span biological and social levels of organization. Our goal here is not to review these statistical models but to provide a more generic discussion of the conceptual issues that arise in the analysis of social and biological determinants of a healthy life span. We suggest that we must move beyond associations to mechanisms to meet the challenge of identifying the social and behavioral factors that influence the likelihood of remaining healthy and functional for the entire life span. The identification of associations and mechanisms depends on the accurate mapping of biological measures (e.g., biomarkers) to social and behavioral constructs in surveys. Such mappings will be aided by experimental or statistical controls for other factors (e.g., medications, time of day, activity level, body mass index) that influence biomarker expressions; attention to contextual variables (e.g., ethnicity) that may moderate the nature of the mappings; and a careful consideration of the sensitivity, specificity, and generality of the mapping in any given investigation. Before delving into these points, however, we describe briefly the nature of the data sets increasingly available to biological and social scientists.
The contributions to this volume demonstrate that scientific and technological advances have dramatically altered the data available to study complex behaviors and healthy aging. Estimates among biologists a decade ago were that 100,000 genes were needed for the cellular processes that are responsible for human behavior and aging, but humans have only a quarter that number of genes (Pennisi, 2005). This finding has fostered a recognition that a gene may have multiple small effects (pleiotropy), that many genes may act in additive and configural fashions to produce small effects both on specific abilities and on general abilities, and that genetic expression can be altered by the social as well as the physical environment in which humans live and work. The advent of single-nucleotide polymorphism (SNP) microarrays permits genome-wide association studies that would have been considered impossible less than a decade ago, and microarrays are on the horizon with which to study many if not all functional DNA polymorphisms in the genome (Butcher, Kennedy, and Plomin, 2006).
In addition to the global analysis of genes (genomics), technologies now exist for large-scale analyses of gene transcripts (transcriptomics), proteins (proteomics), and metabolites (metabolomics) in cells, tissues, and organisms. Among the important advances in quantitative analyses of these data is multivariate genetic analysis, which goes beyond analyzing the variance of each phenotype considered separately to analyze the covariance between them (Butcher, Kennedy, and Plomin, 2006). The