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.
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 FIG. 1A. (Though there are other cases of 0, 1, or many alleles that this invention. addresses, this if 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
PQRST,
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, fib clearly show the alleles of marker for each individual""s genome. Further, these sizes can be determined quantitatively by reference to molecular weight markers 112.
However, the PCR amplification of a CA-repeat location produces an artifact, often termed xe2x80x9cPCR stutterxe2x80x9d. Most likely due to slippage of the polymerase molecule on the nucleic acid polymer go 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)nST,
an entire population of different size bands
{PQ(CA)nST, PQ(CA)nxe2x88x921ST, PQ(CA)nxe2x88x922ST, . . . }
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, xe2x80x9cThe Human Genome Project: Current and future impact,xe2x80x9d 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 xe2x80x9cmicrosatellitesxe2x80x9d) (J. Weber and P. May, xe2x80x9cAbundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction,xe2x80x9d Am J Hum Genet, vol. 44, pp. 388-396, 1989; J. Weber, xe2x80x9cLength Polymorphisms in dC-dA . . . dG-dT Sequences,xe2x80x9d Marshfield Clinic, Marshfield, Wis., assignee code 354770, U.S. Pat. #5,075,217, 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 informativeness, with most loci in common use having PIC values of over 0.70 (J. Weissenbach, c. Gyapay, C. Dib, A. Vignal, J. Morissette, P. Millasseau, G. Vaysseix, and M. Lathrop, xe2x80x9cA second generation linkage map of the human genome,xe2x80x9d Nature, vol. 359, pp. 794-801, 1992; G. Gyapay, et. al., Nature Genetics, vol. 7, pp. 246-239, 1994), incorporated by reference. Finally, these markers 43; 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 10 cM 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, xe2x80x9cA rippling muscle disease gene is localized to 1q41: evidence for multiple genes,xe2x80x9d Neurology, 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 FIG. 2, CA repeat loci often show secondary xe2x80x9cstutterxe2x80x9d or xe2x80x9cshadowxe2x80x9d 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 xe2x80x9cnoisexe2x80x9d on the resulting autoradiograph xe2x80x9csignalsxe2x80x9d, referring to FIG. 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 polynucleotide 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)=(x3+x1), p(x).
The multiplication of the polynomial expressions (x3+x1) 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 10 cM 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, xe2x80x9cIsolation of candidate cDNAs for portions of the Duchenne muscular dystrophy gene,xe2x80x9d Nature, vol. 323, pp. 646-650, 1986; M. Koenig, E. P. Hoffman, C. J. Bertelson, A. P. Monaco, C. Feener, and L. M. Kunkel, xe2x80x9cComplete cloning of the Duchenne muscular dystrophy cDNA and preliminary genomic organization of the DMD gene in normal and affected individuals,xe2x80x9d 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 millionbase pairs); it has a high intragenic recombination rate (10 cM, i.e., 10% recombination between the 5xe2x80x2 and 3xe2x80x2 ends of the gene); and it has a considerable spontaneous mutation rate (10xe2x88x924 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, xe2x80x9cAn informative polymorphism detectable by polymerase chain reaction at the 3xe2x80x2 end of the dystrophin gene,xe2x80x9d 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, xe2x80x9cA 10-megabase physical map of human Xp21, including the Duchenne muscular dystrophy gene,xe2x80x9d 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, xe2x80x9cA polymorphic CACA repeat in the 3xe2x80x2 untranslated region of dystrophin,xe2x80x9d Nucleic Acids Res, vol. 18, pp. 1931, 1990; C. Oudet, R. Heilig, and J. Mandel, xe2x80x9cAn informative polymorphism detectable by polymerase chain reaction at the 3xe2x80x2 end of the dystrophin gene,xe2x80x9d Hum Genet, vol. 84, pp. 283-285, 1990; P. Clemens, R. Fenwick, J. Chamberlain, R. Gibbs, M. de Andrade, R. Chakraborty, and C. Caskey, xe2x80x9cLinkage analysis for Duchenne and Becker muscular dystrophies using dinucleotide repeat polymorphisms,xe2x80x9d Am J Hum Genet, vol. 49, pp. 951-960, 1991; C. Feener, F. Boyce, and L. Kunkel, xe2x80x9cRapid detection of CA polymorphisms in cloned DNA: application to the 5xe2x80x2 region of the dystrophin gene,xe2x80x9d 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 acquisition on automated sequencers (L. S. Schwartz, J. Tarleton, B. Popovich, W. K. Seltzer, and E. P. Hoffman, xe2x80x9cFluorescent Multiplex Linkage Analysis and Carrier Detection for Duchenne/Becker Muscular Dystrophy,xe2x80x9d 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 do not deteriorate for several years following the fluorescent labeling reactions. In this previous report, referring to FIG. 2, alleles were manually interpreted from the automated sequencer traces. Coverage of the entire human genome at 10 cM resolution in fluorescently labeled polynucleotide markers for use in semiautomated genotyping is available. (Map Pairs, Research Genetics, Huntsville, Ala.; P. W. Reed, J. L. Davies, J. B. Copeman, S. T. Bennett, S. M. Palmer, L. E. Pritchard, S. C. L. Gough, Y. Kawaguchi, H. J. Cordell, K. M. Balfour, S. C. Jenkins, E. E. Powell, A. Vignal, and J. A. Todd, xe2x80x9cChromosome-specific microsatellite sets for fluorescence-based, semi-automated genome mapping,xe2x80x9d Nature Genetics, in press, 1994), incorporated by reference.
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 xe2x80x9cstutteredxe2x80x9d 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 xe2x80x9cstutteredxe2x80x9d 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, xe2x80x9cAutomated construction of genetic linkage maps using an expert system (MultiMap): application to 1268 human microsatellite markers,xe2x80x9d 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, xe2x80x9cIdentification of the cystic fibrosis gene: genetic analysis,xe2x80x9d 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. Drumm, M. C. Iannuzzi, F. S. Collins, and L.-C. Tsui, xe2x80x9cIdentification of the cystic fibrosis gene: cloning and characterization of complementary DNA,xe2x80x9d Science, vol. 245, pp. 1066-1073, 1989), incorporated by reference.
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 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.