DETERMINATION OF COPY NUMBER VARIATIONS USING BINOMIAL PROBABILITY CALCULATIONS

• US 20130060483A1
• Filed: 09/06/2012
• Published: 03/07/2013
• Est. Priority Date: 09/07/2011
• Status: Active Grant

• Current Assignee: Roche Molecular Systems (Roche Holding AG)
• Original Assignee: Ariosa Diagnostics Incorporated (Roche Holding AG)
• Inventors: Struble, Craig, Stuelpnagel, John
• Granted Patent: US 8,712,697 B2
• Time in Patent Office: Days
• US Class Current: 702/20
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/10 Ploidy or copy number detection
• 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 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 5/20 Probabilistic models

Abstract

This invention relates to a binomial calculation of copy number of data obtained from a mixed sample having a first source and a second source.

Claims

1. A computer-implemented process for estimating copy number variation (CNV) of one or more genomic regions from a single source in a mixed sample, wherein at least one processor coupled to a memory executes a software component that performs the process comprising:

accessing by the software component a first data set comprising frequency data for two or more informative loci from a first source in the mixed sample;

accessing by the software component a second data set comprising frequency data for two or more informative loci from a second source in the mixed sample;

calculating by the software component an estimated source contribution of cell free nucleic acids in the mixed sample based on the first and second data sets;

accessing by the software component frequency data for one or more genomic regions from a single source in the mixed sample; and

calculating by the software component a presence or absence of CNV for the one or more genomic regions in the single source based on the frequency data and the estimated source contribution of cell free nucleic acids in the mixed sample.

2. The process of claim 1, wherein the CNV is calculated based on empirical frequency data of the genomic regions from the mixed sample.

3. The process of claim 2, wherein the CNV is determined for a genomic region in the second source of the mixed sample, and wherein the CNV for the one or more loci is calculated based on the contribution of the second source to the mixed sample.

4. The process of claim 1, wherein the CNV is calculated based on the frequency data of selected loci from a reference mixed sample having a source contribution substantially the same as the source contribution of the first source and the second source in the mixed sample.

5. The process of claim 1, wherein the frequency data comprises counts for a set of loci located in or associated with a genomic region.

6. The process of claim 1, wherein the mixed sample comprises cell free nucleic acids from both normal and putative genetically atypical cells.

7. The process of claim 1, wherein the mixed sample comprises cell free nucleic acids from two or more different organisms.

8. The process of claim 1, wherein the mixed sample comprises cell free nucleic acids from a donor cell source and a host recipient cell source.

9. The process of claim 1, wherein the estimated contribution of cell free nucleic acids in the mixed sample is calculated using a binomial distribution.

10. The process of claim 9, wherein contribution is quantified by calculating a maximum likelihood estimate based on the quantity of the informative locus from the first source and the second source.

11. The process of claim 10, wherein the maximum likelihood estimate is modeled by the equation:

12. The process of claim 11, wherein the p corresponding to the maximum likelihood estimate is calculated using an optimization algorithm.

13. The process of claim 1, wherein the frequency data for two or more informative loci from the first source and the second source does not include the data from the selected locus.

14. The process of claim 13, wherein the maximum likelihood estimate is modeled by the equation:

15. The process of claim 14, wherein the p corresponding to the maximum likelihood estimate is calculated using an optimization algorithm.

16. A computer-implemented process for copy number variation (CNV) of a one or more genomic regions in a single source in the mixed sample, wherein at least one processor coupled to a memory executes a software component that performs the process comprising:

accessing by the software component a first data set comprising frequency data based on identification of distinguishing regions of two or more informative loci from a first source in the single source in the mixed sample;

accessing by the software component a second data set comprising frequency data based on identification of distinguishing regions of two or more informative loci from a second source in the single source in the mixed sample;

calculating by the software component an estimated source contribution of cell free nucleic acids based on a binomial distribution of counts of the distinguishing regions from first and second data sets;

accessing by the software component a third data set comprising frequency data for one or more genomic regions from the single source in the mixed sample; and

calculating by the software component a presence or absence of a CNV for the one or more genomic regions by comparison of the frequency data from the single source to the estimated contribution of cell free nucleic acids in the mixed sample.

17. The process of claim 16, wherein the CNV is calculated based on the empirical frequency data of the genomic regions in the mixed sample.

18. The process of claim 16, wherein the CNV is determined for a genomic region in the second source of the mixed sample, and wherein the CNV for the one or more loci is calculated based on the contribution of the second source to the mixed sample.

19. The process of claim 16, wherein the CNV is calculated based on the frequency data of the genomic region from a reference sample having a source contribution substantially the same as the mixed sample.

20. The process of claim 16, wherein the frequency data comprises counts for a set of loci located in or associated with a genomic region

21. The process of claim 16, wherein the mixed sample comprises cell free nucleic acids from both normal and putative genetically atypical cells.

22. The process of claim 16, wherein the mixed sample comprises cell free nucleic acids from two or more different organisms.

23. The process of claim 16, wherein the mixed sample comprises cell free nucleic acids from a donor cell source and a host recipient cell source.

24. The process of claim 16, wherein the distinguishing regions comprise single nucleotide polymorphisms.

25. The process of claim 16, wherein the distinguishing regions comprise differences in methylation.

26. The process of claim 16, wherein the distinguishing regions comprise short tandem repeats.

27. The process of claim 16, wherein the calculation is performed by an algorithm that calculates the binomial distribution of the data from the first and second data sets.

28. The process of claim 16, wherein contribution is quantified by calculating the maximum likelihood estimate based on the quantity of the informative loci from the first source and the second source.

29. The process of claim 28, wherein the maximum likelihood estimate is modeled by the equation:

30. The process of claim 29, wherein the p corresponding to the maximum likelihood estimate is calculated using an optimization algorithm.

31. The process of claim 27, wherein frequency data for two or more informative loci from the first source and the second source includes the data from the genomic region.

32. The process of claim 28, wherein the maximum likelihood estimate is modeled by the equation:

33. The process of claim 32, wherein the p corresponding to the maximum likelihood estimate is calculated using an optimization algorithm.

34. A computer-implemented process for copy number variation (CNV) of one or more genomic regions a single source in the mixed sample, wherein at least one processor coupled to a memory executes a software component that performs the process, the process comprising:

accessing by the software component a first data set comprising frequency data for one or more informative loci from a maternal source in the mixed sample;

accessing by the software component a second data set comprising frequency data for one or more informative loci from a fetal source in the mixed sample;

calculating by the software component an estimated fetal source contribution of cell free nucleic based on a binomial distribution of the counts of distinguishing regions from first and second data sets; and

accessing by the software component a third data set comprising frequency data for one or more genomic regions from the single source in the mixed sample; and

calculating by the software component the presence or absence of a CNV for the one or more genomic regions in the fetus by comparison of the frequency of the genomic regions from the single source to the estimated fetal source contribution of cell free nucleic acids in the mixed sample.

35. The process of claim 34, wherein the distinguishing regions comprise single nucleotide polymorphisms.

36. The process of claim 34, wherein the distinguishing regions comprise differences in methylation.

37. The process of claim 34, wherein the distinguishing regions comprise short tandem repeats.

38. The process of claim 34, wherein the calculation is performed by an algorithm that calculates the binomial distribution of the data from the first and second data sets.

39. The process of claim 38, wherein contribution is quantified by calculating the maximum likelihood estimate based on the quantity of the informative loci from the maternal source and the fetal source.

40. The process of claim 39, wherein the contribution is modeled by the equation:

41. The process of claim 40, wherein the p corresponding to the maximum likelihood estimate is calculated using an optimization algorithms.

42. The process of claim 38, wherein frequency data for two or more informative loci from the maternal source and the fetal source includes the data from the genomic region.

43. The process of claim 42, wherein the maximum likelihood estimate is modeled by the equation:

44. The process of claim 43, wherein the p corresponding to the maximum likelihood estimate is calculated using an optimization algorithm.

45. An executable software product stored on a computer-readable medium containing program instructions for estimating copy number variation (CNV) of one or more genomic regions in a mixed sample, the program instructions for:

accessing by the software component a first data set comprising frequency data based on identification of distinguishing regions from copies of one or more informative loci from a first source;

accessing by the software component a second data set comprising frequency data based on identification of distinguishing regions from copies of one or more informative loci from a second source;

calculating by the software component an estimated source contribution of cell free nucleic acids based on a binomial distribution of the first and second data sets; and

calculating by the software component the CNV for the one or more genomic regions by comparison of the at least one of the first source and the second source counts for the genomic region to the estimated source contribution of cell free nucleic acids from the at least one of the first source and the second source.

46. The process of claim 45, wherein the CNV is calculated based on the empirical frequency data of the genomic regions in the mixed sample.

47. The process of claim 45, wherein the CNV is calculated based on the frequency data of the genomic region from a reference sample having a source contribution substantially the same as the mixed sample.

48. A system, comprising:

a memory;

a processor coupled to the memory; and

a software component executed by the processor that is configured to;

acess a first data set comprising frequency data based on identification of distinguishing regions from copies of one or more informative loci from a first source in a mixed sample;

access a second data set comprising frequency data based on identification of distinguishing regions from copies of one or more informative loci from a second source in the mixed sample;

calculate an estimated contribution of cell free nucleic acids from at least one of the first source and the second source based on a binomial distribution of counts of the distinguishing regions from the first and second data sets; and

calculate a copy number variation for one or more genomic regions of a single source in the mixed sample by comparison of the frequency data for the one or more genomic regions to the estimated source contribution of cell free nucleic acids in the mixed sample.

49. The system of claim 48, wherein the at least one of the first source and the second source counts for the genomic region comprise counts for a set of loci located in or associated with a larger genomic region.

50. A computer software product including a non-transitory computer-readable storage medium having fixed therein a sequence of instructions which when executed by a computer direct performance of steps of:

creating a first data set representing a quantity of informative loci from a first source in a mixed sample;

creating a second data set representing a quantity of informative loci from a second source in a mixed sample;

calculating an estimated source contribution of cell free nucleic acids based on a binomial distribution of the quantities of informative loci from the first and second data sets; and

calculating a presence or absence of a copy number variation for a genomic region by comparison of the quantity of one or more informative loci to the estimated source contribution of cell free nucleic acids in the mixed sample.