Estimating the number and size of the main effects in genome-wide case-control association studies

It has recently become possible to screen thousands of markers to detect genetic causes of common diseases. Along with this potential comes analytical challenges, and it is important to develop new statistical tools to identify markers with causal effects and accurately estimate their effect sizes. Knowledge of the proportion of markers without true effects (p0) and the effect sizes of markers with effects provides information to control for false discoveries and to design follow-up studies. We apply newly developed methods to simulated Genetic Analysis Workshop 15 genome-wide case-control data sets, including a maximum likelihood (ML) and a quasi-ML (QML) approach that incorporate the test statistic distribution and estimates effect size simultaneously with p0, and two conservative estimators of p0 that do not rely on the test statistic distribution under the alternative. Compared with four existing commonly used estimators for p0, our results illustrated that all of our estimators have favorable properties in terms of the standard deviation with which p0 is estimated. On average, the ML method performed slightly better than the QML method; the conservative method performed well and was even slightly more precise than the ML estimators, and can be more robust in less optimal conditions (small sample sizes and small number of markers). Further improvements and extensions of the proposed methods are conceivable, such as estimating the distribution of effect sizes and taking population stratification into account when obtain estimates of p0 and effect size

Nemkonvex és diszkrét sztochasztikus programozási feladatok megoldása és alkalmazása = Solution and applications of nonconvex and discrete stochastic programming problems

A nemkonvex feladatok megoldására három területen is lényeges haladást értünk el: a valószínűségek kiszámítása, általános sztochasztikus feladatok optimalizálásában és a diszkrét sztochasztikus programozási feladatok megoldásában. A normális valószínűségek kiszámítására használt számítógépes szubrutinok olyan gyors működését értük el, hogy normális eloszlás esetén még 1000 dimenziós egyszerű konvex alakzatok valószínűségét is meg lehet határozni egy másodperc körüli időben. A poliéderek használatán alapuló módszer egy új elvi alapokat felhasználó eljárás valószínűségek kiszámítására. Ezen kívül a Dirichlet és a gamma eloszlás valószínűségeinek kiszámításában sikerült eredményeket elérni. Sztochasztikus feladatok megoldó algoritmusaira négy új eljárást dolgoztunk ki: a megengedett megoldások halmazának közelitésén (Bukszár), a szukcesszív regressziós approximációk véletlen egyenletrendszerekre való alkalmazása (Deák), metszősík algoritmusokat használó algoritmus (Fábián), a valószínűségi korláton belül tetszőleges helyen véletlent tartalmazó modell megoldása (Vizvári). A többdimenziós momentumproblémák megoldására kifejlesztett eljárásokat hasznossági függvény becslésére alkalmaztuk. | In our research for solving nonconvex problems we achieved progress in three areas: computing probabilities, optimizing general stochastic programming problems and discrete programming problems. The computer subroutines determining multinormal probabilities became so fast, that even for 1000 dimensional simple convex sets we were able to compute probabilities in about 1 sec. Employing polyhedra is a theoretically new path in computing probabilities. Also we developed some algorithms for computing probabilities for the Dirichlet and the gamma distribution. Four new procedures have been developed fo optimizing stochastic programming models: approximating the set of feasible solutions (Bukszár), applying the successive regression approximations for solving random linear systems of equations (Deák), cutting plane techniques (Fábián), solving problems where the random variables may be in any place inside the probabilistic constraint (Vizvari. In the multidimensional discrete moment problems we proved some theorems, and using these results new algorithm could be presented for approximating the expected utility function

Genome-wide association study of patient and clinician rated global impression severity during antipsychotic treatment

Examine the unique and congruent findings between multiple raters in a genome-wide association studies (GWAS) in the context of understanding individual differences in treatment response during antipsychotic therapy for schizophrenia

Genomewide Association Study of Movement-Related Adverse Antipsychotic Effects

Understanding individual differences in the development of extra-pyramidal side effects (EPS) as a response to antipsychotic therapy is essential to individualize treatment