Use of field observations to characterise genotypic flowering responses to photoperiod and temperature: a soyabean exemplar

Photoperiod-temperature phase lag: A universal environmental context of seasonal developmental plasticity

Seasonal developmental plasticity, which consists of season-dependent alternations of developmental processes, has evolved to produce optimal phenotypes depending on specific periods in a year. For example, many phenological events in plants, such as flowering, fruiting, bud blast, bud formation, and growth cessation, are often controlled seasonally. Although temperature and photoperiod are the two major seasonal cues for such responses, the importance of phase lag between annual oscillations of the two signals has been unexplored, despite its universal nature in the context of seasonal environments. In this article, the phase-lag calendar hypothesis (New Phytologist, 210, 2016, 399), especially the one between temperature and photoperiod, is explained using meteorological data obtained from central Japan as an example. We set forth to show how, for a narrow window in time of a couple of weeks in a year, simple threshold responses to these two signals that differ in annual oscillation phases are enough to make developmental plasticity to be expressed as phenological events. The properties of the underlying mechanisms of the events in different seasons are further predicted, and the responses are compared with reported empirical examples. Because many organisms have evolved under the phase lag between photoperiod and temperature, the developmental plasticity in response to the phase lag should be evaluated for diverse organisms.

Phenotypic Characterization and Genetic Dissection of Growth Period Traits in Soybean (Glycine max) Using Association Mapping

The growth period traits are important traits that affect soybean yield. The insights into the genetic basis of growth period traits can provide theoretical basis for cultivated area division, rational distribution, and molecular breeding for soybean varieties. In this study, genome-wide association analysis (GWAS) was exploited to detect the quantitative trait loci (QTL) for number of days to flowering (ETF), number of days from flowering to maturity (FTM), and number of days to maturity (ETM) using 4032 single nucleotide polymorphism (SNP) markers with 146 cultivars mainly from Northeast China. Results showed that abundant phenotypic variation was presented in the population, and variation explained by genotype, environment, and genotype by environment interaction were all significant for each trait. The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province. GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL. Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits. The QTL identified with high resolution in this study will enrich our genomic understanding of growth period traits and could then be explored as genetic markers to be used in genomic applications in soybean breeding.

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.

SSR and AFLP based genetic diversity of soybean germplasm differing in photoperiod sensitivity

Forty-four soybean genotypes with different photoperiod response were selected after screening of 1000 soybean accessions under artificial condition and were profiled using 40 SSR and 5 AFLP primer pairs. The average polymorphism information content (PIC) for SSR and AFLP marker systems was 0.507 and 0.120, respectively. Clustering of genotypes was done using UPGMA method for SSR and AFLP and correlation was 0.337 and 0.504, respectively. Mantel's correlation coefficients between Jaccard's similarity coefficient and the cophenetic values were fairly high in both the marker systems (SSR = 0.924; AFLP = 0.958) indicating very good fit for the clustering pattern. UPGMA based cluster analysis classified soybean genotypes into four major groups with fairly moderate bootstrap support. These major clusters corresponded with the photoperiod response and place of origin. The results indicate that the photoperiod insensitive genotypes, 11/2/1939 (EC 325097) and MACS 330 would be better choice for broadening the genetic base of soybean for this trait.

Climate change and the flowering time of annual crops

Crop production is inherently sensitive to variability in climate. Temperature is a major determinant of the rate of plant development and, under climate change, warmer temperatures that shorten development stages of determinate crops will most probably reduce the yield of a given variety. Earlier crop flowering and maturity have been observed and documented in recent decades, and these are often associated with warmer (spring) temperatures. However, farm management practices have also changed and the attribution of observed changes in phenology to climate change per se is difficult. Increases in atmospheric [CO(2)] often advance the time of flowering by a few days, but measurements in FACE (free air CO(2) enrichment) field-based experiments suggest that elevated [CO(2)] has little or no effect on the rate of development other than small advances in development associated with a warmer canopy temperature. The rate of development (inverse of the duration from sowing to flowering) is largely determined by responses to temperature and photoperiod, and the effects of temperature and of photoperiod at optimum and suboptimum temperatures can be quantified and predicted. However, responses to temperature, and more particularly photoperiod, at supraoptimal temperature are not well understood. Analysis of a comprehensive data set of time to tassel initiation in maize (Zea mays) with a wide range of photoperiods above and below the optimum suggests that photoperiod modulates the negative effects of temperature above the optimum. A simulation analysis of the effects of prescribed increases in temperature (0-6 degrees C in +1 degree C steps) and temperature variability (0% and +50%) on days to tassel initiation showed that tassel initiation occurs later, and variability was increased, as the temperature exceeds the optimum in models both with and without photoperiod sensitivity. However, the inclusion of photoperiod sensitivity above the optimum temperature resulted in a higher apparent optimum temperature and less variability in the time of tassel initiation. Given the importance of changes in plant development for crop yield under climate change, the effects of photoperiod and temperature on development rates above the optimum temperature clearly merit further research, and some of the knowledge gaps are identified herein.

Symbiotic N2 fixation activity in relation to C economy of Pisum sativum L. as a function of plant phenology

The relationships between symbiotic nitrogen fixation (SNF) activity and C fluxes were investigated in pea plants (Pisum sativum L. cv. Baccara) using simultaneous 13C and 15N labelling. Analysis of the dynamics of labelled CO2 efflux from the nodulated roots allowed the different components associated with SNF activity to be calculated, together with root and nodule synthetic and maintenance processes. The carbon costs for the synthesis of roots and nodules were similar and decreased with time. Carbon lost by turnover, associated with maintenance processes, decreased with time for nodules while it increased in the roots. Nodule turnover remained higher than root turnover until flowering. The effect of the N source on SNF was investigated using plants supplied with nitrate or plants only fixing N2. SNF per unit nodule biomass (nodule specific activity) was linearly related to the amount of carbon allocated to the nodulated roots regardless of the N source, with regression slopes decreasing across the growth cycle. These regression slopes permitted potential values of SNF specific activity to be defined. SNF activity decreased as the plants aged, presumably because of the combined effects of both increasing C costs of SNF (from 4.0 to 6.7 g C g-1 N) and the limitation of C supply to the nodules. SNF activity competed for C against synthesis and maintenance processes within the nodulated roots. Synthesis was the main limiting factor of SNF, but its importance decreased as the plant aged. At seed-filling, SNF was probably more limited by nodule age than by C supply to the nodulated roots.

The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley

Crop modelling has so far contributed little to the genetic analysis of a quantitative trait. This study illustrates how a simple model for crop phenological development, which assumes that crop development rate is affected by daily effective temperature, can assist the identification of Quantitative Trait Loci (QTLs), using specific leaf area (SLA) in barley as an example. The SLA was measured in a field experiment six times during the growing season of 94 recombinant inbred lines (RILs) derived from a cross between cultivars Prisma and Apex. Of the six measurements, one was conducted at the same physiological age for all RILs (at flowering), four were undertaken at specific chronological days prior to flowering, and the last one was taken at 14 days after flowering. When the measured SLA was directly used as the quantitative trait, one to three QTLs were detected for SLA at each measurement time. The major dwarfing gene denso segregating in the population was found to affect SLA strongly at all measurement times except at flowering. If SLA of the different RILs was corrected for differences in physiological age at the time of measurement, by the use of the crop development model, QTLs were detected for SLA at only three stages. Furthermore, the effect of the denso gene was no longer significant during the preflowering stages. The effect of the denso gene detected in the first instance was therefore the consequence of its direct effect on the duration of the preflowering period. This demonstrates the important role that crop development models can play in QTL analysis of a trait that varies with developmental stage. Potential uses of ecophysiological crop growth models in QTL analysis are briefly discussed.