterations of T-cell function, along with activation of the inflammatory response system, appear to be linked not only to treatment-resistant schizophrenia, but also to functional psychoses and mood disorders. Because there is a relatively high comorbidity between rheumatoid arthritis (RA), schizophrenia and major depression, the question arises whether there is a common, genetically modulated inflammatory process involved in these disorders. On the basis of three family studies from the U.S. and Europe which were ascertained through an index case suffering from RA (599 nuclear families, 1868 subjects), we aimed to predict the inter-individual variation of autoantibody IgM levels, as an unspecific indicator of inflammatory processes, through molecular-genetic factors. In a three-stage strategy, we first used nonparametric linkage (NPL) analysis to construct an initial configuration of genomic loci showing a sufficiently high NPL score in all three populations. This initial configuration was then modified by iteratively adding or removing genomic loci such that genotype-phenotype correlations were improved. Finally, neural network analysis (NNA) was applied to derive classifiers that predicted the phenotype from the multidimensional genotype. Our analysis led to an activation model that predicted individual IgM levels from the subjects' multidimensional genotypes very reliably. This allowed us to use the activation model for an analysis of the DNA of an existing sample of 1003 psychiatric patients in order to test, in a first approach, whether a deviant, genetically modulated inflammatory process is involved in the pathogenesis of major psychiatric disorders.

0 Bookmarks
 · 
81 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.
    Genetic Epidemiology 02/2007; 31 Suppl 1:S51-60. · 4.02 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: ABSTRACT : Based on a "training" sample of 1,042 subjects genotyped for 5,728 single-nucleotide polymorphisms (SNPs) of a conventional 0.4-Mb genome scan and a "test" sample of 746 subjects genotyped for 545,080 SNPs on a 500k-chip, we investigated the extent to which the subjects' immunoglobulin M levels can be reproducibly predicted from a multilocus genotype. We were specifically interested in the reproducibility of predictors across populations (1,042 versus 746 subjects) and across SNP sets (conventional genome scan versus anonymous 500k-chip) because this is a prerequisite for clinical application. For the training sample, neural network (NN) analysis yielded classifiers that predicted immunoglobulin M levels from the subjects' multilocus genotypes at acceptable error rates through a configuration of 15 genomic loci (61 SNPs). With the test sample (746 subjects) we addressed the question of reproducibility across populations and across SNP sets by means of a novel "competitive SNP set" approach. However, the chip data contained several sources of distortion, including greatly elevated noise levels and artifact-prone SNP regions, thus complicating attempts to verify the reproducibility of NN predictors. Though 5 of 15 genomic loci from the training samples appeared to be reproducible, the NN classifiers derived so far from the test samples are insufficiently compatible with the training samples. Nonetheless, our results are promising enough to justify further investigations. Because the underlying algorithm can easily be split into parallel tasks, the proposed "competitive SNP set" approach has turned out to be well suited for computers with today's 64-bit multiprocessor architectures and to offer a valuable extension to genome-wide association analyses.
    BMC proceedings 01/2009; 3 Suppl 7:S66.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Treatment with antidepressants and antipsychotics, though effective, is unspecific as agents that differ greatly in their biochemical and pharmacological actions have virtually the same efficacy. Half of the patients with initial improvement show incomplete response, while a large proportion of patients exhibit a refractory clinical picture which is resistant to all treatment modalities. Our analyses were based on a reference study of 2,848 depressive inpatients under monotherapeutic treatment with 7 different antidepressants or placebo, along with a naturalistic study of depressive and schizophrenic patients (296 inpatients, 363 outpatients) under today's "standard" polypharmaceutic treatment regimens. The empirical data suggested the following predictors of response: (1) severity at baseline, (2) early onset of improvement, (3) unwanted side-effects, and (4) medical comorbidity. A combination of these predictors with Therapeutic Drug Monitoring (TDM) methods has direct clinical relevance. Evidence-based approaches to personalized treatment help improving the unsatisfactory situation patients and clinicians are faced with, given today's incomplete treatments and the fact that the mechanisms by which antidepressants and antipsychotics ultimately exert their therapeutic effects are only marginally understood.
    Pharmacopsychiatry 09/2011; 44(6):263-72. · 2.11 Impact Factor

Full-text (2 Sources)

Download
1 Download
Available from