scholarly journals Interest of phenomic prediction as an alternative to genomic prediction in grapevine

2021 ◽  
Author(s):  
Charlotte Brault ◽  
Juliette Lazerges ◽  
Agnès Doligez ◽  
Miguel Thomas ◽  
Martin Ecarnot ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Reflectance Spectrum ◽  
Predictive Ability ◽  
Relationship Matrix ◽  
Diversity Panel ◽  
Cereal Species ◽  
Genetic Signal ◽  
First Time ◽  
Half Diallel ◽  
Two Populations

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of -0.2, thus suggesting that phenomic prediction could be applied for any trait.

scholarly journals Across-population genomic prediction in grapevine opens up promising prospects for breeding

2021 ◽  
Author(s):  
Charlotte Brault ◽  
Vincent Segura ◽  
Patrice This ◽  
Loïc Le Cunff ◽  
Timothée Flutre ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Predictive Ability ◽  
Vitis Vinifera L ◽  
Individual Values ◽  
Population Genomic ◽  
Genome Wide ◽  
Diallel Mating Design ◽  
Genetic Value ◽  
Berry Composition ◽  
Half Diallel

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

scholarly journals Genetic Parameter Estimation and Genomic Prediction of Duroc Boars’ Sperm Morphology Abnormalities

Animals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 710 ◽  
Author(s):  
Yunxiang Zhao ◽  
Ning Gao ◽  
Jian Cheng ◽  
Saeed El-Ashram ◽  
Lin Zhu ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genetic Parameters ◽  
Sperm Morphology ◽  
Genetic Parameter ◽  
Predictive Ability ◽  
Single Step ◽  
Swine Production ◽  
Forward Prediction ◽  
Best Linear Unbiased ◽  
First Time

Artificial insemination (AI) has been used globally as a routine technology in the swine production industry. However, genetic parameters and genomic prediction accuracy of semen traits have seldom been reported. In this study, we estimated genetic parameters and conducted genomic prediction for five types of sperm morphology abnormalities in a large Duroc boar population. The estimated heritability of the studied traits ranged from 0.029 to 0.295. In the random cross-validation scenario, the predictive ability ranged from 0.212 to 0.417 for genomic best linear unbiased prediction (GBLUP) and from 0.249 to 0.565 for single-step GBLUP (ssGBLUP). In the forward prediction scenario, the predictive ability ranged from 0.069 to 0.389 for GBLUP and from 0.085 to 0.483 for ssGBLUP. In conclusion, the studied sperm morphology abnormalities showed moderate to low heritability. Both GBLUP and ssGBLUP showed comparative predictive abilities of breeding values, and ssGBLUP outperformed GBLUP under many circumstances in respect to predictive ability. To our knowledge, this is the first time that the genetic parameters and genomic predictive ability of these traits were reported in such a large Duroc boar population.

scholarly journals Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

2021 ◽  
Vol 11 ◽  
Author(s):  
Diego Jarquin ◽  
Natalia de Leon ◽  
Cinta Romay ◽  
Martin Bohn ◽  
Edward S. Buckler ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Combining Ability ◽  
Prediction Models ◽  
Predictive Ability ◽  
Weather Data ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Environment Interaction ◽  
Environmental Covariates ◽  
Genotype By Environment

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

scholarly journals Genomic prediction of arsenic tolerance and grain yield in rice. Contribution of trait-specific markers and multi environment models

2020 ◽  
Author(s):  
Nourollah Ahmadi ◽  
Tuong-Vi Cao ◽  
Julien Frouin ◽  
Gareth J. Norton ◽  
Adam H. Price
Keyword(s):  
Grain Yield ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Computing Time ◽  
Predictive Ability ◽  
Arsenic Content ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Base Line ◽  
Rice Varieties

AbstractMany rice-growing areas are affected by high concentrations of arsenic (As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability (PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic (1600 K SNP) and phenotypic (grain arsenic content, grain yield and days to flowering, observed under two irrigation systems over two years) data of the Bengal and Assam Aus Panel (BAAP). Under the base-line single environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As respectively, the three prediction methods (BL, GBLUP and RKHS) considered performed similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17 K, without negative effect on PA of genomic predictions. Single environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for G × E interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest PA (0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the BAAP. It also applies to breeding for other complex traits evaluated under multiple environments.

scholarly journals On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Theo Meuwissen ◽  
Irene van den Berg ◽  
Mike Goddard
Keyword(s):  
Variable Selection ◽  
Genome Sequence ◽  
Genomic Prediction ◽  
Milk Fat ◽  
Genotype Imputation ◽  
Whole Genome Sequence ◽  
Genomic Relationship Matrix ◽  
Polygenic Effect ◽  
Relationship Matrix ◽  
Whole Genome

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

scholarly journals Identification of pigments in artworks by inverse tangent derivative of spectrum and a new filtering method

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
F. Fazlali ◽  
S. Gorji Kandi
Keyword(s):  
Reflectance Spectrum ◽  
Practical Method ◽  
Chemical Methods ◽  
Munk Function ◽  
Absorption Of Light ◽  
Spectrophotometric Data ◽  
First Time ◽  
Non Destructive ◽  
Derivatives Of

Abstract Employing an economical and non-destructive method for identifying pigments utilized in artworks is a significant aspect for preserving their antiquity value. One of the non-destructive methods for this purpose is spectrophotometry, which is based on the selected absorption of light. Mathematical descriptive methods such as derivatives of the reflectance spectrum, the Kubelka–Munk function and logarithm have been employed for the characterization of the peak features corresponding to the spectrophotometric data. In the present study, the mentioned mathematical descriptive methods were investigated with the aim to characterize the constituents of an Iranian artwork but were not efficient for the samples. Therefore, inverse tangent derivative equation was developed on spectral data for the first time, providing considerable details in the profile of reflectance curves. In the next part, to have a simpler and more practical method it was suggested to use filters made up of pure pigments. By using these filters and placing them on the samples, imaging was done. Then, images of samples with and without filter were evaluated and pure pigments were distinguished. The mentioned methods were also used to identify pigments in a modern Iranian painting specimen. The results confirmed these methods with reliable answers indicating that physical methods (alongside chemical methods) can also be effective in determining the types of pigments.

scholarly journals High Genetic Differentiation among European White Oak Species (Quercus spp.) at a Dehydrin Gene

2015 ◽  
Vol 43 (2) ◽  
pp. 582-588 ◽  
Author(s):  
Iacob CRĂCIUNESC ◽  
Barbara VORNAM ◽  
Ludger LEINEMANN ◽  
Reiner FINKELDEY ◽  
Neculae ȘOFLETEA ◽  
...  
Keyword(s):  
Genetic Differentiation ◽  
Related Species ◽  
Population Level ◽  
Quercus Petraea ◽  
White Oak ◽  
Closely Related Species ◽  
Significant Differentiation ◽  
Dehydrin Gene ◽  
First Time ◽  
Two Populations

Dehydryn genes are involved in plant response to environmental stress and may be useful to examine functional diversity in relation to adaptive variation. Recently, a dehydrin gene (DHN3) was isolated in Quercus petraea and showed little differentiation between populations of the same species in an altitudinal transect. In the present study, inter- and intraspecific differentiation patterns in closely related and interfertile oaks were investigated for the first time at the DHN3 locus. A four-oak-species stand (Quercus frainetto Ten., Q. petraea (Matt.) Liebl., Q. pubescens Willd., Q. robur L.) and two populations for each of five white oak species (Q. frainetto Ten., Q. petraea (Matt.) Liebl., Q. pubescens Willd., Q. robur L. and Q. pedunculiflora K. Koch) were analyzed. Three alleles shared by all five oak species were observed. However, only two alleles were present in each population, but with different frequencies according to the species. At population level, all interspecific pairs of populations showed significant differentiation, except for pure Q. robur and Q. pedunculiflora populations. In contrast, no significant differentiation (p > 0.05) was found among conspecific populations. The DHN3 locus proved to be very useful to differentiate Q. frainetto and Q. pubescens from Q. pedunculiflora (FST = 0.914 and 0.660, respectively) and Q. robur (FST = 0.858 and 0.633, respectively). As expected, the lowest level of differentiation was detected between the most closely related species, Q. robur and Q. pedunculiflora (FST = 0.020). Our results suggest that DHN3 can be an important genetic marker for differentiating among European white oak species.

scholarly journals First records of Brachythemis impartita in peninsular Italy (Odonata: Libellulidae)

2017 ◽  
pp. 133-136
Author(s):  
Emanuele Guido Condello ◽  
Edoardo Razzetti ◽  
Cristiano Liuzzi ◽  
Vittoria D’Agostino ◽  
Fabio Mastropasqua
Keyword(s):  
Habitat Preferences ◽  
Artificial Lake ◽  
Northward Expansion ◽  
First Time ◽  
Two Populations

Two populations of Brachythemis impartita (Karsch, 1890) are here reported in peninsular Italy. The species was found for the first time in 2015 in Calabria in the area of the Angitola artificial lake (Maierato and Monterosso Calabro municipalities) not far from the Tyrrhenian coast. In 2016 the species was also observed in southern Apulia, along the banks of two artificial lagoons in the municipality of Ugento. Information are provided that confirm the habitat preferences of the species and a northward expansion.

scholarly journals A new Palaearctic species of the subgenus Lunatipula (Diptera, Tipulidae) from the West Caucasus with a survey of the caucasica species group

ZooKeys ◽  
2021 ◽  
Vol 1048 ◽  
pp. 145-175
Author(s):  
Vladimir I. Lantsov ◽  
Valentin E. Pilipenko
Keyword(s):  
New Species ◽  
Parallel Evolution ◽  
Distribution Patterns ◽  
Species Group ◽  
Palaearctic Species ◽  
The West ◽  
The Caucasus ◽  
Males And Females ◽  
First Time ◽  
Two Populations

The caucasica species group in the subgenus Lunatipula is redefined and now consists of five species native to the Caucasus. Tipula (L.) eleniyasp. nov. is described as new to science, and variations in the male terminalia in two populations are noted. Two subspecies (quadridentataquadridentata and quadridentatapaupera) are elevated to species rank. Detailed photo’s complement the descriptions of all five species (caucasica, eleniya, paupera, quadridentata, talyshensis), and data on ecology and distribution patterns are included as well as identification keys to males and females. Tipula caucasica is recorded from the West Caucasus and Tipula quadridentata is recorded from Dagestan (Russia) for the first time. Parallel evolution is traced in the male terminalia of the new species and in several non caucasica species group of Palaearctic Lunatipula.

scholarly journals Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

2021 ◽  
Author(s):  
Raysa Gevartosky ◽  
Humberto Fanelli Carvalho ◽  
Germano Costa-Neto ◽  
Osval A. Montesinos-Lopez ◽  
Jose Crossa ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Genomic Prediction ◽  
Predictive Ability ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Apply Genetic ◽  
Ear Height ◽  
Predictor Model ◽  
Training Sets

Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.

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