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BMC Research Notes

, 5:465

First Online: 28 August 2012Received: 11 February 2012Accepted: 21 August 2012DOI: 10.1186-1756-0500-5-465

Cite this article as: Sugaya, Y. BMC Res Notes 2012 5: 465. doi:10.1186-1756-0500-5-465


BackgroundLinkage analysis is a useful tool for detecting genetic variants that regulate a trait of interest, especially genes associated with a given disease. Although penetrance parameters play an important role in determining gene location, they are assigned arbitrary values according to the researcher’s intuition or as estimated by the maximum likelihood principle. Several methods exist by which to evaluate the maximum likelihood estimates of penetrance, although not all of these are supported by software packages and some are biased by marker genotype information, even when disease development is due solely to the genotype of a single allele.

FindingsPrograms for exploring the maximum likelihood estimates of penetrance parameters were developed using the R statistical programming language supplemented by external C functions. The software returns a vector of polynomial coefficients of penetrance parameters, representing the likelihood of pedigree data. From the likelihood polynomial supplied by the proposed method, the likelihood value and its gradient can be precisely computed. To reduce the effect of the supplied dataset on the likelihood function, feasible parameter constraints can be introduced into maximum likelihood estimates, thus enabling flexible exploration of the penetrance estimates. An auxiliary program generates a perspective plot allowing visual validation of the model’s convergence. The functions are collectively available as the MLEP R package.

ConclusionsLinkage analysis using penetrance parameters estimated by the MLEP package enables feasible localization of a disease locus. This is shown through a simulation study and by demonstrating how the package is used to explore maximum likelihood estimates. Although the input dataset tends to bias the likelihood estimates, the method yields accurate results superior to the analysis using intuitive penetrance values for disease with low allele frequencies. MLEP is part of the Comprehensive R Archive Network and is freely available

KeywordsPenetrance Maximum likelihood estimate Linkage analysis Polynomial evaluation Electronic supplementary materialThe online version of this article doi:10.1186-1756-0500-5-465 contains supplementary material, which is available to authorized users.

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Autor: Yuki Sugaya


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