Arabidopsis thaliana is the most widely-studied plant today. The concerted efforts of over 11 000 researchers and 4000 organizations around the world are generating a rich diversity and quantity of information and materials. This information is made available through a comprehensive on-line resource called the Arabidopsis Information Resource (TAIR) (http://arabidopsis.org), which is accessible via commonly used web browsers and can be searched and downloaded in a number of ways. In the last two years, efforts have been focused on increasing data content and diversity, functionally annotating genes and gene products with controlled vocabularies, and improving data retrieval, analysis and visualization tools. New information include sequence polymorphisms including alleles, germplasms and phenotypes, Gene Ontology annotations, gene families, protein information, metabolic pathways, gene expression data from microarray experiments and seed and DNA stocks. New data visualization and analysis tools include SeqViewer, which interactively displays the genome from the whole chromosome down to 10 kb of nucleotide sequence and AraCyc, a metabolic pathway database and map tool that allows overlaying expression data onto the pathway diagrams. Finally, we have recently incorporated seed and DNA stock information from the Arabidopsis Biological Resource Center (ABRC) and implemented a shopping-cart style on-line ordering system.

Arabidopsis thaliana, a small annual plant belonging to the mustard family, is the subject of study by an estimated 7000 researchers around the world. In addition to the large body of genetic, physiological and biochemical data gathered for this plant, it will be the first higher plant genome to be completely sequenced, with completion expected at the end of the year 2000. The sequencing effort has been coordinated by an international collaboration, the Arabidopsis Genome Initiative (AGI). The rationale for intensive investigation of Arabidopsis is that it is an excellent model for higher plants. In order to maximize use of the knowledge gained about this plant, there is a need for a comprehensive database and information retrieval and analysis system that will provide user-friendly access to Arabidopsis information. This paper describes the initial steps we have taken toward realizing these goals in a project called The Arabidopsis Information Resource (TAIR) (www.arabidopsis.org).

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

Plant breeders have an interest in the identification of genomic regions associated with the expression of quantitative traits because they recognize that such information could be used to improve realized heritability and reduce time per cycle of selection. The development of molecular marker technologies may provide tools to accomplish these goals. Maize (Zea mays L.) breeders develop new inbred parents of hybrids through top‐crossed and per se evaluation of numerous quantitative traits in segregating progeny from planned breeding crosses. This study was conducted to determine if regions of the genome associated with variability of agronomicaily important traits could be identified by a small, but typical, sample of top‐crossed and F2:4 progeny from the cross B73×MO17. We identified genomic regions in 24 agronomic traits, using interval mapping and simultaneous estimation of multiple quantitative trait locus models. The estimated numbers of quantitative trait loci (QTL) identified per trait were about three five. Many of the identified QTL were the same for correlated traits. Interestingly, yield QTL were not in the same regions previously reported for B73×MO17. This comparison contains a number of potential confounding factors: source of parental inbreds, type of progeny, different genotype × environment interaction effects, and different small samples of progeny from the cross. Consideration of these factors and the power of the tests to identify QTL suggests that the sampling of progeny is the most likely explanation for the differences.

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Microsatellites are important tools for plant breeding, genetics, and evolution, but few studies have analyzed their mutation pattern in plants. In this study, we estimated the mutation rate for 142 microsatellite loci in maize (Zea mays subsp. mays) in two different experiments of mutation accumulation. The mutation rate per generation was estimated to be 7.7 x 10(-4) for microsatellites with dinucleotide repeat motifs, with a 95% confidence interval from 5.2 x 10(-4) to 1.1 x 10(-3). For microsatellites with repeat motifs of more than 2 bp in length, no mutations were detected; so we could only estimate the upper 95% confidence limit of 5.1 x 10(-5) for the mutation rate. For dinucleotide repeat microsatellites, we also determined that the variance of change in the number of repeats (sigma(m)2) is 3.2. We sequenced 55 of the 73 observed mutations, and all mutations proved to be changes in the number of repeats in the microsatellite or in mononucleotide tracts flanking the microsatellite. There is a higher probability to mutate to an allele of larger size. There is heterogeneity in the mutation rate among dinucleotide microsatellites and a positive correlation between the number of repeats in the progenitor allele and the mutation rate. The microsatellite-based estimate of the effective population size of maize is more than an order of magnitude less than previously reported values based on nucleotide sequence variation.

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE.

Resistance to the soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) is difficult to evaluate in soybean [Glycine max (L.) Merr.] breeding. PI 437.654 has resistance to more SCN race isolates than any other known soybean. We screened 298 F6∶7 recombinant-inbred lines from a cross between PI 437.654 and 'BSR101' for SCN race-3 resistance, genetically mapped 355 RFLP markers and the I locus, and tested these markers for association with resistance loci. The Rhg 4 resistance locus was within 1 cM of the I locus on linkage group A. Two additional QTLs associated with SCN resistance were located within 3cM of markers on groups G and M. These two loci were not independent because 91 of 96 lines that had a resistant-parent marker type on group G also had a resistant-parent marker type on group M. Rhg 4 and the QTL on G showed a significant interaction by together providing complete resistance to SCN race-3. Individually, the QTL on G had greater effect on resistance than did Rhg 4, but neither locus alone provided a degree of resistance much different from the susceptible parent. The nearest markers to the mapped QTLs on groups A and G had allele frequencies from the resistant parent indicating 52 resistant lines in this population, a number not significantly different from the 55 resistant lines found. Therefore, no QTLs from PI 437.654 other than those mapped here are expected to be required for resistance to SCN race-3. All 50 lines that had the PI 437.654 marker type at the nearest marker to each of the QTLs on groups A and G were resistant to SCN race-3. We believe markers near to these QTLs can be used effectively to select for SCN race-3 resistance, thereby improving the ability to breed SCN-resistant soybean varieties.