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.
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").
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.
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