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A set of nested association mapping (NAM) families was developed by crossing 40 diverse soybean [ (L.) Merr.] genotypes to the common cultivar. The 41 parents were deeply sequenced for SNP discovery. Based on the polymorphism of the single-nucleotide polymorphisms (SNPs) and other selection criteria, a set of SNPs was selected to be included in the SoyNAM6K BeadChip for genotyping the parents and 5600 RILs from the 40 families. Analysis of the SNP profiles of the RILs showed a low average recombination rate. We constructed genetic linkage maps for each family and a composite linkage map based on recombinant inbred lines (RILs) across the families and identified and annotated 525,772 high confidence SNPs that were used to impute the SNP alleles in the RILs. The segregation distortion in most families significantly favored the alleles from the female parent, and there was no significant difference of residual heterozygosity in the euchromatic vs. heterochromatic regions. The genotypic datasets for the RILs and parents are publicly available and are anticipated to be useful to map quantitative trait loci (QTL) controlling important traits in soybean.

b-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 pres - ent an opportunity to investigate new selection methods for polygenic traits such as b -glucan concentration. our objectives in this study were to compare genomic, marker-assisted, and best linear unbiased prediction (BLUp )-based phe- notypic selection for short-term response to selection and ability to maintain genetic variance for b-glucan concentration. Starting with a col - lection of 446 elite oat lines from North America, each method was conducted for two cycles. The average b-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 aver - ages of marker-based selection methods in Cycle 2 were greater than those of phenotypic selection (0.08). progenies with the highest b-glucan came from the marker-based selection methods. Marker-assisted selection (MAS) for higher b-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 b-glucan concentration.

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 tradi

Plant breeders have an interest in the identification of genomic regions associated with the expression of quantitative traits because they recognize that such information coud be used to improve realized heritability and reduce time per cyle 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 topcrossed and per se evaluation of numerous quantitative traits in segregating progeny from planned breeding crosses [...]

Segregating soybean [ (L.) Merr.] breeding populations are frequently inbred by either single-seed descent or by bulking progeny at each generation. During inbreeding, repetitive sampling of individual genotypes, or natural selection, may decrease the potential for genetic gain in a population. This is most likely to occur in populations that are inbred by the bulk method. This study examines the genetic structure of two soybean populations, each containing 261 segregating lines. One population was derived from a cross of two elite lines and was inbred by a multiple-seed descent (MSD) strategy that is frequently employed by soybean breeders. A second population was derived from an elite × unadapted genotype cross that was inbred by single-seed descent (SSD). The genetic composition of each line and the genetic distances among lines were estimated using 100 RFLP markers. The genetic contribution of each parent to each population was equivalent regardless of the inbreeding procedure. No individual in either population obtained greater than 75% of its genes from a particular parent. Close genetic relationships among lines were not observed in the SSD population, but were common in the MSD population. Cluster analysis of individual lines within the MSD population indicated that about 18% of the lines were due to repetitive sampling and, therefore, did not represent independent lineages. The repetitive sampling appeared to be caused by stochastic processes, as opposed to systematic factors such as differential fecundity among genotypes. Genetic marker analysis of population structure provides a method for directly assessing the effectiveness of inbreeding strategies.

Gray leaf spot (GLS) (caused by Cercospora zeae-maydis Theon & Daniels) is a fungal disease of the maize (Zea mays L.) that can cause significant grain yield reductions. Quantitative resistance to GLS does exist; therefore, one method of controlling the disease is to develop hybrids with genetic resistance. The objective of this study was to identify quantitative trait loci (QTL) and type-of-gene action for GLS resistance in segregating families of three single-cross populations of maize using restriction fragment length polymorphisms (RFLPs) (.)

Genomics and bioinformatics are expected to revolutionize crop improvement. We have to admit, howere, tha while information from the various "omics" is being used by developmental and evolutionary biologists, the information is not being used routinely byn translational plant biologists or applied plant breeders. This is due to a failure to provide information from omics technologies in formats that can be used to develop more effective and efficient assays that the breeder can use for selection. A similar situation exits in biomedical research where more efficacious therapies will be realized if omics information is translated into biomarker-based diagnostics. Because data are stil lacking in plant science, herein we describe the development of an integrated web-based system that supports translational research using a biomedical example that can serve as a model for translational plant research. We also discuss how the bioinformatic system is agnostic with respect to data content and is capable of accepting omics data that eventually will be generated by plant biologists for use by plant breeders to develop diagnostic biomarkers for use in selection.

Applied breeding programs evaluate large numbers of progeny derived from multiple related crosses for a wide range of agronomic traits and for tens to hundreds of molecular markers. This study was conducted to determine how these phenotypic and genetic data could be used for routinely mapping quantitative trait loci (QTLs). With dense maps, haplotype sharing of parents in a certain region is a good indicator for QTL-allele sharing, albeit not 100% perfect. With this in mind, an approximate and simple method has been developed where ancestral genome blocks in the parents of the crosses can be identified via haplotype analysis and where the effect of a putative QTL is then modeled and estimated per ancestral genome block. A simulation of an early-generation maize breeding scheme demonstrates the potential of the present approach for QTL detection in existing breeding programs. With this new QTL mapping strategy, the power, precision, and accuracy associated with large numbers of progeny may be attained, inferences about QTLs can be drawn across the breeding program rather than being limited to the sample of progeny from a single cross, and results may be much more valuable for marker-assisted breeding because the QTLs apply to agronomically challenging situations in the field.