The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

Sporadic cases are the norm for complex disease

The results of genome-wide association studies have revealed that most human complex diseases (for example, cancer, diabetes and psychiatric disorders) are affected by a large number of variants, each of which explains a small increase in disease risk, suggesting a pattern of polygenic inheritance. At the same time, it has been argued that most complex diseases are genetically heterogeneous because many sporadic cases are observed, as well as cases with a family history. In this study, under the assumption of polygenic inheritance, we derive the expected proportion of sporadic cases using analytical methods and simulation. We show how the proportion of sporadic cases depends on disease prevalence (K) and heritability on the underlying liability scale (hL2). We predict the underlying heritability and the proportion of sporadic cases for a range of human complex diseases, and show that this proportion is typically large. For a disease with hL2=63% and K=0.4%, such as schizophrenia, >83% of proband cases are predicted to be sporadic (no affected first-, second- and third-degree relatives) in typical families (on an average, two children per couple). For the majority of these diseases, a large proportion of sporadic cases is expected under the polygenic model, implying that the observed large proportion of sporadic cases is not informative to the causal mechanism of a complex genetic disease