Other disease traits do not have well-established quantitative measures. In these circumstances, individuals are usually classified as either affected or unaffected – a binary categorical variable. Consider the vast difference in measurement error associated with classifying individuals as either “case” or “control” versus precisely measuring a quantitative trait. For example, multiple sclerosis is a complex clinical phenotype that is often diagnosed over a long period of time by ruling out other possible conditions. However, despite the “loose” classification of case and control, GWAS of multiple sclerosis have been enormously successful, implicating more than 10 new genes for the disorder [22]. So while quantitative outcomes are preferred, they are not required for a successful study.
