Improved computations for relationship inference using low-coverage sequencing data

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 compu...

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Bibliographic Details
Published in:BMC bioinformatics 2023-03, Vol.24 (1), p.90-90, Article 90
Main Authors: Mostad, Petter, Tillmar, Andreas, Kling, Daniel
Format: Article
Language:English
Subjects:
Algorithms
Bayesian
Biochemistry and Molecular Biology
Biokemi och molekylärbiologi
Computer applications
Genetic Linkage
Genetic markers
Genetics
Genotype
Genotype & phenotype
Genotypes
Hypotheses
Inference
LcNGS
LcNGS Pedigree inference Bayesia
Mathematical analysis
Methods
Missing data
Models, Genetic
Mutation
Next-generation sequencing
Pedigree
Pedigree inference
Polymorphism, Single Nucleotide
Probability
Simulation
Software
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Summary: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.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-023-05217-z