Evaluation of linkage disequilibrium, population structure, and genetic diversity in the U.S. peanut mini core collection

A chromosome-level genome of Chenghua pig provides new insights into the domestication and local adaptation of pigs

As a country with abundant genetic resources of pigs, the domestication history of pigs in China and the adaptive evolution of Chinese pig breeds at different latitudes have rarely been elucidated at the genome-wide level. To fill this gap, we first assembled a high-quality chromosome-level genome of the Chenghua pig and used it as a benchmark to analyse the genomes of 272 samples from three genera of three continents. The divergence of the three species belonging to three genera, Phacochoerus africanus, Potamochoerus porcus, and Sus scrofa, was assessed. The introgression of pig breeds redefined that the migration routes were basically from southern China to central and southwestern China, then spread to eastern China, arrived in northern China, and finally reached Europe. The domestication of pigs in China occurred ∼12,000 years ago, earlier than the available Chinese archaeological domestication evidence. In addition, FBN1 and NR6A1 were identified in our study as candidate genes related to extreme skin thickness differences in Eurasian pig breeds and adaptive evolution at different latitudes in Chinese pig breeds, respectively. Our study provides a new resource for the pig genomic pool and refines our understanding of pig genetic diversity, domestication, migration, and adaptive evolution at different latitudes.

Genetic Dissection of Diverse Seed Coat Patterns in Cowpea through a Comprehensive GWAS Approach

This study investigates the genetic determinants of seed coat color and pattern variations in cowpea (Vigna unguiculata), employing a genome-wide association approach. Analyzing a mapping panel of 296 cowpea varieties with 110,000 single nucleotide polymorphisms (SNPs), we focused on eight unique coat patterns: (1) Red and (2) Cream seed; (3) White and (4) Brown/Tan seed coat; (5) Pink, (6) Black, (7) Browneye and (8) Red/Brown Holstein. Across six GWAS models (GLM, SRM, MLM, MLMM, FarmCPU from GAPIT3, and TASSEL5), 13 significant SNP markers were identified and led to the discovery of 23 candidate genes. Among these, four specific genes may play a direct role in determining seed coat pigment. These findings lay a foundational basis for future breeding programs aimed at creating cowpea varieties aligned with consumer preferences and market requirements.

Genetic control of root morphology in response to nitrogen across rapeseed diversity

Rapeseed (Brassica napus L.) is an oil-containing crop of great economic value but with considerable nitrogen requirement. Breeding root systems that efficiently absorb nitrogen from the soil could be a driver to ensure genetic gains for more sustainable rapeseed production. The aim of this study is to identify genomic regions that regulate root morphology in response to nitrate availability. The natural variability offered by 300 inbred lines was screened at two experimental locations. Seedlings grew hydroponically with low or elevated nitrate levels. Fifteen traits related to biomass production and root morphology were measured. On average across the panel, a low nitrate level increased the root-to-shoot biomass ratio and the lateral root length. A large phenotypic variation was observed, along with important heritability values and genotypic effects, but low genotype-by-nitrogen interactions. Genome-wide association study and bulk segregant analysis were used to identify loci regulating phenotypic traits. The first approach nominated 319 SNPs that were combined into 80 QTLs. Three QTLs identified on the A07 and C07 chromosomes were stable across nitrate levels and/or experimental locations. The second approach involved genotyping two groups of individuals from an experimental F2 population created by crossing two accessions with contrasting lateral root lengths. These individuals were found in the tails of the phenotypic distribution. Co-localized QTLs found in both mapping approaches covered a chromosomal region on the A06 chromosome. The QTL regions contained some genes putatively involved in root organogenesis and represent selection targets for redesigning the root morphology of rapeseed.

Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models

Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to reduce the size of genetic data while retaining relevant information. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We develop a mechanistic model to simulate forward-in-time divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles. We investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. To expand the parameter space of positions under selection, we enhance the model by implementing an outlier scan on summarized observed data. We evaluate the usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and emphasize its importance in break down of linkage disequilibrium (LD). We answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection, and determine whether population differentiation-based summary statistics or LD-based summary statistics perform well in estimating selection.

Mapping yield and yield-related traits using diverse common bean germplasm

Common bean (bean) is one of the most important legume crops, and mapping genes for yield and yield-related traits is essential for its improvement. However, yield is a complex trait that is typically controlled by many loci in crop genomes. The objective of this research was to identify regions in the bean genome associated with yield and a number of yield-related traits using a collection of 121 diverse bean genotypes with different yields. The beans were evaluated in replicated trials at two locations, over two years. Significant variation among genotypes was identified for all traits analyzed in the four environments. The collection was genotyped with the BARCBean6K_3 chip (5,398 SNPs), two yield/antiyield gene-based markers, and seven markers previously associated with resistance to common bacterial blight (CBB), including a Niemann-Pick polymorphism (NPP) gene-based marker. Over 90% of the single-nucleotide polymorphisms (SNPs) were polymorphic and separated the panel into two main groups of small-seeded and large-seeded beans, reflecting their Mesoamerican and Andean origins. Thirty-nine significant marker-trait associations (MTAs) were identified between 31 SNPs and 15 analyzed traits on all 11 bean chromosomes. Some of these MTAs confirmed genome regions previously associated with the yield and yield-related traits in bean, but a number of associations were not reported previously, especially those with derived traits. Over 600 candidate genes with different functional annotations were identified for the analyzed traits in the 200-Kb region centered on significant SNPs. Fourteen SNPs were identified within the gene model sequences, and five additional SNPs significantly associated with five different traits were located at less than 0.6 Kb from the candidate genes. The work confirmed associations between two yield/antiyield gene-based markers (AYD1m and AYD2m) on chromosome Pv09 with yield and identified their association with a number of yield-related traits, including seed weight. The results also confirmed the usefulness of the NPP marker in screening for CBB resistance. Since disease resistance and yield measurements are environmentally dependent and labor-intensive, the three gene-based markers (CBB- and two yield-related) and quantitative trait loci (QTL) that were validated in this work may be useful tools for simplifying and accelerating the selection of high-yielding and CBB-resistant bean cultivars.

Association mapping in bambara groundnut [Vigna subterranea (L.) Verdc.] reveals loci associated with agro-morphological traits

BACKGROUND

Genome-wide association studies (GWAS) are important for the acceleration of crop improvement through knowledge of marker-trait association (MTA). This report used DArT SNP markers to successfully perform GWAS on agro-morphological traits using 270 bambara groundnut [Vigna subterranea (L.) Verdc.] landraces sourced from diverse origins. The study aimed to identify marker traits association for nine agronomic traits using GWAS and their candidate genes. The experiment was conducted at two different locations laid out in alpha lattice design. The cowpea [Vigna unguiculata (L.) Walp.] reference genome (i.e. legume genome most closely related to bambara groundnut) assisted in the identification of candidate genes.

RESULTS

The analyses showed that linkage disequilibrium was found to decay rapidly with an average genetic distance of 148 kb. The broadsense heritability was relatively high and ranged from 48.39% (terminal leaf length) to 79.39% (number of pods per plant). The GWAS identified a total of 27 significant marker-trait associations (MTAs) for the nine studied traits explaining 5.27% to 24.86% of phenotypic variations. Among studied traits, the highest number of MTAs was obtained from seed coat colour (6) followed by days to flowering (5), while the least is days to maturity (1), explaining 5.76% to 11.03%, 14.5% to 19.49%, and 11.66% phenotypic variations, respectively. Also, a total of 17 candidate genes were identified, varying in number for different traits; seed coat colour (6), days to flowering (3), terminal leaf length (2), terminal leaf width (2), number of seed per pod (2), pod width (1) and days to maturity (1).

CONCLUSION

These results revealed the prospect of GWAS in identification of SNP variations associated with agronomic traits in bambara groundnut. Also, its present new opportunity to explore GWAS and marker assisted strategies in breeding of bambara groundnut for acceleration of the crop improvement.

Quantitative Trait Locus Analysis of Hessian Fly Resistance in Soft Red Winter Wheat

The Hessian fly (HF) is an invasive insect that has caused millions of dollars in yield losses to southeastern US wheat farms. Genetic resistance is the most sustainable solution to control HF. However, emerging biotypes are quickly overcoming resistance genes in the southeast; therefore, identifying novel sources of resistance is critical. The resistant line "UGA 111729" and susceptible variety "AGS 2038" were crossbred to generate a population of 225 recombinant inbred lines. This population was phenotyped in the growth chamber (GC) during 2019 and 2021 and in field (F) trials in Georgia during the 2021-2022 growing seasons. Visual scoring was utilized in GC studies. The percentage of infested tillers and number of pupae/larvae per tiller, and infested tiller per sample were measured in studies from 2021 to 2022. Averaging across all traits, a major QTL on chromosome 3D explained 42.27% (GC) and 10.43% (F) phenotypic variance within 9.86 centimorgans (cM). SNP marker IWB65911 was associated with the quantitative trait locus (QTL) peak with logarithm of odds (LOD) values of 14.98 (F) and 62.22 (GC). IWB65911 colocalized with resistance gene H32. KASP marker validation verified that UGA 111729 and KS89WGRC06 express H32. IWB65911 may be used for marker-assisted selection.

Population size in QTL detection using quantile regression in genome-wide association studies

The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, considering different population sizes. For this, simulated data was used, with traits of different levels of heritability (0.30 and 0.50), and controlled by 3 and 100 QTLs. Populations of 1,000 to 200 individuals were defined, with a random reduction of 100 individuals for each population. The power of detection of QTLs and the false positive rate were obtained by means of QR considering three different quantiles (0.10, 0.50 and 0.90) and also by means of the General Linear Model (GLM). In general, it was observed that the QR models showed greater power of detection of QTLs in all scenarios evaluated and a relatively low false positive rate in scenarios with a greater number of individuals. The models with the highest detection power of true QTLs at the extreme quantils (0.10 and 0.90) were the ones with the highest detection power of true QTLs. In contrast, the analysis based on the GLM detected few (scenarios with larger population size) or no QTLs in the evaluated scenarios. In the scenarios with low heritability, QR obtained a high detection power. Thus, it was verified that the use of QR in GWAS is effective, allowing the detection of QTLs associated with traits of interest even in scenarios with few genotyped and phenotyped individuals.

Genome-wide association studies reveal novel loci for resistance to groundnut rosette disease in the African core groundnut collection

We identified markers associated with GRD resistance after screening an Africa-wide core collection across three seasons in Uganda Groundnut is cultivated in several African countries where it is a major source of food, feed and income. One of the major constraints to groundnut production in Africa is groundnut rosette disease (GRD), which is caused by a complex of three agents: groundnut rosette assistor luteovirus, groundnut rosette umbravirus and its satellite RNA. Despite several years of breeding for GRD resistance, the genetics of the disease is not fully understood. The objective of the current study was to use the African core collection to establish the level of genetic variation in their response to GRD, and to map genomic regions responsible for the observed resistance. The African groundnut core genotypes were screened across two GRD hotspot locations in Uganda (Nakabango and Serere) for 3 seasons. The Area Under Disease Progress Curve combined with 7523 high quality SNPs were analyzed to establish marker-trait associations (MTAs). Genome-Wide Association Studies based on Enriched Compressed Mixed Linear Model detected 32 MTAs at Nakabango: 21 on chromosome A04, 10 on B04 and 1 on B08. Two of the significant markers were localised on the exons of a putative TIR-NBS-LRR disease resistance gene on chromosome A04. Our results suggest the likely involvement of major genes in the resistance to GRD but will need to be further validated with more comprehensive phenotypic and genotypic datasets. The markers identified in the current study will be developed into routine assays and validated for future genomics-assisted selection for GRD resistance in groundnut.

Genome-wide association mapping for LLS resistance in a MAGIC population of groundnut (Arachis hypogaea L.)

The identified 30 functional nucleotide polymorphisms or genic SNP markers would offer essential information for marker-assisted breeding in groundnut. A genome-wide association study (GWAS) on component traits of LLS resistance in an eight-way multiparent advance generation intercross (MAGIC) population of groundnut in the field and in a light chamber (controlled conditions) was performed via an Affymetrix 48 K single-nucleotide polymorphism (SNP) 'Axiom Arachis' array. Multiparental populations with high-density genotyping enable the detection of novel alleles. In total, five quantitative trait loci (QTLs) with marker - log10(p value) scores ranging from 4.25 to 13.77 for the incubation period (IP) and six QTLs with marker - log10(p value) scores ranging from 4.33 to 10.79 for the latent period (LP) were identified across the A- and B-subgenomes. A total of 62 markers‒trait associations (MTAs) were identified across the A- and B-subgenomes. Markers for LLS scores and the area under the disease progression curve (AUDPC) recorded for plants in the light chamber and under field conditions presented - log10 (p value) scores ranging from 4.22 to 27.30. The highest number of MTAs (six) was identified on chromosomes A05, B07 and B09. Out of a total of 73 MTAs, 37 and 36 MTAs were detected in subgenomes A and B, respectively. Taken together, these results suggest that both subgenomes have equal potential genomic regions contributing to LLS resistance. A total of 30 functional nucleotide polymorphisms or genic SNP markers were detected, among which eight genes were found to encode leucine-rich repeat (LRR) receptor-like protein kinases and putative disease resistance proteins. These important SNPs can be used in breeding programmes for the development of cultivars with improved disease resistance.

Genome-wide analysis-based single nucleotide polymorphism marker sets to identify diverse genotypes in cabbage cultivars (Brassica oleracea var. capitata)

Plant variety protection is essential for breeders' rights granted by the International Union for the Protection of New Varieties of Plants. Distinctness, uniformity, and stability (DUS) are necessary for new variety registration; to this end, currently, morphological traits are examined, which is time-consuming and laborious. Molecular markers are more effective, accurate, and stable descriptors of DUS. Advancements in next-generation sequencing technology have facilitated genome-wide identification of single nucleotide polymorphisms. Here, we developed a core set of single nucleotide polymorphism markers to identify cabbage varieties and traits of test guidance through clustering using the Fluidigm assay, a high-throughput genotyping system. Core sets of 87, 24, and 10 markers are selected based on a genome-wide association-based approach. All core markers could identify 94 cabbage varieties and determine 17 DUS traits. A genotypes database was validated using the Fluidigm platform for variety identification, population structure analysis, cabbage breeding, and DUS testing for plant cultivar protection.

G-DIRT: a web server for identification and removal of duplicate germplasms based on identity-by-state analysis using single nucleotide polymorphism genotyping data

Maintaining duplicate germplasms in genebanks hampers effective conservation and utilization of genebank resources. The redundant germplasm adds to the cost of germplasm conservation by requiring a large proportion of the genebank financial resources towards conservation rather than enriching the diversity. Besides, genome-wide-association analysis using an association panel with over-represented germplasms can be biased resulting in spurious marker-trait associations. The conventional methods of germplasm duplicate removal using passport information suffer from incomplete or missing passport information and data handling errors at various stages of germplasm enrichment. This limitation is less likely in the case of genotypic data. Therefore, we developed a web-based tool, Germplasm Duplicate Identification and Removal Tool (G-DIRT), which allows germplasm duplicate identification based on identity-by-state analysis using single-nucleotide polymorphism genotyping information along with pre-processing of genotypic data. A homozygous genotypic difference threshold of 0.1% for germplasm duplicates has been determined using tetraploid wheat genotypic data with 94.97% of accuracy. Based on the genotypic difference, the tool also builds a dendrogram that can visually depict the relationship between genotypes. To overcome the constraint of high-dimensional genotypic data, an offline version of G-DIRT in the interface of R has also been developed. The G-DIRT is expected to help genebank curators, breeders and other researchers across the world in identifying germplasm duplicates from the global genebank collections by only using the easily sharable genotypic data instead of physically exchanging the seeds or propagating materials. The web server will complement the existing methods of germplasm duplicate identification based on passport or phenotypic information being freely accessible at http://webtools.nbpgr.ernet.in/gdirt/.

Assessment of genetic diversity and SNP marker development within peanut germplasm in Taiwan by RAD-seq

The cultivated peanut (Arachis hypogaea L.) is an important oil crop but has a narrow genetic diversity. Molecular markers can be used to probe the genetic diversity of various germplasm. In this study, the restriction site associated DNA (RAD) approach was utilized to sequence 31 accessions of Taiwanese peanut germplasm, leading to the identification of a total of 17,610 single nucleotide polymorphisms (SNPs). When we grouped these 31 accessions into two subsets according to origin, we found that the "global" subset (n = 17) was more genetically diverse than the "local" subset (n = 14). Concerning botanical varieties, the var. fastigiata subset had greater genetic diversity than the other two subsets of var. vulgaris and var. hypogaea, suggesting that novel genetic resources should be introduced into breeding programs to enhance genetic diversity. Principal component analysis (PCA) of genotyping data separated the 31 accessions into three clusters largely according to the botanical varieties, consistent with the PCA result for 282 accessions genotyped by 14 kompetitive allele-specific PCR (KASP) markers developed in this study. The SNP markers identified in this work not only revealed the genetic relationship and population structure of current germplasm in Taiwan, but also offer an efficient tool for breeding and further genetic applications.

Genome-wide association mapping reveals genomic regions frequently associated with lettuce field resistance to downy mildew

KEY MESSAGE

GWAS identified 63 QTLs for resistance to downy mildew. Though QTLs were distributed across all chromosomes, the genomic regions frequently associated with resistance were located on chromosomes 4 and 5. Lettuce downy mildew is one of the most economically important diseases of cultivated lettuce worldwide. We have applied the genome-wide association mapping (GWAS) approach to detect QTLs for field resistance to downy mildew in the panel of 496 accessions tested in 21 field experiments. The analysis identified 131 significant marker-trait associations that could be grouped into 63 QTLs. At least 51 QTLs were novel, while remaining 12 QTLs overlapped with previously described QTLs for lettuce field resistance to downy mildew. Unlike race-specific, dominant Dm genes that mostly cluster on three out of nine lettuce chromosomes, QTLs (qDMR loci) for polygenic resistance are randomly distributed across all nine chromosomes. The genomic regions frequently associated with lettuce field resistance to downy mildew are located on chromosomes 4 and 5 and could be used for detailed study of the mechanism of polygenic resistance. The most resistant accessions identified in the current study (cvs. Auburn, Grand Rapids, Romabella, PI 226514, and PI 249536) are being incorporated into our breeding program. Markers closely linked to the resistance QTLs could be potentially used for marker-assisted selection, or in combination with other markers in the genome, for a combined genomic and marker-assisted selection. Up to date this is the most comprehensive study of QTLs for field resistance to downy mildew and the first study that uses GWAS for mapping disease resistance loci in lettuce.

GWAS and bulked segregant analysis reveal the Loci controlling growth habit-related traits in cultivated Peanut (Arachis hypogaea L.)

Background

Peanut (Arachis hypogaea L.) is a grain legume crop that originated from South America and is now grown around the world. Peanut growth habit affects the variety’s adaptability, planting patterns, mechanized harvesting, disease resistance, and yield. The objective of this study was to map the quantitative trait locus (QTL) associated with peanut growth habit-related traits by combining the genome-wide association analysis (GWAS) and bulked segregant analysis sequencing (BSA-seq) methods.

Results

GWAS was performed with 17,223 single nucleotide polymorphisms (SNPs) in 103 accessions of the U.S. mini core collection genotyped using an Affymetrix version 2.0 SNP array. With a total of 12,342 high-quality polymorphic SNPs, the 90 suggestive and significant SNPs associated with lateral branch angle (LBA), main stem height (MSH), lateral branch height (LBL), extent radius (ER), and the index of plant type (IOPT) were identified. These SNPs were distributed among 15 chromosomes. A total of 597 associated candidate genes may have important roles in biological processes, hormone signaling, growth, and development. BSA-seq coupled with specific length amplified fragment sequencing (SLAF-seq) method was used to find the association with LBA, an important trait of the peanut growth habit. A 4.08 Mb genomic region on B05 was associated with LBA. Based on the linkage disequilibrium (LD) decay distance, we narrowed down and confirmed the region within the 160 kb region (144,193,467–144,513,467) on B05. Four candidate genes in this region were involved in plant growth. The expression levels of Araip.E64SW detected by qRT-PCR showed significant difference between ‘Jihua 5’ and ‘M130’.

Conclusions

In this study, the SNP (AX-147,251,085 and AX-144,353,467) associated with LBA by GWAS was overlapped with the results in BSA-seq through combined analysis of GWAS and BSA-seq. Based on LD decay distance, the genome range related to LBA on B05 was shortened to 144,193,467–144,513,467. Three candidate genes related to F-box family proteins (Araip.E64SW, Araip.YG1LK, and Araip.JJ6RA) and one candidate gene related to PPP family proteins (Araip.YU281) may be involved in plant growth and development in this genome region. The expression analysis revealed that Araip.E64SW was involved in peanut growth habits. These candidate genes will provide molecular targets in marker-assisted selection for peanut growth habits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08640-3.

Chasing genetic correlation breakers to stimulate population resilience to climate change

Global climate change introduces new combinations of environmental conditions, which is expected to increase stress on plants. This could affect many traits in multiple ways that are as yet unknown but will likely require the modification of existing genetic relationships among functional traits potentially involved in local adaptation. Theoretical evolutionary studies have determined that it is an advantage to have an excess of recombination events under heterogeneous environmental conditions. Our study, conducted on a population of radiata pine (Pinus radiata D. Don), was able to identify individuals that show high genetic recombination at genomic regions, which potentially include pleiotropic or collocating QTLs responsible for the studied traits, reaching a prediction accuracy of 0.80 in random cross-validation and 0.72 when whole family was removed from the training population and predicted. To identify these highly recombined individuals, a training population was constructed from correlation breakers, created through tandem selection of parents in the previous generation and their consequent mating. Although the correlation breakers showed lower observed heterogeneity possibly due to direct selection in both studied traits, the genomic regions with statistically significant differences in the linkage disequilibrium pattern showed higher level of heretozygosity, which has the effect of decomposing unfavourable genetic correlation. We propose undertaking selection of correlation breakers under current environmental conditions and using genomic predictions to increase the frequency of these ’recombined’ individuals in future plantations, ensuring the resilience of planted forests to changing climates. The increased frequency of such individuals will decrease the strength of the population-level genetic correlations among traits, increasing the opportunity for new trait combinations to be developed in the future.

Genetic Diversity, Population Structure and Linkage Disequilibrium Analyses in Tropical Maize Using Genotyping by Sequencing

Several maize breeding programs in India have developed numerous inbred lines but the lines have not been characterized using high-density molecular markers. Here, we studied the molecular diversity, population structure, and linkage disequilibrium (LD) patterns in a panel of 314 tropical normal corn, two sweet corn, and six popcorn inbred lines developed by 17 research centers in India, and 62 normal corn from the International Maize and Wheat Improvement Center (CIMMYT). The 384 inbred lines were genotyped with 60,227 polymorphic single nucleotide polymorphisms (SNPs). Most of the pair-wise relative kinship coefficients (58.5%) were equal or close to 0, which suggests the lack of redundancy in the genomic composition in the majority of inbred lines. Genetic distance among most pairs of lines (98.3%) varied from 0.20 to 0.34 as compared with just 1.7% of the pairs of lines that differed by <0.20, which suggests greater genetic variation even among sister lines. The overall average of 17% heterogeneity was observed in the panel indicated the need for further inbreeding in the high heterogeneous genotypes. The mean nucleotide diversity and frequency of polymorphic sites observed in the panel were 0.28 and 0.02, respectively. The model-based population structure, principal component analysis, and phylogenetic analysis revealed three to six groups with no clear patterns of clustering by centers-wise breeding lines, types of corn, kernel characteristics, maturity, plant height, and ear placement. However, genotypes were grouped partially based on their source germplasm from where they derived.

Genotyping-by-sequencing and genomic selection applications in hexaploid triticale

Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n = 289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) > 0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r² > 0.18 (P < 0.05). Genome-wide LD declined rapidly to r² < 0.1 beyond 10 kb physical distance. The three sub-genomes (A, B, and R) showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement.

Genomic insights into the genetic signatures of selection and seed trait loci in cultivated peanut

INTRODUCTION

Cultivated peanut (Arachis hypogaea L.) is an important oil crop for human nutrition and is cultivated in >100 countries. However, the present knowledge of its genomic diversity, evolution, and loci related to the seed traits is limited.

OBJECTIVES

Our study intended to (1) uncover the population structure and the demographic history of peanuts, (2) identify signatures of selection that occurred during peanut improvement breeding, and (3) detect and verify the functions of candidate genes associated with seed traits.

METHODS

We explored the population relationship and the evolution of peanuts using a largescale single nucleotide polymorphism dataset generated from the genome-wide resequencing of 203 cultivated peanuts. Genetic diversity and genomic scan analyses were applied to identify selective loci for genomic-selection breeding. Genome-wide association studies, transgenic experiments, and RNA-seq were employed to identify the candidate genes associated with seed traits.

RESULTS

Our study revealed that the 203 resequenced accessions were divided into four genetic groups, consistent with their botanical classification. Moreover, the var. peruviana and var. fastigiata subpopulations have diverged to a greater extent than the others, and var. peruviana may be the earliest variant in the evolution from tetraploid ancestors. A recent dramatic expansion in the effective population size of the cultivated peanuts ca. 300-500 years ago was also noted. Selective sweeps underlying quantitative trait loci and genes of seed size, plant architecture, and disease resistance coincide with the major goals of improved peanut breeding compared with the landrace and cultivar populations. Genome-wide association testing with functional analysis led to the identification of two genes involved in seed weight and seed length regulation.

CONCLUSION

Our study provides valuable information for understanding the genomic diversity and the evolution of peanuts and serves as a genomic basis for improving peanut cultivars.

Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values

The principal phenotypic determinants of market class in carrot-the size and shape of the root-are under primarily additive, but also highly polygenic, genetic control. The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided.

Assessment of the genetic diversity and population structure of groundnut germplasm collections using phenotypic traits and SNP markers: Implications for drought tolerance breeding

Profiling the genetic composition and relationships among groundnut germplasm collections is essential for the breeding of new cultivars. The objectives of this study were to assess the genetic diversity and population structure among 100 improved groundnut genotypes using agronomic traits and high-density single nucleotide polymorphism (SNP) markers. The genotypes were evaluated for agronomic traits and drought tolerance at the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT)/India across two seasons. Ninety-nine of the test genotypes were profiled with 16363 SNP markers. Pod yield per plant (PY), seed yield per plant (SY), and harvest index (HI) were significantly (p < 0.05) affected by genotype × environment interaction effects. Genotypes ICGV 07222, ICGV 06040, ICGV 01260, ICGV 15083, ICGV 10143, ICGV 03042, ICGV 06039, ICGV 14001, ICGV 11380, and ICGV 13200 ranked top in terms of pod yield under both drought-stressed and optimum conditions. PY exhibited a significant (p ≤ 0.05) correlation with SY, HI, and total biomass (TBM) under both test conditions. Based on the principal component (PC) analysis, PY, SY, HSW, shelling percentage (SHP), and HI were allocated in PC 1 and contributed to the maximum variability for yield under the two water regimes. Hence, selecting these traits could be successful for screening groundnut genotypes under drought-stressed and optimum conditions. The model-based population structure analysis grouped the studied genotypes into three sub-populations. Dendrogram for phenotypic and genotypic also grouped the studied 99 genotypes into three heterogeneous clusters. Analysis of molecular variance revealed that 98% of the total genetic variation was attributed to individuals, while only 2% of the total variance was due to variation among the subspecies. The genetic distance between the Spanish bunch and Virginia bunch types ranged from 0.11 to 0.52. The genotypes ICGV 13189, ICGV 95111, ICGV 14421, and ICGV 171007 were selected for further breeding based on their wide genetic divergence. Data presented in this study will guide groundnut cultivar development emphasizing economic traits and adaptation to water-limited agro-ecologies, including in Ethiopia.

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut (Arachis hypogaea L.)

The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect-increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanut, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold (α = 1%). We detected 7,682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (p-value < 1.0E-16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil.

Genome-Wide Scanning Enabled SNP Discovery, Linkage Disequilibrium Patterns and Population Structure in a Panel of Fonio (Digitaria exilis [Kippist] Stapf) Germplasm

White fonio (Digitaria exilis) is a staple food for millions of people in arid and semi-arid areas of West Africa. Knowledge about nutritional and health benefits, insights into morphological diversity, and the recent development of genomic resources call for a better understanding of the genetic structure of the extant germplasm gathered throughout the region in order to set up a robust breeding program. We assessed the genetic diversity and population structure of 259 fonio individuals collected from six countries from West Africa (Nigeria, Benin, Guinea, Mali, Burkina Faso and Niger) in this study using 688 putative out of 21,324 DArTseq-derived SNP markers. Due to the inbreeding and small population size, the results revealed a substantial level of genetic variability. Furthermore, two clusters were found irrespective of the geographic origins of accessions. Moreover, the high level of linkage disequilibrium (LD) between loci observed resulted from the mating system of the crop, which is often associated with a low recombination rate. These findings fill the gaps about the molecular diversity and genetic structure of the white fonio germplasm in West Africa. This was required for the application of genomic tools that can potentially speed up the genetic gain in fonio millet breeding for complex traits such as yield, and other nutrient contents.

Genetic diversity analysis of Korean peanut germplasm using 48 K SNPs 'Axiom_Arachis' Array and its application for cultivar differentiation

Cultivated peanut (Arachis hypogaea) is one of the important legume oilseed crops. Cultivated peanut has a narrow genetic base. Therefore, it is necessary to widen its genetic base and diversity for additional use. The objective of the present study was to assess the genetic diversity and population structure of 96 peanut genotypes with 9478 high-resolution SNPs identified from a 48 K 'Axiom_Arachis' SNP array. Korean set genotypes were also compared with a mini-core of US genotypes. These sets of genotypes were used for genetic diversity analysis. Model-based structure analysis at K = 2 indicated the presence of two subpopulations in both sets of genotypes. Phylogenetic and PCA analysis clustered these genotypes into two major groups. However, clear genotype distribution was not observed for categories of subspecies, botanical variety, or origin. The analysis also revealed that current Korean genetic resources lacked variability compared to US mini-core genotypes. These results suggest that Korean genetic resources need to be expanded by creating new allele combinations and widening the genetic pool to offer new genetic variations for Korean peanut improvement programs. High-quality SNP data generated in this study could be used for identifying varietal contaminant, QTL, and genes associated with desirable traits by performing mapping, genome-wide association studies.

Genomic patterns of structural variation among diverse genotypes of Sorghum bicolor and a potential role for deletions in local adaptation

Genomic structural mutations, especially deletions, are an important source of variation in many species and can play key roles in phenotypic diversification and evolution. Previous work in many plant species has identified multiple instances of structural variations (SVs) occurring in or near genes related to stress response and disease resistance, suggesting a possible role for SVs in local adaptation. Sorghum [Sorghum bicolor (L.) Moench] is one of the most widely grown cereal crops in the world. It has been adapted to an array of different climates as well as bred for multiple purposes, resulting in a striking phenotypic diversity. In this study, we identified genome-wide SVs in the Biomass Association Panel, a collection of 347 diverse sorghum genotypes collected from multiple countries and continents. Using Illumina-based, short-read whole-genome resequencing data from every genotype, we found a total of 24,648 SVs, including 22,359 deletions. The global site frequency spectrum of deletions and other types of SVs fit a model of neutral evolution, suggesting that the majority of these mutations were not under any types of selection. Clustering results based on single nucleotide polymorphisms separated the genotypes into eight clusters which largely corresponded with geographic origins, with many of the large deletions we uncovered being unique to a single cluster. Even though most deletions appeared to be neutral, a handful of cluster-specific deletions were found in genes related to biotic and abiotic stress responses, supporting the possibility that at least some of these deletions contribute to local adaptation in sorghum.

Genetic diversity and population structure of advanced clones selected over forty years by a potato breeding program in the USA

Knowledge regarding genetic diversity and population structure of breeding materials is essential for crop improvement. The Texas A&M University Potato Breeding Program has a collection of advanced clones selected and maintained in-vitro over a 40-year period. Little is known about its genetic makeup and usefulness for the current breeding program. In this study, 214 potato clones were genotyped with the Infinium Illumina 22 K V3 Potato Array. After filtering, a total of 10,106 single nucleotide polymorphic (SNP) markers were used for analysis. Heterozygosity varied by SNP, with an overall average of 0.59. Three groups of tetraploid clones primarily based on potato market classes, were detected using STRUCTURE software and confirmed by discriminant analysis of principal components. The highest coefficient of differentiation observed between the groups was 0.14. Signatures of selection were uncovered in genes controlling potato flesh and skin color, length of plant cycle and tuberization, and carbohydrate metabolism. A core set of 43 clones was obtained using Core Hunter 3 to develop a sub-collection that retains similar genetic diversity as the whole population, minimize redundancies, and facilitates long-term conservation of genetic resources. The comprehensive molecular characterization of our breeding clone bank collection contributes to understanding the genetic diversity of existing potato resources. This analysis could be applied to other breeding programs and assist in the selection of parents, fingerprinting, protection, and management of the breeding collections.

Genotyping tools and resources to assess peanut germplasm: smut-resistant landraces as a case study

Peanut smut caused by Thecaphora frezii is a severe fungal disease currently endemic to Argentina and Brazil. The identification of smut resistant germplasm is crucial in view of the potential risk of a global spread. In a recent study, we reported new sources of smut resistance and demonstrated its introgression into elite peanut cultivars. Here, we revisited one of these sources (line I0322) to verify its presence in the U.S. peanut germplasm collection and to identify single nucleotide polymorphisms (SNPs) potentially associated with resistance. Five accessions of Arachis hypogaea subsp. fastigiata from the U.S. peanut collection, along with the resistant source and derived inbred lines were genotyped with a 48K SNP peanut array. A recently developed SNP genotyping platform called RNase H2 enzyme-based amplification (rhAmp) was further applied to validate selected SNPs in a larger number of individuals per accession. More than 14,000 SNPs and nine rhAmp assays confirmed the presence of a germplasm in the U.S. peanut collection that is 98.6% identical (P < 0.01, bootstrap t-test) to the resistant line I0322. We report this germplasm with accompanying genetic information, genotyping data, and diagnostic SNP markers.

Genetic Diversity and Genome-Wide Association Study of Seed Aspect Ratio Using a High-Density SNP Array in Peanut (Arachis hypogaea L.)

Peanut (Arachis hypogaea L.) is one of the important oil crops of the world. In this study, we aimed to evaluate the genetic diversity of 384 peanut germplasms including 100 Korean germplasms and 284 core collections from the United States Department of Agriculture (USDA) using an Axiom_Arachis array with 58K single-nucleotide polymorphisms (SNPs). We evaluated the evolutionary relationships among 384 peanut germplasms using a genome-wide association study (GWAS) of seed aspect ratio data processed by ImageJ software. In total, 14,030 filtered polymorphic SNPs were identified from the peanut 58K SNP array. We identified five SNPs with significant associations to seed aspect ratio on chromosomes Aradu.A09, Aradu.A10, Araip.B08, and Araip.B09. AX-177640219 on chromosome Araip.B08 was the most significantly associated marker in GAPIT and Regularization method. Phosphoenolpyruvate carboxylase (PEPC) was found among the eleven genes within a linkage disequilibrium (LD) of the significant SNPs on Araip.B08 and could have a strong causal effect in determining seed aspect ratio. The results of the present study provide information and methods that are useful for further genetic and genomic studies as well as molecular breeding programs in peanuts.

GCViT: a method for interactive, genome-wide visualization of resequencing and SNP array data

Background

Large genotyping datasets have become commonplace due to efficient, cheap methods for SNP identification. Typical genotyping datasets may have thousands to millions of data points per accession, across tens to thousands of accessions. There is a need for tools to help rapidly explore such datasets, to assess characteristics such as overall differences between accessions and regional anomalies across the genome.

Results

We present GCViT (Genotype Comparison Visualization Tool), for visualizing and exploring large genotyping datasets. GCViT can be used to identify introgressions, conserved or divergent genomic regions, pedigrees, and other features for more detailed exploration. The program can be used online or as a local instance for whole genome visualization of resequencing or SNP array data. The program performs comparisons of variants among user-selected accessions to identify allele differences and similarities between accessions and a user-selected reference, providing visualizations through histogram, heatmap, or haplotype views. The resulting analyses and images can be exported in various formats.

Conclusions

GCViT provides methods for interactively visualizing SNP data on a whole genome scale, and can produce publication-ready figures. It can be used in online or local installations. GCViT enables users to confirm or identify genomics regions of interest associated with particular traits.

GCViT is freely available at https://github.com/LegumeFederation/gcvit. The 1.0 version described here is available at 10.5281/zenodo.4008713.

Genotypic Characterization of the U.S. Peanut Core Collection

Cultivated peanut (Arachis hypogaea) is an important oil, food, and feed crop worldwide. The USDA peanut germplasm collection currently contains 8,982 accessions. In the 1990s, 812 accessions were selected as a core collection on the basis of phenotype and country of origin. The present study reports genotyping results for the entire available core collection. Each accession was genotyped with the Arachis_Axiom2 SNP array, yielding 14,430 high-quality, informative SNPs across the collection. Additionally, a subset of 253 accessions was replicated, using between two and five seeds per accession, to assess heterogeneity within these accessions. The genotypic diversity of the core is mostly captured in five genotypic clusters, which have some correspondence with botanical variety and market type. There is little genetic clustering by country of origin, reflecting peanut’s rapid global dispersion in the 18^th and 19^th centuries. A genetic cluster associated with the hypogaea/aequatoriana/peruviana varieties, with accessions coming primarily from Bolivia, Peru, and Ecuador, is consistent with these having been the earliest landraces. The genetics, phenotypic characteristics, and biogeography are all consistent with previous reports of tetraploid peanut originating in Southeast Bolivia. Analysis of the genotype data indicates an early genetic radiation, followed by regional distribution of major genetic classes through South America, and then a global dissemination that retains much of the early genetic diversity in peanut. Comparison of the genotypic data relative to alleles from the diploid progenitors also indicates that subgenome exchanges, both large and small, have been major contributors to the genetic diversity in peanut.

Identification of QTLs for resistance to leaf spots in cultivated peanut (Arachis hypogaea L.) through GWAS analysis

Two QTLs on ChrB09 significantly associated with both early and late leaf spots were identified by genome-wide association study in the US peanut mini-core collection. Early leaf spot (ELS) and late leaf spot (LLS) are two serious peanut diseases in the USA, causing tens of millions of dollars of annual economic losses. However, the genetic factors underlying resistance to those diseases in peanuts have not been well-studied. We conducted a genome-wide association study for the two peanut diseases using Affymetrix version 2.0 SNP array with 120 genotypes mainly coming from the US peanut mini-core collection. A total of 46 quantitative trait loci (QTLs) were identified with phenotypic variation explained (PVE) from 10.19 to 24.11%, in which eighteen QTLs are for resistance to ELS and 28 QTLs for LLS. Among the 46 QTLs, there were four and two major QTLs with PVE higher than 16.99% for resistance ELS and LLS, respectively. Of the six major QTLs, five were located on the B sub-genome and only one was on the A sub-genome, which suggested that the B sub-genome has more potential resistance genomic regions than the A sub-genome. In addition, two genomic regions on chromosome B09 were found to provide significant resistance to both ELS and LLS. A total of 74 non-redundant genes were identified as resistance genes, among which, twelve candidate genes were in significant genomic regions including two candidate genes for both ELS and LLS, and other ten candidate genes for ELS. The QTLs and candidate genes obtained from this study will be useful to breed peanuts for resistances to the diseases.