A METHOD AND SYSTEM FOR GENOTYPING

Abstract: Method for genotyping comprising obtaining nucleic acid material, amplifying a location of a material and assaying the amplified material by converting the material into a first set of electrical signals corresponding to size and concentration of the amplified products. The first set of electrical signals are operated on with a second set of electrical signals to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material at the location. A system for genotyping is disclosed comprising a mechanism for using said method. The assaying mechanism is in comunication with the amplifying mechanism. A converting mechanism in communication with the assaying mechanism converts the material into a first set of electrical signals corresponding to the size and concentration of the amplified product. An operating mechanism is provided which operates on a first set of electrical signals with a second set of signals corresponding to a response pattern of the location to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material at the location. A method for analyzing genetic material and producing a gene is also disclosed.

Description: A METHOD AND SYSTEM FOR GENOTYPING FIELD OF THE INVENTION The present invention pertains to a process which can be fully automated for accurately determining the alleles of STR genetic markers. More specifically, the present invention is related to performing PCR amplification on DNA, assaying the PCR products, and then determining the genotype of the PCR products. The invention also pertains to systems which can effectively use this genotyping information. BACKGROUND OF THE INVENTION To study polymorphisms in genomes, reliable allele determination of genetic markers is required for accurate genotyping. . A genetic marker corresponds to a relatively unique location on a genome, with normal mammalian individuals having two (possibly identical) alleles 104 for a marker on an autosomal chromosome 102, referring to figure 1A. (Though there are other cases of 0, 1, or many alleles that this invention addresses, this characterization suffices for the background introduction.) One important class of markers is the CA-repeat loci. This class is abundantly represented throughout the genomes of many species, including humans. A CA-repeat marker allele is comprised of a nucleic acid word 106 PQRΞT, where P is the left PCR primer, T defines the right PCR primer, Q and S are relatively fixed sequences, and the primary variation occurs in the sequence R, which is a tandemly repeated sequence 108 of the dinucleotide CA, i.e., R = (CA) n , where is n is an integer that generally ranges between ten and fifty. Thus, the length of the allele sequence uniquely determines the content of the sequence, since the only polymorphism is in the length of R. One can therefore obtain genomic DNA, perform PCR amplification of a CA-repeat genetic marker location, and then assay the length of the allele sequences by differential sizing, typically done by differential migration of DNA molecules using gel electrophoresis. The resulting gel 110 should, in principle, clearly show the alleles of marker for each individual's genome. Further, these sizes can be quantitated by using reference markers 112. However, the PCR amplification of a CA-repeat location produces an artifact, often termed "PCR stutter". Most likely due to slippage of the polymerase molecule on the nucleic acid polymer in the highly repetitive CA-repeat region, the result is that PCR products are produced that correspond to deletions of tandem CA molecules in the repeat region. Thus, instead of a single band on a gel corresponding to the one molecule PQ (CA) n ST, an entire population of different size bands { PQ (CA). ST, PQ (C )^ ST, PQ (CA)__- ST, ... } in varying concentrations is observed. This PCR stuttering 114 can be viewed as a spatial pattern p(x) , or, alternatively, as a response function r(t) of an impulse signal corresponding to the assayed allele. The stutter artifact can be extremely problematic when the two alleles of an autosomal CA-repeat marker are close in size. Then, their two stutter patterns overlap, producing a complex signal 116. In the presence of background measurement noise, this complexity often precludes unambiguous determination of the two alleles. To date, this has prevented reliable automated (or even manual) genotyping of CA-repeat markers from differential sizing assays. This overlap of stutter patterns can be modeled as a superposition of two corrupted signals. Importantly, (1) the corrupting response function is roughly identical for two closely sized alleles of the same CA-repeat marker, and (2) this response function is largely determined by the specific CA-repeat marker, the PCR conditions, and possibly the relative size of the allele. Thus, the response functions 114 can be assayed separately from the genotyping experiment 116. By combining 118 the corrupted signal together with the determined response functions of the CA-repeat marker, the true uncorrupted allele sizes can be determined, and reliable genotyping can be performed. A primary goal of the NIH/DOE Human Genome Project during its initial 5 year phase of operation was to develop a genetic map of humans with markers spaced 2 to 5 cM apart (E. P. Hoffman, "The Human Genome Project: Current and future impact," Am. J. Hum . Genet . , vol. 54, pp. 129-136, 1994), incorporated by reference. This task has already been largely accomplished in half the time anticipated, with markers that are far more informative than originally hoped for. In these new genetic maps, restriction fragment length polymorphism (RFLP) loci have been entirely replaced by CA repeat loci (dinucleotide repeats, also termed "microsatellites") (J. Weber and P. May, "Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction," Am J Hum Genet , vol. 44, pp. 388- 396, 1989; J. Weber, "Length Polymorphisms in dC-dA...dG-dT Sequences," Marshfield Clinic, Marshfield, WI, assignee code 354770, Patent # 5075217, 1991), incorporated by reference, and other short tandem repeat markers (STRs) . It is expected that at least 30,000 CA-repeat markers will be made available in public databases in the form of PCR primer sequences and reaction conditions. One of the advantages of CA repeat loci is their high density in the genome, with about 1 informative CA repeat every 50,000 bp: this permits a theoretical density of approximately 20 loci per centimorgan. Another advantage of CA repeat polymorphisms is their infor ativeness, with most loci in common use having PIC values of over 0.70 (J. Weissenbach, G. Gyapay, C. Dib, A. Vignal, J. Moriεsette, P. Millasseau, G. Vaysseix, and M. Lathrop, "A second generation linkage map of the human genome," Nature, vol. 359, pp. 794- 801, 1992) , incorporated by reference. Finally, these markers are PCR-based, permitting rapid genotyping using minute quantities of input genomic DNA. Taken together, these advantages have facilitated linkage studies by orders of magnitude: a single full-time scientist can cover the entire genome at a lOcM resolution and map a disease gene in an autosomal dominant disease family in about 1 year (D. A. Stephan, N. R. M. Buist, A. B. Chittenden, K. Ricker, J. Zhou, and E. P. Hoffman, "A rippling muscle disease gene is localized to lq41: evidence for multiple genes," Neurology, vol. (in press) , 1994) , incorporated by reference. The CA repeat-based genetic maps are not without disadvantages. First, alleles are detected by size differences in PCR products, which often differ by as little as 2 bp in a 300 bp PCR product. Thus, these alleles must be distinguished using high resolution sequencing gels, which are more labor intensive and technically demanding to use than most other electrophoresis systems. Second, referring to figure 2, CA repeat loci often show secondary "stutter" or "shadow" bands in addition to the band corresponding to the primary allele, thereby complicating allele interpretation. These stutter bands may be due to errors in Taq polymerase replication during PCR, secondary structure in PCR products, or somatic mosaicism for allele size in a patient. Allele interpretation is further complicated by the differential mobility of the two complementary DNA strands of the PCR products when both are labelled. Finally, sequencing gels often show inconsistencies in mobility of DNA fragments, making it difficult to compare alleles of individuals between gels and often within a single gel. The most common experimental approach used for typing CA repeat alleles involves incorporation of radioactive nucleotide precursors into both strands of the PCR product. The combined consequence of stutter peaks and visualization of both strands of alleles differing by 2 bp often leads to considerable "noise" on the resulting autoradiograph "signals", referring to figure 2, which then requires careful subjective interpretation by an experienced scientist in order to determine the true underlying two alleles. The stuttered signals of di-, tri-, tetra-, and other multi-nucleotide repeats can be modeled as the convolution of the true allele sizes with a stutter pattern p(x). Under this model, the complex quantitative banding signal q(x) observed on a gel can be understood as the summation of shifted patterns p(x) , with one shifted pattern for each allele size. A key fact is that generally only one p(x) function is associated with a given genetic marker, its PCR primers and conditions, and the allele size. In the important case of two alleles, where the two allele sizes are denoted by s and t, one can write the expression q(x) = (X s + x l ) p(x) . The multiplication of the polynomial expressions (x s + x*) and p(x) is one implementation of the underlying (shift and add) convolution process. Given the observed data q(x) and the known stutter pattern p(x) , one can therefore determine the unknown allele sizes s and t via a deconvolution procedure. (Note that this convolution/deconvolution model extends to analyses with more than two alleles.) A corollary of highly dense and informative genetic maps is the need to accurately acquire, analyze and store large volumes of data on each individual or family studied. For example, a genome-wide linkage analysis on a 30 member pedigree at lOcM resolution would generate data for approximately 30,000 alleles, with many markers showing five or more alleles. Currently, alleles are visually interpreted and then manually entered into spreadsheets for analysis and storage. This approach requires a large amount of time and effort, and introduces the high likelihood of human error. Moreover, future studies of complex multifactorial disease loci will require large-scale genotyping on hundreds or thousands of individuals. Finally, manual genotyping is arduous, boring, time consuming, and highly error prone. Each of these features suggests that automation of genotype data generation, acquisition, interpretation, and storage is required to fully utilize the developing genetic maps. Some effort has been made to assist in allele identification and data storage (ABI Genotyper manual and software, Applied Biosystems Inc.), incorporated by reference. However, this software still requires substantial user interaction to place manually assigned alleles into a spreadsheet, and is unable to deconvolve (hence cannot accurately genotype) closely spaced alleles or perform other needed analyses. Importantly, no essential use is made of a CA-repeat marker's PCR stutter response pattern by the ABI software or by any other disclosed method or system for genotyping. The Duchenne/Becker muscular dystrophy (DMD/BMD) gene locus (dystrophin gene) (A. P. Monaco, R. L. Neve, C. Colletti-Feener, C. J. Bertelson, D. M. Kurnit, and L. M. Kunkel, "Isolation of candidate cDNAs for portions of the Duchenne muscular dystrophy gene," Nature, vol. 323, pp. 646- 650, 1986; M. Koenig, E. P. Hoffman, C. J. Bertelson, A. P. Monaco, C. Feener, and L. M. Kunkel, "Complete cloning of the Duchenne muscular dystrophy cDNA and preliminary genomic organization of the DMD gene in normal and affected individuals," Cell , vol. 50, pp. 509-517, 1987), incorporated by reference, is a useful experimental system for illustrating the automation of genetic analysis. The dystrophin gene can be considered a mini-genome: it is by far the largest gene known to date (2.5 million base pairs); it has a high intragenic recombination rate (10 cM, i.e., 10% recombination between the 5' and 3' ends of the gene) ; and it has a considerable spontaneous mutation rate (10 " meioses) . Mutation of the dystrophin gene results in one of the most common human lethal genetic diseases, and the lack of therapies for DMD demands that molecular diagnostics be optimized. The gene is very well characterized, with both precise genetic maps (C. Oudet, R. Heilig, and J. Mandel, "An informative polymorphism detectable by polymerase chain reaction at the 3' end of the dystrophin gene," Hum Genet , vol. 84, pp. 283-285, 1990), incorporated by reference, and physical maps (M. Burmeister, A. Monaco, E. Gillard, G. van Ommen, N. Affara, M. Ferguson-Smith, L. Kunkel, and H. Lehrach, "A 10-megabase physical map of human Xp21, including the Duchenne muscular dystrophy gene," Genomics , vol. 2, pp. 189-202, 1988), incorporated by reference. Finally, approximately one dozen CA repeat loci distributed throughout the dystrophin gene have been isolated and characterized (A. Beggs and L. Kunkel, "A polymorphic CACA repeat in the 3' untranslated region of dystrophin," Nucleic Acids Res , vol. 18, pp. 1931, 1990; C. Oudet, R. Heilig, and J. Mandel, "An informative polymorphism detectable by polymerase chain reaction at the 3' end of the dystrophin gene," Hum Genet , vol. 84, pp. 283-285, 1990; P. Clemens, R. Fenwick, J. Chamberlain, R. Gibbs, M. de Andrade, R. Chakraborty, and C. Caskey, "Linkage analysis for Duchenne and Becker muscular dystrophies using dinucleotide repeat polymorphisms," Am J Hum Genet , vol. 49, pp. 951-960, 1991; C. Feener, F. Boyce, and L. Kunkel, "Rapid detection of CA polymorphisms in cloned DNA: application to the 5' region of the dystrophin gene," Am J Hum Genet , vol. 48, pp. 621-627, 1991), incorporated by reference. Many of the problems with interpretation of dystrophin gene CA repeat allele data can be overcome by single or multiplex fluorescent PCR and data acquistion on automated sequencers (L. S. Schwartz, J. Tarleton, B. Popovich, W. K. Seltzer, and E. P. Hoffman, "Fluorescent Multiplex Linkage Analysis and Carrier Detection for Duchenne/Becker Muscular Dystrophy," Am. J. Hum . Genet . , vol. 51, pp. 721-729, 1992), incorporated by reference. This approach uses fluorescently labeled PCR primers to simultaneously amplify four CA repeat loci in a single reaction. By visualizing only a single strand of the PCR product, and by reducing the cycle number, much of the noise associated with these CA repeat loci was eliminated. Moreover, the production of fluorescent multiplex reaction kits provides a standard source of reagents which, have not deteriorated 3 years after the fluorescent labeling reactions were performed. In this previous report, referring to figure 2, alleles were manually interpreted from the automated sequencer traces. This invention pertains to automating data acquisition and interpretation for any STR genetic marker. In the preferred embodiment, the invention: identifies each of the marker alleles at an STR locus in an organism; deconvolves complex "stuttered" alleles which differ by as few as two bp (i.e., at the limits of signal/noise); makes this genotyping information available for further genetic analysis. For example, to establish DMD diagnosis by linkage analysis in pedigrees, the application system: identifies each of the dystrophin gene alleles in pedigree members; deconvolves complex "stuttered" alleles which differ by only two bp where signal/noise is a particular problem; reconstructs the pedigrees from lane assignment information; sets phase in females; propagates haplotypes through the pedigree; identifies female carriers and affected males in the pedigree based on computer derivation of an at-risk haplotype; detects and localizes recombination events within the pedigree. Other uses of automatically acquired STR genetic marker data are the construction of genetic maps (T. C. Matise, M. W. Perlin, and A. Chakravarti, "Automated construction of genetic linkage maps using an expert system (MultiMap) : application to 1268 human microsatellite markers," Nature Genetics , vol. 6, no. 4, pp. 384-390, 1994), incorporated by reference, the localization of genetic traits onto chromosomes (J. Ott, Analysis of Human Genetic Linkage, Revised Edition . Baltimore, Maryland: The Johns Hopkins University Press, 1991) , incorporated by reference, and the positional cloning of genes derived from such localizations (B.-S. Kerem, J. M. Rommens, J. A. Buchanan, D. Markiewicz, T. K. Cox, A. Chakravarti, M. Buchwald, and L.-C. Tsui, "Identification of the cystic fibrosis gene: genetic analysis," Science , vol. 245, pp. 1073-1080, 1989; J. R. Riordan, J. M. Rommens, B.-S. Kerem, N. Alon, R. Rozmahel, Z. Grzelczak, J. Zielenski, S. Lok, N. Plavsic, J.-L. Chou, M. L. Drum , M. C. Iannuzzi, F. S. Collins, and L.-C. Tsui, "Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA," Science , vol. 245, pp. 1066-1073, 1989), incorporated by reference. SUMMARY OF THE INVENTION The present invention pertains to a method for genotyping. The method comprises the steps of obtaining nucleic acid material from a genome. Then there is the step of amplifying a location of the material. Next there is the step of assaying the amplified material based on size and concentration. Then there is the step of converting the assayed amplified material into a first set of electrical signals corresponding to size and concentration of the amplified material at the location. Then there is the step of operating on the first set of electrical signals produced from the amplified material with a second set of electrical signals corresponding to a response pattern of the location to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material on the genome at the location. The present invention also pertains to a system for genotyping. The system comprises means or a mechanism for obtaining nucleic acid material from a genome. The system also comprises means or a mechanism for amplifying a location of the material. The amplified means or mechanism is in communication with the nucleic acid material. Additionally, the system comprises means or a mechanism for assaying the amplified material based on the size and concentration. The assaying means or mechanism is in communication with the amplifying means or mechanism. The system moreover comprises means or a mechanism for converting the assayed amplified material into a first set of electrical signals corresponding to size and concentration of the amplified material at the location. The converting means or mechanism is in communication with the assaying means. The system for genotyping comprises means or a mechanism for operating on the first set of electrical signals produced from the amplified material with a second set of electrical signals corresponding to a response pattern of the location to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material on the genome at the location. The operating means or mechanism is in communication with the sets of electrical signals. The present invention also pertains to a method of analyzing genetic material of an organism. The present invention additionally pertains to a method for producing a gene. BRIEF DESCRIPTION OF THE DRAWINGS In the accompanying drawings, the preferred embodiment of the invention and preferred methods of practicing the invention are illustrated in which: Figure 1A is a schematic of the problem that this invention solves. Shown is (a) a pair autosomal chromosome and a marker location, (b) a CA-repeat genetic marker location, (c) a sizing assay done by gel electrophoresis, (d) the PCR corruption response pattern of one allele, (e) the superimposed corrupted pattern of two alleles, and (f) the recovery of the allele sizes by combining the two allele corrupted pattern with the one allele response pattern. Figure IB is a flow chart of a method for genotyping polymorphic genetic loci. Figure 2 is a PMT voltage versus time data used for input into automated genotyping. Shown is a Becker muscular dystrophy family (family #40) , with representative lane data from the automated sequencer shown below. Multiplex fluorescent CA repeat analysis was done as previously described (Schwartz et al. 1992) . The time windows corresponding to each of four dinucleotide repeat loci are shown above the data traces. The four dystrophin gene CA repeat loci show the full range of different patterns observed with most CA-repeatε: 3'CA shows very clean, distinct alleles but is not very informative, whereas STR-49 and STR-45 show complex patterns of 6-7 peaks for each allele. Reprinted from Schwartz et al. (1992) . Figure 3A shows computed base size vs. peak area for representative individuals and loci from the image analysis. The DNA concentrations shown were detected and quantitated at every DNA length (rows) for each genotyped individual (columns) . The peak area values were computed by the system from the raw data files corresponding to Figure 2, are in arbitrary units, and have been rounded to the nearest integer. Zero values denote minimal signal. The numbers illustrate the three classes of CA-repeat genotype data: hemizygote/homozygote alleles, distinct heterozygote alleles, or superimposed heterozygote alleles. Figure 3B shows the determination of allele sizes and concentrations by applying a grid of expected locations to the data image using relaxation methods and local quantitation. This is done both for (a) finding molecular weight markers and (b) finding genetic marker data locations. Figure 4 is the output from the pedigree construction and genotyping modules. Shown are the genotypes that the software automatically computed for each tested member of Family #40 (Figure 2) . The software automatically applied one of three methods (maximum of single peak, maxima of double peaks, or allele deconvolution) most appropriate to the locus data. This diagram was drawn by the graphical display component of the system. Figure 5 is a schematic representation of a system for genotyping polymorphic genetic loci. Figure 6 is a flow chart of a system for diagnosing genetic disease. Figure 7 shows the setting of phase in the inheritance graph. The links between the individuals in Family #40 show the X-chromosome inheritance paths between parents and children. These links are traversed to generate the vertical, in phase, haplotypes shown. This is done by applying the haplotyping rules when graph nodes (i.e., individuals) are reached in the graph traversal. This diagram was drawn by the graphical display component of the system. Figure 8 shows phenotypic identification of individuals having the at-risk haplotype. All individuals who share a chromosomal haplotype with proband A are inferred to carry the disease gene. A's haplotype is the allele sequence <207,171,233,13l>. Male G has this haplotype, and is presumed to be affected. Females D, E, and F have this haplotype on one of their X chromosomes, and are inferred to be carriers. This diagram was drawn by the graphical display component of the system. DESCRIPTION OF THE PREFERRED EMBODIMENT A genome is any portion of the inherited nucleic acid material, or its derivatives, of one or more individuals of any species. In particular, it is used as a sample for characterization or assay. A nucleic acid material from a genome is a sampling of nucleic acids derived from individuals having some portion of that genome. This represents the unknown material that is to be genotyped. A location on a genome is a physical region that does not exceed 10 megabases that is defined by a set of nucleic acid sequences that characterize the amplification of that region. In the preferred embodiment, a location is more specifically a polymorphic multi-nucleotide repeat locus that is defined by its pair of PCR primers. A set of electrical signals entails electromagnetic energies, including electrity and light, that serves as a physical mechanism for containing and transferring information, preferrably in a computing device. The first set of electrical signals corresponds to a series of nucleic acid size and concentration features that assay the amplification products of a location on a genome. For instance, these signals can include artifacts such as PCR stutter or background noise. The second set of electrical signals corresponds to a series of nucleic acid size and concentration features that characterize the response pattern of a single sequence of a location when distorted by an amplification procedure. These features may vary as a function of the size of the sequence at the location, and there is at least one (though not more than fifty) response pattern associated with the location. For instance, these response patterns can include a PCR stutter artifact of a location on a genome, or background noise. The third set of clean electrical signals corresponds to the size and multiplicities of the genome material at a location on a genome. More specifically, the clean electrical signals corresponds to the different alleles present at a location on a genome, and their relative numbers. For instance, these clean signals may have the artifacts (such as PCR stutter or background noise) removed. (1) A method and system for genotyping polymorphic genetic loci. Referring to figure IB, a method for genotyping polymorphic genetic loci is described which comprises the steps of (1) acquiring an individual's genomic DNA, (2) performing PCR amplification at an STR locus of this DNA, (3) assaying the amplified PCR products, (4) analyzing the peaks of the resulting assay into DNA size and concentration features, and then (5) deconvolving the analyzed PCR product to determine the genotype of the individual at the STR locus, or (5') deconvolution using Fourier domain signal processing techniques. The further step (6) of employing a PCR stutter pattern library in steps 5 or 5' is also described. More precisely, a method is described for genotyping that is comprised of the steps: (1) obtaining nucleic acid material from a genome; (2) amplifying a location of the material; (3) assaying the amplified material based on size and concentration; (4) converting the assayed amplified material into a first set of electrical signals corresponding to size and concentration of the amplified material at the location; (5 or 5', 6) operating on the first set of electrical signals produced from the amplified material with a second set of electrical signals corresponding to a response pattern of the location to produce a third set of clean electrical signals corresponding to the size and multiplicities of the material at the location. Referring to figure IB, step 1 is for acquiring genomic DNA. More precisely, obtaining nucleic acid material from a genome. The process begins by extracting DNA from blood or tissue. There are numerous standard methods to isolate DNA including whole blood, isolated lymphocytes, tissue, and tissue culture (Ausubel, F.M. , Brent, R. , Kingston, R.E., Moore, D.D., Seidman, J.G., Smith, J.A., and Struhl, K. , ed. 1993. Current Protocols in Molecular Biology. New York, NY: John Wiley and Sons; Sambrook, J. , Fritsch, E.F., and Maniatis, T. 1989. Molecular Cloning, second edition . Plainview, NY: Cold Spring Harbor Press; Nordvag 1992. Direct PCR of Washed Blood Cells. BioTechniqueε , 12(4): 490-492), incorporated by reference. In the preferred embodiment, DNA is extracted from anticoagulated human blood removed by standard venipuncture and collected in tubes containing either EDTA or sodium citrate. The red cells are lysed by a gentle detergent and the leukocyte nuclei are pelleted and washed with the lysis buffer. The nuclei are then resuspended in a standard phosphate buffered saline (pH=7.5) and then lysed in a solution of sodium dodecyl sulfate, EDTA and tris buffer pH 8.0 in the presence of proteinase K 100 ug/m 1. The proteinase K digestion is performed for 2 hours to overnight at 50°C. The solution is then extracted with an equal volume of buffered phenol-chloroform. The upper phase is reextracted with chloroform and the DNA is precipitated by the addition of NaAcetate pH 6.5 to a final concentration of 0.3M and one volume of isopropanol. The precipitated DNA is spun in a desktop centrifuge at approximately 15,000 g, washed with 70% ethanol, partially dried and resuspended in TE (lO M Tris pH 7.5, 1 mM EDTA) buffer. There are numerous other methods for isolating eukaryotic DNA, including methods that do not require organic solvents, and purification by adsorption to column matrices. None of these methods are novel, and the only requirement is that the DNA be of sufficient purity to serve as templates in PCR reactions and in sufficient quantity. Referring to figure IB, step 2 is for performing PCR amplification. More precisely, amplifying a location of the material. The genomic DNA is then amplifed at one or more locations on a genome via a PCR reaction. Size standards are used to calibrate the quantitative analysis. The methods for this PCR amplification given here are standard, and can be readily applied to every microsatellite or multinucleotide repeat marker that corresponds to a (relatively unique) location on a genome. Polymorphic genetic markers are locations on a genome that are selected for examining a genome region of interest. The genetic markers to be used for each multinucleotide repeat are obtained as PCR primer sequences pairs and PCR reaction conditions from available databases (Genbank, GDB, EMBL; Hilliard, Davison, Doolittle, and Roderick, Jackson laboratory mouse genome database, Bar Harbor, ME; SSLP genetic map of the mouse, Map Pairs, Research Genetics, Huntsville, AL) , incorporated by reference. Alternatively, some or all of these microsatellite locations can also be constructed using existing techniques (Sambrook, J. , Fritsch, E.F., and Maniatis, T. 1989. Molecular Cloning, second edition . Plainview, NY: Cold Spring Harbor Press; N. J. Dracopoli, J. L. Haines, B. R. Korf, C. C. Morton, C. E. Seidman, J. G. Seidman, D. T. Moir, and D. Smith, ed. , Current Protocols in Human Genetics . New York: John Wiley and Sons, 1994) , incorporated by reference. The oligonucleotide primers for each multinucleotide repeat genetic marker are synthesized (Haralambidis, J., Duncan, L. , Angus, K. , and Tregear, G.W. 1990. The synthesis of polya ide-oligonucleotide conjugate molecules. Nucleic Acids Research , 18(3): 493-9. Nelson, P.S., Kent, M. , and Muthini, S. 1992. Oligonucleotide labeling methods. 3. Direct labeling of oligonucleotides employing a novel, non-nucleosidic, 2-aminobutyl-l,3- propanediol backbone. Nucleic Acids Research, 20(23): 6253-9. Roget, A., Bazin, H., and Teoule, R. 1989. Synthesis and use of labelled nucleoside phosphora idite building blocks bearing a reporter group: biotinyl, dinitrophenyl, pyrenyl and dansyl. Nucleic Acids Research , 17(19): 7643-51. Schubert, F. , Cech, D. , Reinhardt, R. , and Wiesner, P. 1992. Fluorescent labelling of sequencing primers for automated oligonucleotide synthesis. Dna Sequence , 2(5): 273-9. Theisen, P., McCollum, C. , and Andrus, A. 1992. Fluorescent dye phosphoramidite labelling of oligonucleotides. Nucleic Acids Symposium Series , 1992(27): 99-100.), incorporated by reference. These primers may be derivatized with a fluorescent detection molecule or a ligand for immunochemical detection such as digoxigenin. Alternatively, these oligonucleotides and their derivatives can be ordered from a commercial vendor (Research Genetics, Huntsville, AL) . In the preferred embodiment, the genomic DNA is mixed with the other components of the PCR reaction at 4°C. These other components include, but are not limited to, the standard PCR buffer (containing Tris pH8.0, 50 mM KCl, 2.5 mM magnesium chloride, albumin) , triphosphate deoxynucleotides (dTTP, dCTP, dATP, dGTP) , the thermostable polymerase (e.g., Taq polymerase) . The total amount of this mixture is determined by the final volume of each PCR reaction (say, 10 ul) and the number of reactions. The PCR reactions are performed on all of the reactions by heating and cooling to specific locus-dependent temperatures that are given by the known PCR conditions. The entire cycle of annealing, extension, and denaturation is repeated multiple times (ranging from 20-40 cycles depending on the efficiencies of the reactions and sensitivity of the detection system) (Innis, M.A. , Gelfand, D.H., Sninsky, J.J., and White, T.J. 1990. PCR Protocols: A Guide to Methods and Applications . San Diego, CA: Academic Press.), incorporated by reference. In the preferred embodiment, for STR CA-repeat loci, the thermocycling protocol on the Perkin- Elmer PCR System 9600 machine is: a) Heat to 94°C for 3' b) Repeat 3Ox: 94°C for 1/2' (denature) 53°C for 1/2' (anneal) 65°C for 4' (extend) c) 65°C for 7 ' (extend) d) 4°C soak ad librum The PCR cycles are completed, with each reaction tube containing the amplified DNA from a specific location of the genome. Each mixture includes the DNA that was synthesized from the two alleles of the diploid genome (a single allele from haploid chromosomes as is the case with the sex chromosomes in males or in instances of cells in which a portion of the chromosome has been lost such as occurs in tumors, or no alleles when both are lost) . If desired, the free deoxynucleotides and primers may be separated from the PCR products by filtration using commercially available filters (Amicon, "Purification of PCR Products in Microcon Microconcentrators, " Amicon, Beverly, MA, Protocol Publication 305; A. M. Krowczynska and M. B. Henderson, "Efficient Purification of PCR Products Using Ultrafiltration," BioTeciinigues, vol. 13, no. 2, pp. 286-289, 1992) , incorporated by reference. In the preferred embodiment, these PCR reactions generate quantifiable signals, and are done either separately or in multiplexed fashion. In one multiplexed embodiment for DMD diagnosis, four CA-repeat markers [3'-CA (C. Oudet, R. Heilig, and J. Mandel, "An informative polymorphism detectable by polymerase chain reaction at the 3' end of the dystrophin gene," Hum Genet , vol. 84, pp. 283- 285, 1990), 5 , DYSII (C. Feener, F. Boyce, and L. Kunkel, "Rapid detection of CA polymorphisms in cloned DNA: application to the 5' region of the dystrophin gene," Am J Hum Genet , vol. 48, pp. 621-627, 1991), and STRs 45 and 49 (P. Clemens, R. Fenwick, J. Chamberlain, R. Gibbs, M. de Andrade, R. Chakraborty, and C. Caskey, "Linkage analysis for Duchenne and Becker muscular dystrophies using dinucleotide repeat polymorphisms," Am J Hum Genet , vol. 49, pp. 951-960, 1991) , incorporated by reference, distributed throughout the 2.5Mb dystrophin gene are used. The forward primer of each pair of PCR amplimers is covalently linked to fluorescein, and all four loci are amplified in a single 25 cycle multiplex PCR reaction (L. S. Schwartz, J. Tarleton, B. Popovich, W. K. Seltzer, and E. P. Hoffman, "Fluorescent Multiplex Linkage Analysis and Carrier Detection for Duchenne/Becker Muscular Dystrophy," Am. J. Hum . Genet . , vol. 51, pp. 721-729, 1992), incorporated by reference. The mixed fluorescent primers can be stored for over three years with no loss of label intensity, obviating the need for relabelling prior to each experiment. Two fluorescent molecular weight standards (dystrophin gene exons 50 (271 bp) and 52 (113 bp) (A. Beggs and L. Kunkel, "A polymorphic CACA repeat in the 3' untranslated region of dystrophin," Nucleic Acids Res , vol. 18, pp. 1931, 1990; L. S. Schwartz, J. Tarleton, B. Popovich, W. K. Seltzer, and E. P. Hoffman, "Fluorescent Multiplex Linkage Analysis and Carrier Detection for Duchenne/Becker Muscular Dystrophy," Am. J. Hum . Genet . , vol. 51, pp. 721-729, 1992), incorporated by reference, are added to samples prior to electrophoresis. These four markers cover the full spectrum of CA-repeat sizes, signals, stutter patterns, and polymorphisms, which demonstrates that the data generation and analysis methods described in this patent applications are applicable to the entire class of di- and multi-nucleotide repeat markers. Referring to figure IB, step 3 is for size separation of the PCR products. More precisely, assaying the amplified material based on size and concentration. In the preferred embodiment, size separation of the labeled PCR products is done by gel electrophoresis on polyacryla ide gels (Ausubel, F.M. , Brent, R. , Kingston, R.E., Moore, D.D. , Seidman, J.G., Smith, J.A. , and Struhl, K., ed. 1993. Current Protocols in Molecular Biology. New York, NY: John Wiley and Sons; N. J. Dracopoli, J. L. Haines, B. R. Korf, C. C. Morton, C. E. Seidman, J. G. Seidman, D. T. Moir, and D. Smith, ed. , Current Protocols in Human Genetics . New York: John Wiley and Sons, 1994; Sambrook, J. , Fritsch, E.F., and Maniatis, T. 1989. Molecular Cloning, second edition . Plainview, NY: Cold Spring Harbor Press), incorporated by reference. The gel image is then put into machine readable digital format. This is done by electronic scanning of a gel image (e.g., autoradiograph) using a conventional gray scale or color scanner, by phosphor imaging, or by direct electronic acquisition using an automated DNA sequencer (e.g., fluorescence-based) for sizing DNA products. This sizing assay acquires signals that enable the eventual quantitation of the nucleic acid sizes and concentrations present in the amplified material. This is done by obtaining features (related to size and concentration) of the differentially sized nucleic acid products in the amplified material that can be converted into electrical signals. This acquisition may be accomplished by generating images that can be scanned into electronic pixels, by applying a photomultiplier tube to fluorescently labeled amplified material thereby generating electrical signals, by measuring labeled amplified material in microcapillary tubes, by performing differential hybridization of nucleic acid probes with the amplified material, or by any other physical means of detecting relative concentrations of nucleic acid species. The acquisition of the sizing assay data may be effected in real-time, or be postponed to allow increased accumulation of nucleic acid signals. A preferred embodiment using an automated DNA sequencer is given for the specific case of DMD diagnosis; this procedure can be used for any STR PCR product. The PCR products of each of the four DMD CA-repeat loci may lie their own individual lane, or be multiplexed into multiple (e.g., four) minimally overlapping size windows of a single lane. In the latter case, the alleles for all four loci and the molecular weight markers can be read out as a size- multiplexed signal in one lane of a DNA sequencer. The DuPont Genesis DNA sequencer can generate fluorescent intensity data for 10-12 lanes, with one lane assigned to each individual. In an alternative embodiment, the multiple lanes of the Applied Biosystems sequencer (ABI 373A, with optional Genotyper software) , incorporated by reference, the Pharmacia sequencer, the Millipore sequencer, or any comparable system for direct electronic acquisition of electrophoretic gel images is used. With the DuPont system, at least ten family members can be haplotyped for the dystrophin gene with a single sequencer run. Each lane's signal intensity is observed as photomultiplier tube (PMT) voltage units (12 bit resolution) , and is sampled by the sequencer every 3 seconds, providing roughly 20 data points per base of DNA. Gels are run for a total of 4 hours, generating approximately 5,000 data points per lane (individual) . Machine readable data files from the sequencer runs, recorded as a linear fluorescence signal (PMT voltage) trace for each lane (individual) , are automatically generated by the Genesis 2000 software. The traces for the running example analysis of Family #40 is shown in figure 2. These time vs. voltage files are entered into the system, as described below. Referring to figure IB, step 4 is for analyzing the assayed DNA peaks (i.e., bands) into DNA size vs. concentration information. More precisely, converting the assayed amplified material into a first set of electrical signals corresponding to size and concentration of the amplified material at the location. Signals were obtained in step 3 from the differentially sized amplified nucleic acid material of a location on a genome. In this step, these signals are then converted into a first set of electrical signals corresponding to size and concentration features of the amplified material. The conversion is effected using a computer device with memory via a program in memory that examines the values of the assay signals residing in memory locations. These values are assessed for features corresponding to the detection of a discrete size region of amplified nucleic acid material, such as a peak or band in the differential sizing assay. The relative concentration of nucleic acid material is then quantitated in such regions. These size/concentration features are then stored as a first set of electrical signals in the computer's memory, for use in step 5. In a preferred embodiment, each individual's preprocessed DuPont data file contains a time vs. intensity trace of the single or multiplexed PCR sequencer run generated from the corresponding gel lane. For quantitative processing, these data must be converted to DNA size vs. DNA concentration units. The system first searches predetermined time regions to find the molecular weight markers (dystrophin gene exons 50 [271 bp] and 52 [113 bp]). A linear interpolation is then performed to construct a time vs. size mapping grid. Each predefined CA-repeat locus is then processed independently within its predefined size window. Every peak within the CA-repeat marker region is identified, and is assigned a time and an area. The apex of a peak is defined as the point of change between a monotonically increasing series and a monotonically decreasing series, left to right. The onotonicity predicate holds when the difference between an average of right values and an average of left values exceeds a predetermined threshold. Using the linear time-to-size interpolation from the grid, the time of each peak apex's occurrence is converted to a DNA size estimate. The areas are computed as the full-width at half- ax peak from the intensity data, and are considered to be proportional to the approximate DNA concentration for any specific locus. Figure 3A shows partial DNA size/intensity results from the machine vision analysis of example Family #40. In an alternative embodiment, the two dimensional image data (rather than the one dimensional preprocessed lane data) is analyzed to produce size vs. intensity information. First, the image locations of the molecular weight (MW) markers are found in every lane in which they were placed. This is done by searching for peaks of the proper shapes in the expected image locations. By comparing the observed MW marker peak locations to their expected peak locations, a linear interpolation is established that maps each two dimensional image location to a unique lane and DNA size. Second, the data peaks of the stuttered genetic marker alleles are found on the image. For each peak, its lane and DNA size is determined by linear interpolation, and the observed intensity is summed over the peak region; the lane, DNA size, and signal intensity are then recorded. With superimposed signals (e.g., using multiple fluorescent probes) in each lane, the image plane is noted as well. To adjust background levels, standard machine vision techniques such as iterative thresholding are used (J. R. Parker, Practical Computer Vision Using C. New York: John Wiley and Sons, 1994) , incorporated by reference. In an alternative embodiment, a general expectation-based architecture is used. The expected locations of MW and genetic markers is made representationally explicit, and relaxation methods are then employed. First, referring to figure 3B, the known expected locations 302 of the MW markers are arranged into a data structure, which makes explicit the local horizontal and vertical pairwise distance relationships between neighboring markers. The image locations 304 of the MW markers are then found in every lane with MW markers, by searching for peaks of the proper shapes in the expected locations. The observed MW marker peak locations are then compared with their expected peak locations. A relaxation process is then performed which heuristically minimizes the local horizontal and vertical pairwise distances, adapting the expected grid to the observed data, and produces a "best fit" 306 of the observed locations to the expected locations. This produces a local linear interpolation mapping in each region of the grid, that maps each two dimensional image location to a unique lane and DNA size. Second, the data peaks of the stuttered genetic marker alleles are found on the image. All the possible expected locations 312 of genetic marker peaks are arranged into a data structure, which makes explicit the local horizontal and vertical pairwise distance relationships between markers as interpolated from the MW marker analysis. The image locations 314 of the genetic markers are then found by searching for peaks of the proper shapes in the image locations predicted by the expectation grid. A relaxation process is then performed which assures heuristic minimization of the local horizontal and vertical pairwise distances between observed data peaks, adapting the expected data position grid to the observed data positions, and produces a "best fit" 316 of the observed locations to the expected locations. This determines, for each observed data peak, the lane/plane position and the DNA size; the observed intensity at that point is then summed over the peak region, and the lane/plane, DNA size, and signal intensity are recorded. When inheritance information between related individuals is available, the consistency between the predicted inheritance of alleles and observed allele peak patterns can be used to further align the predicted and observed data peak grids. Referring to figure IB, step 5 is for genotyping by deconvolution. More precisely, operating on the first set of electrical signals produced from the amplified material with a second set of electrical signals (described in step 6) corresponding to a response pattern of the location to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material on the genome at the location. The measured first set of electrical signals produced from the amplified material is corrupted by the response pattern of the location on the genome. The objective is to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material on the genome at the location. This is done by operating on the first set of electrical signals, together with the second set of electrical signals detailed in step 6, using a program residing in the memory of the computer. In the preferred embodiment, this operation is a deconvolution procedure. The pattern of measured peaks (DNA sizes vs. DNA concentrations) is classified into one of three classes: hemizygote/homozygote alleles, distinct heterozygote alleles, or superimposed heterozygote alleles. These three classes of peak patterns are defined as follows. A hemizygote/homozygote allele comprises a single decay pattern of decreasing peak amplitudes, with DNA size decreasing from right to left (figure 2); the rightmost and largest peak is considered to be the primary peak. For example, individual A of family #40 is a male X-linked hemizygote. At locus STR- 45, using the values shown in figure 3A, the peak occurs at length 171 nucleotides, with a concentration of 101,299. Thus, the genotype of individual A at locus STR-45 is assigned the value 171. The peak pattern is classified as distinct heterozyogote when two such decay patterns are found within the marker window, and the two primary peaks are of similar amplitude. For example, individual D of family #40 is heterozygotic at locus STR-49. As seen in figure 3A, there is one peak at length 233, and a second peak at length 264. The stutter peaks are widely separated, so there was no overlap in their stutter patterns, and the genotype was readily determined from the two distinct simple signals to be (233, 264). The third class, superimposed heterozygote alleles, is invoked when no simple pattern of alleles satisfying the hemizygotic/homozygotic or distinct heterozygotic criteria is detected. In this class, present in heterozygote loci, the alleles are closely spaced, and produce a complex pattern of overlapping peaks. Deconvolution of the peak pattern is then invoked to identify the two alleles. Since the peak decay patterns are similar for any given locus, the deconvolution of a complex heterozygous pattern at a locus can be done with respect to the hemizygous decay pattern (of a different individual) at the same locus. With superimposed heterozygote alleles, the overlapping stutter peaks of proximate alleles at a locus are deconvolved, thereby computing a single peak per allele. For any given STR marker locus, the allele stutter pattern is relatively fixed. The DNA concentrations for one allele at each discrete DNA size can be written as the pattern vector <P n , ••• , P 2 , P P 0 >, or, equivalently, as the polynomial p(x) , P(x) = P n *x n + ... + P 2 *x 2 + Pτ*x + P 0 . Each coefficient p k is the observed peak area in the allele's pattern for the k th stutter peak. The superimposed stutter patterns observed in the sequencer data of heterozygotic markers can be similarly described by a polynomial q(x) . The coefficients of q(x) are the superimposed peak areas produced by PCR stuttering of the two alleles. The PCR stutter of each allele has a fixed pattern described by the polynomial p(x) . When the allele contains precisely r repeated dinucleotideε, the pattern is shifted 2r bases on the sequencer gel lane. (With repeated trinucleotides, tetranucleotides, and other non-dinucleotide STRs, this factor may be different from "2", but the method still obtains.) A shift in the stutter pattern by 2r bases mathematically corresponds to multiplication of the polynomial p(x) by x 2r . Therefore, if the two allele sizes are s and t, then the two stuttered alleles produce the shifted polynomials x s * p(x), and x* * p(x) , respectively. Superimposing these two allele stutter patterns produces the observed sum q ( x ) = x s * p(x) + x t * p(x) , or = (x s + x l ) * p(x) . Direct deconvolution to obtain the allele sizes s and t (hence, the genotype) by polynomial division via q( χ )/P(x) = x s + x* is not sufficiently robust with real data containing noise. Therefore, statistical moment computations are used. In addition to robustness, this has the second advantage of linear time computation. The k -t t h n moment of a polynomial u (x) is k = u (k) ( l) , where u (k) is the k th algebraic derivative of u(x) . u k can be rapidly computed by weighted summation of the coefficients of u(x)'s k th derivative. As derived below, s+t = (q. - 2^ ) /p 0 f s 2 +t 2 = { [q- - 2p 2 ] + (s+t)[p 0 - 2 Pl ] } / p 0 , and (s-t) 2 = 2(s+t 2 ) - (s+t) 2 . Therefore, one can directly calculate the allele sizes as s = [ (s+t) + (s-t) ] /2, and t = [ (s+t) - (s-t) ] /2. This computation has the effect of deconvolving the superimposed PCR stutter patterns of the heterozygotic alleles into the two discrete peaks, having size s and t, needed for straightforward genotyping. The real numbers s and t are rounded (up or down) to the nearest integer occurring in the observed peak data. Consider, for example, the STR-45 locus of individual E of Family #40. The DNA concentrations at the PCR product sizes 161 through 173 are given in figure 3A. The sizes and concentrations can be represented by the polynomial q(x) = 61326X 173 + 948523. 71 + 47391__ , ° + 18115Ψ + 5896X 165 + 1928X 163 + 93Ox 161 . This pattern does not conform to a simple uniform decay. In Family #40, individual A's hemizygotic locus STR-45, does (as expected) have a simple decay pattern from the peak at size 171 down through size 161, as seen in figure 3A. This data can similarly be represented by the polynomial p(X) = 101299X 171 + 55373-.° + 2079_*X + 72 1 !_X + 2171X 163 + 821X 161 , and can be used to help recover the two alleles at individual E's STR-45 locus. As just described, individual E's peak pattern at locus STR- 5 can be viewed as the superposition of two shifted copies of A's peak pattern at STR-45. Conceptually, the observed q(x) pattern is the sum of two shifted copies of p(x): q(x) = x s * p(x) + x r * p(x), or = (x s + x*) * p(x) . Deconvolution of q(x) with respect to p(x) determines (x s + l ) , where ε and t are the peaks of the shifted patterns. That is, s and t provide the genotype. The polynomical coefficients are first renormalized to account for the expectation that p(x) measureε a single chromosome dosage, whereas q(x) measureε two doses. Then, using the polynomial moment technique detailed above, and shifting the sizeε to their correct origin, compute s = 173.061, and t = 170.832. Rounding these numbers to the closeεt integerε in the peak pattern, yields the genotype (173, 171). This example reεult illustrates how PCR stutter peaks can be effectively exploited using the described deconvolution approach to automatically reεolve CA-repeatε of cloεe sizes. Figure 4 shows the genotyping results using these methods for every member of example Family #40. The following is a detailed derivation of this deconvolution procedure for recovering the alleles s and t in the presence of PCR stutter peaks from the data q(x) , uεing p(x) . p(x) is immediately known in X chromosome family data from (haploid) male individualε, and can be derived via εimilar deconvolution procedureε for autosomal loci. One proceeds in four stepε. Step 5a. Computing an expression for the allele εum ε+t. Taking the derivatives of both sides of q(x) = p(x) * (x s + x l ) , yieldε d/dx [q(x)] = d/dx [ p(x) * (x s + x ) ] = d/dx [p(x) ] * (x s + x l ) + p(x) * d/dx [x s + χ l ] , = p (υ (x) * (x s + x*) + p(x) * [ s*x s' + t*x t_1 ]. Evaluating at x=l, q (1 (l) = p (1) (l) * (I s + 1*) + p(l) * [ s*l s"1 + t*^ *1 ] , = p c1) (l) * (2) + p<°>(l) * [ s + t ]. The n th moment of a polynomial u(x) is u_ = u (n) (1) Thiε may be very efficiently computed in linear time as the sum of the coefficients of the polynomial's n th derivative. The moments are related to more intuitive function statiεtics, such aε the mean and variance: E(u) = u ι/ u o' anc Rewrite the above derivation aε (eaεily computable) moment εtatiεtics: q 1 = 2p. + (s+t)p 0 , or, so: ε + t = q./p- - 2p 1 /p 0 , (q, - , ) /.P (*) Thus, given the hemizygous (or homozygous) diεtribution p(x), and the sequencer data q(x), if either s or t iε known, then εo iε the other. When the poεition t of the larger allele iε determined by identifying the peak of the largeεt PCR product in the locus region, this procedure will determine the location s of the smaller allele. Step 5b. Computing an expression for the allele sum ε 2 +t 2 . To extract second moments, compute the second derivative of the relation q(x) = p(x) * (X s + x*) . After simplification, thiε produceε: q (2) (x) = p (2> (x) * (X s + x l ) + 2 [p (1 (x) * (sx s"1 + tx* "1 )] + p(x) [s(s-l)x s"2 + tft-ljx 1"2 ] . Setting x=l to calculate momentε , and rearranging to group the conεtant, linear, and quadratic terms in s and t, yieldε the equality: 0 = [2p 2 - q 2 ] + (s+t) [2p 1 - p 0 ] + (s 2 +t 2 ) p 0 . Rearranging thiε equality gives the equivalence: s 2 +t 2 = { [q 2 - 2,p ] + (s+tj [p - 2p ] r> ^ / p Each right hand εide term iε directly or indirectly computable from moment properties of the data. For example, "s+t" is known via equation (*) . Step 5c. Computing an expresεion for the allele difference ε- t. From (ε+t) given in (*) , and (s+t 2 ) given in (**) , (s-t) is obtained as follows: (s-t) 2 = s 2 - 2st + t 2 = ε 2 + t 2 - 2st = 2s 2 + 2t 2 - [s 2 + t 2 + 2εt] = 2(s 2 +t 2 ) - (s+t) 2 . This provideε a closed form expreεεion for ε-t, aε the εquare root of 2(ε 2 +t 2 ) - (s+t) 2 . Step 5d. Computing the alleles s and t. Combining ε+t and ε-t: ε = [ (ε+t) + (ε-t) ] /2, and t = [ (ε+t) - (ε-t) ] /2. Thuε, by taking zeroth, first, and second moments of the multiallelic εequence data q(x), together with the known haplotype p(x) , the abεolute poεitionε of nucleotide repeat alleleε ε and t can be rapidly computed. Since computing the momentε iε juεt linear in the size of the data, the produre is fast, and is aεymptotically better than simple (and noiεe intolerant) quadratic time polynomial diviεion; thiε speed advantage is useful in on-line real-time automated genotyping. Referring to figure IB, step 5' iε for deconvolution using Fourier domain techniques. More precisely, operating on the first set of electrical εignalε produced from the amplified material with a second set of electrical signals (described in step 6) correεponding to a reεponεe pattern of the location to produce a third set of clean electrical signalε corresponding to the size and multiplicities of the unamplified material on the genome at the location. The measured first set of electrical signalε produced from the amplified material iε corrupted by the response pattern of the location on the genome. The objective is to produce a third set of clean electrical signals corresponding to the εize and multiplicitieε of the unamplified material on the genome at the location. This is done by operating on the first set of electrical signalε, together with the εecond set of electrical signals detailed in step 6, using a program reεiding in the memory of the computer. In another preferred embodiment, this operation iε Fourier domain deconvolution. In another preferred embodiment, Fourier domain εignal proceεsing methods can be used for deconvolution and allele determination from stuttered PCR reactions. Here, each discrete time unit correεpondε to a DNA εize; this size measured in base pair (bp) units iε obεerved on an electrophoretic gel trace. Uεing conventional signal processing notation, (1) the uncorrupted allele signal is the function u(t), which maps each DNA size t into the number of alleleε of that εize present in the sample; (2) the known PCR stutter pattern of a given genetic marker is r(t), the responεe function deεcribing the εpatial appearance of one marker'ε εtutter on the gel; (3) the observed data from one or more alleles iε the smeared signal s(t), which iε the appearance of the multiple εuperimposed alleles u(t) distorted by the stutter artifact r(t) . (4) That is: s(t) = r(t) * u(t), where "*" denotes convolution, and, in the Fourier domain, S(f) = R(f) U(f) , where the c'apital letters denote the Fourier transforms of the signal functions. The objective is to genotype by determining the allele distribution u(t) from the observed data s(t), exploiting the known responεe function r(t) . In the abεence of noiεe, thiε deconvolution can be implemented by the polynomial diviεion propoεed in step 5 of figure IB, which is equivalent to Fourier domain division. For measured data, though, noise cannot be ignored, and a more robust method such as Optimal (Wiener) Filtering with the (fast) Fourier transform is needed (D. F. Elliot and K. R. Rao, Fast Transforms : Algorithms, Analyses, Applications . New York: Academic Press, 1982; H. J. Nuεεbaumer, Fast Fourier Transform and Convolution Algorithms . New York: Springer-Verlag, 1982; A. Papoulis, Signal Analysis . New York: McGraw-Hill Book Company, 1977; L. R. Rabiner and B. Gold, Theory and Application of Digital Signal Processing. Englewood Cliffε, New Jersey: Prentice-Hall, 1975) , incorporated by reference. The polynomial moment method described in step 5 of figure IB can be understood in this context as well (A. Papoulis, "Approximations of Point Spreads for Deconvolution," J. Opt . Soc. Am. , vol. 62, no. 1, pp. 77-80, 1972) , incorporated by reference. The following method is derived using the approach in εection 12.6 of Press (W. H. Presε, B. P. Flannery, S. A. Teukolεky, and W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing. Cambridge: Cambridge Univerεity Preεε, 1988) , incorporated by reference. When noiεe iε present, the measured signal c(t) is corrupted, and adds a component of noise n(t) to s(t): c(t) = s(t) + n(t) . The optimal filter ø(t) or Φ(f) is applied to the measured signal c(t) or C(f) , and is then deconvolved by the marker- dependent r(t) or R(f) , to produce a signal v(t) or V(f) that iε aε close aε poεεible to the uncorrupted allele signal u(t ' ) or U(f) . That is, the true εignal U(f) iε estimated (in the Fourier domain) by The "closeneεε" is least square minimization of v(t) and u(t) , or, equivalently in the Fourier domain, V(f) and U(f) . The optimal filter Φ(f) is given by Φ(f) = |S(f)! 2 / (|S(f)! 2 + |N(f) j 2 ), where N(f) is the Fourier transform of the noise function n(t) . N(f) can be determined from calibration data in the abεence of allele εignal, or by the straightforward extrapolation scheme deεcribed in pp. 434-437 and figure 1 2.6.1 of (W. H. Preεs, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing. Cambridge: Cambridge University Press, 1988) . Inverse Fourier transformation of the computed V(f) produceε v(t) , which iε the optimal eεtimate of the allele distribution u(t) . This method can used for an unlimited number (specifically, more than two) of alleles in a sample. Referring to figure IB, step 6 is for building and using a PCR stutter pattern library. More precisely, providing a second set of electrical signals corresponding to a responεe pattern of the location that is used when (see stepε 5 and 5') operating on the firεt set of electrical signalε produced from the amplified material to produce a third εet of clean electrical εignalε correεponding to the size and multiplicities of the unamplified material on the genome at the location. A second set of electrical εignalε correεponding to a response pattern of the location on a genome is used in recovering the clean third set of electrical εignalε from the corrupted firεt εet of electrical εignalε. Thiε εecond εet of electrical signalε is generated by deconvolution of routine firεt εets of electrical signalε, aε deεcribed above, or by a εimple laboratory aεsay, as described next, and is stored in the memory of a computer. The genotype of an individual at an STR can be determined without typing other relativeε. Thiε iε because the stutter pattern of an STR locus is largely independent of the particular individuals or families, and dependε primarily on the locuε, the PCR conditions, and the allele size. Thus, by building and using a library of PCR stutter patterns, all STR loci can be genotyped by the described deconvolution method. Specifically, this includes all STRε on autoεomeε or εex chromosomes, for DNA from single individualε or from pooled individual samples. In the preferred embodiment, each locus pattern in the STR library is determined by PCR amplification and subsequent quantitative analysiε of the εize separation distribution. There are three cases, hemizygote/homozygote, distinct heterozygote, or superimpoεed heterozygote. When an individual is found whose genotype asεay iε claεεified into one of the firεt two caεeε, the observed distinct allele pattern can be directly stored in the library. When only superimposed heterozygotes are found, the following is done: (a) A small finite number of candidate εolution allele pairε (ε,t) that include the correct allele pair are made, baεed on the localized region of the aεεay. (b) Each allele pair candidate εolution (s,t) is used to deconvolve the obεerved fit. Thiε iε done by respecting the relationship p(x) = q(x)/(x s + ) to compute a candidate p(x) . (c) The beεt allele candidate εolution (ε,t) which fitε the data, in accordance with the allele εuperpoεition principle, computes the stutter patternε p(x) of the locuε. (d) Thiε determination iε preferrentially repeated with additional individualε. It iε preferrable for the deconvolution determination that theεe individualε be related. Further, the obεerved data or reεulting εtutter patternε are preferrentially combined to reduce noise. (e) The resulting allele size dependent stutter patterns p(x) of the locus are stored in the STR library. In an alternative embodiment, individual haploid chromosomeε are obtained by microdiεεection, with an optional εubεequent cloning step. PCR of single chromosomes (or their clones) produces a single allele stutter pattern. These patterns p(x) are then recorded in the library. To genotype an individual's STR locus (particularly in the superimposed heterozygote case) , the stutter pattern of the locus iε retrieved from the library. This pattern, posεibly dependent on allele size, is combined with the individual'ε locuε data (uεing the allele deconvolution methodε detailed in εtepε 5 and 5' of figure IB) to determine the genotype. Referring to figure 5, a εyεtem for genotyping polymorphic genetic loci compriεed of a computer device with memory and an inputting meanε iε deεcribed. Referring now to the drawingε wherein like reference numeralε refer to εimilar or identical partε throughout the εeveral viewε, and more εpecifically to figure 5 thereof, there iε shown a schematic repreεentation of a εyεtem 500 for genotypting polymorphic genetic loci. The system 500 compriseε a meanε 502 for obtaining nucleic acid material from a genome. The εyεtem 500 comprises a means 504 for PCR amplification of one or more STR loci of the acquired genomic DNA. The system 500 also comprises a means 506 for assaying the differential sizes and concentrations of the PCR amplified DNA. In the preferred embodiment, meanε 508 iε effected by gel electrophoresis and the formation of an image. The syεtem 500 compriεeε a computer 508 with an inputting meanε 510, a memory 512, and an outputting meanε 520. The aεsayed differential DNA sizeε and concentrations are entered into the computer 508 via the inputting means 510. The εyεtem 500 compriεeε a meanε 514 for analyzing images into DNA size and concentration features at locations on the image, thereby converting the asεayed amplified material into a first set of electrical signals corresponding to size and concentration of the amplified material at the location. The system 500 also compriseε a meanε 516 for deconvolving the DNA εize and concentration featureε into their underlying genotypeε, thereby removing PCR εtutter artifact. Thiε deconvolving meanε 516 may make uεe of a means 518 (that uses the memory 512) for conεtructing, recording, and retrieving PCR εtutter patternε. More generally, the meanε 516 iε for operating on the first set of electrical εignalε produced from the amplified material with a εecond εet of electrical εignalε correεponding to a reεponεe pattern of the location to produce a third set of clean electrical signalε correεponding to the εize and multiplicitieε of the unamplified material on the genome at the location. The εyεtem 500 compriεes an outputting means 520 that makes the computed genotypeε available for further proceεεing; theεe genotypeε are derived from the third εet of clean electrical εignalε corresponding to the size and multiplicities of the unamplified material on the genome at the location. The syεtem 500 may optionally compriεe a meanε 522 for further characterizing chromoεomeε from the outputted genotypeε. Such meanε 522 include genetic diagnosiε, the conεtruction or uεe of genetic mapε, the poεitional cloning of geneε, genetic monitoring of cancerous materials, genetic fingerprinting, and the genotyping of populations. (2) A system for diagnosing genetic disease. Referring to figure 6, step 1 determines genotypeε of related individualε. Thiε iε done uεing the method of figure IB. Referring to figure 6, εtep 2 εetε chromosome phase by graph propagation, deductive methods, or likelihood analyεiε. For linkage-baεed molecular diagnoεiticε, it iε often uεeful to know the phaεe of the chromoεomeε. The example of DMD iε preεented aε one preferred embodiment. Once the genotypeε have been determined for a DMD pedigree, phase is eaεily εet on the X chromoεome. Thiε is done by treating the pedigree as a graph, where the nodes are the individuals, and the links are the inheritance paths between them. Starting from a male deεcendant (e.g., the proband) , the neighboring nodeε that are one inheritance link away (whether child or parent) are explored. Individual haplotypes are locally determined from haplotyped neighbors, as follows: * Male individuals are given the haplotype of their hemizygotic genotype. * Female individuals are εet from a male neighbor by aεεigning one haplotype to the male'ε haplotype. and aεεigning the second haplotype as the difference at each marker of the individual's genotype and the male haplotype. * Female individuals are set from a haplotyped female neighbor by first determining which (if either) of the neighbor's haplotypes iε contained within the individual'ε genotype. Thiε haplotype becomeε the firεt haplotype of the individual, and the second haplotype is obtained aε the difference at each marker of the individual'ε genotype and the first haplotype. Other local computations can be done when viεiting each node, εuch aε aεεessing conεiεtency. Since the graph traverεal only propagateε to unhaplotyped neighbors, the process terminates when all individualε have been consistently haplotyped. Independent graph propagations from each male descendant are done. The propagation locally terminates at an individual when a parent-child haplotype inconεiεtency iε detected. Thiε early termination can εuggeεt where recombination (or other eventε) occur in the pedigree, and how to correct for their occurrence. An example of εetting phase from the allele data is illustrated with female individual D and male proband A from Family #40. The genotype of D acrosε the four dyεtrophin markerε 5 DYS-II, STR-45, STR-49, 3-CA iε the allele sequence (207, 215), (171, 175), (233, 264), (131, 131) . A's haplotype is 207, 171, 233, 131. Extracting thiε haplotype from D'ε genotype leaveε 215, 175, 264, 131; These two sequences describe D's two haplotypes. Figure 7 showε the complete haplotyping for example Family #40 uεing this method for setting phase. For autosomal chromoεomeε, phase is set by likelihood methods (G. M. Lathrop and J.-M. Lalouel, "Efficient computations in multilocuε linkage analyεis," Amer. J. Hum . Genet . , vol. 42, pp. 498-505, 1988; J. Ott, Analysis of Human Genetic Linkage, Revised Edition . Baltimore, Maryland: The Johns Hopkins Univerεity Press, 1991) , incorporated by reference, or by deductive analysis (E. M. Wijsman, "A Deductive Method of Haplotype Analyεiε in Pedigreeε," Am. J. Hum . Genet . , vol. 41, pp. 356-373, 1987), incorporated by reference. Referring to figure 6, step 3 determineε the phenotypic riεk of disease for the individuals. The phenotype is inferred by comparing the proband'ε εignature haplotype with the haplotypeε of other related individualε in the pedigree. The multiple informative markers assures that, with high probability, identity-by- state of the multiple markers implies identity-by-descent. Thus, an identical signature at a related individual in the pedigree implies a εhared chromoεomal segment, including the diseased gene region(s) . For example, with X-linked diεorderε, maleε εharing an affected proband'ε εignature are preεumed to be affected, whereaε femaleε sharing this signature are preεumed carriers. Once the entire pedigree has been haplotyped, the affected, unaffected, and carrier (with X-linked disease) individuals are inferred. If no recombination events are found, then the disease gene haplotype of the proband serves as a signature that indicates an affected diseaεe gene. Related perεonε with the disease gene haplotype are thus inferred to have carry the disease gene. The phenotypic status of disease gene carriers dependε on the mode of genetic transmission: with purely recessive disorders, one disease gene dose causeε diεeaεe, whereaε with purely dominant disorders, all chromosomes must be affected. With variable expreεsivity, variable penetrance, and multigenic or multifactorial disorderε, having the diεeaεe gene doeε not neceεεarily imply phenotypic diεeaεe. Phenotypes are then determined. In Family #40, for example, proband A's allele εignature at the four markers 5 DYS-II, STR-45, STR-49, 3-CA is the allele sequence 207, 171, 233, 131. All individuals in Family #40 εharing thiε εequence on one of their haplotyped chromosomes are presumed to also εhare the affected proband'ε disease gene. Thus, individual G iε inferred to be another affected male, and the individualε D, E, and F are inferred to be carrier femaleε. The phenotyped pedigree iε εhown in Figure 8. In non-X-linked diεorders, the multiple linked markers enable phenotype determination via Bayeεian analyεiε. Thiε iε done uεing conventional (I. D. Young, Introduction to Risk Calculation in Genetic Counselling. Oxford: Oxford Univerεity Preεs, 1991) , incorporated by reference, or rule-based (D. K. Pathak and M. W. Perlin, "Automatic Computation of Genetic Riεk," in Proceedings of the Tenth Conference on Artificial Intelligence for Applications , San Antonio, Texaε, 1994, pp. 164-170) , incorporated by reference, techniqueε. Referring to figure 6, step 4 presentε the reεultε. The reεults of the molecular diagnosticε analyεiε iε then preεented in a uεable form. In one preferred embodiment, a graphical computer interface iε uεed to preεent the pedigree, annotated with the reεultε of the geneticε computationε. A preferred implementation iε to uεe object- oriented programming techniqueε, and to associate an object with each individual in the pedigree, and an object with each link between individuals in the pedigree. These objectε are uεed to acceεε the individual-εpecific data, to perform the interindividual graph proceεεing, and to execute all diεplay functionality ' by having objectε diεplay repreεentationε of themεelveε in the appropriate contextε. Such display representationε include graphical objectε (e.g., circleε, εquares, and lineε) , and textual annotationε. (3) A system for constructing genetic maps. A system for constructing genetic linkage mapε compriεing the steps of: 1. Determining genotypes automatically from STR loci using the method of figure IB. 2. Entering data and pedigree information into a computer device with memory. This data entry can be done manually, or automatically, as in (D. K. Pathak and M. W. Perlin, "Intelligent Interpretation of PCR Products in ID Gelε for Automatic Molecular Diagnostics," in Seventh Annual IEEE Symposium on Computer-based Medical Systems , Winston-Salem, North Carolina, 1994) , incorporated by reference. 3a. Running the LINKAGE program to build a genetic map (G. M. Lathrop and J.-M. Lalouel, "Efficient computations in multilocus linkage analysis," Amer. J. Hum . Genet . , vol. 42, pp. 498-505, 1988; J. Ott, Analysis of Human Genetic Linkage, Revised Edition . Baltimore, Maryland: The Johns Hopkins University Presε, 1991) , incorporated by reference. 3b. In an alternative embodiment, applying the automated MultiMap program (T. C. Matise, M. W. Perlin, and A. Chakravarti, "Automated construction of genetic linkage mapε using an expert system (MultiMap) : application to 1268 human microsatellite markers," Nature Genetics , vol. 6, no. 4, pp. 384-390, 1994; P. Green, "Rapid conεtruction of multilocuε genetic linkage mapε. I. Maximum likelihood eεtimation," Department of Geneticε, Washington University School of Medicine, draft manuscript, 1988.), incorporated by reference, to the data. (4) A system for genetically localizing genetic traits. A system for localizing genetic traits on a genome map comprising the εtepε of: 1. Determining genotypeε automatically from STR loci uεing the method of figure IB. 2. Entering data and pedigree information into a computer device with memory. Thiε data entry can be done manually, or automatically, aε in (D. K. Pathak and M. W. Perlin, "Intelligent Interpretation of PCR Products in ID Gelε for Automatic Molecular Diagnostics," in Seventh Annual IEEE Symposium on Computer-based Medical Systems, Winston-Salem, North Carolina, 1994) , incorporated by reference. 3a. Running the LINKAGE program to localize traits on the genetic map (G. M. Lathrop and J.-M. Lalouel, "Efficient computations in multilocus linkage analysis," A er. J. Hum. Genet . , vol. 42, pp. 498-505, 1988), incorporated by reference. 3b. In an alternative embodiment, applying the automated MultiMap program (T. C. Matise, M. W. Perlin, and A. Chakravarti, "Automated construction of genetic linkage maps fusing an expert εyεtem (MultiMap) : application to 1268 human microεatellite markerε," Nature Genetics , vol. 6, no. 4, pp. 384-390, 1994) , incorporated by reference, to the data. 3c. In another alternative embodiment, using linked genetic markers to determine location (E. S. Lander and D. Botstein, "Mapping Complex Genetic Traits in Humanε: New Methodε Using a Complete RFLP Linkage Map," in Cold Spring Harbor Symposia on Quantitative Biology, vol. LI, Cold Spring Harbor, Cold Spring Harbor Laboratory, 1986, pp. 49-62), incorporated by reference. Other approaches include Inner Product Mapping (IPM) superpoεition of alleleε, aε deεcribed next. For a (εmall) chromoεomal region that includeε the cauεative gene, termed the concordant region , all affected/carrier individualε in a pedigree will share (roughly) identical chromosomal material, whereas each unaffected/noncarrier individual will have nonidentical material. A highly informative genetic marker that lieε within the concordant region will exhibit complete concordance, markerε that lie near the concordant region will show high (though incomplete) concordance, and markers far from the concordant region will have random concordance. From a linkage analysis perspective, fully haplotyped chro osomeε for an X-linked trait can be viewed as radiation hybrids (D. R. Cox, M. Burmeister, E. R. Price, S. Kim, and R. M. Myerε, "Radiation hybrid mapping: a εomatic cell genetic method for constructing high-resolution maps of mammalian chromosomeε," Science , vol. 250, pp. 245-250, 1990) , incorporated by reference. Inner product mapping (IPM) (M. W. Perlin and A. Chakravarti, "Efficient Construction of High-Resolution Phyεical Mapε from Yeast Artificial Chromosomeε uεing Radiation Hybridε: Inner Product Mapping," Genomics , vol. 18, pp. 283-289, 1993), incorporated by reference, iε a phyεical mapping method for localizing DNA probeε baεed on concordance of radiation hybrid probings which can be adapted to localizing X-linked diseaεe genes on a genetic map. With fully informative genetic markers, identity-by- state (IBS) analysiε uses allele information directly from the genotyping data. For haplotyped X-linked traits, an individual is concordant for a marker allele when either the individual iε phenotypically affected/carrier and εhareε the allele with the affected/carrier founder, or the individual iε phenotypically unaffected/noncarrier and doeε not εhare the allele with the affected/carrier founder. For every marker, IPM-concordance analyzes each founder allele separately, forming the εum of concordant individualε in the pedigree; the greateεt εum iε the concordance value of the marker. When genetic markerε are not fully informative, an identity-by-descent (IBD) analyεiε of a marker allele weights each individual in the sum by the probability that the allele was inherited from the founder. When a fully concordant value is detected at a candidate marker, the marker's significance for linkage can be measured by examining the concordance at nearby linked markers. Specifically, the concordance is considered significant when the observed concordance valueε for multiple markerε in an interval agree with the predicted concordance valueε, as determined by a χ 2 test (P. G. Hoel, Introduction to Mathematical Statistics. New York: John Wiley & Sons, 1971), incorporated by reference. To predict concordance at a nearby marker having recombination distance θ from the candidate marker, each individual with an affected/carrier parent is considered to be an independent Bernoulli trial for linkage. Since (1-0) iε the probability that the offεpring re ainε linked at the nearby marker, with n aε the total (unweighted IBS or weighted IBD) number of conεidered individuals, the binomial distribution provides the predicted concordance mean and variance parameters μ = n * (1 - θ) , and σ 2 = n * θ * (1 - θ) . From these predicted distribution parameters, the χ 2 teεt can be performed by evaluating a εet of neighboring markerε. (5) A system for positionally cloning disease genes. A syεtem for poεitionally cloning a disease gene comprising the εtepε of: 1. Determining genotypeε automatically from STR loci uεing the method of figure IB. 2. Entering data and pedigree information into a computer device with memory. Thiε data entry can be done manually, or automatically, aε in (D. K. Pathak and M. W. Perlin, "Intelligent Interpretation of PCR Productε in ID Gelε for Automatic Molecular Diagnoεtics," in Seventh Annual IEEE Symposium on Computer-based Medical Systems , Winston-Salem, North Carolina, 1994) , incorporated by reference. 3. Running a computer program εuch aε LINKAGE to localize traitε on the genetic map (G. M. Lathrop and J.-M. Lalouel, "Efficient computationε in multilocuε linkage analysis," Amer . J. Hum . Genet . , vol. 42, pp. 498-505, 1988), incorporated by reference. 4. Use an integrated genetic/physical map to positionally clone the diεeaεe gene uεing εtandard techniqueε (B.-S. Kerem, J. M. Rommenε, J. A. Buchanan, D. Markiewicz, T. K. Cox, A. Chakravarti, M. Buchwald, and L.-C. Tεui, "Identification of the cyεtic fibroεiε gene: genetic analyεiε," Science , vol. 245, pp. 1073-1080, 1989; J. R. Riordan, J. M. Rommenε, B.-S. Kerem, N. Alon, R. Rozmahel, Z. Grzelczak, J. Zielenεki, S. Lok, N. Plavεic, J.-L. Chou, M. L. Drumm, M. C. Iannuzzi, F. S. Collinε, and L.-C. Tεui, "Identification of the cyεtic fibroεiε gene: cloning and characterization of complementary DNA," Science , vol. 245, pp. 1066-1073, 1989), incorporated by reference. 5. Determine the εequence of the cloned gene. 6. Uεe the εequence of the cloned gene for diagnostic testing, for treating diεeaεe, and for developing pharmaceutical reagents. (6) A system for genetically monitoring cancerous materials or other diseases. A εyεtem for genetically monitoring cancerous materials or other diεeaεes comprising the steps of: 1. Determining genotypes of cancerous tissues automatically from STR loci using the method of figure IB. In one preferred embodiment, the STRs are diagnostic tri- or tetra- nucleotide repeats asεociated with tumor progression and severity. In another preferred embodiment, the STRs are multi-nucleotide repeats used to quantitate the number chromosomal regionε preεent in one sample, thereby determining chromosomal deletionε and replicated chromoεome regionε. 2. Entering data and pedigree information into a computer device with memory. This data entry can be done manually, or automatically, as in (D. K. Pathak and M. W. Perlin, "Intelligent Interpretation of PCR Products in ID Gels for Automatic Molecular Diagnosticε, " in Seventh Annual IEEE Symposium on Computer-based Medical Systems , Winεton-Salem, North Carolina, 1994) , incorporated by reference. 3. Evaluate the temporal courεe of the determined genotypeε of tumorε to facilitate accurate diagnoεiε, (Zhang, Y., Coyne, M.Y., Will, S.G., Levenεon, CH. , and Kawaεaki, E.S. (1991) . Single-baεe mutational analyεiε of cancer and genetic diεeases uεing membrane bound modified oligonucleotides. Nucleic Acids Research , 19(14): 3929-33), incorporated by reference. (7) A system for genetic fingerprinting. A syεtem for genetic fingerprinting compriεing the εtepε of: 1. Determining genotypeε of cancerouε tiεεueε automatically from STR loci uεing the method of figure IB. 2. Entering data and pedigree information into a computer device with memory. Thiε data entry can be done manually, or automatically, aε in (D. K. Pathak and M. W. Perlin, "Intelligent Interpretation of PCR Productε in ID Gelε for Automatic Molecular Diagnostics," in Seventh Annual IEEE Symposium on Computer-based Medical Systems , Winston-Salem, North Carolina, 1994) , incorporated by reference. 3. Storing, retrieving, comparing, and processing genetic STR-based fingerprints (Jeffreys, A.J., Brookfield, J.F.Y., and Semeonoff, R. 1985. Positive identification of an immigration teεt-case using human DNA fingerprints. Nature , 317: 818-819.), incorporated by reference. (8) A system for performing population genotyping studies. A syεtem for performing population genotyping studieε compriεing the εtepε of: 1. Determining automatically the genotypes of STR loci for sampleε containing multiple chromoεomeε from STR loci uεing the method of figure IB. Theεe εampleε are pooled DNAε from one or more individualε. 2. Entering data and pedigree information into a computer device with memory. Thiε data entry can be done manually, or automatically, aε in (D. K. Pathak and M. W. Perlin, "Intelligent Interpretation of PCR Productε in ID Gelε for Automatic Molecular Diagnoεticε," in Seventh Annual IEEE Symposium on Computer-based Medical Systems , Winston-Salem, North Carolina, 1994) , incorporated by reference. 3. Performing further population-based analyses such as asεociation or linkage (A. E. H. Emery, Methodology in Medical Genetics : an introduction to statistical methods, Second Edition Edition . Edinburgh: Churchill Livingεtone, 1986; J. Ott, Analysis of Human Genetic Linkage, Revised Edition . Baltimore, Maryland: The Johnε Hopkins University Preεε, 1991) , incorporated by reference, or newer techniques based on dense genotyping (E. Feingold, P. O. Brown, and D. Siegmund, "Gauεεian Models for Genetic Linkage Analyεis Using Complete High-Resolution Maps of Identity by Deεcent," Am. J. Hum . Genet . , vol. 53, pp. 234-252, 1993; D. E. Goldgar, "Multipoint analyεiε of human quantitative genetic variation," Am. J. Hum . Genet . , vol. 47, pp. 957-967, 1990; S.-W. Guo, "Computation of Identity-by-Deεcent Proportionε Shared by Two Siblingε," Am . J. Hum . Genet . , vol. 54, pp. 1104-1109, 1994; N. Riεch, "Linkage strategies for genetically complex traits. In three parts," Am . J. Hum . Genet . , vol. 46, pp. 222-253, 1990; N. J. Schork, "Extended Multipoint Identity-by-Descent Analysiε of Human Quantitative Traitε: Efficiency, Power, and Modeling Conεiderationε," Am. J. Hum . Genet . , vol. 53, pp. 1306-1319, 1993), incorporated by reference, to localize genetic patterns of inheritance on the genome in poputations. (9) A system for assessing genetic risk in individuals. εyεtem for assessing genetic risk comprising the steps of: 1. Determining the genotypes of STR loci for multiple related individuals automatically from STR loci using the method of figure IB. 2. Entering data and pedigree information into a computer device with memory. Thiε data entry can be done manually, or automatically, aε in (D. K. Pathak and M. W. Perlin, "Intelligent Interpretation of PCR Products in ID Gels for Automatic Molecular Diagnoεticε," in Seventh Annual IEEE Symposium on Computer-based Medical Systems , Winεton-Salem, North Carolina, 1994) , incorporated by reference. 3. Uεing the genotypic information to asεess risk in individualε for multigenic traits (E. Feingold, P. O. Brown, and D. Siegmund, "Gausεian Models for Genetic Linkage Analysis Using Complete High-Resolution Maps of Identity by Deεcent," Am . J. Hum . Genet . , vol. 53, pp. 234-252, 1993; D. E. Goldgar, "Multipoint analysis of human quantitative genetic variation," Am. J. Hum . Genet . , vol. 47, pp. 957-967, 1990; S.-W. Guo, "Computation of Identity-by-Descent Proportions Shared by Two Siblings," Am . J. Hum . Genet . , vol. 54, pp. 1104-1109, 1994; N. Risch, "Linkage εtrategieε for genetically complex traitε. In three parts," Am . J. Hum . Genet . , vol. 46, pp. 222-253, 1990; N. J. Schork, "Extended Multipoint Identity-by-Descent Analysis of Human Quantitative Traits: Efficiency, Power, and Modeling Conεiderationε," Am . J. Hum . Genet . , vol. 53, pp. 1306-1319, 1993), incorporated by reference. Performing further riεk aεεeεεment uεing claεsical methods (A. E. H. Emery, Methodology in Medical Genetics: an introduction to statistical methods, Second Edition Edition . Edinburgh: Churchill Livingstone, 1986; A. E. H. Emery and D. L. Rimoin, ed. , Principles and practice of medical genetics . Edinburgh: Churchill Livingstone, 1983; J. Ott, Analysis of Human Genetic Linkage, Revised Edition . Baltimore, Maryland: The Johns Hopkinε Univerεity Press, 1991; I. D. Young, Introduction to Risk Calculation in Genetic Counselling. Oxford: Oxford University Presε, 1991), incorporated by reference, to aεsesε genetic risk of multigenic traitε in individualε or groupε. Herein, meanε or mechaniεm for language haε been used. The presence of meanε iε purεuant to 35 U.S.C. §112 paragraph and iε εubject thereto. The presence of mechanism is outside of 35 U.S.C. §112 and is not εubject thereto. Although the invention has been described in detail in the foregoing embodiments for the purpose of illustration, it is to be understood that such detail is solely for that purpose and that variations can be made therein by those skilled in the art without departing from the spirit and scope of the invention except aε it may be deεcribed by the following claimε.

Claims: WHAT IS CLAIMED IS: 1. A method for genotyping comprised of the steps: (a) obtaining nucleic acid material from a genome; (b) amplifying a location of the material; (c) assaying the amplified material based on size and concentration; (d) converting the aεεayed amplified material into a firεt εet of electrical εignalε corresponding to size and concentration of the amplified material at the location; and (e) operating on the first εet of electrical signals produced from the amplified material with a second set of electrical signalε correεponding to a response pattern of the location to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material on the genome at the location. 2. A method aε deεcribed in Claim 1 wherein the εecond εet of electrical εignalε corresponds to a PCR stutter responεe pattern of the location. 3. A method aε deεcribed in Claim 1 wherein the operating εtep on the firεt εet of electrical signals with the second set of electrical εignalε includes the step of a deconvolution procedure. 4. A method as described in Claim 1 wherein the operating step on the first set of electrical signalε with the second set of electrical signalε includeε the step of deconvolving using computed properties of the electrical signals. 5. A method as described in Claim 1 wherein the operating step on the first set of electrical signals with the second εet of electrical εignalε includes the step of deconvolving using computed moment properties of the electrical signals. 6. A method as described in Claim 1 wherein the operating step on the first εet of electrical signals with the second set of electrical signalε includeε the step of deconvolving using Fourier techniques. 7. A method aε deεcribed in Claim 1 wherein the operating step on the first set of electrical signals with the second set of electrical signals includes the step of deconvolving using a noise tolerant procedure. 8. A method as described in Claim 1 wherein the operating step on the first εet of electrical signals with the second set of electrical signals includes the step of optimal (Wiener) filtering. 9. A method aε deεcribed in Claim 1 wherein the third εet of clean electrical εignals corresponds to no more than two allele sizeε. 10. A method aε deεcribed in Claim 1 wherein the amplification step is polymerase chain reaction using two oligonucleotide primers sufficiently complementary to non- repeated εequenceε at the endε of the multinucleotide repeat location. 11. A method aε deεcribed in Claim 1 wherein the amplified material iε labeled. 12. A method as described in Claim 1 wherein the oligonucleotide primerε are obtained by direct synthesiε. 13. A method aε deεcribed in Claim 1 wherein the oligonucleotide primerε are end-labeled. 14. A method aε described in Claim 1 wherein more than one multinucleotide location on a genome is characterized simultaneously. 15. A method as deεcribed in Claim 1 used to establiεh the identity, pedigree, or relationεhip of an individual comprising comparison of the individual's third set of clean electrical εignalε with the third set of clean electrical signalε from the DNA of one or more control individualε. 16. A method aε deεcribed in Claim 1 wherein the aεεaying εtep of amplified material is separating and characterizing by gel electrophoreεis. 17. A method as described in Claim 1 wherein the nucleic acid material is genomic DNA or cloned DNA. 18. A syεtem for genotyping compriεing: (a) meanε or mechanism for obtaining nucleic acid material from a genome; (b) means or mechanism for amplifying a location of the material, said amplifying means or mechanism in communication with the nucleic acid material; (c) means or mechanism for asεaying the amplified material baεed on size and concentration, said assaying means or mechanism in communication with amplifying means or mechanism; (d) means or mechanism for converting the assayed amplified material into a first set of electrical signalε correεponding to εize and concentration of the amplified material at the location, said converting means or mechanism in communication with the assaying means; and (e) means or mechanism for operating on the first set of electrical signalε produced from the amplified material with a second set of electrical signals corresponding to a response pattern of the location to produce a third set of clean electrical signals corresponding to the size and multiplicities of the unamplified material on the genome at the location, εaid operating meanε or mechanism in communication with the sets of electrical signalε. 19. A εyεtem aε deεcribed in Claim 18 wherein: (a) the amplifying meanε or mechanism includes polymerase chain reaction, or harveεting cloned cellε; (b) the assaying means or mechaniεm includes gel electrophoresis, or mass spectroεcopy, or denaturing gradient gel electrophoreεiε, or differential hybridization, or εequencing by hybridization; (c) the converting means or mechanism employs labeling with detection including radioactivity, or fluorescence, or phosphoreεcence, or chemilumineεcence, or visible light, or ions, or pH, or electricity, or reεistivity, or biotinylation, or antibodies; and includes the detecting means or mechaniεm which includes a photomultiplier tube; a radioactivity counter, a resistivity sensor, a pH meter, or an optical detector; and (d) the operating means or mechanism includes statiεtical moment determinationε, or Fourier transformation, or optimal filtering, or polynomial calculations, or matrix computations. 20. A method for analyzing genetic material of an organism comprising the steps of: (a) amplifying the genetic material; (b) assaying size and concentration features of the amplified genetic material; and (c) characterizing the amplified genetic material in a region having a radius of lesε than five feet at a rate exceeding 100 multi-nucleotide genetic markers per hour per organism. 21. A method for producing a gene compriεed of the εtepε: (a) obtaining nucleic acid materialε from genomes of a firεt εet of individualε expreεεing a trait, or of a second set of individuals, each of whom may be related to a member of the first εet of individuals; (b) amplifying locations of the materials; (c) asεaying the amplified materialε based on size and concentration; (d) converting the assayed amplified materialε into firεt εets of electrical signals corresponding to sizes and concentrations of the amplified materials at the locations; (e) operating on the first εetε of electrical signals produced from the amplified materials with second sets of electrical εignalε correεponding to the PCR εtutter reεponse patterns of the locationε using a deconvolution procedure to produce third εetε of clean electrical signals corresponding to the one or more sizes of the unamplified material on the genome at the locations of each individual; (f) genetically localizing the trait on the genome; (g) using a genetic/phyεical genome map to iεolate a εmall εet of phyεical DNA εequenceε, one or more of which containε the cauεative gene; (h) cloning the gene from thiε set of DNA sequenceε; (i) determining the DNA εequence of the gene; (j) verifying that the gene iε cauεative or tightly linked by analyεiε of mutationε on the firεt or εecond εetε of individualε; and (k) purifying derivativeε of the gene.

Inventors: PERLIN MARK W, PERLIN, MARK, W.
Applicants: PERLIN MARK W, PERLIN, MARK, W.
Application year: 1995
Main classification: G06F17
IPC codes: C12Q1, G06F19