Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
1999-10-14
2002-06-04
Brusca, John S. (Department: 1631)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C435S006120, C435S091100, C435S091200
Reexamination Certificate
active
06401043
ABSTRACT:
BACKGROUND OF THE INVENTION
This application concerns the field of identification of genetic variances in genomes of mammalians. In the cases of gene sequence variances within the human genome in human populations, the identification of said sequence variation has utility in determining response to drug therapy.
Many drugs or other treatments are known to have highly variable safety and efficacy in different individuals. A consequence of such variability is that a given drug or other treatment may be highly effective in one individual, and ineffective or not well tolerated in another individual. Thus, administration of such a drug to an individual in whom the drug would be ineffective would result in wasted cost and time during which the patient's condition may significantly worsen. Also, administration of a drug to an individual in whom the drug would not be tolerated could result in a direct worsening of the patient's condition and could even result in the patient's death.
For some drugs, up to 99% of the measurable variation in selected pharmacokinetic parameters has been shown to be inherited, or associated with genetic factors. For a limited number of drugs, discrete gene sequence variances have been identified in specific genes that are involved in drug action, and these variances have been shown to account for the variable efficacy or safety of the drug in different individuals.
The exponentially growing number of publicly available expressed sequence tags (ESTs) have led to the development of approaches to rapidly identify high-throughput methods for the detection of single nucleotide polymorphisms (SNPs). These methods focus on the manipulation of available sequence data to identify the most common form of DNA sequence variation, SNPs. Sequence fragments from many different individuals can be assembled into overlapping sequence assemblies and genetic differences at the single nucleotide base level can be identified. A variety of different techniques have been developed for rapidly identifying potential SNPs from these assemblies and for scoring these polymorphisms in such a way to distinguish them from sequencing error or other experimental artifact. In such a way, sequence scanning can identify potentially informative genetic markers which can then be correlated to physiologic function, pathophysiologic disease, or drug or therapeutic intervention response.
Manual methods to determine the polymorphisms within a gene or gene family are best suited for a gene or gene family having small numbers of base pairs. This method is user or investigator dependent and requires significant effort in the analysis and interpretation.
Automated methods that make use of sequencing chromatograms to assist in the determination of quality and locations of polymorphisms allow analysis of additional sequences. Unfortunately, automated polymorphism detection is complemented with visual inspection of the chromatogram traces and such methods are best suited for polymorphism detection in gene families or in genomic loci where the base pair number is in the tens of thousands. A further limitation to this method is that not all chromatograms are available for each of the ESTs. Thus, this method may frequently require further substantial sequence data and corresponding information.
In establishing a link between drug response and genetic polymorphism, one must use all available ESTs and thus a computational method for the high-throughput analysis of this sequence data for the identification of these potentially critical genetic polymorphisms. Not all of the identified differences in the ESTs data are SNPs, therefore, it is critical to establish reasonable and statistically stringent strategies to ensure that this analysis results in SNP detection within legitimate confidence limits.
Recently there have been several papers describing computational methods for the detection of genetic polymorphism. One, (Picoult-Newberg et al.) describes a staged-filter model. The method employs the sequence alignment capabilities of the PHRAP computer program (Phil Green, University of Washington) for assembly, and does not attempt to optimize this step. The method also utilize certain calculations in order to remove patches of low-quality sequence having particular characteristics. The method does not include use of any statistical scoring techniques, relying instead on confirmation by laboratory methods.
A second (Buetow et al.) method utilizes sequence data for which quality scores and chromatograms are available that is used as the basis for assembly, again with the use of the PHRAP program. Certain calculations are performed to remove particular types of low-quality sequence. The method makes use of the quality and chromatogram data within these processes, presumably to improve error rates, at the cost of not being able to use sequence data directly from the database, especially sequence data for which such additional information may not be available. The method follows the filtration process with a statistical scoring method based on Baysian statistics.
SUMMARY OF THE INVENTION
The inventors have determined that the identification of gene sequence variances within genes that may be involved in drug action is important for determining whether genetic variances account for variable drug efficacy and safety and for determining whether a given drug or other therapy may be safe and effective in an individual patient. Provided in this invention is a method for the identification of such gene sequence variances which can be useful in connection with predicting differences in response to treatment and selection of appropriate treatment of a disease or condition.
In the present invention, we have identified a computational method for the rapid determination of genetic sequence variation for the purposes of determining the correlation of drug response with genetic variation for a population. In addition, the method can also be used in other applications for which detection of sequence variances is desired. This invention has utility, for example, in programs including drug development, medical management programs, and retrospective analysis of a human population to drug therapy.
In a first aspect, this invention provides a method for identifying at least one variance in at least one gene. The method involves obtaining at least three independent electronic nucleic acid sequences with sequence overlap regions for each gene, comparing the sequence overlap regions for each gene to identify sequence differences; and analyzing the sequences or sequence differences or both to discriminate sequencing errors from sequence variances for each said gene.
Preferably the analyzing includes comparing the at least 3 electronic nucleic acid sequences to identify sequence differences between said sequences, and then applying at least one of the following filters that are helpful for distinguishing true variances from artifacts or sequence errors. One filter involves identifying and removing or discounting sequence differences in portions of the sequences in which the number of sequence differences in an analysis window exceeds a predetermined limit. A second filter involves identifying and removing or discounting of consecutive mismatches. A third filter involves assigning sequence differences a probability of representing a true variance based on sequence context. A fourth filter involves performing a calculation utilizing the detection of particular sequence differences at the same sites in multiple sequences as an indication that each such sequence difference represents a true variance. Preferably the analysis result is a score that is derived from the probability that a detected sequence difference represents a true variance, the above filters can be used singly or in any combination, and can also be used with other filters or sequence quality information.
Thus, in preferred embodiments, the variance scanning method of this invention generally follows four steps. First, the cDNA fragment sequences (ESTs) which are
Adams Robert Mark
Stanton, Jr. Vincent P.
Steffen David
Brusca John S.
Fish & Richardson P.C.
Kim Young
Variagenics Inc.
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