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High-order Interactions (high-order + interaction)
Selected AbstractsElevated plasma fibrinogen ,, concentration is associated with myocardial infarction: effects of variation in fibrinogen genes and environmental factorsJOURNAL OF THROMBOSIS AND HAEMOSTASIS, Issue 4 2007M. N. MANNILA Summary., Background:, Fibrinogen ,,, a fibrinogen ,-chain variant generated via alternative mRNA processing, has been associated with susceptibility to thrombotic disease. Objective:, The present case,control study searched for potential determinants of the plasma fibrinogen ,, concentration and examined the relationship between this variant and risk of myocardial infarction (MI). Patients and methods:, The Stockholm Coronary Artery Risk Factor study, comprising 387 postinfarction patients and 387 healthy individuals, was employed. The fibrinogen gamma (FGG) 9340T > C [rs1049636], fibrinogen alpha (FGA) 2224G > A [rs2070011] and fibrinogen beta (FGB) 1038G > A [rs1800791] polymorphisms were determined. The plasma fibrinogen ,, concentration was measured by enzyme-linked immunosorbent assay. The multifactor dimensionality reduction method was used for interaction analyses on risk of MI. Results:, The FGG 9340T > C and FGA 2224G > A polymorphisms, total plasma concentrations of fibrinogen, insulin and high-density lipoprotein, and gender appeared to be independent determinants of plasma fibrinogen ,, concentration in patients, and the corresponding determinants in controls included FGG 9340T > C and FGA 2224G > A polymorphisms and plasma fibrinogen concentration. An elevated plasma fibrinogen ,, concentration proved to be an independent predictor of MI [adjusted odds ratio (OR) (95% CI): 1.24 (1.01, 1.52)]. The plasma fibrinogen ,, concentration was involved in a high-order interaction with total plasma fibrinogen and the FGG 9340T > C and FGA 2224G > A polymorphisms, associated with a further increased risk of MI [OR (95% CI): 3.22 (2.35, 4.39)]. Conclusions:, Plasma fibrinogen ,, concentration influences the risk of MI, and this relationship seems to be strengthened by the presence of an elevated total plasma fibrinogen concentration and the FGG 9340T and FGA 2224G alleles. [source] A new efficient mixture screening design for optimization of mediaBIOTECHNOLOGY PROGRESS, Issue 4 2009Fred Rispoli Abstract Screening ingredients for the optimization of media is an important first step to reduce the many potential ingredients down to the vital few components. In this study, we propose a new method of screening for mixture experiments called the centroid screening design. Comparison of the proposed design with Plackett-Burman, fractional factorial, simplex lattice design, and modified mixture design shows that the centroid screening design is the most efficient of all the designs in terms of the small number of experimental runs needed and for detecting high-order interaction among ingredients. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source] A novel method to identify gene,gene effects in nuclear families: the MDR-PDTGENETIC EPIDEMIOLOGY, Issue 2 2006E.R. Martin Abstract It is now well recognized that gene,gene and gene,environment interactions are important in complex diseases, and statistical methods to detect interactions are becoming widespread. Traditional parametric approaches are limited in their ability to detect high-order interactions and handle sparse data, and standard stepwise procedures may miss interactions that occur in the absence of detectable main effects. To address these limitations, the multifactor dimensionality reduction (MDR) method [Ritchie et al., 2001: Am J Hum Genet 69:138,147] was developed. The MDR is wellsuited for examining high-order interactions and detecting interactions without main effects. The MDR was originally designed to analyze balanced case-control data. The analysis can use family data, but requires a single matched pair be selected from each family. This may be a discordant sib pair, or may be constructed from triad data when parents are available. To take advantage of additional affected and unaffected siblings requires a test statistic that measures the association of genotype with disease in general nuclear families. We have developed a novel test, the MDR-PDT, by merging the MDR method with the genotype-Pedigree Disequilibrium Test (geno-PDT)[Martin et al., 2003: Genet Epidemiol 25:203,213]. MDR-PDT allows identification of single-locus effects or joint effects of multiple loci in families of diverse structure. We present simulations to demonstrate the validity of the test and evaluate its power. To examine its applicability to real data, we applied the MDR-PDT to data from candidate genes for Alzheimer disease (AD) in a large family dataset. These results show the utility of the MDR-PDT for understanding the genetics of complex diseases. Genet. Epidemiol. 2006. © 2005 Wiley-Liss, Inc. [source] A Combinatorial Searching Method for Detecting a Set of Interacting Loci Associated with Complex TraitsANNALS OF HUMAN GENETICS, Issue 5 2006Qiuying Sha Summary Complex diseases are presumed to be the results of the interaction of several genes and environmental factors, with each gene only having a small effect on the disease. Mapping complex disease genes therefore becomes one of the greatest challenges facing geneticists. Most current approaches of association studies essentially evaluate one marker or one gene (haplotype approach) at a time. These approaches ignore the possibility that effects of multilocus functional genetic units may play a larger role than a single-locus effect in determining trait variability. In this article, we propose a Combinatorial Searching Method (CSM) to detect a set of interacting loci (may be unlinked) that predicts the complex trait. In the application of the CSM, a simple filter is used to filter all the possible locus-sets and retain the candidate locus-sets, then a new objective function based on the cross-validation and partitions of the multi-locus genotypes is proposed to evaluate the retained locus-sets. The locus-set with the largest value of the objective function is the final locus-set and a permutation procedure is performed to evaluate the overall p-value of the test for association between the final locus-set and the trait. The performance of the method is evaluated by simulation studies as well as by being applied to a real data set. The simulation studies show that the CSM has reasonable power to detect high-order interactions. When the CSM is applied to a real data set to detect the locus-set (among the 13 loci in the ACE gene) that predicts systolic blood pressure (SBP) or diastolic blood pressure (DBP), we found that a four-locus gene-gene interaction model best predicts SBP with an overall p-value = 0.033, and similarly a two-locus gene-gene interaction model best predicts DBP with an overall p-value = 0.045. [source] Fractional factorial designs for legal psychologyBEHAVIORAL SCIENCES & THE LAW, Issue 1-2 2002Dennis P. Stolle J.D., Ph.D. Researchers considering novel or exploratory psycholegal research are often able to easily generate a sizable list of independent variables (IVs) that might influence a measure of interest. Where the research question is novel and the literature is not developed, however, choosing from among a long list of potential variables those worthy of empirical investigation often presents a formidable task. Many researchers may feel compelled by legal psychology's heavy reliance on full-factorial designs to narrow the IVs under investigation to two or three in order to avoid an expensive and unwieldy design involving numerous high-order interactions. This article suggests that fractional factorial designs provide a reasonable alternative to full-factorial designs in such circumstances because they allow the psycholegal researcher to examine the main effects of a large number of factors while disregarding high-order interactions. An introduction to the logic of fractional factorial designs is provided and several examples from the social sciences are presented. Copyright © 2002 John Wiley & Sons, Ltd. [source] | ||||||||||