Document Detail


Maximum Likelihood Pedigree Reconstruction Using Integer Linear Programming.
MedLine Citation:
PMID:  23034892     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Large population biobanks of unrelated individuals have been highly successful in detecting common genetic variants affecting diseases of public health concern. However, they lack the statistical power to detect more modest gene-gene and gene-environment interaction effects or the effects of rare variants for which related individuals are ideally required. In reality, most large population studies will undoubtedly contain sets of undeclared relatives, or pedigrees. Although a crude measure of relatedness might sometimes suffice, having a good estimate of the true pedigree would be much more informative if this could be obtained efficiently. Relatives are more likely to share longer haplotypes around disease susceptibility loci and are hence biologically more informative for rare variants than unrelated cases and controls. Distant relatives are arguably more useful for detecting variants with small effects because they are less likely to share masking environmental effects. Moreover, the identification of relatives enables appropriate adjustments of statistical analyses that typically assume unrelatedness. We propose to exploit an integer linear programming optimisation approach to pedigree learning, which is adapted to find valid pedigrees by imposing appropriate constraints. Our method is not restricted to small pedigrees and is guaranteed to return a maximum likelihood pedigree. With additional constraints, we can also search for multiple high-probability pedigrees and thus account for the inherent uncertainty in any particular pedigree reconstruction. The true pedigree is found very quickly by comparison with other methods when all individuals are observed. Extensions to more complex problems seem feasible.
Authors:
James Cussens; Mark Bartlett; Elinor M Jones; Nuala A Sheehan
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-3
Journal Detail:
Title:  Genetic epidemiology     Volume:  -     ISSN:  1098-2272     ISO Abbreviation:  Genet. Epidemiol.     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-4     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8411723     Medline TA:  Genet Epidemiol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© 2012 Wiley Periodicals, Inc.
Affiliation:
Department of Computer Science, University of York, York, North Yorkshire, United Kingdom.
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