Determination of copy number variations using binomial probability calculations
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.
1. A computer-implemented process for calculating copy number variation (CNV) of one or more genomic regions in a single source in a mixed sample of DNA from blood, serum or plasma of a patient, the mixed sample including a minor source and a major source, the process comprising the steps of:
a) measuring a frequency of thirty-two or more informative loci from a first source comprising the minor source and a frequency of thirty-two or more informative loci from a second source comprising the major source;
b) accessing, by a computer processor, a first data set comprising frequency data based on identification of distinguishing regions of the thirty-two or more informative loci from the first source as the single source in the mixed sample;
c) accessing, by the computer processor, a second data set comprising frequency data based on identification of distinguishing regions of the thirty-two or more informative loci from the second source as the single source in the mixed sample;
d) calculating, by the computer processor, an estimated source contribution of cell free nucleic acids based on a binomial distribution of counts of the distinguishing regions from the first and second data sets;
e) accessing, by the computer processor, a third data set comprising frequency data for two or more genomic regions from the combined first and second sources in the mixed sample; and
f) calculating, by the computer processor, a presence or absence of a CNV for one or more genomic regions by comparison of the frequency data from the two or more genomic regions from the combined first and second sources and the estimated contribution of cell free nucleic acids in the mixed sample.
2. The computer-implemented process of claim 1 , wherein the CNV is determined for one or more genomic regions in the second source of the mixed sample, and wherein the CNV for the one or more genomic regions is calculated based on the contribution of the second source to the mixed sample.
3. The computer-implemented process of claim 1 , wherein the frequency data comprises counts for a set of loci located in or associated with a genomic region.
4. The computer-implemented process of claim 1 , wherein the mixed sample comprises cell free nucleic acids from both normal and putative genetically atypical cells.
5. The computer-implemented process of claim 1 , wherein the contribution is quantified by calculating a maximum likelihood estimate based on the quantity of the thirty-two or more informative loci from the first source and the second source.
6. The computer-implemented process of claim 5 , wherein the maximum likelihood estimate is modeled by the equation:
Binomial
(
A
,
B
,
p
)
=
(
A
+
B
)
!
A
!
B
!
p
A
(
1
-
p
)
B
wherein A is the quantity of the thirty-two or more informative loci from the second source, B is the quantity of the thirty-two or more informative loci from the first source, and p is the maximum likelihood estimate for the binomial distribution with quantities A and B.
7. The computer-implemented process of claim 6 , wherein the p corresponding to the maximum likelihood estimate is calculated using an optimization algorithm.
8. The computer-implemented process of claim 1 , wherein the frequency data for thirty-two or more informative loci from the first source and the second source does not include the data from the two or more genomic regions.
9. The computer-implemented process of claim 1 , wherein the first data set comprising frequency data is based on the identification of distinguishing regions of at least 96 informative loci.
10. The computer-implemented process of claim 1 , wherein the first data set comprising frequency data is based on the identification of distinguishing regions of at least 64 informative loci.
11. The computer-implemented process of claim 1 , wherein the patient includes a diagnosed or undiagnosed condition.
12. The computer-implemented process of claim 11 , further comprising:
assisting in a communication of the presence or absence of the CNV to the patient, the presence or absence of the CNV being related to the diagnosed or undiagnosed condition of the patient.
13. The computer-implemented process of claim 1 , wherein the patient is a pregnant mammal.
14. The computer-implemented process of claim 1 , wherein the major source comprises maternal cell free nucleic acids and the minor source comprises fetal cell free nucleic acids.