Comparison of Population-Based Association Study Methods Correcting for Population Stratification

Population stratification can cause spurious associations in population–based association studies. Several statistical methods have been proposed to reduce the impact of population stratification on population–based association studies. We simulated a set of stratified populations based on the real haplotype data from the HapMap ENCODE project, and compared the relative power, type I error rates, accuracy and positive prediction value of four prevailing population–based association study methods: traditional case-control tests, structured association (SA), genomic control (GC) and principal components analysis (PCA) under various population stratification levels. Additionally, we evaluated the effects of sample sizes and frequencies of disease susceptible allele on the performance of the four analytical methods in the presence of population stratification. We found that the performance of PCA was very stable under various scenarios. Our comparison results suggest that SA and PCA have comparable performance, if sufficient ancestral informative markers are used in SA analysis. GC appeared to be strongly conservative in significantly stratified populations. It may be better to apply GC in the stratified populations with low stratification level. Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies

Neural networks for modeling gene-gene interactions in association studies

<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p

Growth of nanostructures by cluster deposition : a review

This paper presents a comprehensive analysis of simple models useful to analyze the growth of nanostructures obtained by cluster deposition. After detailing the potential interest of nanostructures, I extensively study the first stages of growth (the submonolayer regime) by kinetic Monte-Carlo simulations. These simulations are performed in a wide variety of experimental situations : complete condensation, growth with reevaporation, nucleation on defects, total or null cluster-cluster coalescence... The main scope of the paper is to help experimentalists analyzing their data to deduce which of those processes are important and to quantify them. A software including all these simulation programs is available at no cost on request to the author. I carefully discuss experiments of growth from cluster beams and show how the mobility of the clusters on the surface can be measured : surprisingly high values are found. An important issue for future technological applications of cluster deposition is the relation between the size of the incident clusters and the size of the islands obtained on the substrate. An approximate formula which gives the ratio of the two sizes as a function of the melting temperature of the material deposited is given. Finally, I study the atomic mechanisms which can explain the diffusion of the clusters on a substrate and the result of their mutual interaction (simple juxtaposition, partial or total coalescence...)Comment: To be published Rev Mod Phys, Oct 99, RevTeX, 37 figure

Plasma concentrations of anserine, carnosine and pi-methylhistidine as biomarkers of habitual meat consumption.

Background/Objectives Dietary intake of red and processed meat has been associated with disease risk. Since dietary intake assessment methods are prone to measurement errors, identifying biomarkers of meat intake in bio-samples could provide more valid intake estimates. We examined associations of habitual red and processed meat, poultry, fish, and dairy products consumption with plasma concentrations of anserine, carnosine, pi-methylhistidine (Pi-MH), tau-methylhistidine (T-MH), and the ratio of T-MH to Pi-MH in a cross-sectional study.Subjects/Methods Plasma anserine, carnosine, Pi-MH, and T-MH concentrations were measured using ion-pair LC-MS/MS in 294 participants in the second Bavarian Food Consumption Survey (BVS II). Habitual food consumption was assessed using three 24-h dietary recalls. Associations between plasma metabolites concentrations and meat, fish, eggs, and dairy products consumption were assessed by fitting generalized linear model, adjusted for age, sex, and BMI.Results Total meat intake was associated with plasma concentrations of anserine, carnosine, Pi-MH and, the ratio of T-MH to Pi-MH. Red meat intake was related to carnosine (p-trend = 0.0028) and Pi-MH plasma levels (p-trend = 0.0493). Poultry (p-trend = 0.0006) and chicken (p-trend = 0.0003) intake were associated with Pi-MH. The highest anserine concentrations were observed in individuals consuming processed meat or turkey. For T-MH we did not observe any association with meat intake.Conclusions Our results indicate an association between habitual meat consumption and plasma concentrations of anserine, carnosine, Pi-MH and the ratio of T-MH to Pi-MH. Intervention studies should clarify whether the analyzed plasma metabolites are indicative for a specific type of meat before proposing them as biomarkers of habitual meat intake in epidemiologic studies