03249naa a2200469 a 450000100080000000500110000800800410001902200150006002400310007510000130010624501310011926000090025050000670025952018010032665000100212765000220213765300100215965300220216965300340219165300290222565300120225465300390226665300370230565300390234265300080238165300290238965300250241865300260244365300350246965300230250465300100252770000140253770000190255170000180257070000150258870000160260370000170261970000160263670000150265270000220266777300900268910505862019-11-06       2013    bl uuuu     u00u1 u    #d  a2160-1836.7 a10.1534/g3.113.0078072DOI1 aLADO, B.  aIncreased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.h[electronic resource]  c2013  aArticle history: Received 2013 Aug 26 // Accepted 2013 Sep 18.  aAbstract: In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.  aTRIGO  aTRITICUM AESTIVUM  aGBLUP  aGENOMIC SELECTION  aGENOTIPADO POR SECUENCIACIÓN  aGENOTYPING BY SEQUENCING  aGENPRED  aLOCUS DE UN CARÁCTER CUANTITATIVO  aMEJOR PREDICTOR LINEAR INSESGADO  aPOLIMORFISMO DE NUCLEÓTICO SIMPLE  aQTL  aQUANTITATIVE TRAIT LOCUS  aSELECCIÓN GENÓMICA  aSHARED DATA RESOURCES  aSINGLE NUCLEOTIDE POLYMORPHISM  aSPATIAL CORRECTION  aWHEAT1 aMATUS, I.1 aRODRIGUEZ,  A.1 aINOSTROZA, L.1 aPOLAND, J.1 aBELZILE ,F.1 aDEL POZO, A.1 aQUINCKE, M.1 aCASTRO, M.1 aVON ZITZEWITZ, J.  tG3: Genes, Genomes, Genetics (Bethesda)gv. 3, n,12,  p. 2105-2114, 2013.OPEN ACCESS.