Best linear unbiased prediction
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Robustness of linear mixed‐effects models to violations of distributional assumptions Open
Linear mixed‐effects models are powerful tools for analysing complex datasets with repeated or clustered observations, a common data structure in ecology and evolution. Mixed‐effects models involve complex fitting procedures and make sever…
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Software Selegen-REML/BLUP: a useful tool for plant breeding Open
The software Selegen-REML/BLUP uses mixed models, and was developed to optimize the routine of plant breeding programs. It addresses the following plants categories: allogamous, automagous, of mixed mating system, and of clonal propagation…
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GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction Open
Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critica…
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Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method Open
Analytical properties of cross-validation measures are presented. We present a new method named LR for cross-validation that is automatic, easy to use, and which yields the quantities of interest. The method compares predictions based on p…
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Genomic selection for tolerance to heat stress in Australian dairy cattle Open
Temperature and humidity levels above a certain threshold decrease milk production in dairy cattle, and genetic variation is associated with the amount of lost production. To enable selection for improved heat tolerance, the aim of this st…
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Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs Open
The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that w…
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Genomic Selection for Processing and End‐Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program Open
Wheat ( Triticum aestivum L.) cultivars must possess suitable end‐use quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount o…
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Multitrait index based on factor analysis and ideotype‐design: proposal and application on elephant grass breeding for bioenergy Open
This study proposes a new multitrait index based on factor analysis and ideotype‐design ( FAI ‐ BLUP index), and validates its potential on the selection of elephant grass genotypes for energy cogeneration. Factor analysis was carried out,…
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Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture Open
Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the con…
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Purging putative siblings from population genetic data sets: a cautionary view Open
Interest has surged recently in removing siblings from population genetic data sets before conducting downstream analyses. However, even if the pedigree is inferred correctly, this has the potential to do more harm than good. We used compu…
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Hybrid breeding of rice via genomic selection Open
Summary Hybrid breeding is the main strategy for improving productivity in many crops, especially in rice and maize. Genomic hybrid breeding is a technology that uses whole‐genome markers to predict future hybrids. Predicted superior hybri…
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Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations Open
Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increase…
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Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle Open
The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals…
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Bayesian Genomic Prediction with Genotype <b>×</b> Environment Interaction Kernel Models Open
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and…
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Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS Open
Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation …
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Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials Open
Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational pr…
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Multilevel Analysis with Few Clusters: Improving Likelihood-Based Methods to Provide Unbiased Estimates and Accurate Inference Open
Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggest…
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Deep Learning for Predicting Complex Traits in Spring Wheat Breeding Program Open
Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines repre…
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Evaluation of Genome-Enabled Selection for Bacterial Cold Water Disease Resistance Using Progeny Performance Data in Rainbow Trout: Insights on Genotyping Methods and Genomic Prediction Models Open
Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation…
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Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline Open
Prediction of single-cross performance has been a major goal of plant breeders since the beginning of hybrid breeding. Recently, genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy o…
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Evaluation of RR‐BLUP Genomic Selection Models that Incorporate Peak Genome‐Wide Association Study Signals in Maize and Sorghum Open
Certain agronomic crop traits are complex and thus governed by many small‐effect loci. Statistical models typically used in a genome‐wide association study (GWAS) and genomic selection (GS) quantify these signals by assessing genomic marke…
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Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load Open
Inbreeding depression, the deterioration in mean trait value in progeny of related parents, is a fundamental quantity in genetics, evolutionary biology, animal and plant breeding, and conservation biology. The magnitude of inbreeding depre…
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Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90 Open
Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis…
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Genomic Prediction Accuracy for Resistance Against <i>Piscirickettsia salmonis</i> in Farmed Rainbow Trout Open
Salmonid rickettsial syndrome (SRS), caused by the intracellular bacterium Piscirickettsia salmonis, is one of the main diseases affecting rainbow trout (Oncorhynchus mykiss) farming. To accelerate genetic progress, genomic selection metho…
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A 100-Year Review: Methods and impact of genetic selection in dairy cattle—From daughter–dam comparisons to deep learning algorithms Open
In the early 1900s, breed society herdbooks had been established and milk-recording programs were in their infancy. Farmers wanted to improve the productivity of their cattle, but the foundations of population genetics, quantitative geneti…
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HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data Open
Human diseases and agricultural traits can be predicted by modeling a genetic random polygenic effect in linear mixed models. To estimate variance components and predict random effects of the model efficiently with limited computational re…
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A Stacking Ensemble Learning Framework for Genomic Prediction Open
Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the geno…
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An experimental validation of genomic selection in octoploid strawberry Open
The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the pote…
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Genome-Wide Association Study and Cost-Efficient Genomic Predictions for Growth and Fillet Yield in Nile Tilapia ( <i>Oreochromis niloticus</i> ) Open
Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits with…
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Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.) Open
Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction i…