Sharon L. Reilly

Biological Complexity and Strategies for Finding DNA Variations Responsible for Inter-individual Variation in Risk of a Common Chronic Disease, Coronary Artery Disease

Most common chronic diseases of humans aggregate, but do not segregate, in families. The segregation-linkage research paradigm has not provided great insights into their genetic etiology. In this paper, using coronary artery disease as an example, we discuss hierarchical organization, coherence, emergent properties and dynamism as features that characterize the complexity of genotype-phenotype relationships. We summarize a research strategy for evaluating the contribution of genetic and environmental factors to the prediction of inter-individual variation in risk of disease. We then review a statistical strategy that employs cladistic theory to identify individuals carrying mutant DNA sequences responsible for an observed association between marker variation in a gene and inter-individual variation in biological traits that determine risk of a common multifactorial disease. Finding these DNA sequences is a necessary step in our search for an understanding of the nature of the mapping of genetic variation into variation in risk of a disease like coronary artery disease.

The effects of generation and gender on the joint distributions of lipid and apolipoprotein phenotypes in the population at large

The generation and gender effects on the joint distributions of total plasma cholesterol (Total-C), ln triglycerides (lnTrig), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), apolipoproteins AI (Apo AI), AII (Apo AII), and E (lnApo E) were studied in 184 male grandparents (MGP), 242 female grandparents (FGP), 237 male parents (MP), 235 female parents (FP), 202 male children (MC), and 200 female children (FC). Homogeneity of variance tests revealed that lipid variances were gender and/or generation specific while apolipoprotein variances were homogeneous across strata. In the absence of heterogeneity of variance, significant heterogeneity in LDL:lnTrig and lnTrig:Apo AII covariation was found between genders in the parental generation. In the presence of heterogeneity of variance, significant heterogeneity of correlation between genders and/or across generations was found for the HDL-C:LDL-C, Total-C:LDL-C, Total-C:lnTrig, lnTrig:LDL-C, Total-C:lnApo E and HDL-C:lnApo E bivariate distributions. Analyses of principal components revealed that the generation and gender specific cohorts have similar eigenvalues but distinct eigenvectors for the first two principal components underlying the seven dimensional lipid and apolipoprotein distribution. We conclude that the amount of variability explained by the first two principal components is the same across cohorts but how the interindividual variability is distributed among the lipid and apolipoprotein traits is generation and gender specific. This study documents the role that variance and covariance might play in determining risk of disease for special subgroups of the population at large. It also demonstrates how variances and covariances between risk factors traits characterize life processes of aging and sexual dimorphism. This study argues that future biometrical genetic and epidemiological studies of coronary artery disease must take into account age and gender effects on interindividual variability and covariability of risk factors.

Traversing the biological complexity in the hierarchy between genome and CAD endpoints in the population at large

An emerging challenge facing those who are concerned about the efficacy of public health programs is to understand how information from the DNA revolution might be used to improve our ability to predict the initiation, progression and severity of a common disease having a complex multifactorial etiology. In the course of research to evaluate the role of information about DNA, combinations of genome types and environmental exposures that predispose to disease will be identified. Such information is expected to be useful in efforts to identify individuals and families at higher risk of disease and to predict their responses to a proposed therapy. This paper begins with a discussion of the features of a realistic biological model for the study of a common multifactorial disease. We present evidence for the complexity in the relationship between genome type variation and variation in risk of coronary artery disease (CAD) and review the preliminary results of our studies to determine whether information about genome type variation can improve our ability to predict the distribution of CAD among individuals in the population at large. Such studies make it apparent that new analytical strategies are necessary to deal with the plethora of genome type information available for the evaluation of risk of a common disease like CAD. This shift in the research paradigm will build upon new strategies to understand the organization of natural systems that are coming from outside the mainstream of genetic research.

The role of the apolipoprotein E polymorphism in the prediction of coronary artery disease age of onset

We investigated the role of the apolipoprotein (Apo) E polymorphism in the prediction of CAD age of onset in a sample of unrelated living male (n = 65) and female (n = 54) Caucasian subjects diagnosed with CAD. Cumulative distributions of age at the first diagnosis of CAD were estimated for each Apo E genotype and tested for homogeneity using the log‐rank test. The Apo e33 genotype was used as a reference group for all hypothesis tests. Analyses were performed separately in males and females. We found evidence suggesting that the presence of the Apo s32 genotype in males is associated with a significantly earlier CAD age of onset. These results suggest that the Apo E polymorphism may be a gender‐specific predictor of CAD age of onset.

Analysis of Systems Influencing Renal Hemodynamics and Sodium Excretion. I. Biochemical Systems Theory

In this article we present a new methodology—Biochemical Systems Theory and Analysis—as an alternative to traditional parametric statistical procedures for investigating differences between risk groups in a population. We review the systems theory and how it can be used to represent a model of processes influencing renal hemodynamics and sodium (Na+) excretion. We also discuss the potential for new measures of the biology of common diseases that can emerge from a synergism between systems theory and population-based statistical approaches.