Computation method to identify differential allelic gene expression and novel imprinted genes.
BACKGROUND
Genomic imprinting plays an important role in both normal development and diseases. Abnormal imprinting is strongly associated with several human diseases including cancers. Most of the imprinted genes were discovered in the neighborhood of the known imprinted genes. This approach is difficult to extend to analyze the whole genome. We have decided to take a computational approach to systematically search the whole genome for the presence of mono-allelic expressed genes and imprinted genes in human genome.


RESULTS
A computational method was developed to identify novel imprinted or mono-allelic genes. Individuals represented in human cDNA libraries were genotyped using Bayesian statistics, and differential expression of polymorphic alleles was identified. A significant reduction in the number of libraries that expressed both alleles, measured by Z-statistics, is a strong indicator for an imprinted or a mono-allelic gene.


BACKGROUND
The data sets are available at http://leelab.nci.nih.gov/leelab/jsp/IGDM/IGDM.html
Yang
Howard H
HH
Laboratory of Population Genetics, National Cancer Institute, Gaithersburg, MD 20877, USA.
Hu
Ying
Y
Edmonson
Michael
M
Buetow
Kenneth
K
Lee
Maxwell P
MP
D000465:Algorithms; D000483:Alleles; D020224:Expressed Sequence Tags; D005786:Gene Expression Regulation / Q000235:genetics; D005787:Gene Frequency / Q000235:genetics*; D015723:Gene Library; D018392:Genomic Imprinting / Q000235:genetics*; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software