An alternative to examining the association between individual markers and endophenotypes is to construct a polygenic risk score, which captures the aggregate effect of many variants. Such scores are weighted composites of allele counts, with the weights consisting of the regression coefficients associated with the endophenotype from GWAS or meta-analysis. The development, or training, sample and test sample should ideally be independent, of course. If not, then cross-validation techniques (e.g., k-fold or leave-one-out cross-validation) should be used. Weights can be based on the results from the consortia that are forming. The regression coefficients may thus be for predicting a related clinical phenotype, rather than an endophenotype. The simplest approach is to use all markers to generate a risk score, but researchers more commonly construct a set of risk scores by using increasingly stringent p-value thresholds are commonly used to identify “significant” markers, such as p-values ranging from .50 to very small values. At a minimum, significant associations between such risk scores based on clinical outcomes and endophenotypes attest to the construct validity of the endophenotype for the clinical outcome in
