Machine learning algorithms translate big data into predictive breeding accuracy
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and
environmental data. ML algorithms automatically identify relevant features and use cross …
environmental data. ML algorithms automatically identify relevant features and use cross …
Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize
Quantitative genetics states that phenotypic variation is a consequence of the interaction
between genetic and environmental factors. Predictive breeding is based on this statement …
between genetic and environmental factors. Predictive breeding is based on this statement …
Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction
C Carré, JB Carluer, C Chaux, C Estoup-Streiff… - Genome Biology, 2024 - Springer
The problem of missing heritability requires the consideration of genetic interactions among
different loci, called epistasis. Current GWAS statistical models require years to assess the …
different loci, called epistasis. Current GWAS statistical models require years to assess the …
Genomic prediction of synthetic hexaploid wheat upon tetraploid durum and diploid Aegilops parental pools
S Dreisigacker, JWR Martini, J Cuevas… - The Plant …, 2024 - Wiley Online Library
Bread wheat (Triticum aestivum L.) is a globally important food crop, which was
domesticated about 8–10,000 years ago. Bread wheat is an allopolyploid, and it evolved …
domesticated about 8–10,000 years ago. Bread wheat is an allopolyploid, and it evolved …
Megavariate Methods Capture Complex Genotype-by-Environment Interactions
Genomic prediction models that capture genotype-by-environment interaction are useful for
predicting site-specific performance by leveraging information among related individuals …
predicting site-specific performance by leveraging information among related individuals …
Estimation of complex-trait prediction accuracy from the different holo-omics interaction models
QR Qadri, Q Zhao, X Lai, Z Zhang, W Zhao, Y Pan… - Genes, 2022 - mdpi.com
Statistical models play a significant role in designing competent breeding programs related
to complex traits. Recently; the holo-omics framework has been productively utilized in trait …
to complex traits. Recently; the holo-omics framework has been productively utilized in trait …
Professor Heinz Neudecker and matrix differential calculus
Abstract The late Professor Heinz Neudecker (1933–2017) made significant contributions to
the development of matrix differential calculus and its applications to econometrics …
the development of matrix differential calculus and its applications to econometrics …
A simulation-based assessment of the efficiency of QTL mapping under environment and genotype x environment interaction effects
The objective of this simulation-based study was to assess how genes, environments, and
genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping …
genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping …
Linear mixed models
OA Montesinos López, A Montesinos López… - … learning methods for …, 2022 - Springer
The linear mixed model framework is explained in detail in this chapter. We explore three
methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and …
methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and …
Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping
Increasing the number of environments for phenotyping of crop lines in earlier stages of
breeding programs can improve selection accuracy. However, this is often not feasible due …
breeding programs can improve selection accuracy. However, this is often not feasible due …