Literature DB >> 22059578

Use of haplotypes to estimate Mendelian sampling effects and selection limits.

J B Cole1, P M VanRaden.   

Abstract

Limits to selection and Mendelian sampling (MS) terms can be calculated using haplotypes by summing the individual additive effects on each chromosome. Haplotypes were imputed for 43 382 single-nucleotide polymorphisms (SNP) in 1455 Brown Swiss, 40 351 Holstein and 4064 Jersey bulls and cows using the Fortran program findhap.f90, which combines population and pedigree haplotyping methods. Lower and upper bounds of MS variance were calculated for daughter pregnancy rate (a measure of fertility), milk yield, lifetime net merit (a measure of profitability) and protein yield assuming either no or complete linkage among SNP on the same chromosome. Calculated selection limits were greater than the largest direct genomic values observed in all breeds studied. The best chromosomal genotypes generally consisted of two copies of the same haplotype even after adjustment for inbreeding. Selection of animals rather than chromosomes may result in slower progress, but limits may be the same because most chromosomes will become homozygous with either strategy. Selection on functions of MS could be used to change variances in later generations.
© 2011 Blackwell Verlag GmbH.

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Year:  2011        PMID: 22059578     DOI: 10.1111/j.1439-0388.2011.00922.x

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


  10 in total

1.  Selection on Optimal Haploid Value Increases Genetic Gain and Preserves More Genetic Diversity Relative to Genomic Selection.

Authors:  Hans D Daetwyler; Matthew J Hayden; German C Spangenberg; Ben J Hayes
Journal:  Genetics       Date:  2015-06-19       Impact factor: 4.562

2.  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

3.  Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses.

Authors:  Christina Lehermeier; Simon Teyssèdre; Chris-Carolin Schön
Journal:  Genetics       Date:  2017-10-16       Impact factor: 4.562

4.  Breeding Top Genotypes and Accelerating Response to Recurrent Selection by Selecting Parents with Greater Gametic Variance.

Authors:  Piter Bijma; Yvonne C J Wientjes; Mario P L Calus
Journal:  Genetics       Date:  2019-11-26       Impact factor: 4.562

5.  Improving Response in Genomic Selection with a Population-Based Selection Strategy: Optimal Population Value Selection.

Authors:  Matthew Goiffon; Aaron Kusmec; Lizhi Wang; Guiping Hu; Patrick S Schnable
Journal:  Genetics       Date:  2017-05-19       Impact factor: 4.562

6.  Increasing long-term response by selecting for favorable minor alleles.

Authors:  Chuanyu Sun; Paul M VanRaden
Journal:  PLoS One       Date:  2014-02-05       Impact factor: 3.240

7.  Mendelian sampling covariability of marker effects and genetic values.

Authors:  Sarah Bonk; Manuela Reichelt; Friedrich Teuscher; Dierck Segelke; Norbert Reinsch
Journal:  Genet Sel Evol       Date:  2016-04-23       Impact factor: 4.297

8.  Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms.

Authors:  Ling-Yun Chang; Sajjad Toghiani; Samuel E Aggrey; Romdhane Rekaya
Journal:  BMC Genet       Date:  2019-02-22       Impact factor: 2.797

9.  Changes in genomic predictions when new information is added.

Authors:  Jorge Hidalgo; Daniela Lourenco; Shogo Tsuruta; Yutaka Masuda; Stephen Miller; Matias Bermann; Andre L S Garcia; Ignacy Misztal
Journal:  J Anim Sci       Date:  2021-02-01       Impact factor: 3.159

10.  Prediction of expected genetic variation within groups of offspring for innovative mating schemes.

Authors:  Dierck Segelke; Friedrich Reinhardt; Zengting Liu; Georg Thaller
Journal:  Genet Sel Evol       Date:  2014-07-02       Impact factor: 4.297

  10 in total

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