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\begin{document}

\title{An Algorithm for Optimal Testing in Co-segregation Analysis}



\author[1]{Ronald Buie}%
\author[2]{John Rañola}%
\author[1]{Annie Chen}%
\author[1]{Brian Shirts}%
\affil[1]{University of Washington School of Medicine}%
\affil[2]{Ambry Genetics Corp}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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