Institute for Response-Genetics (e.V.), University of Zurich

Assessing Deviations from "Normality"

For traits displaying a norm of variation rather than a fixed feature it is generally accepted that there is a clear need for normative data in order to distinguish on the phenotype level between "random" fluctuations and "significant" deviations. Much less known is the fact that hidden population substructure on the genotype level can substantially reduce the statistical power for linking a given trait to genomic loci through standard linkage or association techniques. The respective results can even be misleading if the observed case-control differences originate to a larger extent from unknown population stratification. One has to deal with similar problems when narrowing down on linkage results by means of an association design.

Biological Ethnicity

Our quantitative approach to ethnicity is based on the genotypic differences in genetic similarity between individuals. The genetic similarity between first-degree relatives is one half, and the genetic similarity between subjects of the same ethnic group is higher than that between subjects of different ethnic groups. Thus, it becomes possible to structurally decompose an ethnically diverse population into genetically homogeneous subgroups. Principal component analysis then leads to a representation of the multidimensional feature vectors in such a way that the (orthogonal) axes of the transformed vector space optimally account for the variance of the underlying features ("genetic diversity").

Ethnicity-Independent Features

Using data from 15,000 ethnically diverse subjects along with specifically selected polymorphisms, we have constructed a "normative" genetic vector space which not only allows us to classify subjects according to their "biological ethnicity" but also enables a distiction between (1) ethnicity-specific features — such as unwanted side effects under psychotropic drug treatment — and (2) ethnicity-independent effects — such as response to psychotropic drug treatment.

References

Stassen HH, Bridler R, Hägele S, Hergersberg M, Mehmann B, Schinzel A, Weisbrod M, Scharfetter C: Schizophrenia and smoking: evidence for a common neurobiological basis? Am J Med Genetics B 2000; 96: 173-177

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

Tadic A, Rujescu D, Muller MJ, Kohnen R, Stassen HH, Dahmen N, Szegedi A: A monoamine oxidase B gene variant and short-term antidepressant treatment response. Prog Neuropsychopharmacol Biol Psychiatry. 2007; 31(7): 1370-1377

Tadic A, Muller MJ, Rujescu D, Kohnen R, Stassen HH, Dahmen N, Szegedi A: The MAOA T941G polymorphism and short-term treatment response to mirtazapine and paroxetine in major depression. Am J Med Genet B Neuropsychiatr Genet. 2007; 144(3): 325-331

Tadic A, Rujescu D, Dahmen N, Stassen HH, Muller MJ, Kohnen R, Szegedi A: Association Analysis between Variants of the Interleukin-1? and the Interleukin-1 Receptor Antagonist Gene and Antidepressant Treatment Response in Major Depression. Neuropsychiatr Dis Treat 2008; 4(1): 269-276

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

Institute for Response-Genetics (e.V.)

Chairman: Prof. Dr. Hans H. Stassen

Psychiatric Hospital (KPPP), University of Zurich

Assessing Deviations from "Normality"

Biological Ethnicity

Ethnicity-Independent Features

References