Genetic variants in root architecture-related genes in a Glycine soja accession, a potential resource to improve cultivated soybean.

BACKGROUND: Root system architecture is important for water acquisition and nutrient acquisition for all crops. In soybean breeding programs, wild soybean alleles have been used successfully to enhance yield and seed composition traits, but have never been investigated to improve root system architecture. Therefore, in this study, high-density single-feature polymorphic markers and simple sequence repeats were used to map quantitative trait loci (QTLs) governing root system architecture in an inter-specific soybean mapping population developed from a cross between Glycine max and Glycine soja. RESULTS: Wild and cultivated soybean both contributed alleles towards significant additive large effect QTLs on chromosome 6 and 7 for a longer total root length and root distribution, respectively. Epistatic effect QTLs were also identified for taproot length, average diameter, and root distribution. These root traits will influence the water and nutrient uptake in soybean. Two cell division-related genes (D type cyclin and auxin efflux carrier protein) with insertion/deletion variations might contribute to the shorter root phenotypes observed in G. soja compared with cultivated soybean. Based on the location of the QTLs and sequence information from a second G. soja accession, three genes (slow anion channel associated 1 like, Auxin responsive NEDD8-activating complex and peroxidase), each with a non-synonymous single nucleotide polymorphism mutation were identified, which may also contribute to changes in root architecture in the cultivated soybean. In addition, Apoptosis inhibitor 5-like on chromosome 7 and slow anion channel associated 1-like on chromosome 15 had epistatic interactions for taproot length QTLs in soybean. CONCLUSION: Rare alleles from a G. soja accession are expected to enhance our understanding of the genetic components involved in root architecture traits, and could be combined to improve root system and drought adaptation in soybean.

The impact of population structure on genomic prediction in stratified populations.

Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize. Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5% of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65% improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.

Accuracy of across-environment genome-wide prediction in maize nested association mapping populations.

Most of previous empirical studies with genome-wide prediction were focused on within-environment prediction based on a single-environment (SE) model. In this study, we evaluated accuracy improvements of across-environment prediction by using genetic and residual covariance across correlated environments. Predictions with a multienvironment (ME) model were evaluated for two corn polygenic leaf structure traits, leaf length and leaf width, based on within-population (WP) and across-population (AP) experiments using a large maize nested association mapping data set consisting of 25 populations of recombinant inbred-lines. To make our study more applicable to plant breeding, two cross-validation schemes were used by evaluating accuracies of (CV1) predicting unobserved phenotypes of untested lines and (CV2) predicting unobserved phenotypes of lines that have been evaluated in some environments but not others. We concluded that (1) genome-wide prediction provided greater prediction accuracies than traditional quantitative trait loci-based prediction in both WP and AP and provided more advantages over quantitative trait loci -based prediction for WP than for AP. (2) Prediction accuracy with ME was significantly greater than that attained by SE in CV1 and CV2, and gains with ME over SE were greater in CV2 than in CV1. These gains were also greater in WP than in AP in both CV1 and CV2. (3) Gains with ME over SE attributed to genetic correlation between environments, with little effect from residual correlation. Impacts of marker density on predictions also were investigated in this study.

Evaluation of genome-wide selection efficiency in maize nested association mapping populations.

In comparison to conventional marker-assisted selection (MAS), which utilizes only a subset of genetic markers associated with a trait to predict breeding values (BVs), genome-wide selection (GWS) improves prediction accuracies by incorporating all markers into a model simultaneously. This strategy avoids risks of missing quantitative trait loci (QTL) with small effects. Here, we evaluated the accuracy of prediction for three corn flowering traits days to silking, days to anthesis, and anthesis-silking interval with GWS based on cross-validation experiments using a large data set of 25 nested association mapping populations in maize (Zea mays). We found that GWS via ridge regression-best linear unbiased prediction (RR-BLUP) gave significantly higher predictions compared to MAS utilizing composite interval mapping (CIM). The CIM method may be selected over multiple linear regression to decrease over-estimations of the efficiency of GWS over a MAS strategy. The RR-BLUP method was the preferred method for estimating marker effects in GWS with prediction accuracies comparable to or greater than BayesA and BayesB. The accuracy with RR-BLUP increased with training sample proportion, marker density, and heritability until it reached a plateau. In general, gains in accuracy with RR-BLUP over CIM increased with decreases of these factors. Compared to training sample proportion, the accuracy of prediction with RR-BLUP was relatively insensitive to marker density.

Replication of nonautonomous retroelements in soybean appears to be both recent and common.

Retrotransposons and their remnants often constitute more than 50% of higher plant genomes. Although extensively studied in monocot crops such as maize (Zea mays) and rice (Oryza sativa), the impact of retrotransposons on dicot crop genomes is not well documented. Here, we present an analysis of retrotransposons in soybean (Glycine max). Analysis of approximately 3.7 megabases (Mb) of genomic sequence, including 0.87 Mb of pericentromeric sequence, uncovered 45 intact long terminal repeat (LTR)-retrotransposons. The ratio of intact elements to solo LTRs was 8:1, one of the highest reported to date in plants, suggesting that removal of retrotransposons by homologous recombination between LTRs is occurring more slowly in soybean than in previously characterized plant species. Analysis of paired LTR sequences uncovered a low frequency of deletions relative to base substitutions, indicating that removal of retrotransposon sequences by illegitimate recombination is also operating more slowly. Significantly, we identified three subfamilies of nonautonomous elements that have replicated in the recent past, suggesting that retrotransposition can be catalyzed in trans by autonomous elements elsewhere in the genome. Analysis of 1.6 Mb of sequence from Glycine tomentella, a wild perennial relative of soybean, uncovered 23 intact retroelements, two of which had accumulated no mutations in their LTRs, indicating very recent insertion. A similar pattern was found in 0.94 Mb of sequence from Phaseolus vulgaris (common bean). Thus, autonomous and nonautonomous retrotransposons appear to be both abundant and active in Glycine and Phaseolus. The impact of nonautonomous retrotransposon replication on genome size appears to be much greater than previously appreciated.

Differential accumulation of retroelements and diversification of NB-LRR disease resistance genes in duplicated regions following polyploidy in the ancestor of soybean.

The genomes of most, if not all, flowering plants have undergone whole genome duplication events during their evolution. The impact of such polyploidy events is poorly understood, as is the fate of most duplicated genes. We sequenced an approximately 1 million-bp region in soybean (Glycine max) centered on the Rpg1-b disease resistance gene and compared this region with a region duplicated 10 to 14 million years ago. These two regions were also compared with homologous regions in several related legume species (a second soybean genotype, Glycine tomentella, Phaseolus vulgaris, and Medicago truncatula), which enabled us to determine how each of the duplicated regions (homoeologues) in soybean has changed following polyploidy. The biggest change was in retroelement content, with homoeologue 2 having expanded to 3-fold the size of homoeologue 1. Despite this accumulation of retroelements, over 77% of the duplicated low-copy genes have been retained in the same order and appear to be functional. This finding contrasts with recent analyses of the maize (Zea mays) genome, in which only about one-third of duplicated genes appear to have been retained over a similar time period. Fluorescent in situ hybridization revealed that the homoeologue 2 region is located very near a centromere. Thus, pericentromeric localization, per se, does not result in a high rate of gene inactivation, despite greatly accelerated retrotransposon accumulation. In contrast to low-copy genes, nucleotide-binding-leucine-rich repeat disease resistance gene clusters have undergone dramatic species/homoeologue-specific duplications and losses, with some evidence for partitioning of subfamilies between homoeologues.