Improved computations for relationship inference using low-coverage sequencing data
Journal article, 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 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.

Bayesian

LcNGS Pedigree inference Bayesia

Pedigree inference

lcNGS

Author

Petter Mostad

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Andreas O Tillmar

Linköping University

Daniel Kling

Norwegian University of Life Sciences

Oslo University Hospital

BMC Bioinformatics

14712105 (eISSN)

Vol. 24 1 90

Areas of Advance

Health Engineering

Subject Categories

Bioinformatics (Computational Biology)

Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)

Bioinformatics and Systems Biology

DOI

10.1186/s12859-023-05217-z

PubMed

36894920

More information

Latest update

3/23/2023