Near-perfect identification of half-sibling versus niece/nephew–avuncular pairs without pedigree information or genotyped relatives

2026 · doi:10.64898/2026.01.06.697070
preprint OA: closed
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This study developed a computational framework using haplotype-level sharing to accurately distinguish half-sibling from avuncular pairs in large genomic biobanks without pedigree data.

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The paper studies how to identify second-degree relative pairs—specifically half-siblings versus avuncular (aunt/uncle–niece/nephew) pairs—using only genotype data in large biobanks that lack pedigree information and may have cryptic relatedness. The authors develop a genotype-only framework that uses haplotype-level sharing features from across-chromosome phasing and models them with a multivariate Gaussian mixture model to separate these relationship types, also distinguishing nieces/nephews from aunts/uncles within avuncular pairs. Validation is performed against ground-truth labels inferred via a multi-step process from a family graph built using high-confidence first-degree relationships, achieving 100% classification of avuncular pairs and identification of 60/61 half-sibling pairs (98.4% sensitivity, 100% specificity). The study’s main caveat is that the ground-truth relies on inferred labels from first-degree relationship inference rather than experimentally established pedigrees. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Motivation Large-scale genomic biobanks contain thousands of second-degree relatives without pedigree data. Accurately distinguishing half-sibling and avuncular pairs–both of which share approximately 25% of the genome–remains a significant challenge. Current SNP-based methods rely on aggregate Identical-By-Descent (IBD) segment counts and age differences, but substantial overlap in these distributions leads to high misclassification rates. There is a need for a scalable, genotype-only method that can resolve these second-degree ambiguities without requiring observed pedigrees or phenotypic information. Results We present a novel computational framework that achieves complete separation of half-siblings and avuncular pairs using haplotype-level sharing features based on data that has been computationally phased across-chromosomes. These features also allow differentiation of nieces/nephews from aunts/uncles within avuncular pairs. By modeling these features with a multivariate Gaussian mixture model, we achieve exceptional classification performance in biobank-scale data. Ground-truth labels for validation were established through a multi-step inference process within a family graph constructed from high-confidence first, second and third-degree relationships. All 352 ground-truth avuncular pairs and all but one of the 80 half-sibling pairs were correctly classified. Treating avuncular status as the positive class, this implies a specificity of 98.75% and sensitivity of 1. The mixture-implied Bayes error of those without ground-truth labels was higher, estimated at 9.3% under the our modeling assumptions. Our results suggest that the framework could perform comparably on other datasets of similar size. This method provides a robust, scalable solution for pedigree reconstruction and the control of cryptic relatedness in large-scale genomic studies. Contact [email protected] , [email protected] , and [email protected]
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Abstract

Motivation Large-scale genomic biobanks contain thousands of second-degree relatives without pedigree data. Accurately distinguishing half-siblings from avuncular pairs–both sharing approximately 25% of the genome–remains a significant challenge. Current SNP-based methods rely on aggregate Identical-By-Descent (IBD) segment counts and age differences, but substantial overlap in these distributions leads to high misclassification rates. There is a need for a scalable, genotype-only method that can resolve these second-degree ambiguities without requiring observed pedigrees or phenotypic information.

Results

We present a novel computational framework that achieves near-complete separation of half-siblings and avuncular pairs using haplotype-level sharing features derived from across-chromosome phasing. These features also allow differentiation of nieces/nephews from aunts/uncles within avuncular pairs. By modeling these features with a multivariate Gaussian mixture model (GMM), we demonstrate exceptional classification performance in biobank-scale data. Ground-truth labels for validation were established through a multi-step inference process within a family graph constructed from high-confidence first-degree relationships. All 395 ground-truth avuncular pairs were correctly classified, and 60 of 61 half-sibling pairs were correctly identified. Treating (e.g.) half-sibling status as the positive class, this corresponds to 98.4% sensitivity and 100% specificity. Our results suggest that the framework could perform comparably on other datasets of similar size. This method provides a robust, scalable solution for pedigree reconstruction and the control of cryptic relatedness in large-scale genomic studies. Contact emmanuel.sapin{at}colorado.edu, kristen.kelly{at}colorado.edu, and matthew.c.keller{at}colorado.edu Competing Interest Statement The authors have declared no competing interest. Footnotes the gaussian mixture model was updated

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