Use Of Quantitative Trait Loci Research Articles

QTL mapping in sesame (Sesamum indicum L.): A review.

Sesame (Sesamum indicum L.) is an important oilseed crop used for food, feed, medicinal, and industrial applications. Inherently low genetic yield potential and susceptibility to biotic and abiotic stresses contribute to low productivity in sesame. Development of stress resistant varieties coupled with high yield is a viable option to raise the genetic potential of sesame. Conventional phenotype-based breeding methods have made an important role in the last couple of decades by developing several sesame varieties with improved quality, yield, and tolerance to biotic and abiotic stresses. However, due to adverse environmental effects, time consuming to develop new variety, and low genetic gain, conventional phenotype-based approach is not adequate to satisfy the rising population growth. In this context, advanced method of genotype selection via modern techniques of biotechnology plays essential roles in reducing the constraints and boosting sesame production to satisfy the huge demand. In line to this, quantitative trait loci (QTL) mapping is considered as a promising method to address the problems of sesame breeding. Previously, huge data have been generated in the practical use of QTL for sesame improvement. Therefore, this paper aims to review recent advances in the area of QTL mapping for yield and yield related traits in sesame for enhancing and sustaining sesame production. In this section, we present an intensive review on the identification and mapping of the most desirable potential candidate genes/QTLs associated with desirable traits. Moreover, this review focuses on the major QTL regions and/or potential candidate genes and associated molecular markers that could provide potential genetic resources for molecular marker-assisted selection and further cloning of functional genes for yield and yield-related traits as well as various biotic and abiotic stress tolerances. Finally, the summarized QTL mapping data shed light on future directions for enhanced sesame breeding programs.

A Systemic Investigation of Genetic Architecture and Gene Resources Controlling Kernel Size-Related Traits in Maize.

Grain yield is the most critical and complex quantitative trait in maize. Kernel length (KL), kernel width (KW), kernel thickness (KT) and hundred-kernel weight (HKW) associated with kernel size are essential components of yield-related traits in maize. With the extensive use of quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) analyses, thousands of QTLs and quantitative trait nucleotides (QTNs) have been discovered for controlling these traits. However, only some of them have been cloned and successfully utilized in breeding programs. In this study, we exhaustively collected reported genes, QTLs and QTNs associated with the four traits, performed cluster identification of QTLs and QTNs, then combined QTL and QTN clusters to detect consensus hotspot regions. In total, 31 hotspots were identified for kernel size-related traits. Their candidate genes were predicted to be related to well-known pathways regulating the kernel developmental process. The identified hotspots can be further explored for fine mapping and candidate gene validation. Finally, we provided a strategy for high yield and quality maize. This study will not only facilitate causal genes cloning, but also guide the breeding practice for maize.

High‐throughput NGS‐based genotyping and phenotyping: Role in genomics‐assisted breeding for soybean improvement

AbstractSoybean is an important food crop that provides edible protein and oil for human and animal nutrition. Conventional phenotypic‐based breeding approaches have made significant contribution in the last century by developing many improved soybean varieties. However, due to the longer time taken to develop a variety, low genetic gain per unit time, and adverse environmental influence of phenotypic‐based selection, conventional approaches are not sufficient to maintain pace with growing population and climate change. In this context, the recent method of genotypic selection, that is, genomics‐assisted breeding (GAB) is considered a promising approach to address the challenges in soybean breeding. However, to harness the true potential of GAB in soybean improvement, the great coverage and precision in the genotyping and phenotyping are required. Previously, a huge gap was observed between the discovery and practical use of quantitative trait loci (QTLs) in soybean improvement. It has been suggested that low marker density and manually collected phenotypes are the major reasons for this failure. Hence, high‐throughput genotyping (HTG) providing higher genome‐wide marker density, as well as accurate and precise phenotyping using high‐throughput digital phenotyping (HTP) platforms, can significantly increase the success of QTL and candidate gene identification in soybean. These approaches can greatly increase the practical utility of GAB in soybean and also offer a faster characterization of germplasm and breeding materials. This review provides the detailed information on how the recent innovations in genomics and phenomics can assists in improving the efficiency and potential of GAB in soybean improvement.

Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants

Development of drought-tolerant cultivars is one of the challenging tasks for the plant breeders due to its complex inheritance and polygenic regulation. Evaluating genetic material for drought tolerance is a complex process due to its spatiotemporal interactions with environmental factors. The conventional breeding approaches are costly, lengthy, and inefficient to achieve the expected gain in drought tolerance. In this regard, genomics-assisted breeding (GAB) offers promise to develop cultivars with improved drought tolerance in a more efficient, quicker, and cost-effective manner. The success of GAB depends upon the precision in marker-trait association and estimation of genomic estimated breeding values (GEBVs), which mostly depends on coverage and precision of genotyping and phenotyping. A wide gap between the discovery and practical use of quantitative trait loci (QTL) for crop improvement has been observed for many important agronomical traits. Such a limitation could be due to the low accuracy in QTL detection, mainly resulting from low marker density and manually collected phenotypes of complex agronomic traits. Increasing marker density using the high-throughput genotyping (HTG), and accurate and precise phenotyping using high-throughput digital phenotyping (HTP) platforms can improve the precision and power of QTL detection. Therefore, both HTG and HTP can enhance the practical utility of GAB along with a faster characterization of germplasm and breeding material. In the present review, we discussed how the recent innovations in HTG and HTP would assist in the breeding of improved drought-tolerant varieties. We have also discussed strategies, tools, and analytical advances made on the HTG and HTP along with their pros and cons.

Use of Targeted Amplicon Sequencing in Peanut to Generate Allele Information on Allotetraploid Sub-Genomes.

The use of molecular markers in plant breeding has become a routine practice, but the cost per accession can be a hindrance to the routine use of Quantitative Trait Loci (QTL) identification in breeding programs. In this study, we demonstrate the use of targeted re-sequencing as a proof of concept of a cost-effective approach to retrieve highly informative allele information, as well as develop a bioinformatics strategy to capture the genome-specific information of a polyploid species. SNPs were identified from alignment of raw transcriptome reads (2 × 50 bp) to a synthetic tetraploid genome using BWA followed by a GATK pipeline. Regions containing high polymorphic SNPs in both A genome and B genomes were selected as targets for the resequencing study. Targets were amplified using multiplex PCR followed by sequencing on an Illumina HiSeq. Eighty-one percent of the SNP calls in diploids and 68% of the SNP calls in tetraploids were confirmed. These results were also confirmed by KASP validation. Based on this study, we find that targeted resequencing technologies have potential for obtaining maximum allele information in allopolyploids at reduced cost.

Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax.

Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Comparing Single-SNP, Multi-SNP, and Haplotype-Based Approaches in Association Studies for Major Traits in Barley.

The multiple single nucleotide polymorphism (multi-SNP) and haplotype-based approaches that jointly consider multiple markers unveiled a larger number of associations, some of which were shared with the single-SNP approach. A larger overlap of quantitative trait loci (QTLs) between the single-SNP and haplotype-based approaches was obtained than with the multi-SNP approach. Despite a limited overlap between the QTLs detected by these approaches, each uncovered QTLs reported previously, suggesting that each approach is capable of uncovering a different subset of QTLs. We demonstrated the efficiency of an integrated genome-wide association study (GWAS) procedure, combining single-locus and multilocus approaches to improve the capacity and reliability of association analysis to detect key QTLs. The efficiency of barley breeding programs may be improved by the practical use of QTLs identified in this study. Genome-wide association studies (GWAS) have been widely used to identify quantitative trait loci (QTLs) underlying complex agronomic traits. The conventional GWAS model is based on a single-locus model, which may prove inaccurate if a trait is controlled by multiple loci, which is the case for most agronomic traits in barley (Hordeum vulgare L.). Additionally, an individual single nucleotide polymorphism (SNP) will prove incapable of capturing underlying allelic diversity. A multilocus model could potentially represent a better alternative for QTL identification. This study aimed to explore different GWAS approaches (single-SNP, multi-SNP, and haplotype-based) to establish SNP-trait associations and to potentially describe the complex genetic architecture of seven key traits in spring barley. The multi-SNP and haplotype-based approaches unveiled a larger number of significant associations, some of which were shared with the single-SNP approach. Globally, the multi-SNP approach explained more of the phenotypic variance (cumulative R2 ) and provided the best fit with the genetic model [Bayesian information criterion (BIC)]. Compared with the multi-SNP approach, the single-SNP and haplotype-based approaches were relatively similar in terms of cumulative R2 and BIC, with an improvement with the haplotype-based approach. Despite limited overlap between detected QTLs, each approach discovered QTLs that had been validated previously, suggesting that each approach can uncover a different subset of QTLs. An integrated GWAS procedure, considering single-locus and multilocus GWAS approaches jointly, may improve the capacity of association studies to detect key QTLs and to provide a more complete picture of the genetic architecture of complex traits in barley.

Stilbenes: biomarkers of grapevine resistance to fungal diseases

Abstract: Since the introduction of powdery and downy mildews in Europe in the late 19th century, breeding resistant cultivars by hybridizing V. vinifera (susceptible) with other Vitis species (resistant) has been largely used and led, in 1947, to the cultivation of > 350,000 ha (23%) of grapevine area in France. Because of the poor wine quality of this first generation of hybrids, legislation prohibited their cultivation for the production of quality wines. Recent investigations allowed sequencing the entire grapevine genome, but no precise resistance genes are yet known for further introduction in susceptible V. vinifera cultivars. At the molecular level, the use of QTL (Quantitative Trait Loci) as resistance markers is ongoing and could be correlated to resistant gene expression and further define metabolite production in resistance mechanisms. Stilbenic phytoalexins are key defence molecules implicated in the resistance of grapevine cultivars to three major fungal pathogens, Botrytis cinerea (grey mould), Plasmopara viticola (downy mildew) and Erysiphe necator (powdery mildew). HPLC analysis of stilbenes is an efficient method to evaluate the ability of the vine plants to inhibit the development of fungal pathogens. Resistant grapevine varieties react very rapidly to infections by producing high concentrations of the most toxic stilbenes, d-viniferin and pterostilbene, at the sites of infection. Monitoring of such stress biomarkers is also of great interest for evaluating the efficiency of priming molecules at inducing the grapevines’ natural defence responses. In addition, these compounds have various beneficial effects on human health, acting as anti-oxidants and also as potential chemopreventive agents. The diversity of stilbenes is intriguing, and new holistic analytical approaches, such as metabolomics, that are widely used for wine classification also have great potential for the comprehensive study of responses of Vitaceae to biotic and abiotic stress.

Maize Combined Insect Resistance Genomic Regions and Their Co-localization With Cell Wall Constituents Revealed by Tissue-Specific QTL Meta-Analyses.

Combinatorial insect attacks on maize leaves, stems, and kernels cause significant yield losses and mycotoxin contaminations. Several small effect quantitative trait loci (QTL) control maize resistance to stem borers and storage pests and are correlated with secondary metabolites. However, efficient use of QTL in molecular breeding requires a synthesis of the available resistance information. In this study, separate meta-analyses of QTL of maize response to stem borers and storage pests feeding on leaves, stems, and kernels along with maize cell wall constituents discovered in these tissues generated 24 leaf (LIR), 42 stem (SIR), and 20 kernel (KIR) insect resistance meta-QTL (MQTL) of a diverse genetic and geographical background. Most of these MQTL involved resistance to several insect species, therefore, generating a significant interest for multiple-insect resistance breeding. Some of the LIR MQTL such as LIR4, 17, and 22 involve resistance to European corn borer, sugarcane borer, and southwestern corn borer. Eleven out of the 42 SIR MQTL related to resistance to European corn borer and Mediterranean corn borer. There KIR MQTL, KIR3, 15, and 16 combined resistance to kernel damage by the maize weevil and the Mediterranean corn borer and could be used in breeding to reduce insect-related post-harvest grain yield loss and field to storage mycotoxin contamination. This meta-analysis corroborates the significant role played by cell wall constituents in maize resistance to insect since the majority of the MQTL contain QTL for members of the hydroxycinnamates group such as p-coumaric acid, ferulic acid, and other diferulates and derivates, and fiber components such as acid detergent fiber, neutral detergent fiber, and lignin. Stem insect resistance MQTL display several co-localization between fiber and hydroxycinnamate components corroborating the hypothesis of cross-linking between these components that provide mechanical resistance to insect attacks. Our results highlight the existence of combined-insect resistance genomic regions in maize and set the basis of multiple-pests resistance breeding.

Regional Association Analysis of MetaQTLs Delineates Candidate Grain Size Genes in Rice.

Molecular mapping studies which aim to identify genetic basis of diverse agronomic traits are vital for marker-assisted crop improvement. Numerous Quantitative Trait Loci (QTLs) mapped in rice span long genomic intervals with hundreds to thousands of genes, which limits their utilization for marker-assisted genetic enhancement of rice. Although potent, fine mapping of QTLs is challenging task as it requires screening of large number of segregants to identify suitable recombination events. Association mapping offers much higher resolution as compared to QTL mapping, but detects considerable number of spurious QTLs. Therefore, combined use of QTL and association mapping strategies can provide advantages associated with both these methods. In the current study, we utilized meta-analysis approach to identify metaQTLs associated with grain size/weight in diverse Indian indica and aromatic rice accessions. Subsequently, attempt has been made to narrow-down identified grain size/weight metaQTLs through individual SNP- as well as haplotype-based regional association analysis. The study identified six different metaQTL regions, three of which were successfully revalidated, and substantially scaled-down along with GS3 QTL interval (positive control) by regional association analysis. Consequently, two potential candidate genes within two reduced metaQTLs were identified based on their differential expression profiles in different tissues/stages of rice accessions during seed development. The developed strategy has broader practical utility for rapid delineation of candidate genes and natural alleles underlying QTLs associated with complex agronomic traits in rice as well as major crop plants enriched with useful genetic and genomic information.

QTL Analysis for Drought Tolerance in Wheat: Present Status and Future Possibilities

In recent years, with climate change, drought stress has been witnessed in many parts of the world. In many irrigated regions also, shortage of water supply allows only limited irrigation. These conditions have an adverse effect on the productivity of many crops including cereals such as wheat. Therefore, genetics of drought/water stress tolerance in different crops has become a priority area of research. This research mainly involves use of quantitative trait locus (QTL) analysis (involving both interval mapping and association mapping) for traits that are related to water-use efficiency. In this article, we briefly review the available literature on QTL analyses in wheat for traits, which respond to drought/water stress. The outlook for future research in this area and the possible approaches for utilizing the available information on genetics of drought tolerance for wheat breeding are also discussed.

Genomewide association study of body weight traits in Baluchi sheep.

The body weight is an economically important trait in sheep. We performed a genomewide association study using Ovine 50 K SNP chip to identify the genes and chromosome regions associated with body weight in Baluchi sheep. A total of 96 blood samples from two herds along with data on weight at birth (BW), weaning (WW), six month (SMW) and yearling (YW) were collected. Markers were tested for association based on linear regression using the PLINK software. Thirteen different SNP markers reached 5% Bonferroni chromosome-wide significance levels. In this study we detected one SNP with genomewide significance effect on yearling weight on chromosome 8. All significant SNPs at chromosome-wide significance level were within or close to known ovine genes. The SNP at genomewide significance level was within gene SYNE1. Thus, we suggest more investigation to prove these genes as candidate genes for body weight traits in sheep. Growth traits are economically important traits for sheep. Mapping of quantitative trait loci (QTL) is an appropriate approach and provides useful information for marker assisted selection (MAS) and gene-based selection in sheep breeding strategies. Relatively few number of QTLs have been reported in sheep. The current release (February 2014) of the Sheep QTLdb (http://www.animalgenome.org/cgi-bin/ QTLdb) contains 129 QTLs for growth traits reported from a genomic study based on marker-QTL linkage analysis. QTL mapping using linkage map QTLs to large confidence intervals on the genome. As a result, use of QTL in MAS is complicated. It would be possible to exploit linkage disequilibrium (LD) to map QTL if dense markers were available. With the advent of next-generation sequencing

Five decades of invasion genetics.

Five decades of invasion genetics.

Lz-0 × Berkeley: a new Arabidopsis recombinant inbred line population for the mapping of complex traits

This study describes the generation and test of a genetic resource suited to identify determinants of cell biological traits in plants. The use of quantitative trait loci (QTL) mapping for a better genetic understanding of cell biological traits is still at an early stage, even for biotechnologically important cell properties, such as the dimensions of fiber cells. A common strategy, the mapping of QTLs in recombinant inbred line (RIL) populations, is limited by the fact that the existing RIL populations exploit only a small fraction of the existing natural variation. Here, we report the mapping of QTLs impacting on the length of fiber cells in Arabidopsis inflorescence stems in a newly generated RIL population derived from a cross between the accessions Berkeley and the little known Lz-0. Through inbreeding of individual F(2) plants, a total of 159 new F8 lines were produced and genotyped with a set of 49 single nucleotide polymorphism markers. The population was successfully used not only for the mapping of three QTLs controlling fiber length, but also to map five QTL controlling flowering time under short and long-day conditions. Our study demonstrates the usefulness of this new genetic resource by mapping in it QTLs underlying a poorly explored cellular trait as well as an already better explored regulatory pathway. The new RIL population and an online platform for the continuous supplementation of genetic markers will be generally available to substantially broaden the genetic diversity through which loci with impact on plant quantitative traits can be identified.

Identification of genetic loci associated with fire blight resistance in Malus through combined use of QTL and association mapping

Fire blight, incited by the enterobacterium Erwinia amylovora, is a destructive disease of Rosaceae, particularly of apples and pears. There are reports on the molecular mechanisms underlying E. amylovora pathogenesis and how the host activates its resistance mechanism. The host's resistance mechanism is quantitatively controlled, although some major genes might also be involved. Thus far, quantitative trait loci (QTL) mapping and differential expression studies have been used to elucidate those genes and/or genomic regions underlying quantitative resistance present in the apple genome. In this study, an effort is undertaken to dissect the genetic basis of fire blight resistance in apple using both QTL and genome-wide association mapping. On the basis of an F1 pedigree of 'Coop 16' × 'Coop 17' and a genome-wide association study (GWAS) mapping population of Malus accessions (species, old and new cultivars and selections), new QTLs and associations have been identified. A total of three QTLs for resistance to fire blight, with above 95% significant logarithm of odds threshold value of 2.5, have been identified on linkage groups (LGs) 02, 06, and 15 of the apple genome with phenotypic variation explained values of 14.7, 20.1 and 17.4, respectively. Although elevated P-values with signals for marker-trait associations are observed for some LGs, these are not found to be significant. However, a total of 34 significant associations, with P-values ≥0.02, have been detected including 8 for lesion length at 7 days following inoculation (PL1), 14 for lesion length at 14 days following inoculation (PL2), and 12 for shoot length.

Identifying permethrin resistance loci in malaria vectors by genetic mapping

Identification of the major loci responsible for insecticide resistance in malaria vectors would aid the development and implementation of effective resistance management strategies, which are urgently needed to tackle the growing threat posed by resistance to the limited insecticides available for malaria control. Genome-wide association studies in the major malaria vector, Anopheles gambiae, have been hindered by the high degree of within-population structuring and very low levels of linkage disequilibrium hence we revisited the use of quantitative trait loci (QTL) mapping to study resistance phenotypes in this vector species. Earlier work, identified two major QTL associated with pyrethroid resistance in A. gambiae s.s. from East Africa using genetic crossing of laboratory-colonized resistant and susceptible strains. In this study, we report the results from genetic mapping of pyrethroid resistance in three isofemale pedigrees established from wild-caught female A. gambiae s.s. mosquitoes from Benin. We identified two QTL on chromosomes 2L and 3R in these field populations, in similar genomic locations to the QTL identified in laboratory strains. The relative merits of two alternative study designs are discussed and suggestions made for future genetic mapping studies of insecticide resistance in mosquitoes.

Ionomic Characterization of Maize Kernels in the Intermated B73 × Mo17 Population

Dietary mineral deficiencies affect nearly half of the people on our planet, largely due to poverty. Enhancing nutritional quality—or biofortification—represents an efficient and sustainable potential solution to this massive public health problem. To create biofortified crops, one must understand the genetic and environmental factors that influence the ionome, or collection of mineral nutrients, in the target organism and tissue. We describe the use of quantitative trait loci (QTL) mapping to characterize the maize (Zea mays L.) grain ionome illustrated by the intermated B73 × Mo17 (IBM) recombinant inbred population. Ionomic profiling was applied to field grown materials from Florida, North Carolina, and New York. Twenty‐seven QTL were detected for 10 traits derived from the North Carolina and New York grown maize explaining between 4 and 46% of the variance observed. For biofortification to succeed, QTL effective in multiple environments need to be the targets for improvement efforts. Florida grown maize were sampled shallowly to provide a low cost dataset to evaluate the models based on the combined North Carolina and New York traits. Twenty‐five QTL were detected as significant in two or more locations using ANOVA on the original single location and/or site data; 12 QTL were found to be significant in Florida. While this strategy may have not detected every potential QTL from our data, we suggest these QTL effective in multiple environments represent a starting position for the biofortification of maize grain.

Genetic Selection and Conservation of Genetic Diversity*

For 100s of years, livestock producers have employed various types of selection to alter livestock populations. Current selection strategies are little different, except our technologies for selection have become more powerful. Genetic resources at the breed level have been in and out of favour over time. These resources are the raw materials used to manipulate populations, and therefore, they are critical to the past and future success of the livestock sector. With increasing ability to rapidly change genetic composition of livestock populations, the conservation of these genetic resources becomes more critical. Globally, awareness of the need to steward genetic resources has increased. A growing number of countries have embarked on large scale conservation efforts by using in situ, ex situ (gene banking), or both approaches. Gene banking efforts have substantially increased and data suggest that gene banks are successfully capturing genetic diversity for research or industry use. It is also noteworthy that both industry and the research community are utilizing gene bank holdings. As pressures grow to meet consumer demands and potential changes in production systems, the linkage between selection goals and genetic conservation will increase as a mechanism to facilitate continued livestock sector development.

Mapping and use of QTLs controlling pod dehiscence in soybean.

While the cultivated soybean, Glycine max (L.) Merr., is more recalcitrant to pod dehiscence (shattering-resistant) than wild soybean, Glycine soja Sieb. & Zucc., there is also significant genetic variation in shattering resistance among cultivated soybean cultivars. To reveal the genetic basis and develop DNA markers for pod dehiscence, several research groups have conducted quantitative trait locus (QTL) analysis using segregated populations derived from crosses between G. max accessions or between a G. max and G. soja accession. In the populations of G. max, a major QTL was repeatedly identified near SSR marker Sat_366 on linkage group J (chromosome 16). Minor QTLs were also detected in several studies, although less commonality was found for the magnitudes of effect and location. In G. max × G. soja populations, only QTLs with a relatively small effect were detected. The major QTL found in G. max was further fine-mapped, leading to the development of specific markers for the shattering resistance allele at this locus. The markers were used in a breeding program, resulting in the production of near-isogenic lines with shattering resistance and genetic backgrounds of Japanese elite cultivars. The markers and lines developed will hopefully contribute to the rapid production of a variety of shattering-resistant soybean cultivars.

Options for Developing Salt-tolerant Crops

Soil salinization is an increasing problem worldwide and is often intensified by irrigation. Unfortunately, few new crop cultivars have been developed resistant to saline soils, a consequence, in part, of the complexity of plant responses to salt stress. There are now, however, several non-traditional options to improving salt tolerance as a result of recent progress in better understanding the mechanisms involved. These mechanisms include 1) exclusion of Na+ and Cl– from plant tissues; 2) inclusion of these ions in inert compartments or tissues; and/or 3) some means of osmotic adjustment with solutes that are compatible with the metabolic machinery of the cell. Although there are very few horticultural examples, several lines of evidence indicate that reductions in salt sensitivity through exclusion or inclusion can be achieved by single gene modifications of the ion transport system. Similarly, single genes resulting in osmotic adjustment with solutes compatible with the metabolic machinery of the cell have resulted in significant increases in salt tolerance. Recent advances in sequencing, use of quantitative trait loci, and marker-assisted selection promise to provide other options for improving salt tolerance.