title: An integrated Bayesian analysis of LOH and copy number data.
abstract: BACKGROUND
  Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events.
  
  
  RESULTS
  To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation.
  
  
  CONCLUSIONS
  We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.
authors.affiliation: Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Manno-Lugano, Switzerland. paola@idsia.ch
authors.forename: Paola M V
authors.initials: PM
authors.lastname: Rancoita
authors.forename: Marcus
authors.initials: M
authors.lastname: Hutter
authors.forename: Francesco
authors.initials: F
authors.lastname: Bertoni
authors.forename: Ivo
authors.initials: I
authors.lastname: Kwee
mesh_terms: D000483:Alleles; D001483:Base Sequence; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics; D004247:DNA / Q000235:genetics; D018628:Gene Dosage; D015894:Genome, Human; D006801:Humans; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000235:genetics; D019656:Loss of Heterozygosity; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA; D024182:Uniparental Disomy
chemical_list: D004247:DNA