Linkage mapping and QTL analysis of flowering time using ddRAD sequencing with genotype error correction in Brassica napus

Construction of a high-density genetic linkage map and QTL mapping for bioenergy-related traits in sweet sorghum [Sorghum bicolor (L.) Moench]

Sorghum is an important but arguably undervalued cereal crop, grown in large areas in Asia and Africa due to its natural resilience to drought and heat. There is growing demand for sweet sorghum as a source of bioethanol as well as food and feed. The improvement of bioenergy-related traits directly affects bioethanol production from sweet sorghum; therefore, understanding the genetic basis of these traits would enable new cultivars to be developed for bioenergy production. In order to reveal the genetic architecture behind bioenergy-related traits, we generated an F₂ population from a cross between sweet sorghum cv. 'Erdurmus' and grain sorghum cv. 'Ogretmenoglu'. This was used to construct a genetic map from SNPs discovered by double-digest restriction-site associated DNA sequencing (ddRAD-seq). F₃ lines derived from each F₂ individual were phenotyped for bioenergy-related traits in two different locations and their genotypes were analyzed with the SNPs to identify QTL regions. On chromosomes 1, 7, and 9, three major plant height (PH) QTLs (qPH1.1, qPH7.1, and qPH9.1) were identified, with phenotypic variation explained (PVE) ranging from 10.8 to 34.8%. One major QTL (qPJ6.1) on chromosome 6 was associated with the plant juice trait (PJ) and explained 35.2% of its phenotypic variation. For fresh biomass weight (FBW), four major QTLs (qFBW1.1, qFBW6.1, qFBW7.1, and qFBW9.1) were determined on chromosomes 1, 6, 7, and 9, which explained 12.3, 14.5, 10.6, and 11.9% of the phenotypic variation, respectively. Moreover, two minor QTLs (qBX3.1 and qBX7.1) of Brix (BX) were mapped on chromosomes 3 and 7, explaining 8.6 and 9.7% of the phenotypic variation, respectively. The QTLs in two clusters (qPH7.1/qBX7.1 and qPH7.1/qFBW7.1) overlapped for PH, FBW and BX. The QTL, qFBW6.1, has not been previously reported. In addition, eight SNPs were converted into cleaved amplified polymorphic sequences (CAPS) markers, which can be easily detected by agarose gel electrophoresis. These QTLs and molecular markers can be used for pyramiding and marker-assisted selection studies in sorghum, to develop advanced lines that include desirable bioenergy-related traits.

Structural variations and environmental specificities of flowering time-related genes in Brassica napus

We found that the flowering time order of accessions in a genetic population considerably varied across environments, and homolog copies of essential flowering time genes played different roles in different locations. Flowering time plays a critical role in determining the life cycle length, yield, and quality of a crop. However, the allelic polymorphism of flowering time-related genes (FTRGs) in Brassica napus, an important oil crop, remains unclear. Here, we provide high-resolution graphics of FTRGs in B. napus on a pangenome-wide scale based on single nucleotide polymorphism (SNP) and structural variation (SV) analyses. A total of 1337 FTRGs in B. napus were identified by aligning their coding sequences with Arabidopsis orthologs. Overall, 46.07% of FTRGs were core genes and 53.93% were variable genes. Moreover, 1.94%, 0.74%, and 4.49% FTRGs had significant presence-frequency differences (PFDs) between the spring and semi-winter, spring and winter, and winter and semi-winter ecotypes, respectively. SNPs and SVs across 1626 accessions of 39 FTRGs underlying numerous published qualitative trait loci were analyzed. Additionally, to identify FTRGs specific to an eco-condition, genome-wide association studies (GWASs) based on SNP, presence/absence variation (PAV), and SV were performed after growing and observing the flowering time order (FTO) of plants in a collection of 292 accessions at three locations in two successive years. It was discovered that the FTO of plants in a genetic population changed a lot across various environments, and homolog copies of some key FTRGs played different roles in different locations. This study revealed the molecular basis of the genotype-by-environment (G × E) effect on flowering and recommended a pool of candidate genes specific to locations for breeding selection.

Integrate QTL Mapping and Transcription Profiles Reveal Candidate Genes Regulating Flowering Time in Brassica napus

Flowering at the proper time is an important part of acclimation to the ambient environment and season and maximizes the plant yield. To reveal the genetic architecture and molecular regulation of flowering time in oilseed rape (Brassica napus), we performed an RNA-seq analysis of the two parents after vernalization at low temperature and combined this with quantitative trait loci (QTL) mapping in an F₂ population. A genetic linkage map that included 1,017 markers merged into 268 bins and covered 793.53 cM was constructed. Two QTLs associated with flowering time were detected in the F₂ population. qFTA06 was the major QTL in the 7.06 Mb interval on chromosome A06 and accounted for 19.3% of the phenotypic variation. qFTC08 was located on chromosome C06 and accounted for 8.6% of the phenotypic variation. RNA-seq analysis revealed 4,626 differentially expressed genes (DEGs) between two parents during vernalization. Integration between QTL mapping and RNA-seq analysis revealed six candidate genes involved in the regulation of flowering time through the circadian clock/photoperiod, auxin and ABA hormone signal, and cold signal transduction and vernalization pathways. These results provide insights into the molecular genetic architecture of flowering time in B. napus.

High-resolution mapping of the quantitative trait locus (QTLs) conferring resistance to false smut disease in rice

Rice false smut (RFS), an emerging major fungal disease worldwide caused by Ustilaginoidea virens, affects rice grain quality and yield. RFS cause 2.8-49% global yield loss depending upon disease severity and cultivars. In India, the yield loss due to RFS ranged from 2 to 75%. Identification of the genes or quantitative trait loci (QTLs) governing disease resistance would be of utmost importance towards mitigating the economic losses incurred due to RFS. Here, we report mapping of RFS resistance QTLs from a resistant breeding line RYT2668. The mapping population was evaluated for RFS resistance under the field condition in three cropping seasons 2013, 2015, and 2016. A positive correlation among infected panicle/plant, total smut ball/panicle, and disease score was observed in the years 2013, 2015, and the mean data. A total of seven QTLs were mapped on rice chromosomes 2, 4, 5, 7, and 9 using 2326 single nucleotide polymorphism markers. Of these, two QTLs, qRFSr5.3 and qRFSr7.1a, were associated with the infected panicle per plant, one QTL qRFsr9.1 with total smut ball per panicle, and four QTLs qRFSr2.2, qRFSr4.3, qRFSr5.4, and qRFSr7.1b with disease score. Among them, a novel QTL qRFSr9.1 on chromosome 9 exhibits the largest phenotypic effect. The prediction of putative candidate genes within the qRFSr9.1 revealed four nucleotide-binding sites-leucine-rich repeat (NBS-LRR) domain-containing disease resistance proteins. In summary, our findings mark the hotspot region of rice chromosomes carrying genes/QTLs for resistance to the RFS disease.

Construction of a High-Density Genetic Map and Identification of Leaf Trait-Related QTLs in Chinese Bayberry (Myrica rubra)

Chinese bayberry (Myrica rubra) is an economically important fruit tree that is grown in southern China. Owing to its over 10-year seedling period, the crossbreeding of bayberry is challenging. The characteristics of plant leaves are among the primary factors that control plant architecture and potential yields, making the analysis of leaf trait-related genetic factors crucial to the hybrid breeding of any plant. In the present study, molecular markers associated with leaf traits were identified via a whole-genome re-sequencing approach, and a genetic map was thereby constructed. In total, this effort yielded 902.11 Gb of raw data that led to the identification of 2,242,353 single nucleotide polymorphisms (SNPs) in 140 F1 individuals and parents (Myrica rubra cv. Biqizhong × Myrica rubra cv. 2012LXRM). The final genetic map ultimately incorporated 31,431 SNPs in eight linkage groups, spanning 1,351.85 cM. This map was then used to assemble and update previous scaffold genomic data at the chromosomal level. The genome size of M. rubra was thereby established to be 275.37 Mb, with 94.98% of sequences being assembled into eight pseudo-chromosomes. Additionally, 18 quantitative trait loci (QTLs) associated with nine leaf and growth-related traits were identified. Two QTL clusters were detected (the LG3 and LG5 clusters). Functional annotations further suggested two chlorophyll content-related candidate genes being identified in the LG5 cluster. Overall, this is the first study on the QTL mapping and identification of loci responsible for the regulation of leaf traits in M. rubra, offering an invaluable scientific for future marker-assisted selection breeding and candidate gene analyses.

QTL Analysis of Five Morpho-Physiological Traits in Bread Wheat Using Two Mapping Populations Derived from Common Parents

Traits such as plant height (PH), juvenile growth habit (GH), heading date (HD), and tiller number are important for both increasing yield potential and improving crop adaptation to climate change. In the present study, these traits were investigated by using the same bi-parental population at early (F₂ and F₂-derived F₃ families) and late (F₆ and F₇, recombinant inbred lines, RILs) generations to detect quantitative trait loci (QTLs) and search for candidate genes. A total of 176 and 178 lines were genotyped by the wheat Illumina 25K Infinium SNP array. The two genetic maps spanned 2486.97 cM and 3732.84 cM in length, for the F₂ and RILs, respectively. QTLs explaining the highest phenotypic variation were found on chromosomes 2B, 2D, 5A, and 7D for HD and GH, whereas those for PH were found on chromosomes 4B and 4D. Several QTL detected in the early generations (i.e., PH and tiller number) were not detected in the late generations as they were due to dominance effects. Some of the identified QTLs co-mapped to well-known adaptive genes (i.e., Ppd-1, Vrn-1, and Rht-1). Other putative candidate genes were identified for each trait, of which PINE1 and PIF4 may be considered new for GH and TTN in wheat. The use of a large F₂ mapping population combined with NGS-based genotyping techniques could improve map resolution and allow closer QTL tagging.