ArticleiScience2020
Maximal Perfect Haplotype Blocks with Wildcards.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Haplotype Matching with GBWT for Pangenome Graphs.bioRxiv : the preprint server for biology · 2025Article
- Fast inference of genetic recombination rates in biobank scale data.Genome research · 2023Article
- HaploBlocks: Efficient Detection of Positive Selection in Large Population Genomic Datasets.Molecular biology and evolution · 2023Article
- FastRecomb: Fast inference of genetic recombination rates in biobank scale data.bioRxiv : the preprint server for biology · 2023Article
- Computational graph pangenomics: a tutorial on data structures and their applications.Natural computing · 2022Article
- P-smoother: efficient PBWT smoothing of large haplotype panels.Bioinformatics advances · 2022Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent work provides the first method to measure the relative fitness of genomic variants within a population that scales to large numbers of genomes. A key component of the computation involves finding maximal perfect haplotype blocks from a set of genomic samples for which SNPs (single-nucleotide polymorphisms) have been called. Often, owing to low read coverage and imperfect assemblies, some of the SNP calls can be missing from some of the samples. In this work, we consider the problem of finding maximal perfect haplotype blocks where some missing values may be present. Missing values are treated as wildcards, and the definition of maximal perfect haplotype blocks is extended in a natural way. We provide an output-linear time algorithm to identify all such blocks and demonstrate the algorithm on a large population SNP dataset. Our software is publicly available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.