Literature DB >> 34755217

Population-tailored mock genome enables genomic studies in species without a reference genome.

Felipe Sabadin1, Humberto Fanelli Carvalho2, Giovanni Galli2, Roberto Fritsche-Neto2.   

Abstract

Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  GBLUP; Genomic selection; Genotyping-by-sequencing; Orphan crops; SNP-array

Mesh:

Year:  2021        PMID: 34755217     DOI: 10.1007/s00438-021-01831-9

Source DB:  PubMed          Journal:  Mol Genet Genomics        ISSN: 1617-4623            Impact factor:   3.291


  37 in total

1.  The impact of genetic architecture on genome-wide evaluation methods.

Authors:  Hans D Daetwyler; Ricardo Pong-Wong; Beatriz Villanueva; John A Woolliams
Journal:  Genetics       Date:  2010-04-20       Impact factor: 4.562

2.  Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens.

Authors:  R Abdollahi-Arpanahi; A Nejati-Javaremi; A Pakdel; M Moradi-Shahrbabak; G Morota; B D Valente; A Kranis; G J M Rosa; D Gianola
Journal:  J Anim Breed Genet       Date:  2014-01-08       Impact factor: 2.380

Review 3.  Bioinformatics in the orphan crops.

Authors:  Ian Armstead; Lin Huang; Adriana Ravagnani; Paul Robson; Helen Ougham
Journal:  Brief Bioinform       Date:  2009-09-04       Impact factor: 11.622

4.  Effect of different genomic relationship matrices on accuracy and scale.

Authors:  C Y Chen; I Misztal; I Aguilar; A Legarra; W M Muir
Journal:  J Anim Sci       Date:  2011-03-31       Impact factor: 3.159

5.  A One-Penny Imputed Genome from Next-Generation Reference Panels.

Authors:  Brian L Browning; Ying Zhou; Sharon R Browning
Journal:  Am J Hum Genet       Date:  2018-08-09       Impact factor: 11.025

Review 6.  Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Authors:  José Crossa; Paulino Pérez-Rodríguez; Jaime Cuevas; Osval Montesinos-López; Diego Jarquín; Gustavo de Los Campos; Juan Burgueño; Juan M González-Camacho; Sergio Pérez-Elizalde; Yoseph Beyene; Susanne Dreisigacker; Ravi Singh; Xuecai Zhang; Manje Gowda; Manish Roorkiwal; Jessica Rutkoski; Rajeev K Varshney
Journal:  Trends Plant Sci       Date:  2017-09-28       Impact factor: 18.313

7.  A comparison between genotyping-by-sequencing and array-based scoring of SNPs for genomic prediction accuracy in winter wheat.

Authors:  Ibrahim S Elbasyoni; A J Lorenz; M Guttieri; K Frels; P S Baenziger; J Poland; E Akhunov
Journal:  Plant Sci       Date:  2018-02-21       Impact factor: 4.729

8.  Genome-Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping-by-Sequencing SNPs and Whole-Genome Prediction.

Authors:  Shiliang Cao; Alexander Loladze; Yibing Yuan; Yongsheng Wu; Ao Zhang; Jiafa Chen; Gordon Huestis; Jingsheng Cao; Vijay Chaikam; Michael Olsen; Boddupalli M Prasanna; Felix San Vicente; Xuecai Zhang
Journal:  Plant Genome       Date:  2017-07       Impact factor: 4.089

9.  Marker density and read depth for genotyping populations using genotyping-by-sequencing.

Authors:  Timothy M Beissinger; Candice N Hirsch; Rajandeep S Sekhon; Jillian M Foerster; James M Johnson; German Muttoni; Brieanne Vaillancourt; C Robin Buell; Shawn M Kaeppler; Natalia de Leon
Journal:  Genetics       Date:  2013-02-14       Impact factor: 4.562

Review 10.  Whole-genome regression and prediction methods applied to plant and animal breeding.

Authors:  Gustavo de Los Campos; John M Hickey; Ricardo Pong-Wong; Hans D Daetwyler; Mario P L Calus
Journal:  Genetics       Date:  2012-06-28       Impact factor: 4.562

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