Identification of major consensus QTLs for seed size and minor QTLs for pod traits in cultivated groundnut (Arachis hypogaea L.)

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.

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.

Mapping quantitative trait loci and predicting candidate genes for Striga resistance in maize using resistance donor line derived from Zea diploperennis

The parasitic weed, Striga is a major biological constraint to cereal production in sub-Saharan Africa (SSA) and threatens food and nutrition security. Two hundred and twenty-three (223) F_2:3 mapping population involving individuals derived from TZdEI 352 x TZEI 916 were phenotyped for four Striga-adaptive traits and genotyped using the Diversity Arrays Technology (DArT) to determine the genomic regions responsible for Striga resistance in maize. After removing distorted SNP markers, a genetic linkage map was constructed using 1,918 DArTseq markers which covered 2092.1 cM. Using the inclusive composite interval mapping method in IciMapping, twenty-three QTLs influencing Striga resistance traits were identified across four Striga-infested environments with five stable QTLs (qGY4, qSC2.1, qSC2.2, qSC5, and qSC6) detected in more than one environment. The variations explained by the QTLs ranged from 4.1% (qSD2.3) to 14.4% (qSC7.1). Six QTLs each with significant additive × environment interactions were also identified for grain yield and Striga damage. Gene annotation revealed candidate genes underlying the QTLs, including the gene models GRMZM2G077002 and GRMZM2G404973 which encode the GATA transcription factors, GRMZM2G178998 and GRMZM2G134073 encoding the NAC transcription factors, GRMZM2G053868 and GRMZM2G157068 which encode the nitrate transporter protein and GRMZM2G371033 encoding the SBP-transcription factor. These candidate genes play crucial roles in plant growth and developmental processes and defense functions. This study provides further insights into the genetic mechanisms of resistance to Striga parasitism in maize. The QTL detected in more than one environment would be useful for further fine-mapping and marker-assisted selection for the development of Striga resistant and high-yielding maize cultivars.

Multi-Omics Profiling Identifies Candidate Genes Controlling Seed Size in Peanut

Seed size is the major yield component and a key target trait that is selected during peanut breeding. However, the mechanisms that regulate peanut seed size are unknown. Two peanut mutants with bigger seed size were isolated in this study by ⁶⁰Co treatment of a common peanut landrace, Huayu 22, and were designated as the "big seed" mutant lines (hybs). The length and weight of the seed in hybs were about 118% and 170% of those in wild-type (WT), respectively. We adopted a multi-omics approach to identify the genomic locus underlying the hybs mutants. We performed whole genome sequencing (WGS) of WT and hybs mutants and identified thousands of large-effect variants (SNPs and indels) that occurred in about four hundred genes in hybs mutants. Seeds from both WT and hybs lines were sampled 20 days after flowering (DAF) and were used for RNA-Seq analysis; the results revealed about one thousand highly differentially expressed genes (DEGs) in hybs compared to WT. Using a method that combined large-effect variants with DEGs, we identified 45 potential candidate genes that shared gene product mutations and expression level changes in hybs compared to WT. Among the genes, two candidate genes encoding cytochrome P450 superfamily protein and NAC transcription factors may be associated with the increased seed size in hybs. The present findings provide new information on the identification and functional research into candidate genes responsible for the seed size phenotype in peanut.

Comparative transcriptomics analysis of developing peanut (Arachis hypogaea L.) pods reveals candidate genes affecting peanut seed size

Pod size is one of the most important agronomic features of peanuts, which directly affects peanut yield. Studies on the regulation mechanism underpinning pod size in cultivated peanuts remain hitherto limited compared to model plant systems. To better understand the molecular elements that underpin peanut pod development, we conducted a comprehensive analysis of chronological transcriptomics during pod development in four peanut accessions with similar genetic backgrounds, but varying pod sizes. Several plant transcription factors, phytohormones, and the mitogen-activated protein kinase (MAPK) signaling pathways were significantly enriched among differentially expressed genes (DEGs) at five consecutive developmental stages, revealing an eclectic range of candidate genes, including PNC, YUC, and IAA that regulate auxin synthesis and metabolism, CYCD and CYCU that regulate cell differentiation and proliferation, and GASA that regulates seed size and pod elongation via gibberellin pathway. It is plausible that MPK3 promotes integument cell division and regulates mitotic activity through phosphorylation, and the interactions between these genes form a network of molecular pathways that affect peanut pod size. Furthermore, two variant sites, GCP4 and RPPL1, were identified which are stable at the QTL interval for seed size attributes and function in plant cell tissue microtubule nucleation. These findings may facilitate the identification of candidate genes that regulate pod size and impart yield improvement in cultivated peanuts.

Transcriptome Analysis and Gene Expression Profiling of the Peanut Small Seed Mutant Identified Genes Involved in Seed Size Control

Seed size is a key factor affecting crop yield and a major agronomic trait concerned in peanut (Arachis hypogaea L.) breeding. However, little is known about the regulation mechanism of peanut seed size. In the present study, a peanut small seed mutant1 (ssm1) was identified through irradiating peanut cultivar Luhua11 (LH11) using ⁶⁰Coγ ray. Since the globular embryo stage, the embryo size of ssm1 was significantly smaller than that of LH11. The dry seed weight of ssm1 was only 39.69% of the wild type LH14. The seeds were wrinkled with darker seed coat. The oil content of ssm1 seeds were also decreased significantly. Seeds of ssm1 and LH11 were sampled 10, 20, and 40 days after pegging (DAP) and were used for RNA-seq. The results revealed that genes involved in plant hormones and several transcription factors related to seed development were differentially expressed at all three stages, especially at DAP10 and DAP20. Genes of fatty acid biosynthesis and late embryogenesis abundant protein were significantly decreased to compare with LH11. Interestingly, the gene profiling data suggested that PKp2 and/or LEC1 could be the key candidate genes leading to the small seed phenotype of the mutant. Our results provide valuable clues for further understanding the mechanisms underlying seed size control in peanut.

High-Density Genetic Variation Map Reveals Key Candidate Loci and Genes Associated With Important Agronomic Traits in Peanut

Peanut is one of the most important cash crops with high quality oil, high protein content, and many other nutritional elements, and grown globally. Cultivated peanut (Arachis hypogaea L.) is allotetraploid with a narrow genetic base, and its genetics and molecular mechanisms controlling the agronomic traits are poorly understood. Here, we report a comprehensive genome variation map based on the genotyping of a panel of 178 peanut cultivars using Axiom_Arachis2 SNP array, including 163 representative varieties of different provinces in China, and 15 cultivars from 9 other countries. According to principal component analysis (PCA) and phylogenetic analysis, the peanut varieties were divided into 7 groups, notable genetic divergences between the different areas were shaped by environment and domestication. Using genome-wide association study (GWAS) analysis, we identified several marker-trait associations (MTAs) and candidate genes potentially involved in regulating several agronomic traits of peanut, including one MTA related with hundred seed weight, one MTA related with total number of branches, and 14 MTAs related with pod shape. This study outlines the genetic basis of these peanut cultivars and provides 13,125 polymorphic SNP markers for further distinguishing and utility of these elite cultivars. In addition, the candidate loci and genes provide valuable information for further fine mapping of QTLs and improving the quality and yield of peanut using a genomic-assisted breeding method.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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