Single Nucleotide Polymorphism Data Research Articles

Phenotypic simulation for fruit-related traits in F1 progenies of chili peppers (Capsicum annuum) using genomic prediction based solely on parental information.

Chili pepper (Capsicum spp.) fruits are used as vegetables, spices, and ornamental plants, necessitating various fruit characteristics. However, their genetic improvement is challenging through conventional crossbreeding due to the quantitative traits, which makes it difficult to predict phenotypes in the progeny. As a breakthrough, we focused on phenotypic simulation via genomic prediction (GP) and aimed to clarify its utility for fruit-related traits in chili peppers. The present study used 291 C. annuum accessions, including two populations: inbred lines and F1 accessions derived from 20 inbred parents. We collected data of fruit length, width, shape index (length/width), weight, and pericarp thickness, and obtained single nucleotide polymorphism data via multiplexed inter-simple sequence repeat genotyping by sequencing. We simulated the fruit-related traits in the F1 accessions by inputting their estimated genotypes (based on their parents) into the GP model using the GBLUP-GAUSS model, which was shown to be the most accurate regardless of population or trait differences in the present study. As a result, we observed strong positive correlations (r = 0.833-0.908) between the simulated and observed phenotypic values across all traits, suggesting that accurate ranking of F1 progenies based on fruit-related traits can be achieved using parental information. This is the first report demonstrating the utility of phenotypic simulation via GP in chili pepper breeding, offering valuable insights for its application in this field.

Molecular and Biochemical Mechanisms of Scutellum Color Variation in Bactrocera dorsalis Adults (Diptera: Tephritidae).

Bactrocera dorsalis (Hendel) is an invasive fruit and vegetable pest, infesting citrus, mango, carambola, etc. We observed that the posterior thoracic scutella of some B. dorsalis adults are yellow, some light yellow, and some white in China. Compared with the B. dorsalis races with a yellow scutellum (YS) and white scutellum (WS), the race with a light-yellow scutellum (LYS) is dominant in citrus and carambola orchards. To reveal genetic correlates among the three races, the genomes of 22 samples (8 with YS, 7 with LYS, and 7 with WS) were sequenced by high-throughput sequencing technology. Single-nucleotide polymorphism (SNP) annotation showed that there were 17,580 non-synonymous mutation sites located in the exonic region. Principal component analysis based on independent SNP data revealed that the SNPs with LYS were more similar to that with YS when compared with WS. Most genes associated with scutellum color variation were involved in three pathways: oxidative phosphorylation, porphyrin and chlorophyll metabolism, and terpenoid backbone biosynthesis. By comparing the sequences among the three races, we screened out 276 differential genes (DGs) in YS vs. WS, 185 DGs in LYS vs. WS, and 104 DGs in YS vs. LYS. Most genes determining color variation in B. dorsalis scutella were located on chromosomes 2-5. Biochemical analysis showed that β-carotene content in YS and LYS was significantly higher than that in WS at any stage of adult days 1, 10, and 20. No significant differences were observed in cytochrome P450 or melanin content in YS, LYS, or WS. Our study provides results on aspects of scutellum color variation in B. dorsalis adults, providing molecular and physiological information for revealing the adaptation and evolution of the B. dorsalis population.

Introduction to Integrated Proteogenomic Pipeline for Dealing with Pathogenic Missense SNPs.

Proteogenomics is a multi-omics setup combining mass spectrometry and next-generation sequencing (NGS) technologies (using genomics and/or transcriptomics) with main aims of improving genome annotation and facilitating characterization of proteo-isoforms. However, working with proteogenomic approach is a very challenging task as it is generating multi-omics data and integrating these data for interpretation of results for biological or clinical implications. There is an urgent need for the development of protocols for integrated proteogenomics approaches. Genome resequencing yields massive data for missense single-nucleotide polymorphisms (SNP), and SNPs are yet not fully covered for their pathogenic nature using proteogenomic approaches. In this chapter, we present such a protocol for dealing with pathogenic missense SNPs using an integrated proteogenomics pipeline combining several steps: DNA-Seq, RNA-Seq, mass spectroscopy (MS), making customized databases of produced datasets, and screening and filtering for useful MS spectrums. This protocol also provides users with tricks and tips for the modifications, based on the requirements of the projects.

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more effective prediction methods based on artificial intelligence (AI) are needed to assist physicians in timely diagnosis. An explainable deep learning-based early Parkinson's disease diagnostic model, Parkinson's Integrative Diagnostic Gated Network (PIDGN), was designed by fusing Single Nucleotide Polymorphism (SNP) and brain sMRI data. Firstly, unimodal internal information was extracted using EmsembleTree dimensionality reduction method, Transformer encoder and 3D ResNet. Secondly, gated attention fusion technique was utilized to explore the inter-modal interactions. Finally, the classification results were output through the fully connected layer. SHapley additive interpretation (SHAP) values and Gradient-weighted Class Activation Mapping (Grad-CAM) techniques were used to help explain the importance of SNPs and brain regions for PD. The results showed that the PIDGN model achieved the best results with the accuracy of 0.858 and AUROC of 0.897. Top 20 SNPs and the brain regions near the midbrain potentially related to PD were identified using two explainable techniques via SHAP values and Grad-CAM respectively. The PIDGN model trained by fusing genetic and imaging data outperforms 13 other commonly used unimodal or bimodal models. Explainable PIDGN model helps deepen understanding of several SNPs and sMRI key factors that may affect PD. This study provides a potentially effective solution for automated early diagnosis of PD using AI.

Chromosome 1 QTLs associated with response to bacterial leaf spot in Beta vulgaris

AbstractBacterial leaf spot (BLS), caused by Pseudomonas syringae pathovar aptata (Psa), is a seedborne, foliar disease affecting members of the Amaranthaceae and Cucurbitaceae families, including table beet and Swiss chard crops. There is no known resistance to BLS in beet or chard. A diversity panel, modified from the Wisconsin Beta Diversity Panel (WBDP) and comprised of 219 accessions from the Beta vulgaris crop complex, was assembled and genotyped for single nucleotide polymorphism data. These accessions were screened by foliar inoculation of Psa and visually evaluated for percentage of diseased leaf tissue. Overall, sugar beet and Beta vulgaris subsp. maritima accessions had the lowest BLS response, whereas table beet accessions had the largest range of responses. Phenotypic means were adjusted using best linear unbiased estimates, and two different software programs, GWASpoly and GAPIT3, were utilized to conduct a genome‐wide association study (GWAS). Leaf color was found to be significantly associated with and correlated with BLS response scores, and was used as a covariate in GWAS analysis. An association with BLS response was detected on chromosome 1 in the full WBDP, explaining upward of 21% of the variation in the phenotype. The marker associated with this quantitative trait locus (QTL), Chr1_61344476, showed an additive relationship between dosage and BLS response. Eleven candidate genes, described and annotated in sugar beet, were associated with this QTL. Some of these include F Box domains, RNA‐binding proteins, and calcium‐dependent kinases, all of which have roles in plant defense responses. Marker Chr1_61344476 may be useful in breeding for BLS resistance in members of the Beta vulgaris crop complex.

Development and validation of a nomogram model based on blood-based genomic mutation signature for predicting the risk of brain metastases in non-small cell lung cancer

PurposeAvailable research indicates that the mammalian target of rapamycin complex 1 (mTORC1) signaling pathway is significantly correlated with lung cancer brain metastasis (BM). This study established a clinical predictive model for assessing the risk of BM based on the mTORC1-related single nucleotide polymorphisms (SNPs).MethodsIn this single-center retrospective study, 395 patients with non-small cell lung cancer were included. Clinical, pathological, imaging, and mTORC1-related single nucleotide polymorphism data were collected. Lasso regression was used to identify variables related to the risk of BM in lung cancer, and a nomogram was constructed. Internal validation was performed using 1,000 bootstrap samples. We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). The calibration of the model was assessed using calibration curves and the Hosmer–Lemeshow goodness-of-fit test, and decision curve analysis (DCA) was plotted to evaluate the net clinical benefit.ResultsThe nomogram's predictive factors included lung cancer histology, clinical N stage, CEA, neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), RPTOR: rs1062935, and RPTOR: rs3751934. The AUC of the model in the training set and internal validation were 0.849 and 0.801, respectively. The calibration curves and Hosmer–Lemeshow test both indicated a good fit.ConclusionThe nomogram has practicality and efficacy in predicting the high risk of BM in lung cancer patients, confirming that single nucleotide polymorphisms in the mTORC1 pathway genes may be good predictors in clinical prediction models.

Evaluating Genomic Selection in beef cattle: Insights from computer simulations using real SNP data

Genomic Selection (GS) is a method that employs genomic data to estimate breeding values and rank candidates for selection. Despite its numerous advantages, its application in cattle breeding programs remains in the early stages in many livestock systems developed in tropical and subtropical environments, such as those in Paraguay. Computational simulations are powerful tools that enhance our understanding of GS applications in different scenarios and are invaluable as an initial step before implementing this technique in "real" genetic improvement programs. In this study, real data from single nucleotide polymorphisms (SNPs) of the Indicus and Taurus breeds were employed to simulate three crossing schemes: F1 crosses, grading up, and rotational crosses. Phenotypes were selected for traits related to shear force, growth, and tolerance. The predictive accuracy of three 50k SNP chips, differing in their SNP selection methodologies, was compared: random selection, selection based on minimum allele frequency differences between breeds, and selection based on minimum allele frequency differences between breeds with a threshold of 0.09 in Taurus. The findings indicate that rotational crossing demonstrates optimal predictive accuracy (0.38), while marker selection based on allele frequency differences between breeds (0.18 and 0.17, respectively) does not benefit predictions significantly.

Breaking the Hybrid Myth of Paphiopedilum wenshanense: Double Bifurcated Divergence Followed by Adaptive Introgression Formed a Morphological Intermediate.

Species with intermediate phenotypes are usually hypothesised to have originated from hybrid speciation. However, other mechanisms, such as double bifurcated speciation with introgression may create the same phenotypes. Clarifying the underlying process generating the observed intermediate morphology is important for conservation efforts, as it provides insights into the species' evolutionary background and adaptability. Here, we utilised genome-wide single nucleotide polymorphism data to test evolutionary history models for Paphiopedilum wenshanense Z. J. Liu & J. Yong Zhang, an orchid species that is widely considered to be a hybrid with intermediate morphology of other two closely related species, P. concolor Pfitzer and P. bellatulum Pfitzer, distributed in Indochina. Our results rejected the hybrid speciation hypothesis and ascribed the intermediate floral phenotypes of P. wenshanese to introgression from P. concolor after it diverged from P. bellatulum during the last glacial maximum. Excluding the geographic covariate, the current genetic mixing rate of P. wenshanense from P. concolor is stronger in the south part of its range and is associated with precipitation in the early summer, indicating that population divergence in P. wenshanense is being driven by adaptive introgression. These genetic patterns also correspond to the observed floral variation across P. wenshaense populations. The introgression from P. concolor, along with the intermediate and varying floral morphologies, may expand the ecological opportunities for P. wenshanense, providing an explanation for why high genetic diversity has been maintained despite a high level of inbreeding and small census population size. Our study provides a framework for future research to investigate the characteristics and underlying mechanisms of intermediate morphologies in general.

Forward selection models for classifying mild cognitive impairment and Alzheimer’s disease based on single nucleotide polymorphisms

Early detection of Alzheimer’s disease (AD) is crucial for patients to begin treatment early to slow the disease’s progression. While mild cognitive impairment (MCI) is considered an early translational stage of AD, clinically diagnosing MCI is difficult due to its inconsistent symptoms and the lack of standardized diagnostic tests. In this work, we proposed forward selection models to classify patients with AD, patients with MCI and healthy controls (HCs) based on single nucleotide polymorphisms (SNPs). In the proposed method, the initial SNP data were prescreened via genome-wide association studies with a suggestive significance threshold. Then, the qualified SNPs were reselected using the forward SNP selection algorithm to create classification models. Consequently, the forward selection models significantly outperformed the preselection models, those based on all prescreened SNPs, with an area under the precision-recall curve (AUPRC) value of 0.93 in the AD-HC classification, an AUPRC value of 0.94 in the MCI-HC classification, and an AUPRC value of 0.81 in the AD-MCI classification. Moreover, the proposed method could identify AD-associated and MCI-associated SNPs, which would support the clinical diagnosis of AD and MCI in the future.

KASP Markers for Identifying Roselle (Hibiscus sabdariffa L.) Key Varieties Based on Genetic Polymorphisms Revealed by ddRAD-Seq

Variety characterization is crucial in the seed trade, particularly for protecting variety rights. However, the identification of roselle (Hibiscus sabdariffa L.) varieties, known for their beneficial effects on human health and high processing potential, has traditionally relied on morphological traits due to limited genetic information. To investigate genetic polymorphisms of roselle germplasms and to develop breeder-accessible genotyping tools, this study first phenotyped a roselle collection from diverse geographical origins for the selection of core varieties, and then utilized double-digest restriction-associated DNA sequencing (ddRAD-seq) to identify 53,746 single nucleotide polymorphisms (SNPs) across 17 core varieties. Cluster analysis of the SNP data effectively grouped varieties with similar genetic backgrounds. From this genetic information, we selected nine SNPs as a toolkit to simplify core variety discrimination. These SNPs were then converted into breeder-friendly kompetitive allele-specific PCR (KASP) markers, facilitating the classification of an additional 54 roselle accessions. In conclusion, this research contributes novel insights into the genetic relationships among roselle varieties, and establishes a robust framework utilizing ddRAD-seq and KASP markers for improved genetic resource identification and application in breeding programs.

Association between hypothyroidism and obstructive sleep apnea: a bidirectional Mendelian randomization study combined with the geo database

BackgroundThe causal relationship between hypothyroidism and obstructive sleep apnea (OSA) remains controversial. Therefore, our research used a bidirectional Mendelian randomization (MR) method in an attempt to determine the causal relationship between hypothyroidism and OSA.MethodsFrom the publicly accessible genome-wide association analysis (GWAS) summary database, we obtained single nucleotide polymorphism (SNPs) data pertaining to hypothyroidism and OSA. Inverse variance weighting (IVW) was the principal method of analysis utilized, with validation also conducted via weighted median, MR-Egger, simple model, and weighted model approaches. To further evaluate the robustness of the results, heterogeneity testing, pleiotropy testing, and the “leave-one-out” sensitivity analysis were performed. Differentially expressed genes (DEGs) from the OSA dataset (GSE135917) and hypothyroidism dataset (GSE176153) derived from the Gene Expression Omnibus (GEO) database were screened using the “limma” package. The “clusterProfiler” and “GO plot” packages were used for further enrichment analysis in order to validate the findings of the MR study. The Cytoscape software was utilized to build a protein–protein interaction (PPI) network of DEGs and to screen for hub genes.ResultsThe MR analysis showed that genetically predicted hypothyroidism was associated with an increased risk of OSA [IVW odds ratio (OR) = 1.734; 95% confidence interval (CI) = 1.073–2.801; p = 0.025]. The trend of the outcomes of the other approaches is consistent with the trend of the IVW outcome. However, the reverse MR analysis suggested no evidence for the causal effect of OSA on hypothyroidism (IVW OR = 1.002, 95% CI: 0.996–1.009, p = 0.454). The robustness of the results was confirmed by the sensitivity analysis. Bioinformatics analysis revealed that there were DEGs that hypothyroidism and OSA have in common.ConclusionOur findings suggested that hypothyroidism may increase the risk of OSA, while the effect of OSA on hypothyroidism was not found in this MR study. Thus, patients with hypothyroidism should be enhanced with screening for OSA for early diagnosis and appropriate treatment.

Endangered Pinto/Northern Abalone (Haliotis kamtschatkana) are Panmictic Across Their 3700 km Range Along the Pacific Coast of North America.

Connectivity is integral to the dynamics of metapopulations through dispersal and gene flow, and understanding these processes is essential for guiding conservation efforts. Abalone, broadcast-spawning marine snails associated with shallow rocky habitats, have experienced widespread declines, and all seven North American species are threatened. We investigated the connectivity and population genomics of pinto/northern abalone (Haliotis kamtschatkana), the widest-ranging of abalone species. We employed reduced representation sequencing (RADseq) to generate single nucleotide polymorphism (SNP) data, assessing population connectivity and potential adaptive variation at 12 locations across the full range from Alaska to Mexico. Despite depleted populations, our analysis of over 6000 SNPs across nearly 300 individuals revealed that pinto abalone maintains a high genetic diversity with no evidence of a genetic bottleneck. Neutral population structure and isolation by distance were extremely weak, indicating panmixia across the species' range (global F ST = 0.0021). Phylogenetic analysis, principal components analysis, and unsupervised clustering methods all supported a single genetic population. However, slight population differentiation was noted in the Salish Sea and Inside Passage regions, with evidence for higher barriers to dispersal relative to outer coastal areas. This north-central region may also represent the species' ancestral range based on relatively low population-specific F ST values; the northern and southern extremes of the range likely represent range expansions. Outlier analysis did not identify consensus loci implicated in adaptive variation, suggesting limited adaptive differentiation. Our study sheds light on the evolutionary history and contemporary gene flow of this threatened species, providing key insights for conservation strategies, particularly in sourcing broodstock for ongoing restoration efforts.

Extensive cytonuclear discordance revealed by phylogenomic analyses suggests complex evolutionary history in the holly genus Ilex (Aquifoliaceae)

Ilex L., the exclusive genus of Aquifoliaceae, encompasses over 600 dioecious wood species with a highly irregular distribution, predominantly found in South America and Asia. The phylogeny and classification of this genus remain enigmatic due to significant early extinctions, constrained morphological diversity, recent hybridization/introgression, and conflicting signals from previously utilized markers. This study presents phylogenetic reconstructions based on complete chloroplast genome sequences and single nucleotide polymorphisms (SNPs) derived from genome resequencing data. A total of 116 accessions of Ilex, representing approximately 108 taxa, were included as the ingroup, with five accessions of two species serving as outgroups. Analysis of the chloroplast genome and nuclear SNP data individually resulted in two robust phylogenetic trees, revealing substantial discrepancies between the chloroplast genome and nuclear SNP phylogenies at both the species and clade levels. The chloroplast genome sequences successfully resolved relationships within this genus into eight strongly supported major clades, while the nuclear SNPs resolved relationships into seven highly supported major clades. Our nuclear SNP phylogenetic tree, in comparison to the chloroplast genome tree, aligns more closely with the recently updated classification of Ilex in multiple instances. The extensive cytonuclear discordance identified may be attributed to recent hybridization events and incomplete lineage sorting (ILS).

Populations of Latvia and Lithuania in the context of some Indo-European and non-Indo-European speaking populations of Europe and India: insights from genetic structure analysis.

The aim of this study was to investigate the relationship among Lithuanian, Latvian, Indian, and some other populations through a genome-wide data analysis of single nucleotide polymorphisms (SNPs). Limited data of Baltic populations were mostly compared with geographically closer modern and ancient populations in the past, but no previous investigation has explored their genetic relationships with distant populations, like the ones of India, in detail. To address this, we collected and merged genome-wide SNP data from diverse publicly available sources to create a comprehensive dataset with a substantial sample size especially from Lithuanians and Latvians. Principal component analysis (PCA) and admixture analysis methods were employed to assess the genetic structure and relationship among the populations under investigation. Additionally, we estimated an effective population size (Ne) and divergence time to shed light on potential past events between the Baltic and Indian populations. To gain a broader perspective, we also incorporated ancient and modern populations from different continents into our analyses. Our findings revealed that the Balts, unsurprisingly, have a closer genetic affinity with individuals from Indian population who speak Indo-European languages, compared to other Indian linguistic groups (such as speakers of Dravidian, Austroasiatic, and Sino-Tibetan languages). However, when compared to other populations from the European continent, which also speak Indo-European and some Uralic languages, the Balts did not exhibit a stronger resemblance to Indo-European-speaking Indians. In conclusion, this study provides an overview of the genetic relationship and structure of the populations investigated, along with insights into their divergence times.

Strong Small-Scale Differentiation but No Cryptic Species Within the Two Isopod Species Asellus aquaticus and Proasellus coxalis in a Restored Urban River System (Emscher, Germany).

Worldwide, humans have strongly altered river networks. Key changes resulted in modified hydromorphology, poor habitat quality and availability, migration barriers, and pollution. Restoration measures aim at mitigating anthropogenic stressors and at restoring connectivity, but the biological success of these measures is not guaranteed. Analyzing genetic diversity and metapopulation structure of target species in the river network with genetic markers can help to understand recolonization processes and to identify persisting gene flow barriers. Here, we studied the population genetic structure of the two pollution-tolerant detritivorous isopod species, Asellus aquaticus and Proasellus coxalis, in the former heavily degraded and polluted, but now mostly restored Emscher catchment in Germany. For both species, we analyzed mitochondrial cytochrome c oxidase I (COI) gene sequences and nuclear genome-wide single nucleotide polymorphism (SNP) data. Surprisingly, we found a strong metapopulation structure for both species with several isolated populations on a small-scale of few kilometers, but a still high genetic diversity, especially in the COI gene. For both taxa, potentially cryptic species are known, but our SNP data showed that the mitochondrial lineages represent only one species, each, in the study area. This highlights the importance of integrating high-resolution nuclear markers into species identification because species diversity may otherwise be overestimated. While we could identify some migration barriers and find indications for passive dispersal by birds or humans, these factors could not fully explain the local metapopulation structure, suggesting that also other drivers, such as isolation by adaptation, priority effects, or biotic interactions, play a role in shaping the population genetic structure.

CE-GAN: Community Evolutionary Generative Adversarial Network for Alzheimer's Disease Risk Prediction.

In the studies of neurodegenerative diseases such as Alzheimer's Disease (AD), researchers often focus on the associations among multi-omics pathogeny based on imaging genetics data. However, current studies overlook the communities in brain networks, leading to inaccurate models of disease development. This paper explores the developmental patterns of AD from the perspective of community evolution. We first establish a mathematical model to describe functional degeneration in the brain as the community evolution driven by entropy information propagation. Next, we propose an interpretable Community Evolutionary Generative Adversarial Network (CE-GAN) to predict disease risk. In the generator of CE-GAN, community evolutionary convolutions are designed to capture the evolutionary patterns of AD. The experiments are conducted using functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data. CE-GAN achieves 91.67% accuracy and 91.83% area under curve (AUC) in AD risk prediction tasks, surpassing advanced methods on the same dataset. In addition, we validated the effectiveness of CE-GAN for pathogeny extraction. The source code of this work is available at https://github.com/fmri123456/CE-GAN.

Genomic regions associated with embryonic loss in primiparous Holstein cows

IntroductionReproductive efficiency in dairy cattle bred by artificial insemination (AI) or via embryo transfer (ET) is important as a means of facilitating the rate of genetic improvement by utilizing superior genetics from sires and donor dams. Fertility is especially challenging in primiparous cows as they have recently calved, are still growing, and are lactating. Fertility, measured by the conception rate from the first service (CCR1), is the number of cows that become pregnant as a result of the first AI or ET compared to all cows serviced, whereas times bred (TBRD) is the number of inseminations cows bred by AI needed to achieve a successful pregnancy.MethodsThis study aimed to identify the loci associated with and gene sets enriched for the CCR1 in primiparous cows that were bred by AI (n = 882) or were ET recipients (n = 353), and to identify the loci associated with the TBRD (n= 1,851) in primiparous cows. A genome-wide association analysis and gene set enrichment analysis were performed using single-nucleotide polymorphism (SNP) data for the CCR1 and TBRD in primiparous cows. In cows bred by AI, two loci containing five positional candidate genes were associated with the CCR1 (p < 1 × 10−5) and one gene set with 18 leading-edge genes was enriched (NES ≥3) for the CCR1.Results and discussionIn cows that were ET recipients, five loci were associated with the CCR1, but no gene sets were enriched for the CCR1. For the second phenotype, five loci with nine positional candidate genes were associated with the TBRD and two gene sets with eight leading-edge genes were enriched for the TBRD. Identifying the loci associated with or gene sets enriched for the CCR1 and TBRD provides an opportunity to better understand embryonic loss in AI-bred and ET-recipient cows and to use this information to select for higher reproductive efficiency.

Genomic selection in forest trees comes to life: unraveling its potential in an advanced four-generation Eucalyptus grandis population.

Genomic Selection (GS) in tree breeding optimizes genetic gains by leveraging genomic data to enable early selection of seedlings without phenotypic data reducing breeding cycle and increasing selection intensity. Traditional assessments of the potential of GS in forest trees have typically focused on model performance using cross-validation within the same generation but evaluating effectively realized predictive ability (RPA) across generations is crucial. This study estimated RPAs for volume growth (VOL), wood density (WD), and pulp yield (PY) across four generations breeding of Eucalyptus grandis. The training set spanned three generations, including 34,461 trees with three-year growth data, 6,014 trees with wood quality trait data, and 1,918 trees with 12,695 SNPs (single nucleotide polymorphisms) data. Employing single-step genomic BLUP, we compared the genomic predictions of breeding values (GEBVs) for 1,153 fourth-generation full-sib seedlings in the greenhouse with their later-collected phenotypic estimated breeding values (EBVs) at age three years. RPAs were estimated using three GS targets (individual trees, trees within families, and families), two selection criteria (single- and multiple-trait), and training populations of either all 1,918 genotyped trees or the 67 direct ancestors of the selection candidates. RPAs were higher for wood quality traits (0.33 to 0.59) compared to VOL (0.14 to 0.19) and improved for wood traits (0.42 to 0.75) but not for VOL when trained only with direct ancestors, highlighting the challenges in accurately predicting growth traits. GS was more effective at excluding bottom-ranked candidates than selecting top-ranked ones. The between-family GS approach outperformed individual-tree selection for VOL (0.11 to 0.16) and PY (0.72 to 0.75), but not for WD (0.43 vs. 0.42). Furthermore, higher levels of relatedness and lower genotype by environment (G × E) interaction between training and testing populations enhanced RPAs for VOL (0.39). In summary, despite limited effectiveness in ranking top VOL individuals, GS effectively identified low-performing individuals and families. These multi-generational findings underscore GS's potential in tree breeding, stressing the importance of considering relatedness and G × E interaction for optimal performance.

Protein quantitative trait loci identifies genetic biomarkers of proteins associated with measures of myocardial fibrosis in new-onset, treatment naive rheumatoid arthritis

Abstract Introduction The excess risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) compared to the general population is attributed to chronic inflammation. Identifying the underlying mechanisms and shared inflammatory pathways more precisely would enable tailored treatment. Purpose To identify genetic variants associated with the cardio-metabolic-inflammatory proteomic signature in patients with early, treatment-naïve RA and low CVD risk. Methods The Coronary Artery Disease Evaluation in RA (CADERA) study was a bolt-on to a RCT of patients with early, treatment-naïve RA and low CVD risk profile (maximum of 1 traditional CVD risk factor and no diabetes mellitus). Normalised expression of 334 proteins from 75 CADERA patients who underwent cardiovascular magnetic resonance imaging (CMR) was measured using the Olink Proximity Extension Immunoassay across Inflammation, Cardiovascular II, III and Cardiometabolic panels. Genome-wide single nucleotide polymorphism (SNP) data were generated using the Illumina Global Screening Array and was imputed and checked for data quality using standard approaches. Proteins associated with CMR parameters were identified using Bayesian mixed effects linear regression. Protein quantitative trait locus (pQTL) analysis was then performed using linear regression models adjusted for age, gender, and systolic blood pressure. Cis-pQTLs were defined as SNPs within 1Mb of the protein-encoding gene, and significance threshold was set at 1e-5. Due to sample size, trans-pQTLs were not considered in this analysis. Linkage disequilibrium (LD) clumping was performed using PLINK software to identify index SNPs defined as the most significant variant in LD (r2 <0.5) within a region of +/- 250Kb. Results Following quality control 318 proteins and 5,971,781 SNPs were available for analysis in 75 CADERA patients. The median age was 51 years [inter-quartile range (IQR) 40 - 61] with 52/75 (69%) females, 40/75 (53%) current/ex-smokers, and median body mass index (BMI) 26.4kg/m2 (IQR 23.3 - 28). Participants had a median RA symptom duration of 21.9 weeks (IQR 14.4 - 32.7). Only 8/75 (10%) reported a history of hypertension, hypercholesteroaemia or family history of CVD. 54 out of 318 proteins were associated with CMR measures of myocardial fibrosis and/or vascular stiffness. 460 cis-pQTLs were identified for 20 out of the 318 proteins. LD clumping revealed 40 cis-pQTLs independently associated with these 20 proteins. Of these, NOTCH3 (rs34763630) was positively associated with extracellular volume, and SELPLG (rs7980427) was negatively associated with late gadolinium enhancement as detected on CMR (table 1). No other proteins with genetic support were associated with CMR measures. Conclusion This study for the first time, implicates genetic markers associated with two proteins as putative candidate biomarkers of CMR-identified myocardial fibrosis in new-onset RA that now warrant validation in an independent cohort.

The influence of remnant cholesterol on ischemic stroke and its subtypes

Abstract Background Established studies have firmly identified elevated low-density lipoprotein (LDL) as a key risk factor for stroke. Nevertheless, an emerging body of evidence suggests that residual stroke risk endures even with rigorous LDL control. Recent investigations propose a potential pivotal role of remnant cholesterol (RC) in this lingering risk. However, the causative relationship between RC and ischemic stroke, encompassing its subtypes, remains elusive. In this study, we employed a two-sample Mendelian Randomization (MR) analysis to delve into the causal association between RC and ischemic stroke, aiming to shed light on its various subtypes. Methods We sourced single nucleotide polymorphism (SNP) data associated with stroke and its subtypes from the MEGASTROKE consortium's comprehensive genome-wide association study (GWAS). SNPs relevant to RC were acquired from the MRC Integrative Epidemiology Unit (IEU) Open GWAS database. Our investigation involved the evaluation of the correlation between RC and ischemic stroke, encompassing 40,585 patients and 406,111 controls. Subtypes considered included large artery atherosclerosis (n=4,373), small artery occlusion (n=5,386), and cardioembolic stroke (n=7,193). A meticulous screening process was implemented to select instrumental variables (IVs) for RC, followed by five Mendelian Randomization (MR) analyses utilizing inverse-variance-weighted (IVW), weighted median, MR-Egger regression, simple mode, and weighted mode methods. Rigorous sensitivity analyses, along with assessments for heterogeneity and pleiotropy, were conducted to ensure the robustness and credibility of our study results. Results Our investigation unveiled a causal relationship between RC and large artery atherosclerosis-type ischemic stroke (IVW method, OR=1.267, 95% CI: 1.017-1.578, P=0.035). This association was further substantiated through rigorous sensitivity analyses, assessments for heterogeneity, and pleiotropy tests. In contrast, no evidence supported a causal link between RC and overall ischemic stroke (IVW method, OR=1.072, 95% CI: 0.986-1.164, P=0.102), small artery occlusion-type stroke (IVW method, OR=1.127, 95% CI: 0.939-1.353, P=0.198), and cardioembolic stroke (IVW method, OR=1.083, 95% CI: 0.931-1.259, P=0.301). Conclusions Our findings robustly establish a causal connection between elevated levels of RC and the occurrence of large artery strokes. This highlights the potential efficacy of reducing RC as a preventive strategy against large artery atherosclerosis. Nevertheless, it is crucial to note that such mitigation efforts may not necessarily result in a decreased risk associated with small artery occlusion and cardioembolic strokes.