Maximum likelihood pedigree reconstruction using integer programming

12 pages•Published: May 15, 2012

Abstract

Pedigrees are `family trees' relating groups of individuals which can usefully be seen as Bayesian networks. The problem of finding a maximum likelihood pedigree from genotypic data is encoded as an integer linear programming problem. Two methods of ensuring that pedigrees are acyclic are considered. Results on obtaining maximum likelihood pedigrees relating 20, 46 and 59 individuals are presented. Running times for larger pedigrees depend strongly on the data used but generally compare well with those in the literature. Solving is particularly fast when allele frequency is uniform.

Keyphrases: bayesian networks, integer programming, pedigrees

In: Agostino Dovier, Alessandro Dal Palù and Sebastian Will (editors). WCB10. Workshop on Constraint Based Methods for Bioinformatics, vol 4, pages 8-19.

Links:	https://easychair.org/publications/paper/bTM
	https://doi.org/10.29007/sghd

BibTeX entry

@inproceedings{WCB10:Maximum_likelihood_pedigree_reconstruction,
  author    = {James Cussens},
  title     = {Maximum likelihood pedigree reconstruction using integer programming},
  booktitle = {WCB10. Workshop on Constraint Based Methods for Bioinformatics},
  editor    = {Agostino Dovier and Alessandro Dal Palù and Sebastian Will},
  series    = {EPiC Series in Computing},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/bTM},
  doi       = {10.29007/sghd},
  pages     = {8-19},
  year      = {2012}}

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