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The integral of the receiver operating characteristic's true positive rate with respect to its false positive rate. Equivalently, the probability that a randomly selected positive is ranked above a randomly selected negative, with half weight for ties. It measures discrimination rather than probability calibration or utility at a particular threshold.

Choose a cutoff on the ranked score and call every eligible locus above it positive. Compare those calls with the fixed reference labels, count $TP,FP,TN,FN$, and plot $(FP/(FP+TN),TP/(TP+FN))$. Repeating over cutoffs generates the receiver operating characteristic.

A very restrictive cutoff starts near $(0,0)$; a cutoff calling everything positive ends at $(1,1)$. The reference labels and eligible locus set must stay fixed during this sweep. Tied scores need a consistent treatment. A single cutoff gives one operating point, whereas the curve describes the trade-off across many possible cutoffs.

A good receiver operating characteristic rises toward the upper-left corner: many reference-positive loci are detected while few reference-negative loci are accepted. The area under the ROC curve summarizes this discrimination over cutoffs. Higher is better in the stated score direction; perfect separation gives one. The displayed value near 0.86 indicates substantial ranking discrimination against this reference.

The area can also be interpreted as the probability that a randomly chosen positive receives a higher score than a randomly chosen negative, with half credit for ties. It does not select a cutoff or demonstrate that most retained calls are correct: those practical questions also depend on prevalence and the relative costs of errors.