IP Library Granted Patent US 11,817,176
Granted Patent B2
US 11,817,176 · App. 17/444,989 · Granted Nov 14, 2023

Ancestry composition determination

Inventors: Peter Richard Wilton (Los Gatos, CA); Gabriel David Poznik (Menlo Park, CA); Kimberly Faith McManus (San Francisco, CA); Ethan Macneil Jewett (San Jose, CA); William Allen Freyman (Menlo Park, CA); Adam Auton (Menlo Park, CA)
Assignee: 23andMe, Inc.
G16B10/00G06F16/285G06N7/01G16B5/20G16B40/00
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Quick Facts
Patent No.
US 11,817,176
App. No.
17/444,989
Granted
Nov 14, 2023
Kind
B2
Abstract

Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.

Claims (31)

1. A method of error correction in an individual's ancestry composition, the method comprising:

obtaining, from a classifier, initial ancestry classifications of a plurality of segments of chromosomes of the individual;

performing error correction on the initial ancestry classifications using one or more computer processors, wherein the error correction comprises (a) generating an error correction model from chromosome data of the individual, and (b) applying the error correction model to the initial ancestry classifications to correct one or more of the initial ancestry classifications; and

providing corrected ancestry classifications having the initial ancestry classifications corrected.

2. The method of claim 1 , wherein generating the error correction model from the chromosome data of the individual comprises using the chromosome data from a plurality of chromosomes of the individual.

3. The method of claim 1 , wherein generating the error correction model from the chromosome data of the individual comprises using training data from only the chromosome data of the individual.

4. The method of claim 1 , wherein the error correction model comprises a Hidden Markov Model (HMM).

5. The method of claim 4 , wherein generating the error correction model from the chromosome data of the individual comprises (i) identifying an initial HMM from a pool of pretrained HMMs, and (ii) refining parameters of the initial HMM with the chromosome data of the individual.

6. The method of claim 4 , wherein generating the error correction model from the chromosome data of the individual comprises determining transition parameter values of the HMM.

7. The method of claim 1 , wherein the corrected ancestry classifications comprise ancestry assignments and posterior probabilities associated with the corrected ancestry assignments.

8. The method of claim 7 , wherein the corrected ancestry assignments are ancestry assignments from a multi-level population hierarchy that groups populations within continents and sub-continental regions, and wherein the posterior probabilities are determined for paths from leaves to a root of the multi-level population hierarchy, which paths contain the corrected ancestry assignments.

9. The method of claim 8 , further comprising selecting the corrected ancestry assignments for positions on the paths that correspond to posterior probabilities of greater than a defined threshold.

10. The method of claim 1 , wherein the corrected ancestry classifications comprise information pertaining to a proportion of genotype data of the individual that is deemed to correspond to a geographical or ethnic ancestry.

11. The method of claim 1 , wherein the segments of the chromosomes of the individual comprises phased haplotypes.

12. The method of claim 1 , wherein the corrected ancestry classifications comprise proportions of genotype data of the individual that is deemed to correspond to a geographical or ethnic ancestry.

13. The method of claim 1 , wherein obtaining, from the classifier, the initial ancestry classifications comprises clustering, based on a geographical hierarchy, a plurality of probabilities to determine a geographical or ethnic ancestry for a proportion of genotype data of the individual, wherein each probability is a probability that the proportion of the genotype data of the individual corresponds to one of a plurality of predicted geographical or ethnic ancestries.

14. The method of claim 1 , further comprising recalibrating the corrected ancestry classifications.

15. The method of claim 14 , wherein recalibrating the corrected ancestry classification establishes confidence levels associated with ancestry assignments of the corrected ancestry classifications.

16. The method of claim 1 , wherein the method does not include recalibrating the corrected ancestry classifications.

17. The method of claim 1 , wherein the segments of the chromosomes of the individual are windows comprising sets of sequential single nucleotide polymorphisms (SNPs) of the chromosomes.

18. The method of claim 1 , further comprising

dividing haplotypes of the individual into the plurality of segments, each of the segments including a set of sequential single nucleotide polymorphisms (SNPs); and

applying a model to the plurality of segments to generate the initial ancestry classifications.

19. The method of claim 1 , wherein performing error correction on the initial ancestry classifications comprises applying a Pair Hidden Markov Model (PHMM) in which an observed state corresponds to the initial ancestry classifications associated with a portion of one of two haplotypes of the individual, and a hidden state corresponds to ancestries associated with a portion of the haplotypes of the individual.

20. The method of claim 19 , wherein performing error correction on the initial ancestry classifications comprises determining a likely sequence of hidden states given the initial ancestry classifications.

21. The method of claim 20 , wherein determining a likely sequence includes performing dynamic programming based on the PHMM.

22. The method of claim 19 , wherein the PHMM is an Autoregressive Pair Hidden Markov Model (APHMM) in which the observed state is dependent on its corresponding hidden state and on a previous observed state.

23. A system for performing error correction in ancestry compositions, the system comprising one or more processors configured to:

obtain, from a classifier, initial ancestry classifications of a plurality of segments of chromosomes of an individual;

perform error correction on the initial ancestry classifications using the one or more computer processors, wherein the error correction comprises (a) generating an error correction model from chromosome data of the individual, and (b) applying the error correction model to the initial ancestry classifications to correct one or more of the initial ancestry classifications; and

provide corrected ancestry classifications having the initial ancestry classifications corrected.

Assignments (4)
CORRECTIVE ASSIGNMENT TO CORRECT THE APP. NO. 63806415 TO 63806145 AND APPL NO. 17721779 TO 17731779 PREVIOUSLY RECORDED ON REEL 73168 FRAME 531. ASSIGNOR(S) HEREBY CONFIRMS THE CHANGE OF NAME. Recorded Jan 6, 2026
From: 23ANDME PGS LLC
To: 23ANDME GENOMICS LLC
Reel/Frame 074434/0334 →
CHANGE OF NAME Recorded Oct 22, 2025
From: 23ANDME PGS LLC
To: 23ANDME GENOMICS LLC
Reel/Frame 073168/0531 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 26, 2025
From: 23ANDME, INC.
To: 23ANDME PGS LLC
Reel/Frame 072562/0795 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 13, 2021
From: WILTON, PETER RICHARD; POZNIK, GABRIEL DAVID; MCMANUS, KIMBERLY FAITH; JEWETT, ETHAN MACNEIL; FREYMAN, WILLIAM ALLEN; AUTON, ADAM
To: 23ANDME, INC.
Reel/Frame 057778/0080 →
Continuity (3)
Provisional Application 63093039 · Oct 16, 2020
Provisional Application 62706396 · Aug 13, 2020
Related Publication 20220051751A1 · Feb 17, 2022
Cited By (1)
US 12,711,324