Complex Adaptive System Models and the Genetic Analysis of Plasma HDL-Cholesterol Concentration

Abstract

Despite remarkable advances in diagnosis and therapy, ischemic heart disease (IHD) remains a leading cause of morbidity and mortality in industrialized countries. Recent efforts to estimate the influence of genetic variation on IHD risk have focused on predicting individual plasma high-density lipoprotein cholesterol (HDL-C) concentration. Plasma HDL-C concentration (mg/dl), a quantitative risk factor for IHD, has a complex multifactorial etiology that involves the actions of many genes. Single gene variations may be necessary but are not individually sufficient to predict a statistically significant increase in risk of disease. The complexity of phenotype-genotype-environment relationships involved in determining plasma HDL-C concentration has challenged commonly held assumptions about genetic causation and has led to the question of which combination of variations, in which subset of genes, in which environmental strata of a particular population significantly improves our ability to predict high or low risk phenotypes. We document the limitations of inferences from genetic research based on commonly accepted biological models, consider how evidence for real-world dynamical interactions between HDL-C determinants challenges the simplifying assumptions implicit in traditional linear statistical genetic models, and conclude by considering research options for evaluating the utility of genetic information in predicting traits with complex etiologies.We are moving from an Age of Reductionism to an Age of Emergence, a time when the search for ultimate causes of things shifts from the behavior of parts to the behavior of the collective.-R. B. Laughlin (2005) A WIDELY ACCEPTED MODEL of living organisms, which has its roots in 19th-century thought, emphasizes a deterministic and programmatic basis of biological structure and function (Mayr 1982, Woese 2004. This model has led to a research strategy whose goals have been to reduce the organism and its characteristic functional properties to presumed essential component agents, to model the relationships between these components and biological function by physical, chemical, or statistical methods, and to test the utility of models obtained for predicting, controlling, and understanding the contributions of the individual components to functional properties by comparing their impact on function in well-controlled experimental situations with their influence in intact, free-living organisms. Significant progress has been achieved in resolving the essential component agents to higher and higher resolution (e.g., metabolic pathways and products, enzymes, protein structures, encoding genes, polynucleotide sequences, nucleotide sequence variations). Modeling approaches have extended the limits of the mechanistic metaphor.

Subsets of SNPs define rare genotype classes that predict ischemic heart disease

“…Caution must be exercised in drawing biological inferences from statistical models of interaction (Moore and Williams 2005) because they cannot reXect the separate actions of the many participating interacting agents, most of which cannot be measured, that are involved in the causation of phenotypic variation (Cordell 2002;Rea et al 2006). Although the biological mechanisms of epistasis cannot be directly extrapolated from the observed statistical evidence for non-additivity of SNP eVects, each SNP involved in a high-risk pairwise combination has been implicated in marking biological eVects in complementary studies.…”

Genetic causes of high and low serum HDL-cholesterol

“…Furthermore, simplifi ed models, such as main effects, may fail to capture the complex inheritance of HDL regulation ( 233 ). Notably, HDL-C levels are strongly associated with a cluster of inherited factors that contribute to CAD (i.e., component traits of the metabolic syndrome), that should also be taken into account to improve existing models.…”

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases

“…This can lead to an increase in type I and type II errors due to parameter estimates with very large standard errors. It is evident that we need research strategies that embrace, rather than ignore, the complexity of the relationship between genotype and phenotype (Templeton 2000;Moore 2003;Sing et al 2003;Thornton-Wells et al 2004;Moore and Williams 2005;Rea et al 2006). …”