IP Library Granted Patent US 10,692,587
Granted Patent B2
US 10,692,587 · App. 16/567,957 · Granted Jun 23, 2020

Global ancestry determination system

Inventors: Shiya Song (San Mateo, CA); Keith D. Noto (San Francisco, CA); Yong Wang (Foster City, CA)
Assignee: Ancestry.com DNA, LLC
G16B5/20G06N20/00G16B40/00G16B50/10G06F17/18
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Quick Facts
Patent No.
US 10,692,587
App. No.
16/567,957
Granted
Jun 23, 2020
Kind
B2
Abstract

An input genotype is divided into a plurality of windows, each including a sequence of SNPs. For each window, a diploid HMM is computed based on genotypes and/or phased haplotypes to determine a probability of a haplotype sequence being associated with a particular label. For example, the diploid HMM for a window is used to determine the emission probability that the window corresponds to a set of labels. An inter-window HMM, with a set of states for each window, is computed. Labels are assigned to the input genotype based on the inter-window HMM. Upper and lower bounds are estimated to produce a range of likely percentage values an input can be assigned to a given label. Confidence values are determined indicating a likelihood that an individual inherits DNA from a certain population. Maps are generated with polygons representing regions where a measure of ethnicity of population falls within specific ranges.

Claims (611)

1. A computer implemented method comprising:

accessing a genotype dataset associated with an individual;

dividing the genotype dataset into a plurality of windows, each window comprising a set of single nucleotide polymorphisms (SNPs);

determining a pair of phased haplotype datasets from the plurality of windows of genotype dataset;

generating, using one or more processors and the pair of phased haplotype datasets, a directed acyclic graph that comprises:

(i) a plurality of node groups, each node group representing one of the windows, each node group comprising a plurality of nodes, each node representing a state of the window represented by the node group, the state comprising:

(1) a first parent label,

(2) a second parent label, and

(3) a switch label representing a switching of order of the first parent label and the second parent label,

(ii) a plurality of edges, each edge connecting a first node to a second node, each edge representing a transition from the first node to the second node,

wherein each node is associated with an emission probability determined based on the pair of phased haplotype datasets; and

generating information on ethnic origin of the individual using the directed acyclic graph.

2. The computer implemented method of claim 1 , wherein the emission probability associated with a particular node corresponding to a particular window represents a likelihood of the particular window corresponding to a pair of haplotype sequences given the state having the first parent label, the second parent label, and the switch label.

3. The computer implemented method of claim 2 , wherein the plurality of nodes in each node group represents permutations of different first parent labels, second parent labels, and switch labels.

4. The computer implemented method of claim 1 , wherein the switching of order of the first parent label and the second parent label corresponds to a switching of the first and second parent labels from one node group to a next node group.

5. The computer implemented method of claim 1 , wherein generating the directed acyclic graph further comprises:

initiating label probabilities, each label probability for a particular label representing a probability distribution of the particular label among other possible labels;

initiating label change probabilities, each label change probability representing a likelihood of a change of label from one window to another window;

initiating a label switch probability representing a likelihood of a switching of order of the first parent label and the second parent label from one window to another window;

adjusting the label probabilities, label change probabilities, and the label switch probability through iterations;

determining transition probabilities based on the label probabilities, the label change probabilities, and the label switch probability, each transition probability associated with a particular edge and representing a likelihood of the first node connected by the particular edge from one node group transitioning to the second node connected by the particular edge from another node group; and

connecting the nodes with edges, each edge corresponding to a determined transition probability.

6. The computer implemented method of claim 1 , wherein the information of ethnic origin comprises a set of possible ethnic origins of the individual and percentage compositions of the set of the possible ethnic origins.

7. The computer implemented method of claim 1 , where generating the information on ethnic origin of the individual using the directed acyclic graph comprises:

determining a Viterbi path for the directed acyclic graph based on a likelihood of the pair of phased haplotype datasets of the individual traversing nodes in the directed acyclic graph along the Viterbi path;

determining a likelihood of the individual having a particular ethnic origin based on the Viterbi path; and

displaying the likelihood of the individual having the particular ethnic origin as the information of ethnic origin.

8. A computer implemented method, comprising:

accessing a set of reference panel samples;

dividing each reference panel sample into a plurality of windows, each window comprising a set of single nucleotide polymorphisms (SNPs);

computing, for each window, a diploid hidden Markov model (HMM) based on the set of reference panel samples that correspond to the each window;

calculating, for each diploid state in each diploid HMM, diploid state probabilities using the diploid HMM;

calculating, for each window, a set of annotations based on the diploid state probabilities, each annotation corresponding to a label; and

determining emission probabilities of a pair of phased haplotypes for an inter-window HMM based on the set of annotations and the pair of phased haplotypes.

9. The computer implemented method of claim 8 , wherein the inter-window HMM comprises:

(i) a plurality of node groups, each node group representing one of the windows, each node group comprising a plurality of nodes, each node representing a state of the window represented by the node group, the state comprising:

(1) a first parent label,

(2) a second parent label, and

(3) a switch label representing a switching of order of the first parent label and the second parent label,

(ii) a plurality of edges, each edge connecting a first node to a second node, each edge representing a transition from the first node to the second node,

wherein each node is associated with one of the emission probabilities.

10. The computer implemented method of claim 9 , further comprising:

computing the inter-window HMM based on the emission probabilities; and

using a Viterbi algorithm to determine transition probabilities between the nodes.

11. The computer implemented method of claim 9 , wherein the emission probability associated with a particular node corresponding to a particular window represents a likelihood of the particular window corresponding to a pair of haplotype sequences given the state having the first parent label, the second parent label, and the switch label.

12. The computer implemented method of claim 9 , further comprising:

computing, for each window, a haploid Markov model, wherein each diploid state in the diploid hidden Markov model corresponds to a pair of haploid states from the haploid Markov model.

13. The computer implemented method of claim 10 , wherein using the Viterbi algorithm to determine the transition probabilities between the nodes are iteratively repeated for a predetermined number of times.

14. The computer implemented method of claim 10 , wherein using the Viterbi algorithm to determine the transition probabilities between the nodes comprises:

determining a Viterbi path that traverses the inter-window HMM based on a probability of traversing a particular sequence of nodes that represent the path.

15. A non-transitory computer readable storage medium storing a directed acyclic graph and instructions, when executed by one or more processors, cause the processor to use the directed acyclic graph to assign one or more labels to a sample of genotype data, the directed acyclic graph comprising:

a plurality of node groups, each node group representing a window of genetic segment, each window comprising a set of single nucleotide polymorphisms (SNPs), each node group comprising a plurality of nodes, each node representing a state represented by the node group, the state comprises:

(1) a first parent label,

(2) a second parent label, and

(3) switch label representing a switching of order of the first parent label and the second parent label,

wherein each node is associated with an emission probability representing a likelihood of a pair of haplotypes corresponding to the window given the first parent label, the second parent label, and the switch label for the each node; and

a plurality of edges, each edge connecting a first node of a first node group to a second node of a second node group, each edge being associated with a transition probability representing a likelihood of transition from the first node to the second node.

16. The non-transitory computer readable storage medium of claim 15 , wherein the plurality of nodes in each node group represents permutations of different first parent labels, second parent labels, and switch labels.

17. The non-transitory computer readable storage medium of claim 15 , wherein each of the first parent labels and the second parent labels corresponds to an ethnic origin group.

18. The non-transitory computer readable storage medium of claim 15 , wherein the transition probability is determined based on:

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19. The non-transitory computer readable storage medium of claim 18 , wherein τ m , τ ƒ , and τ z are determined based on an iterative process.

20. The non-transitory computer readable storage medium of claim 15 , wherein the emission probability is determined based on:

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21. The computer implemented method of claim 1 , wherein the directed acyclic graph is a hidden Markov model (HMM).

22. The non-transitory computer readable storage medium of claim 15 , wherein the directed acyclic graph is a hidden Markov model (HMM).

Assignments (3)
SECURITY INTEREST Recorded Dec 7, 2020
From: ANCESTRY.COM DNA, LLC; ANCESTRY.COM OPERATIONS INC.; IARCHIVES, INC.; ANCESTRYHEALTH.COM, LLC
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH
Reel/Frame 054627/0212 →
SECURITY INTEREST Recorded Dec 7, 2020
From: ANCESTRY.COM DNA, LLC; ANCESTRY.COM OPERATIONS INC.; IARCHIVES, INC.; ANCESTRYHEALTH.COM, LLC
To: WILMINGTON TRUST, NATIONAL ASSOCIATION
Reel/Frame 054627/0237 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 24, 2019
From: SONG, SHIYA; NOTO, KEITH D.; WANG, YONG
To: ANCESTRY.COM DNA, LLC
Reel/Frame 050819/0680 →
Cited By (1)
US 12,626,778