GBS-SNP and SSR based genetic mapping and QTL analysis for drought tolerance in upland cotton

Exploring molecular variation and combining ability of local and exotic bread wheat genotypes under well-watered and drought conditions

Drought is one of the most environmental stressors, significantly affecting wheat production, particularly in the face of accelerating climate change. Therefore, developing drought-resistant, high-yielding wheat varieties is essential to ensure sustainable production and maintain global food security as the world population rapidly grows. This study aimed to evaluate the genetic variation of local and imported bread wheat genotypes through simple sequence repeat (SSR) markers and assess their combining ability to identify top-performing genotypes under both normal and drought-stress environments. SSR markers revealed significant genetic diversity among the parental genotypes, which were utilized to develop 28 F1 crosses utilizing diallel mating design. Field trials under well-watered and drought-stressed environments demonstrated that drought significantly reduced all measured agronomic traits. The genotypes were categorized into five clusters based on their drought tolerance, ranging from highly sensitive (group-E) to robustly drought-resistant (group-A). The local variety Sids-12 (P2) was identified as an excellent combiner for breeding shorter and early-maturing cultivars and Line-117 (P3), Line-144 (P4), and Line-123 (P5) for improving grain yield and related traits under drought conditions. The crosses P1×P5, P3×P8, P4×P5, and P6×P7 possessed superior performance under both conditions. Key traits, including plant height, grains per spike, 1,000-grain weight, and spikes per plant, displayed strong correlations with grain yield, providing an effective approach for indirect selection in drought-prone environments.

The complete mitochondrial genome assembly of Capsicum pubescens reveals key evolutionary characteristics of mitochondrial genes of two Capsicum subspecies

BACKGROUND

Pepper (Capsicum pubescens), one of five domesticated pepper species, has unique characteristics, such as numerous hairs on the epidermis of its leaves and stems, black seeds, and vibrant purple flowers. To date, no studies have reported on the complete assembly of the mitochondrial genome (mitogenome) of C. pubescens. Understanding the mitogenome is crucial for further research on C. pubescens.

RESULTS

In our study, we successfully assembled the first mitogenome of C. pubescens, which was assigned the GenBank accession number OP957066. This mitogenome has a length of 454,165 bp and exhibits the typical circular structure observed in most mitogenomes. We annotated a total of 70 genes, including 35 protein-coding genes (PCGs), 30 tRNA genes, 3 rRNA genes, and 2 pseudogenes. Compared to the other three pepper mitogenomes (KJ865409, KJ865410, and MN196478), C. pubescens OP957066 exhibited four unique PCGs (atp4, atp8, mttB, and rps1), while two PCGs (rpl10 and rps3) were absent. Notably, each of the three pepper mitogenomes from C. annuum (KJ865409, KJ865410, and MN196478) experienced the loss of four PCGs (atp4, atp8, mttB, and rps1). To further explore the evolutionary relationships, we reconstructed a phylogenetic tree using the mitogenomes of C. pubescens and fourteen other species. Structural comparison and synteny analysis of the above four pepper mitogenomes revealed that C. pubescens shares high sequence similarity with KJ865409 and that C. pubescens has rearranged with the other three pepper mitogenomes. Interestingly, we observed 72 similar sequences between the mitochondrial and chloroplast genomes, which accounted for 12.60% of the mitogenome, with a total length of 57,207 bp. These sequences encompassed 12 tRNA genes and the rRNA gene (rrn18). Remarkably, selective pressure analysis suggested that the nad5 gene underwent obvious positive selection. Furthermore, a single-base mutation in three genes (nad1, nad2, and nad4) resulted in an amino acid change.

CONCLUSION

This study provides a high-quality mitogenome of pepper, providing valuable molecular data for future investigations into the exchange of genetic information between pepper organelle genomes.

Consensus genetic linkage map and QTL mapping allow to capture the genomic regions associated with agronomic traits in pearl millet

MAIN CONCLUSION

A genetic linkage map representing the pearl millet genome was constructed with SNP markers. Major and stable QTL associated with flowering, number of productive tillers, ear head length, and test weight were mapped on chromosomes 1 and 3. Pearl millet (Pennisetum glaucum) is a major cereal and fodder crop in arid and semi-arid regions of Asia and Africa. Agronomic traits are important traits in pearl millet breeding and genetic and environmental factors highly influence them. In the present study, an F9 recombinant inbred line (RIL) population derived from a cross between PT6029 and PT6129 was evaluated for agronomic traits in three environments. Utilizing a genotyping by sequencing approach, a dense genetic map with 993 single nucleotide polymorphism markers covering a total genetic distance of 1035.4 cM was constructed. The average interval between the markers was 1.04 cM, and the seven chromosomes varied from 115.39 to 206.72 cM. Quantitative trait loci (QTL) mapping revealed 35 QTL for seven agronomic traits, and they were distributed on all pearl millet chromosomes. These QTL individually explained 11.35 to 26.71% of the phenotypic variation, with LOD values ranging from 2.74 to 5.80. Notably, four QTL (qDFF1.1, qNPT3.1, qEHL3.1, and qTW1.1) associated with days to fifty percent flowering, the number of productive tillers, ear head length, and test weight were found to be major and stable QTL located on chromosomes 1 and 3. Collectively, our results provide an important base for understanding the genetic architecture of agronomic traits in pearl millet, which is useful for accelerating the genetic gain toward crop improvement.

Genetic Analysis of Cotton Fiber Traits in Gossypium Hybrid Lines

Cotton plays a crucial role in the progress of the textile industry and the betterment of human life by providing natural fibers. In our study, we explored the genetic determinants of cotton architecture and fiber yield and quality by crossbreeding Gossypium hirsutum and Gossypium barbadense, creating a recombinant inbred line (RIL) population. Utilizing SNP markers, we constructed an extensive genetic map encompassing 7,730 markers over 2,784.2 cM. We appraised two architectural and seven fiber traits within six environments, identifying 58 QTLs, of which 49 demonstrated stability across these environments. These encompassed QTLs for traits such as lint percentage (LP), boll weight (BW), fiber strength (STRENGTH), seed index (SI), and micronaire (MIC), primarily located on chromosomes chr-A07, chr-D06, and chr-D07. Notably, chr-D07 houses a QTL region affecting SI, corroborated by multiple studies. Within this region, the genes BZIP043 and SEP2 were identified as pivotal, with SEP2 particularly showing augmented expression in developing ovules. These discoveries contribute significantly to marker-assisted selection, potentially elevating both the yield and quality of cotton fiber production. These findings provide valuable insights into marker-assisted breeding strategies, offering crucial information to enhance fiber yield and quality in cotton production.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Genome-wide association study of fiber yield-related traits uncovers the novel genomic regions and candidate genes in Indian upland cotton (Gossypium hirsutum L.)

Upland cotton (Gossypium hirsutum L.) is a major fiber crop that is cultivated worldwide and has significant economic importance. India harbors the largest area for cotton cultivation, but its fiber yield is still compromised and ranks 22nd in terms of productivity. Genetic improvement of cotton fiber yield traits is one of the major goals of cotton breeding, but the understanding of the genetic architecture underlying cotton fiber yield traits remains limited and unclear. To better decipher the genetic variation associated with fiber yield traits, we conducted a comprehensive genome-wide association mapping study using 117 Indian cotton germplasm for six yield-related traits. To accomplish this, we generated 2,41,086 high-quality single nucleotide polymorphism (SNP) markers using genotyping-by-sequencing (GBS) methods. Population structure, PCA, kinship, and phylogenetic analyses divided the germplasm into two sub-populations, showing weak relatedness among the germplasms. Through association analysis, 205 SNPs and 134 QTLs were identified to be significantly associated with the six fiber yield traits. In total, 39 novel QTLs were identified in the current study, whereas 95 QTLs overlapped with existing public domain data in a comparative analysis. Eight QTLs, qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, qGhBN_SCY_D6-3, qGhSI_LI_A5, qGhLI_SI_A13, qGhLI_SI_D9, qGhBW_SCY_A10, and qGhLP_BN_A8 were identified. Gene annotation of these fiber yield QTLs revealed 2,509 unique genes. These genes were predominantly enriched for different biological processes, such as plant cell wall synthesis, nutrient metabolism, and vegetative growth development in the gene ontology (GO) enrichment study. Furthermore, gene expression analysis using RNAseq data from 12 diverse cotton tissues identified 40 candidate genes (23 stable and 17 novel genes) to be transcriptionally active in different stages of fiber, ovule, and seed development. These findings have revealed a rich tapestry of genetic elements, including SNPs, QTLs, and candidate genes, and may have a high potential for improving fiber yield in future breeding programs for Indian cotton.

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses

The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.

Genome-wide association analysis revealed genetic variation and candidate genes associated with the yield traits of upland cotton under drought conditions

Drought is one of the major abiotic stresses seriously affecting cotton yield. At present, the main cotton-producing areas in China are primarily arid and semiarid regions. Therefore, the identification of molecular markers and genes associated with cotton yield traits under drought conditions is of great importance for stabilize cotton yield under such conditions. In this study, resequencing data were used to conduct a genome-wide association study (GWAS) on 8 traits of 150 cotton germplasms. Under drought stress, 18 SNPs were significantly correlated with yield traits (single-boll weight (SBW) and seed (SC)), and 8 SNPs were identified as significantly correlated with effective fruit shoot number (EFBN) traits (a trait that is positively correlated with yield). Finally, a total of 15 candidate genes were screened. The combined results of the GWAS and transcriptome data analysis showed that four genes were highly expressed after drought stress, and these genes had significantly increased expression at 10, 15 and 25 DPA of fiber development. qRT-PCR was performed on two samples with drought tolerance extremes (drought-resistant Xinluzao 45 and drought-sensitive Xinluzao 26), revealing that three of the genes had the same differential expression pattern. This study provides a theoretical basis for the genetic analysis of cotton yield traits under drought stress, and provides gene resources for improved breeding of cotton yield traits under drought stress.

Identification of Stable and Multiple Environment Interaction QTLs and Candidate Genes for Fiber Productive Traits Under Irrigated and Water Stress Conditions Using Intraspecific RILs of Gossypium hirsutum var. MCU5 X TCH1218

Cotton productivity under water-stressed conditions is controlled by multiple quantitative trait loci (QTL). Enhancement of these productivity traits under water deficit stress is crucial for the genetic improvement of upland cotton, Gossypium hirsutum. In the present study, we constructed a genetic map with 504 single nucleotide polymorphisms (SNPs) covering a total span length of 4,416 cM with an average inter-marker distance of 8.76 cM. A total of 181 intra-specific recombinant inbred lines (RILs) were derived from a cross between G. hirsutum var. MCU5 and TCH1218 were used. Although 2,457 polymorphic SNPs were detected between the parents using the CottonSNP50K assay, only 504 SNPs were found to be useful for the construction of the genetic map. In the SNP genotyping, a large number of SNPs showed either >20% missing data, duplication, or segregation distortion. However, the mapped SNPs of this study showed collinearity with the physical map of the reference genome (G. hirsutum var.TM-1), indicating that there was no chromosomal rearrangement within the studied mapping population. RILs were evaluated under multi-environments and seasons for which the phenotypic data were acquired. A total of 53 QTL controlling plant height (PH), number of sympodial branches, boll number (BN), and boll weight (BW) were dissected by QTL analysis under irrigated and water stress conditions. Additionally, it was found that nine QTL hot spots not only co-localized for more than one investigated trait but were also stable with major QTL, i.e., with > 10% of phenotypic variation. One QTL hotspot on chromosome 22 flanked by AX-182254626-AX-182264770 with a span length of 89.4 cM co-localized with seven major and stable QTL linked to a number of sympodial branches both under irrigated and water stress conditions. In addition, putative candidate genes associated with water stress in the QTL hotspots were identified. Besides, few QTL from the hotspots were previously reported across various genetic architects in cotton validating the potential applications of these identified QTL for cotton breeding and improvement. Thus, the major and stable QTL identified in the present study would improve the cotton productivity under water-limited environments through marker-assisted selection.

Construction of a high-density genetic linkage map and QTL analysis of morphological traits in an F1 Malusdomestica × Malus baccata hybrid

Apple is considered the most commonly grown fruit crop in temperate regions that brings great economic profits to fruit growers. Dwarfing rootstocks have been extensively used in apple breeding as well as commercial orchards, but the molecular and genetic basis of scion dwarfing and other morphological traits induced by them is still unclear. At present, we report a genetic map of Malusdomestica × Malus baccata with high density. The F₁ population was sequenced by a specific length amplified fragment (SLAF). In the genetic map, 5064 SLAF markers spanning 17 linkage groups (LG) were included. Dwarf-related and other phenotypic traits of the scion were evaluated over a 3-year growth period. Based on quantitative trait loci (QTL) evaluation of plant height and trunk diameter, two QTL clusters were found on LG 11, which exhibited remarkable influences on dwarfing of the scion. In this analysis, QTL DW2, which was previously reported as a locus that controls dwarfing, was confirmed. Moreover, three novel QTLs for total flower number and branching flower number were detected on LG2 and LG4, exhibited the phenotypic variation that has been explained by QTL ranging from 8.80% to 34.80%. The findings of the present study are helpful to find scion dwarfing and other phenotypes induced by rootstock in the apple.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-01069-0.