Literature DB >> 25282421

Comparison of single-trait to multi-trait national evaluations for yield, health, and fertility.

P M VanRaden1, M E Tooker2, J R Wright2, C Sun3, J L Hutchison2.   

Abstract

Flexible software was designed to replace the current animal model programs used for national genetic evaluations. Model improvements included (1) multi-trait processing, (2) multiple fixed class and regression variables, (3) differing models for different traits, (4) random regressions, and (5) foreign data included using pseudo-records. Computational improvements included (6) parallel processing, (7) renumbering class variables to equation numbers within the program so that estimated effects are output with original identification numbers, and (8) reliability computed within the same program. When applied to 3 fertility traits of 27,971,895 cows and heifers, the new model used daughter pregnancy rate as a correlated trait to improve heifer and cow conception rate evaluations for older animals and in herd-years where records are missing, and also added information from crossbreds. When applied to 7 traits and 76,846,327 lactation records of 30,064,300 cows, gains in accuracy were small for yield and somatic cell score, moderate for daughter pregnancy rate, and larger for productive life for recent bulls compared with single-trait evaluations. For very old bulls, multi-trait gains were also large for protein because lactation records were available only for milk and fat. Multi-trait productive life was computed with exact rather than approximate methods; however, correlated information from conformation was excluded, reducing advantages of the new model over the previous software. Estimates of breed differences, inbreeding depression, and heterosis were similar to previous estimates; new estimates were obtained for conception rates. Predictions were compared by truncating 4 yr of data, and genetic trend validation was applied to all breed-trait combinations. The estimates of trend account for increases in inbreeding across time. Incorporation of foreign data gave correlations above 0.98 for new with previous evaluations of foreign Holstein bulls, but lower for other breeds. The 7-trait model required 35 GB of memory and 3 d to converge using 7 processors. The new software was implemented for fertility traits in 2013 and is scheduled for implementation with yield, somatic cell score, and productive life in 2014. Further revision of the models and software may be needed in the near future to account for genomic preselection.
Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  animal model; best linear unbiased prediction; correlated trait; genetic evaluation

Mesh:

Year:  2014        PMID: 25282421     DOI: 10.3168/jds.2014-8489

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


  8 in total

1.  Genomic Prediction Using Individual-Level Data and Summary Statistics from Multiple Populations.

Authors:  Jeremie Vandenplas; Mario P L Calus; Gregor Gorjanc
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2.  Beef trait genetic parameters based on old and recent data and its implications for genomic predictions in Italian Simmental cattle.

Authors:  Alberto Cesarani; Jorge Hidalgo; Andre Garcia; Lorenzo Degano; Daniele Vicario; Yutaka Masuda; Ignacy Misztal; Daniela Lourenco
Journal:  J Anim Sci       Date:  2020-08-01       Impact factor: 3.159

3.  Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat.

Authors:  Kassa Semagn; José Crossa; Jaime Cuevas; Muhammad Iqbal; Izabela Ciechanowska; Maria Antonia Henriquez; Harpinder Randhawa; Brian L Beres; Reem Aboukhaddour; Brent D McCallum; Anita L Brûlé-Babel; Amidou N'Diaye; Curtis Pozniak; Dean Spaner
Journal:  Theor Appl Genet       Date:  2022-06-23       Impact factor: 5.574

4.  Emerging issues in genomic selection.

Authors:  Ignacy Misztal; Ignacio Aguilar; Daniela Lourenco; Li Ma; Juan Pedro Steibel; Miguel Toro
Journal:  J Anim Sci       Date:  2021-06-01       Impact factor: 3.159

5.  Accuracies of univariate and multivariate genomic prediction models in African cassava.

Authors:  Uche Godfrey Okeke; Deniz Akdemir; Ismail Rabbi; Peter Kulakow; Jean-Luc Jannink
Journal:  Genet Sel Evol       Date:  2017-12-04       Impact factor: 4.297

6.  Modeling honey yield, defensive and swarming behaviors of Italian honey bees (Apis mellifera ligustica) using linear-threshold approaches.

Authors:  Sreten Andonov; Cecilia Costa; Aleksandar Uzunov; Patrizia Bergomi; Daniela Lourenco; Ignacy Misztal
Journal:  BMC Genet       Date:  2019-10-21       Impact factor: 2.797

Review 7.  Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review.

Authors:  Miguel A Gutierrez-Reinoso; Pedro M Aponte; Manuel Garcia-Herreros
Journal:  Animals (Basel)       Date:  2021-02-25       Impact factor: 3.231

8.  Genetic inbreeding depression load for fertility traits in Pura Raza Española mares.

Authors:  Davinia I Perdomo-González; Antonio Molina; María J Sánchez-Guerrero; Ester Bartolomé; Luis Varona; Mercedes Valera
Journal:  J Anim Sci       Date:  2021-12-01       Impact factor: 3.159

  8 in total

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