Abstract
Large-scale computational analyses of the growing wealth of genome-variation data consistently tell two distinct stories. The first is expected: coding variants reported in disease-related databases significantly alter the function of affected proteins. The second is surprising: the genomes of healthy individuals appear to carry many variants that are predicted to have some effect on function. As long as the complete experimental analysis of all human genome variants remains impossible, computational methods, such as PolyPhen, SNAP, and SIFT, might provide important insights. These methods capture the effects of particular variants very well and can highlight trends in populations of variants. Diseases are, arguably, extreme phenotypic variations and are often attributable to one or a few severely functionally disruptive variants. Our findings suggest a genomic basis of the different nondisease phenotypes. Prediction methods indicate that variants in seemingly healthy individuals tend to be neutral or weakly disruptive for protein molecular function. These variant effects are predicted to be largely either experimentally undetectable or are not deemed significant enough to be published. This may suggest that nondisease phenotypes arise through combinations of many variants whose effects are weakly nonneutral (damaging or enhancing) to the molecular protein function but fall within the wild-type range of overall physiological function.