Linkage Mapping for Foliar Necrosis of Soybean Sudden Death Syndrome.

Sudden death syndrome (SDS) foliar symptoms consist of foliar chlorosis, foliar necrosis, leaf marginal curling, and premature defoliation, but resistance screening has been evaluated mostly based on the overall SDS foliar severity rather than on a specific foliar symptom. This study generated an F2 population derived from crossing the susceptible variety Sloan and the resistant germplasm line PI 243518, which exhibits resistance to both foliar chlorosis and necrosis. A total of 400 F2 lines were evaluated for foliar chlorosis, foliar necrosis, and overall SDS foliar symptoms, separately. Genotyping-by-sequencing was applied to obtain single nucleotide polymorphisms (SNPs) in the F2 population, and linkage mapping using 135 F2 lines with 969 high-quality SNPs identified a locus on chromosome 13 for foliar necrosis and SDS foliar symptoms. The locus partially overlaps with loci previously reported for SDS on chromosome 13, which is the third time the region from 15.98 to 21.00 Mbp has been reproduced independently and therefore qualifies this locus for a new nomenclature proposed as Rfv13-02. In summary, this study generated a new biparental population that enables not only the discovery of a locus for foliar necrosis and SDS foliar symptoms on chromosome 13 but also the potential for advanced exploration of SDS foliar resistance derived from the germplasm line PI 243518.

Different loci associated with root and foliar resistance to sudden death syndrome (Fusarium virguliforme) in soybean.

KEY MESSAGE: Different loci associated with root resistance to F. virguliforme colonization and foliar resistance to phytotoxin damage in soybean. Use of resistant cultivars is the most efficacious approach to manage soybean sudden death syndrome (SDS), caused by Fusarium virguliforme. The objectives of this study were to (1) map the loci associated with root and foliar resistance to F. virguliforme infection and (2) decipher the relationships between root infection, foliar damage, and plot yield. A mapping population consisting of 153 F4-derived recombinant inbred lines from the cross U01-390489 × E07080 was genotyped by SoySNP6 K BeadChip assay. Both foliar damage and F. virguliforme colonization in roots were investigated in the field, and a weak positive correlation was identified between them. Foliar damage had a stronger negative correlation with plot yield than F. virguliforme colonization. Twelve loci associated with foliar damage were identified, and four of them were associated with multiple traits across environments. In contrast, only one locus associated with root resistance to F. virguliforme colonization was identified and mapped on Chromosome 18. It colocalized with the locus associated with foliar damage in the same environment. The locus on Chromosome 6, qSDS6-2, and the locus on Chromosome 18, qSDS18-1, were associated with resistance to SDS phytotoxins and resistance to F. virguliforme colonization of roots, respectively. Both loci affected plot yield. Foliar damage-related traits, especially disease index, are valuable indicators for SDS resistance breeding because of consistency of the identified loci and their stronger correlation with plot yield. The information provided by this study will facilitate marker-assisted selection to improve SDS resistance in soybean.

Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean.

White mould of soya bean, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is a necrotrophic fungus capable of infecting a wide range of plants. To dissect the genetic architecture of resistance to white mould, a high-density customized single nucleotide polymorphism (SNP) array (52 041 SNPs) was used to genotype two soya bean diversity panels. Combined with resistance variation data observed in the field and greenhouse environments, genome-wide association studies (GWASs) were conducted to identify quantitative trait loci (QTL) controlling resistance against white mould. Results showed that 16 and 11 loci were found significantly associated with resistance in field and greenhouse, respectively. Of these, eight loci localized to previously mapped QTL intervals and one locus had significant associations with resistance across both environments. The expression level changes in genes located in GWAS-identified loci were assessed between partially resistant and susceptible genotypes through a RNA-seq analysis of the stem tissue collected at various time points after inoculation. A set of genes with diverse biological functionalities were identified as strong candidates underlying white mould resistance. Moreover, we found that genomic prediction models outperformed predictions based on significant SNPs. Prediction accuracies ranged from 0.48 to 0.64 for disease index measured in field experiments. The integrative methods, including GWAS, RNA-seq and genomic selection (GS), applied in this study facilitated the identification of causal variants, enhanced our understanding of mechanisms of white mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean.

QTL mapping and epistatic interaction analysis of field resistance to sudden death syndrome (Fusarium virguliforme) in soybean.

KEY MESSAGE: Two interactive quantitative trait loci (QTLs) controlled the field resistance to sudden death syndrome (SDS) in soybean. The interaction between them was confirmed. Sudden death syndrome (SDS), caused by Fusarium virguliforme, is a major disease of soybean [Glycine max (L.) Merr.] in the United States. Breeding for soybean resistance to SDS is the most cost-effective method to manage the disease. The objective of this study was to identify and characterize quantitative trait loci (QTLs) underlying field resistance to SDS in a recombinant inbred line population from the cross GD2422 × LD01-5907. This population was genotyped with 1786 polymorphic single nucleotide polymorphisms (SNPs) using SoySNP6 K iSelect BeadChip and evaluated for SDS resistance in a naturally infested field. Four SDS resistance QTLs were mapped on Chromosomes 4, 8, 12 and 18. The resistant parent, LD01-5907, contributed the resistance alleles for the QTLs on Chromosomes 8 and 18 (qSDS-8 and qSDS-18), while the other parent, GD2422, provided the resistance alleles for the QTLs on Chromosomes 4 and 12 (qSDS-4 and qSDS-12). The minor QTL on Chromosome 12 (qSDS-12) is novel. The QTL on Chromosomes 8 and 18 (qSDS-8 and qSDS-18) overlapped with two soybean cyst nematode resistance-related loci, Rhg4 and Rhg1, respectively. A significant interaction between qSDS-8 and qSDS-18 was detected by disease incidence. Individual effects together with the interaction effect explained around 70% of the phenotypic variance. The epistatic interaction of qSDS-8 and qSDS-18 was confirmed by the field performance across multiple years. Furthermore, the resistance alleles at qSDS-8 and qSDS-18 were demonstrated to be recessive. The SNP markers linked to these QTLs will be useful for marker-assisted breeding to enhance the SDS resistance.

Genome-wide association mapping of quantitative resistance to sudden death syndrome in soybean.

BACKGROUND: Sudden death syndrome (SDS) is a serious threat to soybean production that can be managed with host plant resistance. To dissect the genetic architecture of quantitative resistance to the disease in soybean, two independent association panels of elite soybean cultivars, consisting of 392 and 300 unique accessions, respectively, were evaluated for SDS resistance in multiple environments and years. The two association panels were genotyped with 52,041 and 5,361 single nucleotide polymorphisms (SNPs), respectively. Genome-wide association mapping was carried out using a mixed linear model that accounted for population structure and cryptic relatedness. RESULT: A total of 20 loci underlying SDS resistance were identified in the two independent studies, including 7 loci localized in previously mapped QTL intervals and 13 novel loci. One strong peak of association on chromosome 18, associated with all disease assessment criteria across the two panels, spanned a physical region of 1.2 Mb around a previously cloned SDS resistance gene (GmRLK18-1) in locus Rfs2. An additional variant independently associated with SDS resistance was also found in this genomic region. Other peaks were within, or close to, sequences annotated as homologous to genes previously shown to be involved in plant disease resistance. The identified loci explained an average of 54.5% of the phenotypic variance measured by different disease assessment criteria. CONCLUSIONS: This study identified multiple novel loci and refined the map locations of known loci related to SDS resistance. These insights into the genetic basis of SDS resistance can now be used to further enhance durable resistance to SDS in soybean. Additionally, the associations identified here provide a basis for further efforts to pinpoint causal variants and to clarify how the implicated genes affect SDS resistance in soybean.

Sleep disturbances are associated with anxiety, depression, and decreased quality of life in patients with coronary heart disease.

BACKGROUND: Studies have shown that sleep disorders are closely related to anxiety and depression, and the quality of life (QoL) of patients with sleep disorders is generally poor. AIM: To examine the occurrence of sleep disorders in people with coronary heart disease (CHD) and their relationships with QoL, depression, and anxiety. METHODS: As per the sleep condition, 240 CHD individuals were separated into two groups: non-sleep disorder group (n = 128) and sleep disorder group (n = 112). The self-rating anxiety scale (SAS), self-rating depression scale (SDS), and World Health Organization criteria for the Quality of Life Brief scale (WHOQOL-BREF) scores of the two groups were compared. Logistic regression method was used to analyze the independent risk factors of CHD patients with sleep disorders. Multivariate logistic regression analysis was employed to develop the risk prediction model. The association among the Pittsburgh Sleep Quality Index, SAS, and SDS was examined using Spearman's correlation analysis. RESULTS: The incidence of sleep disorder was 46.67% in 240 patients. The scores of SAS and SDS in the sleep disorder group were higher than those in the non-sleep disorder group, and the WHOQOL-BREF scores were lower than those in the non-sleep disorder group (P < 0.05). The risk prediction model of sleep disturbances in CHD patients was constructed using the outcomes of multivariate logistic regression analysis, P = 1/[1 + e (-2.160 + 0.989 × (female) + 0.001 × (new rural cooperative medical insurance) + 2.219 × (anxiety) + 2.157 × depression)]. The results of a Spearman's correlation study revealed that sleep quality was strongly adversely connected with the physiological field, psychological field, and social relation scores in QoL, and was considerably positively correlated with SAS and SDS (P < 0.05). CONCLUSION: A multivariate logistic regression model can better predict the occurrence of sleep disorders in CHD patients. Sleep disorders in CHD patients are significantly correlated with QoL, depression, and anxiety.

Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.

Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.