Improved computations for relationship inference using low-coverage sequencing data
Artikel i vetenskaplig tidskrift, 2023

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 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.


LcNGS Pedigree inference Bayesia

Pedigree inference



Petter Mostad

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Andreas O Tillmar

Linköpings universitet

Daniel Kling

Norges miljø- og biovitenskapelige universitet

Oslo universitetssykehus

BMC Bioinformatics

1471-2105 (ISSN)

Vol. 24 1 90


Hälsa och teknik


Bioinformatik (beräkningsbiologi)

Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci)

Bioinformatik och systembiologi





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