The clinical validity and utility of predictive testing for disease based on genetic information currently varies dramatically depending on the mode of inheritance of the disease, what is known about the specific genetic variants implicated, protective variants that may be important, as well as the degree of redundancy of genetic information with other traditional risk factors that are routinely, easily, and more inexpensively assessed clinically (e.g., family history of disease). For instance, work that has uncovered the genetic variations involved in diseases that are inherited according to simple Mendelian principles has led to the development and availability of predictive genetic tests that have nearly 100% accuracy. Thus, a decade after the Huntington’s disease (HD) gene was mapped to chromosome 4, the pathogenic mutation was localized and identified as a CAG-repeat expansion for which testing is now available to offspring of individuals with the disease. This predictive test has high reliability, validity, and clinical utility given the mode of inheritance of the disease and the fact that the causal mutation is known and can be measured. Furthermore, test results indicating the presence of the CAG-repeat provide information that is not redundant with standard clinical risk factors such as family history (i.e., one can have a family history and still not inherit HD), and therefore, has high clinical and personal utility.
Similarly, genetic linkage studies in families with hereditary breast, ovarian, and colon cancers have identified several important high penetrance genetic variants, which are now currently being used for screening, disease risk counseling, and preventive treatment programs for breast cancer . For example, although breast cancer is a complex disease (i.e., non-Mendelian), the high penetrance susceptibility genetic variations identified, specifically variations in the BRCA1 and BRCA2 genes, confer a 50–80% chance of developing breast cancer by age 70 . Thus, a positive test result in this case provides critical additional information (beyond clinical risk factors) with respect to degree of disease risk. Furthermore, based on genetic test results, changes in surveillance practices (e.g., frequency of mammography) or even decisions as to whether to undergo prophylactic surgery to decrease risk are often recommended by health care providers.
In contrast, susceptibility testing in the case of common, complex diseases for which no high penetrance risk variants have been identified is highly controversial (though currently performed in some contexts). As alluded to previously, it is likely that most complex diseases are caused by multiple environmental factors and multiple low penetrance common genetic factors, possibly together with rare variations that are yet to be identified. This is illustrated by the fact that most of the risk variants underlying complex diseases that have been identified through GWAS thus far have been characterized by effect sizes that are small, with odds ratios typically between 1.1 and 1.5. In addition, for most diseases, the variants identified explain little of the total genetic variance known from heritability estimates (Table 1). Nevertheless, work in this area continues in order to determine the feasibility of combining information from many small-effect risk variants in order to develop more complex algorithms that can accurately predict an individual’s genetic risk for common, complex diseases , including risk for neuropsychiatric disease . Early studies suggest, however, that while the use of combined genotypes of small-effect variants identified to date is informative, this approach does not necessarily confer improved risk predictions when compared with traditional clinical risk factors alone as there is often much redundancy. Thus, this hinders, to some extent, broader-scale preventive efforts based on susceptibility testing for complex diseases using genomic information derived from GWAS.
Susceptibility testing using genomic information may also benefit from utilization of protective genetic variants; however, research to discover such variants is incomplete. For example, although much work is ongoing to identify variants associated with healthy aging, findings are controversial and to our knowledge, broadly applicable algorithms to predict longevity that take into account the frequency of the phenotype have not been constructed. In addition, identification of genetic variants associated with protective traits such as optimism and resilience may serve to further clarify risk predictions, particularly for neuropsychiatric disorders; however, to date there has been a lack of needed studies in this area.