High-Density Genetic Map Construction and Identification of QTLs Controlling Oleic and Linoleic Acid in Peanut using SLAF-seq and SSRs

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.

Omics-driven advances in the understanding of regulatory landscape of peanut seed development

Peanuts (Arachis hypogaea) are an essential oilseed crop known for their unique developmental process, characterized by aerial flowering followed by subterranean fruit development. This crop is polyploid, consisting of A and B subgenomes, which complicates its genetic analysis. The advent and progression of omics technologies-encompassing genomics, transcriptomics, proteomics, epigenomics, and metabolomics-have significantly advanced our understanding of peanut biology, particularly in the context of seed development and the regulation of seed-associated traits. Following the completion of the peanut reference genome, research has utilized omics data to elucidate the quantitative trait loci (QTL) associated with seed weight, oil content, protein content, fatty acid composition, sucrose content, and seed coat color as well as the regulatory mechanisms governing seed development. This review aims to summarize the advancements in peanut seed development regulation and trait analysis based on reference genome-guided omics studies. It provides an overview of the significant progress made in understanding the molecular basis of peanut seed development, offering insights into the complex genetic and epigenetic mechanisms that influence key agronomic traits. These studies highlight the significance of omics data in profoundly elucidating the regulatory mechanisms of peanut seed development. Furthermore, they lay a foundational basis for future research on trait-related functional genes, highlighting the pivotal role of comprehensive genomic analysis in advancing our understanding of plant biology.

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.

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.

Identification of Quantitative Trait Nucleotides and Development of Diagnostic Markers for Nine Fatty Acids in the Peanut

The cultivated peanut (Arachis hypogaea L.) is an important oilseed crop worldwide, and fatty acid composition is a major determinant of peanut oil quality. In the present study, we conducted a genome-wide association study (GWAS) for nine fatty acid traits using the whole genome sequences of 160 representative Chinese peanut landraces and identified 6-1195 significant SNPs for different fatty acid contents. Particularly for oleic acid and linoleic acid, two peak SNP clusters on Arahy.09 and Arahy.19 were found to contain the majority of the significant SNPs associated with these two fatty acids. Additionally, a significant proportion of the candidate genes identified on Arahy.09 overlap with those identified in early studies, among which three candidate genes are of special interest. One possesses a significant missense SNP and encodes a known candidate gene FAD2A. The second gene is the gene closest to the most significant SNP for linoleic acid. It codes for an MYB protein that has been demonstrated to impact fatty acid biosynthesis in Arabidopsis. The third gene harbors a missense SNP and encodes a JmjC domain-containing protein. The significant phenotypic difference in the oleic acid/linoleic acid between the genotypes at the first and third candidate genes was further confirmed with PARMS analysis. In addition, we have also identified different candidate genes (i.e., Arahy.ZV39IJ, Arahy.F9E3EA, Arahy.X9ZZC1, and Arahy.Z0ELT9) for the remaining fatty acids. Our findings can help us gain a better understanding of the genetic foundation of peanut fatty acid contents and may hold great potential for enhancing peanut quality in the future.

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.

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.

Discovery of common loci and candidate genes for controlling salt-alkali tolerance and yield-related traits in Brassica napus L

KEY MESSAGE

Common loci and candidate genes for controlling salt-alkali tolerance and yield-related traits were identified in Brassica napus combining QTL mapping with transcriptome under salt and alkaline stresses. The yield of rapeseed (Brassica napus L.) is determined by multiple yield-related traits, which are susceptible to environmental factors. Many yield-related quantitative trait loci (QTLs) have been reported in Brassica napus; however, no studies have been conducted to investigate both salt-alkali tolerance and yield-related traits simultaneously. Here, specific-locus amplified fragment sequencing (SLAF-seq) technologies were utilized to map the QTLs for salt-alkali tolerance and yield-related traits. A total of 65 QTLs were identified, including 30 QTLs for salt-alkali tolerance traits and 35 QTLs for yield-related traits, accounting for 7.61-27.84% of the total phenotypic variations. Among these QTLs, 18 unique QTLs controlling two to four traits were identified by meta-analysis. Six novel and unique QTLs were detected for salt-alkali tolerance traits. By comparing these unique QTLs for salt-alkali tolerance traits with those previously reported QTLs for yield-related traits, seven co-localized chromosomal regions were identified on A09 and A10. Combining QTL mapping with transcriptome of two parents under salt and alkaline stresses, thirteen genes were identified as the candidates controlling both salt-alkali tolerance and yield. These findings provide useful information for future breeding of high-yield cultivars resistant to alkaline and salt stresses.

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.

Construction of a high-resolution genetic map and identification of single nucleotide polymorphism markers relevant to flower stalk height in onion

INTRODUCTION

Onion (Allium cepa L., 2n=16) is an economically and nutritionally important vegetable crop worldwide. Construction of a high-resolution genetic map and map-based gene mining in onion have lagged behind other vegetable crops such as tomato and pepper.

METHODS

In this study, we constructed a high-resolution genetic map of onion using 321 F2 individuals from a cross between two double haploid lines DH-1×DH-17 and employing specific length amplified fragment (SLAF)-seq technology. The genetic map containing 10,584 polymorphic SLAFs with 21,250 single nucleotide polymorphism (SNP) markers and 8 linkage groups was developed for onion, which spanned 928.32 cM, with an average distance of 0.09 cM between adjacent markers.

RESULTS

Using this map, we carried out QTL mapping of Ms locus related to the male-fertile trait and reproduced previous mapping results, which proved that this map was of good quality. Then, four QTLs (located on LG2, LG5, and LG8) were detected for flower stalk height, explaining 26.60% of the phenotypic variance. Among them, we proposed that 20 SLAF markers (in three QTLs) of flower stalk height trait were effective favorable allelic variant markers associated with heterosis.

DISCUSSION

Overall, the genetic map was structured using SLAF-seq based on DH lines, and it is the highest-quality and highest-resolution linkage map of onion to date. It lays a foundation for the fine mapping and candidate gene identification of flower stalk height, and provides new insights into the developmental genetic mechanisms in onion breeding.

Genetic dissection of fatty acid components in the Chinese peanut (Arachis hypogaea L.) mini-core collection under multi-environments

Peanut (Arachis hypogaea L.) is an important source of edible oil and protein for human nutrition. The quality of peanut seed oil is mainly determined by the composition of fatty acids, especially the contents of oleic acid and linoleic acid. Improving the composition of fatty acids in the seed oil is one of the main objectives for peanut breeding globally. To uncover the genetic basis of fatty acids and broaden the genetic variation in future peanut breeding programs, this study used genome-wide association studies (GWAS) to identify loci associated with target traits and developed diagnostic marker. The contents of eight fatty acid components of the Chinese peanut mini-core collection were measured under four environments. Using the phenotypic information and over one hundred thousand single nucleotide polymorphisms (SNPs), GWAS were conducted to investigate the genetics basis of fatty acids under multi-environments. Overall, 75 SNPs were identified significant trait associations with fatty acid components. Nineteen associations were repeatedly identified in multiple environments, and 13 loci were co-associated with two or three traits. Three stable major associated loci were identified, including two loci for oleic acid and linoleic acid on chromosome A09 [mean phenotypic variation explained (PVE): 38.5%, 10.35%] and one for stearic acid on B06 (mean PVE: 23%). According to functional annotations, 21 putative candidate genes related to fatty acid biosynthesis were found underlying the three associations. The allelic effect of SNP A09-114690064 showed that the base variation was highly correlated with the phenotypic variation of oleic acid and linoleic acid contents, and a cost-effective Kompetitive allele-Specific PCR (KASP) diagnostic marker was developed. Furthermore, the SNP A09-114690064 was found to change the cis-element CAAT (-) in the promoter of ahFAD2A to YACT (+), leading dozens of times higher expression level. The enhancer-like activity of ahFAD2A promoter was identified that was valuable for enriching the regulation mechanism of ahFAD2A. This study improved our understanding on the genetic architecture of fatty acid components in peanut, and the new effective diagnostic marker would be useful for marker-assisted selection of high-oleic peanut breeding.

Rapid Identification of a Stripe Rust Resistance Gene YrXK in Chinese Wheat Line Xike01015 Using Specific Locus Amplified Fragment (SLAF) Sequencing

Wheat stripe rust, an airborne fungal disease caused by Puccinia striiformis Westend. f. sp. tritici, is one of the most devastating diseases of wheat. Chinese wheat cultivar Xike01015 displays high levels of all-stage resistance (ASR) to the current predominant P. striiformis f. sp. tritici race CYR33. In this study, a single dominant gene, designated YrXk, was identified in Xike01015 conferring resistance to CYR33 with genetic analysis of F₂ and BC₁ populations from a cross of Mingxian169 (susceptible) and Xike01015. The specific length amplified fragment sequencing (SLAF-seq) strategy was used to construct a linkage map in the F₂ population. Quantitative trait loci (QTL) analysis mapped YrXk to a 12.4-Mb segment on chromosome1 BS, explaining >86.96% of the phenotypic variance. Gene annotation in the QTL region identified three differential expressed candidate genes, TraesCS1B02G168600.1, TraesCS1B02G170200.1, and TraesCS1B02G172400.1. The qRT-PCR results showed that TraesCS1B02G172400.1 and TraesCS1B02G168600.1 are upregulated and that TraesCS1B02G170200.1 is slightly downregulated after inoculation with CYR33 in the seedling stage, which indicates that these genes may function in wheat resistance to stripe rust. The results of this study can be used in wheat breeding for improving resistance to stripe rust.

High-Density Genetic Linkage Map Construction Using Whole-Genome Resequencing for Mapping QTLs of Resistance to Aspergillus flavus Infection in Peanut

The cultivated peanut (Arachis hypogaea L.), which is rich in edible oil and protein, is widely planted around the world as an oil and cash crop. However, aflatoxin contamination seriously affects the quality safety of peanuts, hindering the development of the peanut industry and threatening the health of consumers. Breeding peanut varieties with resistance to Aspergillus flavus infection is important for the control of aflatoxin contamination, and understanding the genetic basis of resistance is vital to its genetic enhancement. In this study, we reported the quantitative trait locus (QTL) mapping of resistance to A. flavus infection of a well-known resistant variety, J11. A mapping population consisting of 200 recombinant inbred lines (RILs) was constructed by crossing a susceptible variety, Zhonghua 16, with J11. Through whole-genome resequencing, a genetic linkage map was constructed with 2,802 recombination bins and an average inter-bin distance of 0.58 cM. Combined with phenotypic data of an infection index in 4 consecutive years, six novel resistant QTLs with 5.03-10.87% phenotypic variances explained (PVE) were identified on chromosomes A05, A08, B01, B03, and B10. The favorable alleles of five QTLs were from J11, while that of one QTL was from Zhonghua 16. The combination of these favorable alleles significantly improved resistance to A. flavus infection. These results could contribute greatly to the understanding of the genetic basis of A. flavus resistance and could be meaningful in the improvement of further resistance in peanuts.

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.

Genetic mapping and QTL analysis for peanut smut resistance

BACKGROUND

Peanut smut is a disease caused by the fungus Thecaphora frezii Carranza & Lindquist to which most commercial cultivars in South America are highly susceptible. It is responsible for severely decreased yield and no effective chemical treatment is available to date. However, smut resistance has been identified in wild Arachis species and further transferred to peanut elite cultivars. To identify the genome regions conferring smut resistance within a tetraploid genetic background, this study evaluated a RIL population {susceptible Arachis hypogaea subsp. hypogaea (JS17304-7-B) × resistant synthetic amphidiploid (JS1806) [A. correntina (K 11905) × A. cardenasii (KSSc 36015)] × A. batizocoi (K 9484)^4×} segregating for the trait.

RESULTS

A SNP based genetic map arranged into 21 linkage groups belonging to the 20 peanut chromosomes was constructed with 1819 markers, spanning a genetic distance of 2531.81 cM. Two consistent quantitative trait loci (QTLs) were identified qSmIA08 and qSmIA02/B02, located on chromosome A08 and A02/B02, respectively. The QTL qSmIA08 at 15.20 cM/5.03 Mbp explained 17.53% of the phenotypic variance, while qSmIA02/B02 at 4.0 cM/3.56 Mbp explained 9.06% of the phenotypic variance. The combined genotypic effects of both QTLs reduced smut incidence by 57% and were stable over the 3 years of evaluation. The genome regions containing the QTLs are rich in genes encoding proteins involved in plant defense, providing new insights into the genetic architecture of peanut smut resistance.

CONCLUSIONS

A major QTL and a minor QTL identified in this study provide new insights into the genetic architecture of peanut smut resistance that may aid in breeding new varieties resistant to peanut smut.

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.

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.

Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut

A deep understanding of the genetic control of drought tolerance and iron deficiency tolerance is essential to hasten the process of developing improved varieties with higher tolerance through genomics-assisted breeding. In this context, an improved genetic map with 1205 loci was developed spanning 2598.3 cM with an average 2.2 cM distance between loci in the recombinant inbred line (TAG 24 × ICGV 86031) population using high-density 58K single nucleotide polymorphism (SNP) “Axiom_Arachis” array. Quantitative trait locus (QTL) analysis was performed using extensive phenotyping data generated for 20 drought tolerance- and two iron deficiency tolerance-related traits from eight seasons (2004–2015) at two locations in India, one in Niger, and one in Senegal. The genome-wide QTL discovery analysis identified 19 major main-effect QTLs with 10.0–33.9% phenotypic variation explained (PVE) for drought tolerance- and iron deficiency tolerance- related traits. Major main-effect QTLs were detected for haulm weight (20.1% PVE), SCMR (soil plant analytical development (SPAD) chlorophyll meter reading, 22.4% PVE), and visual chlorosis rate (33.9% PVE). Several important candidate genes encoding glycosyl hydrolases; malate dehydrogenases; microtubule-associated proteins; and transcription factors such as MADS-box, basic helix-loop-helix (bHLH), NAM, ATAF, and CUC (NAC), and myeloblastosis (MYB) were identified underlying these QTL regions. The putative function of these genes indicated their possible involvement in plant growth, development of seed and pod, and photosynthesis under drought or iron deficiency conditions in groundnut. These genomic regions and candidate genes, after validation, may be useful to develop molecular markers for deploying genomics-assisted breeding for enhancing groundnut yield under drought stress and iron-deficient soil conditions.

Enhancing the Nutritional Quality of Major Food Crops Through Conventional and Genomics-Assisted Breeding

Nutritional stress is making over two billion world population malnourished. Either our commercially cultivated varieties of cereals, pulses, and oilseed crops are deficient in essential nutrients or the soils in which these crops grow are becoming devoid of minerals. Unfortunately, our major food crops are poor sources of micronutrients required for normal human growth. To overcome the problem of nutritional deficiency, greater emphasis should be laid on the identification of genes/quantitative trait loci (QTLs) pertaining to essential nutrients and their successful deployment in elite breeding lines through marker-assisted breeding. The manuscript deals with information on identified QTLs for protein content, vitamins, macronutrients, micro-nutrients, minerals, oil content, and essential amino acids in major food crops. These QTLs can be utilized in the development of nutrient-rich crop varieties. Genome editing technologies that can rapidly modify genomes in a precise way and will directly enrich the nutritional status of elite varieties could hold a bright future to address the challenge of malnutrition.

Next Generation Sequencing Based Forward Genetic Approaches for Identification and Mapping of Causal Mutations in Crop Plants: A Comprehensive Review

The recent advancements in forward genetics have expanded the applications of mutation techniques in advanced genetics and genomics, ahead of direct use in breeding programs. The advent of next-generation sequencing (NGS) has enabled easy identification and mapping of causal mutations within a short period and at relatively low cost. Identifying the genetic mutations and genes that underlie phenotypic changes is essential for understanding a wide variety of biological functions. To accelerate the mutation mapping for crop improvement, several high-throughput and novel NGS based forward genetic approaches have been developed and applied in various crops. These techniques are highly efficient in crop plants, as it is relatively easy to grow and screen thousands of individuals. These approaches have improved the resolution in quantitative trait loci (QTL) position/point mutations and assisted in determining the functional causative variations in genes. To be successful in the interpretation of NGS data, bioinformatics computational methods are critical elements in delivering accurate assembly, alignment, and variant detection. Numerous bioinformatics tools/pipelines have been developed for such analysis. This article intends to review the recent advances in NGS based forward genetic approaches to identify and map the causal mutations in the crop genomes. The article also highlights the available bioinformatics tools/pipelines for reducing the complexity of NGS data and delivering the concluding outcomes.

A New Intra-Specific and High-Resolution Genetic Map of Eggplant Based on a RIL Population, and Location of QTLs Related to Plant Anthocyanin Pigmentation and Seed Vigour

Eggplant is the second most important solanaceous berry-producing crop after tomato. Despite mapping studies based on bi-parental progenies and GWAS approaches having been performed, an eggplant intraspecific high-resolution map is still lacking. We developed a RIL population from the intraspecific cross ‘305E40’, (androgenetic introgressed line carrying the locus Rfo-Sa1 conferring Fusarium resistance) x ‘67/3’ (breeding line whose genome sequence was recently released). One hundred and sixty-three RILs were genotyped by a genotype-by-sequencing (GBS) approach, which allowed us to identify 10,361 polymorphic sites. Overall, 267 Gb of sequencing data were generated and ~773 M Illumina paired end (PE) reads were mapped against the reference sequence. A new linkage map was developed, including 7249 SNPs assigned to the 12 chromosomes and spanning 2169.23 cM, with iaci@liberoan average distance of 0.4 cM between adjacent markers. This was used to elucidate the genetic bases of seven traits related to anthocyanin content in different organs recorded in three locations as well as seed vigor. Overall, from 7 to 17 QTLs (at least one major QTL) were identified for each trait. These results demonstrate that our newly developed map supplies valuable information for QTL fine mapping, candidate gene identification, and the development of molecular markers for marker assisted selection (MAS) of favorable alleles.

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.

Genomics-assisted breeding for pigeonpea improvement

The review outlines advances in pigeonpea genomics, breeding and seed delivery systems to achieve yield gains at farmers' field. Pigeonpea is a nutritious and stress-tolerant grain legume crop of tropical and subtropical regions. Decades of breeding efforts in pigeonpea have resulted in development of a number of high-yielding cultivars. Of late, the development of CMS-based hybrid technology has allowed the exploitation of heterosis for yield enhancement in this crop. Despite these positive developments, the actual on-farm yield of pigeonpea is still well below its potential productivity. Growing needs for high and sustainable pigeonpea yields motivate scientists to improve the breeding efficiency to deliver a steady stream of cultivars that will provide yield benefits under both ideal and stressed environments. To achieve this objective in the shortest possible time, it is imperative that various crop breeding activities are integrated with appropriate new genomics technologies. In this context, the last decade has seen a remarkable rise in the generation of important genomic resources such as genome-wide markers, high-throughput genotyping assays, saturated genome maps, marker/gene-trait associations, whole-genome sequence and germplasm resequencing data. In some cases, marker/gene-trait associations are being employed in pigeonpea breeding programs to improve the valuable yield and market-preferred traits. Embracing new breeding tools like genomic selection and speed breeding is likely to improve genetic gains. Breeding high-yielding pigeonpea cultivars with key adaptation traits also calls for a renewed focus on systematic selection and utilization of targeted genetic resources. Of equal importance is to overcome the difficulties being faced by seed industry to take the new cultivars to the doorstep of farmers.

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.

Mapping quantitative trait loci (QTLs) and estimating the epistasis controlling stem rot resistance in cultivated peanut (Arachis hypogaea)

A total of 33 additive stem rot QTLs were identified in peanut genome with nine of them consistently detected in multiple years or locations. And 12 pairs of epistatic QTLs were firstly reported for peanut stem rot disease. Stem rot in peanut (Arachis hypogaea) is caused by the Sclerotium rolfsii and can result in great economic loss during production. In this study, a recombinant inbred line population from the cross between NC 3033 (stem rot resistant) and Tifrunner (stem rot susceptible) that consists of 156 lines was genotyped by using 58 K peanut single nucleotide polymorphism (SNP) array and phenotyped for stem rot resistance at multiple locations and in multiple years. A linkage map consisting of 1451 SNPs and 73 simple sequence repeat (SSR) markers was constructed. A total of 33 additive quantitative trait loci (QTLs) for stem rot resistance were detected, and six of them with phenotypic variance explained of over 10% (qSR.A01-2, qSR.A01-5, qSR.A05/B05-1, qSR.A05/B05-2, qSR.A07/B07-1 and qSR.B05-1) can be consistently detected in multiple years or locations. Besides, 12 pairs of QTLs with epistatic (additive × additive) interaction were identified. An additive QTL qSR.A01-2 also with an epistatic effect interacted with a novel locus qSR.B07_1-1 to affect the percentage of asymptomatic plants in a row. A total of 193 candidate genes within 38 stem rot QTLs intervals were annotated with functions of biotic stress resistance such as chitinase, ethylene-responsive transcription factors and pathogenesis-related proteins. The identified stem rot resistance QTLs, candidate genes, along with the associated SNP markers in this study, will benefit peanut molecular breeding programs for improving stem rot resistance.

Construction of a SNP-Based Genetic Map Using SLAF-Seq and QTL Analysis of Morphological Traits in Eggplant

Eggplant (Solanum melongena; 2n = 24) is an economically important fruit crop of the family Solanaceae that was domesticated in India and Southeast Asia. Construction of a high-resolution genetic map and map-based gene mining in eggplant have lagged behind other crops within the family such as tomato and potato. In this study, we conducted high-throughput single nucleotide polymorphism (SNP) discovery in the eggplant genome using specific length amplified fragment (SLAF) sequencing and constructed a high-density genetic map for the quantitative trait locus (QTL) analysis of multiple traits. An interspecific F2 population of 121 individuals was developed from the cross between cultivated eggplant "1836" and the wild relative S. linnaeanum "1809." Genomic DNA extracted from parental lines and the F2 population was subjected to high-throughput SLAF sequencing. A total of 111.74 Gb of data and 487.53 million pair-end reads were generated. A high-resolution genetic map containing 2,122 SNP markers and 12 linkage groups was developed for eggplant, which spanned 1530.75 cM, with an average distance of 0.72 cM between adjacent markers. A total of 19 QTLs were detected for stem height and fruit and leaf morphology traits of eggplant, explaining 4.08-55.23% of the phenotypic variance. These QTLs were distributed on nine linkage groups (LGs), but not on LG2, 4, and 9. The number of SNPs ranged from 2 to 11 within each QTL, and the genetic interval varied from 0.15 to 10.53 cM. Overall, the results establish a foundation for the fine mapping of complex QTLs, candidate gene identification, and marker-assisted selection of favorable alleles in eggplant breeding.

Identification of candidate genes for key fibre-related QTLs and derivation of favourable alleles in Gossypium hirsutum recombinant inbred lines with G. barbadense introgressions

Fine mapping QTLs and identifying candidate genes for cotton fibre-quality and yield traits would be beneficial to cotton breeding. Here, we constructed a high-density genetic map by specific-locus amplified fragment sequencing (SLAF-seq) to identify QTLs associated with fibre-quality and yield traits using 239 recombinant inbred lines (RILs), which was developed from LMY22 (a high-yield Gossypium hirsutumL. cultivar) × LY343 (a superior fibre-quality germplasm with G. barbadenseL. introgressions). The genetic map spanned 3426.57 cM, including 3556 SLAF-based SNPs and 199 SSR marker loci. A total of 104 QTLs, including 67 QTLs for fibre quality and 37 QTLs for yield traits, were identified with phenotypic data collected from 7 environments. Among these, 66 QTLs were co-located in 19 QTL clusters on 12 chromosomes, and 24 QTLs were detected in three or more environments and determined to be stable. We also investigated the genomic components of LY343 and their contributions to fibre-related traits by deep sequencing the whole genome of LY343, and we found that genomic components from G. hirsutum races (which entered LY343 via its G. barbadense parent) contributed more favourable alleles than those from G. barbadense. We further identified six putative candidate genes for stable QTLs, including Gh_A03G1147 (GhPEL6), Gh_D07G1598 (GhCSLC6) and Gh_D13G1921 (GhTBL5) for fibre-length QTLs and Gh_D03G0919 (GhCOBL4), Gh_D09G1659 (GhMYB4) and Gh_D09G1690 (GhMYB85) for lint-percentage QTLs. Our results provide comprehensive insight into the genetic basis of the formation of fibre-related traits and would be helpful for cloning fibre-development-related genes as well as for marker-assisted genetic improvement in cotton.

Quantitative Trait Locus (QTLs) Mapping for Quality Traits of Wheat Based on High Density Genetic Map Combined With Bulked Segregant Analysis RNA-seq (BSR-Seq) Indicates That the Basic 7S Globulin Gene Is Related to Falling Number

Numerous quantitative trait loci (QTLs) have been identified for wheat quality; however, most are confined to low-density genetic maps. In this study, based on specific-locus amplified fragment sequencing (SLAF-seq), a high-density genetic map was constructed with 193 recombinant inbred lines derived from Chuanmai 42 and Chuanmai 39. In total, 30 QTLs with phenotypic variance explained (PVE) up to 47.99% were identified for falling number (FN), grain protein content (GPC), grain hardness (GH), and starch pasting properties across three environments. Five NAM genes closely adjacent to QGPC.cib-4A probably have effects on GPC. QGH.cib-5D was the only one detected for GH with high PVE of 33.31-47.99% across the three environments and was assumed to be related to the nearest pina-D1 and pinb-D1genes. Three QTLs were identified for FN in at least two environments, of which QFN.cib-3D had relatively higher PVE of 16.58-25.74%. The positive effect of QFN.cib-3D for high FN was verified in a double-haploid population derived from Chuanmai 42 × Kechengmai 4. The combination of these QTLs has a considerable effect on increasing FN. The transcript levels of Basic 7S globulin and Basic 7S globulin 2 in QFN.cib-3D were significantly different between low FN and high FN bulks, as observed through bulk segregant RNA-seq (BSR). These QTLs and candidate genes based on the high-density genetic map would be beneficial for further understanding of the genetic mechanism of quality traits and molecular breeding of wheat.

High-resolution mapping of a major and consensus quantitative trait locus for oil content to a ~ 0.8-Mb region on chromosome A08 in peanut (Arachis hypogaea L.)

KEY MESSAGE: ddRAD-seq-based high-density genetic map comprising 2595 loci identified a major and consensus QTL with a linked marker in a 0.8-Mb physical interval for oil content in peanut. Enhancing oil content is an important breeding objective in peanut. High-resolution mapping of quantitative trait loci (QTLs) with linked markers could facilitate marker-assisted selection in breeding for target traits. In the present study, a recombined inbred line population (Xuhua 13 × Zhonghua 6) was used to construct a genetic map based on double-digest restriction-site-associated DNA sequencing (ddRAD-seq). The resulting high-density genetic map contained 2595 loci, and spanned a length of 2465.62 cM, with an average distance of 0.95 cM/locus. Seven QTLs for oil content were identified on five linkage groups, including the major and stable QTL qOCA08.1 on chromosome A08 with 10.14-27.19% phenotypic variation explained. The physical interval of qOCA08.1 was further delimited to a ~ 0.8-Mb genomic region where two genes affecting oil synthesis had been annotated. The marker SNPOCA08 was developed targeting the SNP loci associated with oil content and validated in peanut cultivars with diverse oil contents. The major and stable QTL identified in the present study could be further dissected for gene discovery. Furthermore, the tightly linked marker for oil content would be useful in marker-assisted breeding in peanut.

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 Assessment of Avocado Germplasm Determined from Specific Length Amplified Fragment Sequencing and Transcriptomes: Population Structure, Genetic Diversity, Identification, and Application of Race-Specific Markers

Genomic data is a powerful tool. However, the phylogenetic relationships among different ecological races of avocado remain unclear. Here, we used the results from specific length amplified fragment sequencing (SLAF-seq) and transcriptome data to infer the population structure and genetic diversity of 21 avocado cultivars and reconstructed the phylogeny of three ecological races and two interracial hybrids. The results of the three analyses performed (unweighted pair-group methods with arithmetic means (UPGMA) cluster, Principal coordinate analysis (PCoA), and STRUCTURE) based on single nucleotide polymorphisms (SNPs) from SLAF-seq all indicated the existence of two populations based on botanical race: Mexican–Guatemalan and West Indian genotype populations. Our results based on SNPs from SLAF-seq indicated that the Mexican and Guatemalan races were more closely related to each other than either was to the West Indian race, which also was confirmed in the UPGMA cluster results based on SNPs from transcriptomic data. SNPs from SLAF-seq provided strong evidence that the Guatemalan, Mexican, and Guatemalan × Mexican hybrid accession possessed higher genetic diversity than the West Indian races and Guatemalan × West Indian hybrid accessions. Six race-specific Kompetitive allele specific PCR (KASP) markers based on SNPs from SLAF-seq were then developed and validated.

Identification of male-specific SNP markers and development of PCR-based genetic sex identification technique in crucifix crab (Charybdis feriatus) with implication of an XX/XY sex determination system

In this study, we first identified male-specific SNP markers using restriction site-associated DNA sequencing, and further developed a PCR-based sex identification technique for Charybdis feriatus. A total of 296.96 million clean reads were obtained, with 114.95 and 182.01 million from females and males. After assembly and alignment, 10 SNP markers were identified being heterozygous in males but homozygous in females. Five markers were further confirmed to be male-specific in a large number of individuals. Moreover, two male-specific sense primers and a common antisense primer were designed, using which, a PCR-based genetic sex identification method was successfully developed and used to identify the sex of 103 individuals, with a result of 49 females and 54 males. The presence of male-specific SNP markers suggests an XX/XY sex determination system for C. feriatus. These findings should be helpful for better understanding sex determination mechanism, and drafting artificial breeding program in crustaceans.

Construction of a high-density linkage map and QTL mapping for important agronomic traits in Stylosanthes guianensis (Aubl.) Sw

Stylosanthes guianensis (Aubl.) Sw. is an economically important pasture and forage legume in tropical regions of the world. Genetic improvement of the crop can be enhanced through marker-assisted breeding. However, neither single nucleotide polymorphism (SNP) markers nor SNP-based genetic linkage map has been previously reported. In this study, a high-quality genetic linkage map of 2572 SNP markers for S. guianensis is generated using amplified-fragment single nucleotide polymorphism and methylation (AFSM) approach. The genetic map has 10 linkage groups (LGs), which spanned 2226.6 cM, with an average genetic distance of 0.87 cM between adjacent markers. Genetic mapping of quantitative trait loci (QTLs) for important agronomic traits such as yield-related and nutritional or quality-related traits was performed using F₂ progeny of a cross between a male-sterile female parent TPRC1979 and male parent TPRCR273 with contrasting phenotypes for morphological and physiological traits. A total of 30 QTLs for 8 yield-related traits and 18 QTLs for 4 nutritional or quality-related traits are mapped on the linkage map. Both the high-quality genetic linkage map and the QTL mapping for important agronomic traits described here will provide valuable genetic resources for marker-assisted selection for S. guianensis.

Genetic Mapping With Allele Dosage Information in Tetraploid Urochloa decumbens (Stapf) R. D. Webster Reveals Insights Into Spittlebug (Notozulia entreriana Berg) Resistance

Urochloa decumbens (Stapf) R. D. Webster is one of the most important African forage grasses in Brazilian beef production. Currently available genetic-genomic resources for this species are restricted mainly due to polyploidy and apomixis. Therefore, crucial genomic-molecular studies such as the construction of genetic maps and the mapping of quantitative trait loci (QTLs) are very challenging and consequently affect the advancement of molecular breeding. The objectives of this work were to (i) construct an integrated U. decumbens genetic map for a full-sibling progeny using GBS-based markers with allele dosage information, (ii) detect QTLs for spittlebug (Notozulia entreriana) resistance, and (iii) seek putative candidate genes involved in defense against biotic stresses. We used the Setaria viridis genome a reference to align GBS reads and selected 4,240 high-quality SNP markers with allele dosage information. Of these markers, 1,000 were distributed throughout nine homologous groups with a cumulative map length of 1,335.09 cM and an average marker density of 1.33 cM. We detected QTLs for resistance to spittlebug, an important pasture insect pest, that explained between 4.66 and 6.24% of the phenotypic variation. These QTLs are in regions containing putative candidate genes related to defense against biotic stresses. Because this is the first genetic map with SNP autotetraploid dosage data and QTL detection in U. decumbens, it will be useful for future evolutionary studies, genome assembly, and other QTL analyses in Urochloa spp. Moreover, the results might facilitate the isolation of spittlebug-related candidate genes and help clarify the mechanism of spittlebug resistance. These approaches will improve selection efficiency and accuracy in U. decumbens molecular breeding and shorten the breeding cycle.

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

Plant growth habit is an important and complex agronomic trait and is associated with yield, disease resistance, and mechanized harvesting in peanuts. There are at least two distinct growth habits (erect and prostrate) and several intermediate forms existing in the peanut germplasm. A recombinant inbred line population containing 188 individuals was developed from a cross of "Jihua 5" and "M130" for genetically dissecting the architecture of the growth habit. A new high-density genetic linkage map was constructed by using specific locus amplified fragment sequencing technology. The map contains 2,808 single-nucleotide polymorphism markers distributed on 20 linkage groups with a total length of 1,308.20 cM and an average inter-marker distance of 0.47 cM. The quantitative trait locus (QTL) analysis of the growth habit-related traits was conducted based on phenotyping data from seven environments. A total of 39 QTLs for growth habit-related traits was detected on 10 chromosomes explaining 4.55-27.74% of the phenotypic variance, in which 6 QTLs were for lateral branch angle, 8 QTLs were for extent radius, 7 QTLs were for the index of plant type, 11 QTLs were for main stem height, and 7 QTLs were for lateral branch length. Among these QTLs, 12 were co-localized on chromosome B05 spanning an approximately 0.17 Mb physical interval in comparison with the allotetraploid reference genome of "Tifrunner." Analysis of the co-localized genome region has shown that the putative genes are involved in light and hormones and will facilitate peanut growth habit molecular breeding and study of peanut domestication.