In genomewide selection, the expected correlation between predicted performance and true genotypic value is a function of the training population size (N), heritability on an entry-mean basis (h 2

Over the past two decades many quantitative trait loci (QTL) have been detected; however, very few have been incorporated into breeding programs. The recent development of genome-wide association studies (GWAS) in plants provides the opportunity to detect QTL in germplasm collections such as unstructured populations from breeding programs. The overall goal of the barley Coordinated Agricultural Project was to conduct GWAS with the intent to couple QTL detection and breeding. The basic idea is that breeding programs generate a vast amount of phenotypic data and combined with cheap genotyping it should be possible to use GWAS to detect QTL that would be immediately accessible and used by breeding programs. There are several constraints to using breeding program-derived phenotype data for conducting GWAS namely: limited population size and unbalanced data sets. We chose the highly heritable trait heading date to study these two variables. We examined 766 spring barley breeding lines (panel #1) grown in balanced trials and a subset of 384 spring barley breeding lines (panel #2) grown in balanced and unbalanced trials. In panel #1, we detected three major QTL for heading date that have been detected in previous bi-parental mapping studies. Simulation studies showed that population sizes greater than 384 individuals are required to consistently detect QTL. We also showed that unbalanced data sets from panel #2 can be used to detect the three major QTL. However, unbalanced data sets resulted in an increase in the false-positive rate. Interestingly, one-step analysis performed better than two-step analysis in reducing the false-positive rate. The results of this work show that it is possible to use phenotypic data from breeding programs to detect QTL, but that careful consideration of population size and experimental design are required.

Multienvironment trials (METs) enable the evaluation of the same genotypes under a variety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 33 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors (BLUPs) for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad‐sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

Semidwarf maize (Zea mays L.) could be grown in new areas of production or in alternative crop rotations. Our objectives were to determine (i) if genomewide selection is useful for the rapid improvement of an exotic × adapted cross, (ii) if genomewide selection is more effective than phenotypic backcrossing for a trait with major genes, and (iii) the extent to which the high grain yield of nondwarf maize can be combined with the reduced stature and adaptability to high plant population densities of semidwarf maize. We conducted four cycles of genomewide selection in two semidwarf × adapted crosses. Phenotypic backcrossing was also done until the BC4 with selection for short plants. The accuracy of genomewide predictions in Cycle 0 was 0.67 to 0.70 for plant height and 0.57 to 0.70 for grain yield. Genomewide selection from Cycle 1 until Cycle 5 either maintained or improved on the gains from phenotypic selection achieved in Cycle 1. Observed gains generally agreed with predicted gains. Cycle 5 did not always have the best mean performance. Unfavorable genetic correlations made it difficult to select for short plants with high grain yield. Linkage disequilibrium between markers declined as selection progressed. Compared with phenotypic backcrossing, genomewide selection led to better mean performance and a higher proportion of exotic germplasm introgressed. To our knowledge, this is the first empirical study on genomewide selection to improve an exotic × adapted cross.

While filling vases with water and observing volume and height relationships, students learn the fundamentals of functions.