Construction of High-Density Genetic Map and Mapping Quantitative Trait Loci for Growth Habit-Related Traits of Peanut (Arachis hypogaea L.)

Advances in the evolution research and genetic breeding of peanut

Peanut is an important cash crop used in oil, food and feed in our country. The rapid development of sequencing technology has promoted the research on the related aspects of peanut genetic breeding. This paper reviews the research progress of peanut origin and evolution, genetic breeding, molecular markers and their applications, genomics, QTL mapping and genome selection techniques. The main problems of molecular genetic breeding in peanut research worldwide include: the narrow genetic resources of cultivated species, unstable genetic transformation and unclear molecular mechanism of important agronomic traits. Considering the severe challenges regarding the supply of edible oil, and the main problems in peanut production, the urgent research directions of peanut are put forward: The de novo domestication and the exploitation of excellent genes from wild resources to improve modern cultivars; Integration of multi-omics data to enhance the importance of big data in peanut genetics and breeding; Cloning the important genes related to peanut agronomic traits and analyzing their fine regulation mechanisms; Precision molecular design breeding and using gene editing technology to accurately improve the key traits of peanut.

Identification of a major QTL underlying sugar content in peanut kernels based on the RIL mapping population

High sugar content in peanut seeds is one of the major breeding objectives for peanut flavor improvement. In order to explore the genetic control of sugar accumulation in peanut kernels, we constructed a recombinant inbred line population of 256 F2:6-7 lines derived from the Luhua11 × 06B16 cross. A high-resolution genetic map was constructed with 3692 bin markers through whole genome re-sequencing. The total map distance was 981.65 cM and the average bin marker distance was 0.27cM. A major stable QTL region (qSCB09/qSSCB09) was identified on linkage group (LG) B09 associated with both sucrose content (SC) and soluble sugar content (SSC) explaining 21.51-33.58% phenotypic variations. This major QTL region was consistently detected in three environments and mapped within a physical interval of 1.56 Mb on chromosome B09, and six candidate genes were identified. These results provide valuable information for further map-based cloning of favorable allele for sugar content in peanut.

Designing future peanut: the power of genomics-assisted breeding

KEY MESSAGE

Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

A genomic variation map provides insights into peanut diversity in China and associations with 28 agronomic traits

Peanut (Arachis hypogaea L.) is an important allotetraploid oil and food legume crop. China is one of the world's largest peanut producers and consumers. However, genomic variations underlying the migration and divergence of peanuts in China remain unclear. Here we reported a genome-wide variation map based on the resequencing of 390 peanut accessions, suggesting that peanuts might have been introduced into southern and northern China separately, forming two cultivation centers. Selective sweep analysis highlights asymmetric selection between the two subgenomes during peanut improvement. A classical pedigree from South China offers a context for the examination of the impact of artificial selection on peanut genome. Genome-wide association studies identified 22,309 significant associations with 28 agronomic traits, including candidate genes for plant architecture and oil biosynthesis. Our findings shed light on peanut migration and diversity in China and provide valuable genomic resources for peanut improvement.

Detection of two homologous major QTLs and development of diagnostic molecular markers for sucrose content in peanut

KEY MESSAGE

We identified two stable and homologous major QTLs for sucrose content in peanut, and developed breeder-friendly molecular markers for marker-assisted selection breeding. Sucrose content is a crucial quality trait for edible peanuts, and increasing sucrose content is a key breeding objective. However, the genetic basis of sucrose content in peanut remains unclear, and major quantitative trait loci (QTLs) for sucrose content have yet to be identified. In this study, a high-density genetic map was constructed based on whole-genome re-sequencing data from a peanut RIL population. This map consisted of 2,042 bins and 24,142 SNP markers, making it one of the most comprehensive maps to date in terms of marker density. Two major QTLs (qSCA06.2 and qSCB06.2) were identified, explaining 31.41% and 24.13% of the phenotypic variance, respectively. Notably, these two QTLs were located in homologous genomic regions between the A and B subgenomes. The elite allele of qSCA06.2 was exclusive to Valencia-type, while the elite allele of qSCB06.2 existed in other peanut types. Importantly, the distribution of alleles from two homologous QTLs in the RIL population and diverse germplasm accessions consistently demonstrated that only the combination of elite allelic genotypes from both QTLs/genes resulted in a significantly dominant phenotype, accompanied by a substantial increase in sucrose content. The newly developed diagnostic markers for these QTLs were confirmed to be reliable and could facilitate future breeding efforts to enhance sucrose content using marker-assisted selection techniques. Overall, this study highlights the co-regulation of sucrose content by two major homologous QTLs/genes and provides valuable insights into the genetic basis of sucrose in peanuts.

The use of weighted multiple linear regression to estimate QTL × QTL × QTL interaction effects of winter wheat (Triticum aestivum L.) doubled-haploid lines

Knowledge of the magnitude of gene effects and their interactions, their nature, and contribution to determining quantitative traits is very important in conducting an effective breeding program. In traditional breeding, information on the parameter related to additive gene effect and additive-additive interaction (epistasis) and higher-order additive interactions would be useful. Although commonly overlooked in studies, higher-order interactions have a significant impact on phenotypic traits. Failure to account for the effect of triplet interactions in quantitative genetics can significantly underestimate additive QTL effects. Understanding the genetic architecture of quantitative traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL-QTL interactions, and QTL-QTL-QTL interactions. This paper proposes using weighted multiple linear regression to estimate the effects of triple interaction (additive-additive-additive) quantitative trait loci (QTL-QTL-QTL). The material for the study consisted of 126 doubled haploid lines of winter wheat (Mandub × Begra cross). The lines were analyzed for 18 traits, including percentage of necrosis leaf area, percentage of leaf area covered by pycnidia, heading data, and height. The number of genes (the number of effective factors) was lower than the number of QTLs for nine traits, higher for four traits and equal for five traits. The number of triples for unweighted regression ranged from 0 to 9, while for weighted regression, it ranged from 0 to 13. The total aaagu effect ranged from - 14.74 to 15.61, while aaagw ranged from - 23.39 to 21.65. The number of detected threes using weighted regression was higher for two traits and lower for four traits. Forty-nine statistically significant threes of the additive-by-additive-by-additive interaction effects were observed. The QTL most frequently occurring in threes was 4407404 (9 times). The use of weighted regression improved (in absolute value) the assessment of QTL-QTL-QTL interaction effects compared to the assessment based on unweighted regression. The coefficients of determination for the weighted regression model were higher, ranging from 0.8 to 15.5%, than for the unweighted regression. Based on the results, it can be concluded that the QTL-QTL-QTL triple interaction had a significant effect on the expression of quantitative traits. The use of weighted multiple linear regression proved to be a useful statistical tool for estimating additive-additive-additive (aaa) interaction effects. The weighted regression also provided results closer to phenotypic evaluations than estimator values obtained using unweighted regression, which is closer to the true values.

Mapping Quantitative Trait Loci (QTLs) for Hundred-Pod and Hundred-Seed Weight under Seven Environments in a Recombinant Inbred Line Population of Cultivated Peanut (Arachis hypogaea L.)

The cultivated peanut (Arachis hypogaea L.) is a significant oil and cash crop globally. Hundred-pod and -seed weight are important components for peanut yield. To unravel the genetic basis of hundred-pod weight (HPW) and hundred-seed weight (HSW), in the current study, a recombinant inbred line (RIL) population with 188 individuals was developed from a cross between JH5 (JH5, large pod and seed weight) and M130 (small pod and seed weight), and was utilized to identify QTLs for HPW and HSW. An integrated genetic linkage map was constructed by using SSR, AhTE, SRAP, TRAP and SNP markers. This map consisted of 3130 genetic markers, which were assigned to 20 chromosomes, and covered 1998.95 cM with an average distance 0.64 cM. On this basis, 31 QTLs for HPW and HSW were located on seven chromosomes, with each QTL accounting for 3.7-10.8% of phenotypic variance explained (PVE). Among these, seven QTLs were detected under multiple environments, and two major QTLs were found on B04 and B08. Notably, a QTL hotspot on chromosome A08 contained seven QTLs over a 2.74 cM genetic interval with an 0.36 Mb physical map, including 18 candidate genes. Of these, Arahy.D52S1Z, Arahy.IBM9RL, Arahy.W18Y25, Arahy.CPLC2W and Arahy.14EF4H might play a role in modulating peanut pod and seed weight. These findings could facilitate further research into the genetic mechanisms influencing pod and seed weight in cultivated peanut.

Uncovering mechanisms governing stem growth in peanut (Arachis hypogaea L.) with varying plant heights through integrated transcriptome and metabolomics analyses

The mechanisms responsible for stem growth in peanut (Arachis hypogaea L.) cultivars with varying plant heights remain unclear, despite the significant impact of plant height on peanut yield. Therefore, this study aimed to investigate the underlying mechanisms of peanut stem growth using phenotypic, physiological, transcriptomic, and metabolomic analyses. The findings revealed that the tallest cultivar, HY33, exhibited the highest rate of stem growth and accumulated the most stem dry matter, followed by the intermediate cultivar, SH108, while the dwarf cultivar, Df216, displayed the lowest values. Furthermore, SH108 exhibited a higher harvest index, as well as superior pod and kernel yields compared to both HY33 and Df216. Transcriptome and metabolome analyses identified differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs) associated with phenylpropanoid and flavonoid biosynthesis. Notably, downregulated DEGs in Df216/HY33 and Df216/SH108 included phenylalanine ammonia-lyase (PAL), caffeoyl-CoA O-methyltransferase (COMT), and ferulate-5-hydroxylase (F5H), while downregulated DEMs included p-coumaryl alcohol, chlorogenic acid, and L-epicatechin. Compared to HY33, the reduced activities of PAL, COMT, and F5H resulted in a decreased stem lignin content in Df216. Additionally, downregulated DEGs involved in gibberellin (GA) and brassinosteroid (BR) biosynthesis were identified in Df216/HY33, which contributed to the lowest levels of GA1, GA3, and BR contents in Df216. The results suggest that the dwarf phenotype arises from impaired GA and BR biosynthesis and signaling, resulting in a slower stem growth rate and reduced lignin accumulation.

Simultaneously mapping loci related to two plant architecture traits by phenotypic recombination BSA/BSR in peanut (Arachis hypogaea L.)

KEY MESSAGE

We developed a new method phenotypic recombination BSA/BSR (PR-BSA/BSR), which could simultaneously identify the candidate genomic regions associated with two traits in a segregating population. Bulked segregant analysis sequencing (BSA-seq) has been widely used for identifying the genomic regions affecting a certain trait. In this study, we developed a modified BSA/bulked segregant RNA-sequencing (BSR-seq) method, which we named phenotypic recombination BSA/BSR (PR-BSA/BSR), to simultaneously identify candidate genomic regions associated with two traits in a segregating population. Lateral branch angle (LBA) and flower-branch pattern (FBP) are two important traits associated with the peanut plant architecture because they affect the planting density and light use efficiency. We generated an F₆ population (with two segregating traits) derived from a cross between the inbred lines Pingdu9616 (erect and sequential; ES-type) and Florunner (spreading and alternating; SA-type). The selection of bulks with extreme phenotypes was a key step in this study. Specifically, 30 individuals with recombinant phenotypes [i.e., spreading and sequential (SS-type) and erect and alternating (EA-type)] were selected to generate two bulks. The transcriptomes of individuals were sequenced and then the loci related to LBA and FBP were simultaneously detected via a ΔSNP-index strategy, which involved the direction of positive and negative peaks in the ∆SNP-index plot. The LBA-related locus was mapped to a 6.82 Mb region (101,743,223-108,564,267 bp) on chromosome 15, whereas the FBP-related locus was mapped to a 2.16 Mb region (117,682,534-119,846,824 bp) on chromosome 12. Furthermore, the marker-based classical QTL mapping method was used to analyze the PF-F₆ population, which confirmed our PR-BSA/BSR results. Therefore, the PR-BSA/BSR method produces accurate and reliable data.

An Overview of Mapping Quantitative Trait Loci in Peanut (Arachis hypogaea L.)

Quantitative Trait Loci (QTL) mapping has been thoroughly used in peanut genetics and breeding in spite of the narrow genetic diversity and the segmental tetraploid nature of the cultivated species. QTL mapping is helpful for identifying the genomic regions that contribute to traits, for estimating the extent of variation and the genetic action (i.e., additive, dominant, or epistatic) underlying this variation, and for pinpointing genetic correlations between traits. The aim of this paper is to review the recently published studies on QTL mapping with a particular emphasis on mapping populations used as well as traits related to kernel quality. We found that several populations have been used for QTL mapping including interspecific populations developed from crosses between synthetic tetraploids and elite varieties. Those populations allowed the broadening of the genetic base of cultivated peanut and helped with the mapping of QTL and identifying beneficial wild alleles for economically important traits. Furthermore, only a few studies reported QTL related to kernel quality. The main quality traits for which QTL have been mapped include oil and protein content as well as fatty acid compositions. QTL for other agronomic traits have also been reported. Among the 1261 QTL reported in this review, and extracted from the most relevant studies on QTL mapping in peanut, 413 (~33%) were related to kernel quality showing the importance of quality in peanut genetics and breeding. Exploiting the QTL information could accelerate breeding to develop highly nutritious superior cultivars in the face of climate change.

Identification of quantitative trait loci and development of diagnostic markers for growth habit traits in peanut (Arachis hypogaea L.)

KEY MESSAGE

QTLs for growth habit are identified on Arahy.15 and Arahy.06 in peanut, and diagnostic markers are developed and validated for further use in marker-assisted breeding. Peanut is a unique legume crop because its pods develop and mature underground. The pegs derive from flowers following pollination, then reach the ground and develop into pods in the soil. Pod number per plant is influenced by peanut growth habit (GH) that has been categorized into four types, including erect, bunch, spreading and prostrate. Restricting pod development at the plant base, as would be the case for peanut plants with upright lateral branches, would decrease pod yield. On the other hand, GH characterized by spreading lateral branches on the ground would facilitate pod formation on the nodes, thereby increasing yield potential. We describe herein an investigation into the GH traits of 521 peanut recombinant inbred lines grown in three distinct environments. Quantitative trait loci (QTLs) for GH were identified on linkage group (LG) 15 between 203.1 and 204.2 cM and on LG 16 from 139.1 to 139.3 cM. Analysis of resequencing data in the identified QTL regions revealed that single nucleotide polymorphism (SNP) or insertion and/or deletion (INDEL) at Arahy15.156854742, Arahy15.156931574, Arahy15.156976352 and Arahy06.111973258 may affect the functions of their respective candidate genes, Arahy.QV02Z8, Arahy.509QUQ, Arahy.ATH5WE and Arahy.SC7TJM. These SNPs and INDELs in relation to peanut GH were further developed for KASP genotyping and tested on a panel of 77 peanut accessions with distinct GH features. This study validates four diagnostic markers that may be used to distinguish erect/bunch peanuts from spreading/prostrate peanuts, thereby facilitating marker-assisted selection for GH traits in peanut breeding.

Genetic analysis and exploration of major effect QTLs underlying oil content in peanut

KEY MESSAGE

AhyHOF1, likely encoding a WRI1 transcription factor, plays critical roles in peanut oil synthesis. Although increasing the oil content of peanut to meet growing demand has long been a primary aim of breeding programs worldwide, the mining of genetic resources to achieve this objective has obviously lagged behind that of other oil crops. In the present study, we developed an advanced recombinant inbred line population containing 192 F9:11 families derived from parents JH5 and KX01-6. We then constructed a high-resolution genetic map covering 3,706.382 cM, with an average length of 185.32 cM per linkage group, using 2840 polymorphic SNPs. Two stable QTLs, qCOA08_1 and qCOA08_2 having the highest contributions to genetic variation (16.1% and 20.7%, respectively), were simultaneously detected in multiple environments and closely mapped within physical intervals of approximately 2.9 Mb and 1.7 Mb, respectively, on chromosome A08. In addition, combined analysis of whole-genome and transcriptome resequencing data uncovered a strong candidate gene encoding a WRI1 transcription factor and differentially expressed between the two parents. This gene, designated as High Oil Favorable gene 1 in Arachis hypogaea (AhyHOF1), was hypothesized to play roles in oil accumulation. Examination of near-inbred lines of #AhyHOF1/#Ahyhof1 provided further evidence that AhyHOF1 increases oil content, mainly by affecting the contents of several fatty acids. Taken together, our results provide valuable information for cloning the favorable allele for oil content in peanut. In addition, the closely linked polymorphic SNP markers within qCOA08_1 and qCOA08_2 loci may be useful for accelerating marker-assisted selection breeding of peanut.

BSA‑seq and genetic mapping identified candidate genes for branching habit in peanut

The candidate gene AhLBA1 controlling lateral branch angel of peanut was fine-mapped to a 136.65-kb physical region on chromosome 15 using the BSA-seq and QTL mapping. Lateral branch angel (LBA) is an important plant architecture trait of peanut, which plays key role in lodging, peg soil penetration and pod yield. However, there are few reports of fine mapping and quantitative trait loci (QTLs)/cloned genes for LBA in peanut. In this project, a mapping population was constructed using a spreading variety Tifrunner and the erect variety Fuhuasheng. Through bulked segregant analysis sequencing (BSA-seq), a major gene related to LBA, named as AhLBA1, was preliminarily mapped at the region of Chr.15: 150-160 Mb. Then, using traditional QTL approach, AhLBA1 was narrowed to a 1.12 cM region, corresponding to a 136.65-kb physical interval of the reference genome. Of the nine genes housed in this region, three of them were involved in hormone metabolism and regulation, including one "F-box protein" and two "2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase (2OG oxygenase)" encoding genes. In addition, we found that the level of some classes of cytokinin (CK), auxin and ethylene showed significant differences between spreading and erect peanuts at the junction of main stem and lateral branch. These findings will aid further elucidation of the genetic mechanism of LBA in peanut and facilitating marker-assisted selection (MAS) in the future breeding program.

De novo genes in Arachis hypogaea cv. Tifrunner: systematic identification, molecular evolution, and potential contributions to cultivated peanut

De novo genes are derived from non-coding sequences, and they can play essential roles in organisms. Cultivated peanut (Arachis hypogaea) is a major oil and protein crop derived from a cross between Arachis duranensis and Arachis ipaensis. However, few de novo genes have been documented in Arachis. Here, we identified 381 de novo genes in A. hypogaea cv. Tifrunner based on comparison with five closely related Arachis species. There are distinct differences in gene expression patterns and gene structures between conserved and de novo genes. The identified de novo genes originated from ancestral sequence regions associated with metabolic and biosynthetic processes, and they were subsequently integrated into existing regulatory networks. De novo paralogs and homoeologs were identified in A. hypogaea cv. Tifrunner. De novo paralogs and homoeologs with conserved expression have mismatching cis-acting elements under normal growth conditions. De novo genes potentially have pluripotent functions in responses to biotic stresses as well as in growth and development based on quantitative trait locus data. This work provides a foundation for future research examining gene birth processes and gene function in Arachis and related taxa.

QTL identification, fine mapping, and marker development for breeding peanut (Arachis hypogaea L.) resistant to bacterial wilt

A major QTL, qBWA12, was fine mapped to a 216.68 kb physical region, and A12.4097252 was identified as a useful KASP marker for breeding peanut varieties resistant to bacterial wilt. Bacterial wilt, caused by Ralstonia solanacearum, is a major disease detrimental to peanut production in China. Breeding disease-resistant peanut varieties is the most economical and effective way to prevent the disease and yield loss. Fine mapping the QTLs for bacterial wilt resistance is critical for the marker-assisted breeding of disease-resistant varieties. A recombinant inbred population comprising 521 lines was used to construct a high-density genetic linkage map and to identify QTLs for bacterial wilt resistance following restriction-site-associated DNA sequencing. The genetic map, which included 5120 SNP markers, covered a length of 3179 cM with an average marker distance of 0.6 cM. Four QTLs for bacterial wilt resistance were mapped on four chromosomes. One major QTL, qBWA12, with LOD score of 32.8-66.0 and PVE of 31.2-44.8%, was stably detected in all four development stages investigated over the 3 trial years. Additionally, qBWA12 spanned a 2.7 cM region, corresponding to approximately 0.4 Mb and was fine mapped to a 216.7 kb region by applying KASP markers that were polymorphic between the two parents based on whole-genome resequencing data. In a large collection of breeding and germplasm lines, it was proved that KASP marker A12.4097252 can be applied for the marker-assisted breeding to develop peanut varieties resistant to bacterial wilt. Of the 19 candidate genes in the region covered by qBWA12, nine NBS-LRR genes should be further investigated regarding their potential contribution to the resistance of peanut against bacterial wilt.

Proteome evaluation of homolog abundance patterns in Arachis hypogaea cv. Tifrunner

BACKGROUND

Cultivated peanut (Arachis hypogaea, AABB genome), an allotetraploid from a cross between A. duranensis (AA genome) and A. ipaensis (BB genome), is an important oil and protein crop with released genome and RNA-seq sequence datasets. These datasets provide the molecular foundation for studying gene expression and evolutionary patterns. However, there are no reports on the proteomic data of A. hypogaea cv. Tifrunner, which limits understanding of its gene function and protein level evolution.

RESULTS

This study sequenced the A. hypogaea cv. Tifrunner leaf and root proteome using the tandem mass tag technology. A total of 4803 abundant proteins were identified. The 364 differentially abundant proteins were estimated by comparing protein abundances between leaf and root proteomes. The differentially abundant proteins enriched the photosystem process. The number of biased abundant homeologs between the two sub-genomes A (87 homeologs in leaf and root) and B (69 and 68 homeologs in leaf and root, respectively) was not significantly different. However, homeologous proteins with biased abundances in different sub-genomes enriched different biological processes. In the leaf, homeologs biased to sub-genome A enriched biosynthetic and metabolic process, while homeologs biased to sub-genome B enriched iron ion homeostasis process. In the root, homeologs with biased abundance in sub-genome A enriched inorganic biosynthesis and metabolism process, while homeologs with biased abundance in sub-genome B enriched organic biosynthesis and metabolism process. Purifying selection mainly acted on paralogs and homeologs. The selective pressure values were negatively correlated with paralogous protein abundance. About 77.42% (24/31) homeologous and 80% (48/60) paralogous protein pairs had asymmetric abundance, and several protein pairs had conserved abundances in the leaf and root tissues.

CONCLUSIONS

This study sequenced the proteome of A. hypogaea cv. Tifrunner using the leaf and root tissues. Differentially abundant proteins were identified, and revealed functions. Paralog abundance divergence and homeolog bias abundance was elucidated. These results indicate that divergent abundance caused retention of homologs in A. hypogaea cv. Tifrunner.

Regression based fast multi-trait genome-wide QTL analysis

Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance-covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.

Identification of fruit size associated quantitative trait loci featuring SLAF based high-density linkage map of goji berry (Lycium spp.)

BACKGROUND

Goji (Lycium spp., 2n = 24) is a fruit bearing woody plant popular as a superfood for extensive medicinal and nutritional advantages. Fruit size associated attributes are important for evaluating small-fruited goji berry and plant architecture. The domestication traits are regulated quantitatively in crop plants but few studies have attempted on genomic regions corresponding to fruit traits.

RESULTS

In this study, we established high-resolution map using specific locus amplified fragment (SLAF) sequencing for de novo SNPs detection based on 305 F₁ individuals derived from L. chinense and L. barbarum and performed quantitative trait loci (QTL) analysis of fruit size related traits in goji berry. The genetic map contained 3495 SLAF markers on 12 LGs, spanning 1649.03 cM with 0.47 cM average interval. Female and male parents and F₁ individuals` sequencing depth was 111.85-fold and 168.72-fold and 35.80-fold, respectively. The phenotype data were collected for 2 successive years (2018-2019); however, two-year mean data were combined in an extra year (1819). Total 117 QTLs were detected corresponding to multiple traits, of which 78 QTLs in 2 individual years and 36 QTLs in extra year. Six Promising QTLs (qFW10-6.1, qFL10-2.1, qLL10-2.1, qLD10-2.1, qLD12-4.1, qLA10-2.1) were discovered influencing fruit weight, fruit length and leaf related attributes covering an interval ranged from 27.32-71.59 cM on LG10 with peak LOD of 10.48 and 14.6% PVE. Three QTLs targeting fruit sweetness (qFS3-1, qFS5-2) and fruit firmness (qFF10-1) were also identified. Strikingly, various traits QTLs were overlapped on LG10, in particular, qFL10-2.1 was co-located with qLL10-2.1, qLD10-2.1 and qLA10-2.1 among stable QTLs, harbored tightly linked markers, while qLL10-1 was one major QTL with 14.21 highest LOD and 19.3% variance. As LG10 harbored important traits QTLs, we might speculate that it could be hotspot region regulating fruit size and plant architectures.

CONCLUSIONS

This report highlights the extremely saturated linkage map using SLAF-seq and novel loci contributing fruit size-related attributes in goji berry. Our results will shed light on domestication traits and further strengthen molecular and genetic underpinnings of goji berry; moreover, these findings would better facilitate to assemble the reference genome, determining potential candidate genes and marker-assisted breeding.

Translational genomics for achieving higher genetic gains in groundnut

KEY MESSAGE

Groundnut has entered now in post-genome era enriched with optimum genomic and genetic resources to facilitate faster trait dissection, gene discovery and accelerated genetic improvement for developing climate-smart varieties. Cultivated groundnut or peanut (Arachis hypogaea), an allopolyploid oilseed crop with a large and complex genome, is one of the most nutritious food. This crop is grown in more than 100 countries, and the low productivity has remained the biggest challenge in the semiarid tropics. Recently, the groundnut research community has witnessed fast progress and achieved several key milestones in genomics research including genome sequence assemblies of wild diploid progenitors, wild tetraploid and both the subspecies of cultivated tetraploids, resequencing of diverse germplasm lines, genome-wide transcriptome atlas and cost-effective high and low-density genotyping assays. These genomic resources have enabled high-resolution trait mapping by using germplasm diversity panels and multi-parent genetic populations leading to precise gene discovery and diagnostic marker development. Furthermore, development and deployment of diagnostic markers have facilitated screening early generation populations as well as marker-assisted backcrossing breeding leading to development and commercialization of some molecular breeding products in groundnut. Several new genomics applications/technologies such as genomic selection, speed breeding, mid-density genotyping assay and genome editing are in pipeline. The integration of these new technologies hold great promise for developing climate-smart, high yielding and more nutritious groundnut varieties in the post-genome era.

Discovery of two novel and adjacent QTLs on chromosome B02 controlling resistance against bacterial wilt in peanut variety Zhonghua 6

Two novel and adjacent genomics and candidate genes for bacterial wilt resistance were identified on chromosome B02 in peanut variety Zhonghua 6 using both traditional QTL mapping and QTL-seq methods. Peanut (Arachis hypogaea) is an important oilseed crop worldwide. Utilization of genetic resistance is the most economic and effective approach to control bacterial wilt, one of the most devastating plant diseases, in peanut production. To accelerate the genetic improvement of bacterial wilt resistance (BWR) in peanut breeding programs, quantitative trait locus (QTL) mapping has been conducted for two resistant varieties. In this context, we deployed linkage mapping as well as sequencing-based mapping approach, QTL-seq, to identify genomic regions and candidate genes for BWR in another highly resistant variety Zhonghua 6. The recombination inbred line population (268 progenies) from the cross Xuhua 13 × Zhonghua 6 was used in BWR evaluation across five environments. QTL mapping using both SSR- and SNP-based genetic maps identified a stable QTL (qBWRB02-1) on chromosome B02 with 37.79-78.86% phenotypic variation explained (PVE) across five environments. The QTL-seq facilitated further dissection of qBWRB02-1 into two adjacent genomic regions, qBWRB02-1-1 (2.81-4.24 Mb) and qBWRB02-1-2 (6.54-8.75 Mb). Mapping of newly developed Kompetitive allele-specific PCR (KASP) markers on the genetic map confirmed their stable expressions across five environments. The effects of qBWRB02-1-1 (49.43-68.86% PVE) were much higher than qBWRB02-1-2 (3.96-6.48% PVE) and other previously reported QTLs. Nineteen putative candidate genes affected by 49 non-synonymous SNPs were identified for qBWRB02-1-1, and ten of them were predicted to code for disease resistance proteins. The major and stable QTL qBWRB02-1-1 and validated KASP markers could be deployed in genomics-assisted breeding (GAB) to develop improved peanut varieties with enhanced BWR.

QTL identification for seed weight and size based on a high-density SLAF-seq genetic map in peanut (Arachis hypogaea L.)

BACKGROUND

The cultivated peanut is an important oil and cash crop grown worldwide. To meet the growing demand for peanut production each year, genetic studies and enhanced selection efficiency are essential, including linkage mapping, genome-wide association study, bulked-segregant analysis and marker-assisted selection. Specific locus amplified fragment sequencing (SLAF-seq) is a powerful tool for high density genetic map (HDGM) construction and quantitative trait loci (QTLs) mapping. In this study, a HDGM was constructed using SLAF-seq leading to identification of QTL for seed weight and size in peanut.

RESULTS

A recombinant inbred line (RIL) population was advanced from a cross between a cultivar 'Huayu36' and a germplasm line '6-13' with contrasting seed weight, size and shape. Based on the cultivated peanut genome, a HDGM was constructed with 3866 loci consisting of SLAF-seq and simple sequence repeat (SSR) markers distributed on 20 linkage groups (LGs) covering a total map distance of 1266.87 cM. Phenotypic data of four seed related traits were obtained in four environments, which mostly displayed normal distribution with varied levels of correlation. A total of 27 QTLs for 100 seed weight (100SW), seed length (SL), seed width (SW) and length to width ratio (L/W) were identified on 8 chromosomes, with LOD values of 3.16-31.55 and explaining phenotypic variance (PVE) from 0.74 to 83.23%. Two stable QTL regions were identified on chromosomes 2 and 16, and gene content within these regions provided valuable information for further functional analysis of yield component traits.

CONCLUSIONS

This study represents a new HDGM based on the cultivated peanut genome using SLAF-seq and SSRs. QTL mapping of four seed related traits revealed two stable QTL regions on chromosomes 2 and 16, which not only facilitate fine mapping and cloning these genes, but also provide opportunity for molecular breeding of new peanut cultivars with improved seed weight and size.