SNIP: An adaptation of sorted neighborhood methods for deduplicating pedigree data
Theodore Huang, Matthew Ploenzke, Danielle Braun
The Annals of Applied Statistics · 2023 · Volume 17 · Issue 3
Abstract
Pedigree data contain family history information that is used to analyze hereditary diseases. These clinical data sets may contain duplicate records due to the same family visiting a clinic multiple times or a clinician entering multiple versions of the family for testing purposes. Inferences drawn from the data or using them for training or validation without removing the duplicates could lead to invalid conclusions, and hence identifying the duplicates is essential. Since family structures can be complex, direct application of existing deduplication algorithms may not be straightforward. We first motivate the importance of deduplication by examining the impact of pedigree duplicates on model performance when training and validating a familial risk prediction model. We then introduce an unsupervised algorithm, which we call SNIP (Sorted NeIghborhood for Pedigrees), that builds on the sorted neighborhood method to find efficiently and to classify pair comparisons by leveraging the inherent hierarchical nature of the pedigrees. We conduct a simulation study to assess the performance of the algorithm and find parameter configurations where the algorithm is able to accurately detect the duplicates. We then apply the method to data from the Risk Service, which includes over 300,000 pedigrees at high risk of hereditary cancers, and uncover large clusters of potential duplicate families. After removing 104,520 pedigrees (33% of original data), the resulting Risk Service data set can now be used for future analysis, training, and validation. The algorithm is available as an R package snipR at https://github.com/bayesmendel/snipR.
Keywords
Deduplication, entity matching, pedigree data, sorted neighborhood
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