Verschiedene Studien haben aufgezeigt, dass der Einfluss genetischer Faktoren auf psychische Eigenschaften (Turkheimer et al., 2003) oder Verhaltensweisen (Tuvblad et al., 2006) in einer sozial-ökonomisch bevorzugten Umwelt stärker ausgeprägt ist als in einer sozial-ökonomisch benachteiligten Umwelt. Begründet wird dies dadurch, dass Menschen aus privilegierten „Sozialschichten“ den sozialen und ökonomischen Risikofaktoren, die für bestimmte Phänotypen prädisponieren, nicht in dem Maße ausgesetzt sind.
Es werden unterschiedliche Theorien diskutiert, wie der genetische Hintergrund und der Sozio-ökonomische Status (SES) zusammen auf den Phänotypen wirken können. South und Krueger (2011) sprechen von der „Social Causation“ und der „Social Selection“ Theorie.
Bisherige Studien konzentrieren sich auf Zwillingsstudien, die nicht explizit genetisches Material erhoben haben, um ein Zusammenspiel zwischen Genetik und SES zu analysieren. In der Regel werden dafür Strukturgleichungsmodelle (SGM) verwendet, die den Vorteil haben sowohl latente Variablen, wie den SES, als auch kausale Abläufe über mehrere Schritte modellieren zu können.
Ziel unserer Untersuchung ist, ob der genetische Einfluss des FTO Gens auf Adipositas auch über sozio-ökonomische Faktoren sowie über weitere Sozialfaktoren (TV/PC-Konsum, Essverhalten, körperliche Aktivität) moderiert wird. Dafür werden SGMs auf genetische Assoziationsdaten angewendet und die Theorien der Social Causation und Social Selection in entsprechende Modelle umgesetzt und miteinander verglichen.
 
South SC, Krueger RF (2011) Genetic and environmental influences on internalizing psychopathology vary as a function of economic status. Psychol Med, 41, 107-117.
Turkheimer E, Haley A, Waldron M, D'Onofrio B, Gottesman II (2003) Socioeconomic status modifies heritability of IQ in young children. Psychol Sci, 14, 623-628.
Tuvblad C, Grann M, Lichtenstein P (2006) Heritability for adolescent antisocial behavior differs with socioeconomic status: gene-environment interaction. J Child Psychol Psychiatry, 47, 734-743.

 

Microarray technology is a key component in the study of cancer. The bioinformatics platform presented in this talk enables scientists to structure, analyze and communicate data gathered in this area of research. The software covers the pathology, molecular genetics and biostatistics of microarray analysis in detail. It is used by scientists from these three different fields of science.

The robust approach to statistical modeling aims at deriving methods that produce reliable estimates not only when data follow a given distribution exactly, but also when this happens approximately. The generalized linear model (GLM) allows the distribution of the dependent variable to belong to the exponential family, which also includes not continuous distributions. Other than simple linear relationships between response and explanatory variables are permitted.

I have explored current facilities of the free software environment for statistical computing R to identify influential cases in GLMs and to carry out robust GLMs. During the presentation, I will summarize relevant theoretical and technical details and apply R to investigate real datasets by robust and standard GLMs, including robust variance estimates. 

Methods and software for robust estimation of GLM are still sparse and mainly limited to Logistic and Poisson regression. The “car” and “robustbase” packages provide convenient functions for diagnostic plots and for fitting robust GLMs. In many practical situations, the implementation of robust GLMs is relatively straightforward and the comparison between standard and robust estimates may be advantageous.

(Folien)

Visual inspection of the clusterplot for each trait-associated SNP is still the recommended strategy for ascertaining the accuracy of the genotyping (Pearson and Manolio, 2008). This requires two independent reviewers and is time consuming for genome wide association studies. Automated procedures are necessary with the advent of large-scale genotyping, which assays at least hundreds of thousands of SNPs. Here a model based calling algorithm which can be performed unsupervised is presented based on finite mixture models is presented. In contrast to the approach by Teo et al (2007) the number of mixture components does not need to be fixed in advance. This is achieved by applying the VEM-algorithm (Schlattmann, 2009) for bivariate normal data.
The validity of the method is investigated in a large scale simulation study.
 

References
Pearson, T. A. & Manolio, T. A. (2008). How to interpret a genome-wide association study.
Journal of the American Medical Association, 299, 1335-1344..

Schlattmann, P. (2009). Medical Applications of Finite Mixture Models. Berlin: Springer.

Teo, Y. Y., Inouye, M., Small, K. S., Gwilliam, R., Deloukas, P., Kwiatkowski, D. P. & Clark, T. G.
(2007). A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics,
23, 2741-2746.
 

The partial least squares method is a multivariate data analysis method which is being used in many fields such as; physics, chemistry, industry, medicine, bioinformatics, and so on.
Since articles of PLS are often difficult to understand, in this lecture I am going to describe this method using other data analysis methods, step by step. First, I will give a short introduction to the Ordinary Least Squares (OLS) and Principal Component Regression (PCR), because they help us understanding and interpreting the PLS method and results easier and better. Secondly, two general algorithms, NIPALS (nonlinear iterative partial least squares) and SIMPLS (simple iteration partial least squares) will be explained. Finally, I am going to present an application of this theory together with an example.
 

P.S.: I hope that we will learn useful points from our discussion after the talk
and have a nice time together.

Für die Beurteilung der klinischen Relevanz von Ergebnissen aus klinischen Studien gibt es bisher nur wenige allgemein anerkannte Kriterien. Hier sind vielleicht in erster Linie zu nennen, dass Therapieeffekte umso relevanter eingeschätzt werden, je größer sie sind, und dass auch kleine Therapieeffekte als relevant eingestuft werden können, wenn das zugehörige Krankheitsbild schwerwiegend ist. Sicher ist, dass in Europa keine Arzneimittel zugelassen werden ohne dass zugleich die klinische Relevanz der erzielten Effekte bestätigt wird. Diese Bestätigung ist oft qualitativer Natur, d.h. ohne eine zugehörige befriedigende quantitative Analyse. Ich will in meinem Vortrag über quantitative Ansätze in der Literatur und Praxis berichten und diese Ansätze kritisch beleuchten.

(Folien)

Meta-analyses for diagnostic accuracy studies are complicated by the fact that two parameters of interest (sensitivity and specificity) are given by each study, leading to statistical models with bivariate responses. We propose a new model using beta-binomial marginal distributions and bivariate copulas to this task. The model comes with the advantage that sensitivity and specificity are modelled on their original scale while still allowing for (1) these being correlated within each study, (2) these being heterogeneous across studies, (3) accessing the individual patient data, (4) allowing extreme values of 100% sensitivity and specificity, and (5) using standard software (e.g., SAS PROC NLMIXED). Compared to the current standard model [1,2], our model has a closed-form likelihood, thus facilitating parameter estimation. Finally, by using different copulas, the model allows for different correlation patterns between sensitivity and specificity.
We illustrate the methods by the classical example of Glas et al. [3] on the diagnostic accuracy of a urinary tumor marker (telomerase) for the diagnosis of primary bladder cancer. Moreover, we report on simulation results comparing our model to the current standard model.

[1] Chu H, Cole SR. Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol. 2006;59:1331–3.
[2] Hamza TH, van Houwelingen HC, Stijnen T. The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol. 2008;61:41–51.
[3] Glas AS, Roos D, Deutekom M, Zwinderman AH, Bossuyt PM, Kurth KH. Tumor markers in the diagnosis of primary bladder cancer. A systematic review. J. of Urol. 2003;169(6):1975-82.
 

 (Folien)

When linking a large number of single nucleotide polymorphisms (SNPs) to a phenotype, testbased techniques often consider each SNP separately. In contrast, regularized multivariable
regression techniques can incorporate all SNPs simultaneously. While the lasso is a prominent technique in this class, which can also select important SNPs, we consider componentwise
likelihood-based boosting as an alternative. The latter is available for continuous and binary phenotypes, as well as for time-to-event endpoints. We show that it can provide results similar to
the lasso when there is little linkage between the SNPs, using simulated time-to-event data. For strong linkage disequilibrium, the set of SNPs selected by the boosting approach is seen to be
more stable compared to the lasso. A second issue with regularization techniques is standardization of covariates. Without standardization, SNPs with large minor allele frequencies are
preferentially selected, which might not always be wanted. We propose a modified boosting approach to control preferential selection, without requiring artificial standardization of ordinal or
binary SNP covariates. The properties of componentwise likelihood-based boosting approach are illustrated using real SNP data with a time-to-event endpoint, where established clinical predictors
provide a performance reference. Specifically, the various strategies of tailoring componentwise boosting for SNP data are evaluated by prediction error curves to measure performance gains over
a purely clinical model.

(Folien)