ArticleAmerican journal of human genetics2019
Fast and Accurate Shared Segment Detection and Relatedness Estimation in Un-phased Genetic Data via TRUFFLE.
Article in American journal of human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Blocks of Identity by Descent Reveal the Dual Role Played by Reef Fish Larvae for Retention and Dispersal.Molecular ecology · 2026Article
- IBS-CNN: a novel computer vision-based kinship classification model for high-density SNP microarray.International journal of legal medicine · 2026Article
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- Ancient genomes reveal early-stage admixture and genetic diversity in the Northwestern Kyushu Yayoi.Scientific reports · 2026Article
- clusIBD: Robust Detection of Identity-by-descent Segments Using Unphased Genetic Data from Poor-quality Samples.Genomics, proteomics & bioinformatics · 2025Article
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- Scalable analysis of large multi-ancestry biobanks by leveraging sparse ancestry-adjusted sample-relatedness.Research square · 2024Article
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- Unraveling the genomic diversity and admixture history of captive tigers in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Biobank-scale inference of multi-individual identity by descent and gene conversion.American journal of human genetics · 2024Article
- Characterizing identity by descent segments in Chinese interpopulation unrelated individual pairs.Molecular genetics and genomics : MGG · 2024Article
- Whole-exome sequencing in familial type 2 diabetes identifies an atypical missense variant in the RyR2 gene.Frontiers in endocrinology · 2024Article
- Biobank-scale inference of multi-individual identity by descent and gene conversion.bioRxiv : the preprint server for biology · 2023Article
- Whole-genome sequencing of multiple related individuals with type 2 diabetes reveals an atypical likely pathogenic mutation in the PAX6 gene.European journal of human genetics : EJHG · 2023Article
- Parent-offspring inference in inbred populations.Molecular ecology resources · 2022Article
- Including diverse and admixed populations in genetic epidemiology research.Genetic epidemiology · 2022Article
- A generalized robust allele-based genetic association test.Biometrics · 2022Article
- Evaluating the utility of identity-by-descent segment numbers for relatedness inference via information theory and classification.G3 (Bethesda, Md.) · 2022Article
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3 authors.
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Abstract
Relationship estimation and segment detection between individuals is an important aspect of disease gene mapping. Existing methods are either tailored for computational efficiency or require phasing to improve accuracy. We developed TRUFFLE, a method that integrates computational techniques and statistical principles for the identification and visualization of identity-by-descent (IBD) segments using un-phased data. By skipping the haplotype phasing step and, instead, relying on a simpler region-based approach, our method is computationally efficient while maintaining inferential accuracy. In addition, an error model corrects for segment break-ups that occur as a consequence of genotyping errors. TRUFFLE can estimate relatedness for 3.1 million pairs from the 1000 Genomes Project data in a few minutes on a typical laptop computer. Consistent with expectation, we identified only three second cousin or closer pairs across different populations, while commonly used methods identified a large number of such pairs. Similarly, within populations, we identified many fewer related pairs. Compared to methods relying on phased data, TRUFFLE has comparable accuracy but is drastically faster and has fewer broken segments. We also identified specific local genomic regions that are commonly shared within populations, suggesting selection. When applied to pedigree data, we observed 99.6% accuracy in detecting 1
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