Accuracy Of Genomic Prediction Research Articles

Residual networks without pooling layers improve the accuracy of genomic predictions.

Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction accuracy by genomic selection (GS) continues to improve. Residual networks, a widely validated deep learning technique, are introduced to deep learning for GS. Since each locus has a different weighted impact on the phenotype, strided convolutions are more suitable for GS problems than pooling layers. Through the above technological innovations, we propose a GS deep learning algorithm, residual neural network for genomic selection (ResGS). ResGS is the first neural network to reach 35 layers in GS. In 15 cases from four public data, the prediction accuracy of ResGS is higher than that of ridge-regression best linear unbiased prediction, support vector regression, random forest, gradient boosting regressor, and deep neural network genomic prediction in most cases. ResGS performs well in dealing with gene-environment interaction. Phenotypes from other environments are imported into ResGS along with genetic data. The prediction results are much better than just providing genetic data as input, which demonstrates the effectiveness of GS multi-modal learning. Standard deviation is recommended as an auxiliary GS evaluation metric, which could improve the distribution of predicted results. Deep learning for GS, such as ResGS, is becoming more accurate in phenotype prediction.

Accuracy of genomic prediction using multiple Atlantic salmon populations

BackgroundThe accuracy of genomic prediction is partly determined by the size of the reference population. In Atlantic salmon breeding programs, four parallel populations often exist, thus offering the opportunity to increase the size of the reference set by combining these populations. By allowing a reduction in the number of records per population, multi-population prediction can potentially reduce cost and welfare issues related to the recording of traits, particularly for diseases. In this study, we evaluated the accuracy of multi- and across-population prediction of breeding values for resistance to amoebic gill disease (AGD) using all single nucleotide polymorphisms (SNPs) on a 55K chip or a selected subset of SNPs based on the signs of allele substitution effect estimates across populations, using both linear and nonlinear genomic prediction (GP) models in Atlantic salmon populations. In addition, we investigated genetic distance, genetic correlation estimated based on genomic relationships, and persistency of linkage disequilibrium (LD) phase across these populations.ResultsThe genetic distance between populations ranged from 0.03 to 0.07, while the genetic correlation ranged from 0.19 to 0.99. Nonetheless, compared to within-population prediction, there was limited or no impact of combining populations for multi-population prediction across the various models used or when using the selected subset of SNPs. The estimates of across-population prediction accuracy were low and to some extent proportional to the genetic correlation estimates. The persistency of LD phase between adjacent markers across populations using all SNP data ranged from 0.51 to 0.65, indicating that LD is poorly conserved across the studied populations.ConclusionsOur results show that a high genetic correlation and a high genetic relationship between populations do not guarantee a higher prediction accuracy from multi-population genomic prediction in Atlantic salmon.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits

Abstract With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g., decreased vigor).

225 Genomic diversity of Katahdin sheep and impacts on genomic prediction

Abstract In the late 1950s, Katahdin hair sheep were developed as a composite breed of medium size and moderate prolificacy, with potential to express resistance to gastrointestinal nematodes. With its increasing popularity and the recent adoption of genomic prediction in the Katahdin breeding program, evaluating the genetic diversity in this breed is necessary. Prediction accuracies rely on the strength of genetic relationships. That strength often increases with selection using genomically-enhanced estimated breeding values; however, it carries the risk of decreasing diversity. We aim to characterize the genetic diversity present in the breed and the subsequent impact on the accuracies of genomic predictions. Genotypes on Katahdin sheep from member flocks in the National Sheep Improvement Program were used. Data from two medium density 50k bead chips and one high density 600k bead chip were merged and limited to shared autosomal single nucleotide polymorphisms (SNP). After quality control, there were 9,705 animals and 31,984 SNP remaining. Only for analyses of inbreeding and heterozygosity, linkage disequilibrium (LD) pruning was performed; variants with an LD score exceeding 0.8 were removed with 29,904 SNP remaining. To determine the extent of population structure, principal component (PC) analysis was conducted with PC one and PC two explaining 4.9% and 3.8% of the variation in the population, respectively. When examining subpopulations, a single ancestral population explained 99.9% of the genetic variation among animals. When clustered by identity-by-state, all animals were placed in a single cluster. Inbreeding coefficients were estimated with four methods: method of moments, variance-standardized relationship minus one, excess homozygosity, and correlation between uniting gametes. Across methods, average inbreeding coefficients ranged from 1.14% to 1.20% with Pearson and Spearman correlations ranging from -0.51 to 0.91 (Table 1). Historical effective population sizes (Ne) were estimated for earlier generations, and a linear regression fit to predict more recent Ne. At the founding of the breed, historical Ne was estimated to be 310, and the current Ne was estimated to be 150. Effects of Ne on the accuracy of genomic predictions were examined for reference population sizes ranging from 1k to 25k for traits of low (0.1), moderate (0.3), and high (0.5) heritabilities. The corresponding number of effective chromosomal segments were 2NeL, where L was the length of the genome in morgans (26M). Accuracies of genomic predictions increased as the reference population size grew, with only marginal gains once the population exceeded 15k. Overall, there were no detectable genetic subpopulations in the Katahdin breed. Current levels of inbreeding were low. With a small Ne the breed can achieve high accuracy for genomic predictions by aiming for a reference population of 15k animals. Negative impacts on genetic diversity due to genomic selection are not of immediate concern.

Effect of genetic and sex effect on genomic prediction for horn development in Nellore cattle

This study aimed to evaluate the influence of phenotypic classification of horn development, animal sex effect, and non-autosomal SNP (single nucleotide polymorphism) markers on the genetic parameters and genomic prediction ability for horn development in Nellore cattle using the single-step genomic best linear unbiased prediction method. The polled phenotype was evaluated in two (presence and absence of horns), three (scurs and polled offspring from a horned parent, and the polled and horned animals), and four (absence of horn, polled born to a parent with horn, scurs, and presence of horn) phenotypic categories. A total of 12 statistical models were evaluated. The variance components were estimated using the THRGIBBS1F90 software, and a threshold animal model was used for genomic prediction analyses with the single-step genomic BLUP (ssGBLUP) procedure. Accuracy, bias, and dispersion parameters were evaluated based on the linear regression (LR) method. The highest heritability (0.84) was obtained when the polled character was evaluated as a binary trait. The lowest heritability estimates (0.44 to 0.45) for horn development were obtained when the phenotype was classified into three categories. For the same horn development classification method, the heritability estimates were similar regardless of the genomic evaluated models and fixed effects included in the model. For models considering four and three phenotypic categories for horn development, the inclusion of the sex effect as a fixed effect within the CG did not improve the accuracy, bias, and dispersion of genomic predictions for horn development. Analyzing the trait with binary expression, the highest prediction accuracy was observed when the effect of sex was not included in the CG and without the SNPs in the sex chromosomes. These models displayed the highest dispersion, pointing out the low robustness of genomic prediction. In addition, models that use less than four categories to classify the horn development phenotype, with no discrimination between polled and homozygous polled displayed lower prediction ability. The inclusion of non-autosomal SNPs in the analyses for the models considering four phenotypic categories leads to an improvement in prediction accuracy in 5,26%, bias, and dispersion reduction, 37% and 4,55%, respectively, compared with models that only considered autosomal SNPs. The selection using genomic information for the polled trait is feasible, and it is an alternative to obtaining polled Nellore animals. The binary coding of horn development is an unsuitable oversimplification of polled phenotype, and probably, the genetic background of horn development is more complex than previously proposed. The most adequate prediction model to evaluate the horn development in Nellore cattle was considering four phenotypic categories and including non-autosomal SNP in the analyses for genomic prediction purposes of naturally genetically polled animals. Genetic dehorning can be adopted on a large scale as a low-cost and non-invasive approach to increase the frequency of hornless animals using genomic information and mating strategies.

Boosting genome-wide association power and genomic prediction accuracy for date palm fruit traits with advanced statistics

The date palm is economically vital in the Middle East and North Africa, providing essential fibres, vitamins, and carbohydrates. Understanding the genetic architecture of its traits remains complex due to the tree's perennial nature and long generation times. This study aims to address these complexities by employing advanced genome-wide association (GWAS) and genomic prediction models using previously published data involving fruit acid content, sugar content, dimension, and colour traits. The multivariate GWAS model identified seven QTL, including five novel associations, that shed light on the genetic control of these traits. Furthermore, the research evaluates different genomic prediction models that considered genotype by environment and genotype by trait interactions. While colour- traits demonstrate strong predictive power, other traits display moderate accuracies across different models and scenarios aligned with the expectations when using small reference populations. When designing the cross-validation to predict new individuals, the accuracy of the best multi-trait model was significantly higher than all single-trait models for dimension traits, but not for the remaining traits, which showed similar performances. However, the cross-validation strategy that masked random phenotypic records (i.e., mimicking the unbalanced phenotypic records) showed significantly higher accuracy for all traits except acid contents. The findings underscore the importance of understanding genetic architecture for informed breeding strategies. The research emphasises the need for larger population sizes and multivariate models to enhance gene tagging power and predictive accuracy to advance date palm breeding programs. These findings support more targeted breeding in date palm, improving productivity and resilience to various environments.

Genomic prediction of resistance to Hymenoscyphus fraxineus in common ash (Fraxinus excelsior L.) populations.

The increase in introduced insect pests and pathogens due to anthropogenic environmental changes has become a major concern for tree species worldwide. Common ash (Fraxinus excelsior L.) is one of such species facing a significant threat from the invasive fungal pathogen Hymenoscyphus fraxineus. Some studies have indicated that the susceptibility of ash to the pathogen is genetically determined, providing some hope for accelerated breeding programs that are aimed at increasing the resistance of ash populations. To address this challenge, we used a genomic selection strategy to identify potential genetic markers that are associated with resistance to the pathogen causing ash dieback. Through genome-wide association studies (GWAS) of 300 common ash individuals from 30 populations across Poland (ddRAD, dataset A), we identified six significant SNP loci with a p-value ≤1 × 10-4 associated with health status. To further evaluate the effectiveness of GWAS markers in predicting health status, we considered two genomic prediction scenarios. Firstly, we conducted cross-validation on dataset A. Secondly, we trained markers on dataset A and tested them on dataset B, which involved whole-genome sequencing of 20 individuals from two populations. Genomic prediction analysis revealed that the top SNPs identified via GWAS exhibited notably higher prediction accuracies compared to randomly selected SNPs, particularly with a larger number of SNPs. Cross-validation analyses using dataset A showcased high genomic prediction accuracy, predicting tree health status with over 90% accuracy across the top SNP sets ranging from 500 to 10,000 SNPs from the GWAS datasets. However, no significant results emerged for health status when the model trained on dataset A was tested on dataset B. Our findings illuminate potential genetic markers associated with resistance to ash dieback, offering support for future breeding programs in Poland aimed at combating ash dieback and bolstering conservation efforts for this invaluable tree species.

Genome-wide association study and genomic prediction of resistance to summer mortality in Pacific oyster (Crassostrea gigas) using whole genome resequencing

The Pacific oyster Crassostrea gigas is one of the most important farmed marine shellfish worldwide. However, massive mortality during the summer months caused significant economic losses to the C. gigas farming industry. Understanding the genetic architecture of resistance traits has been an ongoing research issue, and the incorporation of genomic information into breeding programs is expected to accelerate the process of genetic improvement. Genomic selection (GS), as a new approach of genetic improvement, has great potential for breeding new lines with adaptive advantages. In this study, we conducted a genome-wide association study (GWAS) for summer mortality resistance and estimated the accuracy of genomic predictions using different methods. The heritability of the C. gigas summer mortality resistance was low, with heritability of 0.108 ± 0.036, 0.161 ± 0.102 and 0.134 ± 0.043 for PBLUP, GBLUP and ssGBLUP, respectively. In addition, we detected 9,783,674 SNPs and used a mixed linear model to identify 18 significant SNPs associated with summer mortality resistance. The phenotypic variance explained (PVE) for these SNPs ranged from 8.25% to 10.14%. Based on these significantly related SNPs, nine significant candidate genes were identified (TLR4, HEX, SLC22A8, PDE2A, HUWE1, SLMAP, RAD52, TBK1 and RAPH1). The prediction accuracy using PBLUP, GBLUP, ssGBLUP, and weight ssGBLUP (WssGBLUP) was 0.549 ± 0.026, 0.381 ± 0.074, 0.544 ± 0.026, and 0.869 ± 0.018, respectively. Therefore, WssGBLUP models are more suitable for genomic prediction of summer mortality resistance in C. gigas. Our results suggest a polygenic genetic architecture that provide new perspectives for studying candidate genes for resistance to summer mortality, which may facilitate the genetic improvement for resistance lines in C. gigas.

Multi-Trait Bayesian Models Enhance the Accuracy of Genomic Prediction in Multi-Breed Reference Populations

Multi-Trait Bayesian Models Enhance the Accuracy of Genomic Prediction in Multi-Breed Reference Populations

Accuracy of Genomic prediction for fleece traits in Inner Mongolia Cashmere goats

The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P < 0.01). All of these factors should be considered when the genetic parameters of fleece traits in IMCGs are evaluated. The heritabilities of FL, CL, CP and CD with ABLUP, GBLUP and ssGBLUP methods were 0.26 ~ 0.31, 0.05 ~ 0.08, 0.15 ~ 0.20 and 0.22 ~ 0.28, respectively. Therefore, it can be inferred that the genetic progress of CL is relatively slow. The predictive ability of fleece traits in IMCGs with GBLUP (56.18% to 69.06%) and ssGBLUP methods (66.82% to 73.70%) was significantly higher than that of ABLUP (36.73% to 41.25%). For the ssGBLUP method is significantly (29% ~ 33%) higher than that with ABLUP, and which is slightly (4% ~ 14%) higher than that of GBLUP. The ssGBLUP will be as an superiors method for using genomic selection of fleece traits in Inner Mongolia Cashmere goats.

Pooled DNA sequencing in hairy vetch (Vicia villosa Roth) reveals QTL for seed dormancy but not pod dehiscence.

Hairy vetch (Vicia villosa Roth) is a promising legume cover crop, but its use is limited by high rates of pod dehiscence and seed dormancy. We used phenotypically contrasting pooled DNA samples (n=24 with 29-74 individuals per sample) from an ongoing cover crop breeding program across four environments (site-year combinations: Maryland 2020, Maryland 2022, Wisconsin 2021, Wisconsin 2022) to find genetic associations and genomic prediction accuracies for pod dehiscence and seed dormancy. We also combined pooled DNA sample genetic association results with the results of a prior genome-wide association study. Genomic prediction resulted in positive predictive abilities for both traits between environments and with an independent dataset (0.34-0.50), but reduced predictive ability for DNA pools with divergent seed dormancy in the Maryland environments (0.07-0.15). The pooled DNA samples found six significant (false discovery rate q-value<0.01) quantitative trait loci (QTL) for seed dormancy and four significant QTL for pod dehiscence. Unfortunately, the minor alleles of the pod dehiscence QTL increased the rate of pod dehiscence and are not useful for marker-assisted selection. When combined with a prior association study, sixteen seed dormancy QTL and zero pod dehiscence QTL were significant. Combining the association studies did not increase the detection of useful QTL.

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

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

Enhancing Genomic Prediction Accuracy for Body Conformation Traits in Korean Holstein Cattle.

The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

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

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

Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population.

Maintaining or introducing genetic diversity into plant breeding programs is necessary for continual genetic gain; however, diversity at the cost of reduced performance is not something sought by breeders. To this end, backcross-nested association mapping (BC-NAM) populations, in which the recurrent parent is an elite line, can be employed as a strategy to introgress diversity from unadapted accessions while maintaining agronomic performance. This study evaluates (i) the hybrid performance of sorghum lines from 18 BC1-NAM families and (ii) the potential of genomic prediction to screen lines from BC1-NAM families for hybrid performance prior to phenotypic evaluation. Despite the diverse geographical origins and agronomic performance of the unadapted parents for BC1-NAM families, many BC1-derived lines performed significantly better in the hybrid trials than the elite recurrent parent, R.Tx436. The genomic prediction accuracies for grain yield, plant height, and days to mid-anthesis were acceptable, but the prediction accuracies for plant height were lower than expected. While the prediction accuracies increased when including more individuals in the training set, improvements tended to plateau between two and five lines per family, with larger training sets being required for more complex traits such as grain yield. Therefore, genomic prediction models can be optimized in a large BC1-NAM population with a relatively low fraction of individuals needing to be evaluated. These results suggest that genomic prediction is an effective method of pre-screening lines within BC1-NAM families prior to evaluation in extensive hybrid field trials.

Integration of genome-wide association and genomic prediction for dissecting seed protein and amino acid in foxtail millet

ContextThe exploitation and utilization of genetic variation in crop germplasm benefit genomics-informed breeding programs. The integration of genome-wide association study (GWAS) and genomic prediction (GP) holds promise for illustrating genetic basis of phenotypic variation. Currently, the use of this integrative approach for dissecting grain protein and amino acid traits remains limited in crop species. ObjectiveThe research objectives were to identify significant single nucleotide polymorphisms (SNPs) associated with seed protein and amino acids and to assess the genomic predictive ability for nutritional traits in natural population of foxtail millet. MethodsIn this study, the concentrations of seed protein and 17 amino acids were assessed in a training population consisting of 238 diverse accessions of foxtail millet. The significant SNPs associated with these phenotypes were identified by GWAS with a mix-linear model. Extensive cross-validation was performed to evaluate the accuracy of genomic prediction of seed nutritional traits. Furthermore, the genomic prediction model was optimized and externally validated in 211 untested materials with the most significant SNPs obtained from GWAS. ResultsA significant genotype variance was detected for seed protein and amino acids and broad-sense heritability (h2) ranged from 0.45 to 0.87 (P < 0.05). The GWAS identified 39 significant SNPs associated with 15 amino acids (p < 1e-06), explaining an average of 12.2% phenotypic variation per locus. The number of significant SNPs ranged from 1 (Asp, Ile, Glu) to 23 for Phe amino acid, including six SNPs associated with one or multiple amino acids. In the training population, the prediction accuracies of protein and amino acids, tested using the 10-fold cross-validation model with whole genome-wide SNPs, ranged from 0.23 (Glu) to 0.60 (Lys) with an average value of 0.44%. Population structure had little effect on the prediction accuracy. In an empirical validation experiment involving 211 untested accessions, the averaged prediction accuracy increased by 12.3% across all traits when using the 1000 top SNPs obtained from GWAS, compared with the same number of randomly selected SNPs from the entire genome. ConclusionsLarge variations of seed protein and amino acids were found in a natural population of foxtail millet. GWAS identified 39 significant SNPs associated with 15 amino acids, and six SNPs were linked to more than one trait. The 10-fold cross-validation model produced the relatively higher prediction accuracies in seed quality traits of diverse accessions. By applying the significant SNPs from GWAS to the untested accessions, the use of 1000 GWAS-top SNPs resulted in more powerful predictions for complex nutritional traits than those using the same number of randomly selected SNPs across the genome. SignificanceThe research highlights the importance and effectiveness of integrative GWAS and genomic prediction in revealing the genetic architecture of seed protein and amino acid variation in foxtail millet. The results provide valuable insights into assessment and improvement of germplasm resources for advancing precision crop phenotyping and enhancing breeding programs for crop nutritional traits.

Genetic factors underlying host resistance to Rhipicephalus microplus tick infestation in Braford cattle: a systems biology perspective.

Approximately 80% of the world's cattle are raised in regions with a high risk of tick-borne diseases, resulting in significant economic losses due to parasitism by Rhipicephalus (Boophilus) microplus. However, the lack of a systemic biology approach hampers a comprehensive understanding of tick-host interactions that mediate tick resistance phenotypes. Here, we conducted a genome-wide association study (GWAS) of 2933 Braford cattle and found 340 single-nucleotide polymorphisms (SNPs) associated with tick counts. Gene expression analyses were performed on skin samples obtained from previously tick-exposed heifers with extremely high or low estimated breeding values for R. microplus counts. Evaluations were performed both before and after artificial infestation with ticks. Differentially expressed genes were found within 1-Mb windows centered at significant SNPs from GWAS. A total of 330 genes were related to the breakdown of homeostasis that was induced by larval attachment to bovine skin. Enrichment analysis pointed to a key role of proteolysis and signal transduction via JAK/STAT, NFKB and WNT/beta catenin signaling pathways. Integrative analysis on matrixEQTL revealed two cis-eQTLs and four significant SNPs in the genes peptidyl arginine deiminase type IV (PADI4) and LOC11449251. The integration of genomic data from QTL maps and transcriptome analyses has identified a set of twelve key genes that show significant associations with tick loads. These genes could be key candidates to improve the accuracy of genomic predictions for tick resistance in Braford cattle.

Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality. Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait. Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection. We performed a genome-wide association study (GWAS) and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpeño and non-Tuxpeño heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms (SNPs). Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses. More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines. Incorporating genotype-by-environment (G × E) interaction increased genomic prediction accuracy.

Genomic dissection of the correlation between milk yield and various health traits using functional and evolutionary information about imputed sequence variants of 34,497 German Holstein cows

BackgroundOver the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM.ResultsIn general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits’ genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets’ high effect.ConclusionOur results confirm the connection of milk production and health traits in dairy cattle via the animals’ metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits’ connection in more detail.