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Institute for Response-Genetics (e.V.)

Prof. Dr. Hans H. Stassen, Chairman

(Formerly Associated Institute of the University of Zurich)

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Master.GEN Version 7.3

Linking Multi-Locus Genotype Data to Complex Phenotypes

In order to meet the requirements of complex molecular-genetic studies, we have developed this program package Master.GEN which is based on the experiences from our previous systems Master.EEG for investigations into the structural properties of brain wave patterns, and Master.VOX for investigations into the nonverbal aspects of human speech. For easy use, Master.GEN has been built around a databank, thus facilitating the storage and retrieval of genotype and phenotype data at the different stages of analysis. As a key feature, this program package supports the structural decomposition of genetic diversity by means of adaptive algorithms. Standard statistical analyses, on the other hand, are accessed by interfacing to generally available programs, such as the statistical packages SAS and SPSS, amongst others.

Support for Adaptive Strategies

Once oligogenic configurations of genomic loci have been detected, Neural Network Analysis (NNA) provides powerful tools for modeling pre-specified responses to complex, multidimensional input stimuli. Thus, a classification of patients treated with antidepressants or antipsychotics into early/late/non-responders, for example, can be accomplished using oligogenic configurations of candidate genes as input data. It is the specific advantage of NNA that no causal relationship between stimuli and responses is required, so that experienced users can fit virtually any set of nondegenerate stimuli to any set of responses, provided a sufficiently large and representative set of learning probes is available.

 

Data Retrieval

  • MSELECT  Select Markers
  • CSELECT  Select Cases
  • GSELECT  Subdivides Samples Selected by CSELECT into Subgroups

Consistency of Genotype Data

  • ERRORS  Redundancies, Errors and Quality Control
  • FAMILIES  Test Genetic Consistency Within Families
  • MISSING  Replace Missing Genotypes Within Families

Basic Statistics

  • HETERO  Genetic Diversity Across Genomic Loci
  • MRANGE  Intervals of Marker Allele Sizes
  • FREQ  Frequency Distributions of Markers
  • FREQ2  Allele Distributions from Genotype Data
  • CHISQ  Chi-Square Tests Between Allele Distributions
  • RANDOM  Generating Random Permutations of Numbers 1,2,... nobs
  • SAMPLE  Random Splitting of Samples into Subsamples
  • POWER  Estimation of Statistical Power by Simulation

Principal Components of Genotype Data

  • VECTORS  Set-up of Genotype Feature Vectors, Principal Components
  • CORR  Correlation Analysis, Scatter Plots

Genetic Similarity/Diversity

  • SIMI  Genetic Distances, Similarities and Concordances
  • PSIM  Genetic Similarity Within and Between Populations
  • FSIM  Genetic Similarity Within Families
  • COMPARE  Genetic Similarity: Systematic Genome Scans
  • SEARCH  Search for Subspaces of a Genetic Vector Space
  • RSIM  Similarity Matrices from Feature Vectors
  • OPTI  Iterative Optimization of Feature Vectors (Haplotypes)
  • SEARCH2  Iterative Maximization of Between-Group Differences

Simulation of Genotype Data

  • GENERATE  Generate Genotype Data from Allele Distributions
  • INSTAB  Instability of k-Nucleotide Repeats

Structural Analyses

  • NEURO  Neural Nets for Genotype-Phenotype Associations
  • MATRIX  Matrix of Genotype Feature Vectors
  • CLUSTER  Cluster Analyses
  • CLUSINTR  Interpretation of Clusters in Terms of Basic Features
  • DISCR  Multiple Linear Discriminant Analysis
  • DISCRTST  Performance Test of Discriminant Functions
  • KYSTPLUS  Metric/Nonmetric Multidimensional Scaling
  • PCOMP  Principal Component Analysis

 

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Molecular-Genetic Neural Net
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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