Variability in traits forming the Leaf Economics Spectrum (LES) among and within crop species play a key role in governing agroecosystem processes. However, studies evaluating the extent, causes, and consequences of within-species variation in LES traits for some of the world’s most common crops remain limited. We quantified variation in nine leaf traits measured across 90 vines of five wine grape (Vitis vinifera) varieties at two ontogenetic stages. Grape traits covary along an intraspecific LES, in patterns similar to those documented in wild plants. Across varieties, high rates of photosynthesis (A), and leaf nitrogen (N) concentrations, are coupled with low leaf mass per area (LMA), while the opposite suite of traits defines the “resource conserving end” of this intraspecific LES in grape. Variety identity predicted of leaf physiological (A) and morphological traits (i.e., leaf area and leaf mass), while leaf chemical traits and LMA were best explained by ontogenetic stage. All varieties expressed greater resource conserving trait syndromes (i.e., higher LMA, lower N, lower Amass) later in the growing season. Traits related to leaf hydraulics, including instantaneous water-use efficiency (WUE), were unrelated to LES and other resource capture traits, and were better explained by spatial location. Our results highlight the relative contributions of genetic vs. phenotypic factors in structuring this variation and point to a key role of domestication in governing trait relationships in the world’s crops.

Wrist shape varies greatly across primates and previous studies indicate that the numerous morphological differences among them are related to a complex mixture of phylogeny and function. However, little is known about whether the variation in these various anatomical differences is linked and to what extent the wrist bones vary independently. Here, we used 3D geometric morphometrics on a sample of extant hominids (Homo sapiens, Pan troglodytes, Gorilla gorilla, and Gorilla beringei), to find the model that best describes the covariation patterns among four of the eight carpals (i.e., capitate, lunate, scaphoid, and trapezium). For this purpose, 15 modular hypotheses were tested using the Covariation Coefficient. Results indicate that there is a covariation structure common to all hominids, which corresponds to stronger covariation within each carpal as compared to the covariation between carpals. However, the results also indicate that that there is a degree of codependence in the variation of some carpals, which is unique in humans, chimpanzees, and gorillas, respectively. In humans there is evidence of associated shape changes between the lunate and capitate, and between the scaphoid and trapezium. This covariation between lunate and capitate is also apparent in gorillas, while chimpanzees display the greatest disassociation among carpals, showing low covariation values in all pairwise comparisons. Our analyses indicate that carpals have an important level of variational independence which might suggest a high degree of independent evolvability in the wrists of hominids, and that although weak, the structure of associated changes of these four carpals varies across genera. To our knowledge this is the first report on the patterns of modularity between these four wrist bones in the Homininae and future studies might attempt to investigate whether the anatomical shape associations among carpals are functionally related to locomotion and manipulation.

Barnyardgrass (Echinochloa spp.) is a prevalent type of weed in rice fields worldwide. Despite the growing knowledge concerning allelopathic interference with barnyardgrass, little is understood regarding the competitive mechanisms between allelopathic rice and herbicide-resistant barnyardgrass at the plant physiological level. In this context, a hydroponic system was employed to investigate the root morphological traits and different phytohormones levels of two rice cultivars, i.e., the allelopathic rice cultivar “PI312777” (PI) and the non-allelopathic rice cultivar “Lemont” (LE), when co-planted with quinclorac-resistant and -susceptible barnyardgrass, respectively. The results showed that the shoot and root biomass were greater in the allelopathic rice cultivar. Moreover, the treatments at the two time points induced an increase in shoot and root biomass of PI when subjected to barnyardgrass stress. In terms of root morphology, PI exhibited significantly higher fine-root length in diameters <0.5 mm, a greater number of root tips, and longer root tips compared to LE. In addition, the levels of different plant hormones, including auxin (IAA), abscisic acid (ABA), jasmonic acid (JA), and salicylic acid (SA), known for their diverse adaptation strategies to biotic stress, were analysed. The response to quinclorac-resistant barnyardgrass stress was consistent in terms of the content of well-known stress-related hormones, namely SA and JA. The accumulation of SA and JA was observed in both rice cultivars under the stress of quinclorac-resistant barnyardgrass, with higher contents of these hormones in PI than that in LE. The most significant change was observed in IAA and ABA contents in rice, which decreased significantly from 7 days to 14 days under co-culture stress. Additionally, PI contained higher levels of IAA and ABA than LE in the presence of barnyardgrass stress. This research may aid in the development of strategies for reducing the environmental impact of herbicides through the prudent selection of non-chemical herbicide control tactics.

Pasmo is one of the most widespread diseases threatening flax production. To identify genetic regions associated with pasmo resistance (PR), a genome-wide association study was performed on 370 accessions from the flax core collection. Evaluation of pasmo severity was performed in the field from 2012 to 2016 in Morden, MB, Canada. Genotyping-by-sequencing has identified 258,873 single nucleotide polymorphisms (SNPs) distributed on all 15 flax chromosomes. Marker-trait associations were identified using ten different statistical models. A total of 692 unique quantitative trait nucleotides (QTNs) associated with 500 putative quantitative trait loci (QTL) were detected from six phenotypic PR datasets (five individual years and average across years). Different QTNs were identified with various statistical models and from individual PR datasets, indicative of the complementation between analytical methods and/or genotype × environment interactions of the QTL effects. The single-locus models tended to identify large-effect QTNs while the multi-loci models were able to detect QTNs with smaller effects. Among the putative QTL, 67 had large effects (3-23%), were stable across all datasets and explained 32-64% of the total variation for PR in the various datasets. Forty-five of these QTL spanned 85 resistance gene analogs including a large toll interleukin receptor, nucleotide-binding site, leucine-rich repeat (TNL) type gene cluster on chromosome 8. The number of positive effect QTL (NPQTL) in accessions was significantly correlated with PR (R²=0.55), suggesting additive effects. NPQTL was also significantly associated with morphotype (R²=0.52) and major positive effect QTL were present in the fiber type accessions. The 67 large effect QTL are suited for marker-assisted selection and the 500 QTL for effective genomic prediction in PR molecular breeding.

Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk compositon, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focusses primarily on QTL discovered in cattle (Bos taurus) as a model species for lactation biology, with occasional references to other ruminant species, and some comparisons with human milk composition are also presented. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype data bases continue to grow and diversify, new QTL will continue to be discovered, and, although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of lactation biology.

Purpose: To clarify the mechanism of state anxiety on intertemporal decision-making, two studies were conducted to investigate the role of trait anxiety and inhibitory control. Study 1 examined the moderating effect of trait anxiety. Study 2 continued to investigate the mediating effect of inhibition control on the basis of study 1. Methods: A total of 266 Chinese college students participated. Participants’ state anxiety, trait anxiety and intertemporal decision-making were measured by questionnaires (scales) respectively. And inhibition control was evaluated by two-choice oddball paradigm. Results: The results of Study 1 indicated that state anxiety could predict intertemporal decision-making and that the moderating effect of trait anxiety on this relationship was significant. Study 2 found that inhibitory control could completely moderate the relationship between state anxiety and intertemporal decision-making and that this indirect effect was affected by trait anxiety. Conclusion: 1)Individuals with higher trait anxiety are more inclined to make more impulsive and intertemporal decisions when they have a high level of state anxiety; 2)state anxiety allows inhibitory control ability to have a further effect on intertemporal decision-making; and 3)the higher trait anxiety in individuals, the stronger mediating effect of inhibitory control on the relationship between state anxiety and intertemporal decision-making.

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or ‘multi‐level convergent evolution’. To investigate multi‐level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long‐standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi‐level convergent evolution.

Root rot diseases remain a major global threat to the productivity of agricultural crops. They are usually caused by more than one type of pathogen and are thus often referred to as a root rot complex. Fungal and oomycete species are the predominant participants in the complex, while bacteria and viruses are also known to cause root rot. Incorporating genetic resistance in cultivated crops is considered as the most efficient and sustainable solution to counter root rot; however, resistance is often quantitative in nature. Several genetics studies in various crops have identified quantitative trait loci associated with resistance. With access to whole genome sequences, the identity of the genes within the reported loci is becoming available. Several of the identified genes have been implicated in pathogen response. However, it is becoming apparent that at the molecular level, each pathogen engages a unique set of proteins to either infest the host successfully or be defeated or contained in doing so. In this review, a comprehensive summary of the genes and potential mechanisms underlying resistance or susceptibility against the most investigated root rots of important agricultural crops is presented.

Eating disorders (ED) are characterized by alterations in eating behavior. The genetic factors shared between ED diagnoses have been underexplored. The present study aimed to perform a genome-wide association study on individuals with disordered eating behaviors in the Mexican population, blood methylation quantitative trait loci (blood-meQTL) analysis, and in silico function prediction by different algorithms. The analysis included a total of 1803 individuals. Genome-wide association study and blood-meQTL analysis were performed by logistic and linear regression. In silico functional variant prediction, phenome-wide, and transcriptome-wide association studies by different algorithms were analyzed. In the genome-wide association study, we identified 44 single-nucleotide polymorphisms (SNP) associated at a nominal value and 7 blood-meQTL at a genome-wide umbral. The SNPs were enriched in genome-wide associations of the metabolic and immunologic domains. In the in silico analysis, the SNP rs10419198 located on an enhancer mark could change the expression of PRR12 on blood, adipocytes, and brain areas that regulate food intake. The present study supports the previous associations of genetic variation in the metabolic domain with ED.

In recent decades, attention has been directed at anemia classification for various medical purposes, such as thalassemia screening and predicting iron deficiency anemia (IDA). In this study, a new method has been successfully tested for discrimination between IDA and β-thalassemia trait (β-TT). The method is based on a Dynamic Harmony Search (DHS). Complete blood count (CBC), a fast and inexpensive laboratory test, is used as the input of the system. Other models, such as a genetic programming method called structured representation on genetic algorithm in non-linear function fitting (STROGANOFF), an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a support vector machine (SVM), k-nearest neighbor (KNN), and certain traditional methods, are compared with the proposed method.

The evaluation of forage crops for adaptability and performance across production systems and environments is one of the main strategies used to improve forage production. To enhance the genetic resource base and identify traits responsible for increased feed potential of Napier grass, forty-five genotypes from EMBRAPA, Brazil, were evaluated for forage biomass yield and feed nutritional quality in a replicated trial under wet and dry season conditions in Ethiopia. The results revealed significant variation in forage yield and feed nutritional qualities among the genotypes and between the wet and dry seasons. Feed fibre components were lower in the dry season while crude protein, in vitro organic matter digestibility and metabolizable energy were higher. Based on the cumulative biomass yield and metabolizable energy yield, top performing genotypes were identified that are candidates for future forage improvement studies. Furthermore, the marker-trait association study identified diagnostic SNP and SilicoDArT markers and potential candidate genes that could differentiate high biomass yielding and high metabolizable energy genotypes in the collection.

In order to integrate genomics in breeding and development of drought tolerant groundnut genotypes, identification of genomic regions/genetic markers for drought surrogate traits is essential. We used SNP markers for a genetic analysis of the ICRISAT groundnut minicore collection for genome wide marker-trait association for some physiological traits and to determine the magnitude of linkage disequilibrium (LD) present in the genetic resources. The LD analysis showed that about 36% of loci pairs were in significant LD (P < 0.05 and r2 > 0.2) and 3.14% of the pairs were in complete LD. There was rapid decline in LD with distance and the LD was <0.2 at a distance of 41635 bp. The marker trait association (MTAs) studies revealed 20 significant MTAs (p <0.001) with 11 markers for leaf area index (4), canopy temperature (13), chlorophyll content (1) and NDVI (2). The markers explained 2 to 21% of the phenotypic variation observed. Most of the MTAs identified on the A subgenome were also identified on the respective homeologous chromosome on the B subgenome. The duplications of effect observed could be due to common ancestor of the A and B genome which explains the linkage detected between markers lying on different chromosomes seen in the current study. The present study identified a total of 20 highly significant marker trait associations with 11 markers for four physiological traits of importance in groundnut; LAI, CT, SCMR and NDVI. The markers identified in this study can serve as useful genomic resources to initiate marker-assisted selection and trait introgression of groundnut for drought tolerance. The identified markers in this study may be useful for marker assisted selection after further validation.

Invasion ecology has a long tradition of searching for traits distinguishing alien invasive and non-invasive plants. Surprisingly, this approach has been so far poorly used to understand why some arable weeds are abundant and widespread while others are rare and narrowly distributed. In the present study, we focused on the characteristics of successful weeds occurring in maize fields, one of the most import crop worldwide. Two national weed surveys conducted in France were used to identify increasing and decreasing species based on 175 and 484 surveyed fields in the 1970s and the 2000s, respectively. Weed trait values related to regional frequency, local abundance and specialization to maize were identified with Phylogenetic Generalized Least-Squares (PGLS). We found a positive relationship between regional frequency and local abundance, i.e. the most widespread weeds were also locally more abundant. We highlighted that weeds with the C4 photosynthetic pathway and summer emergence were more abundant, more frequent and more specialized to maize crops. More generally, we highlighted two successful strategies: on the one hand, traits related to a general weediness syndrome with rapid resource acquisition (high SLA and Ellenberg-N) and high colonization capacity (seed longevity, fecundity and wind dispersal), on the other hand, traits related to specific adaptation to spring cultivation (thermophilous species with summer emergence, late flowering and C4 photosynthetic pathway). Deviations from the abundancy-frequency relationships also indicated that species of the Panicoideae sub-family, species with Triazine-resistant populations and neophyte species were more abundant than expected by their regional frequency. To some extent, it is therefore possible to predict which species can be troublesome in maize crops and use this information in weed risk assessment tools to prevent new introductions or favor early detection and eradication. This study showed how tools developed in functional and macro-ecology can be used to improve our understanding of weed ecology and to develop more preventive management strategies.

Various distance metrics and their induced norms are employed in the quantitative modeling of evolutionary dynamics. Minimization of these distance metrics when applied to evolutionary optimization are hypothesized to result in different outcomes. Here, we apply the different distance metrics to the evolutionary trait dynamics brought about by the interaction between two competing species infected by parasites (exploiters). We present deterministic cases showing the distinctive selection outcomes under the Manhattan, Euclidean and Chebyshev norms. Specifically, we show how they differ in the time of convergence to the desired optima (e.g., no disease), and in the egalitarian sharing of carrying capacity between the competing species. However, when randomness is introduced to the population dynamics of parasites and to the trait dynamics of the competing species, the distinctive characteristics of the outcomes under the three norms become indistinguishable. Our results provide theoretical cases when evolutionary dynamics using different distance metrics exhibit similar outcomes.

Bacterial wilt (BW), caused by Ralstonia solanacearum is one of the major biotic factors limiting tomato production in the humid tropics. Pyramiding of resistance genes through marker-assisted selection is an efficient way to develop durable BW resistant cultivars. Tomato line ‘Hawaii 7996’ (H7996) is a stable and robust resistance source against various R. solanacearum strains. Major BW resistance quantitative trait loci (QTLs) Bwr-12 and Bwr-6, and several minor or strain specific QTLs have been coarse-mapped in this line, but none has been fine-mapped and validated. The objective of the current study was to construct a high density genetic map using single-nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing, fine-map Bwr-12 and Bwr-6 and determine the effects of these QTLs using a near isogenic line (NIL) population. A high density genetic map using 1,604 SNP markers with an average distance of 0.82 cM was developed for 188 F9 recombinant inbred lines derived from the cross H7996 × WVa700. A total of seven QTLs associated with BW resistance to race 1-phylotype I or/and race 3-phylotype II strains were located on chromosomes 6 (Bwr-6.1, 6.2, 6.3 and 6.4) and 12 (Bwr-12.1, Bwr-12.2 and Bwr-12.3) with logarithm of odds (LOD) scores of 6.2-15.6 and 6.2-31.1, explaining 14.2-33.4% and 15.9-53.9% of the total phenotypic variation contributed from H7996, respectively. To validate the genetic effects of the two QTL regions, a set of 80 BC3F3 NILs containing different sections of Bwr-6 with or without Bwr-12 was phenotyped for disease severity after challenge with either race 1-phylotype I Pss4 or race 3-phylotype II Pss1632 BW strains over two seasons. Bwr-6.1 specific to Pss4 and Bwr-6.3 specific to Pss1632 were mapped to an interval of 5.0 cM (P < 0.05) between 6_33,444,000_SLM6-47 and 6_33,868,000_SLM6-124 SNP marker, and to 2.7 cM (P < 0.01) between positions 6_35,949,000 _SLM6-107 to 6_36,750,000_SLM6-82 marker, respectively. In addition, the specific effect of Bwr-12 for resistance to Pss4 (LOD score of 5.8-16.1, P < 0.01) was confirmed and markers for this QTL have already been made available previously.

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as reference panel to detect associations with the phenotype at the gene level and were gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as reference panel to conduct tissue specific TWAS on cigarettes per day (CPD) over 13 brain tissues in two large cohorts: UK Biobank (UKBB; N=142,202) and the GWAS &amp; Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N=143,210), and then meta-analyzed the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues, partially homogeneous genes associated with CPD in cortex, cerebellum and hippocampus tissues, and lastly the tissue-specific genes associated with CPD in only few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

The aim of this article has been to evaluate the impact of a physical-sport education programme, based on the pedagogical model of Sport Education within the framework of quality Physical Education and approached from the field of social and emotional learning, on a set of psychological variables: subjective well-being (quality of life related to health, positive affect and negative affect); trait emotional intelligence and social anxiety. A total number of 113 Compulsory Secondary Education students were involved, aged 12 to 15 years old, 44 of whom belonged to the control group (CG) and 69 to the experimental group (EG). A quasiexperimental design of repeated pretest and posttest measures with the CG was used. The results obtained in this investigation revealed that the intervention programme caused significant improvements in the subjective well-being and the trait emotional intelligence for the EG. These findings reinforce the pedagogical efficiency of the programme with regards to the aim that has been set. Likewise, the findings also highlight the suitability and appropriateness in terms of innovative teaching proposals. In the same way, the results showed relevant empirical contributions in this given school context due to its psychological benefits and the encouragement of healthy living.

Background: Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. Methods: We conducted summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are po-tentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes that were pleiotropically/potentially causally associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, PSMR=2.10×10-18), ILMN_1682717 (tagging IER3, PSMR=1.07×10-16) and ILMN_1661439 (tagging FLOT1, PSMR=1.16×10-14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, PSMR=1.44×10-10), ENSG00000186470.9 (tagging BTN3A2, PSMR=2.28×10-10), and ENSG00000224389.4 (tagging C4B, PSMR=1.23×10-9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes. Conclusion: We identified many genetic variants/loci that are potentially involved in the patho-genesis of IgAN.

Ecologists use the net biotic interactions among plants to predict fundamental ecosystem features. Following this approach, ecologists have built a giant body of theory founded on observational evidence. However, due to the limitations that a phenomenological approach raises both in empirical and theoretical studies, an increasing number of scientists claim the need for a mechanistic understanding of plant interaction outcomes, and a few studies have taken such a mechanistic approach. In this synthesis, we propose a modeling framework to study the plant interaction mechanistically. We first establish a conceptual ground to frame plant-plant interactions, and then, we propose to formalize this research line theoretically developing a family of individual-based, spatially-explicit models in which biotic interactions are an emergent property mediated by the interaction between plants’ functional traits and the environment. These models allow researchers to evaluate the strength and sign of biotic interactions under different environmental scenarios and thus constitute a powerful tool to investigate the mechanisms underlying facilitation, species coexistence, or the formation of vegetation spatial patterns.

Miscanthus holds a great potential in the frame of the bioeconomy and yield prediction can help improving Miscanthus logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. Random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass and standing biomass) using VIs time series and predicted yield using peak descriptor derived from VIs time series with 2.3 Mg DM ha-1 of RMSE. The study demonstrates the potential of UAVs multispectral in HTP applications and in yield prediction for providing important information needed to increase sustainable biomass production.

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding .

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality, and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134, and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

Weed competitive ability (WCA) is a desirable key trait for the improvement of grain yield under direct-seeded rice (DSR) and the aerobic rice ecosystem. The present study targeted screening of 167 introgression lines (ILs) of a Green Super Rice (GSR) IR2-6 population derived from a cross between Weed Tolerant Rice 1 (WTR1) as the recipient parent and Y134 as the donor parent developed at IRRI for weed competitiveness in screen house conditions (SHC). The ILs were phenotyped for WCA traits such as early seed germination (ESG) and early seedling vigor (ESV) in Petri dishes and pot experiment conditions. The results of phenotypic variance revealed ESG-related traits, especially first germination count (1st GC) that positively correlated with second germination count (2nd GC), germination percentage (GP), total dry weight (TDW), total fresh weight (TFW), and vigor index (VI-1), whereas, in ESV, all the traits were positively correlated with each other except for three traits: root dry weight (RDW), 1st GC, and GP-2. The ESG and ESV traits are vital for weed competitiveness. A 6K SNP array was used to study the genetic association for the WCA traits. Forty-four QTLs for WCA traits were mapped on all chromosomes (except on chromosomes 4 and 8) through single marker analysis (SMA). Out of 44 QTLs, 29 were associated with ESG traits and 15 with ESV traits, with LOD scores of 2.93 to 8.03 and 2.93 to 5.04 and explained phenotypic variance ranging from 7.85% to 19.9% and from 7.85% to 13.2%, respectively. However, 31 QTLs were contributed by a negative additive allele from Y134, whereas a positive additive allele was contributed by WTR1 in 13 QTLs. Among them, two QTL hotspot regions were mapped on chromosome 11 (24.7-27.9 Mb) and chromosome 12 (14.8-17.4 Mb). The majority of the QTLs related to WCA traits were grouped into two QTL hotspots: QTL hotspot-I (qAFW11.1, qFC11.1, qFC11.2, qSC11.1, qGP-111.1, qGP-111.2, qTFGS11.1, qVI-111.1, and qVI-111.2) and QTL hotspot-II (qFC12.1, qFC12.2, qSC12.1, qFC12.2, qGP-112.1, qGP-112.2, qTFGS12.1, qTFGS12.2, qVI-112.1, qIV12.2, qFC12.1, and qGC12.2), and a few of them were co-localized on chromosomes 11 and 12. Further, we fine-tuned in the genomic regions of QTL hotspots and identified a total of 13 putative candidate genes on chromosomes 11 and 12 collectively. The present study is the first report on the genetic basis of WCA-related traits and the co-localized QTLs, which could be highly valuable in future breeding programs aiming to improve WCA in rice.

Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits in a population. To date, a total of 267 QTL for 29 quantitative traits have been reported in 13 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, scaffold sequences, or pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but were mainly based on simple sequence repeat (SSR) or single nucleotide polymorphism (SNP) markers. This article provides methods with software tools and database files to uniquely map SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules. Using these methods, 195 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters. Mapping of QTL from different studies to the same reference enables comparisons and facilitates genome-wide QTL analysis, candidate gene scanning, and breeding applications.

Fusarium wilt of banana is a devastating disease that has decimated banana production worldwide. Host resistance to Fusarium oxysporum f. sp. cubense (Foc), the causal agent of this disease, is genetically dissected in this study using two Musa acuminata ssp. malaccensis segregating populations segregating for Foc Tropical (TR4) and Subtropical (STR4) race 4 resistance. Marker loci and trait association using 11 SNP-based PCR markers allowed the candidate region to be delimited to a 12.9 cM genetic interval corresponding to a 959 kb region on Chromosome 3 of ‘DH-Pahang’ reference assembly v4. Within this region, there is a cluster of pattern recognition receptors, namely leucine rich repeat ectodomain containing receptor-like protein kinases, cysteine-rich cell wall associated protein kinases, and leaf rust 10 disease-resistance locus receptor-like proteins positioned in an interspersed arrangement. Their transcript levels were rapidly upregulated in the resistant but not in susceptible F2 progenies at the onset of infection. This suggests that one or several of these genes may control resistance at this locus. To confirm the segregation of single-gene resistance, we generated an inter-cross between the resistant parent ‘Ma850’ and a susceptible line ‘Ma848’, to show that the STR4 resistance co-segregated with marker ‘28820’ at this locus. Finally, an informative SNP marker 29730 allowed the locus specific resistance to be assessed in a collection of diploid and polyploid banana plants. Out of the 60 lines screened, 22 lines were predicted to carry resistance at this locus, including lines known to be TR4 resistant, such as ‘Pahang’, ‘SH-3362’, ‘SH-3217’, ‘Ma-ITC0250’, and ‘DH-Pahang/CIRAD 930’. Additional screening in the International Institute for Tropical Agriculture’s collection suggests that the dominant allele is common among in the elite ‘Matooke’ NARITA hybrids, as well as in other triploid or tetraploid hybrids derived from East African highland bananas. Fine-mapping and candidate gene identification will allow characterization of molecular mechanisms underlying TR4 resistance. The markers developed in this study can now aid the marker-assisted selection of TR4 resistance in breeding programs around the world.