Marker Density Research Articles

Accuracy of genotype imputation based on reference population size and marker density in Hanwoo cattle.

Recently, the cattle genome sequence has been completed, followed by developing a commercial single nucleotide polymorphism (SNP) chip panel in the animal genome industry. In order to increase statistical power for detecting quantitative trait locus (QTL), a number of animals should be genotyped. However, a high-density chip for many animals would be increasing the genotyping cost. Therefore, statistical inference of genotype imputation (low-density chip to high-density) will be useful in the animal industry. The purpose of this study is to investigate the effect of the reference population size and marker density on the imputation accuracy and to suggest the appropriate number of reference population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo cattle were divided into reference and validation populations. The reference sets consisted of 50k (38,916) marker data and different population sizes (500, 1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four validation sets (Total 889) and the different marker density (5k [5,000], 10k [10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct comparison of the true genotype and the imputed genotype. In conclusion, when the lowest marker density (5k) was used in the validation set, according to the reference population size, the imputation accuracy was 0.793 to 0.929. On the other hand, when the highest marker density (15k), according to the reference population size, the imputation accuracy was 0.904 to 0.967. Moreover, the reference population size should be more than 1,000 to obtain at least 88% imputation accuracy in Hanwoo cattle.

Higher plasma level of small dense low-density lipoprotein cholesterol in ST-segment-elevation myocardial infarction patients with plaque rupture

Abstract Aim The aim of this study was to compare small dense low-density lipoprotein cholesterol (sd-LDL-c) and various lipid markers between patients exhibiting plaque rupture (PR) in the culprit lesions evaluated by optical coherence tomography (OCT) imaging and those without PR in ST-segment elevation myocardial infarction (STEMI) patients. Methods We studied consecutive 60 de novo culprit lesions in 60 patients with STEMI who underwent pre-intervention OCT. PR was defined as a plaque containing a cavity that had overlying residual fibrous caps. Plasma sd-LDL-c was measured directly by homogeneous assay at the time of primary percutaneous coronary intervention. Results The patients were classified into PR (n=40, 66.7%) or non-PR (n=20, 33.3%). There were no significant differences in low-density lipoprotein cholesterol (LDL-c), non-high-density lipoprotein cholesterol (non-HDL-c) and pre-admission statin therapy between two groups (135.1±38.0 mg/dL vs 129.2±43.8 mg/dL; p=0.35, 157.1±37.4 mg/dL vs 143.0±37.7 mg/dL; p=0.24, 15.0% vs 20.0%; p=0.93). However, sd-LDL-c level was significantly higher in patients with PR than those with non-PR (44.0±18.1 mg/dL vs 28.0±9.3 mg/dL; p=0.0005). On multiple logistic regression analysis, sd-LDL-c was an independent predictor of PR (odds ratio, 1.14 per 1 mg/dL; p=0.0063). Conclusion Sd-LDL-c was significantly associated with PR of the culprit lesion in patients with STEMI. Funding Acknowledgement Type of funding sources: None.

Dense core vesicle markers in CSF and cortical tissues of patients with Alzheimer\u2019s disease

BackgroundNew fluid biomarkers for Alzheimer's disease (AD) that reveal synaptic and neural network dysfunctions are needed for clinical practice and therapeutic trial design. Dense core vesicle (DCV) cargos are promising cerebrospinal fluid (CSF) indicators of synaptic failure in AD patients. However, their value as biomarkers has not yet been determined.MethodsImmunoassays were performed to analyze the secretory proteins prohormone convertases PC1/3 and PC2, carboxypeptidase E (CPE), secretogranins SgIII and SgII, and Cystatin C in the cerebral cortex (n = 45, provided by Bellvitge University Hospital) and CSF samples (n = 66, provided by The Sant Pau Initiative on Neurodegeneration cohort) from AD patients (n = 56) and age-matched controls (n = 55).ResultsIn AD tissues, most DCV proteins were aberrantly accumulated in dystrophic neurites and activated astrocytes, whereas PC1/3, PC2 and CPE were also specifically accumulated in hippocampal granulovacuolar degeneration bodies. AD individuals displayed an overall decline of secretory proteins in the CSF. Interestingly, in AD patients, the CSF levels of prohormone convertases strongly correlated inversely with those of neurodegeneration markers and directly with cognitive impairment status.ConclusionsThese results demonstrate marked alterations of neuronal-specific prohormone convertases in CSF and cortical tissues of AD patients. The neuronal DCV cargos are biomarker candidates for synaptic dysfunction and neurodegeneration in AD.

Genomic Selection and Genome-wide Association Study for Feed-Efficiency Traits in a Farmed Nile Tilapia (Oreochromis niloticus) Population.

Nile tilapia is a key aquaculture species with one of the highest production volumes globally. Genetic improvement of feed efficiency via selective breeding is an important goal, and genomic selection may expedite this process. The aims of this study were to 1) dissect the genetic architecture of feed-efficiency traits in a Nile tilapia breeding population, 2) map the genomic regions associated with these traits and identify candidate genes, 3) evaluate the accuracy of breeding value prediction using genomic data, and 4) assess the impact of the genetic marker density on genomic prediction accuracies. Using an experimental video recording trial, feed conversion ratio (FCR), body weight gain (BWG), residual feed intake (RFI) and feed intake (FI) traits were recorded in 40 full-sibling families from the GIFT (Genetically Improved Farmed Tilapia) Nile tilapia breeding population. Fish were genotyped with a ThermoFisher Axiom 65 K Nile tilapia SNP array. Significant heritabilities, ranging from 0.12 to 0.22, were estimated for all the assessed traits using the genomic relationship matrix. A negative but favourable genetic correlation was found between BWG and the feed-efficiency related traits; −0.60 and −0.63 for FCR and RFI, respectively. While the genome-wide association analyses suggested a polygenic genetic architecture for all the measured traits, there were significant QTL identified for BWG and FI on chromosomes seven and five respectively. Candidate genes previously found to be associated with feed-efficiency traits were located in these QTL regions, including ntrk3a, ghrh and eif4e3. The accuracy of breeding value prediction using the genomic data was up to 34% higher than using pedigree records. A SNP density of approximately 5,000 SNPs was sufficient to achieve similar prediction accuracy as the full genotype data set. Our results highlight the potential of genomic selection to improve feed efficiency traits in Nile tilapia breeding programmes.

Accuracy of genomic prediction for seed oil concentration in high‐oleic soybean populations using a low‐density marker panel

AbstractInsoybean [Glycine max (L.) Merr.], seed oil concentration is a complex quantitative trait, and genomic selection (GS) has been shown to be a valuable tool for performing selection on such traits. The objectives of this study were to evaluate multiple GS models for seed oil concentration using a low‐density marker panel in four biparental soybean populations and to assess predictive ability of the models using six unique training populations (TPs). Individuals were grown as BC1F4:F5 progeny rows in 2014. Genomic estimated breeding values (GEBVs) were calculated for each genotype within a population using genomic best linear unbiased predictor (GBLUP), BayesA, and BayesB models in a biparental specific context. In 2015, 60 individuals from each population were randomly selected and grown at six locations with two replications each to generate a “true” phenotypic value for each genotype. Prediction accuracies for each estimation set were generated by correlating the GEBVs with the “true” phenotypic value. Across all populations, the GS prediction accuracy was greatest using GBLUP; however, no GS prediction model showed a significant advantage in accuracy over the phenotypic values. Generally, TPs consisting of more individuals had higher prediction accuracies; however, variations were observed across populations and models. The results show that GS in a biparental context and with low marker densities can be a valuable tool for breeders focused on making gains for oil concentration; however, consideration must be given as to how to apply these methods to each situation.

Placental Autophagy and Viral Replication Co-localize in Human and Non-human Primate Placentae Following Zika Virus Infection: Implications for Therapeutic Interventions.

Multiple studies have shown both induction and inhibition of autophagy during Zika virus (ZIKV) infection. While some have proposed mechanisms by which autophagic dysregulation might facilitate ZIKV vertical transmission, there is a lack of in situ data in human and non-human primate models. This is an especially pertinent question as autophagy-inhibitors, such as hydroxychloroquine, have been proposed as potential therapeutic agents aimed at preventing vertical transmission of ZIKV and other RNA viruses. Given the paucity of pre-clinical data in support of either autophagic enhancement or inhibition of placental ZIKV viral infection, we sought to assess cellular, spatial, and temporal associations between placental ZIKV infection and measures of autophagy in human primary cell culture and congenital infection cases, as well as an experimental non-human primate (marmoset, Callithrix jacchus) model. Primary trophoblast cells were isolated from human placentae (n = 10) and infected in vitro with ZIKV. Autophagy-associated gene expression (ULK-1, BECN1, ATG5, ATG7, ATG12, ATG16L1, MAP1LC3A, MAP1LC3B, p62/SQSTM1) was then determined by TaqMan qPCR to determine fold-change with ZIKV-infection. In in vivo validation experiments, autophagy genes LC3B and p62/SQSTM1 were probed using in situ hybridization (ISH) in the placentae of human Congenital Zika Syndrome (CZS) cases (n = 3) and ZIKV-infected marmoset placenta (n = 1) and fetal tissue (n = 1). Infected and uninfected villi were compared for mean density and co-localization of autophagic protein markers. Studies of primary cultured human trophoblasts revealed decreased expression of autophagy genes ATG5 and p62/SQSTM1 in ZIKV-infected trophoblasts [ATG5 fold change (±SD) 0.734-fold (±0.722), p = 0.036; p62/SQSTM1 0.661-fold (±0.666), p = 0.029]. Histologic examination by ISH and immunohistochemistry confirmed spatial association of autophagy and ZIKV infection in human congenital infection cases, as well as marmoset placental and fetal tissue samples. When quantified by densitometric data, autophagic protein LC3B, and p62/SQSTM1 expression in marmoset placenta were significantly decreased in in situ ZIKV-infected villi compared to less-infected areas [LC3B mean 0.951 (95% CI, 0.930-0.971), p = 0.018; p62/SQSTM1 mean 0.863 (95% CI, 0.810-0.916), p = 0.024]. In the current study, we observed that in the non-transformed human and non-human primate placenta, disruption (specifically down-regulation) of autophagy accompanies later ZIKV replication in vitro, in vivo, and in situ. The findings collectively suggest that dysregulated autophagy spatially and temporally accompanies placental ZIKV replication, providing the first in situ evidence in relevant primate pre-clinical and clinical models for the importance of timing of human therapeutic strategies aimed at agonizing/antagonizing autophagy. These studies have likely further implications for other congenitally transmitted viruses, particularly the RNA viruses, given the ubiquitous nature of autophagic disruption and dysregulation in host responses to viral infection during pregnancy.

Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation

Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn, Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.

In vitro study on the immunomodulatory effects of differently functionalized silver nanoparticles on human peripheral blood mononuclear cells.

The interaction of silver nanoparticles (AgNPs) with the immune system has not yet been sufficiently elucidated even though they belong to the most investigated and exploited group of nanomaterials. This study aimed to evaluate immunomodulatory effect of four different AgNPs on human peripheral blood mononuclear cells (hPBMCs). Fresh hPBMCs were exposed to the small sized (~ 10 nm) AgNPs immediately after isolation from the whole blood of healthy volunteers. The study considered coating-, time- and dose-dependent response of hPBMSc and stimulation of both early and intermediate activation of lymphocytes and monocytes using flow cytometry. The AgNPs differed in surface charge and were stabilised with polyvinyl pyrrolidone (PVP), poly-L-lysine (PLL), bis(2-ethylhexyl) sulfosuccinate sodium (AOT) or blood serum albumin (BSA). Response of hPBMCs to coating agents and ionic Ag form was evaluated to distinguish their effect from the AgNPs action as they may be released from the nanosurface. There was no significant effect of any tested AgNPs on relative count of hPBMCs subpopulations. The T-cells and monocytes were not activated after treatment with AgNPs, but the highest concentration of PLL- and BSA-AgNPs decreased density of CD4 and CD8 markers on T-helper and T-cytotoxic cells, respectively. The same AgNPs activated B- and NK-cells. Ionic Ag activated T-, B- and NK-cells, but at very higher concentration, whereas only PLL exhibited immunomodulatory activity. This study evidenced immunomodulatory activity of AgNPs that may be fine-tuned by the design of their surface functionalization.Graphic abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s00775-021-01898-0.

Linkage disequilibrium and effective population size in Brazilian Saanen goats

• To know linkage disequilibrium and effective population size permit to determine optimal marker density for genomic studies • The mean value of r 2 for this Saanen goat population in Brazil, between adjacent markers (∼52 kb), was 0.04 ± 0.06. • Results indicate the need to use a denser marker panel (∼ 300k) and to expand the size of the reference population. The knowledge about linkage disequilibrium (LD) and effective population size (N e ) is essential to determine the optimal marker density required for performing genomic studies. In this study, the aim was to estimate the LD and N e of a Saanen goat population using a panel of 62,557 single nucleotide polymorphisms (SNPs), Axiom_OviCap (Caprine), customized by Affimetrix. Twenty-four males and 916 females were genotyped from herds from the Dairy Goats Breeding Program (from Portuguese Programa de Melhoramento Genético de Caprinos Leiteiros - Capragene®). The decay of the LD was analysed using quadratic correlation coefficient (r 2 ) for all pairs of markers on each chromosome. To estimate N e the values of r 2 combined with the distance between the markers was used, based on the physical map used for the LD analysis. The mean value of r 2 between adjacent markers (∼52 kb) was 0.04 ± 0.06. Among chromosomes, the range of values varied from 0.03 ± 0.06 to 0.05 ± 0.08. The LD fell rapidly from the first to the second distance range. In later intervals, the average of r 2 decreased more gradually. Moderate levels of r 2 (> 0.20), on a scale of 0–1, were observed only in the genetic distance classes up to 20 kb. The estimate of N e decreased over time, ranging from 42 animals, 19 past generations, to 15 animals in the current generation. The observed results have practical applications for performing genomic studies, and indicate the need to use a denser marker panel (∼ 300k), since the LD level was relatively small even at short distances, and it is essential to expand the size of the reference population.

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array.

Wheat is one of the most important cereal crops worldwide. A consensus map combines genetic information from multiple populations, providing an effective alternative to improve the genome coverage and marker density. In this study, we constructed a consensus map from three populations of recombinant inbred lines (RILs) of wheat using a 90K single nucleotide polymorphism (SNP) array. Phenotypic data on plant height (PH), spike length (SL), and thousand-kernel weight (TKW) was collected in six, four, and four environments in the three populations, and then used for quantitative trait locus (QTL) mapping. The mapping results obtained using the constructed consensus map were compared with previous results obtained using individual maps and previous studies on other populations. A simulation experiment was also conducted to assess the performance of QTL mapping with the consensus map. The constructed consensus map from the three populations spanned 4558.55 cM in length, with 25,667 SNPs, having high collinearity with physical map and individual maps. Based on the consensus map, 21, 27, and 19 stable QTLs were identified for PH, SL, and TKW, much more than those detected with individual maps. Four PH QTLs and six SL QTLs were likely to be novel. A putative gene called TraesCS4D02G076400 encoding gibberellin-regulated protein was identified to be the candidate gene for one major PH QTL located on 4DS, which may enrich genetic resources in wheat semi-dwarfing breeding. The simulation results indicated that the length of the confidence interval and standard errors of the QTLs detected using the consensus map were much smaller than those detected using individual maps. The consensus map constructed in this study provides the underlying genetic information for systematic mapping, comparison, and clustering of QTL, and gene discovery in wheat genetic study. The QTLs detected in this study had stable effects across environments and can be used to improve the wide adaptation of wheat cultivars through marker-assisted breeding.

Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing

BackgroundGenome-wide data are invaluable to characterize differentiation and adaptation of natural populations. Reduced representation sequencing (RRS) subsamples a genome repeatedly across many individuals. However, RRS requires careful optimization and fine-tuning to deliver high marker density while being cost-efficient. The number of genomic fragments created through restriction enzyme digestion and the sequencing library setup must match to achieve sufficient sequencing coverage per locus. Here, we present a workflow based on published information and computational and experimental procedures to investigate and streamline the applicability of RRS.ResultsIn an iterative process genome size estimates, restriction enzymes and size selection windows were tested and scaled in six classes of Antarctic animals (Ostracoda, Malacostraca, Bivalvia, Asteroidea, Actinopterygii, Aves). Achieving high marker density would be expensive in amphipods, the malacostracan target taxon, due to the large genome size. We propose alternative approaches such as mitogenome or target capture sequencing for this group. Pilot libraries were sequenced for all other target taxa. Ostracods, bivalves, sea stars, and fish showed overall good coverage and marker numbers for downstream population genomic analyses. In contrast, the bird test library produced low coverage and few polymorphic loci, likely due to degraded DNA.ConclusionsPrior testing and optimization are important to identify which groups are amenable for RRS and where alternative methods may currently offer better cost-benefit ratios. The steps outlined here are easy to follow for other non-model taxa with little genomic resources, thus stimulating efficient resource use for the many pressing research questions in molecular ecology.

The simulation of 3D hypervelocity spallation using a hydrocode PAGOSA with FLIP+MPM

In this work, a hydrocode PAGOSA with FLIP+MPM is presented and exercised to investigate the fracture in ductile material. The merit of PAGOSA with FLIP+MPM to solve the advection problem is first illustrated by a solid piston periodically moving in a sealed tube with air. Furthermore, the ability of PAGOSA with FLIP+MPM to capture the fracture in material is shown by a simple stretching fracture in ductile material. In both of two benchmark problems, the PAGOSA results and analytical solutions are also presented for comparison. Then PAGOSA with FLIP+MPM is used to model complex spall in ductile material, which is a challenging problem in engineering applications. The convergences of PAGOSA with FLIP+MPM—based on both the mesh size and the marker density—are investigated by monitoring free surface velocity. To further show the ability of PAGOSA with FLIP+MPM to predict fracture in ductile material, the numerical results are compared with the experimental results and other published numerical results. Moreover, the effect of the spall parameter in PAGOSA with FLIP+MPM on the numerical simulation is also analyzed by investigating the free surface velocity. Finally, the effects of the peak compressive stress, the tensile strain rate, and the loading rate on the spallation are further investigated. The numerical results show that PAGOSA with FLIP+MPM can improve PAGOSA's performance when applied to predicting fracture in ductile materials, and is robust enough to accurately predict spall fracture in ductile materials.

Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K–40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.

Prediction in High-Dimensional Linear Models and Application to Genomic Selection Under Imperfect Linkage Disequilibrium

Abstract Genomic selection (GS) consists in predicting breeding values of selection candidates, using a large number of genetic markers. An important question in GS is to determine the number of markers required for a good prediction. For this purpose, we introduce new proxies for the accuracy of the prediction. These proxies are suitable under sparse genetic map where it is likely to observe some imperfect linkage disequilibrium, that is, the situation where the alleles at a gene location and at a marker located nearby vary. Moreover, our suggested proxies are helpful for designing cost-effective SNP chips based on a moderate density of markers. We analyse rice data from Los Banos, Philippines and focus on the flowering time collected during the dry season 2012. Using different densities of markers, we show that at least 1553 markers are required to implement GS. Finding the optimal number of markers is crucial in order to optimize the breeding program.

Genome-Wide Analyses Reveal the Genetic Architecture and Candidate Genes of Indicine, Taurine, Synthetic Crossbreds, and Locally Adapted Cattle in Brazil

Cattle population history, breeding systems, and geographic subdivision may be reflected in runs of homozygosity (ROH), effective population size (Ne), and linkage disequilibrium (LD) patterns. Thus, the assessment of this information has become essential to the implementation of genomic selection on purebred and crossbred cattle breeding programs. In this way, we assessed the genotype of 19 cattle breeds raised in Brazil belonging to taurine, indicine, synthetic crossbreds, and Iberian-derived locally adapted ancestries to evaluate the overall LD decay patterns, Ne, ROH, and breed composition. We were able to obtain a general overview of the genomic architecture of cattle breeds currently raised in Brazil and other tropical countries. We found that, among the evaluated breeds, different marker densities should be used to improve the genomic prediction accuracy and power of genome-wide association studies. Breeds showing low Ne values indicate a recent inbreeding, also reflected by the occurrence of longer ROH, which demand special attention in the matting schemes to avoid extensive inbreeding. Candidate genes (e.g., ABCA7, PENK, SPP1, IFNAR1, IFNAR2, SPEF2, PRLR, LRRTM1, and LRRTM4) located in the identified ROH islands were evaluated, highlighting biological processes involved with milk production, behavior, rusticity, and fertility. Furthermore, we were successful in obtaining the breed composition regarding the taurine and indicine composition using single-nucleotide polymorphism (SNP) data. Our results were able to observe in detail the genomic backgrounds that are present in each breed and allowed to better understand the various contributions of ancestor breeds to the modern breed composition to the Brazilian cattle.

Learning meaningful controls for fluids

While modern fluid simulation methods achieve high-quality simulation results, it is still a big challenge to interpret and control motion from visual quantities, such as the advected marker density. These visual quantities play an important role in user interactions: Being familiar and meaningful to humans, these quantities have a strong correlation with the underlying motion. We propose a novel data-driven conditional adversarial model that solves the challenging and theoretically ill-posed problem of deriving plausible velocity fields from a single frame of a density field. Besides density modifications, our generative model is the first to enable the control of the results using all of the following control modalities: obstacles, physical parameters, kinetic energy, and vorticity. Our method is based on a new conditional generative adversarial neural network that explicitly embeds physical quantities into the learned latent space, and a new cyclic adversarial network design for control disentanglement. We show the high quality and versatile controllability of our results for density-based inference, realistic obstacle interaction, and sensitive responses to modifications of physical parameters, kinetic energy, and vorticity. Code, models, and results can be found at https://github.com/RachelCmy/den2vel.

Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs.

Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.

Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata).

Disease outbreaks are a major threat to the aquaculture industry, and can be controlled by selective breeding. With the development of high-throughput genotyping technologies, genomic selection may become accessible even in minor species. Training population size and marker density are among the main drivers of the prediction accuracy, which both have a high impact on the cost of genomic selection. In this study, we assessed the impact of training population size as well as marker density on the prediction accuracy of disease resistance traits in European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata). We performed a challenge to nervous necrosis virus (NNV) in two sea bass cohorts, a challenge to Vibrio harveyi in one sea bass cohort and a challenge to Photobacterium damselae subsp. piscicida in one sea bream cohort. Challenged individuals were genotyped on 57K–60K SNP chips. Markers were sampled to design virtual SNP chips of 1K, 3K, 6K, and 10K markers. Similarly, challenged individuals were randomly sampled to vary training population size from 50 to 800 individuals. The accuracy of genomic-based (GBLUP model) and pedigree-based estimated breeding values (EBV) (PBLUP model) was computed for each training population size using Monte-Carlo cross-validation. Genomic-based breeding values were also computed using the virtual chips to study the effect of marker density. For resistance to Viral Nervous Necrosis (VNN), as one major QTL was detected, the opportunity of marker-assisted selection was investigated by adding a QTL effect in both genomic and pedigree prediction models. As training population size increased, accuracy increased to reach values in range of 0.51–0.65 for full density chips. The accuracy could still increase with more individuals in the training population as the accuracy plateau was not reached. When using only the 6K density chip, accuracy reached at least 90% of that obtained with the full density chip. Adding the QTL effect increased the accuracy of the PBLUP model to values higher than the GBLUP model without the QTL effect. This work sets a framework for the practical implementation of genomic selection to improve the resistance to major diseases in European sea bass and gilthead sea bream.

Genomic prediction of growth traits in scallops using convolutional neural networks

Deep learning neural networks applied to the genomic prediction of complex traits have been of great interest in recent years. Previous studies primarily used simulated phenotypes or/and genotypes in plants and animals. The properties of deep learning models used in genomic selection are not well characterized and not well validated with real datasets. In the present study, we evaluated the performance of a class of deep learning methods called convolutional neural networks (CNNs) in the genomic prediction of four quantitative traits (e.g., shell length, shell height, shell width, and total weight) in a Bay scallop (Argopecten irradians irradians) population. The results were compared with those obtained from two linear models, RR-GBLUP and Bayes B, and multilayer perceptron neural networks (MLPs). One-convolutional layer CNNs with an optimal structure, which was obtained by using AIC or BIC method, had roughly comparable prediction accuracies on the four quantitive traits in the scallop population. Overall, CNNs outperformed RR-GBLUP, Bayes B and MLPs on shell height, shell width and total weight, and performed slightly worse than only Bayes B on shell length. MLPs gave the least accurate predictions on average among the four types of models. Because MLPs had far more parameters to estimate than the two linear models, and their predictions were challenged by the overfitting problem. Genomic prediction accuracy varied with SNP panel size and training population size.The impact of varied marker densities and two GWAS-based scenarios for SNP selection on genomic prediction accuracy was investigated as well. The present results provide evidence that supports the use of convolutional neural networks for genomic prediction of complex traits in scallops, yet the optimal structures of CNNs remained to be exploited in future studies.

Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle.

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of 'marker density' seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of 'marker density' based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σd2, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20-30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.