ReviewFrontiers in genetics2018
Non-additive Effects in Genomic Selection.
Review in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
118 citing papers in PubMed.
- Genomic prediction of agronomic traits in a switchgrass (Panicum virgatum L.) half-sib progeny panel evaluated across multiple environments.The plant genome · 2026Article
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Generation-Specific Heterosis in Lactation, Reproduction, and Blood Transcriptomic Profiles of Chinese Simmental × Holstein Crossbred Cows.Animals : an open access journal from MDPI · 2026Article
- Gsformer: a dual-architecture deep learning framework with CNN-self-attention and sparse-attention for genomic selection.Genetics, selection, evolution : GSE · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- On the genetic origins of phenotypes in genome-wide association studies: the SAFE-hBMC bioinformatics · 2026Article
- Feed-forward neural networks for genomic selection: insights into varying levels of dominance heritability.BMC genomics · 2026Article
- Nonlinear genomic selection index accelerates multi-trait crop improvement.Nature communications · 2026Article
- Evidence of a genomic basis for growth rate variation in a natural kelp population.Scientific reports · 2026Article
- Genomic prediction of feed efficiency in boars by deep learning.G3 (Bethesda, Md.) · 2026Article
- Derivation of prediction error variance for non-genotyped individuals in genomic selection.Frontiers in genetics · 2026Article
- Heritability of theFrontiers in plant science · 2026Article
- The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice.The plant genome · 2025Article
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 2025Article
- Evaluating effect of different dominance genotype encodings on genome-wide association studies and genomic selection.Animal bioscience · 2025Article
- Inheritance characteristics and potential of genomic prediction for pungency levels in FBreeding science · 2025Article
- Improvement in genetic evaluation of quantitative traits in sheep by enriching genetic model with dominance effects.Scientific reports · 2025Article
- Consequences of ignoring dominance genetic effects from genomic selection model for discrete threshold traits.Scientific reports · 2025Article
- Multifactor Analysis of a Genome-Wide Selection System inPlants (Basel, Switzerland) · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
58 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism) markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i) they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii) they allow the definition of mate allocation procedures between candidates for selection; and (iii) they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.