Genetic mapping of yield traits using RIL population derived from Fuchuan Dahuasheng and ICG6375 of peanut (Arachis hypogaea L.)

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

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.

Genome-Wide Mapping of Quantitative Trait Loci for Yield-Attributing Traits of Peanut

Peanuts (Arachis hypogaea L.) are important high-protein and oil-containing legume crops adapted to arid to semi-arid regions. The yield and quality of peanuts are complex quantitative traits that show high environmental influence. In this study, a recombinant inbred line population (RIL) (Valencia-C × JUG-03) was developed and phenotyped for nine traits under two environments. A genetic map was constructed using 1323 SNP markers spanning a map distance of 2003.13 cM. Quantitative trait loci (QTL) analysis using this genetic map and phenotyping data identified seventeen QTLs for nine traits. Intriguingly, a total of four QTLs, two each for 100-seed weight (HSW) and shelling percentage (SP), showed major and consistent effects, explaining 10.98% to 14.65% phenotypic variation. The major QTLs for HSW and SP harbored genes associated with seed and pod development such as the seed maturation protein-encoding gene, serine-threonine phosphatase gene, TIR-NBS-LRR gene, protein kinase superfamily gene, bHLH transcription factor-encoding gene, isopentyl transferase gene, ethylene-responsive transcription factor-encoding gene and cytochrome P450 superfamily gene. Additionally, the identification of 76 major epistatic QTLs, with PVE ranging from 11.63% to 72.61%, highlighted their significant role in determining the yield- and quality-related traits. The significant G × E interaction revealed the existence of the major role of the environment in determining the phenotype of yield-attributing traits. Notably, the seed maturation protein-coding gene in the vicinity of major QTLs for HSW can be further investigated to develop a diagnostic marker for HSW in peanut breeding. This study provides understanding of the genetic factor governing peanut traits and valuable insights for future breeding efforts aimed at improving yield and quality.

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.

Fine mapping of qAHPS07 and functional studies of AhRUVBL2 controlling pod size in peanut (Arachis hypogaea L.)

Cultivated peanut (Arachis hypogaea L.) is an important oil and cash crop. Pod size is one of the major traits determining yield and commodity characteristic of peanut. Fine mapping of quantitative trait locus (QTL) and identification of candidate genes associated with pod size are essential for genetic improvement and molecular breeding of peanut varieties. In this study, a major QTL related to pod size, qAHPS07, was fine mapped to a 36.46 kb interval on chromosome A07 using F2 , recombinant inbred line (RIL) and secondary F2 populations. qAHPS07 explained 38.6%, 23.35%, 37.48%, 25.94% of the phenotypic variation for single pod weight (SPW), pod length (PL), pod width (PW) and pod shell thickness (PST), respectively. Whole genome resequencing and gene expression analysis revealed that a RuvB-like 2 protein coding gene AhRUVBL2 was the most likely candidate for qAHPS07. Overexpression of AhRUVBL2 in Arabidopsis led to larger seeds and plants than the wild type. AhRUVBL2-silenced peanut seedlings represented small leaves and shorter main stems. Three haplotypes were identified according to three SNPs in the promoter of AhRUVBL2 among 119 peanut accessions. Among them, SPW, PW and PST of accessions carrying Hap_ATT represent 17.6%, 11.2% and 26.3% higher than those carrying Hap_GAC，respectively. In addition, a functional marker of AhRUVBL2 was developed. Taken together, our study identified a key functional gene of peanut pod size, which provides new insights into peanut pod size regulation mechanism and offers practicable markers for the genetic improvement of pod size-related traits in peanut breeding.

Identification of QTL for kernel weight and size and analysis of the pentatricopeptide repeat (PPR) gene family in cultivated peanut (Arachis hypogaea L.)

Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut.

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.

Genetic mapping identifies genomic regions and candidate genes for seed weight and shelling percentage in groundnut

Seed size is not only a yield-related trait but also an important measure to determine the commercial value of groundnut in the international market. For instance, small size is preferred in oil production, whereas large-sized seeds are preferred in confectioneries. In order to identify the genomic regions associated with 100-seed weight (HSW) and shelling percentage (SHP), the recombinant inbred line (RIL) population (Chico × ICGV 02251) of 352 individuals was phenotyped for three seasons and genotyped with an Axiom_Arachis array containing 58K SNPs. A genetic map with 4199 SNP loci was constructed, spanning a map distance of 2708.36 cM. QTL analysis identified six QTLs for SHP, with three consistent QTLs on chromosomes A05, A08, and B10. Similarly, for HSW, seven QTLs located on chromosomes A01, A02, A04, A10, B05, B06, and B09 were identified. BIG SEED locus and spermidine synthase candidate genes associated with seed weight were identified in the QTL region on chromosome B09. Laccase, fibre protein, lipid transfer protein, senescence-associated protein, and disease-resistant NBS-LRR proteins were identified in the QTL regions associated with shelling percentage. The associated markers for major-effect QTLs for both traits successfully distinguished between the small- and large-seeded RILs. QTLs identified for HSW and SHP can be used for developing potential selectable markers to improve the cultivars with desired seed size and shelling percentage to meet the demands of confectionery industries.

Identification and Pyramiding Major QTL Loci for Simultaneously Enhancing Aflatoxin Resistance and Yield Components in Peanut

Peanut is susceptible to Aspergillus flavus infection, and the consequent aflatoxin contamination has been recognized as an important risk factor affecting food safety and industry development. Planting peanut varieties with resistance to aflatoxin contamination is regarded as an ideal approach to decrease the risk in food safety, but most of the available resistant varieties have not been extensively used in production because of their low yield potential mostly due to possessing small pods and seeds. Hence, it is highly necessary to integrate resistance to aflatoxin and large seed weight. In this study, an RIL population derived from a cross between Zhonghua 16 with high yield and J 11 with resistance to infection of A. flavus and aflatoxin production, was used to identify quantitative trait locus (QTL) for aflatoxin production (AP) resistance and hundred-seed weight (HSW). From combined analysis using a high-density genetic linkage map constructed, 11 QTLs for AP resistance with 4.61–11.42% phenotypic variation explanation (PVE) and six QTLs for HSW with 3.20–28.48% PVE were identified, including three major QTLs for AP resistance (qAFTA05.1, qAFTB05.2 and qAFTB06.3) and three for HSW (qHSWA05, qHSWA08 and qHSWB06). In addition, qAFTA05.1, qAFTB06.3, qHSWA05, qHSWA08 and qHSWB06 were detected in multiple environments. The aflatoxin contents under artificial inoculation were decreased by 34.77–47.67% in those segregated lines harboring qAFTA05.1, qAFTB05.2 and qAFTB06.3, while the HSWs were increased by 47.56–49.46 g in other lines harboring qHSWA05, qHSWA08 and qHSWB06. Conditional QTL mapping indicated that HSW and percent seed infection index (PSII) had no significant influence on aflatoxin content. Interestingly, the QT 1059 simultaneously harboring alleles of aflatoxin content including qAFTA05.1 and qAFTB05.2, alleles of PSII including qPSIIB03.1, qPSIIB03.2, and qPSIIB10 and alleles of HSW including qHSWA05, qHSWB06, qHSWA08 had better resistance to A. flavus infection and to toxin production and higher yield potential compared with the two parents of the RIL. The above identified major loci for AP resistance and HWS would be helpful for marker-assisted selection in peanut breeding.

Impact of Rhamnolipids (RLs), Natural Defense Elicitors, on Shoot and Root Proteomes of Brassica napus by a Tandem Mass Tags (TMTs) Labeling Approach

The rapeseed crop is susceptible to many pathogens such as parasitic plants or fungi attacking aerial or root parts. Conventional plant protection products, used intensively in agriculture, have a negative impact on the environment as well as on human health. There is therefore a growing demand for the development of more planet-friendly alternative protection methods such as biocontrol compounds. Natural rhamnolipids (RLs) can be used as elicitors of plant defense mechanisms. These glycolipids, from bacteria secretome, are biodegradable, non-toxic and are known for their stimulating and protective effects, in particular on rapeseed against filamentous fungi. Characterizing the organ responsiveness to defense-stimulating compounds such as RLs is missing. This analysis is crucial in the frame of optimizing the effectiveness of RLs against various diseases. A Tandem Mass Tags (TMT) labeling of the proteins extracted from the shoots and roots of rapeseed has been performed and showed a differential pattern of protein abundance between them. Quantitative proteomic analysis highlighted the differential accumulation of parietal and cytoplasmic defense or stress proteins in response to RL treatments with a clear effect of the type of application (foliar spraying or root absorption). These results must be considered for further use of RLs to fight specific rapeseed pathogens.

Detection of a major QTL and development of KASP markers for seed weight by combining QTL-seq, QTL-mapping and RNA-seq in peanut

Combining QTL-seq, QTL-mapping and RNA-seq identified a major QTL and candidate genes, which contributed to the development of KASP markers and understanding of molecular mechanisms associated with seed weight in peanut. Seed weight, as an important component of seed yield, is a significant target of peanut breeding. However, relatively little is known about the quantitative trait loci (QTLs) and candidate genes associated with seed weight in peanut. In this study, three major QTLs on chromosomes A05, B02, and B06 were determined by applying the QTL-seq approach in a recombinant inbred line (RIL) population. Based on conventional QTL-mapping, these three QTL regions were successfully narrowed down through newly developed single nucleotide polymorphism (SNP) and simple sequence repeat markers. Among these three QTL regions, qSWB06.3 exhibited stable expression, contributing mainly to phenotypic variance across environments. Furthermore, differentially expressed genes (DEGs) were identified at the three seed developmental stages between the two parents of the RIL population. It was found that the DEGs were widely distributed in the ubiquitin-proteasome pathway, the serine/threonine-protein pathway, signal transduction of hormones and transcription factors. Notably, DEGs at the early stage were mostly involved in regulating cell division, whereas DEGs at the middle and late stages were primarily involved in cell expansion during seed development. The expression patterns of candidate genes related to seed weight in qSWB06.3 were investigated using quantitative real-time PCR. In addition, the allelic diversity of qSWB06.3 was investigated in peanut germplasm accessions. The marker Ah011475 has higher efficiency for discriminating accessions with different seed weights, and it would be useful as a diagnostic marker in marker-assisted breeding. This study provided insights into the genetic and molecular mechanisms of seed weight in peanut.

Integument-Specific Transcriptional Regulation in the Mid-Stage of Flax Seed Development Influences the Release of Mucilage and the Seed Oil Content

Flax (Linum usitatissimum L.) seed oil, which accumulates in the embryo, and mucilage, which is synthesized in the seed coat, are of great economic importance for food, pharmaceutical as well as chemical industries. Theories on the link between oil and mucilage production in seeds consist in the spatio-temporal competition of both compounds for photosynthates during the very early stages of seed development. In this study, we demonstrate a positive relationship between seed oil production and seed coat mucilage extrusion in the agronomic model, flax. Three recombinant inbred lines were selected for low, medium and high mucilage and seed oil contents. Metabolite and transcript profiling (1H NMR and DNA oligo-microarrays) was performed on the seeds during seed development. These analyses showed main changes in the seed coat transcriptome during the mid-phase of seed development (25 Days Post-Anthesis), once the mucilage biosynthesis and modification processes are thought to be finished. These transcriptome changes comprised genes that are putatively involved in mucilage chemical modification and oil synthesis, as well as gibberellic acid (GA) metabolism. The results of this integrative biology approach suggest that transcriptional regulations of seed oil and fatty acid (FA) metabolism could occur in the seed coat during the mid-stage of seed development, once the seed coat carbon supplies have been used for mucilage biosynthesis and mechanochemical properties of the mucilage secretory cells.

Construction of ddRADseq-Based High-Density Genetic Map and Identification of Quantitative Trait Loci for Trans-resveratrol Content in Peanut Seeds

Resveratrol (trans-3,4',5-trihydroxystilbene) is a natural stilbene phytoalexin which is also found to be good for human health. Cultivated peanut (Arachis hypogaea L.), a worldwide important legume crop, is one of the few sources of human's dietary intake of resveratrol. Although the variations of resveratrol contents among peanut varieties were observed, the variations across environments and its underlying genetic basis were poorly investigated. In this study, the resveratrol content in seeds of a recombination inbred line (RIL) population (Zhonghua 6 × Xuhua 13, 186 progenies) were quantified by high performance liquid chromatography (HPLC) method across four environments. Genotypes, environments and genotype × environment interactions significantly influenced the resveratrol contents in the RIL population. A total of 8,114 high-quality single nucleotide polymorphisms (SNPs) were identified based on double-digest restriction-site-associated DNA sequencing (ddRADseq) reads. These SNPs were clustered into bins using a reference-based method, which facilitated the construction of high-density genetic map (2,183 loci with a total length of 2,063.55 cM) and the discovery of several chromosome translocations. Through composite interval mapping (CIM), nine additive quantitative trait loci (QTL) for resveratrol contents were identified on chromosomes A01, A07, A08, B04, B05, B06, B07, and B10 with 5.07-8.19% phenotypic variations explained (PVE). Putative genes within their confidential intervals might play roles in diverse primary and secondary metabolic processes. These results laid a foundation for the further genetic dissection of resveratrol content as well as the breeding and production of high-resveratrol peanuts.

Fine-Mapping of a Wild Genomic Region Involved in Pod and Seed Size Reduction on Chromosome A07 in Peanut (Arachis hypogaea L.)

Fruit and seed size are important yield component traits that have been selected during crop domestication. In previous studies, Advanced Backcross Quantitative Trait Loci (AB-QTL) and Chromosome Segment Substitution Line (CSSL) populations were developed in peanut by crossing the cultivated variety Fleur11 and a synthetic wild allotetraploid (Arachis ipaensis × Arachis duranensis)^4x. In the AB-QTL population, a major QTL for pod and seed size was detected in a ~5 Mb interval in the proximal region of chromosome A07. In the CSSL population, the line 12CS_091, which carries the QTL region and that produces smaller pods and seeds than Fleur11, was identified. In this study, we used a two-step strategy to fine-map the seed size QTL region on chromosome A07. We developed new SSR and SNP markers, as well as near-isogenic lines (NILs) in the target QTL region. We first located the QTL in ~1 Mb region between two SSR markers, thanks to the genotyping of a large F₂ population of 2172 individuals and a single marker analysis approach. We then used nine new SNP markers evenly distributed in the refined QTL region to genotype 490 F₃ plants derived from 88 F₂, and we selected 10 NILs. The phenotyping of the NILs and marker/trait association allowed us to narrowing down the QTL region to a 168.37 kb chromosome segment, between the SNPs Aradu_A07_1148327 and Aradu_A07_1316694. This region contains 22 predicted genes. Among these genes, Aradu.DN3DB and Aradu.RLZ61, which encode a transcriptional regulator STERILE APETALA-like (SAP) and an F-box SNEEZY (SNE), respectively, were of particular interest. The function of these genes in regulating the variation of fruit and seed size is discussed. This study will contribute to a better knowledge of genes that have been targeted during peanut domestication.

Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explore the distribution of recombination and potential chromosomal rearrangements. This linkage map was then used to identify Quantitative Trait Loci (QTL) for seed and pod traits that were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

High-density SNP map facilitates fine mapping of QTLs and candidate genes discovery for Aspergillus flavus resistance in peanut (Arachis hypogaea)

Two novel resistant QTLs mapped and candidate genes identified for Aspergillus flavus resistance in cultivated peanut using SLAF-seq. Aflatoxin contamination in peanuts caused by Aspergillus flavus is a serious food safety issue for human health around the world. Host plant resistance to fungal infection and reduction in aflatoxin are crucial for mitigating this problem. Identification of the resistance-linked markers can be used in marker-assisted breeding for varietal development. Here we report construction of two high-density genetic linkage maps with 1975 SNP loci and 5022 SNP loci, respectively. Two consistent quantitative trait loci (QTL) were identified as qRAF-3-1 and qRAF-14-1, which located on chromosomes A03 and B04, respectively. QTL qRAF-3-1 was mapped within 1.67 cM and had more than 19% phenotypic variance explained (PVE), while qRAF-14-1 was located within 1.34 cM with 5.15% PVE. While comparing with the reference genome, the mapped QTLs, qRAF-3-1 and qRAF-14-1, were located within a physical distance of 1.44 Megabase pair (Mbp) and 2.22 Mbp, harboring 67 and 137 genes, respectively. Among the identified candidate genes, six genes with the same function were found within both QTLs regions. In addition, putative disease resistance RPP13-like protein 1 (RPP13), lipoxygenase (Lox), WRKY transcription factor (WRKY) and cytochrome P450 71B34 genes were also identified. Using microarray analysis, genes responded to A. flavus infection included coding for RPP13, pentatricopeptide repeat-containing-like protein, and Lox which may be possible candidate genes for resistance to A. flavus. The QTLs and candidate genes will further facilitate marker development and validation of genes for deployment in the molecular breeding programs against A. flavus in peanuts.

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.

Genome-wide identification of microsatellite markers from cultivated peanut (Arachis hypogaea L.)

Background

Microsatellites, or simple sequence repeats (SSRs), represent important DNA variations that are widely distributed across the entire plant genome and can be used to develop SSR markers, which can then be used to conduct genetic analyses and molecular breeding. Cultivated peanut (A. hypogaea L.), an important oil crop worldwide, is an allotetraploid (AABB, 2n = 4× = 40) plant species. Because of its complex genome, genomic marker development has been very challenging. However, sequencing of cultivated peanut genome allowed us to develop genomic markers and construct a high-density physical map.

Results

A total of 8,329,496 SSRs were identified, including 3,772,653, 4,414,961, and 141,882 SSRs that were distributed in subgenome A, B, and nine scaffolds, respectively. Based on the flanking sequences of the identified SSRs, a total of 973,984 newly developed SSR markers were developed in subgenome A (462,267), B (489,394), and nine scaffolds (22,323), with an average density of 392.45 markers per Mb. In silico PCR evaluation showed that an average of 88.32% of the SSR markers generated only one in silico-specific product in two tetraploid A. hypogaea varieties, Tifrunner and Shitouqi. A total of 39,599 common SSR markers were identified among the two A. hypogaea varieties and two progenitors, A. duranensis and A. ipaensis. Additionally, an amplification effectiveness of 44.15% was observed by real PCR validation. Moreover, a total of 1276 public SSR loci were integrated with the newly developed SSR markers. Finally, a previously known leaf spot quantitative trait locus (QTL), qLLS_T13_A05_7, was determined to be in a 1.448-Mb region on chromosome A05. In this region, a total of 819 newly developed SSR markers were located and 108 candidate genes were detected.

Conclusions

The availability of these newly developed and public SSR markers both provide a large number of molecular markers that could potentially be used to enhance the process of trait genetic analyses and improve molecular breeding strategies for cultivated peanut.

Identification of genomic regions and diagnostic markers for resistance to aflatoxin contamination in peanut (Arachis hypogaea L.)

Background

Aflatoxin contamination caused by Aspergillus flavus is a major constraint to peanut industry worldwide due to its toxicological effects to human and animals. Developing peanut varieties with resistance to seed infection and/or aflatoxin accumulation is the most effective and economic strategy for reducing aflatoxin risk in food chain. Breeding for resistance to aflatoxin in peanut is a challenging task for breeders because the genetic basis is still poorly understood. To identify the quantitative trait loci (QTLs) for resistance to aflatoxin contamination in peanut, a recombinant inbred line (RIL) population was developed from crossing Zhonghua 10 (susceptible) with ICG 12625 (resistant). The percent seed infection index (PSII), the contents of aflatoxin B₁ (AFB₁) and aflatoxin B₂ (AFB₂) of RILs were evaluated by a laboratory kernel inoculation assay.

Results

Two QTLs were identified for PSII including one major QTL with 11.32–13.00% phenotypic variance explained (PVE). A total of 12 QTLs for aflatoxin accumulation were detected by unconditional analysis, and four of them (qAFB1A07 and qAFB1B06.1 for AFB₁, qAFB2A07 and qAFB2B06 for AFB₂) exhibited major and stable effects across multiple environments with 9.32–21.02% PVE. Furthermore, not only qAFB1A07 and qAFB2A07 were co-localized in the same genetic interval on LG A07, but qAFB1B06.1 was also co-localized with qAFB2B06 on LG B06. Conditional QTL mapping also confirmed that there was a strong interaction between resistance to AFB₁ and AFB₂ accumulation. Genotyping of RILs revealed that qAFB1A07 and qAFB1B06.1 interacted additively to improve the resistance to both AFB₁ and AFB₂ accumulation. Additionally, validation of the two markers was performed in diversified germplasm collection and four accessions with resistance to aflatoxin accumulation were identified.

Conclusions

Single major QTL for resistance to PSII and two important co-localized intervals associated with major QTLs for resistance to AFB₁ and AFB₂. Combination of these intervals could improve the resistance to aflatoxin accumulation in peanut. SSR markers linked to these intervals were identified and validated. The identified QTLs and associated markers exhibit potential to be applied in improvement of resistance to aflatoxin contamination.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0734-z) contains supplementary material, which is available to authorized users.

OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices

Matrices representing genetic relatedness among individuals (i.e., Genomic Relationship Matrices, GRMs) play a central role in genetic analysis. The eigen-decomposition of GRMs (or its alternative that generates fewer top singular values using genotype matrices) is a necessary step for many analyses including estimation of SNP-heritability, Principal Component Analysis (PCA), and genomic prediction. However, the GRMs and genotype matrices provided by modern biobanks are too large to be stored in active memory. To accommodate the current and future "bigger-data", we develop a disk-based tool, Out-of-Core Matrices Analyzer (OCMA), using state-of-the-art computational techniques that can nimbly perform eigen and Singular Value Decomposition (SVD) analyses. By integrating memory mapping (mmap) and the latest matrix factorization libraries, our tool is fast and memory-efficient. To demonstrate the impressive performance of OCMA, we test it on a personal computer. For full eigen-decomposition, it solves an ordinary GRM (N = 10,000) in 55 sec. For SVD, a commonly used faster alternative of full eigen-decomposition in genomic analyses, OCMA solves the top 200 singular values (SVs) in half an hour, top 2,000 SVs in 0.95 hr, and all 5,000 SVs in 1.77 hr based on a very large genotype matrix (N = 1,000,000, M = 5,000) on the same personal computer. OCMA also supports multi-threading when running in a desktop or HPC cluster. Our OCMA tool can thus alleviate the computing bottleneck of classical analyses on large genomic matrices, and make it possible to scale up current and emerging analytical methods to big genomics data using lightweight computing resources.

Chromosomes A07 and A05 associated with stable and major QTLs for pod weight and size in cultivated peanut (Arachis hypogaea L.)

Co-localized intervals and candidate genes were identified for major and stable QTLs controlling pod weight and size on chromosomes A07 and A05 in an RIL population across four environments. Cultivated peanut (Arachis hypogaea L.) is an important legume crops grown in > 100 countries. Hundred-pod weight (HPW) is an important yield trait in peanut, but its underlying genetic mechanism was not well studied. In this study, a mapping population (Xuhua 13 × Zhonghua 6) with 187 recombinant inbred lines (RILs) was developed to map quantitative trait loci (QTLs) for HPW together with pod length (PL) and pod width (PW) by both unconditional and conditional QTL analyses. A genetic map covering 1756.48 cM was constructed with 817 markers. Additive effects, epistatic interactions, and genotype-by-environment interactions were analyzed using the phenotyping data generated across four environments. Twelve additive QTLs were identified on chromosomes A05, A07, and A08 by unconditional analysis, and five of them (qPLA07, qPLA05.1, qPWA07, qHPWA07.1, and qHPWA05.2) showed major and stable expressions in all environments. Conditional QTL mapping found that PL had stronger influences on HPW than PW. Notably, qHPWA07.1, qPLA07, and qPWA07 that explained 17.93-43.63% of the phenotypic variations of the three traits were co-localized in a 5 cM interval (1.48 Mb in physical map) on chromosome A07 with 147 candidate genes related to catalytic activity and metabolic process. In addition, qHPWA05.2 and qPLA05.1 were co-localized with minor QTL qPWA05.2 to a 1.3 cM genetic interval (280 kb in physical map) on chromosome A05 with 12 candidate genes. This study provides a comprehensive characterization of the genetic components controlling pod weight and size as well as candidate QTLs and genes for improving pod yield in future peanut breeding.

Mutant Transcriptome Sequencing Provides Insights into Pod Development in Peanut (Arachis hypogaea L.)

Pod size is the major yield component and a key target trait that is selected for in peanut breeding. However, although numerous quantitative trait loci (QTLs) for peanut pod size have been described, the molecular mechanisms underlying the development of this characteristic remain elusive. A peanut mutant with a narrower pod was developed in this study using ethyl methanesulfonate (EMS) mutagenesis and designated as the "pod width" mutant line (pw). The fresh pod weight of pw was only about 40% of that seen in the wild-type (WT) Zhonghua16, while the hull and seed filling of the mutant both also developed at earlier stages. Pods from both pw and WT lines were sampled 20, 40, and 60 days after flowering (DAF) and used for RNA-Seq analysis; the results revealed highly differentially expressed lignin metabolic pathway genes at all three stages, but especially at DAF 20 and DAF 40. At the same time, expression of genes related to auxin signal transduction was found to be significantly repressed during the pw early pod developmental stage. A genome-wide comparative analysis of expression profiles revealed 260 differentially expressed genes (DEGs) across all three stages, and two candidate genes, c26901_g1 (CAD) and c37339_g1 (ACS), responsible for pod width were identified by integrating expression patterns and function annotation of the common DEGs within the three stages. Taken together, the information provided in this study illuminates the processes underlying peanut pod development, and will facilitate further identification of causal genes and the development of improved peanut varieties with higher yields.