Genome-wide detection of genotype environment interactions for flowering time in Brassica napus
X Han, Q Tang, L Xu, Z Guan, J Tu, B Yi, K Liu… - Frontiers in Plant …, 2022 - frontiersin.org
Flowering time is strongly related to the environment, while the genotype-by-environment
interaction study for flowering time is lacking in Brassica napus. Here, a total of 11,700,689 …
interaction study for flowering time is lacking in Brassica napus. Here, a total of 11,700,689 …
Combining GWAS and comparative genomics to fine map candidate genes for days to flowering in mung bean
KO Chiteri, A Rairdin, K Sandhu, S Redsun, A Farmer… - BMC genomics, 2024 - Springer
Abstract Background Mung bean (Vigna radiata (L.) Wilczek), is an important pulse crop in
the global south. Early flowering and maturation are advantageous traits for adaptation to …
the global south. Early flowering and maturation are advantageous traits for adaptation to …
Application of genomic selection at the early stage of breeding pipeline in tropical maize
In maize, doubled haploid (DH) line production capacity of large-sized maize breeding
programs often exceeds the capacity to phenotypically evaluate the complete set of testcross …
programs often exceeds the capacity to phenotypically evaluate the complete set of testcross …
Artificial neural networks in the prediction of genetic merit to flowering traits in bean cultivars
RDS Rosado, CD Cruz, LD Barili… - Agriculture, 2020 - mdpi.com
Flowering is an important agronomic trait that presents non-additive gene action. Genome-
enabled prediction allow incorporating molecular information into the prediction of individual …
enabled prediction allow incorporating molecular information into the prediction of individual …
Boosting predictive ability of tropical maize hybrids via genotype‐by‐environment interaction under multivariate GBLUP models
M Dalsente Krause, KOG Dias… - Crop …, 2020 - Wiley Online Library
Genomic selection has been implemented in several plant and animal breeding programs
and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of …
and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of …
Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks
CC Westhues, GS Mahone, S da Silva… - Frontiers in plant …, 2021 - frontiersin.org
The development of crop varieties with stable performance in future environmental
conditions represents a critical challenge in the context of climate change. Environmental …
conditions represents a critical challenge in the context of climate change. Environmental …
Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability
P Sarzaeim, F Muñoz-Arriola… - Journal of experimental …, 2022 - academic.oup.com
Despite efforts to collect genomics and phenomics ('omics') and environmental data,
spatiotemporal availability and access to digital resources still limit our ability to predict …
spatiotemporal availability and access to digital resources still limit our ability to predict …
Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials
Adaptation constraints within crop species have resulted in limited genetic diversity in some
breeding programs and areas where new crops have been introduced, for example, for lentil …
breeding programs and areas where new crops have been introduced, for example, for lentil …
Quantile regression for genome-wide association study of flowering time-related traits in common bean
Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit
models for all portions of a probability distribution. In Genome-wide association studies …
models for all portions of a probability distribution. In Genome-wide association studies …
Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids
F Bonnafous, G Fievet, N Blanchet… - Theoretical and applied …, 2018 - Springer
Key message This study compares five models of GWAS, to show the added value of non-
additive modeling of allelic effects to identify genomic regions controlling flowering time of …
additive modeling of allelic effects to identify genomic regions controlling flowering time of …