Genomic selection (GS) is a method to estimate the breeding values of individuals by using markers throughout the genome. We evaluated the accuracies of GS using data from fi ve traits on 446 oat (Avena sativa L.) lines genotyped with 1005 Diversity Array Technology (DArT) markers and two GS methods (ridge regression-best linear unbiased prediction [RR-BLUP] and BayesCπ) under various training designs. Our objectives were to (i) determine accuracy under increasing marker density and training population size, (ii) assess accuracies when data is divided over time, and (iii) examine accuracy in the presence of population structure. Accuracy increased as the number of markers and training size become larger. Including older lines in the training population increased or maintained accuracy, indicating that older generations retained information useful for predicting validation populations. The presence of population structure affected accuracy: when training and validation subpopulations were closely related accuracy was greater than when they were distantly related, implying that linkage disequilibrium (LD) relationships changed across subpopulations. Across many scenarios involving large training populations, the accuracy of BayesCπ and RR-BLUP did not differ. This empirical study provided evidence regarding the application of GS to hasten the delivery of cultivars through the use of inexpensive and abundant molecular markers available to the public sector.T HE DECREASING COST of high-density molecular markers allows saturation of crop genomes with genetic markers and off ers an approach to predict genetic merit. Th ese markers can help capture the eff ects of many quantitative trait loci (QTL) controlling polygenic traits regardless of location of the QTL in the genome by using linkage disequilibrium (LD), the nonrandom association of alleles at diff erent loci (Falconer and Mackay, 1996). Meuwissen et al. (2001) proposed genomic selection (GS) based on prediction of the genetic value of individuals or the genomic estimated breeding values (GEBV) from high-density markers positioned throughout the genome. Because GS includes all markers, major and polygenic eff ects can be captured, potentially explaining more genetic variance (Solberg et al., 2008). Th erefore, the objective of GS is to predict the breeding value of each individual instead of identifying QTL for use in a traditional marker-assisted selection (MAS) program.Selection methods can be evaluated by measuring accuracy, a major component of the response to selection equation, R = irσ A , in which R is the response, i is the selection intensity, r is the accuracy, and σ A is the additive genetic standard deviation (Falconer and Mackay, 1996). As a general term in statistics, accuracy is the degree of similarity

β‐glucan, a soluble fiber found in oat (Avena sativa L.) grain, is good for human health, and selection for higher levels of this compound is regarded as an important breeding objective. Recent advances in oat DNA markers present an opportunity to investigate new selection methods for polygenic traits such as β‐glucan concentration. Our objectives in this study were to compare genomic, marker‐assisted, and best linear unbiased prediction (BLUP)–based phenotypic selection for short‐term response to selection and ability to maintain genetic variance for β‐glucan concentration. Starting with a collection of 446 elite oat lines from North America, each method was conducted for two cycles. The average β‐glucan concentration increased from 4.57 g/100 g in Cycle 0 to between 6.66 and 6.88 g/100 g over the two cycles. The averages of marker‐based selection methods in Cycle 2 were greater than those of phenotypic selection (P < 0.08). Progenies with the highest β‐glucan came from the marker‐based selection methods. Marker‐assisted selection (MAS) for higher β‐glucan concentration resulted in a later heading date. We also found that marker‐based selection methods maintained greater genetic variance than did BLUP phenotypic selection, potentially enabling greater future selection gains. Overall, the results of these experiments suggest that genomic selection is a superior method for selecting a polygenic complex trait like β‐glucan concentration.

Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F2, backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting β-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2 years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with β-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence homology to genes localized on rice chromosome seven adjacent to the CslFgene family, known to have β-glucan synthase function. Results indicate that GWAS in oat can be a successful option for QTL detection, more so with future development of higher-density markers. Abstract Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F 2 , backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting b-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2 years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with b-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence hom...

Genome‐wide association studies (GWAS) can be a useful approach to detect quantitative trait loci (QTL) controlling complex traits in crop plants. Oat (Avena sativa L.) β‐glucan is a soluble dietary fiber and has been shown to have positive health benefits. We report a GWAS involving 446 elite oat breeding lines from North America genotyped with 1005 diversity arrays technology (DArT) markers and with phenotypic data from both historical and balanced 2‐yr data. Association analyses accounting for pair‐wise relationships and population structure were conducted using single‐marker tests and least absolute shrinkage and selection operator (LASSO). Single‐marker tests yielded six and 15 significant markers for the historical and balanced data sets, respectively. The LASSO method selected 24 and 37 markers as the most important in explaining β‐glucan concentration for the historical and balanced data sets, respectively. Comparisons of genetic location showed that 15 of the markers in our study were found on the same linkage groups as QTL identified in previous studies. Four of the markers colocalized to within 4 cM of three previously detected QTL, suggesting concordance between QTL detected in our study and previous studies. Two of the significant markers were also adjacent to a β‐glucan candidate gene in the rice (Oryza sativa L.) genome. Our findings suggest that GWAS can be used for QTL detection for the purpose of gene discovery and for marker‐assisted selection to improve β‐glucan concentration in elite oat.

Cereal Chem. 91(2):183-188Oats (Avena sativa L.) have received significant attention for their positive and consistent health benefits when consumed as a whole grain food, attributed in part to mixed-linkage (1-3,1-4)-β-D-glucan (referred to as β-glucan). Unfortunately, the standard enzymatic method of measurement for oat β-glucan is costly and does not provide the high-throughput capability needed for plant breeding in which thousands of samples are measured over a short period of time. The objective of this research was to test a microenzymatic approach for high-throughput phenotyping of oat β-glucan. Fifty North American elite lines were chosen to span the range of possible values encountered in elite oats. Pearson and Spearman correlations (r) ranged from 0.81 to 0.86 between the two methods. Although the microenzymatic method did contain bias compared with the results for the standard streamlined method, this bias did not substantially decrease its ability to determine β-glucan content. In addition to a substantial decrease in cost, the microenzymatic approach took as little as 6% of the time compared with the streamlined method. Therefore, the microenzymatic method for β-glucan evaluation is an alternative method that can enhance high-throughput phenotyping in oat breeding programs.