Literature DB >> 21512116

The efficiency of genome-wide selection for genetic improvement of net merit.

K Togashi1, C Y Lin, T Yamazaki.   

Abstract

Four methods of selection for net merit comprising 2 correlated traits were compared in this study: 1) EBV-only index (I₁), which consists of the EBV of both traits (i.e., traditional 2-trait BLUP selection); 2) GEBV-only index (I₂), which comprises the genomic EBV (GEBV) of both traits; 3) GEBV-assisted index (I₃), which combines both the EBV and the GEBV of both traits; and 4) GBV-assisted index (I₄), which combines both the EBV and the true genomic breeding value (GBV) of both traits. Comparisons of these indices were based on 3 evaluation criteria [selection accuracy, genetic response (ΔH), and relative efficiency] under 64 scenarios that arise from combining 2 levels of genetic correlation (r(G)), 2 ratios of genetic variances between traits, 2 ratios of the genomic variance to total genetic variances for trait 1, 4 accuracies of EBV, and 2 proportions of r(G) explained by the GBV. Both selection accuracy and genetic responses of the indices I₁, I₃, and I₄ increased as the accuracy of EBV increased, but the efficiency of the indices I₃ and I₄ relative to I₁ decreased as the accuracy of EBV increased. The relative efficiency of both I₃ and I₄ was generally greater when the accuracy of EBV was 0.6 than when it was 0.9, suggesting that the genomic markers are most useful to assist selection when the accuracy of EBV is low. The GBV-assisted index I₄ was superior to the GEBV-assisted I₃ in all 64 cases examined, indicating the importance of improving the accuracy of prediction of genomic breeding values. Other parameters being identical, increasing the genetic variance of a high heritability trait would increase the genetic response of the genomic indices (I₂, I₃, and I₄). The genetic responses to I₂, I₃, and I(4) was greater when the genetic correlation between traits was positive (r(G) = 0.5) than when it was negative (r(G) = -0.5). The results of this study indicate that the effectiveness of the GEBV-assisted index I₃ is affected by heritability of and genetic correlation between traits, the ratio of genetic variances between traits, the genomic-genetic variance ratio of each index trait, the proportion of genetic correlation accounted for by the genomic markers, and the accuracy of predictions of both EBV and GBV. However, most of these affecting factors are genetic characteristics of a population that is beyond the control of the breeders. The key factor subject to manipulation is to maximize both the proportion of the genetic variance explained by GEBV and the accuracy of both GEBV and EBV. The developed procedures provide means to investigate the efficiency of various genomic indices for any given combination of the genetic factors studied.

Mesh:

Substances:

Year:  2011        PMID: 21512116     DOI: 10.2527/jas.2009-2606

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  8 in total

1.  A Genomic Selection Index Applied to Simulated and Real Data.

Authors:  J Jesus Ceron-Rojas; José Crossa; Vivi N Arief; Kaye Basford; Jessica Rutkoski; Diego Jarquín; Gregorio Alvarado; Yoseph Beyene; Kassa Semagn; Ian DeLacy
Journal:  G3 (Bethesda)       Date:  2015-08-18       Impact factor: 3.154

2.  Lactation persistency as a component trait of the selection index and increase in reliability by using single nucleotide polymorphism in net merit defined as the first five lactation milk yields and herd life.

Authors:  K Togashi; K Hagiya; T Osawa; T Nakanishi; T Yamazaki; Y Nagamine; C Y Lin; S Matsumoto; M Aihara; K Hayasaka
Journal:  Asian-Australas J Anim Sci       Date:  2012-08       Impact factor: 2.509

3.  Trait variation and genetic diversity in a banana genomic selection training population.

Authors:  Moses Nyine; Brigitte Uwimana; Rony Swennen; Michael Batte; Allan Brown; Pavla Christelová; Eva Hřibová; Jim Lorenzen; Jaroslav Doležel
Journal:  PLoS One       Date:  2017-06-06       Impact factor: 3.240

4.  Combining grain yield, protein content and protein quality by multi-trait genomic selection in bread wheat.

Authors:  Sebastian Michel; Franziska Löschenberger; Christian Ametz; Bernadette Pachler; Ellen Sparry; Hermann Bürstmayr
Journal:  Theor Appl Genet       Date:  2019-07-01       Impact factor: 5.699

5.  Efficiency of a Constrained Linear Genomic Selection Index To Predict the Net Genetic Merit in Plants.

Authors:  J Jesus Cerón-Rojas; Jose Crossa
Journal:  G3 (Bethesda)       Date:  2019-12-03       Impact factor: 3.154

6.  The statistical theory of linear selection indices from phenotypic to genomic selection.

Authors:  J Jesus Cerón-Rojas; Jose Crossa
Journal:  Crop Sci       Date:  2022-02-06       Impact factor: 2.763

Review 7.  Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits.

Authors:  Pallavi Sinha; Vikas K Singh; Abhishek Bohra; Arvind Kumar; Jochen C Reif; Rajeev K Varshney
Journal:  Theor Appl Genet       Date:  2021-05-20       Impact factor: 5.699

8.  Genomic selection for heterobothriosis resistance concurrent with body size in the tiger pufferfish, Takifugu rubripes.

Authors:  Zijie Lin; Sho Hosoya; Mana Sato; Naoki Mizuno; Yuki Kobayashi; Takuya Itou; Kiyoshi Kikuchi
Journal:  Sci Rep       Date:  2020-11-17       Impact factor: 4.379

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.