scholarly journals Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

2020 ◽  
Author(s):  
Dorcus C Gemenet ◽  
Hannele Lindqvist-Kreuze ◽  
Bode A Olukolu ◽  
Bert De Boeck ◽  
Guilherme da Silva Pereira ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Service Providers ◽  
Predictive Ability ◽  
Genetic Effects ◽  
Additive Models ◽  
Sequencing Depth ◽  
Nucleotide Polymorphisms ◽  
Breeding Populations ◽  
Wide Range ◽  
Additive Genetic Effects

AbstractThe autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: i) do polyploid crop breeders need to invest more for additional sequencing depth? ii) how many markers are required to make selection decisions? iii) does considering non-additive genetic effects improve predictive ability (PA)? iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction, hence it is possible to get this number at the current sequencing depth from most service providers. We also show that considering dosage information and additive-effects only models had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.Key messagePolypoid crop breeders do not need more investment for sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture.

scholarly journals Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

2020 ◽  
Vol 133 (12) ◽  
pp. 3345-3363
Author(s):  
Dorcus C. Gemenet ◽  
Hannele Lindqvist-Kreuze ◽  
Bert De Boeck ◽  
Guilherme da Silva Pereira ◽  
Marcelo Mollinari ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Predictive Ability ◽  
Genetic Effects ◽  
Additive Models ◽  
Sequencing Depth ◽  
Nucleotide Polymorphisms ◽  
Breeding Populations ◽  
Selection Decisions ◽  
Wide Range ◽  
Additive Genetic Effects

Key message Polypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture. Abstract The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene-action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental F1 population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: (i) do polyploid crop breeders need to invest more for additional sequencing depth? (ii) how many markers are required to make selection decisions? (iii) does considering non-additive genetic effects improve predictive ability (PA)? (iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction in the type of populations we analyzed. We also show that considering dosage information and models considering only additive effects had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.

scholarly journals Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 585 ◽  
Author(s):  
Seema Yadav ◽  
Phillip Jackson ◽  
Xianming Wei ◽  
Elizabeth M. Ross ◽  
Karen Aitken ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Cycle Length ◽  
Primary Source ◽  
Genetic Effects ◽  
Breeding Cycle ◽  
Narrow Sense Heritability ◽  
Breeding Values ◽  
Sugarcane Breeding ◽  
Additive Genetic Effects

Sugarcane is a major industrial crop cultivated in tropical and subtropical regions of the world. It is the primary source of sugar worldwide, accounting for more than 70% of world sugar consumption. Additionally, sugarcane is emerging as a source of sustainable bioenergy. However, the increase in productivity from sugarcane has been small compared to other major crops, and the rate of genetic gains from current breeding programs tends to be plateauing. In this review, some of the main contributors for the relatively slow rates of genetic gain are discussed, including (i) breeding cycle length and (ii) low narrow-sense heritability for major commercial traits, possibly reflecting strong non-additive genetic effects involved in quantitative trait expression. A general overview of genomic selection (GS), a modern breeding tool that has been very successfully applied in animal and plant breeding, is given. This review discusses key elements of GS and its potential to significantly increase the rate of genetic gain in sugarcane, mainly by (i) reducing the breeding cycle length, (ii) increasing the prediction accuracy for clonal performance, and (iii) increasing the accuracy of breeding values for parent selection. GS approaches that can accurately capture non-additive genetic effects and potentially improve the accuracy of genomic estimated breeding values are particularly promising for the adoption of GS in sugarcane breeding. Finally, different strategies for the efficient incorporation of GS in a practical sugarcane breeding context are presented. These proposed strategies hold the potential to substantially increase the rate of genetic gain in future sugarcane breeding.

scholarly journals Quantifying the contribution of dominance effects to complex trait variation in biobank-scale data

2020 ◽  
Author(s):  
Ali Pazokitoroudi ◽  
Alec M. Chiu ◽  
Kathryn S. Burch ◽  
Bogdan Pasaniuc ◽  
Sriram Sankararaman
Keyword(s):  
Complex Traits ◽  
Complex Trait ◽  
Genetic Effects ◽  
Trait Variation ◽  
Wide Range ◽  
Unbiased Estimates ◽  
The Uk ◽  
Additive Genetic Effects ◽  
Intense Debate ◽  
Scale Data

AbstractThe proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of Biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method that can partition the variation in complex traits into variance that can be attributed to additive (additive heritability) and dominance (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency, MAF > 1%) and 50 quantitative traits measured in 291, 273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no evidence for dominance heritability ( accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.

Modelling non-additive genetic effects using ridge regression for an Angus–Nellore crossbred population

2019 ◽  
Vol 59 (5) ◽  
pp. 823 ◽  
Author(s):  
C. D. Bertoli ◽  
J. Braccini Neto ◽  
C. McManus ◽  
J. A. Cobuci ◽  
G. S. Campos ◽  
...  
Keyword(s):  
Ridge Regression ◽  
Genetic Effects ◽  
Additive Models ◽  
Additive Effect ◽  
Ratio Test ◽  
Additive Effects ◽  
Complete Model ◽  
Complementarity Effect ◽  
Scrotal Circumference ◽  
Additive Genetic Effects

Data from 294045 records from a crossbred Angus × Nellore population were used to estimate fixed genetic effects (both additive and non-additive) and to test different non-additive models using ridge regression. The traits studied included weaning gain (WG), postweaning gain (PG), phenotypic scores for weaning (WC) and postweaning (PC) conformation, weaning (WP) and postweaning (PP) precocity, weaning (WM) and postweaning (PM) muscling and scrotal circumference (SC). All models were compared using the likelihood-ratio test. The model including all fixed genetic effects (breed additive and complementarity, heterosis and epistatic loss non-additive effects, both direct and maternal) was the best option to analyse this crossbred population. For the complete model, all effects were statistically significant (P < 0.01) for weaning traits, except the direct breed additive effects for WP and WM; direct complementarity effect for WP, WM, PP and PM and maternal epistatic loss for PG. Direct breed additive effect was positive for weaning traits and negative for postweaning. Maternal breed additive effect was negative for SC and WP. Direct complementarity and heterosis were positive for all traits and maternal complementarity and heterosis were also positive for all traits, except for PG. Direct and maternal epistatic loss effects were negative for all traits. We conclude that the fixed genetic effects are mostly significant. Thus, it is important to include them in the model when evaluating crossbred animals, and the model that included breed additive effects, complementarity, heterosis and epistatic loss differed significantly from all reduced models, allowing to infer that it was the best model. The model with only breed additive and heterosis was parsimonious and could be used when the structure or amount of data does not allow the use of complete model.

scholarly journals Phantom Epistasis in Genomic Selection: On the Predictive Ability of Epistatic Models

2020 ◽  
Vol 10 (9) ◽  
pp. 3137-3145 ◽  
Author(s):  
Matías F Schrauf ◽  
Johannes W R Martini ◽  
Henner Simianer ◽  
Gustavo de los Campos ◽  
Rodolfo Cantet ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Association Studies ◽  
Predictive Ability ◽  
Additive Models ◽  
Marker Density ◽  
Interaction Terms ◽  
Biological Interpretation ◽  
Genetic Value

Abstract Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density (“Phantom Epistasis”). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.

scholarly journals Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

2018 ◽  
Author(s):  
Luis Felipe Ventorim Ferrão ◽  
Caillet Dornelles Marinho ◽  
Patricio R. Munoz ◽  
Marcio F. R. Resende
Keyword(s):  
Genomic Selection ◽  
Prediction Models ◽  
Predictive Ability ◽  
Additive Models ◽  
Hybrid Breeding ◽  
Maize Breeding ◽  
Prediction Ability ◽  
Heterotic Groups ◽  
Breeding Programs ◽  
Grain Moisture

AbstractHybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein, we assess modelling approaches where dominance effects and multi-environment statistical are considered for genomic selection in maize hybrid. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three production cycles in different locations. Hybrid genotypes were inferredin silicobased on their parental inbred lines using single-nucleotide polymorphism markers obtained via a 500k SNP chip. We considered the importance to decomposes additive and dominance marker effects into components that are constant across environments and deviations that are group-specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30% in some scenarios. Contrastingly, additive models yielded better results for grain moisture. For multi-environment modelling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype by environment interactions resulted in gains in accuracy and hence could be considered for genomic selection implementation in maize breeding programs.

scholarly journals SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Plants ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 331 ◽  
Author(s):  
Paulina Ballesta ◽  
Carlos Maldonado ◽  
Paulino Pérez-Rodríguez ◽  
Freddy Mora
Keyword(s):  
Genomic Selection ◽  
Eucalyptus Globulus ◽  
Quantitative Traits ◽  
Prediction Models ◽  
Wood Quality ◽  
Predictive Ability ◽  
Breeding Population ◽  
Snp Markers ◽  
Nucleotide Polymorphisms ◽  
Stem Straightness

Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.

scholarly journals Genetic Diversity and Genomic Selection in Eucalyptus Benthamii

2021 ◽  
Author(s):  
BRUNO MARCHETTI DE SOUZA ◽  
Lucas Moura de Abreu ◽  
Marília de Castro Rodrigues Pappas ◽  
Vânia Cristina Rennó Azevedo ◽  
Paulo Eduardo Telles dos Santos ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Genomic Selection ◽  
Natural Populations ◽  
Predictive Ability ◽  
Breeding Population ◽  
Phenotypic Traits ◽  
Breeding Populations ◽  
Kinship Coefficients ◽  
Hardy Weinberg Equilibrium ◽  
The One

Abstract The study investigates the genetic diversity and the ability of genomic-wide selection to predict breeding genomic values of an E. benthamii trial. All individuals (115) of the breeding population were genotyped with 13 microsatellites loci. The diameter at breast height and total height were measured. The data analysis was carried using the softwares: Structure, Popgene, GDA, SPAGeDi1.5 and R. Predictive ability, heritability and standard errors markers were estimated using the RRblup method. The average number of alleles per locus was nine, and the polymorphism level for each locus varied from 3 to 17. The average expected heterozygosity (He=0.655) was very similar to observed heterozygosity and the estimated inbreeding (F = 0.02) was very low. These results corroborate that this population is in Hardy-Weinberg equilibrium for the most loci. The trial genetic diversity is considered high, once the trial sampling demonstrated similar values to the natural populations. The group coancestry (0.085) demonstrate that the trees, in general, related at the half-sib level in this population. By using the Evanno’s method it is inferred that the individuals came from two original populations. The genetic distance calculated among the two groups was low ( =0.21). The heritability estimated from genomic selection for phenotypic traits was very low; however, the heritability estimated using the kinship coefficients was higher. The marker-based heritability using kinship coefficients probably is the more accurate than the one estimated using genomic selection, showing that the population samples can be used to establish breeding populations, hybrids and enriching the species germplasm bank.

Association of the CD14 C159T and the Toll-like receptor 4 Asp299Gly polymorphisms with various phenotypes of asthma in adults from Crimea

2020 ◽  
Vol 41 (2) ◽  
pp. 134-140
Author(s):  
Yuriy Bisyuk ◽  
Andrew Dubovyi ◽  
Ilona DuBuske ◽  
Viktor Litus ◽  
Lawrence M. DuBuske
Keyword(s):  
Late Onset ◽  
Adult Population ◽  
Additive Models ◽  
Atopic Asthma ◽  
Control Group ◽  
Nucleotide Polymorphisms ◽  
Toll Like Receptor ◽  
Toll Like Receptor 4 ◽  
Single Nucleotide ◽  
Gender And Age

Background: This study assessed gene polymorphisms of the CD14 receptor (C-159T) and Toll-like receptor 4 (Asp299Gly) in a patient population in Crimea, Ukraine, stratified by clinical (early versus late onset; frequent versus occasional relapses; fixed versus reversible obstruction) and immunologic (atopic versus nonatopic; eosinophilic; neutrophilic or paucigranulocytic inflammation) subtype. Methods: Two polymorphisms, CD14 C-159T and TLR4 Asp299Gly, were assessed in 331 patients with asthma. The control group included 285 volunteers who were nonatopic. The single nucleotide polymorphisms were studied by using polymerase chain reaction with electrophoretic detection. Results: There were increased odds of asthma development in patients with the Asp299Gly TLR4 mutation compared with the general population underdominant odds ratio (OR) 1.52 [95% confidence interval (CI), 1.00‐2.32] and overdominant (OR 1.55 [95% CI, 1.01‐2.38]) models after adjustment for gender and age. In addition, mutations in this gene decreased the odds of nonatopic asthma in underdominant (OR 0.26 [95% CI, 0.07‐0.93]; p = 0.027), overdominant (OR 0.27 [95% CI, 0.07‐0.96]; p = 0.033), and log-additive models (OR 0.26 [95% CI, 0.07‐0.93]; p = 0.026) compared with the atopic subgroup after adjustment for gender, age, number of exacerbations, and type of airway inflammation. Allele frequencies for CD14 and TLR4 polymorphisms did not show statistical differences between the patients with asthma and the control subjects. Conclusion: CD14 C-159T polymorphisms were not associated with asthma in the adult population in Crimea. TLR4 Asp299Gly polymorphisms were associated with asthma and with decreased odds of nonatopic asthma compared with atopic asthma in the adult population in Crimea.

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