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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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Asthma in the Ethnically Diverse CSGA and Hutterite Populations

Vulnerability to Asthma

Using the genome-wide screening data of the CSGA (226 families, 1'461 genotyped subjects, 323 marker loci) and Hutterite studies (129 families, 690 genotyped subjects, 365 marker loci) we applied a genetic similarity function in order to quantify the inter-individual genetic distances d(Xi,Xj) between feature vectors Xi, Xj made up by the allelic patterns of subjects i, j with respect to loci L1, L2,.. Ln. Based on this similarity function, we structurally decomposed the genetic diversity of the CSGA population in order to address the question of ethnicity-related asthma vulnerability for genetically homogenous CSGA subgroups. The question of ethnicity-independent asthma vulnerability was investigated with all CSGA families as training and the Hutterite families as replication samples.

Oligogenic Models with Interactions

We evaluated the between-sib similarities which were expected to deviate from "0.5" in affected sib pairs if the region of interest contained markers close to disease-causing genes. The reference value "0.5" was derived by determining the parents-offspring similarities which are always "0.5", irrespective of the affection status of parents and offspring. We found 18 vulnerability loci on chromosomes 1, 3, 4, 5, 6, 8, 12, 13 and 14, which were remarkably reproducible in the CSGA and the Hutterite data and constituted an ethnicity-independent oligogenic model.

Ethnicity-Independent Vulnerability

Treating the genome as a single entity we subdivided the genetic map, implicitly defined by the M marker loci, into m segments Si each including 10 markers (i=1,2,.. m). Each segment Si was then systematically combined with each segment Sj into a feature vector of length 20 (i≠j), thus enabling the detection of interactions between any two loci. Based on the 20-dimensional feature vectors and a set-theoretical similarity function we determined the distribution of parent-offspring similarities, the distribution of between-sib similarities of affected sib pairs, and the distribution of between-sib similarities of unaffected sib pairs. Subsequently, the signal detection algorithm looked for significant differences between the parent-offspring similarities and the between-sib similarities of affected sib pairs under the constraint that no such differences showed up between the parent-offspring similarities and the between-sib similarities of unaffected sib pairs. Those loci that contributed significantly to deviations in the expected values of genetic similarity constituted an oligogenic model.

References

Stassen HH, Bachmann S, Bridler R, Cattapan K, Herzig D, Schneeberger A, Seifritz E: Detailing the Effects of Polypharmacy in Psychiatry: Longitudinal Study of 320 Patients Hospitalized for Depression or Schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2021 [submitted for publication]
Stassen HH, Bachmann S, Bridler R, Cattapan K, Herzig D, Schneeberger A, Seifritz E: Inflammatory Processes linked to Major Depression & Schizophrenic Disorders and the Effects of Polypharmacy in Psychiatry: Evidence from a longitudinal Study of 279 Patients under Therapy. Eur Arch Psychiatry Clin Neurosci. 2021; 271(3): 507-520 [get the article]
Pollak TA, Lennox B, Müller S, Benros ME, Prüss H, Tebartz van Elst L, Klein H, Steiner J, Frodl T, Bogerts B, Tian L, Groc L, Hasan A, Baune BT, Endres D, Haroon E, Yolken R, Benedetti F, Halaris A, Meyer J, Stassen HH, Leboyer M, Fuchs D, Otto M, Brown DA, Vincent A, Najjar S, Bechter K: An international consensus on an approach to the diagnosis and management of psychosis of suspected autoimmune origin: the concept of autoimmune psychosis. Lancet Psychiatry 2020; 7(1): 93-108
Zhang M, Bridler R, Mohr C, Moragrega I, Sun N, Xu Z, Yang Z, Possenti M, Stassen HH: Early Detection of the Risk of Developing Psychiatric Disorders: A Study of 461 Chinese University Students under Chronic Stress. Psychopathology 2019; 52(6): 367-377 [get the article]
Bhake R, Kluckner V, Stassen HH, Russell GM, Leendertz J, Stevens K, Linthorst ACE, Lightman S: Continuous Free Cortisol Profiles – Circadian Rhythms in Healthy Men. J Clinical Endocrinology & Metabolism 2019; 104(12): 5935-5947
Stassen HH: Heterogeneity of schizophrenic disorders and link to chronically elevated IgM values. Neurology, psychiatry and brain research 2018; 29: 23-24
Braun S, Bridler R, Müller N, Schwarz MJ, Seifritz E, Weisbrod M, Zgraggen A, Stassen HH: Inflammatory Processes and Schizophrenia: Two Independent Lines of Evidence from a Study of Twins Discordant and Concordant for Schizophrenic Disorders. Eur Arch Psychiatry Clin Neurosci 2017; 267: 377-389
Stassen HH, Delfino JP, Kluckner VJ, Lott P, Mohr C: Vulnerabilität und psychische Erkrankung. Swiss Archives of Neurology and Psychiatry 2014; 165(5): 152-157
Drago A, Giegling I, Schäfer M, Hartmann AM, Friedl M, Konte B, Möller HJ, De Ronchi D, Stassen HH, Serretti A, Rujescu D: AKAP13, CACNA1, GRIK4 and GRIA1 genetic variations may be associated with haloperidol efficacy during acute treatment. Eur Neuropsychopharmacol. 2013; 23(8): 887-894
Giegling I, Balzarro B, Porcelli S, Schäfer M, Hartmann AM, Friedl M, Konte B, Krämer P, Möller HJ, De Ronchi D, Stassen HH, Serretti A, Rujescu D: Influence of ANKK1 and DRD2 polymorphisms in response to haloperidol. Eur Arch Psychiatry Clin Neurosci. 2013; 263(1): 65-74
Drago A, Giegling I, Schäfer M, Hartmann AM, Möller HJ, De Ronchi D, Stassen HH, Serretti A, Rujescu D: No association of a set of candidate genes on haloperidol side effects. PLoS One. 2012; 7(10): e44853
Giegling I, Drago A, Schäfer M, Hartmann AM, Sander T, Toliat MR, Möller HJ, De Ronchi D, Stassen HH, Rujescu D, Serretti A: Lack of association between 71 variations located in candidate genes and response to acute haloperidol treatment. Psychopharmacology 2011; 214(3): 719-728
Giegling I, Drago A, Dolzan V, Plesnicar BK, Schäfer M, Hartmann AM, Sander T, Toliat MR, Möller HJ, Stassen HH, Rujescu D, Serretti A: Glutamatergic gene variants impact the clinical profile of efficacy and side effects of haloperidol. Pharmacogenet Genomics. 2011; 21(4): 206-216
Gravemann S, Schnipper N, Meyer H, Vaya A, Nowaczyk MJM, Rajab A, Hofmann WK, Salewsky B, Tönnies H, Neitzel H, Stassen HH, Sperling K, Hoffmann K: Dosage effect of zero to three functional LBR-genes in vivo and in vitro. Nucleus 2010; 1(2): 1-12
Hoffmann K, Planitz C, Rüschendorf F, Müller-Myhsok B, Stassen HH, Lucke B, Mattheisen M, Stumvoll M, Bochmann R, Zschornack G, Wienker TF, Nürnberg P, Reis A, Luft FC, Lindner TH: A novel locus for arterial hypertension on chromosome 1p36 maps to a metabolic syndrome trait cluster in the Sorbs, a Slavic population isolate in Germany. J Hypertens 2009; 27: 983-990
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
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, Angst J, Hell D, Scharfetter C, Szegedi A: Is there a common resilience mechanism underlying antidepressant drug response? Evidence from 2'848 patients. J Clin Psychiatry 2007; 68(8): 1195-1205
Tadic A, Rujescu D, Müller 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, Müller 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
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
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, Angst J, Scharfetter C, Szegedi A: Therapie mit Antidepressiva: Erfolg von genetischen Faktoren abhängig? Leading Opinions, Neurologie & Psychiatrie 2005; 6: 25-27
Szegedi A, Rujescu D, Tadic A, Müller MJ, Ralf Kohnen R, Stassen HH, Dahmen N: The catechol-O-methyltransferase Val108/158Met-polymorphism affects short-term treatment response to mirtazapine, but not to paroxetine in Major Depression. Pharmacogenomics 2005; 5(1): 49-53
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
Stassen HH, Hoffmann K, Scharfetter C: Similarity by state/descent and genetic vector spaces: Analysis of a longitudinal family study. Genetic Analysis Workshop 13: Analysis of longitudinal family data for complex diseases and related risk factors. BMC Genet 2003; 4, S59: 1-6
Stassen HH, Scharfetter C: Oligogenic approaches to the predisposition of asthma in ethnically diverse populations. Genetic Analysis Workshop 12: Analysis of genetic and environmental factors in common diseases. Genetic Epidemiology 2001; 21(1): 284-289
Hoffmann K, Stassen HH, Reis A: Genkartierung in Isolatpopulationen. Medizinische Genetik 2000; 12,4: 428-437
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 and Scharfetter C: Integration of genetic maps by polynomial transformations. Am J Med Genetics B 2000; 96: 108-113
Stassen HH, Begleiter H, Porjesz B, Rice J, Scharfetter C, Reich T: Structural decomposition of genetic diversity in families with alcohol dependence. Genetic Analysis Workshop 11: Analysis of genetic and environmental factors in common diseases. Genetic Epidemiology 1999; 17: 325-330

 

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Asthma in the Ethnically Diverse Populations
Genetic vector space spanned by 20 polymorphic markers on chromosomes 6, 11 and 22 reveals differences between ethnic groups: circles designate Afro Americans (n=141), triangles NonAfro Americans (n=111), and squares Swiss subjects (n=257). Subjects are projected onto the hyperplane defined through the eigenvectors associated with the 2 largest eigenvalues.
Please note: population stratification is critical in genetic studies. While vulnerability and resilience factors are largely independent of biological ethnicity, unwanted side effects (e.g., weight gain), the inter-individual variation in dose and blood-level are highly ethnicity-specific.
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