ERROR CORRECTION IN ANCESTRY CLASSIFICATION
Error correction in ancestry classification includes obtaining, from a classifier, initial ancestry classifications associated with portions of two phased haplotypes of a chromosome pair of an individual; performing error correction on an initial ancestry classification, including detecting a phasing error in the initial ancestry classifications; and outputting a corrected ancestry classification in which the phasing error is corrected.
1 . A method of error correction in ancestry classification, comprising:
obtaining, from a classifier, initial ancestry classifications associated with portions of two phased haplotypes of a chromosome pair of an individual;
performing error correction on an initial ancestry classification using one or more computer processors, including detecting a phasing error in the initial ancestry classifications; and
outputting a corrected ancestry classification in which the phasing error is corrected.
2 . The method of claim 1 , wherein performing error correction on the initial ancestry classification further includes detecting a correlated prediction error that was caused by the classifier.
3 . The method of claim 1 , wherein performing error correction on the initial ancestry classification further includes detecting a correlated prediction error that was caused by the classifier, and the correlated prediction error is corrected in the corrected ancestry classification.
4 . The method of claim 1 , wherein performing error correction on the initial ancestry classification includes obtaining a Pair Hidden Markov Model (PHMM) in which an observed state corresponds to an initial ancestry classification associated with a portion of one of the two haplotypes, and a hidden state corresponds to ancestries associated with a portion of the two haplotypes.
5 . The method of claim 4 , wherein performing error correction on the initial ancestry classification further includes determining a likely sequence of hidden states given the initial ancestry classifications.
6 . The method of claim 5 , wherein the determination includes performing dynamic programming based on the PHMM.
7 . The method of claim 4 , 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.
8 . The method of claim 1 , wherein performing error correction on the initial ancestry classification includes obtaining a plurality of Pair Hidden Markov Models (PHMMs), and wherein each PHMM corresponds to a distinct reference population.
9 . The method of claim 8 , wherein performing error correction on the initial ancestry classification further includes applying the initial ancestry classifications associated with portions of two phased haplotypes to the plurality of PHMMs, and performing a Bayesian model averaging on outputs of the plurality of PHMMs.
10 . A system of error correction in ancestry classification, comprising:
one or more processors configured to:
obtain, from a classifier, initial ancestry classifications associated with portions of two phased haplotypes of a chromosome pair of an individual;
perform error correction on an initial ancestry classification, including to detect a phasing error in the initial ancestry classifications; and
output a corrected ancestry classification in which the phasing error is corrected;
one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions.
11 . The system of claim 10 , wherein to perform error correction on the initial ancestry classification further includes to detect a correlated prediction error that was caused by the classifier.
12 . The system of claim 10 , wherein to perform error correction on the initial ancestry classification further includes to detect a correlated prediction error that was caused by the classifier, and the correlated prediction error is corrected in the corrected ancestry classification.
13 . The system of claim 10 , wherein to perform error correction on the initial ancestry classification includes to obtain a Pair Hidden Markov Model (PHMM) in which an observed state corresponds to an initial ancestry classification associated with a portion of one of the two haplotypes, and a hidden state corresponds to ancestries associated with a portion of the two haplotypes.
14 . The system of claim 13 , wherein to perform error correction on the initial ancestry classification further includes to determine a likely sequence of hidden states given the initial ancestry classifications.
15 . The system of claim 14 , wherein the determination includes performing dynamic programming based on the PHMM.
16 . The system of claim 13 , 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.
17 . The system of claim 10 , wherein to perform error correction on the initial ancestry classification includes to obtain a plurality of Pair Hidden Markov Models (PHMMs), and wherein each PHMM corresponds to a distinct reference population.
18 . The system of claim 17 , wherein to perform error correction on the initial ancestry classification further includes to apply the initial ancestry classifications associated with portions of two phased haplotypes to the plurality of PHMMs, and to perform a Bayesian model averaging on outputs of the plurality of PHMMs.
19 . A computer program product for of error correction in ancestry classification, the computer program product being embodied in a tangible computer readable storage medium and comprising computer instructions for:
obtaining, from a classifier, initial ancestry classifications associated with portions of two phased haplotypes of a chromosome pair of an individual;
performing error correction on an initial ancestry classification, including detecting a phasing error in the initial ancestry classifications; and
outputting a corrected ancestry classification in which the phasing error is corrected.