Institute for Response-Genetics (e.V.)Chairman: Prof. Dr. Hans H. StassenPsychiatric Hospital (KPPP), University of Zurich |
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From Genotype to PhenotypeStandard (logistic) regression connects genotype with phenotype in a direct way, thus greatly simplifying biology. In fact, genes code for proteins or RNA ("gene products") which may interact in a variety of ways and influence the phenotype only after a cascade of intermediate steps. Molecular-genetic Neural Nets (NNs) generalize standard regression analysis in a very natural way by (1) implementing multistage gene products through one or more intermediate "layer(s)", and (2) allowing for nonlinear interactions between genes and gene products. Molecular-Genetic Neural NetsIt is the advantage of NNs that the specific knowledge about the cascade of intermediate steps, which ultimately lead from genotype to phenotype, can be incomplete or even unknown ("hidden layers"). In this case, the model’s gene product layers lack direct interpretation and act in the sense of a "black box". However, the influence of each single gene on the phenotype, as well as the interactions between genes, can always be quantified and detailed through analysis of the weight matrices of the fitted model. In the simplest form, molecular-genetic NNs connect each gene with its gene product, while these gene products contribute to observed phenotype, for example, IgM level, affectedness (diagnosis), or response to treatment. Backpropagation AlgorithmNN connects the "neurons" of input and output layers via one or more "hidden" layers. All outputs are computed using sigmoid thresholding of the scalar product of the corresponding weight and input vectors. Outputs at stage "s" are connected to each input of stage "s+1". NN connections are realized through (1) weight matrices and (2) model fitting algorithms minimizing an error function in the weight space (goodness of fit). The most popular fitting strategy, the backpropagation algorithm, looks for the minimum of the error function using the method of gradient descent: 
Selecting an Initial ConfigurationIn principal, NNs may be used for selecting genes (SNPs) out of a pool of candidates. The computational burden of such an approach can become unrealistic for larger data sets, in particular when reproducibility has to be tested through k-fold cross-validation. On the other hand, molecular-genetic NNs possess a sufficient performance when an initial gene configuration is available —either through a priori knowledge or derived through other methods— so that the initial configuration can be optimized by systematically adding or removing genes. References
Stassen HH, Bridler R, Hell D, Weisbrod M, Scharfetter C: Ethnicity-independent genetic basis
of functional psychoses. A Genotype-to-phenotype approach. Am J Med Genetics B 2004; 124:
101-112
Berger M, Stassen HH, Köhler K, Krane V, Mönks D, Wanner C, Hoffmann K, Hoffmann MM, Zimmer M,
Bickeböller H, Lindner TH: Hidden population substructures in an apparently homogeneous
population bias association studies. Eur J Hum Genetics 2006; 14: 236-244
Stassen HH, Szegedi A, Scharfetter C: Modeling Activation of Inflammatory Response System.
A Molecular-Genetic Neural Network Analysis. BMC Proceedings 2007, 1 (Suppl 1): S61, 1-6
Stassen HH, Hoffmann K, Scharfetter C: The Difficulties of Reproducing Conventionally Derived
Results through 500k-Chip Technology. BMC Genet Proc. 2009; 3 Suppl 7: S66
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Molecular-genetic Neural Nets may connect multiple genetic factors, as observed in each individual patient, through a layer of gene products to a one-dimensional phenotype, for example, IgM level, within-pair concordance of monozygotic twins, or time to response to treatment under consideration of interactions between all gene products. The model can easily be generalized to multidimensional phenotypes, for example, the syndrome patterns underlying schizophrenic or bipolar illness. |
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