02392naa a2200265 a 450000100080000000500110000800800410001902200140006002400310007410000170010524501300012226000090025250002800026152012740054165300280181565300230184365300220186665300290188870000160191770000160193370000150194970000160196470000160198077301300199610612362020-07-24       2020    bl uuuu     u00u1 u    #d  a2073-44257 a10.3390/genes110707902DOI1 aLOURENCO, D.  aSingle-step genomic evaluations from theory to practicebusing snp chips and sequence data in blupf90.h[electronic resource]  c2020  aArticle history: Received: 19 June 2020 / Revised: 3 July 2020 / Accepted: 6 July 2020 / Published: 14 July 2020. (This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data):  https://www.mdpi.com/journal/genes/special_issues/Genomic_Prediction  aABSTRACT. 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 processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data.  aGenome-wide association  aGenomic prediction  aGenomic selection  aSINGLE-STEP GENOMIC BLUP1 aLEGARRA, A.1 aTSURUTA, S.1 aMASUDA, Y.1 aAGUILAR, I.1 aMISZTAL, I.  tGenes, July 2020. Volume 11, Issue 7, Article number 790, Pages 1-32. Open Access. Doi: https://doi.org/10.3390/genes11070790