Marker Density Research Articles

Comparative Analysis of Predicted SSR Sequences and CpG Islands to Discover Evolutionary Relics of Sex-chromosomes in Divergent Animal Species

Background: DNA markers have high occurrence and mutation rates and are generally located around the controlling regions of some tissue-specific genes and housekeeping genes that can change the expression pattern. Microsatellites and CpG islands are stretches of DNA with repeats and are known to influence gene expression. Methods: In the present study, these DNA markers are mined and an In silico comparison was carried out to understand their occurrence pattern and distribution frequency in sex chromosomes (X and Y) of 12 different animal species using Perl and R programming pipelines. Result: It was found that female-dominant X chromosomes had higher occurrence and distribution frequencies for these DNA markers than that of male-dominant sex chromosome i.e. Y which means that the former has a higher number of the evolutionary sites.The density of DNA markers however, showed remarkable variation for different animal species. The results obtained need validation through wet-lab experimentation. Tri- and hexa-nucleotide repeats are more abundant in exons, whereas other repeats are more abundant in non-coding regions.

Assessment of linkage disequilibrium and haplotype block structure in indigenous cattle populations of Tamil Nadu, India usingwhole genome sequence data

Understanding the nature of linkage disequilibrium (LD) between molecular markers reflects the degree of non-random association between their alleles, which is essential for genome-wide association studies. The main objective of this study was to assess the chromosome-wise linkage disequilibrium and haplotype block structure in indigenous cattle breeds of Tamil Nadu, India. A total of 79 animals belonging to five indigenous cattle breeds were considered as single indigenous cattle population and three samples per breed were sequenced using Illumina NovaSeqTM 6000 and Illumina HiSeq 2500. The LD parameter (r2) was estimated for a total of 62,095,778 pair- wise SNPs for all autosomes. The chromosome-wise number of SNP pairs, mean r2±standard deviation and median r2 values were 2,141,234, 0.484±0.246 and 0.493, respectively. Chromosome-wise mean r2 of different distance bins were calculated and it showed the maximum r2 value of 0.839 at 31-70 kb distance for Chromosome 11 and minimum r2 value of zero for Chromosome 7, 11, 15, 20 and 29, respectively at a distance 71-100 kb. This study also revealed a total of 413, 277 haplotype blocks which covered 2.34% of the autosomal genome. There was a total of 1,589,118 SNPs distributed within the haplotype blocks, covering a total length of 53.62 mb. The results of the study suggest the need for breed-specific reference populations for indigenous cattle breeds, which have a greater density of molecular markers of economic significance and thereby to identify breed-specific haplotypes in future.

DArTseq genotyping facilitates the transfer of "exotic" chromatin from a Secale cereale × S. strictum hybrid into wheat.

Cultivated and wild species of the genus rye (Secale) are important but underexploited gene sources for increasing the genetic diversity of bread wheat. Gene transfer is possible via bridge genetic materials derived from intergeneric hybrids. During this process, it is essential to precisely identify the rye chromatin in the wheat genetic background. In the present study, backcross generation BC2F8 from a cross between Triticum aestivum (Mv9kr1) and S. cereanum ('Kriszta,' a cultivar from the artificial hybrid of S. cereale and S. strictum) was screened using in-situ hybridization (GISH and FISH) and analyzed by DArTseq genotyping in order to select potentially agronomically useful genotypes for prebreeding purposes. Of the 329,267 high-quality short sequence reads generated, 27,822 SilicoDArT and 8,842 SNP markers specific to S. cereanum 1R-7R chromosomes were identified. Heatmaps of the marker densities along the 'Lo7' rye reference pseudomolecules revealed subtle differences between the FISH- and DArTseq-based results. This study demonstrates that the "exotic" rye chromatin of S. cereanum introgressed into wheat can be reliably identified by high-throughput DArTseq genotyping. The Mv9kr1-'Kriszta' addition and translocation lines presented here may serve as valuable prebreeding genetic materials for the development of stress-tolerant or disease-resistant wheat varieties.

Genetic variation associated with adult migration timing in lineages of Steelhead and Chinook Salmon in the Columbia River.

With the discovery of a major effect region (GREB1L, ROCK1) for adult migration timing in genomes of both Chinook Salmon and Steelhead, several subsequent studies have investigated the effect size and distribution of early and late migration alleles among populations in the Columbia River. Here, we synthesize the results of these studies for the major lineages of Chinook Salmon and Steelhead that include highly distinct groups in the interior Columbia River that exhibit atypical life histories from most coastal lineage populations of these two species. Whole-genome studies with high marker density have provided extensive insight into SNPs most associated with adult migration timing, and suites of markers for each species have been genotyped in large numbers of individuals to further validate phenotypic effects. For Steelhead, the largest phenotypic effect sizes have been observed in the coastal lineage (36% of variation for passage timing at Bonneville Dam; 43% of variation for tributary arrival timing) compared to the inland lineage (7.5% of variation for passage timing at Bonneville Dam; 8.4% of variation for tributary arrival timing) that overwinter in freshwater prior to spawning. For Chinook Salmon, large effect sizes have been observed in all three lineages for multiple adult migration phenotypes (Coastal lineage: percentage of variation of 27.9% for passage timing at Bonneville Dam, 28.7% for arrival timing for spawning; Interior ocean type: percentage of variation of 47.6% for passage timing at Bonneville Dam, 39.6% for tributary arrival timing, 77.9% for arrival timing for spawning; Interior stream type: percentage of variation of 35.3% for passage at Bonneville Dam, 9.8% for tributary arrival timing, 4.7% for arrival timing for spawning). Together, these results have extended our understanding of genetic variation associated with life history diversity in distinct populations of the Columbia River, however, much research remains necessary to determine the causal mechanism for this major effect region on migration timing in these species.

Purinergic signaling influences the neuroinflammatory outcomes of a testosterone-derived synthetic in female rats: Resistance training protective effects on brain health

Physical exercise is recognized as a non-pharmacological approach to treat and protect against several neuroinflammatory conditions and thus to prevent brain disorders. However, the interest in ergogenic resources by athletes and bodybuilding practitioners is widespread and on the rise. These substances shorten the process of performance gain and improve aesthetics, having led to the prominent use and abuse of hormones in the past years. Recent evidence has shown that the purinergic system, composed of adenine nucleotides, nucleosides, enzymes, and receptors, participates in a wide range of processes within the brain, such as neuroinflammation, neuromodulation, and cellular communication. Here, we investigated the effects of the anabolic androgenic steroid (AAS) testosterone (TES) at a dose of 70 mg/kg/week in female rats and the neuroprotective effect of resistance exercise related to the purinergic system and oxidative stress parameters. Our findings showed a decrease in ATP and ADO hydrolysis in treated and trained animals. Furthermore, there was an increase in the density of purinoceptors (P2X7 and A2A) and inflammatory markers (IBA-1, NRLP3, CASP-1, IL-1β, and IL-6) in the cerebral cortex of animals that received AAS. On the other hand, exercise reversed neuroinflammatory parameters such as IBA-1, NLRP3, CASP-1, and IL-1β and improved antioxidant response and anti-inflammatory IL-10 cytokine levels. Overall, this study shows that the use of TES without indication or prescription disrupts brain homeostasis, as demonstrated by the increase in neuroinflammation, and that the practice of exercise can protect brain health.

Effects of Diquafosol Sodium Ophthalmic Solution on Tear Film Matrix Metallopeptidase-9 and Corneal Nerve Density in Patients with Type 2 Diabetic Dry Eye.

Purpose: Diabetes mellitus has been associated with increased dry eye disease (DED) and exacerbates DED's pathology. This preliminary short-term study aimed to evaluate the effects of 3% Diquafosol Sodium ophthalmic solution (DQS) on ocular surface inflammation and corneal nerve density in diabetic dry eye (DDE) patients. Methods: In this perspective, participants used 1 drop of 3% DQS (Diquas; Santen Pharmaceutical Co., Ltd., Osaka, Japan) 6 times daily for 8 weeks. Non-invasive tear breakup time (NITBUT), tear film lipid layer (TFLL), conjunctival hyperemia [redness score (RS)], corneoconjunctival staining (CFS), corneal sensitivity (CS), Meibomian gland quality (MGQ) and Meibomian gland expressibility (MGEx), corneal nerve fiber density (CNFD), and Standard Patient Evaluation Eye Dryness (SPEED) questionnaire were assessed at baseline, at weeks 4, and up to 8 weeks. Matrix metalloproteinase-9 (MMP-9) of tear samples was measured at baseline and weeks 8. Results: The mean age was 61.27 ± 11.68 years. At baseline NITBUT = 5.89 ± 2.81 s, tear meniscus height = 0.17 ± 0.05 mm, TFLL = 2.74 ± 0.51, CFS = 4.35 ± 0.68, CS = 53.83 ± 9.63 mm, MMP-9 = 49.10 ± 10.42 ng/mL, RS = 1.65 ± 0.44, MGEx = 1.85 ± 0.72, MGQ = 2.65 ± 0.50, CNFD = 20.36 ± 8.20 no./mm2, and SPEED = 12.62 ± 3.91. At week 4, significant improvements were found in all parameters except RS (1.59 ± 0.46, P = 0.172) and CNFD (21.46 ± 8.41, P = 0.163). Finally, at week 8, all parameters had significant improvements. Conclusion: Preliminary short-term findings suggest that treatment of DDE patients with DQS was found to be safe and efficacious in improving dry eye parameters. In addition, inflammatory marker and corneal nerve density were significantly improved. This study was registered with ClinicalTrials.gov (NCT05193331).

Konjac flour-mediated gut microbiota alleviates insulin resistance and improves placental angiogenesis of obese sows

Our previous study revealed that dietary konjac flour (KF) could remodel gut microbiota and improve reproductive performance of sows, but its underlying mechanisms remain unclear. This experiment aimed to investigate how dietary KF improves reproductive performance of obese sows. Here, 60 sows were assigned into three groups according to their backfat thickness: normal backfat sows fed with control diet (CON-N), high backfat sows fed with control diet (CON-H) and high backfat sows fed with KF inclusion diet (KF-H). The characteristics of sows and piglets were recorded. Next, fecal microbiota transplantation (FMT) was performed on female mice, followed by recording the characteristics of female mice. The results showed that compared with CON-H group, KF-H group showed downtrend in stillbirth rate (P = 0.07), an increase in placental efficiency (P < 0.01) and average piglet weight (P < 0.01); coupled with a decrease in the values of homeostasis model assessment-insulin resistance (P < 0.01); as well as an increase in placental vascular density and protein expression of angiogenesis markers (P < 0.01). As expected, sows fed KF diets had improved abundance and diversity of gut microbiota. More importantly, compared with CON-H(FMT) group, KF-H(FMT) group showed improvement in reproductive performance and insulin sensitivity (P < 0.05), as well as an increase in placental labyrinth zone and protein expression of angiogenesis markers (P < 0.05). Furthermore, we found a content increase (P < 0.05) of SCFAs in both KF-H group sow and KF-H (FMT) group mice. Overall, KF supplementation could alleviate insulin resistance, promote placental angiogenesis, and ultimately improve the reproductive performance of sows via gut microbiota remodeling.

Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp

The Pacific white shrimp is one of the most important species in the aquaculture industry worldwide, and the growth is regarded as primary trait for selective breeding programmes. In this study, the heritability and genetic correlation of two growth traits, including body length (BL) and the ratio of abdomen length to cephalothorax length (AL/CL) were analyzed, and the genomic prediction based on different genomic selection models including machine learning method were evaluated. The heritabilities of BL and AL/CL were 0.25 ± 0.04 and 0.07 ± 0.03, respectively. The two phenotypes showed moderate negative correlations (−0.70 ± 0.14). Comparison of the different prediction models showed that NeuralNet had the highest prediction accuracy. The prediction accuracy of NeuralNet increased by about 10% compared to GBLUP. Furthermore, NeuralNet presented the highest prediction accuracy under different marker densities, and the prediction accuracy using 1000 SNPs was similar to that estimated by total SNPs. When comparing multi-trait models (MTM) and single-trait models (STM), NeuralNet outperformed the other methods, which increased prediction accuracy by around 30%. Overall, the NeuralNet model may have better application prospects for genomic selection breeding in shrimp. These results provide a strong basis for accelerating the application of genomic selection breeding in shrimp improvement programmes.

Immune microenvironment of intimal sarcomas: Adaptive immune resistance with potential therapeutic implications.

Intimal sarcomas are rare, aggressive neoplasms that arise from large blood vessels. Characterization of the tumor immune microenvironment may suggest new treatment strategies. Seventeen specimens from 7 patients were labeled by immunohistochemistry for programmed cell death 1 ligand 1 (PD-L1), CD45, CD8, CD4, FOXP3, CD20, CD68, and LAG3. The immune cell density was scored as a percentage of the tumor area (1+ [<5%], 2+ [5%-50%], 3+ [>50%]); PD-L1 expression was scored on tumor cells and on intratumoral immune cells. Immune marker density was quantified using image analysis software. All intimal sarcomas showed immune cell infiltration (41% were 1+, 53% were 2+, 6% were 3+). Tumor and immune cell PD-L1 labeling was seen in 35% and 76% of cases, respectively; PD-L1+ intimal sarcomas had higher CD45+, CD8+, FOXP3+, CD68+, and leukocyte activation gene 3 (LAG3)+ cell densities (P ≤ .01). Similarly, PD-L1 expression on immune cells correlated with higher densities of CD8+ and FOXP3+ cells (P < .04). Higher LAG3+ cell density correlated with higher CD68+ cell density and necrosis (P < .05). One patient with prolonged survival had the highest immune cell density and PD-L1 expression. These data show that intimal sarcomas have an active tumor microenvironment with an adaptive pattern of PD-L1 expression. Our results suggest that immunotherapy may be an effective treatment option.

Identification and introgression of a novel leaf rust resistance gene from Thinopyrum intermedium chromosome 7Js into wheat.

A novel leaf rust resistance locus located on a terminal segment (0-69.29 Mb) of Thinopyrum intermedium chromosome arm 7JsS has been introduced into wheat genome for disease resistance breeding. Xiaoyan 78829, a wheat-Thinopyrum intermedium partial amphiploid, exhibits excellent resistance to fungal diseases in wheat. To transfer its disease resistance to common wheat (Triticum aestivum), we previously developed a translocation line WTT26 using chromosome engineering. Disease evaluation showed that WTT26 was nearly immune to 14 common races of leaf rust pathogen (Puccinia triticina) and highly resistant to Ug99 race PTKST of stem rust pathogen (P. graminis f. sp. tritici) at the seedling stage. It also displayed high adult plant resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Cytogenetic and molecular marker analysis revealed that WTT26 carried a T4BS·7JsS chromosome translocation. Once transferred into the susceptible wheat genetic background, chromosome 7JsS exhibited its resistance to leaf rust, indicating that the resistance locus was located on this alien chromosome. To enhance the usefulness of this locus in wheat breeding, we further developed several new translocation lines with small Th. intermedium segments using irradiation and developed 124 specific markers using specific-locus amplified fragment sequencing, which increased the marker density of chromosome 7JsS. Furthermore, a refined physical map of chromosome 7JsS was constructed with 74 specific markers, and six bins were thus arranged according to the co-occurrence of markers and alien chromosome segments. Combining data from specific marker amplification and resistance evaluation, we mapped a new leaf rust resistance locus in the 0-69.29Mb region on chromosome 7JsS. The translocation lines carrying the new leaf rust resistance locus and its linked markers will contribute to wheat disease-resistance breeding.

Accurate prediction of quantitative traits with failed SNP calls in canola and maize.

In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.

Assessing the agronomic potential of sorghum B‐lines using genomic prediction

AbstractIn hybrid sorghum breeding, basic agronomic traits, such as days to flowering and plant height, of sorghum seed parents must be within a specific range for hybrid seed production. Because many sorghum programs select new B‐lines outside of commercial seed production environments, the purpose of this study was to determine if genomic prediction is effective to evaluate new lines for their agronomic potential for seed production. Two B‐line RIL populations were evaluated across several environments for days to mid‐anthesis (DY), plant height (PH), panicle length (PL), and seed yield (SY). Across environments and populations, average prediction accuracies were between 0.47 and 0.61 for DY, 0.24 and 0.60 for PH, 0.16 and 0.37 for SY, and 0.37 and 0.57 for PL. The effect of training set size was assessed by subsampling various amounts of data into the training set, ranging from 5% to 65%. Prediction accuracies generally improved as the proportion of the total data in the training set increased for both inter‐ and intrapopulation predictions, but a relatively small portion (15%) of the total data still produced modest prediction accuracies. The effect of the marker density on genomic prediction accuracies was assessed by subsampling various numbers of single nucleotide polymorphisms. It was observed that as few as 500 markers were able to produce prediction accuracies similar to when all markers were used. The results of this study indicated that genomic prediction can be a useful and cost‐effective tool that sorghum breeding programs should incorporate into their pipeline.

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

AbstractThis study evaluates the impact of genomic prediction models on selecting inbred lines as parents in hybrid breeding programs. New parents in a hybrid breeding program are typically selected from early‐stage yield trials based on general combining ability (GCA) from testcrosses. Genomic studies have largely focused on predicting hybrid performance in the late stages of the breeding pipeline and largely ignored the selection of inbred lines as parents of the subsequent breeding cycles. Here, we used stochastic simulations of a maize (Zea mays L.) hybrid breeding program for 20 years to evaluate the performance of genomic prediction models for selecting parents based on their predicted GCA. Five genomic prediction models were evaluated in terms of achieved genetic gain and heterosis under two different single nucleotide polymorphism (SNP) marker densities and the true quantitative trait loci genotypes. The results show that using high‐density SNP markers generated more genetic gain and heterosis than the low‐density SNP markers. The relative performance of genomic prediction models differed across marker scenarios. For genetic gain, we observed more differences between the models at low than high marker density. For heterosis, we observed the opposite, more differences between the models at high than low marker density. Overall, models that fitted the average or additive effects specific to each heterotic pool and dominance effects provide a better fit and hence higher genetic gain in hybrid breeding programs.

Genomic selection to improve husk tightness based on genomic molecular markers in maize.

The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models. An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods. The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness. The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits.

Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat.

Estimation and prediction play a key role in breeding programs. Currently, phenotyping of complex traits such as nitrogen use efficiency (NUE) in wheat is still expensive, requires high-throughput technologies and is very time consuming compared to genotyping. Therefore, researchers are trying to predict phenotypes based on marker information. Genetic parameters such as population structure, genomic relationship matrix, marker density and sample size are major factors that increase the performance and accuracy of a model. However, they play an important role in adjusting the statistically significant false discovery rate (FDR) threshold in estimation. In parallel, there are many genetic hyper-parameters that are hidden and not represented in the given genomic selection (GS) model but have significant effects on the results, such as panel size, number of markers, minor allele frequency, number of call rates for each marker, number of cross validations and batch size in the training set of the genomic file. The main challenge is to ensure the reliability and accuracy of predicted breeding values (BVs) as results. Our study has confirmed the results of bias-variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK, which has the highest performance when estimating genetic parameters and hyper-parameters in a given GS model compared to other models.

Exposure to metam sodium-based pesticide impaired cognitive performances in adult mice: Involvement of oxidative damage and glial activation

Cognitive integrity is a critical aspect of neurological function, and a decline in cognitive function is a hallmark of neurotoxicity. Oxidative stress is a significant pathological feature contributing to cognitive deficits that can arise from exposure to environmental pollutants such as pesticides. Among these, Metam sodium-based pesticides (MS-BP) are an emergent type of pesticide widely used in the agriculture and public health sectors for controlling pests and diseases. Our prior research has shown that animals exposed to MS-BP during the early stages of brain development caused cognitive impairments. In the present study, we tested whether exposure to this compound in a fully matured brain would affect cognitive performance and induce oxidative damage to the central nervous system. In this context, adult mice received chronic treatment with increasing doses of MS-BP and subjected to a set of behavioral paradigms. Following behavioral assessment, oxidative stress and glial activation were evaluated. Our main findings showed that MS-BP chronic exposure impaired recognition and short- and long-term memory. These alterations were accompanied by increased superoxide dismutase activity and malondialdehyde level and a marked decrease in catalase activity in specific brain areas. Moreover, exposure to MS-BP is associated with a significant rise in the density of astrocytic and microglial markers, indicating a possible glial cell response within the prefrontal cortex and hippocampus. The present work demonstrated that MS-BP altered cognitive performance likely through oxidative damage to the brain.

Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.

Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.

Maize (Zea mays L.) is the third most important cereal crop after rice (Oryza sativa) and wheat (Triticum aestivum). Salinity stress significantly affects vegetative biomass and grain yield and, therefore, reduces the food and silage productivity of maize. Selecting salt-tolerant genotypes is a cumbersome and time-consuming process that requires meticulous phenotyping. To predict salt tolerance in maize, we estimated breeding values for four biomass-related traits, including shoot length, shoot weight, root length, and root weight under salt-stressed and controlled conditions. A five-fold cross-validation method was used to select the best model among genomic best linear unbiased prediction (GBLUP), ridge-regression BLUP(rrBLUP), extendedGBLUP, Bayesian Lasso, Bayesian ridge regression, BayesA, BayesB, and BayesC. Examination of the effect of different marker densities on prediction accuracy revealed that a set of low-density single nucleotide polymorphisms obtained through filtering based on a combination of analysis of variance and linkage disequilibrium provided the best prediction accuracy for all the traits. The average prediction accuracy in cross-validations ranged from 0.46 to 0.77 across the four derived traits. The GBLUP, rrBLUP, and all Bayesian models except BayesB demonstrated comparable levels of prediction accuracy that were superior to the other modeling approaches. These findings provide a roadmap for the deployment and optimization of genomic selection in breeding for salt tolerance in maize.

Genetic dissection and genomic prediction for pork cuts and carcass morphology traits in pig

BackgroundAs pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts.ResultsWe identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs.ConclusionsWe performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs.

Hybrid performance evaluation and genome-wide association analysis of root system architecture in a maize association population.

The genetic architecture of RSA traits was dissected by GWAS and coexpression networks analysis in a maize association population. Root system architecture (RSA) is a crucial determinant of water and nutrient uptake efficiency in crops. However, the maize genetic architecture of RSA is still poorly understood due to the challenges in quantifying root traits and the lack of dense molecular markers. Here, an association mapping panel including 356 inbred lines were crossed with a common tester, Zheng58, and the test crosses were phenotyped for 12 RSA traits in three locations. We observed a 1.3 ~ sixfold phenotypic variation for measured RSA in the association panel. The association panel consisted of four subpopulations, non-stiff stalk (NSS) lines, stiff stalk (SS), tropical/subtropical (TST), and mixed. Zheng58 × TST has a 2.1% higher crown root number (CRN) and 8.6% less brace root number (BRN) than Zheng58 × NSS and Zheng58 × SS, respectively. Using a genome-wide association study (GWAS) with 1.25 million SNPs and correction for population structure, 191 significant SNPs were identified for root traits. Ninety (47%) of the significant SNPs showed positive allelic effects, and 101 (53%) showed negative effects. Each locus could explain 0.39% to 11.8% of phenotypic variation. By integrating GWAS results and comparing coexpression networks, 26 high-priority candidate genes were identified. Gene GRMZM2G377215, which belongs to the COBRA-like gene family, affected root growth and development. Gene GRMZM2G468657 encodes the aspartic proteinase nepenthesin-1, related to root development and N-deficient response. Collectively, our research provides progress in the genetic dissection of root system architecture. These findings present the further possibility for the genetic improvement of root traits in maize.