Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA)

• Published in: BMC bioinformatics Vol. 9; no. 1; p. 261
• Main Authors: González, Juan R, Carrasco, Josep L, Armengol, Lluís, Villatoro, Sergi, Jover, Lluís, Yasui, Yutaka, Estivill, Xavier
• Format: Journal Article
• Language: English
• Published: London BioMed Central 04.06.2008; BioMed Central Ltd; BMC
• Subjects: Algorithms; Autistic Disorder - genetics; Bioinformatics; Biomedical and Life Sciences; Biotecnologia; Breast Neoplasms - genetics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Confidence Intervals; DiGeorge Syndrome - genetics; DNA probes; DNA Probes - analysis; DNA testing; Gene amplification; Gene Dosage; Genetic Markers; Genetic Predisposition to Disease - epidemiology; Genetic Variation; Genètica molecular; Humans; Life Sciences; Linear Models; Methodology; Methodology Article; Methods; Microarrays; Molecular probe techniques; Molecular Probe Techniques - statistics & numerical data; Polymerase Chain Reaction; Prader-Willi Syndrome - genetics; Predictive Value of Tests; Reference Standards; Reference Values; Single-Blind Method; Stochastic Processes; Spain; Tolerance Interval; DiGeorge Syndrome; Control Probe; Good Linear Unbiased Predictor
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-9-261

Background MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample. Results Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace. Conclusion Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.