Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction
The first paradigm of plant breeding involves direct selection-based phenotypic observation,
followed by predictive breeding using statistical models for quantitative traits constructed …
followed by predictive breeding using statistical models for quantitative traits constructed …
Genomic prediction of (mal) adaptation across current and future climatic landscapes
Signals of local adaptation have been found in many plants and animals, highlighting the
heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In …
heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In …
Computationally efficient whole-genome regression for quantitative and binary traits
Genome-wide association analysis of cohorts with thousands of phenotypes is
computationally expensive, particularly when accounting for sample relatedness or …
computationally expensive, particularly when accounting for sample relatedness or …
Genomic selection in plant breeding: methods, models, and perspectives
Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates
the breeding cycle. In this review, we discuss the history, principles, and basis of GS and …
the breeding cycle. In this review, we discuss the history, principles, and basis of GS and …
Impacts of salinity stress on crop plants: Improving salt tolerance through genetic and molecular dissection
Improper use of water resources in irrigation that contain a significant amount of salts, faulty
agronomic practices such as improper fertilization, climate change etc. are gradually …
agronomic practices such as improper fertilization, climate change etc. are gradually …
Efficient Bayesian mixed-model analysis increases association power in large cohorts
Linear mixed models are a powerful statistical tool for identifying genetic associations and
avoiding confounding. However, existing methods are computationally intractable in large …
avoiding confounding. However, existing methods are computationally intractable in large …
Genome-wide regression and prediction with the BGLR statistical package
P Pérez, G de Los Campos - Genetics, 2014 - academic.oup.com
Many modern genomic data analyses require implementing regressions where the number
of parameters (p, eg, the number of marker effects) exceeds sample size (n). Implementing …
of parameters (p, eg, the number of marker effects) exceeds sample size (n). Implementing …
Genomic selection: A tool for accelerating the efficiency of molecular breeding for development of climate-resilient crops
Since the inception of the theory and conceptual framework of genomic selection (GS),
extensive research has been done on evaluating its efficiency for utilization in crop …
extensive research has been done on evaluating its efficiency for utilization in crop …
Signatures of negative selection in the genetic architecture of human complex traits
We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide
polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero …
polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero …
Genomic selection: genome-wide prediction in plant improvement
Association analysis is used to measure relations between markers and quantitative trait loci
(QTL). Their estimation ignores genes with small effects that trigger underpinning …
(QTL). Their estimation ignores genes with small effects that trigger underpinning …