Calibration And Artifact Diagnostics For SV Association

Why Calibration Diagnostics Matter

A structural-variant association can look compelling even when the recorded genotypes partly reflect sequencing or calling behavior rather than biological variation. A deletion, duplication, or other structural variant may be harder to call in some samples than others. If that difficulty tracks case status, sequencing batch, or another feature related to the phenotype, the resulting association can be misleading. Calibration diagnostics look for such patterns before an association is treated as credible.

No single check establishes that a signal is genuine. Instead, several checks address different failure modes: whether callability differs between phenotype groups, whether it varies across batches, whether apparently null tests show systematic departures from expectation, and whether negative controls produce unexpected associations. A concerning result is a reason to investigate, not by itself proof that the variant is artifactual.

Compare Call Rates Across Phenotype Groups

For a case-control phenotype, compare the proportion of cases with a callable genotype to the corresponding proportion of controls. A substantial difference matters because the association analysis may then compare different subsets of samples in the two phenotype groups. If callability depends on phenotype group, observed genotype distributions can be distorted, especially when missingness is handled as if it were unrelated to genotype or phenotype.

A simple diagnostic treats each sample's callability as a binary outcome: callable or not callable. Let \(n_1\) and \(n_0\) be the numbers of cases and controls, and let \(x_1\) and \(x_0\) be their callable counts. The observed call rates are \(\hat{p}_1=x_1/n_1\) and \(\hat{p}_0=x_0/n_0\). To test the null hypothesis of equal call rates, use the pooled estimate under that null and a two-sided two-proportion score test, provided the observations are independent and expected counts are adequate.

Under the null and the large-sample conditions, \(z\) is approximately standard normal. The two-sided p-value is \(2P(Z\geq |z|)\), where \(Z\sim N(0,1)\). The test detects a difference in callability; it does not identify its cause. Differences could arise from a technical artifact, but they could also reflect study design or another feature correlated with phenotype.

The test assumes independent samples and sufficiently large expected numbers of callable and noncallable observations in both groups. Here, under the null, each group has an expected \(300(0.925)=277.5\) callable samples and \(300(0.075)=22.5\) noncallable samples, so the large-sample approximation is reasonable. If expected counts are sparse, use an appropriate exact procedure rather than relying on the normal approximation. In matched, related, or otherwise dependent samples, the independent two-proportion test is not appropriate without accounting for that dependence.

Look for Batch-Related Callability

Sequencing batch, laboratory, platform, or calling pipeline can affect whether a variant is detected. Compare call rates across these groups, and check whether phenotype groups are unevenly distributed across them. A batch difference can be harmless if it is unrelated to phenotype and handled appropriately, but it becomes a serious concern when batch and phenotype are entangled: the analysis may then have difficulty separating a biological association from a technical one.

For a table with \(r\) batches and two callability outcomes, the Pearson chi-square test compares observed cell counts \(O_{ij}\) with counts \(E_{ij}\) expected if callability and batch are independent. The statistic is \(\sum_{i,j}(O_{ij}-E_{ij})^2/E_{ij}\), with \((r-1)(2-1)\) degrees of freedom when the usual large-sample approximation applies. This is an omnibus test: evidence of heterogeneity says that not all batch call rates are alike, but does not say which batch differs or why.

A useful follow-up is to tabulate phenotype by batch as well as callability by batch. If most cases were processed in one batch and most controls in another, an association can be difficult to distinguish from a batch effect. A regression adjustment for recorded batch may help when there is adequate overlap, but adjustment cannot reliably separate effects when phenotype and batch are nearly or completely confounded. Recalling samples, restricting to comparable batches, or seeking independent replication may be needed.

Assess Null Association Patterns

A null association analysis uses a phenotype or variant for which no association is expected under the diagnostic design. Under a correctly specified null model and suitable calibration, p-values are approximately uniform between 0 and 1. In particular, among many valid null tests, the expected proportion below a prespecified threshold \(\alpha\) is \(\alpha\). This expectation applies across repeated tests; it does not mean that every individual p-value should be large.

An excess of small p-values across null tests can indicate systematic problems, such as population structure, unmodeled relatedness, phenotype or batch imbalance, or technical artifacts. A single small p-value may occur by chance. Conversely, an apparently uniform set of p-values does not prove that every variant is well called: a localized artifact affecting one variant may not create a broad pattern. Inspect results by batch, phenotype group, call rate, and other relevant technical strata rather than relying on one summary.

Use Negative Controls

A negative control is an outcome, exposure, or genetic feature chosen because the study design gives no plausible causal or biological reason for the association being tested, while preserving relevant sources of bias. A negative-control outcome can reveal whether the variant appears associated with an unrelated phenotype measured through similar procedures. A negative-control variant or region can probe whether the same technical pipeline produces unexpected signals. The control is most informative when it shares the technical vulnerabilities of the target analysis.

Negative controls require justification. A supposedly unrelated phenotype may share causes with the primary phenotype, and a control region may have different mapping or calling properties from the structural variant of interest. A positive association with a negative control is therefore a warning that may reflect residual confounding, a technical problem, chance, or an invalid control choice. It should prompt investigation rather than automatic deletion of the target result.

A Practical Diagnostic Sequence

The checks work best as a connected review. First establish how callability was defined and whether the criteria were applied consistently. Then compare call rates across phenotype groups and technical strata. Next examine whether cases and controls are balanced across batches, and whether the association pattern is stable within comparable strata. Finally, evaluate null analyses and negative controls whose assumptions are explicit.

Common Pitfall: Treating a Diagnostic as a Verdict

A low p-value for a call-rate comparison does not mean that a variant association is definitely false; it means the observed callability pattern is difficult to reconcile with equal rates under the test assumptions. Likewise, a nonsignificant comparison does not prove equal call rates, particularly when sample sizes are small or missingness is substantial. Report the rates and counts so that practical differences remain visible even when a test lacks power.

Another mistake is to count missing genotypes as reference genotypes or noncarriers. A sample without a callable genotype does not provide evidence that the variant is absent. Keep callability distinct from dosage, and explain how missing calls enter each association analysis. Finally, do not regard adjustment for batch as a universal repair: when batch and phenotype have little overlap, the data may not identify their separate contributions.