Literature DB >> 22720970

Short communication: Imputation performances of 3 low-density marker panels in beef and dairy cattle.

R Dassonneville1, S Fritz, V Ducrocq, D Boichard.   

Abstract

Low-density chips are appealing alternative tools contributing to the reduction of genotyping costs. Imputation enables researchers to predict missing genotypes to recreate the denser coverage of the standard 50K (∼50,000) genotype. Two alternative in silico chips were defined in this study that included markers selected to optimize minor allele frequency and spacing. The objective of this study was to compare the imputation accuracy of these custom low-density chips with a commercially available 3K chip. Data consisted of genotypes of 4,037 Holstein bulls, 1,219 Montbéliarde bulls, and 991 Blonde d'Aquitaine bulls. Criteria to select markers to include in low-density marker panels are described. To mimic a low-density genotype, all markers except the markers present on the low-density panel were masked in the validation population. Imputation was performed using the Beagle software. Combining the directed acyclic graph obtained with Beagle with the PHASEBOOK algorithm provides fast and accurate imputation that is suitable for routine genomic evaluations based on imputed genotypes. Overall, 95 to 99% of alleles were correctly imputed depending on the breed and the low-density chip used. The alternative low-density chips gave better results than the commercially available 3K chip. A low-density chip with 6,000 markers is a valuable genotyping tool suitable for both dairy and beef breeds. Such a tool could be used for preselection of young animals or large-scale screening of the female population.
Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22720970     DOI: 10.3168/jds.2011-5133

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  11 in total

1.  Design of a bovine low-density SNP array optimized for imputation.

Authors:  Didier Boichard; Hoyoung Chung; Romain Dassonneville; Xavier David; André Eggen; Sébastien Fritz; Kimberly J Gietzen; Ben J Hayes; Cynthia T Lawley; Tad S Sonstegard; Curtis P Van Tassell; Paul M VanRaden; Karine A Viaud-Martinez; George R Wiggans
Journal:  PLoS One       Date:  2012-03-28       Impact factor: 3.240

2.  Methods of tagSNP selection and other variables affecting imputation accuracy in swine.

Authors:  Yvonne M Badke; Ronald O Bates; Catherine W Ernst; Clint Schwab; Justin Fix; Curtis P Van Tassell; Juan P Steibel
Journal:  BMC Genet       Date:  2013-02-21       Impact factor: 2.797

3.  Accuracy of genome-wide imputation in Braford and Hereford beef cattle.

Authors:  Mario L Piccoli; José Braccini; Fernando F Cardoso; Medhi Sargolzaei; Steven G Larmer; Flávio S Schenkel
Journal:  BMC Genet       Date:  2014-12-29       Impact factor: 2.797

4.  Design of low density SNP chips for genotype imputation in layer chicken.

Authors:  Florian Herry; Frédéric Hérault; David Picard Druet; Amandine Varenne; Thierry Burlot; Pascale Le Roy; Sophie Allais
Journal:  BMC Genet       Date:  2018-12-04       Impact factor: 2.797

5.  Genome-Wide Association Study and Cost-Efficient Genomic Predictions for Growth and Fillet Yield in Nile Tilapia (Oreochromis niloticus).

Authors:  Grazyella M Yoshida; Jean P Lhorente; Katharina Correa; Jose Soto; Diego Salas; José M Yáñez
Journal:  G3 (Bethesda)       Date:  2019-08-08       Impact factor: 3.154

6.  Interest of using imputation for genomic evaluation in layer chicken.

Authors:  Florian Herry; David Picard Druet; Frédéric Hérault; Amandine Varenne; Thierry Burlot; Pascale Le Roy; Sophie Allais
Journal:  Poult Sci       Date:  2020-03-18       Impact factor: 3.352

7.  Inference of Ancestries and Heterozygosity Proportion and Genotype Imputation in West African Cattle Populations.

Authors:  Netsanet Z Gebrehiwot; Hassan Aliloo; Eva M Strucken; Karen Marshall; Mohammad Al Kalaldeh; Ayao Missohou; John P Gibson
Journal:  Front Genet       Date:  2021-03-23       Impact factor: 4.599

8.  Use of partial least squares regression to impute SNP genotypes in Italian cattle breeds.

Authors:  Corrado Dimauro; Massimo Cellesi; Giustino Gaspa; Paolo Ajmone-Marsan; Roberto Steri; Gabriele Marras; Nicolò P P Macciotta
Journal:  Genet Sel Evol       Date:  2013-06-05       Impact factor: 4.297

9.  Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip.

Authors:  Chris Schrooten; Romain Dassonneville; Vincent Ducrocq; Rasmus F Brøndum; Mogens S Lund; Jun Chen; Zengting Liu; Oscar González-Recio; Juan Pena; Tom Druet
Journal:  Genet Sel Evol       Date:  2014-02-04       Impact factor: 4.297

10.  Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers.

Authors:  Guosheng Su; Bernt Guldbrandtsen; Gert P Aamand; Ismo Strandén; Mogens S Lund
Journal:  Genet Sel Evol       Date:  2014-07-30       Impact factor: 4.297

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