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Polymorphic repeats in human genes
| Details |
Inventors: Garner, Harold R.; Wren, Jonathan D.; Minna, John D.; Fondon, III, John W.;
Assignee: Board of Regents, The University of Texas System (Austin, TX)
Primary Examiner: Horlick; Kenneth R.
Assistant Examiner: Strzelecka; Teresa
Attorney, Agent or Firm: Osman; Richard Aron
The invention provides computational methods and compositions for identifying polymorphic repeats in genes. Candidate polymorphic repeats are identified by detecting tandem repeats in a target coding sequence, scoring the repeats for polymorphic probability, and generating a dataset correlating the repeats with polymorphic probability. Actual polymorphic repeat are identified by further detecting the candidate polymorphic repeat in each of a population of different coding sequences, and determining whether the candidate polymorphic repeat is polymorphic in the population. Computationally derived polymorphic repeats are validated with phenotypic variations and these correlates are used to detect the presence or absence of such phenotypic variation in test genes. Variances at polymorphic repeats are identified by detecting in a test gene or coding region the presence or absence of variance at a disclosed unconventional polymorphic repeat. |
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DETAILED DESCRIPTION OF PARTICULAR EMBODIMENTS OF THE INVENTION The following descriptions of particular embodiments and examples are offered by way of illustration and not by way of limitation.
The invention provides tools for identifying genotypic variation by measuring the polymorphism rate for repeats in genes, correlating this data with other genetic and genomic data, selective testing of this class of genes against genetic panels (e.
g.
affected/non-affected, families), and/or through association with homologous genes in model organisms which are directly manipulated to reveal phenotype.
In one aspect, the invention provides methods for identifying a candidate polymorphic repeat within a coding sequence, which comprise the steps of (a) detecting tandem repeats in a target coding sequence; (b) scoring the repeats for polymorphic probability; and (c) generating a dataset correlating the repeats with polymorphic probability to identify a candidate polymorphic repeat.
The repeats may vary from one to hundreds of nucleotides in length and may be present in from two to dozens of copies.
The repeats may be of varying degrees of identity (purity or homogeneity), preferably substantially pure, more preferably pure.
The methods are applied to any coding sequence, including a single coding sequence (e.
g.
a single cDNA sequence), a concatamerized coding sequence and a coding sequence library.
The coding sequence may determined physically, e.
g.
from actual transcribed polynucleotides, or conceptually, e.
g.
inferred in silico from genomic sequence.
The detecting, scoring and/or generating steps may be implemented manually and/or by computer.
In a particular embodiment, the steps are effected by a computer program, such as the Rep-X program described below.
The scoring step generally comprises determining at least the type and number of the repeats, though measures of repeat purity may also be considered.
Rep-X is an algorithm that works by iteratively comparing each base pair, x, in a given nucleic acid sequence with the ones following it
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Drugs 
