Presentation is loading. Please wait.

Presentation is loading. Please wait.

Imputation-based local ancestry inference in admixed populations

Published byMaximillian Horn Modified over 6 years ago

Similar presentations

Presentation on theme: "Imputation-based local ancestry inference in admixed populations"— Presentation transcript:

1 Imputation-based local ancestry inference in admixed populations
Ion Mandoiu Computer Science and Engineering Department University of Connecticut Joint work with J. Kennedy and B. Pasaniuc

2 Outline Motivation and problem definition
Factorial HMM model of genotype data Algorithms for genotype imputation and ancestry inference Preliminary experimental results Summary and ongoing work

3 Population admixture Admixture mapping is a method for localizing disease causing genetic variants that differ in frequency across populations. It is most advantageous to apply this approach to populations that have descended from a recent mix of two ancestral groups that have been geographically isolated for many tens of thousands of years (e.g. African Americans) 3

4 Admixture mapping Admixture mapping is a method for localizing disease causing genetic variants that differ in frequency across populations. It is most advantageous to apply this approach to populations that have descended from a recent mix of two ancestral groups that have been geographically isolated for many tens of thousands of years (e.g. African Americans) Patterson et al, AJHG 74: , 2004 4

5 Inferred local ancestry
Local ancestry inference problem Given: Reference haplotypes for ancestral populations P1,…,Pn Whole-genome SNP genotype data for extant individual Find: Allele ancestries at each locus Reference haplotypes ? ? ?100101? ? ? ? 011?001? ? ? ? ?100101? ? ? ? 011?001? ? ? ? ?100101? ? ? ? 011?001? ? Inferred local ancestry Admixture mapping is a method for localizing disease causing genetic variants that differ in frequency across populations. It is most advantageous to apply this approach to populations that have descended from a recent mix of two ancestral groups that have been geographically isolated for many tens of thousands of years (e.g. African Americans) rs P1 P1 rs P1 P1 rs P1 P1 rs P1 P2 rs P1 P2 rs P1 P2 rs P1 P2 ... SNP genotypes rs T T rs C T rs G G rs G G rs G G rs C C rs A G ... 5

6 Previous work MANY methods Two main classes
Ancestry inference at different granularities, assuming different amounts of info about genetic makeup of ancestral populations Two main classes HMM-based: SABER [Tang et al 06], SWITCH [Sankararaman et al 08a], HAPAA [Sundquist et al. 08], … Window-based: LAMP [Sankararaman et al 08b], WINPOP [Pasaniuc et al. 09] Poor accuracy when ancestral populations are closely related (e.g. Japanese and Chinese) Methods based on unlinked SNPs outperform methods that model LD! Longer terms for observations: For individual from recently admixed populations the local ancestry of a SNP locus is typically shared with a large number of neighboring loci. The accuracy of SNP genotype imputation within such a neighborhood is typically higher when using the factorial HMMs corresponding to the correct local ancestry compared to a mis-specified model.

7 Haplotype structure in panmictic populations

8 HMM model of haplotype frequencies
Similar models proposed in [Schwartz 04, Rastas et al. 05, Kennedy et al. 07, Kimmel&Shamir 05, Scheet&Stephens 06,…]

9 Graphical model representation
F1 F2 Fn … H1 H2 Hn Random variables Fi = founder haplotype at locus i, between 1 and K Hi = observed allele at locus I Model training Based on haplotypes using Baum-Welch algo, or Based on genotypes using EM [Rastas et al. 05] Given haplotype h, P(H=h|M) can be computed in O(nK2) using a forward algorithm, where n=#SNPs, K=#founders

10 Factorial HMM for genotype data in a window with known local ancestry
… F1 F2 Fn H1 H2 Hn … F'1 F'2 F'n The Hierarchical-Factorial HMM we used to describe haplotype sequences is similar to models recently proposed by others. At the core of the model are 2 regular HMM representing haplotype frequencies in the populations of origin of the sequenced individual’s parents. Under this model each haplotype in the population is viewed as a mosaic formed as a result of historical recombination among a set of K founder haplotypes. Increasing the number of founders allows more paths to describe a haplotype sequence, but at a cost of a longer run time. Training & the estimation of this HMM is done via a Baum-Welch algorithm based on haplotype inferred from a panel representing the population of origin of each parent. H'1 H'2 H'n G1 G2 Gn 10

11 HMM Based Genotype Imputation
Probability of missing genotype given the typed genotype data:  gi is imputed as

12 Forward-backward computation
fi … … hi f’i … … h’i gi 12

13 Forward-backward computation
fi … … hi f’i … … h’i gi 13

14 Forward-backward computation
fi … … hi f’i … … h’i gi 14

15 Forward-backward computation
fi … … hi f’i … … h’i gi 15

16 Runtime Direct recurrences for computing forward probabilities:
Runtime reduced to O(nK3) by reusing common terms: where 16

17 Imputation-based ancestry inference
View local ancestry inference as a model selection problem Each possible local ancestry defines a factorial HMM Pick model that re-imputes SNPs most accurately around the locus of interest Fixed-window version: pick ancestry that maximizes the average posterior probability of true SNP genotypes within a fixed-size window centered at the locus Multi-window version: weighted voting over window sizes between , with window weights proportional to average posterior probabilities Longer terms for observations: For individual from recently admixed populations the local ancestry of a SNP locus is typically shared with a large number of neighboring loci. The accuracy of SNP genotype imputation within such a neighborhood is typically higher when using the factorial HMMs corresponding to the correct local ancestry compared to a mis-specified model.

18 HMM imputation accuracy
Missing data rate and accuracy for imputed genotypes at different thresholds (WTCCC 58BC/Hapmap CEU)

19 Window size effect N=2,000 g=7 =0.2 n=38,864 r=10-8

20 Number of founders effect
CEU-JPT N=2,000 g=7 =0.2 n=38,864 r=10-8

21 Comparison with other methods
Alpha is the (X100) percentage of individuals coming from one population, with 1-alpha being the percentage of individuals coming from the second population. N=2,000 g=7 =0.2 n=38,864 r=10-8 21

22 Summary and ongoing work
Imputation-based local ancestry inference achieves significant improvement over previous methods for admixtures between close ancestral populations Code at Ongoing work Evaluating accuracy under more realistic admixture scenarios (multiple ancestral populations/gene flow/drift in ancestral populations) Extension to pedigree data Exploiting inferred local ancestry for more accurate untyped SNP imputation and phasing of admixed individuals Extensions to sequencing data Inference of ancestral haplotypes from extant admixed populations

23 Untyped SNP imputation accuracy in admixed individuals
Alpha is the (X100) percentage of individuals coming from one population, with 1-alpha being the percentage of individuals coming from the second population. N=2,000 g=7 =0.5 n=38,864 r=10-8 23

24 HMM-based phasing … F1 F2 Fn H1 H2 Hn … F'1 F'2 F'n H'1 H'2 H'n G1 G2 Gn Maximum likelihood genotype phasing: given g, find (h1,h2) = argmax h1+h2=g P(h1|M)P(h2|M)

25 HMM-based phasing Bad news: Cannot approximate maxh1+h2=g P(h1|M)P(h2|M) within a factor of O(n1/2 -), unless ZPP=NP [KMP08] Good news: Viterbi-like heuristics yields phasing accuracy comparable to PHASE in practice [Rastas et al. 05]

26 Factorial HMM model for sequencing data
… F1 F2 Fn H1 H2 Hn … F'1 F'2 F'n The Hierarchical-Factorial HMM we used to describe haplotype sequences is similar to models recently proposed by others. At the core of the model are 2 regular HMM representing haplotype frequencies in the populations of origin of the sequenced individual’s parents. Under this model each haplotype in the population is viewed as a mosaic formed as a result of historical recombination among a set of K founder haplotypes. Increasing the number of founders allows more paths to describe a haplotype sequence, but at a cost of a longer run time. Training & the estimation of this HMM is done via a Baum-Welch algorithm based on haplotype inferred from a panel representing the population of origin of each parent. H'1 H'2 H'n G1 G2 Gn R1,1 … R1,c R2,1 … R2,c Rn,1 … Rn,c 1 2 n 26

27 Acknowledgments J. Kennedy and B. Pasaniuc
Work supported in part by NSF awards IIS and DBI

Download ppt "Imputation-based local ancestry inference in admixed populations"

Similar presentations

Imputation for GWAS 6 December 2012.

Imputation for GWAS 6 December 2012.
Hidden Markov Model in Biological Sequence Analysis – Part 2

Hidden Markov Model in Biological Sequence Analysis – Part 2
METHODS FOR HAPLOTYPE RECONSTRUCTION

METHODS FOR HAPLOTYPE RECONSTRUCTION
Multiple Comparisons Measures of LD Jess Paulus, ScD January 29, 2013.

Multiple Comparisons Measures of LD Jess Paulus, ScD January 29, 2013.
Genotype and Haplotype Reconstruction from Low- Coverage Short Sequencing Reads Ion Mandoiu Computer Science and Engineering Department University of Connecticut.

Genotype and Haplotype Reconstruction from Low- Coverage Short Sequencing Reads Ion Mandoiu Computer Science and Engineering Department University of Connecticut.
Objectives Cover some of the essential concepts for GWAS that have not yet been covered Hardy-Weinberg equilibrium Meta-analysis SNP Imputation Review.

Objectives Cover some of the essential concepts for GWAS that have not yet been covered Hardy-Weinberg equilibrium Meta-analysis SNP Imputation Review.
High resolution detection of IBD Sharon R Browning and Brian L Browning Supported by the Marsden Fund.

High resolution detection of IBD Sharon R Browning and Brian L Browning Supported by the Marsden Fund.
Understanding GWAS Chip Design – Linkage Disequilibrium and HapMap Peter Castaldi January 29, 2013.

Understanding GWAS Chip Design – Linkage Disequilibrium and HapMap Peter Castaldi January 29, 2013.
A New Nonparametric Bayesian Model for Genetic Recombination in Open Ancestral Space Presented by Chunping Wang Machine Learning Group, Duke University.

A New Nonparametric Bayesian Model for Genetic Recombination in Open Ancestral Space Presented by Chunping Wang Machine Learning Group, Duke University.
University of Connecticut

University of Connecticut
Admixture Mapping Qunyuan Zhang Division of Statistical Genomics GEMS Course M21-621 Computational Statistical Genetics Computational Statistical Genetics.

Admixture Mapping Qunyuan Zhang Division of Statistical Genomics GEMS Course M Computational Statistical Genetics Computational Statistical Genetics.
Computational Challenges in Whole-Genome Association Studies Ion Mandoiu Computer Science and Engineering Department University of Connecticut.

Computational Challenges in Whole-Genome Association Studies Ion Mandoiu Computer Science and Engineering Department University of Connecticut.
Estimation of alternative splicing isoform frequencies from RNA-Seq data Ion Mandoiu Computer Science and Engineering Department University of Connecticut.

Estimation of alternative splicing isoform frequencies from RNA-Seq data Ion Mandoiu Computer Science and Engineering Department University of Connecticut.
Ion Mandoiu Computer Science and Engineering Department

Ion Mandoiu Computer Science and Engineering Department
DNA Barcode Data Analysis: Boosting Assignment Accuracy by Combining Distance- and Character-Based Classifiers Bogdan Paşaniuc, Sotirios Kentros and Ion.

DNA Barcode Data Analysis: Boosting Assignment Accuracy by Combining Distance- and Character-Based Classifiers Bogdan Paşaniuc, Sotirios Kentros and Ion.
Genotype Error Detection using Hidden Markov Models of Haplotype Diversity Ion Mandoiu CSE Department, University of Connecticut Joint work with Justin.

Genotype Error Detection using Hidden Markov Models of Haplotype Diversity Ion Mandoiu CSE Department, University of Connecticut Joint work with Justin.
. Hidden Markov Models For Genetic Linkage Analysis Lecture #4 Prepared by Dan Geiger.

. Hidden Markov Models For Genetic Linkage Analysis Lecture #4 Prepared by Dan Geiger.
Linkage Disequilibrium-Based Single Individual Genotyping from Low-Coverage Short Sequencing Reads Justin Kennedy 1 Joint work with Sanjiv Dinakar 1, Yozen.

Linkage Disequilibrium-Based Single Individual Genotyping from Low-Coverage Short Sequencing Reads Justin Kennedy 1 Joint work with Sanjiv Dinakar 1, Yozen.
Combinatorial Algorithms for Maximum Likelihood Tag SNP Selection and Haplotype Inference Ion Mandoiu University of Connecticut CS&E Department.

Combinatorial Algorithms for Maximum Likelihood Tag SNP Selection and Haplotype Inference Ion Mandoiu University of Connecticut CS&E Department.
ISBRA 2007 Tutorial A: Scalable Algorithms for Genotype and Haplotype Analysis Ion Mandoiu (University of Connecticut) Alexander Zelikovsky (Georgia State.

ISBRA 2007 Tutorial A: Scalable Algorithms for Genotype and Haplotype Analysis Ion Mandoiu (University of Connecticut) Alexander Zelikovsky (Georgia State.

Similar presentations

Ads by Google