Literature DB >> 22059576

The unified approach to the use of genomic and pedigree information in genomic evaluations revisited.

T H E Meuwissen1, T Luan, J A Woolliams.   

Abstract

Previous proposals for a unified approach for amalgamating information from animals with or without genotypes have combined the numerator relationship matrix A with the genomic relationship G estimated from the markers. These approaches have resulted in biased genomic EBV (GEBV), and methodology was developed to overcome these problems. Firstly, a relationship matrix, G(FG) , based on linkage analysis was derived using the same base population as A, which (i) utilizes the genomic information on the same scale as the pedigree information and (ii) permits the regression coefficients used to propagate the genomic data from the genotyped to ungenotyped individuals to be calculated in the light of the genomic information, rather than ignoring it. Secondly, the elements of G were regressed back towards their expected values in the A matrix to allow for their estimation errors. These developments were combined in a methodology LDLAb and tested on simulated populations where either parents were phenotyped and offspring genotyped or vice versa. The LDLAb method was demonstrated to be a unified approach that maximized accuracy of GEBV compared to previous methodologies and removed the bias in the GEBV. Although LDLAb is computationally much more demanding than MLAC, it demonstrates how to make best use the marker information and also shows the computational problems that need to be solved in the future to make best use of the marker data.
© 2011 Blackwell Verlag GmbH.

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Year:  2011        PMID: 22059576     DOI: 10.1111/j.1439-0388.2011.00966.x

Source DB:  PubMed          Journal:  J Anim Breed Genet        ISSN: 0931-2668            Impact factor:   2.380


  25 in total

1.  The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction.

Authors:  Yvonne C J Wientjes; Roel F Veerkamp; Mario P L Calus
Journal:  Genetics       Date:  2012-12-24       Impact factor: 4.562

2.  A genealogical estimate of genetic relationships.

Authors:  Caoqi Fan; Nicholas Mancuso; Charleston W K Chiang
Journal:  Am J Hum Genet       Date:  2022-04-12       Impact factor: 11.043

3.  Genetic prediction of complex traits: integrating infinitesimal and marked genetic effects.

Authors:  Clément Carré; Fabrice Gamboa; David Cros; John Michael Hickey; Gregor Gorjanc; Eduardo Manfredi
Journal:  Genetica       Date:  2013-05-30       Impact factor: 1.082

4.  A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation.

Authors:  John M Hickey; Brian P Kinghorn; Bruce Tier; Julius H J van der Werf; Matthew A Cleveland
Journal:  Genet Sel Evol       Date:  2012-06-19       Impact factor: 4.297

5.  Single-step genomic prediction of Eucalyptus dunnii using different identity-by-descent and identity-by-state relationship matrices.

Authors:  Esteban J Jurcic; Pamela V Villalba; Pablo S Pathauer; Dino A Palazzini; Gustavo P J Oberschelp; Leonel Harrand; Martín N Garcia; Natalia C Aguirre; Cintia V Acuña; María C Martínez; Juan G Rivas; Esteban F Cisneros; Juan A López; Susana N Marcucci Poltri; Sebastián Munilla; Eduardo P Cappa
Journal:  Heredity (Edinb)       Date:  2021-06-18       Impact factor: 3.832

6.  Genome-wide estimates of coancestry, inbreeding and effective population size in the Spanish Holstein population.

Authors:  Silvia Teresa Rodríguez-Ramilo; Jesús Fernández; Miguel Angel Toro; Delfino Hernández; Beatriz Villanueva
Journal:  PLoS One       Date:  2015-04-16       Impact factor: 3.240

7.  Genomic prediction of traits related to canine hip dysplasia.

Authors:  Enrique Sánchez-Molano; Ricardo Pong-Wong; Dylan N Clements; Sarah C Blott; Pamela Wiener; John A Woolliams
Journal:  Front Genet       Date:  2015-03-13       Impact factor: 4.599

8.  A simple method to separate base population and segregation effects in genomic relationship matrices.

Authors:  Laura Plieschke; Christian Edel; Eduardo Cg Pimentel; Reiner Emmerling; Jörn Bennewitz; Kay-Uwe Götz
Journal:  Genet Sel Evol       Date:  2015-06-23       Impact factor: 4.297

9.  Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation.

Authors:  Ole F Christensen
Journal:  Genet Sel Evol       Date:  2012-12-03       Impact factor: 4.297

10.  Accuracy of the unified approach in maternally influenced traits--illustrated by a simulation study in the honey bee (Apis mellifera).

Authors:  Pooja Gupta; Norbert Reinsch; Andreas Spötter; Tim Conrad; Kaspar Bienefeld
Journal:  BMC Genet       Date:  2013-05-06       Impact factor: 2.797

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