Maximum likelihood inference of imprinting and allele-specific expression from EST data.
BACKGROUND
In a diploid organism the proportion of transcripts that are produced from the two parental alleles can differ substantially due, for example to epigenetic modification that causes complete or partial silencing of one parental allele or to cis acting polymorphisms that affect transcriptional regulation. Counts of SNP alleles derived from EST sequences have been used to identify both novel candidates for genomic imprinting as well as examples of genes with allelic differences in expression.


RESULTS
We have developed a set of statistical models in a maximum likelihood framework that can make highly efficient use of public transcript data to identify genes with unequal representation of alternative alleles in cDNA libraries. We modelled both imprinting and allele-specific expression and applied the models to a large dataset of SNPs mapped to EST sequences. Using simulations, matched closely to real data, we demonstrate significantly improved performance over existing methods that have been applied to the same data. We further validated the power of this approach to detect imprinting using a set of known imprinted genes and inferred a set of candidate imprinted genes, several of which are in close proximity to known imprinted genes. We report evidence that there are undiscovered imprinted genes in known imprinted regions. Overall, more than half of the genes for which the most data are available show some evidence of allele-specific expression.


BACKGROUND
Software is available from the authors on request.
Seoighe
Cathal
C
Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa. cathal@science.uct.ac.za
Nembaware
Victoria
V
Scheffler
Konrad
K
D000483:Alleles; D002874:Chromosome Mapping / Q000379:methods*; D030541:Databases, Genetic; D020224:Expressed Sequence Tags; D015870:Gene Expression / Q000235:genetics; D018392:Genomic Imprinting / Q000235:genetics*; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000235:genetics*
D014157:Transcription Factors