Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.)

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean.

Variation in CD8 T cell IFNγ differentiation to strains of Toxoplasma gondii is characterized by small effect QTLs with contribution from ROP16

Introduction

Toxoplasma gondii induces a strong CD8 T cell response characterized by the secretion of IFNγ that promotes host survival during infection. The initiation of CD8 T cell IFNγ responses in vitro differs widely between clonal lineage strains of T. gondii, in which type I strains are low inducers, while types II and III strains are high inducers. We hypothesized this phenotype is due to a polymorphic "Regulator Of CD8 T cell Response" (ROCTR).

Methods

Therefore, we screened F1 progeny from genetic crosses between the clonal lineage strains to identify ROCTR. Naïve antigen-specific CD8 T cells (T57) isolated from transnuclear mice, which are specific for the endogenous and vacuolar TGD057 antigen, were measured for their ability to become activated, transcribe Ifng and produce IFNγ in response to T. gondii infected macrophages.

Results

Genetic mapping returned four non-interacting quantitative trait loci (QTL) with small effect on T. gondii chromosomes (chr) VIIb-VIII, X and XII. These loci encompass multiple gene candidates highlighted by ROP16 (chrVIIb-VIII), GRA35 (chrX), TgNSM (chrX), and a pair of uncharacterized NTPases (chrXII), whose locus we report to be significantly truncated in the type I RH background. Although none of the chromosome X and XII candidates bore evidence for regulating CD8 T cell IFNγ responses, type I variants of ROP16 lowered Ifng transcription early after T cell activation. During our search for ROCTR, we also noted the parasitophorous vacuole membrane (PVM) targeting factor for dense granules (GRAs), GRA43, repressed the response suggesting PVM-associated GRAs are important for CD8 T cell activation. Furthermore, RIPK3 expression in macrophages was an absolute requirement for CD8 T cell IFNγ differentiation implicating the necroptosis pathway in T cell immunity to T. gondii.

Discussion

Collectively, our data suggest that while CD8 T cell IFNγ production to T. gondii strains vary dramatically, it is not controlled by a single polymorphism with strong effect. However, early in the differentiation process, polymorphisms in ROP16 can regulate commitment of responding CD8 T cells to IFNγ production which may have bearing on immunity to T. gondii.

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.