Biomedical subjects

A S Foulkes

Publications and source records attributed to A S Foulkes.

6 recordsLinked to original sources

PubMed2005

Mixed modelling to characterize genotype-phenotype associations.

We propose using mixed effects models to characterize the association between multiple gene polymorphisms, environmental factors and measures of disease progression. Characterizing high-order gene-gene and gene-environment interactions presents an analytic challenge due to the large number of candidate genes and the complex, undescribed interactions among them. Several approaches have been proposed recently to reduce the number of candidate genes and post hoc approaches to identify gene-gene interactions are described. However, these approaches may be inadequate for identifying high-order interactions in the absence of main effects and generally do not permit us to control for potential confounders. We describe how mixed effects models and related testing procedures overcome these limitations and apply this approach to data from a cohort of subjects at risk for cardiovascular disease. Four (4) genetic polymorphisms in three genes of the same gene family are considered. The proposed modelling approach allows us first to test whether there is a significant genetic contribution to the variability observed in our disease outcome. This contribution may be through main effects of multi-locus genotypes or through an interaction between genotype and environmental factors. This approach also enables us to identify specific multi-locus genotypes that interact with environmental factors in predicting the outcome. Mixed effects models provide a flexible statistical framework for controlling for potential confounders and identifying interactions among multiple genes and environmental factors that explain the variability in measures of disease progression.

Adult↗

PubMed2004

MDR and PRP: a comparison of methods for high-order genotype-phenotype associations.

Complex diseases such as cardiovascular disease are likely due to the effects of high-order interactions among multiple genes and demographic factors. Therefore, in order to understand their underlying biological mechanisms, we need to consider simultaneously the effects of genotypes across multiple loci. Statistical methods such as multifactor dimensionality reduction (MDR), the combinatorial partitioning method (CPM), recursive partitioning (RP), and patterning and recursive partitioning (PRP) are designed to uncover complex relationships without relying on a specific model for the interaction, and are therefore well-suited to this data setting. However, the theoretical overlap among these methods and their relative merits have not been well characterized. In this paper we demonstrate mathematically that MDR is a special case of RP in which (1) patterns are used as predictors (PRP), (2) tree growth is restricted to a single split, and (3) misclassification error is used as the measure of impurity. Both approaches are applied to a case-control study assessing the effect of eleven single nucleotide polymorphisms on coronary artery calcification in people at risk for cardiovascular disease.

Cardiovascular Diseases↗

PubMed2003

Characterizing classes of antiretroviral drugs by genotype.

This paper develops methods for using HIV-1 genotypic information to group patients who are expected to have similar patterns of sensitivity or resistance to two or more drugs. The methods presented are an extension of prediction based classification to handle multiple drug responses. Here, the goal is to determine the probability that one antiretroviral therapy will be more favourable than another for an individual given the specific genotypic or other characteristics of the infecting viral population. This approach requires a model relating genotype to a vector of drug specific phenotypic responses. A comparison of Nelfinavir and Indinavir is provided using 2746 protease sequences and corresponding in vitro sensitivity assays provided to us by the Virco Group.

Amino Acid Sequence↗

PubMed2002

Characterizing the relationship between HIV-1 genotype and phenotype: prediction-based classification.

This paper establishes a framework for understanding the complex relationships between HIV-1 genotypic markers of resistance to antiretroviral drugs and clinical measures of disease progression. A new classification scheme based on the probabilities of how new patients will respond to antiretroviral therapy given the available data is proposed as a method for distinguishing among groups of viral sequences. This approach draws from existing cluster analysis, discriminant analysis, and recursive partitioning techniques and requires a model relating genotypic characteristics to phenotypic response. A data set of 2,746 sequences and the corresponding Indinavir 50% inhibitory concentrations are described and used for illustrative purposes.

Amino Acid Sequence↗

PubMed2000

Methods for investigation of the relationship between drug-susceptibility phenotype and human immunodeficiency virus type 1 genotype with applications to AIDS clinical trials group 333.

Use of human immunodeficiency virus (HIV) drug-resistance testing in therapeutic decision making may be aided by understanding the relationship between results of genotypic and drug-susceptibility phenotypic assays. We investigated this relationship by applying 3 different statistical methods-cluster analysis, recursive partitioning, and linear discriminant analysis-to results for 72 patients followed in the Adult AIDS Clinical Trials Group (ACTG) protocol 333. ACTG 333 was a multicenter, randomized trial comparing 2 formulations of saquinavir (SQV) to indinavir (IDV) in patients with extensive hard-gel SQV experience. Data include protease amino acid sequences and 50% inhibitory concentrations for SQV and IDV at baseline. The 3 methods give similar results showing the association of mutations at codons 10, 63, 71, and 90 with in vitro resistance to IDV and SQV. Recursive partitioning is especially useful because it can identify interactions among mutations at different codons and accommodates many types of data as well as missing observations.

Adult↗

PubMed1999

A murine model of myocardial microvascular thrombosis.

Disorders of hemostasis lead to vascular pathology. Endothelium-derived gene products play a critical role in the formation and degradation of fibrin. We sought to characterize the importance of these locally produced factors in the formation of fibrin in the cardiac macrovasculature and microvasculature. This study used mice with modifications of the thrombomodulin (TM) gene, the tissue-type plasminogen activator (tPA) gene, and the urokinase-type plasminogen activator (uPA) gene. The results revealed that tPA played the most important role in local regulation of fibrin deposition in the heart, with lesser contributions by TM and uPA (least significant). Moreover, a synergistic relationship in fibrin formation existed in mice with concomitant modifications of tPA and TM, resulting in myocardial necrosis and depressed cardiac function. The data were fit to a statistical model that may offer a foundation for examination of hemostasis-regulating gene interactions.

Animals↗