ArticleBMC bioinformatics2023
Improved computations for relationship inference using low-coverage sequencing data.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Unattainable regions in the space of genetic relationships.Journal of mathematical biology · 2026Article
- Individual identification and kinship testing from hair shaft nuclear DNA: leveraging short amplicon strategy and bioinformatics models.International journal of legal medicine · 2026Article
- Comparative assessment of SNP genotyping assays for challenging forensic samples utilizing ancient DNA methods.Genome biology · 2025Article
- Article
- On forensic likelihood ratios from low-coverage sequencing.Forensic science international. Genetics · 2025Article
- Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations.Forensic science international. Genetics · 2025Article
- Article
- A likelihood ratio framework for inferring close kinship from dynamically selected SNPs.Frontiers in genetics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pedigree inference, for example determining whether two persons are second cousins or unrelated, can be done by comparing their genotypes at a selection of genetic markers. When the data for one or more of the persons is from low-coverage next generation sequencing (lcNGS), currently available computational methods either ignore genetic linkage or do not take advantage of the probabilistic nature of lcNGS data, relying instead on first estimating the genotype. We provide a method and software (see familias.name/lcNGS) bridging the above gap. Simulations indicate how our results are considerably more accurate compared to some previously available alternatives. Our method, utilizing a version of the Lander-Green algorithm, uses a group of symmetries to speed up calculations. This group may be of further interest in other calculations involving linked loci.
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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.