One physiology does not fit all: a path from data variability to "physiogenetics"?

Data variability is a costly complication of biomedical experimentation because the same experiment must be repeated a sufficient number of times so that the sample mean becomes a credible representation of the entire population. Since sampling is ideally done randomly in populations normalized for environmental and genetic backgrounds, data variability is viewed as a purely statistical issue reflecting the distribution in the population and captured as the standard deviation of the sampled data. The factors contributing to data variability are not analyzed by statistical methods; for want of a better explanation, data scatter is simply attributed to random noise and/or methodological limitations. In this commentary, evidence is discussed that documents an important role of interindividual biological diversity as a cause for data variability based on studies in which repeated sampling in the same individual permitted statistical comparisons between individuals in the same sample. Significant differences were found for proximal fluid reabsorption and plasma renin concentration between sample means of individuals of the same population. Furthermore, arterial blood pressure varied significantly between individual mice independently of strain and sex. Recognition of the extent of interindividual variability has important implications for data reproducibility, data collection, and data presentation in physiological research. Such nonrandom data variability may have different causes, but DNA modifications by genetic or epigenetic mechanisms could generate phenotype variants without being associated with disease symptoms. Exploration of the heritability of phenotypical diversity in physiology may be defined as "physiogenetics," and it would thus be the physiological corollary of pharmacogenetics and pharmacogenomics.