Proving that a mutation is truly neutral requires ascertaining neutrality for all possible functional assays. Second, proving that a mutation has an effect requires only one successful experiment. First, scientifically, it is more interesting to study variants that have an effect than those that do not. However, the experimental data are inherently biased for variants affecting function (nonneutral enhancing or damaging protein function) over those that do not (neutral no functional difference between wild type and mutant). 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. These variant effects are predicted to be largely either experimentally undetectable or are not deemed significant enough to be published. Prediction methods indicate that variants in seemingly healthy individuals tend to be neutral or weakly disruptive for protein molecular function. Our findings suggest a genomic basis of the different nondisease phenotypes. Diseases are, arguably, extreme phenotypic variations and are often attributable to one or a few severely functionally disruptive variants. These methods capture the effects of particular variants very well and can highlight trends in populations of variants. 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. The second is surprising: the genomes of healthy individuals appear to carry many variants that are predicted to have some effect on function. The first is expected: coding variants reported in disease-related databases significantly alter the function of affected proteins. Large-scale computational analyses of the growing wealth of genome-variation data consistently tell two distinct stories.
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