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Ovulation rate is an important phenotypic trait that is a critical component of litter size in pigs. Despite being moderately heritable in pigs, selection for increased ovulation rate is difficult because it is difficult to measure and is a sex-limited trait. A QTL for ovulation rate residing on the p-terminal end of pig chromosome 8 has been detected in a Meishan-cross resource population. Comparative analysis of this region yielded a positional candidate gene, mannosidase 2B2 ( MAN2B2), for this QTL. The entire coding region of MAN2B2 was resequenced in the Meishan and White Composite founder animals of the resource population to identify SNPs. Eleven polymorphisms that alter the protein product of MAN2B2 were discovered and tested for statistical associations with ovulation rate in three generations of the resource population. The polymorphism located at position 1574 of the mRNA (D28521:c.1574A>G) was the most significant polymorphism tested ( P = 0.00005) where the additive effect of the c.1574A allele was estimated to be −0.89 ova. This polymorphism was determined to be more significantly associated with ovulation rate than the breed-specific analysis conducted during the line-cross QTL discovery. The c.1574A>G marker was not associated with ovulation rate in an occidental population. Therefore, either MAN2B2 has a unique epistatic interaction within the Meishan-cross population or the c.1574A>G SNP is in linkage disequilibrium with the actual causative genetic variation in the Meishan-cross population. [ABSTRACT FROM AUTHOR]

Proper development of a seed requires coordinated exchanges of signals among the three components that develop side by side in the seed. One of these is the maternal integument that encloses the other two zygotic components, i.e., the diploid embryo and its nurturing annex, the triploid endosperm. Although the formation of the embryo and endosperm contains the contributions of both maternal and paternal parents, maternally and paternally derived alleles may be expressed differently, leading to a so-called parent-of-origin or imprinting effect. Currently, the nature of how genes from the maternal and zygotic genomes interact to affect seed development remains largely unknown. Here, we present a novel statistical model for estimating the main and interaction effects of quantitative trait loci (QTLs) that are derived from different genomes and further testing the imprinting effects of these QTLs on seed development. The experimental design used is based on reciprocal backcrosses toward both parents, so that the inheritance of parent-specific alleles could be traced. The computing model and algorithm were implemented with the maximum likelihood approach. The new strategy presented was applied to study the mode of inheritance for QTLs that control endoreduplication traits in maize endosperm. Monte Carlo simulation studies were performed to investigate the statistical properties of the new model with the data simulated under different imprinting degrees. The false positive rate of imprinting QTL discovery by the model was examined by analyzing the simulated data that contain no imprinting QTL. The reciprocal design and a series of analytical and testing strategies proposed provide a standard procedure for genomic mapping of QTLs involved in the genetic control of complex seed development traits in flowering plants. [ABSTRACT FROM AUTHOR]

We present a predicted passerine genome map consisting of 196 microsatellite markers distributed across 25 chromosomes. The map was constructed by assigning chromosomal locations based on the sequence similarity between 550 publicly available passerine microsatellites and the draft chicken genome sequence published by the International Chicken Genome Sequencing Consortium. We compared this passerine microsatellite map with a recently published great reed warbler ( Acrocephalus arundinaceus) linkage map derived from the segregation of marker alleles in a pedigree of a natural population. Twenty-four microsatellite markers were shared between the two maps, distributed across ten chromosomes. Synteny was maintained between the predicted passerine microsatellite map and the great reed warbler linkage map, confirming the validity and accuracy of our approach. Possible applications of the predicted passerine microsatellite map include genome mapping; quantitative trait locus (QTL) discovery; understanding heterozygosity–fitness correlations; investigating avian karyotype evolution; understanding microsatellite mutation processes; and for identifying loci conserved in multiple species, unlinked loci for use in genotyping sets and sex-linked markers. [ABSTRACT FROM AUTHOR]

Drought tolerance (DT) is a very complex trait. For the purpose of developing DT rice cultivars, a research programme was initiated in 1997 in China, consisting of four coherent parts: the development of a DT field screen facility and evaluation standard; the collection, evaluation and enhancement of DT rice resources; DT gene/quantitative trait locus (QTL) discovery; and DT rice breeding. More than 2000 rice accessions, mostly from China, were collected and evaluated in a new DT screening facility with a powerful water management system. Eighty-six entries were selected to serve as a core DT collection. A set of 187 recombinant inbred lines was developed for the genetic mapping of DT and high yield-related QTL under drought conditions. Several DT rice cultivars adapted to southern and central China were released. DT rice CMS lines were developed and distributed to most parts of China to encourage the development of DT or water-saving hybrid rice. [ABSTRACT FROM AUTHOR]

A collection of 134 durum wheat accessions, mainly including cultivars (cvs.) representative of the major gene pools, was assembled and characterized with 70 SSRs for genetic diversity and level of long-range linkage disequilibrium (LD). Results of both a distance-based and a model-based (Bayesian) cluster analysis evidenced the presence of a structured diversity. In the model-based analysis, six to eight main distinct subpopulations were identified based on the molecular data. Only a relatively small portion (20%) of the molecular variation was accounted for by the geographical origin of the accessions. Major differences were detected between the North American and the Mediterranean cvs., while a considerable overlap characterized the cvs. from CIMMYT-ICARDA and Italy. The North American cvs. showed the highest within group mean genetic similarity (GSm = 0.68). French cvs. revealed sizeable similarities with both the North American as well as the Italian and CIMMYT-ICARDA pools. Considering the germplasm as a whole, high levels of LD were found both at locus pairs with an intrachromosomal distance <50 cM as well as at those with distances more than 50 cM and independent (86, 52 and 54% of SSR pairs at p < 0.01, respectively). After re-evaluating LD within each of the three main subgroups identified through the analysis of the germplasm structure, the LD level remained high for tightly to moderately linked locus pairs (<20 cM apart), but was greatly reduced in the loosely linked (more than 50 cM apart) and independent locus pairs. The implications of these findings as to the possibility of using association mapping for gene/QTL discovery in durum wheat are discussed. [ABSTRACT FROM AUTHOR]

Allelic effects observed in QTL discovery experiments must be confirmed to be useful in subsequent breeding efforts. Two QTL affecting seed yield of spring hybrid canola (Brassica napus L.) were previously identified in two populations of inbred backcross lines (IBLs) containing germplasm introgressed from a winter cultivar. The effects of favorable alleles at these QTL were retested by crossing two selected IBLs (M5 and M31) to three spring canola lines having different genetic backgrounds. Doubled haploid (DH) lines derived from each F1 were genotyped with RFLP markers flanking the QTL and grouped into the four possible QTL genotypes. For the first field experiment, DH lines derived by crossing the M5 line to one spring line were crossed to two female testers and evaluated as individual testcross progenies in one environment. QTL genotypes had large variances and were not significantly different. A second field experiment was conducted using the DH lines from the first experiment and two other sets of DH lines derived from the M31 line crossed to two different spring canola lines. Individual lines within each QTL genotype of each set were bulked and crossed to the same testers used in Experiment 1. Bulked hybrid seeds of each QTL genotype were planted in a split-split plot randomized block design and 12 replicates. QTL genotypes had smaller variances in this experiment, and the effects of one QTL were confirmed in some genetic backgrounds. These results suggest that bulking of QTL genotypes and use of an appropriate experimental design with many replicates are needed to detect small differences between QTL genotypes. [ABSTRACT FROM AUTHOR]

A novel set of 50 highly polymorphic microsatellite markers were developed and mapped on existing RAPD framework maps of Eucalyptus grandis and E. urophylla. Together with the twenty previously developed microsatellite markers, these were used to align the existing maps for the two most commercially important Eucalyptus species in the tropics. Sixty-three microsatellite markers were placed on the E. grandis map in 11 linkage groups, and 53 on the E. urophylla map distributed in 10 linkage groups. Approximately 66% of the microsatellite markers segregated in a fully informative fashion, allowing the establishment of colinear syntenic linkage groups between the two maps. The 50 new microsatellite markers were highly informative, with an average of 14 alleles per locus, and average expected heterozygosity between 0.82 and 0.87. Furthermore, within the subgenus Symphyomyrtus, to which the vast majority of commercially important Eucalyptus species belong, these markers display on average 90% transportability. This set of 70 mapped microsatellite markers represents a significant step toward the development of a genus-wide reference linkage map for Eucalyptus. These highly multiallelic and transportable markers constitute a powerful tool for QTL discovery and validation, and can be used in directed searches for QTL allele variation across Eucalyptus pedigrees. [ABSTRACT FROM AUTHOR]

Identifying causal variants from genome-wide association studies (GWAS) is challenging due to widespread linkage disequilibrium (LD) and the possible existence of multiple causal variants in the same genomic locus. Functional annotations of the genome may help to prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. Classical fine-mapping methods conducting an exhaustive search of variant-level causal configurations have a high computational cost, especially when the underlying genetic architecture and LD patterns are complex. SuSiE provided an iterative Bayesian stepwise selection algorithm for efficient fine-mapping. In this work, we build connections between SuSiE and a paired mean field variational inference algorithm through the implementation of a sparse projection, and propose effective strategies for estimating hyperparameters and summarizing posterior probabilities. Moreover, we incorporate functional annotations into fine-mapping by jointly estimating enrichment weights to derive functionally-informed priors. We evaluate the performance of SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved improved power for fine-mapping with reduced computation time. We demonstrate the utility of SparsePro through fine-mapping of five functional biomarkers of clinically relevant phenotypes. In summary, we have developed an efficient fine-mapping method for integrating summary statistics and functional annotations. Our method can have wide utility in understanding the genetics of complex traits and increasing the yield of functional follow-up studies of GWAS. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro. Author summary: Accurately identifying causal variants from genome-wide association studies summary statistics is important for understanding genetic architecture of complex traits and identifying therapeutic targets. Functional annotations are commonly used as additional evidence for prioritizing causal variants. In this study, we present SparsePro to integrate summary statistics and functional annotations for accurate identification of causal variants. SparsePro extends the capabilities of a popular fine-mapping method, SuSiE, with important contributions in hyperparameter estimation, posterior summaries and integration of function annotations. Through extensive simulations, we demonstrate that our proposed approach can effectively integrate summary statistics and functional annotation, leading to improved power for identifying causal variants. Furthermore, we evaluate the benefits of incorporating functional annotations through real data analyses of five functional biomarkers. In summary, by improving power and providing valuable insights into complex disease genetics, SparsePro will have wide utility in advancing our knowledge and facilitating follow-up discoveries. [ABSTRACT FROM AUTHOR]

In wheat (Triticum aestivum L.), the grain size varies according to position within the spike. Exposure to drought and high temperature stress during grain development in wheat reduces grain size, and this reduction also varies across the length of the spike. We developed the phenomics approach involving image-based tools to assess the intra-spike variation in grain size. The grains were arranged corresponding to the spikelet position and the camera of smart phone was used to acquire 333 images. The open-source software ImageJ was used to analyze features of each grain and the image-derived parameters were used to calculate intra-spike variation as standard deviation (ISVAD). The effect of genotype and environment were highly significant on the ISVAD of grain area. Sunstar and Raj 4079 contrasted in the ISVAD of grain area under late sown environment, and RNA sequencing of the spike was done at 25 days after anthesis. The genes for carbohydrate transport and stress response were upregulated in Sunstar as compared to Raj 4079, suggesting that these play a role in intra-spike assimilate distribution. The phenomics method developed may be useful for grain phenotyping and identifying germplasm with low intra-spike variation in grain size for their further validation as parental material in breeding. [ABSTRACT FROM AUTHOR]

Rice cultivation in the low-lying basin-like wetlands, known as the Haor, is often affected by early flash floods during the first two weeks of April. The flooding is mainly caused by heavy rainfall and water surging downstream from the Meghalaya hills in India. This flash flood poses a significant threat to rice production, risking the country's food security. Dry winter (Boro) rice is the primary food source throughout the year in the Haor region. Flash floods are the most catastrophic, affecting about 80% or even the entire rice yield. In 2017, a loss of 0.88 million metric tons of Boro rice in Haor regions cost the nation USD 450 million. To escape flash floods, it is recommended to sow Boro rice earlier, between the last week of October and the first week of November, instead of around 15 November so rice may be harvested by the last week of March before the onset of flash floods. However, early sowing has a possibility of causing grain sterility due to cold spells when the booting and heading stages of rice inevitably coincide with the cold period between 15 January and 7 February. The minimum temperature in the Haor regions ranges from 11 to 15 °C during this time. Rice is especially susceptible to low average temperatures (<20 °C) during the reproductive stage, leading to pollen abortion and the malformation of immature microspores. Low temperatures mainly impact rice cultivation in Haor regions during the reproductive phase, resulting in the degeneration of the spikelets, partial panicle exertion, and increased spikelet sterility, leading to a decrease in grain yield. Over two million hectares of Boro rice have been damaged by extreme cold spells in recent years, resulting in partial or total yield loss. To overcome the threats of flash floods and cold injury, breeding short-duration and cold-tolerant rice varieties is crucial. We assume that an economic benefit of USD 230 million per year could be achieved through the development and adoption of short-duration and cold-tolerant high-yielding rice varieties in the Haor regions of the country. In this review article, the authors summarized the problems and outline a way forward to overcome the flash flood and cold injury of Boro rice cultivation in the Haor districts of the country. Furthermore, the authors discussed the various forms and scenarios of cold damage and the global existence of cold-tolerant rice cultivars. Based on the available data from earlier research, a potential way of mitigating flash floods and cold devastation was suggested. [ABSTRACT FROM AUTHOR]

Simple Summary: By integrating prior biological information into genomic selection methods using appropriate models, it is possible to improve prediction accuracy for complex traits. In this context, we conducted a comparative assessment of two genomic prediction models, namely, genomic best linear unbiased prediction and genomic feature best linear unbiased prediction. The accuracy of these models in predicting the growth traits of backfat thickness and loin muscle area was evaluated. Our results revealed that the genomic feature best linear unbiased prediction model can effectively integrate prior information into the model, which is superior to the genomic best linear unbiased prediction model in some cases. These findings provide valuable ideas for enhancing the genomic prediction accuracy of growth traits in pigs. Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models. [ABSTRACT FROM AUTHOR]

Terminal drought is one of the most common and devastating climatic stress factors affecting durum wheat (Triticum durum Desf.) production worldwide. The wild relatives of this crop are deemed a vast potential source of useful alleles to adapt to this stress. A nested association mapping (NAM) panel was generated using as a recurrent parent the Moroccan variety 'Nachit' derived from Triticum dicoccoides and known for its large grain size. This was recombined to three top-performing lines derived from T. dicoccoides, T. araraticum,and Aegilops speltoides, for a total of 426 inbred progenies. This NAM was evaluated across eight environments (Syria, Lebanon, and Morocco) experiencing different degrees of terminal moisture stress over two crop seasons. Our results showed that drought stress caused on average 41% loss in yield and that 1,000-kernel weight (TKW) was the most important trait for adaptation to it. Genotyping with the 25K TraitGenetics array resulted in a consensus map of 1,678 polymorphic SNPs, spanning 1,723 cM aligned to the reference 'Svevo' genome assembly. Kinship distinguished the progenies in three cladesmatching the parent of origin. A total of 18 stable quantitative trait loci (QTLs) were identified as controlling various traits but independent from flowering time. The most significant genomic regions were namedQ.ICD.NAM-04,Q.ICD.NAM-14, andQ.ICD.NAM-16. Allelic investigation in a second germplasm panel confirmed that carrying the positive allele at all three loci produced an average TKW advantage of 25.6% when field-tested under drought conditions. The underlying SNPs were converted to Kompetitive Allele-Specific PCR (KASP) markers and successfully validated in a third germplasm set, where they explained up to 19% of phenotypic variation for TKW under moisture stress. These findings confirm the identification of critical loci for drought adaptation derived from wild relatives that can now be readily exploited via molecular breeding. [ABSTRACT FROM AUTHOR]

Background: Celiac disease (CeD) is an immune-mediated disorder that develops in genetically predisposed individuals upon gluten consumption. HLA risk alleles explain 40% of the genetic component of CeD, so there have been continuing efforts to uncover non-HLA loci that can explain the remaining heritability. As in most autoimmune disorders, the prevalence of CeD is significantly higher in women. Here, we investigated the possible involvement of the X chromosome on the sex bias of CeD. Methods: We performed a X chromosome-wide association study (XWAS) and a gene-based association study in women from the CeD Immunochip (7062 cases, 5446 controls). We also constructed a database of X chromosome cis-expression quantitative trait loci (eQTLs) in monocytes from unstimulated (n = 226) and lipopolysaccharide (LPS)-stimulated (n = 130) female donors and performed a Summary-data-based MR (SMR) analysis to integrate XWAS and eQTL information. We interrogated the expression of the potentially causal gene (TMEM187) in peripheral blood mononuclear cells (PBMCs) from celiac patients at onset, on a gluten-free diet, potential celiac patients and non-celiac controls. Results: The XWAS and gene-based analyses identified 13 SNPs and 25 genes, respectively, 22 of which had not been previously associated with CeD. The X chromosome cis-eQTL analysis found 18 genes with at least one cis-eQTL in naïve female monocytes and 8 genes in LPS-stimulated female monocytes, 2 of which were common to both situations and 6 were unique to LPS stimulation. SMR identified a potentially causal association of TMEM187 expression in naïve monocytes with CeD in women, regulated by CeD-associated, eQTL-SNPs rs7350355 and rs5945386. The CeD-risk alleles were correlated with lower TMEM187 expression. These results were replicated using eQTLs from LPS-stimulated monocytes. We observed higher levels of TMEM187 expression in PBMCs from female CeD patients at onset compared to female non-celiac controls, but not in male CeD individuals. Conclusion: Using X chromosome genotypes and gene expression data from female monocytes, SMR has identified TMEM187 as a potentially causal candidate in CeD. Further studies are needed to understand the implication of the X chromosome in the higher prevalence of CeD in women. Plain language summary: Celiac disease (CeD) is an immune-related condition triggered by gluten consumption in genetically susceptible individuals. Women present higher prevalence of CeD than men, but the biological explanation of such difference has not been elucidated. In this study, we investigated whether specific genetic variations on the X chromosome were associated with CeD in each sex. Surprisingly, we found 13 genetic variants and 25 genes significantly linked to CeD in women, but not in men. Additionally, we identified genetic variants on the X chromosome associated with gene expression of monocytes, a type of immune cells that is activated in CeD after gluten intake. Integrating these data with our previous findings, we found that lower expression of a gene termed TMEM187 might be associated with a potential increase in CeD risk in women. Finally, validation experiments confirmed higher TMEM187 levels in blood cells from female CeD patients compared to non-celiac women, while no such difference was seen in males. In summary, our study suggests that the X-chromosome gene TMEM187 may play a key role in CeD development, providing insights into the higher prevalence of CeD in females. Highlights: The XWAS and gene-based association study identified 13 genetic variants and 25 genes significantly associated with CeD in women, but not in men. eQTL analyses in naïve and LPS-stimulated female monocytes identified 16 unique genes with at least one cis-eQTL in the naïve condition, 6 in the LPS condition and 2 genes, namely ZNF185 and TMEM187, common to both conditions. SMR identified three SNPs (rs7350355, rs5945386, rs80208125) associated with gene TMEM187. The CeD risk alleles were negatively correlated with TMEM187 expression. PBMCs from active female CeD patients showed significantly higher TMEM187 expression than controls, but no differences were observed in men. [ABSTRACT FROM AUTHOR]

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture. [ABSTRACT FROM AUTHOR]

A large number of QTLs controlling important traits in different crops have been mapped but their deployment in the marker assisted selection (MAS) is highly limited. The possible reason is lack of availability of stable QTLs over diverse genetic background and environment. QTL meta-analysis provides an opportunity to compile QTLs from different studies and identify consistent QTLs across genetic background and environments. These meta-QTLs can be targeted for further fine mapping and used for MAS after validation. QTL meta-analysis involve library search for reported QTL studies for a particular trait in a crop and subsequently a consensus linkage map is constructed projecting QTLs from different studies using simple scaling rule. The Confidence Interval (CI) of QTL is estimated on the consensus map using the formula as CI (95): ¼ 530/(Nλ), where CI (95) is the confidence interval at 95 percent probability level, N is the size of mapping population and λ is the proportion of phenotypic variance explained by the concerned QTL. Finally, detected QTLs are projected for cluster analysis to determine the exact number and positions of true QTLs. The utility of this approach will help to detect the stable meta-QTLs that will be further helpful to identify candidate genes and their application in MAS program. [ABSTRACT FROM AUTHOR]

Anthracnose (ANT) caused by Colletotrichum lindemuthianum is the most devastating seed-borne fungal disease of common bean. In response to fungal infections, it is hypothesized that pathogen–plant interactions typically cause hypersensitive reactions by producing reactive oxygen species, hydrogen peroxide and lipid peroxidation of cell membranes. esent study was conducted by inoculating susceptible bean genotype "SB174" and resistant bean genotype "E10" with pathogen "C. lindemuthianum". Defense-related enzymes (ascorbate peroxidase, peroxidase, lipid peroxidase, and catalase) and C-based compounds (total phenols and flavonoids) were studied using the detached bean leaf method. Comparative defense response was studied in different plant tissues (pod, stem, and seed) in susceptible and resistant bean genotypes under uninoculated and pathogen-inoculated conditions. The host‒pathogen interaction was studied at mock inoculation, 2, 4 and 6 days after inoculation (dai). Comparing the pathogen-inoculated bean leaves to water-treated bean leaves, defense enzymes as well as total phenols and flavonoids exhibited differential expression. In a comparative study, the enzyme activity also displayed differential biochemical responses in pods, stems and seeds in both contrasting genotypes. For example, 5.1-fold (pod), 1.5-fold (stem) and 1.06-fold (seed) increases in ascorbate peroxidase activity were observed in the susceptible genotype at 6 dai compared to mock inoculation. Similarly, catalase activity in pods was upregulated (1.47-fold) in the resistant genotype and downregulated (1.30-fold) in the susceptible genotype at 6 dai. The study revealed that defense-related antioxidative enzymes, phenols and flavonoids are fine-tuned to detoxify important reactive oxygen species (ROS) molecules, induce systemic resistance and are successfully controlled in common bean plants against pathogen invasion. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) production is greatly affected by persistent and/or intermittent droughts in rainfed soybean-growing regions worldwide. Symbiotic N2 fixation (SNF) in soybean can also be significantly hampered even under moderate drought stress. The objective of this study was to identify genomic regions associated with shoot carbon isotope ratio (δ13C) as a surrogate measure for water use efficiency (WUE), nitrogen isotope ratio (δ15N) to assess relative SNF, N concentration ([N]), and carbon/nitrogen ratio (C/N). Genome-wide association mapping was performed with 105 genotypes and approximately 4 million single-nucleotide polymorphism markers derived from whole-genome resequencing information. A total of 11, 21, 22, and 22 genomic loci associated with δ13C, δ15N, [N], and C/N, respectively, were identified in two environments. Nine of these 76 loci were stable across environments, as they were detected in both environments. In addition to the 62 novel loci identified, 14 loci aligned with previously reported quantitative trait loci for different C and N traits related to drought, WUE, and N2 fixation in soybean. A total of 58 Glyma gene models encoding for different genes related to the four traits were identified in the vicinity of the genomic loci. [ABSTRACT FROM AUTHOR]

Background: In agricultural ecosystems, outbreaks of diseases are frequent and pose a significant threat to food security. A successful pathogen undergoes a complex and well-timed sequence of regulatory changes to avoid detection by the host immune system; hence, well-tuned gene regulation is essential for survival. However, the extent to which the regulatory polymorphisms in a pathogen population provide an adaptive advantage is poorly understood. Results: We used Zymoseptoria tritici, one of the most important pathogens of wheat, to generate a genome-wide map of regulatory polymorphism governing gene expression. We investigated genome-wide transcription levels of 146 strains grown under nutrient starvation and performed expression quantitative trait loci (eQTL) mapping. We identified cis-eQTLs for 65.3% of all genes and the majority of all eQTL loci are within 2kb upstream and downstream of the transcription start site (TSS). We also show that polymorphism in different gene elements contributes disproportionally to gene expression variation. Investigating regulatory polymorphism in gene categories, we found an enrichment of regulatory variants for genes predicted to be important for fungal pathogenesis but with comparatively low effect size, suggesting a separate layer of gene regulation involving epigenetics. We also show that previously reported trait-associated SNPs in pathogen populations are frequently cis-regulatory variants of neighboring genes with implications for the trait architecture. Conclusions: Overall, our study provides extensive evidence that single populations segregate large-scale regulatory variation and are likely to fuel rapid adaptation to resistant hosts and environmental change. [ABSTRACT FROM AUTHOR]

Simple Summary: The weight features of Alpine Merino Sheep, particularly their live weight in the 14th month (WT), are crucial in production and breeding, as they live at high altitudes and in highland areas where oxygen is scarce. Genome selection (GS) offers the advantages of minimizing breeding costs and generational intervals, and the main objective of genome selection breeding is the accuracy of GS. Genome-wide association studies (GWASs) were used in this study to extract the variation information related to the 14-month-old body weight trait of Alpine Merino Sheep, identify candidate genes associated with this trait, and employ GWAS results as prior information to conduct GS research on the trait to determine the most suitable GS breeding model for the target trait. Alpine Merino Sheep is a novel breed reared from Australian Merino Sheep as the father and Gansu Alpine Fine-Wool Sheep as the mother, living all year in cold and arid alpine areas with exceptional wool quality and meat performance. Body weight is an important economic trait of the Alpine Merino Sheep, but there is limited research on identifying the genes associated with live weight in the 14th month for improving the accuracy of the genomic prediction of this trait. Therefore, this study's sample comprised 1310 Alpine Merino Sheep ewes, and the Fine Wool Sheep 50K Panel was used for genome-wide association study (GWAS) analysis to identify candidate genes. Moreover, the trial population (1310 ewes) in this study was randomly divided into two groups. One group was used as the population for GWAS analysis and screened for the most significant top 5%, top 10%, top 15%, and top 20% SNPs to obtain prior marker information. The other group was used to estimate the genetic parameters based on the weight assigned by heritability combined with different prior marker information. The aim of this study was to compare the accuracy of genomic breeding value estimation when combined with prior marker information from GWAS analysis with the optimal linear unbiased prediction method for genome selection (GBLUP) for the breeding value of target traits. Finally, the accuracy was evaluated using the five-fold cross-validation method. This research provides theoretical and technical support to improve the accuracy of sheep genome selection and better guide breeding. The results demonstrated that eight candidate genes were associated with GWAS analysis, and the gene function query and literature search results suggested that FAM184B, NCAPG, MACF1, ANKRD44, DCAF16, FUK, LCORL, and SYN3 were candidate genes affecting live weight in the 14th month (WT), which regulated the growth of muscle and bone in sheep. In genome selection analysis, the heritability of GBLUP to calculate the WT was 0.335–0.374, the accuracy after five-fold cross-verification was 0.154–0.190, and after assigning different weights to the top 5%, top 10%, top 15%, and top 20% of the GWAS results in accordance with previous information to construct the G matrix, the accuracy of the WT in the GBLUP model was improved by 2.59–7.79%. [ABSTRACT FROM AUTHOR]

The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single‐nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi‐step method (GBLUP) and single‐step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction. [ABSTRACT FROM AUTHOR]

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits. [ABSTRACT FROM AUTHOR]

Motivation Molecular quantitative trait locus (QTL) mapping has proven to be a powerful approach for prioritizing genetic regulatory variants and causal genes identified by genome-wide association studies. Recently, this success has been extended to circular RNA (circRNA), a potential group of RNAs that can serve as markers for the diagnosis, prognosis, or therapeutic targets of various human diseases. However, a well-developed computational pipeline for circRNA QTL (circQTL) discovery is still lacking. Results We introduce an integrative method for circQTL mapping and implement it as an automated pipeline based on Nextflow, named cscQTL. The proposed method has two main advantages. Firstly, cscQTL improves the specificity by systematically combining outputs of multiple circRNA calling algorithms to obtain highly confident circRNA annotations. Secondly, cscQTL improves the sensitivity by accurately quantifying circRNA expression with the help of pseudo references. Compared to the single method approach, cscQTL effectively identifies circQTLs with an increase of 20%–100% circQTLs detected and recovered all circQTLs that are highly supported by the single method approach. We apply cscQTL to a dataset of human T cells and discover genetic variants that control the expression of 55 circRNAs. By colocalization tests, we further identify circBACH2 and circYY1AP1 as potential candidates for immune disease regulation. Availability and implementation cscQTL is freely available at: https://github.com/datngu/cscQTL and https://doi.org/10.5281/zenodo.7851982. [ABSTRACT FROM AUTHOR]

The outbreak of Fusarium head blight (FHB) poses a serious threat to wheat production as it leads to both significant yield losses and accumulation of several mycotoxins including deoxynivalenol (DON) in the grains, which are harmful to human and livestock. To date, hundreds of FHB-resistance-related quantitative trait loci (QTLs) have been reported, but only a few of them have been cloned and used for breeding. Small interfering RNAs (siRNA) have been reported in plants to mediate host defense against pathogens, but they have rarely been reported in wheat-FHB interaction. In order to identify the key siRNAs that can potentially be used in the improvement of resistance to FHB, siRNAs from the spikes of an FHB-resistant variety Sumai 3 and an FHB-susceptible variety of Chinese Spring (CS) were sequenced after F. graminearum infection and mock inoculation, respectively. The expression patterns of the siRNAs of interest were analyzed. A total of 4019 siRNAs of high-confidence were identified, with 131 being CS-specific, 309 Sumai 3-specific and 3071 being common in both varieties. More than 87% of these siRNAs were 24 nt in length. An overall down-regulation trend was found for siRNAs in the spikes of both varieties after being infected with F. graminearum. The expression patterns for Triticum aestivum Dicer-like 3 (TaDCL3) that synthesizes 24 nt siRNAs were validated by qRT-PCR, which were positively correlated with those of the siRNAs. A total of 85% of the differentially expressed genes putatively targeted by the siRNAs were significantly up-regulated after infection, showing a negative correlation with the overall down-regulated expression of siRNAs. Interestingly, the majority of the up-regulated genes are annotated as disease resistance. These results suggested that the inhibition of siRNA by F. graminearum up-regulated the disease resistance genes, which were putatively suppressed by siRNAs through RNA-directed DNA methylation (RdDM). Consequently, the resistant capability to F. graminearum infection was enhanced. This study provides novel clues for investigating the function of siRNA in wheat-F. graminearum interaction. [ABSTRACT FROM AUTHOR]

Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA sequencing (RNA-seq) using a reference single-nuclear RNA-seq data set. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower BMI, respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best-fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e−6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits. Article Highlights: Our goal was to create a resource of trait-gene expression associations on a genome-wide scale across several cardiometabolic traits that accounts for cell-type heterogeneity. We aimed to determine whether cell-type composition affects trait-gene associations. We found that adjusting for both BMI and cell-type proportion is the best-fitting model for most trait-gene expression associations in adipose tissue. We identified 2,664 significant associations for 1,252 genes and 14 traits using a linear model that accounts for cell-type composition and BMI. Our findings suggest that cell-type composition should be considered when assessing the association between adipose gene expression and cardiometabolic traits. [ABSTRACT FROM AUTHOR]

The coexistence of drought and salinity stresses in field conditions significantly hinders wheat (Triticum aestivum L.) productivity. Understanding the molecular mechanisms governing response and tolerance to these stresses is crucial for developing resilient wheat varieties. Our research, employing a combination of meta-QTL and meta-RNA-Seq transcriptome analyses, has uncovered the genome functional landscape of wheat in response to drought and salinity. We identified 118 meta-QTLs (MQTLs) distributed across all 21 wheat chromosomes, with ten designated as the most promising. Additionally, we found 690 meta-differentially expressed genes (mDEGs) shared between drought and salinity stress. Notably, our findings highlight the Early Responsive to Dehydration 15 (ERD15) gene, located in one of the most promising MQTLs, as a key gene in the shared gene network of drought and salinity stress. ERD15, differentially expressed between contrasting wheat genotypes under combined stress conditions, significantly regulates water relations, photosynthetic activity, antioxidant activity, and ion homeostasis. These findings not only provide valuable insights into the molecular genetic mechanisms underlying combined stress tolerance in wheat but also hold the potential to contribute significantly to the development of stress-resilient wheat varieties., (© 2024 Scandinavian Plant Physiology Society. Published by John Wiley & Sons Ltd.)

Alternative splicing (AS) in human genes is widely viewed as a mechanism for enhancing proteomic diversity. AS can also impact gene expression levels without increasing protein diversity by producing 'unproductive' transcripts that are targeted for rapid degradation by nonsense-mediated decay (NMD). However, the relative importance of this regulatory mechanism remains underexplored. To better understand the impact of AS-NMD relative to other regulatory mechanisms, we analyzed population-scale genomic data across eight molecular assays, covering various stages from transcription to cytoplasmic decay. We report threefold more unproductive splicing compared with prior estimates using steady-state RNA. This unproductive splicing compounds across multi-intronic genes, resulting in 15% of transcript molecules from protein-coding genes being unproductive. Leveraging genetic variation across cell lines, we find that GWAS trait-associated loci explained by AS are as often associated with NMD-induced expression level differences as with differences in protein isoform usage. Our findings suggest that much of the impact of AS is mediated by NMD-induced changes in gene expression rather than diversification of the proteome., (© 2024. The Author(s).)

Key Message: Two genomic regions associated with FFBN and HFFBN and a potential regulatory gene (GhE6) of HFFBN were identified through the integration of RTM-GWAS and meta‑QTL analyses. Abstract The first fruit branch node (FFBN) and the height of the first fruit branch node (HFFBN) are two important traits that are related to plant architecture and early maturation in upland cotton. Several studies have been conducted to elucidate the genetic basis of these traits in cotton using biparental and natural populations. In this study, by using 9,244 SNP linkage disequilibrium block (SNPLDB) loci from 315 upland cotton accessions, we carried out restricted two-stage multilocus and multiallele genome-wide association studies (RTM-GWASs) and identified promising haplotypes/alleles of the four stable and true major SNPLDB loci that were significantly associated with FFBN and HFFBN. Additionally, a meta-quantitative trait locus (MQTL) analysis was conducted on 274 original QTLs that were reported in 27 studies, and 40 MQTLs associated with FFBN and HFFBN were identified. Through the integration of the RTM-GWAS and meta‑QTL analyses, two stable and true major SNPLDBs (LDB&#95;5&#95;15144433 and LDB&#95;16&#95;37952328) that were distributed in the two MQTLs were identified. Ultimately, 142 genes in the two genomic regions were annotated, and three candidate genes associated with FFBN and HFFBN were identified in the genomic region (A05:14.64-15.64 Mb) via RNA-Seq and qRT‒PCR. The results of virus-induced gene silencing (VIGS) experiments indicated that GhE6 was a key gene related to HFFBN and that GhDRM1 and GhGES were important genes associated with early flowering in upland cotton. These findings will aid in the future identification of molecular markers and genetic resources for developing elite early-maturing cultivars with ideal plant characteristics., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The cultivated garden strawberry (Fragaria × ananassa) has a rich history, originating from the hybridization of two wild octoploid strawberry species in the 18th century. Two-step reconstruction of Fragaria × ananassa through controlled crossings between pre-improved selections of its parental species is a promising approach for enriching the breeding germplasm of strawberry for wider adaptability. We created a population of reconstructed strawberry by hybridizing elite selections of F. virginiana and F. chiloensis. A replicated field experiment was conducted to evaluate the population's performance for eleven horticulturally important traits, over multiple years. Population structure analyses based on Fana-50 k SNP array data confirmed pedigree-based grouping of the progenies into four distinct groups. As complex traits are often influenced by environmental variables, and population structure can lead to spurious associations, we tested multiple genome-wide association study (GWAS) models. GWAS uncovered 39 quantitative trait loci (QTL) regions for eight traits distributed across twenty chromosomes, including 11 consistent and 28 putative QTLs. Candidate genes for traits including winter survival, flowering time, runnering vigor, and hermaphrodism were identified within the QTL regions. To our knowledge, this study marks the first comprehensive investigation of adaptive and horticultural traits in a large, multi-familial reconstructed strawberry population using SNP markers., (© 2024 The Author(s). Physiologia Plantarum published by John Wiley & Sons Ltd on behalf of Scandinavian Plant Physiology Society.)

Intumescence is a nonpathogenic physiological disorder characterized by leaf blistering. This disorder can affect growth and development in glasshouses and growth chambers and may be confused with pathogenic diseases. We used quantitative trait loci (QTL) analysis to examine the genetic basis of variation in intumescence severity in Eucalyptus globulus, and test for colocation with previously detected QTLs for pathogen susceptibility. QTL analysis used the phenotype means of open-pollinated (OP) families of an outcrossed F2 mapping family (OP F3; n = 300) of E. globulus and the linkage map constructed in the F2. We validate this phenotyping approach for QTL analysis by assessing a trait previously used for QTL discovery in the F2 and showing the same major QTL was detected with the OP F3. For intumescence severity, five putative QTLs were detected across four linkage groups. Four of these did not colocate with previously reported QTLs for fungal pathogen susceptibility in Eucalyptus, suggesting the mechanisms underlying susceptibility to intumescence and to the two fungal pathogens are largely independent. This study demonstrates there is a genetic basis for variation in intumescence severity, reports the first QTL for intumescence severity in plants, and provides a robust framework for investigating the potential mechanisms involved. [ABSTRACT FROM AUTHOR]

Fruit traits influence consumer’s choice and the market worth of melons. Multiple genes controlled the majority of fruit quality traits and these genes are influenced via differential factors. Cleaved amplified polymorphism sequence (CAPS) molecular markers have been validated as efficient tool for detection of numerous SNPs (single nucleotide polymorphisms). In the current study, genetic linkage map was assembled and QTL discovery was conducted for melon quality traits in F 2:3 population, generated from crossing of MR-1 and M4-7. SNPs were detected and converted into CAPS markers from retrieved data of high-throughput resequencing. Two parental lines (MR-1 and M4-7) of melon were analyzed and resequenced for assembling the novel CAPS markers. Totally, 91.54% and 90.00% of assembled sequences covered the reference genome of these two distinct parental lines, respectively. Analysis of assembled genomic data revealed total 2,761,801 SNPs and 7741 CAPS locus. Overall, 410 novels CAPS molecular markers were assembled along with 4-restriction endonuclease and 160 CAPS pairs displayed polymorphic bands of predictable length after screening by the use of PCR and process of enzyme digestion, with 39% rate of polymorphism. Genetic linkage map construction was done by genotyping of these novel CAPS markers within F 2:3 population and this map totally spanned 2848.68 cM length with 12 chromosomes and 160 CAPS markers. Total 24 minor QTLs were discovered for fruit weight, firmness, flesh firmness, Brix, fruit length, width and shape with different genetic loci on various chromosomal locations. These newly assembled CAPS markers will be valuable and potent for future breeding and genetic schemes in melon. [ABSTRACT FROM AUTHOR]

Advanced backcross QTL analysis is proposed as a method of combining QTL analysis with variety development. It is tailored for the discovery and transfer of valuable QTL alleles from unadapted donor lines (e.g., land races, wild species) into established elite inbred lines. Following this strategy, QTL analysis is delayed until the BC2 or BC3 generation and, during the development of these populations, negative selection is exercised to reduce the frequency of deleterious donor alleles. Simulations suggest that advanced backcross QTL analysis will be effective in detecting additive, dominant, partially dominant, or overdominant QTLs. Epistatic QTLs or QTLs with gene actions ranging from recessive to additive will be detected with less power than in selfing generations. QTL-NILs can be derived from advanced backcross populations in one or two additional generations and utilized to verify QTL activity. These same QTL-NILs also represent commercial inbreds improved (over the original recurrent inbred line) for one or more quantitative traits. The time lapse from QTL discovery to construction and testing of improved QTL-NILs is minimal (1-2 years). If successfully employed, advanced backcross QTL analysis can open the door to exploiting unadapted and exotic germplasm for the quantitative trait improvement of a number of crop plants. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) is a Flavivirus responsible for recent epidemics in Pacific Islands and in the Americas. In humans, the consequences of ZIKV infection range from asymptomatic infection to severe neurological disease such as Guillain-Barré syndrome or fetal neurodevelopmental defects, suggesting, among other factors, the influence of host genetic variants. We previously reported similar diverse outcomes of ZIKV infection in mice of the Collaborative Cross (CC), a collection of inbred strains with large genetic diversity. CC071/TauUnc (CC071) was the most susceptible CC strain with severe symptoms and lethality. Notably, CC071 has been recently reported to be also susceptible to other flaviviruses including dengue virus, Powassan virus, West Nile virus, and to Rift Valley fever virus. To identify the genetic origin of this broad susceptibility, we investigated ZIKV replication in mouse embryonic fibroblasts (MEFs) from CC071 and two resistant strains. CC071 showed uncontrolled ZIKV replication associated with delayed induction of type-I interferons (IFN-I). Genetic analysis identified a mutation in the Irf3 gene specific to the CC071 strain which prevents the protein phosphorylation required to activate interferon beta transcription. We demonstrated that this mutation induces the same defective IFN-I response and uncontrolled viral replication in MEFs as an Irf3 knock-out allele. By contrast, we also showed that Irf3 deficiency did not induce the high plasma viral load and clinical severity observed in CC071 mice and that susceptibility alleles at other genes, not associated with the IFN-I response, are required. Our results provide new insight into the in vitro and in vivo roles of Irf3, and into the genetic complexity of host responses to flaviviruses. Author summary: Recent ZIKV outbreaks led to millions of infected people, with rare but severe complications such as Guillain-Barré syndrome and encephalitis in adults suggesting that host genes influence the susceptibility to severe forms of infection. We previously reported the importance of host genes in ZIKV pathogenesis using a panel of genetically diverse mouse strains and identified CC071 as the most susceptible strain. Importantly, this mouse strain has been shown by others to be also susceptible to several other RNA viruses. Through a combination of functional and genetic approaches in a cellular model, we identified a mutation in the Irf3 gene which plays a key role in activating the expression of interferon beta to induce the type I interferon response, the first line of host defense against the virus. This mutation fully explains the high viral replication observed in CC071 cells. However, it was not able to induce the elevated viremia and the disease signs displayed by CC071 ZIKV-infected mice, unraveling the implication of other host genes which are not associated with the type I interferon response. Because of the broad susceptibility of CC071 to multiple viruses, our results have implications beyond ZIKV infection and contribute to shedding light on the plurality of host mechanisms fighting infectious diseases. [ABSTRACT FROM AUTHOR]

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. Author summary: Genome-wide association studies have identified many non-coding loci associated with complex traits and diseases. While these variants are primarily expected to affect gene regulation, identifying the target genes as well as the tissue and cell type contexts where these variants are active has remained challenging. We have previously developed the eQTL Catalogue resource to systematically curate and reprocess all available RNA sequencing and genotype datasets. Here we present several updates to the resources that increase its utility for interpreting complex trait associations even further. In addition to increasing both the number of studies and datasets covered, we have also implemented statistical fine mapping to improve our ability to link multiple independent genetic variants influencing gene regulation with complex traits and diseases. We demonstrate the utility of these updates by focussing on interpreting genetic variants associated with a single well-characterised complex trait—circulating levels of vitamin D in human plasma. We believe that our publicly available analysis results will greatly facilitate the interpretation of complex trait associations identified by other large-scale human genetics efforts. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) is one the most globally destructive fungal diseases in wheat and other small grains, causing a reduction in grain yield by 10--70%. The present study was conducted in a panel of historical and modern Canadian spring wheat (Triticum aestivum L.) varieties and lines to identify new sources of FHB resistance and map associated quantitative trait loci (QTLs). We evaluated 249 varieties and lines for reaction to disease incidence, severity, and visual rating index (VRI) in seven environments by artificially spraying a mixture of four Fusarium graminearum isolates. A subset of 198 them were genotyped with the Wheat 90K iSelect single nucleotide polymorphisms (SNPs) array. Genome-wide association mapping performed on the overall best linear unbiased estimators (BLUE) computed from all seven environments and the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map of 26,449 polymorphic SNPs out of the 90K identified sixteen FHB resistance QTLs that individually accounted for 5.7--10.2% of the phenotypic variance. The positions of two of the FHB resistance QTLs overlapped with plant height and flowering time QTLs. Four of the QTLs (QFhb.dms-3B.1, QFhb.dms-5A.5, QFhb.dms-5A.7, and QFhb.dms-6A.4) were simultaneously associated with disease incidence, severity, and VRI, which accounted for 27.0--33.2% of the total phenotypic variance in the combined environments. Three of the QTLs (QFhb.dms-2A.2, QFhb.dms-2D.2, and QFhb.dms-5B.8) were associated with both incidence and VRI and accounted for 20.5--22.1% of the total phenotypic variance. In comparison with the VRI of the checks, we identified four highly resistant and thirty-three moderately resistant lines and varieties. The new FHB sources of resistance and the physical map of the associated QTLs would provide wheat breeders valuable information towards their efforts in developing improved varieties in western Canada. [ABSTRACT FROM AUTHOR]

Rose (Rosa spp.) is one of the most economically important ornamental species worldwide. Flower diameter, flower weight, and the number of petals and petaloids are key flower-size parameters and attractive targets for DNA-informed breeding. Pedigree-based analysis (PBA) using FlexQTL software was conducted using two sets of multi-parental diploid rose populations. Phenotypic data for flower diameter (Diam), flower weight (fresh (FWT)/dry (DWT)), number of petals (NP), and number of petaloids (PD) were collected over six environments (seasons) at two locations in Texas. The objectives of this study were to 1) identify new and/or validate previously reported QTL(s); 2) identify SNP haplotypes associated with QTL alleles (Q-/q-) of a trait and their sources; and 3) determine QTL genotypes for important rose breeding parents. Several new and previously reported QTLs for NP and Diam traits were identified. In addition, QTLs associated with flower weight and PD were identified for the first time. Two major QTLs with large effects were mapped for all traits. The first QTL was at the distal end of LG1 (60.44-60.95 Mbp) and was associated with Diam and DWT in the TX2WOB populations. The second QTL was consistently mapped in the middle region on LG3 (30.15-39.34 Mbp) and associated with NP, PD, and flower weight across two multi-parent populations (TX2WOB and TX2WSE). Haplotype results revealed a series of QTL alleles with differing effects at important loci for most traits. This work is distinct from previous studies by conducting co-factor analysis to account for the DOUBLE FLOWER locus while mapping QTL for NP. Sources of high-value (Q) alleles were identified, namely, 'Old Blush' and Rosa wichuraiana from J14-3 for Diam, while 'Violette' and PP-J14-3 were sources for other traits. In addition, the source of the low-value (q) alleles for Diam was 'Little Chief', and Rosa wichuraiana through J14-3 was the source for the remaining traits. Hence, our results can potentially inform parental/seedling selections as means to improve ornamental quality in roses and a step towards implementing DNA-informed techniques for use in rose breeding programs. [ABSTRACT FROM AUTHOR]

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue. Many genetic variants have been associated with human traits, but the mechanism is often unknown. Here, the authors integrate local and distal genetic associations with multi-omics datasets to provide a roadmap to understand the underlying mechanisms of GWAS variants on complex traits. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits. Here the authors combine an experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes to provide a comprehensive map of Atrial Fibrillation risk variants and genes. [ABSTRACT FROM AUTHOR]

Background: Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. Results: We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. Conclusions: By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis. [ABSTRACT FROM AUTHOR]

Inflammation in response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection drives severity of coronavirus disease 2019 (COVID-19) and is influenced by host genetics. To understand mechanisms of inflammation, animal models that reflect genetic diversity and clinical outcomes observed in humans are needed. We report a mouse panel comprising the genetically diverse Collaborative Cross (CC) founder strains crossed to human ACE2 transgenic mice (K18-hACE2) that confers susceptibility to SARS-CoV-2. Infection of CC x K18-hACE2 resulted in a spectrum of survival, viral replication kinetics, and immune profiles. Importantly, in contrast to the K18-hACE2 model, early type I interferon (IFN-I) and regulated proinflammatory responses were required for control of SARS-CoV-2 replication in PWK x K18-hACE2 mice that were highly resistant to disease. Thus, virus dynamics and inflammation observed in COVID-19 can be modeled in diverse mouse strains that provide a genetically tractable platform for understanding anti-coronavirus immunity. Dynamics of type I interferon (IFN) following infection with SARS-CoV-2 are critical in determining disease severity in humans but have been difficult to model in mice. Here, infection of genetically diverse mice reveals how delayed or immediate IFN signaling coordinates antiviral immunity. [ABSTRACT FROM AUTHOR]

Background: Identifying true positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of genomic information may give insights into the optimal number of individuals to be used in GWA. This study investigated different discovery set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). In addition, we investigated the impact on the prediction accuracy by adding variants, which were selected based on different set sizes, to the regular single nucleotide polymorphism (SNP) chips used for genomic prediction. Methods: We simulated sequence data that included 500k SNPs with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one in every ten simulated SNPs. Effective population size (Ne) was set to 20 or 200. GWA were performed using a number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and large-effect size SNPs were added to the 50k panel and used for single-step genomic best linear unbiased prediction (ssGBLUP). Results: Using a number of genotyped animals corresponding to at least EIG98 allowed the identification of QTN with the largest effect sizes when Ne was large. Populations with smaller Ne required more than EIG98. Furthermore, including genotyped animals with a higher reliability (i.e., a higher trait heritability) improved the identification of the most informative QTN. Prediction accuracy was highest when the significant or the large-effect SNPs representing twice the number of simulated QTN were added to the 50k panel. Conclusions: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits. [ABSTRACT FROM AUTHOR]

In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist's vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist's evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level. [ABSTRACT FROM AUTHOR]

Guava (Psidium guajava L.) is an important fruit crop of the Indian sub-continent, with potential for improvements in quality and yield. The goal of the present study was to construct a genetic linkage map in an intraspecific cross between the elite cultivar 'Allahabad Safeda' and the Purple Guava landrace to identify the genomic regions responsible for important fruit quality traits, viz., total soluble solids, titratable acidity, vitamin C, and sugars. This population was phenotyped in field trials (as a winter crop) for three consecutive years, and showed moderateto- high values of heterogeneity coefficients along with higher heritability (60.0%-97.0%) and genetic-advance-over-mean values (13.23%-31.17%), suggesting minimal environmental influence on the expression of fruit-quality traits and indicating that these traits can be improved by phenotypic selection methods. Significant correlations and strong associations were also detected among fruit physico-chemical traits in segregating progeny. The constructed linkage map consisted of 195 markers distributed across 11 chromosomes, spanning a length of 1,604.47 cM (average inter-loci distance of 8.80 markers) and with 88.00% coverage of the guava genome. Fifty-eight quantitative trait loci (QTLs) were detected in three environments with best linear unbiased prediction (BLUP) values using the composite interval mapping algorithm of the BIP (biparental populations) module. The QTLs were distributed on seven different chromosomes, explaining 10.95%-17.77% of phenotypic variance, with the highest LOD score being 5.96 for qTSS.AS.pau-6.2. Thirteen QTLs detected across multiple environments with BLUPs indicate stability and utility in a future breeding program for guava. Furthermore, seven QTL clusters with stable or common individual QTLs affecting two or more different traits were located on six linkage groups (LGs), explaining the correlation among fruit-quality traits. Thus, the multiple environmental evaluations conducted here have increased our understanding of the molecular basis of phenotypic variation, providing the basis for future high-resolution fine-mapping and paving the way for marker-assisted breeding of fruit-quality traits. [ABSTRACT FROM AUTHOR]

Hundreds of quantitative trait loci (QTLs) have been reported in diverse types of hexaploid wheat (Triticum aestivum L.) populations, but direct comparisons of QTLs in different studies and populations are still challenging due to the lack of physical positions for most QTLs. Here, we used the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map of all markers to map QTLs associated with leaf spot (Ls), leaf rust (Lr), stripe rust (Yr), and common bunt (Cbt) resistance in two recombinant inbred line populations. QTL mapping was conducted using the IWGSC physical map of 3158 and 5732 markers and disease severity data of Peace/Carberry and Attila/CDC Go populations evaluated in three to eight environments. We uncovered a total of 82 QTLs associated with Yr (36), Ls (18), Lr (15), and Cbt (13) resistance in the individual and overall means of all combined environments. Among them, 29 were associated with all combined environments, which accounted for 0.5%–20.9% individually and 12.4%–41.2% of the total disease severity per trait. Three (QLr.dms‐2D.2, QLs.dms‐5B, and QYr.dms‐5B.2) of the 29 QTLs were common in both populations. Fourteen out of the 29 QTLs were stable as they were identified both in the overall means and most of the individual environments. Ten chromosome arms harbored a cluster of QTLs associated with resistance to two to four diseases. This methodology would serve as one of the resources to compare QTLs identified in different populations and studies based on the improved physical information of all markers instead of population‐specific and consensus linkage maps. Core Ideas: The methodology described in this study would serve as a basis for easily comparing QTLs in different populations and independent studies using the physical positions of all markers.This study uncovered 29 QTLs associated with the overall mean disease severity of four wheat diseases in two populations.The number of QTLs identified in Peace/Carberry population was over twofold more than Attila/CDC Go population.The total phenotypic variation explained in Peace/Carberry was two‐ to threefold more than Attila/CDC Go.Ten chromosomes harbored a cluster of QTLs associated with resistance for two to four diseases. [ABSTRACT FROM AUTHOR]

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand. [ABSTRACT FROM AUTHOR]