Recommendations for Choosing the Genotyping Method and Best Practices for Quality Control in Crop Genome-Wide Association Studies

Missing genotype imputation in non-model species using self-organizing maps

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

Non-destructive seed genotyping via microneedle-based DNA extraction

Crop breeding plays an essential role in addressing food security by enhancing crop yield, disease resistance and nutritional value. However, the current crop breeding process faces multiple challenges and limitations, especially in genotypic evaluations. Traditional methods for seed genotyping remain labour-intensive, time-consuming and cost-prohibitive outside of large-scale breeding programs. Here, we present a handheld microneedle (MN)-based seed DNA extraction platform for rapid, non-destructive and in-field DNA isolation from crop seeds for instant marker analysis. Using soybean seeds as a case study, we demonstrated the use of polyvinyl alcohol (PVA) MN patches for the successful extraction of DNA from softened soybean seeds. This extraction technology maintained high seed viability, showing germination rates of 82% and 79%, respectively, before and after MN sampling. The quality of MN-extracted DNA was sufficient for various genomic analyses, including PCR, LAMP and whole-genome sequencing. Importantly, this MN patch method also allowed for the identification of specific genetic differences between soybean varieties. Additionally, we designed a 3D-printed extraction device, which enabled multiplexed seed DNA extraction in a microplate format. In the future, this method could be applied at scale and in-field for crop seed DNA extraction and genotyping analysis.

Polygenic risk scores for eGFR are associated with age at kidney failure

BACKGROUND

The genetic architecture of chronic kidney disease (CKD) is complex, including monogenic and polygenic contributions. CKD progression to kidney failure is influenced by factors including male sex, baseline estimated glomerular filtration rate (eGFR), hypertension, diabetes, proteinuria, and the underlying kidney disease. These traits all have strong genetic components, which can be partially quantified using polygenic risk scores. This paper examines the association between polygenic risk scores for CKD-related traits and age at kidney failure development.

METHODS

Genome-wide genotype data from 10,586 patients with kidney failure were compiled from 12 cohorts. Polygenic risk scores for hypertension, albuminuria, rapid decline in eGFR, decreased total kidney volume, and decreased eGFR were calculated using weights from published independent population-scale genome-wide association studies. The association between each polygenic risk score and age at kidney failure was investigated using logistic regression models. The association between polygenic risk score and age at kidney failure was also investigated separately for each primary kidney disease.

RESULTS

Individuals in the highest 10% of polygenic risk score for decreased eGFR developed kidney failure 2 years earlier than those in the bottom 90% (49.9 years and 47.9 years, P = 5e-5). A standard deviation increase in decreased eGFR polygenic risk score was associated with increased odds of developing kidney failure before the age of 60 years (Odds ratio (OR) = 1.05; 95% CI 1.01-1.10; P = 0.01), as was high decreased eGFR polygenic risk score (OR = 1.26; 95% CI 1.08-1.46; P = 0.003).

CONCLUSIONS

We conclude that decreased eGFR polygenic risk score explains a portion of the variation in age at development of kidney failure.

Genetic diversity analysis and core germplasm construction of tea plants in Lu'an

BACKGROUND

Lu'an, as one of the two major tea-producing areas in Anhui Province, has a long history of tea planting and rich tea germplasm resources. However, the genetic diversity and population structure of local tea plants are still unclear. In order to better protect and utilize tea germplasm resources in Lu'an, 217 tea accessions from six geographical origins were used to assess genetic diversity of Lu'an tea plant germplasm through double digest restriction-site associated DNA sequencing (ddRAD-seq) technology.

RESULTS

A total of 306,320 high quality single nucleotide polymorphism (SNP) markers were obtained. Population structure, phylogenetic relationships and principal component analysis (PCA) divided the entire population into three groups. The genetic diversity and population differentiation analysis showed that the mean observed heterozygosity (Ho) was 0.06 ∼ 0.17, average nucleotide diversity (Pi) was 0.13 ∼ 0.26, and pairwise fixation index (Fst) was 0.01 ∼ 0.15. In addition, a core tea germplasm set composed of 50 tea germplasm sets was established.

CONCLUSION

Our study demonstrated that the germplasm resources of the Lu'an tea plants exhibit significant genetic diversity. A core germplasm sets for the Lu'an tea plants has been established, which effectively represents the genetic diversity of the entire tea germplasm collection. This study provided the basis for genetic research, germplasm protection and breeding of tea plants in Lu'an.

PGSXplorer: an integrated nextflow pipeline for comprehensive quality control and polygenic score model development

The rapid development of next-generation sequencing technologies and genomic data sharing initiatives during the post-Human Genome Project-era has catalyzed major advances in individualized medicine research. Genome-wide association studies (GWAS) have become a cornerstone of efforts towards understanding the genetic basis of complex diseases, leading to the development of polygenic scores (PGS). Despite their immense potential, the scarcity of standardized PGS development pipelines limits widespread adoption of PGS. Herein, we introduce PGSXplorer, a comprehensive Nextflow DSL2 pipeline that enables quality control of genomic data and automates the phasing, imputation, and construction of PGS models using reference GWAS data. PGSXplorer integrates various PGS development tools such as PLINK, PRSice-2, LD-Pred2, Lassosum2, MegaPRS, SBayesR-C, PRS-CSx and MUSSEL, improving the generalizability of PGS through multi-origin data integration. Tested with synthetic datasets, our fully Docker-encapsulated tool has demonstrated scalability and effectiveness for both single- and multi-population analyses. Continuously updated as an open-source tool, PGSXplorer is freely available with user tutorials at https://github.com/tutkuyaras/PGSXplorer, making it a valuable resource for advancing precision medicine in genetic research.

Comprehensive insights on association mapping in perennial fruit crops breeding - Its implications, current status and future perspectives

In order to provide food and nutritional security for the world's rapidly expanding population, fruit crop researchers have identified two critical priorities: increasing production and preserving fruit quality during the pre- and post-harvest periods. The genetic basis of these complex, commercially important fruit traits which are uniquely regulated by polygenes or multi-allelic genes that interact with one another and the environment can be analyzed with the aid of trait mapping tools. The most interesting trait mapping approach that offers the genetic level investigation for marker-trait associations (MTAs) for these complex fruit traits, without the development of mapping population, is association mapping. This approach was used during the genetic improvement program, emphasizing the obstacles (breeding strategies adopted, generation interval, and their genomic status) pertaining to perennial fruit crops. This method of studying population diversity and linkage disequilibrium in perennial fruit crops has been made possible by recent developments in genotyping, phenotyping, and statistical analysis. Thus, the purpose of this review is to provide an overview of different trait mapping techniques, with a focus on association mapping (method, essential components, viability, constraints, and future perspective) and its advantages, disadvantages, and possibilities for breeding perennial fruit crops.

Genome-Wide Association Study of Birth Wool Length, Birth Weight, and Head Color in Chinese Tan Sheep Through Whole-Genome Re-Sequencing

The Chinese Tan sheep is a unique breed of sheep that is typical throughout China, mainly used for fur and meat production. They are widely distributed in northwestern China and are famous for their lambskin and shiny white curly wool. In this study, the phenotypic traits of wool length, birth weight, and head coat color were evaluated in 256 Chinese Tan sheep breeds. Whole genome sequencing generated 23.67 million high-quality SNPs for genome-wide association studies (GWAS). We identified 208 significant SNPs associated with birth wool length, implicating RAD50, MACROD2, SAMD5, SASH1, and SPTLC3 as potential candidate genes for this trait. For birth weight, 1056 significant SNPs, with 76.89% of them located on chromosome 2, were identified by GWAS, and XPA, INVS, LOC121818504, GABBR2, LOC101114941, and LOC106990096 were identified as potential candidate genes for birth weight. The GWAS for head coat color identified 1424 significant SNPs across three chromosomes, with 99.65% on chromosome 14, and SPIRE2, TCF25, and MC1R as candidate genes were found to be possibly involved in the development of the black-headed coat color in sheep. Furthermore, we selected head coat color as a representative trait and performed an independent test of our GWAS findings through multiplex PCR SNP genotyping. The findings validated five mutation sites in chromosome 14 (14,251,947 T>A, 14,252,090 G>A, 14,252,158 C>T, 14,252,329 T>G, and 14,252,464 C>T) within the exon1 of the MC1R gene (517 bp), as identified by GWAS in an additional 102 Tan sheep individuals, and revealed that black-headed sheep predominantly exhibited heterozygous genotypes, possibly contributing to their color change. Our results provide a valuable foundation for further study of these three economically important traits, and enhance our understanding of genetic structure and variation in Chinese Tan sheep.

Genome-wide association study uncovers pea candidate genes and metabolic pathways involved in rust resistance

Pea (Pisum sativum L.) is an important temperate legume crop providing plant-based proteins for food and feed worldwide. Pea yield can be limited by several biotic stresses, among which rust represents a major limiting factor in many temperate and subtropical regions. Some efforts have been made to assess the natural variation in pea resistance to rust, but its efficient exploitation in breeding is limited since the resistance loci identified so far are scarce and their responsible gene(s) unknown. To overcome this knowledge gap, a comprehensive genome-wide association study (GWAS) has been performed on pea rust, caused by Uromyces pisi, to uncover genetic loci associated with resistance. Utilizing a diverse collection of 320 pea accessions, we evaluated phenotypic responses to two rust isolates using both traditional methods and advanced image-based phenotyping. We detected 95 significant trait-marker associations using a set of 26,045 Diversity Arrays Technology-sequencing polymorphic markers. Our in silico analysis identified 62 candidate genes putatively involved in rust resistance, grouped into different functional categories such as gene expression regulation, vesicle trafficking, cell wall biosynthesis, and hormonal signaling. This research highlights the potential of GWAS to identify molecular markers associated with resistance and candidate genes against pea rust, offering new targets for precision breeding. By integrating our findings into current breeding programs, we can facilitate the development of pea varieties with improved resistance to rust, contributing to sustainable agricultural practices and food security. This study sets the stage for future functional genomic analyses and the application of genomic selection approaches to enhance disease resistance in peas.

Morpho-Phenological, Chemical, and Genetic Characterization of Italian Maize Landraces from the Lazio Region

In the framework of a Collaboration Agreement between CREA and ARSIAL, a morpho-phenological, chemical, and genetic characterization of maize populations native to the Lazio region was carried out. During 2022 and 2023, a set of 50 accessions, belonging both to ARSIAL and CREA maize collections, were multiplied in Bergamo. Morpho-phenological descriptors were recorded in the field: plant height, ear height, and male and female flowering time. The grain chemical composition in terms of protein, lipid, starch, ash and fiber was evaluated by near-infrared spectroscopy (NIRS). A double-digest restriction-site-associated DNA sequencing (ddRADseq) strategy was used to genotype the landraces. The two collections were not significantly different in terms of grain chemical composition. On the other hand, the ARSIAL and CREA germplasm showed a different distribution in the three cluster-based population structure obtained by ddRADseq, which largely corresponded to the distribution map of their collection sites. The materials from the Lazio region maintained by ARSIAL and CREA were revealed to be different. The comparison between the two groups of landraces showed the importance of characterizing germplasm collections to promote the recovery and valorization of local biodiversity.

Genomic exploration of Iranian almond (Prunus dulcis) germplasm: decoding diversity, population structure, and linkage disequilibrium through genotyping-by-sequencing analysis

This study focuses on the genetic diversity and population structure of Prunus dulcis (almond tree), a crucial agricultural component with widespread cultivation and commercial importance, particularly in Iran, a region with a longstanding tradition of almond cultivation. The diverse almond collection in Iran encompasses many local varieties, breeding selections, rootstocks, and international cultivars. This diversity necessitates advanced genotyping techniques to gain insights into genetic diversity, population structure, and linkage disequilibrium (LD). In this paper, genotyping-by-sequencing (GBS) was employed to analyze 62 almond germplasm samples, identifying approximately 63,537 high-quality single nucleotide polymorphisms (SNPs) distributed across the eight chromosomes of the almond genome. On average, there were 30,225 SNPs per chromosome. The analysis yielded an average polymorphism information content (PIC) of 0.315 and an expected heterozygosity (He) rate of 0.28, indicating a significant level of genetic diversity within the studied almond germplasm. The LD analysis demonstrated a rapid decline, with an average LD decay spanning approximately 300 kb for an r2 value of 0.2. This suggests substantial hybridization among the sampled almond varieties. Principal Component Analysis (PCA) and structure analysis could not differentiate genotypes based on geographical origin, providing further evidence of genetic mixing among the studied almond populations. An Analysis of Molecular Variance (AMOVA) highlighted significant genetic diversity within populations but revealed minimal differences. This comprehensive study of Iran's almond genotypes offers valuable insights for future breeding and conservation efforts, emphasizing this agriculturally significant species abundant genetic diversity and intricate population structure.

Unveiling the Genome-Wide Consequences of Range Expansion and Mating System Transitions in Primula vulgaris

Genetic diversity is heterogeneously distributed among populations of the same species, due to the joint effects of multiple demographic processes, including range contractions and expansions, and mating systems shifts. Here, we ask how both processes shape genomic diversity in space and time in the classical Primula vulgaris model. This perennial herb originated in the Caucasus region and was hypothesized to have expanded westward following glacial retreat in the Quaternary. Moreover, this species is a long-standing model for mating system transitions, exemplified by shifts from heterostyly to homostyly. Leveraging a high-quality reference genome of the closely related Primula veris and whole-genome resequencing data from both heterostylous and homostylous individuals from populations encompassing a wide distribution of P. vulgaris, we reconstructed the demographic history of P. vulgaris. Results are compatible with the previously proposed hypothesis of range expansion from the Caucasus region approximately 79,000 years ago and suggest later shifts to homostyly following rather than preceding postglacial colonization of England. Furthermore, in accordance with population genetic theoretical predictions, both processes are associated with reduced genetic diversity, increased linkage disequilibrium, and reduced efficacy of purifying selection. A novel result concerns the contrasting effects of range expansion versus shift to homostyly on transposable elements, for the former, process is associated with changes in transposable element genomic content, while the latter is not. Jointly, our results elucidate how the interactions among range expansion, transitions to selfing, and Quaternary climatic oscillations shape plant evolution.

Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: a case study with a dataset from an almond germplasm collection

Background

Advances in DNA sequencing revolutionized plant genomics and significantly contributed to the study of genetic diversity. However, predicting phenotypes from genomic data remains a challenge, particularly in the context of plant breeding. Despite significant progress, accurately predicting phenotypes from high-dimensional genomic data remains a challenge, particularly in identifying the key genetic factors influencing these predictions. This study aims to bridge this gap by integrating explainable artificial intelligence (XAI) techniques with advanced machine learning models. This approach is intended to enhance both the predictive accuracy and interpretability of genotype-to-phenotype models, thereby improving their reliability and supporting more informed breeding decisions.

Results

This study compares several ML methods for genotype-to-phenotype prediction, using data available from an almond germplasm collection. After preprocessing and feature selection, regression models are employed to predict almond shelling fraction. Best predictions were obtained by the Random Forest method (correlation = 0.727 ± 0.020, an R 2 = 0.511 ± 0.025, and an RMSE = 7.746 ± 0.199). Notably, the application of the SHAP (SHapley Additive exPlanations) values algorithm to explain the results highlighted several genomic regions associated with the trait, including one, having the highest feature importance, located in a gene potentially involved in seed development.

Conclusions

Employing explainable artificial intelligence algorithms enhances model interpretability, identifying genetic polymorphisms associated with the shelling percentage. These findings underscore XAI's efficacy in predicting phenotypic traits from genomic data, highlighting its significance in optimizing crop production for sustainable agriculture.

Genome-Wide Association Studies on Resistance to Pea Weevil: Identification of Novel Sources of Resistance and Associated Markers

Little resistance to the pea weevil insect pest (Bruchus pisorum) is available in pea (Pisum sativum) cultivars, highlighting the need to search for sources of resistance in Pisum germplasm and to decipher the genetic basis of resistance. To address this need, we screened the response to pea weevil in a Pisum germplasm collection (324 accession, previously genotyped) under field conditions over four environments. Significant variation for weevil seed infestation (SI) was identified, with resistance being frequent in P. fulvum, followed by P. sativum ssp. elatius, P. abyssinicum, and P. sativum ssp. humile. SI tended to be higher in accessions with lighter seed color. SI was also affected by environmental factors, being favored by high humidity during flowering and hampered by warm winter temperatures and high evapotranspiration during and after flowering. Merging the phenotypic and genotypic data allowed genome-wide association studies (GWAS) yielding 73 markers significantly associated with SI. Through the GWAS models, 23 candidate genes were found associated with weevil resistance, highlighting the interest of five genes located on chromosome 6. These included gene 127136761 encoding squalene epoxidase; gene 127091639 encoding a transcription factor MYB SRM1; gene 127097033 encoding a 60S ribosomal protein L14; gene 127092211, encoding a BolA-like family protein, which, interestingly, was located within QTL BpLD.I, earlier described as conferring resistance to weevil in pea; and gene 127096593 encoding a methyltransferase. These associated genes offer valuable potential for developing pea varieties resistant to Bruchus spp. and efficient utilization of genomic resources through marker-assisted selection (MAS).

Sampling strategies for genotyping common bean (Phaseolus vulgaris L.) Genebank accessions with DArTseq: a comparison of single plants, multiple plants, and DNA pools

Introduction

Genotyping large-scale gene bank collections requires an appropriate sampling strategy to represent the diversity within and between accessions.

Methods

A panel of 44 common bean (Phaseolus vulgaris L.) landraces from the Alliance Bioversity and The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) gene bank was genotyped with DArTseq using three sampling strategies: a single plant per accession, 25 individual plants per accession jointly analyzed after genotyping (in silico-pool), and by pooling tissue from 25 individual plants per accession (seq-pool). Sampling strategies were compared to assess the technical aspects of the samples, the marker information content, and the genetic composition of the panel.

Results

The seq-pool strategy resulted in more consistent DNA libraries for quality and call rate, although with fewer polymorphic markers (6,142 single-nucleotide polymorphisms) than the in silico-pool (14,074) or the single plant sets (6,555). Estimates of allele frequencies by seq-pool and in silico-pool genotyping were consistent, but the results suggest that the difference between pools depends on population heterogeneity. Principal coordinate analysis, hierarchical clustering, and the estimation of admixture coefficients derived from a single plant, in silico-pool, and seq-pool successfully identified the well-known structure of Andean and Mesoamerican gene pools of P. vulgaris across all datasets.

Conclusion

In conclusion, seq-pool proved to be a viable approach for characterizing common bean germplasm compared to genotyping individual plants separately by balancing genotyping effort and costs. This study provides insights and serves as a valuable guide for gene bank researchers embarking on genotyping initiatives to characterize their collections. It aids curators in effectively managing the collections and facilitates marker-trait association studies, enabling the identification of candidate markers for key traits.

Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden)

Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.

Genomic analyses elucidate S-locus evolution in response to intra-specific losses of distyly in Primula vulgaris

Distyly, a floral dimorphism that promotes outcrossing, is controlled by a hemizygous genomic region known as the S-locus. Disruptions of genes within the S-locus are responsible for the loss of distyly and the emergence of homostyly, a floral monomorphism that favors selfing. Using whole-genome resequencing data of distylous and homostylous individuals from populations of Primula vulgaris and leveraging high-quality reference genomes of Primula we tested, for the first time, predictions about the evolutionary consequences of transitions to selfing on S-genes. Our results reveal a previously undetected structural rearrangement in CYPᵀ associated with the shift to homostyly and confirm previously reported, homostyle-specific, loss-of-function mutations in the exons of the S-gene CYPᵀ. We also discovered that the promoter and intronic regions of CYPᵀ in distylous and homostylous individuals are conserved, suggesting that down-regulation of CYPᵀ via mutations in its promoter and intronic regions is not a cause of the shift to homostyly. Furthermore, we found that hemizygosity is associated with reduced genetic diversity in S-genes compared with their paralogs outside the S-locus. Additionally, the shift to homostyly lowers genetic diversity in both the S-genes and their paralogs, as expected in primarily selfing plants. Finally, we tested, for the first time, long-standing theoretical models of changes in S-locus genotypes during early stages of the transition to homostyly, supporting the assumption that two copies of the S-locus might reduce homostyle fitness.

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.

Genomic prediction and allele mining of agronomic and morphological traits in pea (Pisum sativum) germplasm collections

Well-performing genomic prediction (GP) models for polygenic traits and molecular marker sets for oligogenic traits could be useful for identifying promising genetic resources in germplasm collections, setting core collections, and establishing molecular variety distinction. This study aimed at (i) defining GP models and key marker sets for predicting 15 agronomic or morphological traits in germplasm collections, (ii) verifying the GP model usefulness also for selection in breeding programs, (iii) investigating the consistency between molecular and phenotypic diversity patterns, and (iv) identifying genomic regions associated with to the target traits. The study was based on phenotyping data and over 41,000 genotyping-by-sequencing-generated SNP markers of 220 landraces or old cultivars belonging to a world germplasm collection and 11 modern cultivars. Non-metric multi-dimensional scaling (NMDS) and an analysis of population genetic structure indicated a high level of genetic differentiation of material from Western Asia, a major West-East diversity gradient, and quite limited genetic diversity of the improved germplasm. Mantel's test revealed a low correlation (r = 0.12) between phenotypic and molecular diversity, which increased (r = 0.45) when considering only the molecular diversity relative to significant SNPs from genome-wide association analyses. These analyses identified, inter alia, several areas of chromosome 6 involved in a largely pleiotropic control of vegetative or reproductive organ pigmentation. We found various significant SNPs for grain and straw yield under severe drought and onset of flowering, and one SNP on chromosome 5 for grain protein content. GP models displayed moderately high predictive ability (0.43 to 0.61) for protein content, grain and straw yield, and onset of flowering, and high predictive ability (0.76) for individual seed weight, based on intra-population, intra-environment cross-validations. The inter-population, inter-environment assessment of the models trained on the germplasm collection for breeding material of three recombinant inbred line (RIL) populations, which was challenged by much narrower diversity of the material, over eight-fold less available markers and quite different test environments, led to an overall loss of predictive ability of about 40% for seed weight, 50% for protein content and straw yield, and 60% for onset of flowering, and no prediction for grain yield. Within-RIL population predictive ability differed among populations.

The challenge of breeding for reduced off-flavor in faba bean ingredients

The growing interest in plant protein sources, such as pulses, is driven by the necessity for sustainable food production and climate change mitigation strategies. Faba bean (Vicia faba L.) is a promising protein crop for temperate climates, owing to its remarkable yield potential (up to 8 tonnes ha-1 in favourable growing conditions) and high protein content (~29% dry matter basis). Nevertheless, the adoption of faba bean protein in plant-based products that aim to resemble animal-derived counterparts is hindered by its distinctive taste and aroma, regarded as "off-flavors". In this review, we propose to introduce off-flavor as a trait in breeding programs by identifying molecules involved in sensory perception and defining key breeding targets. We discuss the role of lipid oxidation in producing volatile and non-volatile compounds responsible for the beany aroma and bitter taste, respectively. We further investigate the contribution of saponin, tannin, and other polyphenols to bitterness and astringency. To develop faba bean varieties with diminished off-flavors, we suggest targeting genes to reduce lipid oxidation, such as lipoxygenases (lox) and fatty acid desaturases (fad), and genes involved in phenylpropanoid and saponin biosynthesis, such as zero-tannin (zt), chalcone isomerase (chi), chalcone synthase (chs), β-amyrin (bas1). Additionally, we address potential challenges, including the need for high-throughput phenotyping and possible limitations that could arise during the genetic improvement process. The breeding approach can facilitate the use of faba bean protein in plant-based food such as meat and dairy analogues more extensively, fostering a transition toward more sustainable and climate-resilient diets.

Genetic diversity and signature of divergence in the genome of grapevine clones of Southern Italy varieties

Sexual reproduction has contributed to a significant degree of variability in cultivated grapevine populations. However, the additional influence of spontaneous somatic mutations has played a pivotal role in shaping the diverse landscape of grapevine agrobiodiversity. These naturally occurring selections, termed 'clones,' represent a vast reservoir of potentially valuable traits and alleles that hold promise for enhancing grape quality and bolstering plant resilience against environmental and biotic challenges. Despite their potential, many of these clones remain largely untapped.In light of this context, this study aims to delve into the population structure, genetic diversity, and distinctive genetic loci within a collection of 138 clones derived from six Campanian and Apulian grapevine varieties, known for their desirable attributes in viticulture and winemaking. Employing two reduced representation sequencing methods, we extracted Single-Nucleotide Polymorphism (SNP) markers. Population structure analysis and fixation index (FST) calculations were conducted both between populations and at individual loci. Notably, varieties originating from the same geographical region exhibited pronounced genetic similarity.The resulting SNP dataset facilitated the identification of approximately two hundred loci featuring divergent markers (FST ≥ 0.80) within annotated exons. Several of these loci exhibited associations with essential traits like phenotypic adaptability and environmental responsiveness, offering compelling opportunities for grapevine breeding initiatives. By shedding light on the genetic variability inherent in these treasured traditional grapevines, our study contributes to the broader understanding of their potential. Importantly, it underscores the urgency of preserving and characterizing these valuable genetic resources to safeguard their intra-varietal diversity and foster future advancements in grapevine cultivation.

High-density linkage mapping and genetic dissection of resistance to broomrape (Orobanche crenata Forsk.) in pea (Pisum sativum L.)

Pea (Pisum sativum L.) is a widely cultivated legume of major importance for global food security and agricultural sustainability. Crenate broomrape (Orobanche crenata Forsk.) (Oc) is a parasitic weed severely affecting legumes, including pea, in the Mediterranean Basin and the Middle East. Previously, the identification of the pea line "ROR12", displaying resistance to Oc, was reported. Two-year field trials on a segregant population of 148 F₇ recombinant inbred lines (RILs), originating from a cross between "ROR12" and the susceptible cultivar "Sprinter", revealed high heritability (0.84) of the "ROR12" resistance source. Genotyping-by-sequencing (GBS) on the same RIL population allowed the construction of a high-density pea linkage map, which was compared with the pea reference genome and used for quantitative trait locus (QTL) mapping. Three QTLs associated with the response to Oc infection, named PsOcr-1, PsOcr-2, and PsOcr-3, were identified, with PsOcr-1 explaining 69.3% of the genotypic variance. Evaluation of the effects of different genotypic combinations indicated additivity between PsOcr-1 and PsOcr-2, and between PsOcr-1 and PsOcr-3, and epistasis between PsOcr-2 and PsOcr-3. Finally, three Kompetitive Allele Specific PCR (KASP) marker assays were designed on the single-nucleotide polymorphisms (SNPs) associated with the QTL significance peaks. Besides contributing to the development of pea genomic resources, this work lays the foundation for the obtainment of pea cultivars resistant to Oc and the identification of genes involved in resistance to parasitic Orobanchaceae.

CRISPR-Cas9-based repeat depletion for high-throughput genotyping of complex plant genomes

High-throughput genotyping enables the large-scale analysis of genetic diversity in population genomics and genome-wide association studies that combine the genotypic and phenotypic characterization of large collections of accessions. Sequencing-based approaches for genotyping are progressively replacing traditional genotyping methods because of the lower ascertainment bias. However, genome-wide genotyping based on sequencing becomes expensive in species with large genomes and a high proportion of repetitive DNA. Here we describe the use of CRISPR-Cas9 technology to deplete repetitive elements in the 3.76-Gb genome of lentil (Lens culinaris), 84% consisting of repeats, thus concentrating the sequencing data on coding and regulatory regions (single-copy regions). We designed a custom set of 566,766 gRNAs targeting 2.9 Gbp of repeats and excluding repetitive regions overlapping annotated genes and putative regulatory elements based on ATAC-seq data. The novel depletion method removed ∼40% of reads mapping to repeats, increasing those mapping to single-copy regions by ∼2.6-fold. When analyzing 25 million fragments, this repeat-to-single-copy shift in the sequencing data increased the number of genotyped bases of ∼10-fold compared to nondepleted libraries. In the same condition, we were also able to identify ∼12-fold more genetic variants in the single-copy regions and increased the genotyping accuracy by rescuing thousands of heterozygous variants that otherwise would be missed because of low coverage. The method performed similarly regardless of the multiplexing level, type of library or genotypes, including different cultivars and a closely related species (L. orientalis). Our results showed that CRISPR-Cas9-driven repeat depletion focuses sequencing data on single-copy regions, thus improving high-density and genome-wide genotyping in large and repetitive genomes.

Review: Status and prospects of association mapping in grapevine

Thanks to current advances in sequencing technologies, novel bioinformatics tools, and efficient modeling solutions, association mapping has become a widely accepted approach to unravel the link between genotype and phenotype diversity in numerous crops. In grapevine, this strategy has been used in the last decades to understand the genetic basis of traits of agronomic interest (fruit quality, crop yield, biotic and abiotic resistance), of special relevance nowadays to improve crop resilience to cope with future climate scenarios. Genome-wide association studies have identified many putative causative loci for different traits, some of them overlapping well-known causal genes identified by conventional quantitative trait loci studies in biparental progenies, and/or validated by functional approaches. In addition, candidate-gene association studies have been useful to pinpoint the causal mutation underlying phenotypic variation for several traits of high interest in breeding programs (like berry color, seedlessness, and muscat flavor), information that has been used to develop highly informative and useful markers already in use in marker-assisted selection processes. Thus, association mapping has proved to represent a valuable step towards high quality and sustainable grape production. This review summarizes current applications of association mapping in grapevine research and discusses future prospects in view of current viticulture challenges.

Genetic approaches to exploit landraces for improvement of Triticum turgidum ssp. durum in the age of climate change

Addressing the challenges of climate change and durum wheat production is becoming an important driver for food and nutrition security in the Mediterranean area, where are located the major producing countries (Italy, Spain, France, Greece, Morocco, Algeria, Tunisia, Turkey, and Syria). One of the emergent strategies, to cope with durum wheat adaptation, is the exploration and exploitation of the existing genetic variability in landrace populations. In this context, this review aims to highlight the important role of durum wheat landraces as a useful genetic resource to improve the sustainability of Mediterranean agroecosystems, with a focus on adaptation to environmental stresses. We described the most recent molecular techniques and statistical approaches suitable for the identification of beneficial genes/alleles related to the most important traits in landraces and the development of molecular markers for marker-assisted selection. Finally, we outline the state of the art about landraces genetic diversity and signature of selection, already identified from these accessions, for adaptability to the environment.

Development and Evaluation of an AxiomTM 60K SNP Array for Almond (Prunus dulcis)

A high-density single nucleotide polymorphism (SNP) array is essential to enable faster progress in plant breeding for new cultivar development. In this regard, we have developed an Axiom 60K almond SNP array by resequencing 81 almond accessions. For the validation of the array, a set of 210 accessions were genotyped and 82.8% of the SNPs were classified in the best recommended SNPs. The rate of missing data was between 0.4% and 2.7% for the almond accessions and less than 15.5% for the few peach and wild accessions, suggesting that this array can be used for peach and interspecific peach × almond genetic studies. The values of the two SNPs linked to the RMja (nematode resistance) and SK (bitterness) genes were consistent. We also genotyped 49 hybrids from an almond F2 progeny and could build a genetic map with a set of 1159 SNPs. Error rates, less than 1%, were evaluated by comparing replicates and by detection of departures from Mendelian inheritance in the F2 progeny. This almond array is commercially available and should be a cost-effective genotyping tool useful in the search for new genes and quantitative traits loci (QTL) involved in the control of agronomic traits.

Genotyping-by-Sequencing Defines Genetic Structure within the “Acquaviva” Red Onion Landrace

Genetic structure and distinctive features of landraces, such as adaptability to local agro-ecosystems and specific qualitative profiles, can be substantially altered by the massive introduction of allochthonous germplasm. The landrace known as “Cipolla rossa di Acquaviva” (Acquaviva red onion, further referred to as ARO) is traditionally cultivated and propagated in a small area of the Apulia region (southern Italy). However, the recent rise of its market value and cultivation area is possibly causing genetic contamination with foreign propagating material. In this work, genotyping-by-sequencing (GBS) was used to characterize genetic variation of seven onion populations commercialized as ARO, as well as one population of the landrace “Montoro” (M), which is phenotypically similar, but originates from another cultivation area and displays different qualitative features. A panel of 5011 SNP markers was used to perform parametric and non-parametric genetic structure analyses, which supported the hypothesis of genetic contamination of germplasm commercialized as ARO with a gene pool including the M landrace. Four ARO populations formed a core genetic group, homogeneous and clearly distinct from the other ARO and M populations. Conversely, the remaining three ARO populations did not display significant differences with the M population. A set of private alleles for the ARO core genetic group was identified, indicating the possibility to trace the ARO landrace by means of a SNP-based molecular barcode. Overall, the results of this study provide a framework for further breeding activities and the traceability of the ARO landrace.

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/.

Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases

PURPOSE

The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases.

METHODS

Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history.

RESULTS

Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%.

CONCLUSION

Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.

Wood Formation under Changing Environment: Omics Approaches to Elucidate the Mechanisms Driving the Early-to-Latewood Transition in Conifers

The global change scenarios highlight the urgency of clarifying the mechanisms driving the determination of wood traits in forest trees. Coniferous xylem is characterized by the alternation between earlywood (EW) and latewood (LW), on which proportions the wood density depend, one of the most important mechanical xylem qualities. However, the molecular mechanisms triggering the transition between the production of cells with the typical features of EW to the LW are still far from being completely elucidated. The increasing availability of omics resources for conifers, e.g., genomes and transcriptomes, would lay the basis for the comprehension of wood formation dynamics, boosting both breeding and gene-editing approaches. This review is intended to introduce the importance of wood formation dynamics and xylem traits of conifers in a changing environment. Then, an up-to-date overview of the omics resources available for conifers was reported, focusing on both genomes and transcriptomes. Later, an analysis of wood formation studies using omics approaches was conducted, with the aim of elucidating the main metabolic pathways involved in EW and LW determination. Finally, the future perspectives and the urgent needs on this research topic were highlighted.

Climate Adaptation, Drought Susceptibility, and Genomic-Informed Predictions of Future Climate Refugia for the Australian Forest Tree Eucalyptus globulus

Understanding the capacity of forest tree species to adapt to climate change is of increasing importance for managing forest genetic resources. Through a genomics approach, we modelled spatial variation in climate adaptation within the Australian temperate forest tree Eucalyptus globulus, identified putative climate drivers of this genomic variation, and predicted locations of future climate refugia and populations at-risk of future maladaptation. Using 812,158 SNPs across 130 individuals from 30 populations (i.e., localities) spanning the species’ natural range, a gradientForest algorithm found 1177 SNPs associated with locality variation in home-site climate (climate-SNPs), putatively linking them to climate adaptation. Very few climate-SNPs were associated with population-level variation in drought susceptibility, signalling the multi-faceted nature and complexity of climate adaptation. Redundancy analysis (RDA) showed 24% of the climate-SNP variation could be explained by annual precipitation, isothermality, and maximum temperature of the warmest month. Spatial predictions of the RDA climate vectors associated with climate-SNPs allowed mapping of genomically informed climate selective surfaces across the species’ range under contemporary and projected future climates. These surfaces suggest over 50% of the current distribution of E. globulus will be outside the modelled adaptive range by 2070 and at risk of climate maladaptation. Such surfaces present a new integrated approach for natural resource managers to capture adaptive genetic variation and plan translocations in the face of climate change.

Genome-wide association study reveals white lupin candidate gene involved in anthracnose resistance

GWAS identifies candidate gene controlling resistance to anthracnose disease in white lupin. White lupin (Lupinus albus L.) is a promising grain legume to meet the growing demand for plant-based protein. Its cultivation, however, is severely threatened by anthracnose disease caused by the fungal pathogen Colletotrichum lupini. To dissect the genetic architecture for anthracnose resistance, genotyping by sequencing was performed on white lupin accessions collected from the center of domestication and traditional cultivation regions. GBS resulted in 4611 high-quality single-nucleotide polymorphisms (SNPs) for 181 accessions, which were combined with resistance data observed under controlled conditions to perform a genome-wide association study (GWAS). Obtained disease phenotypes were shown to highly correlate with overall three-year disease assessments under Swiss field conditions (r > 0.8). GWAS results identified two significant SNPs associated with anthracnose resistance on gene Lalb_Chr05_g0216161 encoding a RING zinc-finger E3 ubiquitin ligase which is potentially involved in plant immunity. Population analysis showed a remarkably fast linkage disequilibrium decay, weak population structure and grouping of commercial varieties with landraces, corresponding to the slow domestication history and scarcity of modern breeding efforts in white lupin. Together with 15 highly resistant accessions identified in the resistance assay, our findings show promise for further crop improvement. This study provides the basis for marker-assisted selection, genomic prediction and studies aimed at understanding anthracnose resistance mechanisms in white lupin and contributes to improving breeding programs worldwide.

Taming the massive genome of Scots pine with PiSy50k, a new genotyping array for conifer research

Pinus sylvestris (Scots pine) is the most widespread coniferous tree in the boreal forests of Eurasia, with major economic and ecological importance. However, its large and repetitive genome presents a challenge for conducting genome-wide analyses such as association studies, genetic mapping and genomic selection. We present a new 50K single-nucleotide polymorphism (SNP) genotyping array for Scots pine research, breeding and other applications. To select the SNP set, we first genotyped 480 Scots pine samples on a 407 540 SNP screening array and identified 47 712 high-quality SNPs for the final array (called 'PiSy50k'). Here, we provide details of the design and testing, as well as allele frequency estimates from the discovery panel, functional annotation, tissue-specific expression patterns and expression level information for the SNPs or corresponding genes, when available. We validated the performance of the PiSy50k array using samples from Finland and Scotland. Overall, 39 678 (83.2%) SNPs showed low error rates (mean = 0.9%). Relatedness estimates based on array genotypes were consistent with the expected pedigrees, and the level of Mendelian error was negligible. In addition, array genotypes successfully discriminate between Scots pine populations of Finnish and Scottish origins. The PiSy50k SNP array will be a valuable tool for a wide variety of future genetic studies and forestry applications.

Broadening the horizon of crop research: a decade of advancements in plant molecular genetics to divulge phenotype governing genes

Advancements in sequencing, genotyping, and computational technologies during the last decade (2011-2020) enabled new forward-genetic approaches, which subdue the impediments of precise gene mapping in varied crops. The modern crop improvement programs rely heavily on two major steps-trait-associated QTL/gene/marker's identification and molecular breeding. Thus, it is vital for basic and translational crop research to identify genomic regions that govern the phenotype of interest. Until the advent of next-generation sequencing, the forward-genetic techniques were laborious and time-consuming. Over the last 10 years, advancements in the area of genome assembly, genotyping, large-scale data analysis, and statistical algorithms have led faster identification of genomic variations regulating the complex agronomic traits and pathogen resistance. In this review, we describe the latest developments in genome sequencing and genotyping along with a comprehensive evaluation of the last 10-year headways in forward-genetic techniques that have shifted the focus of plant research from model plants to diverse crops. We have classified the available molecular genetic methods under bulk-segregant analysis-based (QTL-seq, GradedPool-Seq, QTG-Seq, Exome QTL-seq, and RapMap), target sequence enrichment-based (RenSeq, AgRenSeq, and TACCA), and mutation-based groups (MutMap, NIKS algorithm, MutRenSeq, MutChromSeq), alongside improvements in classical mapping and genome-wide association analyses. Newer methods for outcrossing, heterozygous, and polyploid plant genetics have also been discussed. The use of k-mers has enriched the nature of genetic variants which can be utilized to identify the phenotype-causing genes, independent of reference genomes. We envisage that the recent methods discussed herein will expand the repertoire of useful alleles and help in developing high-yielding and climate-resilient crops.

Merging genotyping-by-sequencing data from two ex situ collections provides insights on the pea evolutionary history

Pea (Pisum sativum L. subsp. sativum) is one of the oldest domesticated species and a widely cultivated legume. In this study, we combined next generation sequencing (NGS) data referring to two genotyping-by-sequencing (GBS) libraries, each one prepared from a different Pisum germplasm collection. The selection of single nucleotide polymorphism (SNP) loci called in both germplasm collections caused some loss of information; however, this did not prevent the obtainment of one of the largest datasets ever used to explore pea biodiversity, consisting of 652 accessions and 22 127 markers. The analysis of population structure reflected genetic variation based on geographic patterns and allowed the definition of a model for the expansion of pea cultivation from the domestication centre to other regions of the world. In genetically distinct populations, the average decay of linkage disequilibrium (LD) ranged from a few bases to hundreds of kilobases, thus indicating different evolutionary histories leading to their diversification. Genome-wide scans resulted in the identification of putative selective sweeps associated with domestication and breeding, including genes known to regulate shoot branching, cotyledon colour and resistance to lodging, and the correct mapping of two Mendelian genes. In addition to providing information of major interest for fundamental and applied research on pea, our work describes the first successful example of integration of different GBS datasets generated from ex situ collections - a process of potential interest for a variety of purposes, including conservation genetics, genome-wide association studies, and breeding.

Biparental Crossing and QTL Mapping for Validation of Genome-Wide Association Studies

Association mapping (AM), also known as genome-wide association studies (GWAS), is increasingly being employed in crop plants for the identification of QTL/genes and marker-trait associations (MTAs) in natural populations. Large numbers of such associations have been identified for variety of traits in different crop plants. However, not many of these associations have been used practically in the crop improvement program due to lack of validation. Although there are different ways through which the results of AM/GWAS could be validated, the best approach is to develop a biparental population for the trait of interest. An overview of the steps involved in the validation of results of AM using biparental mapping population in plants is provided in this chapter.

Screening of Olive Biodiversity Defines Genotypes Potentially Resistant to Xylella fastidiosa

The recent outbreak of the Olive Quick Decline Syndrome (OQDS), caused by Xylella fastidiosa subsp. pauca (Xf), is dramatically altering ecosystem services in the peninsula of Salento (Apulia Region, southeastern Italy). Here we report the accomplishment of several exploratory missions in the Salento area, resulting in the identification of thirty paucisymptomatic or asymptomatic plants in olive orchards severely affected by the OQDS. The genetic profiles of such putatively resistant plants (PRPs), assessed by a selection of ten simple sequence repeat (SSR) markers, were compared with those of 141 Mediterranean cultivars. Most (23) PRPs formed a genetic cluster (K1) with 22 Italian cultivars, including 'Leccino' and 'FS17', previously reported as resistant to Xf. The remaining PRPs displayed relatedness with genetically differentiated germplasm, including a cluster of Tunisian cultivars. Markedly lower colonization levels were observed in PRPs of the cluster K1 with respect to control plants. Field evaluation of four cultivars related to PRPs allowed the definition of partial resistance in the genotypes 'Frantoio' and 'Nocellara Messinese'. Some of the PRPs identified in this study might be exploited in cultivation, or as parental clones of breeding programs. In addition, our results indicate the possibility to characterize resistance to Xf in cultivars genetically related to PRPs.

Polyphenol oxidase genes as integral part of the evolutionary history of domesticated tetraploid wheat

Studying and understanding the genetic basis of polyphenol oxidases (PPO)-related traits plays a crucial role in genetic improvement of crops. A tetraploid wheat collection (T. turgidum ssp., TWC) was analyzed using the 90K wheat SNP iSelect assay and phenotyped for PPO activity. A total of 21,347 polymorphic SNPs were used to perform genome-wide association analysis (GWA) in TWC and durum wheat sub-groups, detecting 23 and 85 marker-trait associations (MTA). In addition, candidate genes responsible for PPO activity were predicted. Based on the 23 MTAs detected in TWC, two haplotypes associated with low and high PPO activity were identified. Four SNPs were developed and validated providing one reliable marker (IWB75732) for marker assisted selection. The 23 MTAs were used to evaluate the genetic divergence (F_ST > 0.25) between the T. turgidum subspecies, providing new information important for understanding the domestication process of Triticum turgidum ssp. and in particular of ssp. carthlicum.

Concordance rate in cattle and sheep between genotypes differing in Illumina GenCall quality score

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12 428 767 genotypes in total post-editing) and 80 sheep (1 557 360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.

Almond diversity and homozygosity define structure, kinship, inbreeding, and linkage disequilibrium in cultivated germplasm, and reveal genomic associations with nut and seed weight

Almond [Prunus dulcis Miller (D.A. Webb)] is the main tree nut species worldwide. Here, genotyping-by-sequencing (GBS) was applied to 149 almond cultivars from the ex situ collections of the Italian Council for Agricultural Research (CREA) and the Spanish National Research Council (CSIC), leading to the detection of 93,119 single-nucleotide polymorphisms (SNPs). The study of population structure outlined four distinct genetic groups and highlighted diversification between the Mediterranean and Californian gene pools. Data on SNP diversity and runs of homozygosity (ROHs) allowed the definition of kinship, inbreeding, and linkage disequilibrium (LD) decay in almond cultivated germplasm. Four-year phenotypic observations, gathered on 98 cultivars of the CREA collection, were used to perform a genome-wide association study (GWAS) and, for the first time in a crop species, homozygosity mapping (HM), resulting in the identification of genomic associations with nut, shell, and seed weight. Both GWAS and HM suggested that loci controlling nut and seed weight are mostly independent. Overall, this study provides insights on the almond cultivation history and delivers information of major interest for almond genetics and breeding. In a broader perspective, our results encourage the use of ROHs in crop science to estimate inbreeding, choose parental combinations minimizing the risk of inbreeding depression, and identify genomic footprints of selection for specific traits.

Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening

PURPOSE

Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants.

METHODS

We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants.

RESULTS

While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32-10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06-5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14-7.46) increased odds of identifying rare variant heterozygotes.

CONCLUSION

Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes.