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Identity-by-descent-based phasing and imputation in founder populations using graphical models.

Palin K, Campbell H, Wright AF, Wilson JF, Durbin R - Genet. Epidemiol. (2011)

Bottom Line: Accurate knowledge of haplotypes, the combination of alleles co-residing on a single copy of a chromosome, enables powerful gene mapping and sequence imputation methods.In this study, we present a new computational model for haplotype phasing based on pairwise sharing of haplotypes inferred to be Identical-By-Descent (IBD).We apply the Bayesian network based model in a new phasing algorithm, called systematic long-range phasing (SLRP), that can capitalize on the close genetic relationships in isolated founder populations, and show with simulated and real genome-wide genotype data that SLRP substantially reduces the rate of phasing errors compared to previous phasing algorithms.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

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Bayesian network for the SLRP model of haplotype phasing and IBD inference. The observed genotype of an individual a at marker j is in variable , which depends on the diplotype . Variable  indicates the type of IBD between a pair of individuals a and b at the marker j. IBD, identity-by-descent; SLRP, systematic long-range phasing.
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fig01: Bayesian network for the SLRP model of haplotype phasing and IBD inference. The observed genotype of an individual a at marker j is in variable , which depends on the diplotype . Variable indicates the type of IBD between a pair of individuals a and b at the marker j. IBD, identity-by-descent; SLRP, systematic long-range phasing.

Mentions: To use the model for phasing, we combine the HMMs for all pairs of individuals into a Bayesian network. The network illustrated in Figure 1, includes observed variables g for the genotypes and hidden variables h for the diplotypes and p for the IBD relationship between pairs of individuals. A variable encodes the diplotype for individual a on marker j. The distribution of the observed genotype depends essentially deterministically on the underlying diplotype but allowing for some noise from the genotyping assay. The network also includes an IBD variable for each SNP j and pair of individuals a and b. This variable encodes the IBD relationship between the two individuals at marker j.


Identity-by-descent-based phasing and imputation in founder populations using graphical models.

Palin K, Campbell H, Wright AF, Wilson JF, Durbin R - Genet. Epidemiol. (2011)

Bayesian network for the SLRP model of haplotype phasing and IBD inference. The observed genotype of an individual a at marker j is in variable , which depends on the diplotype . Variable  indicates the type of IBD between a pair of individuals a and b at the marker j. IBD, identity-by-descent; SLRP, systematic long-range phasing.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368215&req=5

fig01: Bayesian network for the SLRP model of haplotype phasing and IBD inference. The observed genotype of an individual a at marker j is in variable , which depends on the diplotype . Variable indicates the type of IBD between a pair of individuals a and b at the marker j. IBD, identity-by-descent; SLRP, systematic long-range phasing.
Mentions: To use the model for phasing, we combine the HMMs for all pairs of individuals into a Bayesian network. The network illustrated in Figure 1, includes observed variables g for the genotypes and hidden variables h for the diplotypes and p for the IBD relationship between pairs of individuals. A variable encodes the diplotype for individual a on marker j. The distribution of the observed genotype depends essentially deterministically on the underlying diplotype but allowing for some noise from the genotyping assay. The network also includes an IBD variable for each SNP j and pair of individuals a and b. This variable encodes the IBD relationship between the two individuals at marker j.

Bottom Line: Accurate knowledge of haplotypes, the combination of alleles co-residing on a single copy of a chromosome, enables powerful gene mapping and sequence imputation methods.In this study, we present a new computational model for haplotype phasing based on pairwise sharing of haplotypes inferred to be Identical-By-Descent (IBD).We apply the Bayesian network based model in a new phasing algorithm, called systematic long-range phasing (SLRP), that can capitalize on the close genetic relationships in isolated founder populations, and show with simulated and real genome-wide genotype data that SLRP substantially reduces the rate of phasing errors compared to previous phasing algorithms.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

Show MeSH
Related in: MedlinePlus