Literature DB >> 21705746

Imputation of missing genotypes from sparse to high density using long-range phasing.

Hans D Daetwyler1, George R Wiggans, Ben J Hayes, John A Woolliams, Mike E Goddard.   

Abstract

Related individuals share potentially long chromosome segments that trace to a common ancestor. We describe a phasing algorithm (ChromoPhase) that utilizes this characteristic of finite populations to phase large sections of a chromosome. In addition to phasing, our method imputes missing genotypes in individuals genotyped at lower marker density when more densely genotyped relatives are available. ChromoPhase uses a pedigree to collect an individual's (the proband) surrogate parents and offspring and uses genotypic similarity to identify its genomic surrogates. The algorithm then cycles through the relatives and genomic surrogates one at a time to find shared chromosome segments. Once a segment has been identified, any missing information in the proband is filled in with information from the relative. We tested ChromoPhase in a simulated population consisting of 400 individuals at a marker density of 1500/M, which is approximately equivalent to a 50K bovine single nucleotide polymorphism chip. In simulated data, 99.9% loci were correctly phased and, when imputing from 100 to 1500 markers, more than 87% of missing genotypes were correctly imputed. Performance increased when the number of generations available in the pedigree increased, but was reduced when the sparse genotype contained fewer loci. However, in simulated data, ChromoPhase correctly imputed at least 12% more genotypes than fastPHASE, depending on sparse marker density. We also tested the algorithm in a real Holstein cattle data set to impute 50K genotypes in animals with a sparse 3K genotype. In these data 92% of genotypes were correctly imputed in animals with a genotyped sire. We evaluated the accuracy of genomic predictions with the dense, sparse, and imputed simulated data sets and show that the reduction in genomic evaluation accuracy is modest even with imperfectly imputed genotype data. Our results demonstrate that imputation of missing genotypes, and potentially full genome sequence, using long-range phasing is feasible.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21705746      PMCID: PMC3176129          DOI: 10.1534/genetics.111.128082

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  29 in total

1.  Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci.

Authors:  T H Meuwissen; M E Goddard
Journal:  Genetics       Date:  2000-05       Impact factor: 4.562

2.  The impact of genetic architecture on genome-wide evaluation methods.

Authors:  Hans D Daetwyler; Ricardo Pong-Wong; Beatriz Villanueva; John A Woolliams
Journal:  Genetics       Date:  2010-04-20       Impact factor: 4.562

3.  In silico method for inferring genotypes in pedigrees.

Authors:  Joshua T Burdick; Wei-Min Chen; Gonçalo R Abecasis; Vivian G Cheung
Journal:  Nat Genet       Date:  2006-08-20       Impact factor: 38.330

4.  A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

Authors:  Paul Scheet; Matthew Stephens
Journal:  Am J Hum Genet       Date:  2006-02-17       Impact factor: 11.025

5.  Linkage disequilibrium in finite populations.

Authors:  W G Hill; A Robertson
Journal:  Theor Appl Genet       Date:  1968-06       Impact factor: 5.699

6.  A general model for the genetic analysis of pedigree data.

Authors:  R C Elston; J Stewart
Journal:  Hum Hered       Date:  1971       Impact factor: 0.444

7.  Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle.

Authors:  K A Weigel; G de Los Campos; A I Vazquez; G J M Rosa; D Gianola; C P Van Tassell
Journal:  J Dairy Sci       Date:  2010-11       Impact factor: 4.034

8.  Detection of sharing by descent, long-range phasing and haplotype imputation.

Authors:  Augustine Kong; Gisli Masson; Michael L Frigge; Arnaldur Gylfason; Pasha Zusmanovich; Gudmar Thorleifsson; Pall I Olason; Andres Ingason; Stacy Steinberg; Thorunn Rafnar; Patrick Sulem; Magali Mouy; Frosti Jonsson; Unnur Thorsteinsdottir; Daniel F Gudbjartsson; Hreinn Stefansson; Kari Stefansson
Journal:  Nat Genet       Date:  2008-09       Impact factor: 38.330

9.  Genomic evaluations with many more genotypes.

Authors:  Paul M VanRaden; Jeffrey R O'Connell; George R Wiggans; Kent A Weigel
Journal:  Genet Sel Evol       Date:  2011-03-02       Impact factor: 4.297

Review 10.  Genomic imprinting in mammals: emerging themes and established theories.

Authors:  Andrew J Wood; Rebecca J Oakey
Journal:  PLoS Genet       Date:  2006-11-24       Impact factor: 5.917

View more
  35 in total

1.  Whole-genome resequencing of two elite sires for the detection of haplotypes under selection in dairy cattle.

Authors:  Denis M Larkin; Hans D Daetwyler; Alvaro G Hernandez; Chris L Wright; Lorie A Hetrick; Lisa Boucek; Sharon L Bachman; Mark R Band; Tatsiana V Akraiko; Miri Cohen-Zinder; Jyothi Thimmapuram; Iona M Macleod; Timothy T Harkins; Jennifer E McCague; Michael E Goddard; Ben J Hayes; Harris A Lewin
Journal:  Proc Natl Acad Sci U S A       Date:  2012-04-23       Impact factor: 11.205

2.  Power of family-based association designs to detect rare variants in large pedigrees using imputed genotypes.

Authors:  Mohamad Saad; Ellen M Wijsman
Journal:  Genet Epidemiol       Date:  2013-11-15       Impact factor: 2.135

3.  The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1.

Authors:  Bruna P Sollero; Jeremy T Howard; Matthew L Spangler
Journal:  J Anim Sci       Date:  2019-07-02       Impact factor: 3.159

4.  Off the street phasing (OTSP): no hassle haplotype phasing for molecular PGD applications.

Authors:  David A Zeevi; Fouad Zahdeh; Yehuda Kling; Shai Carmi; Gheona Altarescu
Journal:  J Assist Reprod Genet       Date:  2019-01-08       Impact factor: 3.412

5.  GIGI: an approach to effective imputation of dense genotypes on large pedigrees.

Authors:  Charles Y K Cheung; Elizabeth A Thompson; Ellen M Wijsman
Journal:  Am J Hum Genet       Date:  2013-04-04       Impact factor: 11.025

6.  Identification of key ancestors of modern germplasm in a breeding program of maize.

Authors:  F Technow; T A Schrag; W Schipprack; A E Melchinger
Journal:  Theor Appl Genet       Date:  2014-09-11       Impact factor: 5.699

7.  Meuwissen et al. on Genomic Selection.

Authors:  Dirk-Jan de Koning
Journal:  Genetics       Date:  2016-05       Impact factor: 4.562

Review 8.  Haplotype phasing: existing methods and new developments.

Authors:  Sharon R Browning; Brian L Browning
Journal:  Nat Rev Genet       Date:  2011-09-16       Impact factor: 53.242

9.  Estimation of Recombination Rate and Maternal Linkage Disequilibrium in Half-Sibs.

Authors:  Alexander Hampel; Friedrich Teuscher; Luis Gomez-Raya; Michael Doschoris; Dörte Wittenburg
Journal:  Front Genet       Date:  2018-06-05       Impact factor: 4.599

10.  PedBLIMP: extending linear predictors to impute genotypes in pedigrees.

Authors:  Wenan Chen; Daniel J Schaid
Journal:  Genet Epidemiol       Date:  2014-07-12       Impact factor: 2.135

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.