Genotyping marker density reduction is not an effective approach in long-term prediction-based breeding of cross-pollinated crops

Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computational time-consumption and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles, but not for the long term. Moreover, to the best of our knowledge, no GS models consider the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are an important and unexplored issue in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models (additive, additive-dominant, general (GCA), and specific combining ability (SCA)). Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or SCA for reciprocal.

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

Background Genome-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. Results In 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. Conclusions Prior 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.

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.

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.

Evaluation of Bayesian Alphabet and GBLUP Based on Different Marker Density for Genomic Prediction in Alpine Merino Sheep

ABSTRACT The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the genomic prediction of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesC π and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted 5-fold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

Profiling of immune checkpoint biomarkers by multiplex immunofluorescence in breast cancer brain metastases.

2021 Background: Despite potential clinical implications, the complexity of immune microenvironment in breast cancer (BC) brain metastases (BM) is still poorly understood. Multiplex immunofluorescence (mIF) allows simultaneous visualization of several IF labeled proteins while maintaining spatial information. This novel technique can be used to comprehensively describe BCBM immune microenvironment, potentially providing useful information to guide novel therapeutic approaches. Methods: Clinical data and archival BM samples from 60 BC patients undergoing neurosurgery (2003-2018) at three institutions were collected. BCBMs were characterized using a custom mIF panel, including immune checkpoint and co-inhibitory molecules (CD3, PD1, PD-L1, TIM3, LAG3, CD163) and localization (keratin for tumor recognition) markers. Mean marker density was determined by digital image analysis (positive cells/mm2) and classified in tumor and stroma areas. Associations between immune marker densities, BC subtype and overall survival from BM diagnosis (OS) were studied. Results: Sixty BCBM samples were analyzed; 32% HR+/HER2-, 38% HER2+, 30% HR-/HER2-. At a median follow-up of 43 months, the only clinical variable associated with OS was BC subtype (shortest for HR-/HER2- and longest for HER2+, p=0.02). In the total sample area and tumor area, no significant difference in marker density was observed according to BC subtype. In the stroma area, a significant difference in TIM3+ cell density was observed according to BC subtype (highest density in HR+/HER2- and lowest density in HER2+ tumors, Kruskal-Wallis p=0.017). Higher CD163 density (a marker of M2 macrophage polarization), both in the tumor and in the stroma area, was significantly associated with worse OS, even after correction by BC subtype. In the subgroup of patients with HR+/HER2- BCBM, high TIM3+ cell density in the stroma area was significantly associated with longer OS (median OS 54.1 versus 23 months respectively for TIM3+ density above and below median value; p=0.01). Conclusions: In BCBM, stromal TIM3+ immune infiltrate differs according to BC subtype. M2 macrophage polarization is consistently associated with worse OS across all BC subtypes and might represent a potential therapeutic target for these patients.[Table: see text]

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

Genome-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. In 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. Prior 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.

Comprehensive Characterization and Validation of Chromosome-Specific Highly Polymorphic SSR Markers From Pomegranate (Punica granatum L.) cv. Tunisia Genome

The simple sequence repeat (SSR) survey of ‘Tunisia’ genome (296.85 Mb) identified a total of 365,279 perfect SSRs spanning eight chromosomes, with a mean marker density of 1,230.6 SSRs/Mb. We found a positive trend in chromosome length and the SSR abundance as marker density enhanced with a shorter chromosome length. The highest number of SSRs (60,708) was mined from chromosome 1 (55.56 Mb), whereas the highest marker density (1,294.62 SSRs/Mb) was recorded for the shortest chromosome 8 (27.99 Mb). Furthermore, we categorized all SSR motifs into three major classes based on their tract lengths. Across the eight chromosomes, the class III had maximum number of SSR motifs (301,684, 82.59%), followed by the class II (31,056, 8.50%) and the class I (5,003, 1.37%). Examination of the distribution of SSR motif types within a chromosome suggested the abundance of hexanucleotide repeats in each chromosome followed by dinucleotides, and these results are consistent with ‘Tunisia’ genome features as a whole. Concerning major repeat types, AT/AG was the most frequent (14.16%), followed by AAAAAT/AAAAAG (7.89%), A/C (7.54%), AAT/AAG (5.23%), AAAT/AAAG (4.37%), and AAAAT/AAAAG (1.2%) types. We designed and validated a total of 3,839 class I SSRs in the ‘Tunisia’ genome through electronic polymerase chain reaction (ePCR) and found 1,165 (30.34%) SSRs producing a single amplicon. Then, we selected 906 highly variable SSRs (&gt; 40 nt) from the ePCR-verified class I SSRs and in silico validated across multiple draft genomes of pomegranate, which provided us a subset of 265 highly polymorphic SSRs. Of these, 235 primers were validated on six pomegranate genotypes through wet-lab experiment. We found 221 (94%) polymorphic SSRs on six genotypes, and 187 of these SSRs had ≥ 0.5 PIC values. The utility of the developed SSRs was demonstrated by analyzing genetic diversity of 30 pomegranate genotypes using 16 HvSSRs spanning eight pomegranate chromosomes. In summary, we developed a comprehensive set of highly polymorphic genome-wide SSRs. These chromosome-specific SSRs will serve as a powerful genomic tool to leverage future genetic studies, germplasm management, and genomics-assisted breeding in pomegranate.

Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

ABSTRACTThe selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using “static” training sets and populations, which are adequate only to predict a breeding program’s initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequili brium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.