Genomic BLUP Research Articles

Genome-Wide Association and Genomic Prediction of Alfalfa (Medicago sativa L.) Biomass Yield Under Drought Stress.

Developing drought-resistant alfalfa (Medicago sativa L.) that maintains high biomass yield is a key breeding goal to enhance productivity in water-limited areas. In this study, 424 alfalfa breeding families were analyzed to identify molecular markers associated with biomass yield under drought stress and to predict high-merit plants. Biomass yield was measured from 18 harvests from 2020 to 2023 in a field trial with deficit irrigation. A total of 131 significant markers were associated with biomass yield, with 80 markers specifically linked to yield under drought stress; among these, 19 markers were associated with multiple harvests. Finally, genomic best linear unbiased prediction (GBLUP) was employed to obtain predictive accuracies (PAs) and genomic estimated breeding values (GEBVs). Removing low-informative SNPs [SNPs with p-values > 0.05 from the additive Genome-Wide Association (GWAS) model] for GBLUP increased PA by 47.3%. The high number of markers associated with yield under drought stress and the highest PA (0.9) represent a significant achievement in improving yield under drought stress in alfalfa.

Genomic prediction accounting for dominance and epistatic genetic effects on litter size traits in Large White pigs.

Litter size traits of sows are crucial for the economic benefits of the pig industry. Three phenotypic traits of 1,206 Large White (LW) pigs, that is, the total number born (TNB), number born alive (NBA), and number of healthy piglets (NHP), were recorded. We evaluated a series of genomic best linear unbiased prediction (GBLUP) models that sequentially added additive effects (model A), dominance effects (model A+D), and epistatic effects (model A+D+AA, model A+D+AA+AD, and model A+D+AA+AD+DD) using chip data and imputed whole-genome sequencing (WGS) data to estimate genetic parameters and predictive accuracy. The reproductive traits of sows showed low heritability in this study, with narrow heritability of the three traits ranging from 0.030 to 0.064, and broad heritability ranging from 0.125 to 0.145. The inclusion of non-additive effects in the model improved the accuracy of genomic selection. In the chip data, compared with that of the A model, the A+D+AA+AD+DD model showed the greatest increase in accuracy for TNB, NBA, and NHP, with improvements of 1.78, 1.67, and 1.74%, respectively. Additionally, the accuracy of the imputed WGS data was greater compared to the chip data. For the TNB, NBA, and NHP traits, the predictive accuracy of the imputed WGS data improved by 3.26, 7.72, and 3.00%, respectively, compared with that of the chip data. In summary, these results suggest that non-additive effects in genomic selection could improve prediction accuracy and should be considered in pig genomic evaluation procedures.

Optimizing Genomic Selection Methods to Improve Prediction Accuracy of Sugarcane Single-Stalk Weight

Sugarcane (Saccharum spp. Hybrids), serving as a vital sugar and energy crop, holds immense development potential on a global scale. In the process of sugarcane breeding and variety improvement, single-stalk weight stands as a crucial selection criterion. By cultivating sugarcane varieties with heavier single stalks, robust growth, high yields, and superior quality, the planting efficiency and market competitiveness of sugarcane can be further enhanced. Single-stalk weight was determined by measuring individual stalks three times in the field, calculating the average value as the phenotypic expression. The distribution of single-stalk weights in the orthogonal and reciprocal populations revealed coefficients of variation of 19.3% and 17.7%, respectively, with the reciprocal population showing greater genetic stability. After rigorous filtering of Hyper_seq_FD sequencing data from 409 sugarcane samples, we identified 31,204 high-quality single-nucleotide polymorphisms (SNPs) evenly distributed across all 32 chromosomes, providing a comprehensive representation of the sugarcane genome. In this study, we evaluated the predictive performance of various genomic selection (GS) methods for single-stalk weight in the 299 orthogonal population, with the male parent being GZ_73-204 and the female parent being GZ_P72-1210, and in the 108 reciprocal population, with the male parent being GZ_P72-1210 and the female parent being GZ_73-204. Initially, we compared the performance of five prediction approaches, including genomic best linear unbiased prediction (GBLUP), single-step genomic best linear unbiased prediction (SSBLUP), Bayes A, machine learning (ML), and deep learning (DL) approaches. The results showed that the GBLUP model had the highest prediction accuracy, at 0.35, while the deep learning model had the lowest accuracy, at 0.20. To improve prediction accuracy, we assigned different scores to various regions of the sugarcane genome based on gene annotation information, thereby giving different weights to SNPs located in these regions. Additionally, we incorporated inbred and outbred populations as fixed effects into the model. The optimized SSBLUP model achieved a prediction accuracy of 0.44, which was a 17% improvement over the original SSBLUP model and a 9% increase compared to the originally optimal GBLUP model. The research results indicate that it is crucial to fully consider genomic structural regions, population structure characteristics, and fixed effects in GS predictions.

Single-step genomic BLUP (ssGBLUP) effectively models small cattle populations: lessons from the Israeli-Holstein Herdbook

BackgroundRoutine genomic-estimated breeding values (gEBVs) are computed for the Israeli dairy cattle population by a two-step methodology in combination with the much larger Dutch population. Only sire genotypes are included. This work evaluated the contribution of cow genotypes obtained from the Israeli Holstein population to enhance gEBVs predictions via single-step genomic best-linear unbiased prediction (ssGBLUP). The gEBV values of 141 bulls with daughter information and high reliabilities for 305-day lactation yield of milk, fat, and protein were compared with the bulls’ predicted ssGBLUP-gEBVs using a truncated dataset omitting production data of the last five years. We investigated how these sire gEBVs were affected by varying polygenic weights in the genomic relationship matrices and by deleting old phenotypic or genotypic records.ResultsThe correlations of the predicted gEBVs for milk, fat and protein computed from the truncated data with the current gEBVs based also on daughter records of the last five years were 0.64, 0.57, and 0.56, respectively, for a polygenic weight of 0.5, similar to the values achieved by the current two-step methodology. The regressions of the current gEBVs on the predicted values were 0.9 for milk and 0.7 for fat and protein. Genotyping of 1.8-5 cows had the approximate statistical power of one additional bull depending on the trait. Omitting phenotype records earlier than 2000 resulted in similar gEBV values. Omitting genotypes before 1995 improved the regression coefficients. For all experiments, varying the polygenic weights over the range of 0.1 to 0.9 resulted in a trade-off between correlations and overestimation of gEBVs for young bulls.ConclusionsThe model suffers from overestimation of the predicted values for young bulls. The time interval used for inclusion of genotypic and phenotypic records and adjustment of the polygenic weight can improve gEBV predictions and should be tuned to fit the tested population. For relatively small populations, genotyping of cows can significantly increase the reliability of gEBVs computed by single-step methodology. By extrapolation of our results, records of ~ 13,000 genotyped cows should provide a sufficiently large training population to obtain reliable estimates of gEBVs using ssGBLUP.

Genomic Prediction Using Imputed Whole-Genome Sequence Data in Australian Angus Cattle.

Whole-genome sequence (WGS) data was used to estimate genomic breeding values for growth and carcass traits in Australian Angus cattle. The study aimed to compare the accuracy and bias of genomic predictions with three marker densities, including 50K, high-density (HD) and WGS. The dataset used in this study consisted of animals born between 2013 and 2022. Body weight traits included birthweight, weight at 400 days and weight at 600 days of age. The carcass traits were carcass weight, carcass intramuscular fat and carcass marbling score. The accuracy and bias of prediction were assessed using the cross-validation. Further, for the growth traits, animals in the validation group were subdivided into two subgroups, which were moderately or highly related to the reference. Genomic best linear unbiased prediction (GBLUP) was used to compare genomic predictions with the three marker densities. The prediction accuracies were generally similar across the marker densities, ranging between 0.61 and 0.68 for the body weight traits and between 0.40 and 0.52 for the carcass traits. However, the accuracies marginally decreased as the marker density increased for all the traits studied. A similar lack of difference was found when considering the accuracy by the relatedness subgroups. The results indicated that no meaningful difference in prediction accuracy was estimated when comparing the three marker densities due to the population structure. In conclusion, there was no substantial improvement in genomic prediction when using the WGS in this study.

Genomic selection of resistance to acute hepatopancreatic necrosis disease in Litopenaeus vannamei

Acute hepatopancreatic necrosis disease (AHPND) is a major bacterial disease caused by Vibrio parahaemolyticus (VPAHPND) that results in significant economic losses to Pacific white shrimp Litopenaeus vannamei aquaculture. Breeding of disease-resistant broodstocks is regarded as a fundamental strategy for solving the disease problem. This study evaluated the feasibility of genomic selection (GS) for enhancing the AHPND resistance of L.vannamei. Following the VPAHPND challenge, the survival time was utilized as a phenotypic measure to assess the AHPND resistance. The reference population consisted of 447 genotyped and 288 no-genotyped individuals with phenotypes. The candidate population consisted of 206 uninfected and genotyped individuals. The liquid chip “Yellow Sea Chip No.1” with 40 K single nucleotide polymorphism (SNP) generated genotypes for the reference and candidate populations. Heritabilities of survival time obtained using the average information REML method with a relationship matrix (A or H) and an animal model were 0.16 ± 0.06 and 0.22 ± 0.07, respectively. Predictive accuracies of pedigree-based best linear unbiased prediction (PBLUP), single-step genomic BLUP (ssGBLUP), and single-step Bayesian regression (ssBR) were evaluated using random, full-sib and between-family cross-validation. Compared to PBLUP, the predictive accuracies of ssGBLUP and ssBR for the survival time increased by 15.00 % and 67.50 % in random cross-validation, and 13.33 % and 53.33 % in full-sib cross-validation, respectively. All methods exhibited a reduction in predictive accuracy when moving from the random cross-validation or full-sib cross-validation to the between-family cross-validation. The predictive accuracies of Genomic BLUP (GBLUP) and Bayesian A (BayesA) were evaluated using full-sib cross-validation. Compared to GBLUP, the predictive accuracy of BayesA increased by 33.33 %. ssBR was used to predict the candidates' genomic estimated breeding values (GEBVs). 80 candidates with high GEBVs, 86 with medium GEBVs, and 40 with low GEBVs were selected to breed offspring with a spectrum of resistance levels. In the VPAHPND challenge test, the high resistance offspring had mean survival times that were 10.15 % longer than the moderate resistance offspring and 102.01 % longer than the low resistance offspring. This study validates the practicality and success of GS in improving resistance to AHPND in L.vannamei.

Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction.

In plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations. Various sparse testing methods have been explored to optimize resource use. This study employed Incomplete Block Design (IBD) to allocate lines to environments, ensuring not all lines were tested in every environment. We compared IBD to Random line allocation, maintaining a consistent number of environments per line across both methods. The primary objective was to estimate grain yield performance of lines using Genomic Estimated Breeding Values (GEBVs) computed through six Genomic Best Linear Unbiased Predictor (GBLUP) methods. In the first five methods, missing values were predicted before cross-environment adjustment; in the sixth, adjustment was performed directly. Using the Bayesian GBLUP model, we analyzed genotype performance under both IBD and random allocation. Results indicate that computing GEBVs for a target population of environments (TPE) using available phenotype and marker data is effective for selection. The IBD method showed superior performance with less variability compared to random allocation. These findings suggest that using IBD designs can enhance selection accuracy and efficiency, and that pre-adjustment prediction of missing lines may not necessarily improve selection outcomes.

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

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

Genetic Perspectives on Feed Event, Meal and Feed Efficiency Traits in Bos taurus indicus Beef Cattle.

Electronic feeders record feeding behaviour as feed events by tracking the animal's in-out visits to the feeder. Another way to measure feeding behaviour is based on meals. However, the two approaches provide different outcomes. The objectives of this study were to estimate genetic parameters (heritabilities and genetic and phenotypic correlations) for feed event and meal traits, and their genetic and phenotypic correlations with feed efficiency traits in Nellore cattle. The present study analysed six feed event traits (DMIFE: dry matter intake per feed event, FED: feed event duration, TBFE: time between feed events, FTd: feeding time per day, FEd: feed events per day, and FR: feeding rate), six meal traits (DMIME: DMI per meal, MED: meal duration, TBME: time between meals, MC: meal criterion, MTd: meal time per day, and MEd: meals per day), and three feed efficiency traits (ADG: average daily gain, DMI, and RFI: residual feed intake). The traits were measured in feed efficiency tests of Nellore cattle (age = 280 ± 41 days and body weight = 258 ± 47 kg at enrolment). The MC was calculated for each animal and ranged from 1.70 to 64.0 min, i.e., any pair of feed events separated by less than the MC value was considered part of the same meal. The heritabilities and correlations were estimated by fitting univariate and bivariate animal models, respectively, using single-step genomic BLUP. The highest heritabilities for feed event traits were 0.35 ± 0.06 (FED), 0.39 ± 0.06 (FTd), and 0.50 ± 0.05 (FTd), and for meal traits were 0.31 ± 0.06 (MED) and 0.45 ± 0.06 (MTd). The genetic correlation between feed event traits and meal traits were weak. FR, FED, and FTd had moderate genetic correlations with RFI (-0.56 ± 0.11, 0.44 ± 0.11, 0.60 ± 0.08, respectively). These results indicate that more efficient animals spent less time at the feeder per feed event and per day, and eat faster compared to less efficient animals. In conclusion, feed event and meal traits must be treated as distinct groups of traits since the genetic and phenotypic correlations were, in general, weak to moderate. Among feed event versus meal traits, feed event traits are more favourable to explain the genetic relationships of feeding behaviour with feed efficiency-related traits.

Identification of candidate genes and genomic prediction for early heifer pregnancy in Nelore beef cattle

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (λ) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained >0.5 % of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6 % using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the λ values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (∼18 %) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.

Comprehensive genome-wide analysis of genetic loci and candidate genes associated with litter traits in purebred Berkshire pigs of Korea.

The objective of this study was to identify genomic regions and candidate genes associated with the total number of piglets born (TNB), number of piglets born alive (NBA), and total number of stillbirths (TNS) in Berkshire pigs. This study used a total of 11,228 records and 2,843 single-nucleotide polymorphism (SNP) data obtained from Illumina porcine 60 K and 80 K chips. The estimated genomic breeding values (GEBVs) and SNP effects were estimated using weighted single-step genomic BLUP (WssGBLUP). The heritabilities of the TNB, NBA, and TNS were determined using single-step genomic best linear unbiased prediction (ssGBLUP). The heritability estimates were 0.13, 0.12, and 0.015 for TNB, NBA, and TNS, respectively. When comparing the accuracy of breeding value estimates, the results using pedigree-based BLUP (PBLUP) were 0.58, 0.60, and 0.31 for TNB, NBA, and TNS, respectively. In contrast, the accuracy increased to 0.67, 0.66, and 0.42 for TNB, NBA, and TNS, respectively, when using WssGBLUP, specifically in the last three iterations. The results of weighted single-step genome-wide association studies (WssGWAS) showed that the highest variance explained for each trait was predominantly located in the Sus scrofa chromosome 5 (SSC5) region. Specifically, the variance exceeded 4% for TNB, 3% for NBA, and 6% for TNS. Within the SSC5 region (12.26 to 12.76 Mb), which exhibited the highest variance for TNB, 20 SNPs were identified, and five candidate genes were identified: TIMP3, SYN3, FBXO7, BPIFC, and RTCB. The identified SNP markers for TNB, NBA, and TNS were expected to provide valuable information for genetic improvement as an understanding of their expression and genetic architecture in Berkshire pigs. With the accumulation of more phenotype and SNP data in the future, it is anticipated that more effective SNP markers will be identified.

Genomic selection for agronomical phenotypes using genome-wide SNPs and SVs in pearl millet.

Pearl millet is an essential crop worldwide, with noteworthy resilience to abiotic stress, yet the advancement of its breeding remains constrained by the underutilization of molecular-assisted breeding techniques. In this study, we collected 1,455,924 single nucleotide polymorphism (SNP) and 124,532 structural variant (SV) markers primarily from a pearl millet inbred germplasm association panel consisting of 242 accessions including 120 observed phenotypes, mostly related to the yield. Our findings revealed that the SV markers had the capacity to capture genetic diversity not discerned by SNP markers. Furthermore, no correlation in heritability was observed between SNP and SV markers associated with the same phenotype. The assessment of the nine genomic prediction models revealed that SV markers performed better than SNP markers. When using the SV markers as the predictor variable, the genomic BLUP model achieved the best performance, while using the SNP markers, Bayesian methods outperformed the others. The integration of these models enabled the identification of eight candidate accessions with high genomic estimated breeding values (GEBV) across nine phenotypes using SNP markers. Four candidate accessions were identified with high GEBV across 22 phenotypes using SV markers. Notably, accession 'P23' emerged as a consistent candidate predicted based on both SNP and SV markers specifically for panicle number. These findings contribute valuable insights into the potential of utilizing both SNP and SV markers for genomic prediction in pearl millet breeding. Moreover, the identification of promising candidate accessions, such as 'P23', underscores the accelerated prospects of molecular breeding initiatives for enhancing pearl millet varieties.

Cross-ancestry meta-genome-wide association studies provide insights to the understanding of semen traits in pigs

Semen traits play a crucial role in pig reproduction and fertility. However, limited data availability hinder a comprehensive understanding of the genetic mechanisms underlying these traits. In this study, we integrated 597 299 ejaculates and 3 596 sequence data to identify genetic variants and candidate genes related to four semen traits, including sperm progressive motility (MOT), semen volume, sperm concentration (CON), and effective sperm count (SUM). A cross-ancestry meta−genome-wide association study was conducted to detect 163 lead single nucleotide polymorphisms (SNPs) associated with MOT, CON, and SUM. Subsequently, transcriptome-wide association studies and colocalisation analyses were integrated to identify 176 candidate genes, many of which have documented roles in spermatogenesis or male mammal semen traits. Our analysis highlighted the potential involvement of CSM5, PDZD9, and LDAF1 in regulating semen traits through multiple methods. Finally, to validate the function of significant SNPs, we performed genomic feature best linear unbiased prediction in 348 independent pigs using identified trait-related SNP subsets as genomic features. We found that integrating the top 0.1, 1, and 5% significant SNPs as genomic features could enhance genomic prediction accuracy for CON and MOT compared to traditional genomic best linear unbiased prediction. This study contributes to a comprehensive understanding of the genetic mechanisms of boar semen traits and provides insight for developing genomic selection models.

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated to large sections of a genome. By analyzing allele sharing between individuals, one may calculate realized genomic relationships from single-nucleotide polymorphisms (SNPs) data rather than relying on known pedigree relationships under polygenic model. The traditional approaches in genome-wide prediction (GWP) of quantitative phenotypes utilize genomic relationships in fixed global covariance modeling, possibly with some nonlinear kernel mapping (for example Gaussian processes). On the other hand, the DL approaches proposed so far for GWP fail to take into account the non-Euclidean graph structure of relationships between individuals over several generations. In this paper, we propose one global convolutional neural network (GCN) and one local sub-sampling architecture (GCN-RS) that are specifically designed to perform regression analysis based on genomic relationship information. A GCN is tailored to non-Euclidean spaces and consists of several layers of graph convolutions. The GCN-RS architecture is designed to further improve the GCN's performance by sub-sampling the graph to reduce the dimensionality of the input data. Through these graph convolutional layers, the GCN maps input genomic markers to their quantitative phenotype values. The graphs are constructed using an iterative nearest neighbor approach. Comparisons show that the GCN-RS outperforms the popular Genomic Best Linear Unbiased Predictor method on one simulated and three real datasets from wheat, mice and pig with a predictive improvement of 4.4% to 49.4% in terms of test mean squared error. This indicates that GCN-RS is a promising tool for genomic predictions in plants and animals. Furthermore, GCN-RS is computationally efficient, making it a viable option for large-scale applications.

GWAS Enhances Genomic Prediction Accuracy of Caviar Yield, Caviar Color and Body Weight Traits in Sturgeons Using Whole-Genome Sequencing Data.

Caviar yield, caviar color, and body weight are crucial economic traits in sturgeon breeding. Understanding the molecular mechanisms behind these traits is essential for their genetic improvement. In this study, we performed whole-genome sequencing on 673 Russian sturgeons, renowned for their high-quality caviar. With an average sequencing depth of 13.69×, we obtained approximately 10.41 million high-quality single nucleotide polymorphisms (SNPs). Using a genome-wide association study (GWAS) with a single-marker regression model, we identified SNPs and genes associated with these traits. Our findings revealed several candidate genes for each trait: caviar yield: TFAP2A, RPS6KA3, CRB3, TUBB, H2AFX, morc3, BAG1, RANBP2, PLA2G1B, and NYAP1; caviar color: NFX1, OTULIN, SRFBP1, PLEK, INHBA, and NARS; body weight: ACVR1, HTR4, fmnl2, INSIG2, GPD2, ACVR1C, TANC1, KCNH7, SLC16A13, XKR4, GALR2, RPL39, ACVR2A, ADCY10, and ZEB2. Additionally, using the genomic feature BLUP (GFBLUP) method, which combines linkage disequilibrium (LD) pruning markers with GWAS prior information, we improved genomic prediction accuracy by 2%, 1.9%, and 3.1% for caviar yield, caviar color, and body weight traits, respectively, compared to the GBLUP method. In conclusion, this study enhances our understanding of the genetic mechanisms underlying caviar yield, caviar color, and body weight traits in sturgeons, providing opportunities for genetic improvement of these traits through genomic selection.

Integrating large-scale meta-analysis of genome-wide association studies improve the genomic prediction accuracy for combined pig populations.

The strategy of combining reference populations has been widely recognized as an effective way to enhance the accuracy of genomic prediction (GP). This study investigated the efficiency of genomic prediction using prior information and combined reference population. In total, prior information considering trait-associated single nucleotide polymorphisms (SNPs) obtained from meta-analysis of genome-wide association studies (GWAS meta-analysis) was incorporated into three models to assess the performance of GP using combined reference populations. Two different Yorkshire populations with imputed whole genome sequence (WGS) data (9,741,620 SNPs), named as P1 (1259 individuals) and P2 (1018 individuals), were used to predict genomic estimated breeding values for three live carcass traits, including backfat thickness, loin muscle area, and loin muscle depth. A 10 × 5 fold cross-validation was used to evaluate the prediction accuracy of 203 randomly selected candidate pigs from the P2 population and the reference population consisted of the remaining pigs from P2 and the stepwise added pigs from P1. By integrating SNPs with different p-value thresholds from GWAS meta-analysis downloaded from PigGTEx Project, the prediction accuracy of GBLUP, genomic feature BLUP (GFBLUP) and GBLUP given genetic architecture (BLUP|GA) were compared. Moreover, we explored effects of reference population size and heritability enrichment of genomic features on the prediction accuracy improvement of GFBLUP and BLUP|GA relative to GBLUP. The prediction accuracy of GBLUP using all WGS markers showed average improvement of 4.380% using the P1 + P2 reference population compared with the P2 reference population. Using the combined reference population, GFBLUP and BLUP|GA yielded 6.179% and 5.525% higher accuracies than GBLUP using all SNPs based on the single reference population, respectively. Positive regression coefficients were estimated in relation to the improvement in prediction accuracy (between GFBLUP/BLUP|GA and GBLUP) and the size of the reference as well as the heritability enrichment of genomic features. Compared to the classic GBLUP model, GFBLUP and BLUP|GA models integrating GWAS meta-analysis information increase the prediction accuracy and using combined populations with enlarged reference population size further enhances prediction accuracy of the two approaches. The heritability enrichment of genomic features can be used as an indicator to reflect weather prior information is accurately presented.

Unveiling Genetic Potential for Equine Meat Production: A Bioinformatics Approach.

In view of the predicted significant increase in global meat production, alternative sources such as horsemeat are becoming increasingly important due to their lower environmental impact and high nutritional value. This study aimed to identify SNP markers on the GeneSeek® Genomic Profiler™ Equine (Neogen, Lansing, MI, USA) that are important for horsemeat production traits. First, orthologous genes related to meat yield in cattle and common genes between horses and cattle within QTLs for body size and weight were identified. Markers for these genes were then evaluated based on predicted variant consequences, GERP scores, and positions within constrained elements and orthologous regulatory regions in pigs. A total of 268 markers in 57 genes related to meat production were analyzed. This resulted in 27 prioritized SNP markers in 22 genes, including notable markers in LCORL, LASP1, IGF1R, and MSTN. These results will benefit smallholder farmers by providing genetic insights for selective breeding that could improve meat yield. This study also supports future large-scale genetic analyses such as GWAS and Genomic Best Linear Unbiased Prediction (GBLUP). The results of this study may be helpful in improving the accuracy of genomic breeding values. However, limitations include reliance on bioinformatics without experimental validation. Future research can validate these markers and consider a wider range of traits to ensure accuracy in equine breeding.

Automated phenotyping empowered by deep learning for genomic prediction of body size in the tiger pufferfish, Takifugu rubripes

Although genomic selection (GS) is accelerating the genetic improvement of economically important traits in aquaculture species, the accurate phenotyping of thousands of individuals remains a bottleneck in GS programs. Computer vision (CV) technologies with the assistance of deep learning may provide a cost-efficient solution for automated phenotyping of fish body size. In this study, we designed a CV-based automated phenotyping system based on the deep learning model Mask R-CNN (region-based convolutional neural networks) and tested its ability to predict standard length (SL) of tiger pufferfish (Takifugu rubripes) fed low fishmeal (LFM) diet. Simultaneously, we investigated the impact of the automated phenotyping on genetic parameter estimation and genomic prediction for growth-related traits. We raised a test population (n = 1001) that was fed the LFM diet from three months old until harvest at 22 months. Manual phenotyping of the fish was performed at 10-, 14-, 17-, and 22-months-old. At the last two measurement times, images of each fish were collected to train Mask R-CNN for fish detection and pixel-level segmentation of the fish body excluding the tail fin. An automated phenotyping pipeline combining the trained Mask R-CNN and standard CV algorithms were designed to estimate SL. We found a high correlation (0.965–0.971) and low relative difference (0.015–0.027) between the CV-derived and manually measured SL, indicating high precision of our CV system. A high correlation (0.912–0.956) was also observed between CV-derived body area and manual body weight. The heritability estimations and genomic predictions on manual SLs were conducted using a Genomic Best Linear Unbiased Prediction (GBLUP) model using a total of 16,471 single nucleotide polymorphism (SNP). Estimated heritability at the four sampling points ranged from 0.573 to 0.622, and the predictive ability of genomic prediction for harvest size was 0.627. These values are comparable to those reported in previous studies collected from the population fed a normal fishmeal diet. Our heritability estimation and genomic prediction derived from automated SL measurements differed negligibly to those based on manual measurements. This finding indicates the utility of our CV-based automated phenotyping system for GS breeding programs.

To be or not to be tetraploid-the impact of marker ploidy on genomic prediction and GWAS of potato.

Cultivated potato, Solanum tuberosum L., is considered an autotetraploid with 12 chromosomes with four homologous phases. However, recent evidence found that, due to frequent large phase deletions in the genome, gene ploidy is not constant across the genome. The elite cultivar "Otava" was found to have an average gene copy number of 3.2 across all loci. Breeding programs for elite potato cultivars rely increasingly on genomic prediction tools for selection breeding and elucidation of quantitative trait loci underpinning trait genetic variance. These are typically based on anonymous single nucleotide polymorphism (SNP) markers, which are usually called from, for example, SNP array or sequencing data using a tetraploid model. In this study, we analyzed the impact of using whole genome markers genotyped as either tetraploid or observed allele frequencies from genotype-by-sequencing data on single-trait additive genomic best linear unbiased prediction (GBLUP) genomic prediction (GP) models and single-marker regression genome-wide association studies of potato to evaluate the implications of capturing varying ploidy on the statistical models employed in genomic breeding. A panel of 762 offspring of a diallel cross of 18 parents of elite breeding material was used for modeling. These were genotyped by sequencing and phenotyped for five key performance traits: chipping quality, length/width ratio, senescence, dry matter content, and yield. We also estimated the read coverage required to confidently discriminate between a heterozygous triploid and tetraploid state from simulated data. It was found that using a tetraploid model neither impaired nor improved genomic predictions compared to using the observed allele frequencies that account for true marker ploidy. In genome-wide associations studies (GWAS), very minor variations of both signal amplitude and number of SNPs supporting both minor and major quantitative trait loci (QTLs) were observed between the two data sets. However, all major QTLs were reproducible using both data sets.

GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.

Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).