03992naa a2200265 a 450000100080000000500110000800800410001902200140006002400290007410000160010324500650011926000090018450003900019352028240058365300230340765300220343065300340345265300150348665300370350170000160353870000170355470000110357170000180358277301260360010622062021-06-30       2021    bl uuuu     u00u1 u    #d  a1525-31637 a10.1093/jas/skab0922DOI1 aMISZTAL, I.  aEmerging issues in genomic selection.h[electronic resource]  c2021  aArticle history: Received 23 January 2021; Accepted 26 March 2021; Advance Access publication March 27, 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.  aABSTRACT. - Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.  © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science.  aGenomic evaluation  aGenomic selection  aGenomwide association studies  aLarge data  aStability of genomic predictions1 aAGUILAR, I.1 aLOURENCO, D.1 aMA, L.1 aSTEIBEL, J.P.  tJournal of Animal Science, June 2021, Volume 99, Issue 61, skab092. OPEN ACCESS. Doi: https://doi.org/10.1093/jas/skab092