Marker Density Research Articles

Genomic predictions under different genetic architectures are impacted by mating designs

Mating in animal communities must be managed in a way that assures the performance increase in the progenies without increasing the rate of inbreeding. It has currently become possible to identify millions of single nucleotide polymorphisms (SNPs), and it is feasible to select animals based on genome-wide marker profiles. This study aimed to evaluate the impact of five mating designs among individuals (random, positive and negative assortative, minimized and maximized inbreeding) on genomic prediction accuracy. The choice of these five particular mating designs provides a thorough analysis of the way genetic diversity, relatedness, inbreeding, and biological conditions influence the accuracy of genomic predictions. Utilizing a stochastic simulation technique, various marker and quantitative trait loci (QTL) densities were taken into account. The heritabilities of a simulated trait were 0.05, 0.30, and 0.60. A validation population that only had genotypic records was taken into consideration, and a reference population that had both genotypic and phenotypic records was considered for every simulation scenario. By measuring the correlation between estimated and true breeding values, the prediction accuracy was calculated. Computing the regression of true genomic breeding value on estimated genomic breeding value allowed for the examination of prediction bias. The scenario with a positive assortative mating design had the highest accuracy of genomic prediction (0.733 ± 0.003 to 0.966 ± 0.001). In a case of negative assortative mating, the genomic evaluation's accuracy was lowest (0.680 ± 0.011 to 0.899 ± 0.003). Applying the positive assortative mating design resulted in the unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value. Based on the current results, it is suggested to implement positive assortative mating in genomic evaluation programs to obtain unbiased genomic predictions with greater accuracy. This study implies that animal breeding programs can improve offspring performance without compromising genetic health by carefully managing mating strategies based on genetic diversity, relatedness, and inbreeding levels. To maximize breeding results and ensure long-term genetic improvement in animal populations, this study highlights the importance of considering different mating designs when evaluating genomic information. When incorporating positive assortative mating or other mating schemes into genomic evaluation programs, it is critical to consider the complex relationship between gene interactions, environmental influences, and genetic drift to ensure the stability and effectiveness of breeding efforts. Further research and comprehensive analyzes are needed to fully understand the impact of these factors and their possible complex interactions on the accuracy of genomic prediction and to develop strategies that optimize breeding outcomes in animal populations.

A meta-analysis on the effects of marker coverage, status number, and size of training set on predictive accuracy and heritability estimates from genomic selection in tree breeding

Genomic selection (GS) is increasingly used in tree breeding because of the possibility to hasten breeding cycles, increase selection intensity or facilitate multi-trait selection, and to obtain less biased estimates of quantitative genetic parameters such as heritability. However, tree breeders are aiming to obtain accurate estimates of such parameters and breeding values while optimizing sampling and genotyping costs. We conducted a metadata analysis of results from 28 GS studies totalling 115 study-traits. We found that heritability estimates obtained using DNA marker-based information for a variety of traits and species were not significantly related to variation in the total number of markers ranging from about 1500 to 116 000, nor by the marker density, ranging from about 1 to 60 markers/centimorgan, nor by the status number of the breeding populations ranging from about 10 to 620, nor by the size of the training set ranging from 236 to 2458. However, the predictive accuracy of breeding values was generally higher when the status number of the breeding population was smaller, which was expected given the higher level of relatedness in small breeding populations, and the increased ability of a given number of markers to trace the long-range linkage disequilibrium in such conditions. According to expectations, the predictive accuracy also increased with the size of the training set used to build marker-based models. Genotyping arrays with a few to many thousand markers exist for several tree species and with the actual costs, GS could thus be efficiently implemented in many more tree breeding programs, delivering less biased genetic parameters and more accurate estimates of breeding values.

Advancing selective breeding in leopard coral grouper (P. leopardus) through development of a high-throughput image-based growth trait

Utilizing image-based computer vision techniques, many high-throughput phenotyping methods have been employed to capture intricate growth trait characteristics, offering reliable estimates of phenotypic traits crucial for breeding programs. In this study, we explored the application of partial differential equation (PDE)-based level set approaches to introduce image-based body area percentage (IBAP) as a novel growth trait in Plectropomus leopardus, as a substitution for the traditional growth trait body weight (BW). Assessing the genetic parameters essential for robust growth trait improvement in P. leopardus breeding programs, we estimated SNP-based heritability for IBAP and BW using a comprehensive set of SNPs (673,039 SNPs with MAF >2%). Results revealed heritability estimates of 0.515 (S.E. 0.06) for IBAP and 0.542 (S.E. 0.06) for BW. Moreover, strong phenotypic and genetic correlations of 0.812 (S.E. 0.001) and 0.903 (S.E 0.021) between IBAP and BW, respectively, underscored the potential for IBAP as a surrogate trait of BW for the genetic improvement of P. leopardus. We established a linear regression model of IBAP and BW (y ​= ​−730 ​+ ​1700×, (R2 ​= ​0.71)), after rigorous assessments of linearity, normality, and homoscedasticity, to confirm model fit. Evaluation of breeding value prediction accuracies using two linear models (rr-GBLUP and Bayes B) and a non-linear (RKHS) model demonstrated the superior performance of RKHS across IBAP and BW. Exploring the impact of varied marker densities for SNP selection on genomic prediction accuracy for IBAP and BW demonstrated a threshold of 10,000 SNPs for maximal model accuracy. These findings provide essential reference information and methodological groundwork for leveraging image-based traits in P. leopardus breeding endeavors, facilitating more efficient and precise genetic improvement programs.

Dynamics of centipede locomotion revealed by large-scale traction force microscopy.

We present a novel approach to traction force microscopy (TFM) for studying the locomotion of 10 cm long walking centipedes on soft substrates. Leveraging the remarkable elasticity and ductility of kudzu starch gels, we use them as a deformable gel substrate, providing resilience against the centipedes' sharp leg tips. By optimizing fiducial marker size and density and fine-tuning imaging conditions, we enhance measurement accuracy. Our TFM investigation reveals traction forces along the centipede's longitudinal axis that effectively counterbalance inertial forces within the 0-10 mN range, providing the first report of non-vanishing inertia forces in TFM studies. Interestingly, we observe waves of forces propagating from the head to the tail of the centipede, corresponding to its locomotion speed. Furthermore, we discover a characteristic cycle of leg clusters engaging with the substrate: forward force (friction) upon leg tip contact, backward force (traction) as the leg pulls the substrate while stationary, and subsequent forward force as the leg tip detaches to reposition itself in the anterior direction. This work opens perspectives for TFM applications in ethology, tribology and robotics.

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits.

The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.

Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.

To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population's genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection.

Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.)

BackgroundGenomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively.ResultsLarge phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594–0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780–0.835), fruit width (0.791–0.865), and pericarp thickness (0.643–0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768−12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets.ConclusionOur study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.

Immunohistochemical staining with CD117 and PGP9.5 of excised vestibular tissue from patients with neuroproliferative vestibulodynia.

Neuroproliferative vestibulodynia (NPV), a provoked genital pain characterized by severe allodynia and hyperalgesia, is confirmed in excised vestibular tissue by immunohistochemical staining (>8 CD117-positive immunostained cells/100× microscopic field) rather than by hematoxylin and eosin staining. In this study we sought to assess immunostaining of tissue samples obtained during vestibulectomy surgery and to correlate results with patient outcomes. Patients (n = 65) meeting criteria for NPV who underwent vestibulectomy during the period from June 2019 through December 2022 formed the study cohort. We performed assessment of pathology of vestibular tissues by use of immunohistochemical staining, including quantitation of mast cells by CD117 (mast cell marker) and nerve fibers by protein gene product (PGP) 9.5 (neuronal marker). We analyzed 725 photomicrographs of immunostained tissue sections (100× and 200×) by manual counting and computer-assisted histometry and correlated these data to clinical assessments. Outcomes included density of CD117 and PGP9.5 immunostaining in the 1:00-11:00 o'clock and 12:00 o'clock vestibular regions, and patient-reported outcomes assessing sexual function, pain, distress, and symptom improvement. All 65 NPV patients (median age 26 years), 45 with lifelong and 20 with acquired NPV, had severe pain documented by PROs and vulvoscopy and had >8 CD117-immunopositive cells/100× microscopic field. Median cell count values were similar in the 1:00-11:00 o'clock and 12:00 vestibular regions (28.5 and 29.5/100× field, respectively). Likewise, the marker) and nerve fibers by protein gene product (PGP) 9.5 (neuronal marker). We analyzed 725 photomicrographs of immunostained tissue sections (100× and 200×) by manual counting and computer-assisted histometry and correlated these data to clinical assessments. Outcomes included density of CD117 and PGP9.5 immunostaining in the 1:00-11:00 o'clock and 12:00 o'clock vestibular regions, and patient-reported outcomes assessing sexual function, pain, distress, and symptom improvement. All 65 NPV patients (median age 26 years), 45 with lifelong and 20 with acquired NPV, had severe pain documented by PROs and vulvoscopy and had >8 CD117-immunopositive cells/100× microscopic field. Median cell count values were similar in the 1:00-11:00 o'clock and 12:00 vestibular regions (28.5 and 29.5/100× field, respectively). Likewise, the median area of CD117 immunostaining was similar in both regions (0.69% and 0.73%). The median area of PGP9.5 immunostaining was 0.47% and 0.31% in these same regions. Pain scores determined with cotton-tipped swab testing were nominally higher in lifelong vs acquired NPV patients, reaching statistical significance in the 1:00-11:00 o'clock region (P < .001). The median score for the McGill Pain Questionnaire affective subscale dimension was also significantly higher in lifelong vs acquired NPV patients (P = .011). No correlations were observed between hematoxylin and eosin results and density of mast cells or neuronal markers. Of note, 63% of the patient cohort reported having additional conditions associated with aberrant mast cell activity. The pathology of NPV is primarily localized to the vestibular epithelial basement membrane and subepithelial stroma with no visible vulvoscopic findings, making clinical diagnosis challenging. Strengths of this study include the large number of tissues examined with what is to our knowledge the first-ever assessment of the 12:00 vestibule. Major limitations are specimens from a single timepoint within the disease state and lack of control tissues. Performing immunohistochemical staining of excised vestibular tissue with CD117 and PGP9.5 led to histometric confirmation of NPV, indications that NPV is a field disease involving all vestibular regions, validation for patients whose pain had been ignored and who had experienced negative psychosocial impact, and appreciation that such staining can advance knowledge.

Abstract 3627: Volrustomig a novel PD-1/CTLA-4 bispecific antibody leads to robust increase in peripheral and intra-tumoral pharmacodynamic biomarkers in solid tumors from FTiH study

Abstract Background: Volrustomig (MEDI5752) is designed to fully inhibit PD-1 while preferentially inhibiting CTLA-4 on activated PD-1+ T cells. In a FTiH trial, volrustomig demonstrated meaningful clinical activity in patients with solid tumors (including 1L RCC and NSCLC), acceptable safety profile, sustained PD-1 receptor occupancy, and T cell proliferation at levels greater than seen with clinically tolerable doses of CTLA-4 inhibitors with co-administration of anti-PD(L)-1. We sought to further characterize volrustomig pharmacodynamic effect in the periphery and tumor to demonstrate its biological activity and inform dose optimization. Methods: Pre- and on-treatment blood samples from 86 patients enrolled in dose exploration were collected. Peripheral T cell profiling was assayed by flow cytometry, RNAseq, TCRSeq and serum proteomics. Paired tumor biopsies (N=17), pre- and at week 6 on-treatment, were analyzed for T cell infiltration and function using RNAseq and computational image analysis of immune cell marker density detected by histochemistry and multiplex immune fluorescence assays. Results: Volrustomig achieved dose-dependent peripheral pharmacodynamic changes of biomarkers specific to CTLA-4 blockade at ≥ 225mg. At C1D8 with doses ≥ 500mg, increases in CD4 T cell proliferation (ki67), activation (ICOS), and memory T cells (1516%, 321% and 88%, respectively) were seen, each greater than observed with historical controls of patients treated with tremelimumab 3mg/kg (T3) in combination with durvalumab. Similarly, CD8 proliferation and activation were also higher than T3 at C1D8 with 300% and 161% increase respectively. Additional CD4 and CD8 T cell proliferation and activation was observed at C2D8 supporting the benefit of repeat dosing. Higher peripheral T cell clonal expansion at doses ≥ 500mg was seen and associated with PFS and OS benefit. In patients with paired biopsies, volrustomig achieved robust T cell mediated anti-tumor activity including increases in T cell infiltration (CD8+, 2.3 fold, p=0.04), proliferation (CD8+ Ki67+, 2.1 fold, p=0.04) and, T cell cytotoxicity and activation (GrB+, 2.2 fold, p=0.01; T cell effector and IFNg gene signatures, &amp;gt; 2-fold, p&amp;lt; 0.01). Additionally, volrustomig increased CD8 proliferation in tumor compared to historical control durvalumab alone (&amp;gt;2-fold increase in 70% vs 50% patients). Conclusion: Voltrustomig demonstrated robust peripheral and intra-tumoral T cell activation and proliferation, broadening the pool of antigen experienced T cells, at levels greater than seen with dual checkpoint blockade currently used in the clinic. Citation Format: Ikbel Achour, Michael Kuziora, Florian Song, Steven Eck, Lina Meinecke, Jorge Blando, Jennifer Pearson, Megha Saraiya, Christian Eisen, Felix Segerer, Markus Schick, Nicolas Triltsch, Michael Surace, Gisele Silva Boos, Harald Hessel, Mai Bui, Katrin Pratsch, Alma Andoni, Shelby Denise Gainer, Dan Freeman, Deepa Subramaniam, Ben Tran. Volrustomig a novel PD-1/CTLA-4 bispecific antibody leads to robust increase in peripheral and intra-tumoral pharmacodynamic biomarkers in solid tumors from FTiH study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3627.

Elucidation of population stratifying markers and selective sweeps in crossbred Landlly pig population using genome-wide SNP data.

The present study was aimed at the identification of population stratifying markers from the commercial porcine SNP 60K array and elucidate the genome-wide selective sweeps in the crossbred Landlly pig population. Original genotyping data, generated on Landlly pigs, was merged in various combinations with global suid breeds that were grouped as exotic (global pig breeds excluding Indian and Chinese), Chinese (Chinese pig breeds only), and outgroup pig populations. Post quality control, the genome-wide SNPs were ranked for their stratifying power within each dataset in TRES (using three different criteria) and FIFS programs and top-ranked SNPs (0.5K, 1K, 2K, 3K, and 4K densities) were selected. PCA plots were used to assess the stratification power of low-density panels. Selective sweeps were elucidated in the Landlly population using intra- and inter-population haplotype statistics. Additionally, Tajima's D-statistics were calculated to determine the status of balancing selection in the Landlly population. PCA plots showed 0.5K marker density to effectively stratify Landlly from other pig populations. The A-score in DAPC program revealed the Delta statistic of marker selection to outperform other methods (informativeness and FST methods) and that 3000-marker density was suitable for stratification of Landlly animals from exotic pig populations. The results from selective sweep analysis revealed the Landlly population to be under selection for mammary (NAV2), reproductive efficiency (JMY, SERGEF, and MAP3K20), body conformation (FHIT, WNT2, ASRB, DMGDH, and BHMT), feed efficiency (CSRNP1 and ADRA1A), and immunity (U6, MYO3B, RBMS3, and FAM78B) traits. More than two methods suggested sweeps for immunity and feed efficiency traits, thus giving a strong indication for selection in this direction. The study is the first of its kind in Indian pig breeds with a comparison against global breeds. In conclusion, 500 markers were able to effectively stratify the breeds. Different traits under selective sweeps (natural or artificial selection) can be exploited for further improvement.

Effects of marker density on genomic prediction for yield traits in sweet corn

Effects of marker density on genomic prediction for yield traits in sweet corn

Mutational, immune microenvironment, and clinicopathological profiles of diffuse large B-cell lymphoma and follicular lymphoma with BCL6 rearrangement.

BCL6-rearrangement (BCL6-R) is associated with a favorable prognosis of follicular lymphoma (FL), but the mechanism is unknown. We analyzed the clinicopathological, immune microenvironment (immune checkpoint, immuno-oncology markers), and mutational profiles of 10 BCL6-R-positive FL, and 19 BCL6-R-positive diffuse large B-cell lymphoma (DLBCL) cases (both BCL2-R and MYC-R negative). A custom-made panel included 168 genes related to aggressive B-cell lymphomas and FL. FL cases were nodal, histological grade 3A in 70%, low Ki67; and had a favorable overall and progression-free survival. DLBCL cases were extranodal in 60%, IPI high in 63%, non-GCB in 60%, EBER-negative; and had a progression-free survival comparable to that of DLBCL NOS. The microenvironment had variable infiltration of M2-like tumor-associated macrophages (TAMs) that were CD163, CSF1R, LAIR1, PD-L1, and CD85A (LILRB3) positive; but had low IL10 and PTX3 expression. In comparison to FL, DLBCL had higher TAMs, IL10, and PTX3 expression. Both lymphoma subtypes shared a common mutational profile with mutations in relevant pathogenic genes such as KMT2D, OSBPL10, CREBBP, and HLA-B (related to chromatin remodeling, metabolism, epigenetic modification, and antigen presentation). FL cases were characterized by a higher frequency of mutations of ARID1B, ATM, CD36, RHOA, PLOD2, and PRPRD (p < 0.05). DLBCL cases were characterized by mutations of BTG2, and PIM1; and mutations of HIST1H1E and MFHAS1 to disease progression (p < 0.05). Interestingly, mutations of genes usually associated with poor prognosis, such as NOTCH1/2 and CDKN2A, were infrequent in both lymphoma subtypes. Some high-confidence variant calls were likely oncogenic, loss-of-function. MYD88 L265P gain-of-function was found in 32% of DLBCL. In conclusion, both BCL6-R-positive FL and BCL6-R-positive DLBCL had a common mutational profile; but also, differences. DLBCL cases had a higher density of microenvironment markers.

The effect of marker types and density on genomic prediction and GWAS of key performance traits in tetraploid potato.

Genomic prediction and genome-wide association studies are becoming widely employed in potato key performance trait QTL identifications and to support potato breeding using genomic selection. Elite cultivars are tetraploid and highly heterozygous but also share many common ancestors and generation-spanning inbreeding events, resulting from the clonal propagation of potatoes through seed potatoes. Consequentially, many SNP markers are not in a 1:1 relationship with a single allele variant but shared over several alleles that might exert varying effects on a given trait. The impact of such redundant "diluted" predictors on the statistical models underpinning genome-wide association studies (GWAS) and genomic prediction has scarcely been evaluated despite the potential impact on model accuracy and performance. We evaluated the impact of marker location, marker type, and marker density on the genomic prediction and GWAS of five key performance traits in tetraploid potato (chipping quality, dry matter content, length/width ratio, senescence, and yield). A 762-offspring panel of a diallel cross of 18 elite cultivars was genotyped by sequencing, and markers were annotated according to a reference genome. Genomic prediction models (GBLUP) were trained on four marker subsets [non-synonymous (29,553 SNPs), synonymous (31,229), non-coding (32,388), and a combination], and robustness to marker reduction was investigated. Single-marker regression GWAS was performed for each trait and marker subset. The best cross-validated prediction correlation coefficients of 0.54, 0.75, 0.49, 0.35, and 0.28 were obtained for chipping quality, dry matter content, length/width ratio, senescence, and yield, respectively. The trait prediction abilities were similar across all marker types, with only non-synonymous variants improving yield predictive ability by 16%. Marker reduction response did not depend on marker type but rather on trait. Traits with high predictive abilities, e.g., dry matter content, reached a plateau using fewer markers than traits with intermediate-low correlations, such as yield. The predictions were unbiased across all traits, marker types, and all marker densities >100 SNPs. Our results suggest that using non-synonymous variants does not enhance the performance of genomic prediction of most traits. The major known QTLs were identified by GWAS and were reproducible across exonic and whole-genome variant sets for dry matter content, length/width ratio, and senescence. In contrast, minor QTL detection was marker type dependent.

Long-term effects of denosumab on bone mineral density and turnover markers in patients undergoing hemodialysis

IntroductionDenosumab, a fully human anti-RANKL monoclonal antibody, is a widely used osteoporosis treatment that is increasingly being used in patients undergoing dialysis; however, its long-term efficacy and safety in these patients remain unknown.Materials and methodsThis observational study comprised individuals aged ≥ 20 years undergoing hemodialysis and receiving denosumab. After denosumab administration, we analyzed the long-term changes in bone mineral density (BMD) and levels of bone turnover markers (BTMs) and calcium.ResultsThe study included 45 patients who have been receiving denosumab for a median duration of 3.8 (interquartile range, 2.5–6.7) years. Tartrate-resistant acid phosphatase 5b (TRACP-5b) levels decreased from a median of 595 (434–778) mU/dL at baseline to 200 (141–430) mU/dL after 6 months of denosumab administration (P < 0.001) and remained low thereafter. Similarly, bone-specific alkaline phosphatase (BAP) levels decreased from a median of 18.2 (15.9–25.8) μg/L at baseline to 12.4 (9.9–15.6) μg/L after 6 months (P < 0.001) and remained low thereafter. Meanwhile, BMD, as assessed with dual energy X-ray absorptiometry and measured at the distal 1/3 of the radius, did not decrease (0.465 ± 0.112 g/cm2 at baseline vs. 0.464 ± 0.112 g/cm2 after administration; P = 0.616). Regarding hypocalcemia, corrected calcium levels reached were the lowest at 7 days after administration and normalized within 30 days.ConclusionThe study showed long-term suppression of TRACP-5b and BAP levels and sustaining BMD after denosumab administration over an extended period in patients undergoing hemodialysis.

Fhb9, a major QTL for Fusarium head blight resistance improvement in wheat

Fusarium head blight (FHB), mainly caused by Fusarium graminearum, is one of the most devastating diseases of wheat worldwide. Identification and validation of major quantitative trait loci (QTLs) for FHB resistance without negative effects on agronomic traits is critical to success in breeding FHB-resistant cultivars. In this study, a stable major QTL on chromosome arm 2DL was identified by evaluating a recombinant inbred line (RIL) population derived from Shi4185×Shijiazhuang 8 in both field and greenhouse experiments. QTL mapping and pedigree analyses indicated that the 2DL QTL is the same QTL as QFhb-2DL previously identified in Ji5265, therefore, designated Fhb9. Four kompetitive amplicon sequence polymorphism (KASP) markers were developed based on exome capture sequencing data to enhance marker density in the Fhb9 region, and it was delimited to an interval between single nucleotide polymorphism (SNP) markers KASP-12056 (533.8) and KASP-525 (525.9 Mb) explained 26.0-30.1% of the phenotypic variation. Analysis of the geographic distribution of the Fhb9 resistance allele suggested that it originated from Huang-Huai winter wheat region in China, and very low frequency of Fhb9 in modern Chinese cultivars reveals that it has not been widely deployed in breeding programs. Field and greenhouse evaluation of yield-related traits of near-isogenic lines (NILs) contrasting in Fhb9 alleles indicated that Fhb9 resistance allele did not show any adverse effects on those traits. Fhb9 showed an additive effect on enhancing FHB resistance with Fhb1. Therefore, Fhb9 is a valuable major QTL for improving FHB resistance in wheat and the near-diagnostic markers developed in this study will facilitate its deployment in wheat breeding programs.

Detection of two homologous major QTLs and development of diagnostic molecular markers for sucrose content in peanut.

We identified two stable and homologous major QTLs for sucrose content in peanut, and developed breeder-friendly molecular markers for marker-assisted selection breeding. Sucrose content is a crucial quality trait for edible peanuts, and increasing sucrose content is a key breeding objective. However, the genetic basis of sucrose content in peanut remains unclear, and major quantitative trait loci (QTLs) for sucrose content have yet to be identified. In this study, a high-density genetic map was constructed based on whole-genome re-sequencing data from a peanut RIL population. This map consisted of 2,042 bins and 24,142 SNP markers, making it one of the most comprehensive maps to date in terms of marker density. Two major QTLs (qSCA06.2 and qSCB06.2) were identified, explaining 31.41% and 24.13% of the phenotypic variance, respectively. Notably, these two QTLs were located in homologous genomic regions between the A and B subgenomes. The elite allele of qSCA06.2 was exclusive to Valencia-type, while the elite allele of qSCB06.2 existed in other peanut types. Importantly, the distribution of alleles from two homologous QTLs in the RIL population and diverse germplasm accessions consistently demonstrated that only the combination of elite allelic genotypes from both QTLs/genes resulted in a significantly dominant phenotype, accompanied by a substantial increase in sucrose content. The newly developed diagnostic markers for these QTLs were confirmed to be reliable and could facilitate future breeding efforts to enhance sucrose content using marker-assisted selection techniques. Overall, this study highlights the co-regulation of sucrose content by two major homologous QTLs/genes and provides valuable insights into the genetic basis of sucrose in peanuts.

Genotyping-by-Sequencing Reveals Population Differentiation and Linkage Disequilibrium in Alternaria linariae from Tomato.

Alternaria linariae is an economically important foliar pathogen that causes early blight disease in tomatoes. Understanding genetic diversity, population genetic structure, and evolutionary potential is crucial to contemplating effective disease management strategies. We leveraged genotyping-by-sequencing (GBS) technology to compare genome-wide variation in 124 isolates of Alternaria spp. (A. alternata, A. linariae, and A. solani) for comparative genome analysis and to test the hypotheses of genetic differentiation and linkage disequilibrium (LD) in A. linariae collected from tomatoes in western North Carolina. We performed a pangenome-aware variant calling and filtering with GBSapp and identified 53,238 variants conserved across the reference genomes of three Alternaria spp. The highest marker density was observed on chromosome 1 (7 Mb). Both discriminant analysis of principal components and Bayesian model-based STRUCTURE analysis of A. linariae isolates revealed three subpopulations with minimal admixture. The genetic differentiation coefficients (FST) within A. linariae subpopulations were similar and high (0.86), indicating that alleles in the subpopulationsarefixed and the genetic structure is likely due to restricted recombination. Analysis of molecular variance indicated higher variation among populations (89%) than within the population (11%). We found long-range LD between pairs of loci in A. linariae, supporting the hypothesis of low recombination expected for a fungal pathogen with limited sexual reproduction. Our findings provide evidence of a high level of population genetic differentiation in A. linariae, which reinforces the importance of developing tomato varieties with broad-spectrum resistance to various isolates of A. linariae.

Identification of Novel QTL for Mercury Accumulation in Maize Using an Enlarged SNP Panel.

Mercury (Hg) pollution not only poses a threat to the environment but also adversely affects the growth and development of plants, with potential repercussions for animals and humans through bioaccumulation in the food chain. Maize, a crucial source of food, industrial materials, and livestock feed, requires special attention in understanding the genetic factors influencing mercury accumulation. Developing maize varieties with low mercury accumulation is vital for both maize production and human health. In this study, a comprehensive genome-wide association study (GWAS) was conducted using an enlarged SNP panel comprising 1.25 million single nucleotide polymorphisms (SNPs) in 230 maize inbred lines across three environments. The analysis identified 111 significant SNPs within 78 quantitative trait loci (QTL), involving 169 candidate genes under the Q model. Compared to the previous study, the increased marker density and optimized statistical model led to the discovery of 74 additional QTL, demonstrating improved statistical power. Gene ontology (GO) enrichment analysis revealed that most genes participate in arsenate reduction and stress responses. Notably, GRMZM2G440968, which has been reported in previous studies, is associated with the significant SNP chr6.S_155668107 in axis tissue. It encodes a cysteine proteinase inhibitor, implying its potential role in mitigating mercury toxicity by inhibiting cysteine. Haplotype analyses provided further insights, indicating that lines carrying hap3 exhibited the lowest mercury content compared to other haplotypes. In summary, our study significantly enhances the statistical power of GWAS, identifying additional genes related to mercury accumulation and metabolism. These findings offer valuable insights into unraveling the genetic basis of mercury content in maize and contribute to the development of maize varieties with low mercury accumulation.

Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models.

Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.

Genetic parameters and genomic prediction of growth and breast morphological traits in a crossbreed duck population.

Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single-step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54-0.72), whereas moderate-to-high heritabilities for average daily gain (0.21-0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18-0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree-based BLUP method. The single-step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top-performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low-density panel in ducks.