Clinical genetic sequencing tests often identify variants of uncertain significance (VUS). One source of data that can help classify the pothogenicity of variants is familial cosegregation analysis. Identifying and genotyping relatives for cosegregation analysis can be time consuming and costly. We propose an algorithm that describes a single measure of expected variant information gain from genotyping a single additional relative in a family. Then we explore the performance of this algorithm by comparing actual recruitment strategies used in 35 families who had pursued cosegregation analysis with synthetic pedigrees of possible testing outcomes if the families had pursued an optimized testing strategy instead. For each actual and synthetic pedigree, we calculated the likelihood ratio of pathogenicity as each successive test was added to the pedigree. We analyzed the differences in cosegregation likelihood ratio over time resulting from actual versus optimized testing approaches. Employing the testing strategy indicated by the algorithm would have led to maximal information more rapidly in 30 of the 35 pedigrees (86%). Many clinical and research laboratories are involved in targeted cosegregation analysis. The algorithm we present can facilitate a data driven approach to optimal relative recruitment and genotyping for cosegregation analysis and more efficient variant classification.

Many genomic variants are currently classified, but many more are extremely rare, have minimal associated patient data, and are classified as variants uncertain significance (VUS). Accumulating patient data such as family history and de novo status can help classify variants. Understanding potential timelines for data accumulation and variant classification can inform reporting, diagnosis, and treatment decisions. We modeled future clinical data observations with different strategies for sharing and aggregating clinical evidence for variants across multiple sequencing centers over time. Models illustrate how long it takes for variants to be classified when evidence is or is not shared between clinical laboratories and compared to when only variant interpretations are shared. When sequencing centers share evidence the probability of classifying a one in 100,000 pathogenic variant increases from less than 25% to nearly 80% after one year and to nearly 100% with 5 years of observations. Extremely rare variants have a low likelihood of classification using clinical data even with optimal data sharing. Sharing clinical evidence between laboratories will lead to faster and more certain classifications. Modeling can effectively illustrate the likelihood of variant classification under current classification frameworks and may help define realistic provider and patient expectations.