US2002119451A1PendingUtilityA1
System and method for predicting chromosomal regions that control phenotypic traits
Priority: Dec 15, 2000Filed: Dec 15, 2000Published: Aug 29, 2002
Est. expiryDec 15, 2020(expired)· nominal 20-yr term from priority
G16B 20/20C12Q 1/6876G16B 20/00
51
PatentIndex Score
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Claims
Abstract
A method of associating a phenotype with one or more candidate chromosomal regions in a genome of an organism includes the step of deriving a phenotypic data structure that represents differences in phenotypes between different strains of the organism. Further, a genotypic data structure is established. The genotypic data structure corresponds to a locus selected from a plurality of loci in the genome of the organism. The genotypic data structure represents variations of at least one component of the locus between different strains of the organism. The phenotypic data structure is compared to the genotypic data structure to form a correlation value. The process of establishing a genotypic data structure and comparing it to the phenotypic data structure is repeated for each locus in the plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other compared genotypic data structures. The loci that correspond to the one or more genotypic data structures having a high correlation value represent the one or more candidate chromosomal regions.
Claims
exact text as granted — not AI-modifiedWe claim:
1 . A method of associating a phenotype with one or more candidate chromosomal regions in a genome of an organism using a phenotypic data structure that represents a difference in a phenotype between different strains of said organism, said genome including a plurality of loci, said method comprising:
establishing a genotypic data structure, said genotypic data structure corresponding to a locus selected from said plurality of loci, said genotypic data structure representing a variation of at least one component of said locus between different strains of said organism; comparing said phenotypic data structure to said genotypic data structure to form a correlation value; and repeating said establishing and comparing steps for each locus in said plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures that are compared to said phenotypic structure during said comparing step; wherein the loci that correspond to said one or more genotypic structures that form a high correlation value represent said one or more candidate chromosomal regions.
2 . The method of claim 1 , each element in said phenotypic structure representing a difference in a phenotype between different strains of said organism; wherein, for each element in said phenotypic structure, said different strains of said organism are selected from a plurality of strains of said organism.
3 . The method of claim 2 , wherein said difference in said phenotype is determined by a measurement of an attribute corresponding to said phenotype in different strains of said organism.
4 . The method of claim 1 , each element in said phenotypic structure representing a difference in said phenotype between a first cluster of strains of said organism and a different second cluster of strains of said organism; wherein, for each element in said phenotypic structure, said different first and second cluster of strains of said organism are selected from a plurality of clusters of strains of said organism.
5 . The method of claim 1 , each element in said genotypic structure representing a variation of at least one component of said locus between different strains of said organism; wherein, for each element in said genotypic structure, said different strains of said organism are selected from a plurality of strains of said organism.
6 . The method of claim 1 , each element in said genotypic structure representing a variation of at least one component of said locus between a first cluster of strains of said organism and a different second cluster of strains of said organism; wherein, for each element in said genotypic structure, said different first and second clusters of strains of said organism are selected from a plurality of strains of said organism.
7 . The method of claim 1 , wherein said correlation value is formed in accordance with the expression:
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where,
c(P, G L ) is said correlation value;
p(i) is a value of the ith element of said phenotypic data structure;
g(i) is a value of the ith element of said genotypic data structure;
<P> is a mean value of all elements in said phenotypic data structure; and
<G L > is a mean value of all elements in said genotypic data structure.
8 . The method of claim 1 , wherein said correlation value is formed using an algorithm selected from the group consisting of regression analysis, regression analysis with data transformations, a Pearson correlation, a Spearman rank correlation, a regression tree and concomitant data reduction, partial least squares, and canonical analysis.
9 . The method of claim 1 , wherein said repeating step further comprises:
computing (i) a mean correlation value that represents a mean of each said correlation value formed during instances of said comparing step; and (ii) a standard deviation of said mean correlation value based on each said correlation value formed during instances of said comparing step; wherein, said one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures compared to said phenotypic data structure during said comparing step are identified by selecting genotypic data structures that form a correlation value that is a predetermined number of standard deviations above said mean correlation value.
10 . The method of claim 1 , wherein each said variation in said genotypic data structure is obtained from a variation in a single nucleotide polymorphism database, a microsatellite marker database, a restriction fragment length polymorphism database, a short tandem repeat database, a sequence length polymorphism database, or an expression profile database.
11 . A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
a genotypic database for storing variations in genomic sequences of a plurality of strains of an organism; a phenotypic data structure that represents a difference in a phenotype between different strains of said organism; and a program module for associating a phenotype with one or more candidate chromosomal regions in a genome of said organism, said genome including a plurality of loci, said program module comprising:
instructions for establishing a genotypic data structure, said genotypic data structure corresponding to a locus selected from a plurality of loci, said genotypic data structure representing a variation of at least one component of said locus between different strains of said organism stored in said genotypic database;
instructions for comparing said phenotypic data structure to said genotypic data structure to form a correlation value; and
instructions for repeating said instructions for establishing and instructions for comparing for each locus in said plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures that are compared to said phenotypic data structure by said instructions for comparing; wherein the loci that correspond to said one or more genotypic data structures that form a high correlation value represent said one or more candidate chromosomal regions.
12 . The computer program product of claim 11 , each element in said phenotypic data structure representing a difference in said phenotype between different strains of said organism; wherein, for each element in said phenotypic data structure, said different strains of said organism are selected from said plurality of strains of said organism represented in said genotypic database.
13 . The computer program product of claim 12 , wherein said difference in said phenotype is determined by a measurement of an attribute corresponding to said phenotype in said different strains of said organism that are represented in said genotypic database.
14 . The computer program product of claim 11 , each element in said phenotypic data structure representing a difference in said phenotype between a first cluster of strains of said organism and a different second cluster of strains of said organism; wherein, for each element in said phenotypic data structure, said different first and second cluster of strains of said organism are selected from a plurality of clusters of strains of said organism that are represented in said genotypic database.
15 . The computer program product of claim 11 , each element in said genotypic data structure representing a variation of at least one component of said locus between different strains of said organism; wherein, for each element in said genotypic data structure, said different strains of said organism are selected from said plurality of strains of said organism represented in said genotypic database.
16 . The computer program product of claim 11 , each element in said genotypic data structure representing a variation of at least one component of said locus between a first cluster of strains of said organism and a different second cluster of strains of said organism; wherein, for each element in said genotypic data structure, said different first and second clusters of strains of said organisms are selected from said plurality of strains of said organism represented in said genotypic database.
17 . The computer program product of claim 11 , wherein said instructions for comparing include instructions for forming said correlation value in accordance with the expression:
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where,
c(P, G L ) is said correlation value;
p(i) is a value of the i th element of said phenotypic data structure;
g(i) is a value of the i th element of said genotypic data structure;
<P> is a mean value of all elements in said phenotypic data structure; and
<G L > is a mean value of all elements in said genotypic data structure.
18 . The computer program product of claim 11 , wherein said instructions for comparing include instructions for forming said correlation value by an algorithm selected from the group consisting of regression analysis, regression analysis with data transformations, a Pearson correlation, a Spearman rank correlation, a regression tree and concomitant data reduction, partial least squares, and canonical analysis.
19 . The computer program product of claim 11 , wherein said instructions for repeating further comprise:
instructions for computing (i) a mean correlation value that represents a mean of each said correlation value formed during instances of said instructions for comparing; and (ii) a standard deviation of said mean correlation value based on each said correlation value formed during instances of said instructions for comparing; wherein, said one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures compared to said phenotypic data structure by said instructions for comparing are identified by selecting genotypic data structures that form a correlation value that is a predetermined number of standard deviations above said mean correlation value.
20 . The computer program product of claim 11 , wherein said genotypic database is a single nucleotide polymorphism database, a microsatellite marker database, a restriction fragment length polymorphism database, a short tandem repeat database, a sequence length polymorphism database, an expression profile database, or a DNA methylation database; and said variation in said genotypic data structure is obtained from said genotypic database.
21 . A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
a genotypic database for storing variations in genomic sequences of a plurality of strains of an organism; a phenotypic data structure, each element in said phenotypic data structure representing a difference in said phenotype between different strains of said organism; and a program module for associating a phenotype with one or more candidate chromosomal regions in a genome of said organism, said genome including a plurality of loci, said program module comprising:
instructions for identifying a genotypic data structure, said genotypic data structure corresponding to a locus selected from said plurality of loci, each element in said genotypic data structure representing a variation of at least one component of said locus between different strains of said organism;
instructions for comparing said phenotypic data structure to said genotypic data structure to form a correlation value; and
instructions for repeating said instructions for identifying and said instructions for comparing, for each locus in said plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures that are compared to said phenotypic data structure by said instructions for comparing; wherein the loci that correspond to said one or more genotypic data structures that form a high correlation value represent said one or more candidate chromosomal regions.
22 . A computer system for associating a phenotype with one or more candidate chromosomal regions in a genome of an organism, said genome including a plurality of loci, the computer system comprising:
a central processing unit; a memory, coupled to the central processing unit, the memory storing:
a genotypic database for storing variations in genomic sequences of a plurality of strains of said organism;
a phenotypic data structure that represents a difference in a phenotype between different strains of said organism; and
a program module, said program module comprising:
instructions for establishing a genotypic data structure, said genotypic data structure corresponding to a locus selected from a plurality of loci, said genotypic data structure representing a variation of at least one component of said locus between different strains of said organism stored in said genotypic database;
instructions for comparing said phenotypic data structure to said genotypic data structure to form a correlation value; and
instructions for repeating said instructions for establishing and said instructions for comparing, for each locus in said plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures that are compared to said phenotypic data structure by said instructions for comparing; wherein the loci that correspond to said one or more genotypic data structures that form a high correlation value represent said one or more candidate chromosomal regions.
23 . The computer system of claim 22 , each element in said phenotypic data structure representing a variation in said phenotype between different strains of said organism; wherein, for each element in said phenotypic data structure, said different strains of said organism are selected from said plurality of strains of said organism represented in said genotypic database.
24 . The computer system of claim 23 , wherein said difference in a phenotype is determined by a measurement of an attribute corresponding to said phenotype in said different strains of said organism that are represented in said genotypic database.
25 . The computer system of claim 22 , each element in said phenotypic data structure representing a variation in said phenotype between a first cluster of strains of said organism and a different second cluster of strains of said organism; wherein, for each element in said phenotypic data structure, said different first and second cluster of strains of said organism are selected from a plurality of clusters of strains of said organism that are represented in said genotypic database.
26 . The computer system of claim 22 , each element in said genotypic data structure representing a variation of at least one component of said locus between different strains of said organism; wherein, for each element in said genotypic data structure, said different strains of said organism are selected from said plurality of strains of said organism represented in said genotypic database.
27 . The computer system of claim 22 , each element in said genotypic data structure representing a variation of at least one component of said locus between a first cluster of strains of said organism and a different second cluster of strains of said organism; wherein, for each element in said genotypic data structure, said different first and second clusters of strains of said organisms are selected from said plurality of strains of said organism represented in said genotypic database.
28 . The computer system of claim 22 , wherein said instructions for comparing include instructions for forming said correlation value in accordance with the expression:
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where,
c(P, G L ) is said correlation value;
p(i) is a value of the i th element of said phenotypic data structure;
g(i) is a value of the i th element of said genotypic data structure;
<P> is a mean value of all elements in said phenotypic data structure; and
<G L > is a mean value of all elements in said genotypic data structure.
29 . The computer system of claim 22 , wherein said instructions for comparing include instructions for forming said correlation value by an algorithm selected from the group consisting of regression analysis, regression analysis with data transformations, a Pearson correlation, a Spearman rank correlation, a regression tree and concomitant data reduction, partial least squares, and canonical analysis.
30 . The computer system of claim 22 , wherein said instructions for repeating further comprise:
instructions for computing (i) a mean correlation value that represents a mean of each said correlation value formed during instances of said instructions for comparing; and (ii) a standard deviation of said mean correlation value based on each said correlation value formed during instances of said instructions for comparing; wherein, said one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures compared to said phenotypic data structure by said instructions for comparing are identified by selecting genotypic data structures that form a correlation value that is a predetermined number of standard deviations above said mean correlation value.
31 . The computer system of claim 22 , wherein said genotypic database is a single nucleotide polymorphism database, a microsatellite marker database, a restriction fragment length polymorphism database, a short tandem repeat database, a sequence length polymorphism database, an expression profile database, or a DNA methylation database; and said variation in said genotypic data structure is obtained from said genotypic database.
32 . A method of associating a phenotype with one or more candidate chromosomal regions in a genome of an organism using a phenotypic data structure that represents alterations in phenotypes between different strains in a plurality of strains of said organism,
said phenotypic data structure including a description of each said alteration and individual elements of said phenotypic data structure including an amount of alteration between different strains of said organism selected from said plurality of strains of said organism, said genome including a plurality of loci, each said loci representing one or more positions within said genome, said method comprising:
establishing a unique individual variation matrix for each said one or more positions represented by said loci, wherein an element within each said unique individual variation matrix represents an allelic comparison between different strains of said organism that are selected from said plurality of strains of said organism;
summing corresponding elements in each said unique individual matrix to form a genotypic data structure;
comparing said phenotypic data structure to said genotypic data structure to form a correlation value; and
repeating said establishing, summing and comparing steps, for each locus in said plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures that are compared to said phenotypic data structure during said comparing step; wherein the loci that correspond to said one or more genotypic data structures that form a high correlation value represent said one or more candidate chromosomal regions associated with said phenotype.
33 . A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
a genotypic database for storing variations in genomic sequences of a plurality of strains of an organism; a phenotypic data structure that represents alterations in phenotypes between different strains of said organism selected from said plurality of strains of said organism, said phenotypic data structure including a description of each said alteration and individual elements of said phenotypic data structure including an amount of alteration between different strains in said plurality of strains of said organism; and a program module for associating a phenotype with one or more candidate chromosomal regions in a genome of said organism, said genome including a plurality of loci, each said loci representing one or more positions within said genome, said program module comprising:
instructions for establishing a unique individual variation matrix for each said one or more positions represented by said loci, wherein an element within each said unique individual variation matrix represents an allelic comparison of values stored in said genotypic database between different strains of said organism that are selected from said plurality of strains of said organism;
instructions for summing corresponding elements in each said unique individual matrix to form a genotypic data structure;
instructions for comparing said phenotypic data structure to said genotypic data structure to form a correlation value; and
instructions for repeating said instructions for establishing, summing and comparing, for each locus in said plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures that are compared to said phenotypic data structure during said comparing step; wherein the loci that correspond to said one or more genotypic data structures that form a high correlation value represent said one or more candidate chromosomal regions associated with said phenotype.
34 . A computer system for associating a phenotype with one or more candidate chromosomal regions in a genome of an organism, said genome including a plurality of loci, each said loci representing one or more positions within said genome, said program module comprising:
a central processing unit; a memory, coupled to the central processing unit, the memory storing:
a genotypic database for storing variations in genomic sequences of a plurality of strains of said organism;
a phenotypic data structure that represents alterations in phenotypes between different strains in said plurality of strains of said organism, said phenotypic data structure including a description of each said alteration and individual elements of said phenotypic data structure including an amount of alteration between different strains in said plurality of strains of said organism; and
a program module, said program module comprising:
instructions for establishing a unique individual variation matrix for each said one or more positions represented by said loci, wherein an element within each said unique individual variation matrix represents an allelic comparison of values stored in said genotypic database between different strains of said organism that are selected from said plurality of strains of said organism;
instructions for summing corresponding elements in each said unique individual matrix to form a genotypic data structure;
instructions for comparing said phenotypic data structure to said genotypic data structure to form a correlation value; and
instructions for repeating said instructions for establishing, summing and comparing, for each locus in said plurality of loci, thereby identifying one or more genotypic data structures that form a high correlation value relative to all other genotypic data structures that are compared to said phenotypic data structure during said comparing step; wherein the loci that correspond to said one or more genotypic data structures that form a high correlation represent said one or more candidate chromosomal regions associated with said phenotype.Join the waitlist — get patent alerts
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