Whole genome based genetic evaluation and selection process

• US 20080163824A1
• Filed: 08/31/2007
• Published: 07/10/2008
• Est. Priority Date: 09/01/2006
• Status: Abandoned Application

• Current Assignee: Innovative Dairy Products Pty., Ltd.
• Original Assignee: Innovative Dairy Products Pty Limited An Australian Company ACN 098 382 784
• Inventors: Raadsma, Herman W., Moser, Gerhard Christian, Tier, Bruce, Woolaston, Alexander Frederick
• Application Number:
  US11/849,134
• Publication Number:
  US 20080163824A1
• Time in Patent Office: Days
• US Class Current: 119/174
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/40 Population genetics; Linkage disequilibrium
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis

Abstract

The present invention provides a method and system for the prediction of the merit of at least one individual in a population, the method comprising the steps of: (a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; (b) utilising the explanatory variables to generate a predictor function with respect to merit; and (c) utilising the predictor function to predict the merit of the individual.

Claims

1. A method for the prediction of the merit of at least one individual in a population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables;

(b) utilising the explanatory variables to generate a predictor function with respect to merit; and

(c) utilising the predictor function to predict the merit of the individual.

2. A method as claimed in claim 1 for a prediction of a merit of at least one individual, the method comprising the steps of:

(a) in a first population, where genotype and phenotype information of individuals in the first population are known, using dimension reduction on the genotype and phenotype information to determine the complexity of the genotype and phenotype information to minimise prediction error for at least one marker in the first population and thereby generate a set of explanatory variables with respect to the at least one marker;

(b) utilising the explanatory variables to the first population to generate a predictor function with respect to merit;

(c) generating a genotype for the at least one marker in at least one individual of interest from a second population; and

(d) utilising the predictor function to the genotype of the at least one individual of interest to determine the genetic merit of the individual of interest with respect to the at least one marker.

4. A method as claimed in claim 1 wherein step (b) comprises utilising the explanatory variables to generate a plurality of predictor functions for the individuals of the population.

5. A method as claimed in claim 1 wherein the information comprises information for at least one marker.

6. A method as claimed in claim 5 wherein the information comprises information for a plurality of marker s.

7. A method as claimed in claim 1 wherein for a plurality of individuals of interest from the population where information is unknown, generating genotype for at least one individual of interest from population.

8. A method according to claim 1 further comprising the steps of:

(f) determining additional information on the explanatory variables for the at least one individual;

(g) combining the additional information for the at least one individual with the information on the explanatory variables for the individuals of the population; and

(h) repeating steps (b) and (c) for at least one further individual to predict the merit of the further individual.

9. A method according to claim 8 wherein step (f) comprises determining additional information on the explanatory variables on a plurality of individuals.

10. A method according to claim 1, wherein the utilisation of the predictor function is performed on the basis of a desired outcome.

11. A method according to claim 4 wherein the genotype information comprises genetic markers or bio-markers or epigenetic markers.

12. A method according to claim 1, wherein the merit is a genetic merit selected from the group of a molecular breeding value, a quantitative trait locus, or a quantitative trait nucleotide.

41. A method according to claim 1, wherein the information comprises genetic information consisting essentially of marker genotypes.

42. A method according to claim 41 wherein the genetic markers are distributed substantially across the genome.

43. A method according to claim 41, wherein the number of genetic markers genotyped is greater than 1000, greater than 1500, greater than 2500, greater than 5000, greater than 10000, greater than 15000, greater than 20000, greater than 25000, greater than 30000, greater than 35000, greater than 40000, greater than 45000, greater than 50000, greater than 100000, greater than 250000, greater than 500000, or greater than 1000000, greater than 5000000, greater than 10000000 or greater than 15000000.

44. A method according to claim 41, wherein the genetic markers are selected from the group consisting of single nucleotide polymorphism (SNP), tag SNP, microsatellite (simple tandem repeat STR, simple sequence repeat SSR), restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), insertion-deletion polymorphism (INDEL), random amplified polymorphic DNA (RAPD), ligase chain reaction, insertion/deletions and direct sequencing of the gene or a simple sequence conformation polymorphisms (SSCP).

45. A method according to claim 44 wherein the genetic marker is a SNP.

46. A method according to claim 1, wherein the information comprises at least one of the pedigree of the individual;

an estimated breeding value of the individual;

data on genetic markers across the genome for the individual or for relatives of the individual;

at least one index of phenotype for the individual or for relatives of the individual;

at least one marker predictive of phenotype for the individual or for relatives of the individual; and

at least one index of epigenetic modification or status for the individual, or a combination thereof.

48. A method according to claim 1, wherein the dimension reduction is selected from the a technique in the group consisting of principal component analysis (PCA), a genetic algorithm, a neural network, partial least squares (PLS), inverse least squares, kernel PCA, LLE, Hessian LLE, Laplacian Eigenmaps, LTSA, isomap, maximum variance unfolding, Bolzman machines, projection pursuit, a hidden Markov model support vector machines, kernel regression, discriminant analysis and classification, k-nearest-neighbour analysis, fuzzy neural networks, Bayesian networks, or cluster analysis.

49. A method according to claim 48, wherein the dimension reduction technique is principal component analysis.

50. A method according to claim 48, wherein the dimension reduction technique is supervised principal component analysis.

51. A method according to claim 49 wherein the number of principal components is between about 10 and about 40.

52. A method according to claim 49 wherein the number of principal components is about 20.

53. A method according to claim 48 wherein the dimension reduction technique is partial least squares analysis.

54. A method according to claim 53 wherein the number of latent components is between about 4 and about 10.

55. A method according to claim 43 wherein the number of latent components is about 6.

56. A method according to claim 48 wherein the dimension reduction technique is support vector machine analysis.

57. A method according to claim 1 wherein the information does not include the pedigree of the individual.

65. A method as claimed in claim 1 wherein the predictor function is a predictor function with having minimal prediction error.

3. A method for the prediction of the merit of at least one individual in a population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using a genetic algorithm process on the information to generate a set of explanatory variables for all the information, the explanatory variables comprising weighted averages for components of the information; and

(b) utilising the explanatory variables to generate a predictor function with respect to merit;

(c) utilising the predictor function to predict the merit of the individual.

13. A method of predicting trait performance for at least one individual in a population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) utilising the explanatory variables to generate a predictor function with respect to merit;

(c) utilising the predictor function to predict the trait performance for the individual.

14. A method as claimed in claim 13 further comprising the steps of:

(d) for an individual of interest from the population where information is unknown, generating genotype for at least one individual of interest from population; and

(e) applying the predictor function to the genotype of the at least one individual of interest to predict the predict the trait performance for the individual.

15. A method as claimed in claim 13 wherein the information is selected from the group of genotype, phenotype or genotype and phenotype information on individuals in the population.

16. A method as claimed in claim 13 wherein the trait is a quantitative trait.

47. A method according to claim 13, wherein the individual is a dairy cow or bull, and wherein the quantitative trait is selected from the group consisting of APR, ASI, protein kg, protein percent, milk yield, fat kg, fat percent, overall type, mammary system, stature, udder texture, bone quality, angularity, muzzle width, body depth, chest width, pin set, pin sign, foot angle, set sign, rear leg view, udder depth, fore attachment, rear attachment height, rear attachment width, centre ligament, teat placement, teat length, loin strength, milking speed, temperament, like-ability, survival, calving ease, somatic cell count, cow fertility, and gestation length, or a combination of one or more of these traits.

17. A method for selecting at least one individual in a population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) utilising the explanatory variables to generate a predictor function;

(c) utilising the predictor function to select an individual.

18. A method as claimed in claim 17 further comprising the steps of:

(d) for an individual of interest from the population where information is unknown, generating genotype for at least one individual of interest from population; and

(e) applying the predictor function to the genotype of the at least one individual of interest to select an individual.

19. A method as claimed in claim 17 wherein the information is selected from the group of genotype, phenotype or genotype and phenotype information on individuals in the population.

20. A method of diagnosing a condition in at least one individual of interest in a population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) utilising the explanatory variables to generate a predictor function;

(c) utilising the predictor function to diagnose a condition in the individual.

21. A method as claimed in claim 20 further comprising the steps of:

(d) for an individual of interest from the population where information is unknown, generating genotype for at least one individual of interest from population; and

(e) applying the predictor function to the genotype of the at least one individual of interest to diagnose a condition in the individual of interest.

22. A method as claimed in claim 20 wherein the information is selected from the group of genotype, phenotype or genotype and phenotype information on individuals in the population.

23. A method of prediction of a susceptibility to an outcome of at least one individual of interest in a population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) utilising the explanatory variables to generate a predictor function;

(c) utilising the predictor function to predict the susceptibility of the individual to an outcome.

24. A method as claimed in claim 23 further comprising the steps of:

(d) for an individual of interest from the population where information is unknown, generating genotype for at least one individual of interest from population; and

(e) applying the predictor function to the genotype of the at least one individual of interest to predict the susceptibility of the individual to an outcome.

25. A method as claimed in claim 23 wherein the information is selected from the group of genotype, phenotype or genotype and phenotype information on individuals in the population.

26. A method as claimed in claim 23 wherein the outcome is the susceptibility of the individual of interest to a disease.

27. A method as claimed in claim 23 wherein the outcome is the susceptibility of the individual of interest to a response to a stimulus.

28. A method as claimed in claim 27 wherein the stimulus is selected from the group of a medicament, toxin, or an environmental condition.

29. A method as claimed in claim 28 wherein the environmental condition comprises water shortage, feed shortage, stress, sunlight, or other environmental condition.

30. A method of breeding at least one individual in a population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) utilising the explanatory variables to generate a predictor function with respect to merit of the individual;

(c) utilising the predictor function to predict the merit of the individual and

(d) breeding from the individual of interest on the basis of the merit of the individual.

31. A method according to claim 30, further comprising the steps of:

(f) determining information for the descendants of the at least one individual;

(g) correlating the information for the descendants of the at least one individual to the predictor function; and

(h) selecting descendants of said individual on the basis of the relationship between the information for the descendants and the predictor function.

32. A method as claimed in claim 30 wherein the information is selected from the group of genotype, phenotype or genotype and phenotype information on individuals in the population.

33. A system for the prediction of merit of an individual in a population, the system comprising:

(a) in the population, where information of individuals are known, means for using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) means for utilising the explanatory variables to generate a predictor function with respect to merit;

(c) means for utilising the predictor function to predict the merit of the individual.

66. A system according to claim 33 wherein the information comprises genetic information consisting essentially of marker genotypes.

67. A system according to claim 33 wherein the genetic markers are distributed substantially across the genome.

68. A system according to claim 33 wherein the dimension reduction is selected from the a technique in the group consisting of principal component analysis (PCA), a genetic algorithm, a neural network, partial least squares (PLS), inverse least squares, kernel PCA, LLE, Hessian LLE, Laplacian Eigenmaps, LTSA, isomap, maximum variance unfolding, Bolzman machines, projection pursuit, a hidden Markov model support vector machines, kernel regression, discriminant analysis and classification, k-nearest-neighbour analysis, fuzzy neural networks, Bayesian networks, or cluster analysis.

69. A system as claimed in claim 33 wherein the predictor function is a predictor function with having minimal prediction error.

70. A system as claimed in claim 33 wherein the information comprises at least one of the pedigree of the individual;

an estimated breeding value of the individual;

data on genetic markers across the genome for the individual or for relatives of the individual;

at least one index of phenotype for the individual or for relatives of the individual;

at least one marker predictive of phenotype for the individual or for relatives of the individual; and

at least one index of epigenetic modification or status for the individual, or a combination thereof.

34. A system for predicting trait performance of at least one individual in a population, the system comprising;

(a) in the population, where information of individuals are known, means for using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) means for utilising the explanatory variables to generate a predictor function; and

(c) means for utilising the predictor function to predict performance of said trait for the individual of interest.

35. A system as claimed in claim 34 wherein the trait is a quantitative trait.

36. A system for selecting at least one individual in a population, the system comprising;

(a) in the population, where information of individuals are known, means for using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) means for utilising the explanatory variables to generate a predictor function; and

(c) means for utilising the predictor function to select the individual.

37. A system for diagnosing a condition in at least one individual of interest in a population, the system comprising:

(a) in the population, where information of individuals are known, means for using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) means for utilising the explanatory variables to generate a predictor function;

(c) means for utilising the predictor function to diagnose a condition in the individual.

38. A system for prediction of a susceptibility to an outcome of at least one individual of interest in a population, the system comprising:

(a) in the population, where information of individuals are known, means for using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) means for utilising the explanatory variables to generate a predictor function;

(c) means for utilising the predictor function to predict the susceptibility of the at least one individual of interest to an outcome.

39. A system for breeding at least one individual in a population, the system comprising:

(a) in the population, where information of individuals are known, means for using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) means for utilising the explanatory variables to generate a predictor function with respect to merit of the individual;

(c) means for utilising the predictor function to predict the merit of the individual and

(d) means for breeding from the individual of interest on the basis of the merit of the individual.

40. A system as claimed in claim 39, further comprising the steps of:

(f) means for determining information for the descendants of the at least one individual;

(g) means for correlating the information for the descendants of the at least one individual to the predictor function; and

(h) means for selecting descendants of said individual on the basis of the relationship between the information for the descendants and the predictor function.

58. A breeders product comprising at least one gamete with a high prediction of merit for at least one marker, the breeders product selected by a method for the prediction of the merit of at least one individual, the method comprising the steps of:

(a) in a first population, where genotype and phenotype information of individuals in the first population are known, using dimension reduction on the genotype and phenotype information to determine the complexity of the genotype and phenotype information to minimise prediction error for at least one marker in the first population and thereby generate a set of explanatory variables with respect to the at least one marker;

(b) applying the explanatory variables to the first population to generate a predictor function;

(c) generating genotype for the at least one marker in at least one individual of interest from a second population;

(d) applying the predictor function to the genotype of the at least one individual of interest to determine the genetic merit of the individual of interest with respect to the at least one marker.

59. A computer system comprising a computer processor and memory, the memory comprising software code stored therein for execution by the computer processor of a method for the prediction of the merit of at least one individual in a population, the method comprising the steps of:

(a) in a database comprising information about the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables;

(b) utilising the explanatory variables to generate a predictor function with respect to merit; and

(c) utilising the predictor function to predict the merit of the individual.

60. A computer readable medium, having a program recorded thereon, where the program is configured to make a computer execute a procedure for the prediction of the merit of at least one individual in a population, the software product comprising:

(a) in a database comprising information about the population, where information of individuals are known, code for using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables;

(b) code for utilising the explanatory variables to generate a predictor function with respect to merit; and

(c) code for utilising the predictor function to predict the merit of the individual.

61. An information database product comprising information for individuals of a population, the information database for use with a method for the selection of at least one individual in the population, the method comprising the steps of:

(a) in the population, where information of individuals are known, using dimension reduction on the information to project the information to a low dimensional space whilst retaining the complexity of the information to generate a set of explanatory variables; and

(b) utilising the explanatory variables to generate a predictor function with respect to merit;

(c) utilising the predictor function to predict the merit of the individual.

62. An information database product for use with a breeding program, the database comprising information for individuals of a population and a prediction of the merit of the individuals in the population.

63. An information database product comprising information for individuals of a population according to claim 62 wherein a prediction of a merit of the individuals in the population is provided by a dimension reduction method on the genotype and phenotype information of individuals in the population comprising the steps of:

(a) using a dimension reduction method, determining the complexity of genotype and phenotype information of individuals in the population to minimise prediction error and thereby generate a set of explanatory variables;

(b) applying the explanatory variables to the first population to generate a predictor function;

(c) generating genotype for the at least one marker in at least one individual of interest from a second population;

(d) applying the predictor function to the genotype of the individuals of the second population thereby to determine the genetic merit of individuals in the second population individuals with respect to the at least one marker.

64. An information database product according to claim 62 wherein individuals of interest from the second population are selected for use in a breeding program based upon the prediction of merit for the at least one marker.