Salinity stress tolerance prediction for biomass‐related traits in maize (Zea mays L.) using genome‐wide markers

The Plant Genome, Journal Year: 2023, Volume and Issue: 16(4)

Published: Sept. 4, 2023

Maize (Zea mays L.) is the third most important cereal crop after rice (Oryza sativa) and wheat (Triticum aestivum). Salinity stress significantly affects vegetative biomass grain yield and, therefore, reduces food silage productivity of maize. Selecting salt-tolerant genotypes a cumbersome time-consuming process that requires meticulous phenotyping. To predict salt tolerance in maize, we estimated breeding values for four biomass-related traits, including shoot length, weight, root weight under salt-stressed controlled conditions. A five-fold cross-validation method was used to select best model among genomic linear unbiased prediction (GBLUP), ridge-regression BLUP (rrBLUP), extended GBLUP, Bayesian Lasso, ridge regression, BayesA, BayesB, BayesC. Examination effect different marker densities on accuracy revealed set low-density single nucleotide polymorphisms obtained through filtering based combination analysis variance linkage disequilibrium provided all traits. The average cross-validations ranged from 0.46 0.77 across derived rrBLUP, models except BayesB demonstrated comparable levels were superior other modeling approaches. These findings provide roadmap deployment optimization selection

Language: Английский

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Genomic selection for survival under naturally occurring Saprolegnia oomycete infection in farmed European whitefish Coregonus lavaretus

Journal of Animal Science, Journal Year: 2023, Volume and Issue: 101

Published: Jan. 1, 2023

Saprolegnia oomycete infection causes serious economic losses and reduces fish health in aquaculture. Genomic selection based on thousands of DNA markers is a powerful tool to improve traits selective breeding programs. Our goal was develop single nucleotide polymorphism (SNP) marker panel test its use genomic for improved survival against European whitefish Coregonus lavaretus, the second most important farmed species Finland. We used double digest restriction site associated (ddRAD) genotyping by sequencing method produce SNP panel, we tested it analyzing data from cohort 1,335 fish, which were measured at different times mortality weight traits. calculated genetic relationship matrix (GRM) genome-wide data, integrating multivariate mixed models estimation variance components values (GEBVs), carry out Genome-Wide Association Studies presence quantitative trait loci (QTL) affecting phenotypes analysis. identified one major QTL chromosome 6 infection, explaining 7.7% 51.3% variance, 4, 1.8% 5.4% variance. Heritability 0.20 0.43 liability scale, heritability 0.44 0.53. The showed an additive allelic effect. whether as fixed factor, together with new GRM excluding would accuracy GEBVs. This done through cross-validation approach, indicated that inclusion increased mean GEBVs 0.28 points, 0.33 0.61, relative full only. area under curve receiver-operator 0.58 0.67 when included model. effect model correlation between early late mortality, compared did not include QTL. These results validate usability produced highlight opportunity modeling QTLs evaluation due infection.Saprolegnia created novel set reduce fungus. Using markers, estimated how much are determined variation, thus what potential have be selected. observed resistance controlled both variant many other variants small distributed across genome. could increase precision explicitly adding analysis, our results. conclude directly including information about increases predictions, rather than assuming all each explain amount variation.

Language: Английский

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Assessing accuracy of genomic breeding values of selection candidates under biosecurity restrictions by progeny testing in Chinese shrimp Fenneropenaeus chinensis

Aquaculture, Journal Year: 2022, Volume and Issue: 566, P. 739181 - 739181

Published: Dec. 21, 2022

Language: Английский

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Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 12, 2024

Abstract Complementing phenotypic traits and molecular markers with high-dimensional data such as climate soil information is becoming a common practice in breeding programs. This study explored new ways to integrate non-genetic genomic prediction models using machine learning (ML). Using the multi-environment trial from Genomes To Fields initiative, different predict maize grain yield were adjusted various inputs: genetic, environmental, or combination of both, either an additive (genetic-and-environmental; G+E) multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, mean predictive ability increased 7-9% over well-established Factor Analytic Multiplicative Mixed Model (FA) among three cross-validation scenarios evaluated. Moreover, G+E model was more advantageous than GEI given superior, at least comparable, ability, lower usage computational memory time, flexibility accounting for interactions by construction. Our results illustrate provided ML framework, particularly feature engineering. We show that featured engineering stage offers viable option envirotyping generates valuable learning-based models. Furthermore, we verified genotype-by-environment may be considered tree-based approaches without explicitly model. These findings support growing interest merging genotypic into modeling. Key message Incorporating feature-engineered efficient approach interactions.

Language: Английский

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Development and Validation of a 40 K Liquid Snp Array for the Mud Crab (Scylla Paramamosain)

Published: Jan. 1, 2024

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Language: Английский

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