Accuracy Of Genomic Prediction Research Articles

Machine learning methods for genomic prediction of cow behavioral traits measured by automatic milking systems in North American Holstein cattle

Identifying genome-enabled methods that provide more accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In this study, we aimed to compare the predictive performance of traditional genomic prediction methods and deep learning algorithms for genomic prediction of milking refusals (MREF) and milking failures (MFAIL) in North American Holstein cows measured by automatic milking systems (milking robots). A total of 1,993,509 daily records from 4,511 genotyped Holstein cows were collected by 36 milking robot stations. After quality control, 57,600 single nucleotide polymorphisms (SNP) were available for the analyses. Four genomic prediction methods were considered: Bayesian Lasso (LASSO), Multiple Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Genomic Best Linear Unbiased Prediction (GBLUP). We implemented the first 3 methods using the Keras and TensorFlow libraries in Python (v.3.9) while the GBLUP method was implemented using the BLUPF90+ family programs. The accuracy of genomic prediction (Mean Square Error) for MREF and MFAIL was 0.34 (0.08) and 0.27 (0.08) based on LASSO, 0.36 (0.09) and 0.32 (0.09) for MLP, 0.37 (0.08) and 0.30 (0.09) for CNN, and 0.35 (0.09) and 0.31(0.09) based on GBLUP, respectively. Additionally, we observed a lower re-ranking of top selected individuals based on the MLP versus CNN methods compared with the other approaches for both MREF and MFAIL. Although the deep learning methods showed slightly higher accuracies than GBLUP, the results may not be sufficient to justify their use over traditional methods due to their higher computational demand and the difficulty of performing genomic prediction for non-genotyped individuals using deep learning procedures. Overall, this study provides insights into the potential feasibility of using deep learning methods to enhance genomic prediction accuracy for behavioral traits in livestock. Further research is needed to determine their practical applicability to large dairy cattle breeding programs.

Linkage mapping and genomic prediction of grain quality traits in tropical maize (Zea mays L.).

The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18-0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.

Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models.

Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.

Genomic selection accelerates genetic improvement of resistance to Vibriosis in the Pacific oyster, Crassostrea gigas

Due to lack of acquired immune system, the oysters cultured along coasts are subject to frequent pathogen threats, which leads to severe disease outbreaks around the world. It's well recognized that the selection breeding of aquatic animals can be accelerated via the harnessing of genomic tools to increase genetic gain and shorten the breeding time. In this work, we carried out genomic selection breeding in the Pacific oysters (Crassostrea gigas) for genetic improvement of resistance to Vibriosis. The genome-wide variations were genotyped by ddRAD-seq from 295 oysters with contrasted resistance to Vibrio infection. Based on genome-wide SNPs, we performed an estimation of genomic heritability and prediction accuracy for resistance to Vibrio alginolyticus in C. gigas. The genomic heritability of resistance to V. alginolyticus was low to moderate, ranging from 0.1405 to 0.2730. Four genomic selection models including rrBLUP, Bayes A, Bayes B and Bayesian Lasso were evaluated, of which Bayes A showed superior prediction accuracy and computational speed. The genomic estimated breeding value (GEBV) calculated by genomic selection model can effectively distinguish the resistance or susceptibility of oysters to Vibriosis. Selection of individuals with high GEBV as broodstock greatly improved the resistance to Vibriosis of their progeny, resulting in 18.42% increase in relative survival rate and 12.73% increase in relative survival time compared to the control population. For the first time, this work reported the efficiency of genomic selection breeding for genetic improvement for resistance trait to Vibriosis in the C. gigas, which would greatly accelerate the cultivation of Vibriosis resistant oyster strains to support the healthy and sustainable development of aquaculture.

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats.

Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs. This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs. It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

The Effects of Genetic Distance and Genetic Diversity on Genomic Prediction Accuracy for Soybean Quantitative Disease Resistance to Phytophthora sojae

Assessing quantitative disease resistance and other complex traits can be time- and space-consuming, limiting the number of lines that can be evaluated in a breeding program. A potential improvement is the application of genomic prediction models, where genotypic data are used to calculate genomic estimated breeding values, referred to as genomic selection. Genomic prediction uses training datasets, where a germplasm panel is phenotyped and genotyped to calculate genomic estimated breeding values in a validation panel of lines based solely on genotypic data. To develop an initial phenotypic dataset, breeders may consider utilizing previously phenotyped lines in a publicly available dataset, such as plant introductions (PIs) from the USDA Soybean collection. A relevant question is, how effective is genomic prediction across diverse training and prediction panels? To answer this, we used previously collected phenotypic data for quantitative disease resistance toward Phytophthora sojae. Diverse germplasm panels were represented by sets of PIs originating from the United States, the Republic of Korea, and worldwide, for a total collection of 1,768 PIs. The accuracy of the prediction model was significantly influenced by population differentiation between panels, as well as genetic and phenotypic diversity. Low prediction accuracy resulted when the panel used to create the prediction model was highly differentiated from the validation panel. However, the addition of a small number of accessions to the training panel that were more closely related to the validation panel resulted in increased accuracy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Genome-wide association mapping to identify genetic loci governing agronomic traits and genomic prediction prospects in tetraploid potatoes

Genome-wide association studies (GWAS) and Genomic Prediction (GP) have become powerful tools for examining genetically complex traits in crops. The present study was undertaken on a diverse potato panel of 222 tetraploid potato cultivars and breeding lines, genotyped using genotyping with a sequencing approach to identify 120,622 high-quality single nucleotide polymorphism (SNP) markers. The phenotype data was observed on 12 agronomic traits, including morphological (qualitative) and tuber yield traits (quantitative). The population structure showed three different sub-populations with varying levels of admixtures in them. The LD decay was observed at ∼5Mb distance and GWAS using the Q+K model identified 46 marker-trait associations (MTAs), including 20 associations for tuber morphological traits and rest 26 for tuber yield traits. The average phenotypic variance captured by the associated SNPs was around 10 % for most traits. The putative candidate genes identified for different traits were cytochrome P450 71A4 for flesh color, phytochrome-B and ATP synthase subunit for tuber number, and receptor-like kinases and MYB transcription factor for tuber yield. We did not get any marker trait association for tuber skin color and chip score. The GP accuracy was moderate (0.3–0.5) to high (>0.5) for most of the traits except crisp score, tuber shape and marketable tubers, which recorded low GP accuracy. The reduced marker density did not show any decline in the GP accuracy of different traits. Overall, the study presents the genetic analysis of a diverse panel with information on genetic control and genomic predictions of complex traits for use in genomics-assisted breeding and genomic selection in potatoes.

Recurrent selection for Fusarium head blight resistance in a durum wheat population

AbstractFusarium head blight (FHB) is a devastating fungal disease of wheat. Since early 1990s, frequent FHB epidemics in major wheat‐growing regions have caused massive economic losses. Developing FHB‐resistant varieties is key to minimize the loss caused by the disease. In durum wheat (Triticum turgidum L. ssp. durum), however, it is challenged by lack of resistant sources. Introgression of resistance genes from wild relatives and bread wheat has resulted in some durum wheat lines with moderate resistance. Due to its complex genetic nature, integrating more resistance genes with moderate and even minor effects promises to provide high and durable FHB resistance. In this study, a base population was developed using diverse resistant lines and elite durum wheat breeding lines as founders and went through three cycles of phenotypic selection from 2019 to 2022, with FHB severity decreased by 34%. Six S0:1 lines in Cycle 2 and Cycle 3 populations exhibited significantly lower FHB severity than the moderately susceptible check variety ND Riveland. The results suggested that recurrent phenotypic selection could effectively improve FHB resistance. Analysis of the founders and Cycle 2 and Cycle 3 populations with 2706 single nucleotide polymorphism markers indicated that there was little decrease in genetic variation due to selection. Genome‐wide association analysis found no markers significantly associated with FHB severity. Genomic prediction accuracies based on cross‐validation for FHB severity, plant height, and days‐to‐flowering were 0.51, 0.69, and 0.61, respectively. Recurrent genomic selection can shorten the select cycle to 4 months and therefore is promising to accelerate genetic improvement of FHB severity.

Genomic predictions for resistance to Aeromonas hydrophila in pacu (Piaractus mesopotamicus)

Infections caused by the bacterium Aeromonas hydrophila result in high mortality outbreaks in pacu (Piaractus mesopotamicus) production. An alternative to increase the resistance to A. hydrophila infection is the implementation of genomic selection programs, which can contribute to the sustainable development of the Brazilian aquaculture. In addition, low-density SNP panels, optimized by genotype imputation, represent a low-cost tool for implementing genomic selection on a commercial scale. This study aimed to evaluate the accuracy of genomic prediction using low (0.5 and 1 K) and medium (15 K imputed) density SNP panels; and compare breeding values accuracy with restriction-site associated DNA sequencing (18 K) and pedigree-based methods for individuals challenged for A. hydrophila. The genomic prediction analyses were conducted using genotypes from the medium-density SNP panel of pacu as reference and genotypes from a low-density SNP panel (0.5 and 1 K) using target genotype-by-sequencing technology (tGBS) for imputation. Approximately 15 K SNPs were successfully imputed with accuracy (r2) above 0.8. Time to death (TD) and binary survival (BS) against A. hydrophila infection were used as phenotypes for genomic prediction. Genomic values were estimated using the GBLUP model adopting different genotyping methods (SNP array and RAD-seq) and densities of SNPs (0.5 K, 1 K, and 15K_imputed for the array and 18 K for RAD-seq). Accuracies of genomic approaches using different methods and densities were compared to the pedigree method PBLUP to assess the benefits of incorporating genomic information. The accuracy of predicting breeding values using genomic methods outperformed the estimates obtained through PBLUP by 33%, 39%, and 67% for TD, and 36%, 39%, and 82% for BS, considering the densities of 0.5, 1, and 15K_imputed, respectively. The imputation strategy yielded better results in terms of genomic prediction accuracy for both traits. The RAD-seq genotyping method produced low-accuracy estimates (∼30% lower for both traits) compared to PBLUP, indicating that such method may not be suitable for genomic prediction of these traits. Genomic selection using genotype imputation is a viable and cost-effective approach to increase resistance to A. hydrophila infection in pacu and reduce genotyping costs without compromising genomic prediction accuracy.

Leveraging transcriptomics-based approaches to enhance genomic prediction: integrating SNPs and gene networks for cotton fibre quality improvement.

Cultivated cotton plants are the world's largest source of natural fibre, where yield and quality are key traits for this renewable and biodegradable commodity. The Gossypium hirsutum cotton genome contains ~80K protein-coding genes, making precision breeding of complex traits a challenge. This study tested approaches to improving the genomic prediction (GP) accuracy of valuable cotton fibre traits to help accelerate precision breeding. With a biology-informed basis, a novel approach was tested for improving GP for key cotton fibre traits with transcriptomics of key time points during fibre development, namely, fibre cells undergoing primary, transition, and secondary wall development. Three test approaches included weighting of SNPs in DE genes overall, in target DE gene lists informed by gene annotation, and in a novel approach of gene co-expression network (GCN) clusters created with partial correlation and information theory (PCIT) as the prior information in GP models. The GCN clusters were nucleated with known genes for fibre biomechanics, i.e., fasciclin-like arabinogalactan proteins, and cluster size effects were evaluated. The most promising improvements in GP accuracy were achieved by using GCN clusters for cotton fibre elongation by 4.6%, and strength by 4.7%, where cluster sizes of two and three neighbours proved most effective. Furthermore, the improvements in GP were due to only a small number of SNPs, in the order of 30 per trait using the GCN cluster approach. Non-trait-specific biological time points, and genes, were found to have neutral effects, or even reduced GP accuracy for certain traits. As the GCN clusters were generated based on known genes for fibre biomechanics, additional candidate genes were identified for fibre elongation and strength. These results demonstrate that GCN clusters make a specific and unique contribution in improving the GP of cotton fibre traits. The findings also indicate that there is room for incorporating biology-based GCNs into GP models of genomic selection pipelines for cotton breeding to help improve precision breeding of target traits. The PCIT-GCN cluster approach may also hold potential application in other crops and trees for enhancing breeding of complex traits.

Genomic prediction and genome-wide association studies of morphological traits and distraction index in Korean Sapsaree dogs.

The Korean Sapsaree dog is a native breed known for its distinctive appearance and historical significance in Korean culture. The accurate estimation of breeding values is essential for the genetic improvement and conservation of such indigenous breeds. This study aimed to evaluate the accuracy of breeding values for body height, body length, chest width, hair length, and distraction index (DI) traits in Korean Sapsaree dogs. Additionally, a genome-wide association study (GWAS) was conducted to identify the genomic regions and nearby candidate genes influencing these traits. Phenotypic data were collected from 378 Korean Sapsaree dogs, and of these, 234 individuals were genotyped using the 170k Illumina CanineHD BeadChip. The accuracy of genomic predictions was evaluated using the traditional BLUP method with phenotypes only on genotyped animals (PBLUP-G), another traditional BLUP method using a pedigree-based relationship matrix (PBLUP) for all individuals, a GBLUP method based on a genomic relationship matrix, and a single-step GBLUP (ssGBLUP) method. Heritability estimates for body height, body length, chest width, hair length, and DI were 0.45, 0.39, 0.32, 0.55, and 0.50, respectively. Accuracy values varied across methods, with ranges of 0.22 to 0.31 for PBLUP-G, 0.30 to 0.57 for PBLUP, 0.31 to 0.54 for GBLUP, and 0.39 to 0.67 for ssGBLUP. Through GWAS, 194 genome-wide significant SNPs associated with studied Sapsaree traits were identified. The selection of the most promising candidate genes was based on gene ontology (GO) terms and functions previously identified to influence traits. Notable genes included CCKAR and DCAF16 for body height, PDZRN3 and CNTN1 for body length, TRIM63, KDELR2, and SUPT3H for chest width, RSPO2, EIF3E, PKHD1L1, TRPS1, and EXT1 for hair length, and DDHD1, BMP4, SEMA3C, and FOXP1 for the DI. These findings suggest that significant QTL, combined with functional candidate genes, can be leveraged to improve the genetic quality of the Sapsaree population. This study provides a foundation for more effective breeding strategies aimed at preserving and enhancing the unique traits of this Korean dog breed.

Use of whole-genome sequence data for fine mapping and genomic prediction of sea louse resistance in Atlantic salmon.

Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data.

High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models.

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

Effect of population structure on the accuracy of genomic prediction for carcass traits in Hanwoo cattle: a simulation study

Improvement of quantitative traits in livestock is an essential goal of animal breeding. For the achievement of this goal, an accurate breeding value estimation is needed. The genetic relationship between reference and test groups is a key factor in determining the accuracy of genomic estimated breeding value (GEBV) thus, the structure of the reference population is crucial for efficient genomic selection. By the number of sharing parents, the population structure can be divided into half-sibling and full-sibling families. Also, the population structure of Hanwoo cattle primarily consists of half-sibling families because of the production system. Therefore, comparing half-sibling and full-sibling families is challenging in the Hanwoo cattle population, yet an important issue in the direction of breeding strategy in Korea. The objective of this study was to compare the accuracy of GEBV between different family structures and investigate efficient family size in the reference population using simulated data. 6 different populations were simulated using QMSim software, and the individuals in the last generations were separated into reference and test groups. The GEBV was calculated using BLUPF90 software. Practical accuracy was between 0.36-0.52 in half-sibling families and 0.55-0.77 in full-sibling families. The increase rate of accuracy was highest at the sibling size of 20, with practical accuracy of 0.52 in half-sibling families and 0.77 in full-sibling families. As a result, the most efficient population structure for genomic prediction was a sibling size of 20 in a full-sibling family.

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase.

Crop growth and phenology are driven by seasonal changes in environmental variables, with temperature as one important factor. However, knowledge about genotype-specific temperature response and its influence on phenology is limited. Such information is fundamental to improve crop models and adapt selection strategies. We measured the increase in height of 352 European winter wheat varieties in 4 years to quantify phenology, and fitted an asymptotic temperature response model. The model used hourly fluctuations in temperature to parameterize the base temperature (Tmin), the temperature optimum (rmax), and the steepness (lrc) of growth responses. Our results show that higher Tmin and lrc relate to an earlier start and end of stem elongation. A higher rmax relates to an increased final height. Both final height and rmax decreased for varieties originating from the continental east of Europe towards the maritime west. A genome-wide association study (GWAS) indicated a quantitative inheritance and a large degree of independence among loci. Nevertheless, genomic prediction accuracies (GBLUPs) for Tmin and lrc were low (r≤0.32) compared with other traits (r≥0.59). As well as known, major genes related to vernalization, photoperiod, or dwarfing, the GWAS indicated additional, as yet unknown loci that dominate the temperature response.

Genomic Predictions in Korean Hanwoo Cows: A Comparative Analysis of Genomic BLUP and Bayesian Methods for Reproductive Traits.

This study aimed to predict the accuracy of genomic estimated breeding values (GEBVs) for reproductive traits in Hanwoo cows using the GBLUP, BayesB, BayesLASSO, and BayesR methods. Accuracy estimates of GEBVs for reproductive traits were derived through fivefold cross-validation, analyzing a dataset comprising 11,348 animals and employing an Illumina Bovine 50K SNP chip. GBLUP showed an accuracy of 0.26 for AFC, while BayesB, BayesLASSO, and BayesR demonstrated values of 0.28, 0.29, and 0.29, respectively. For CI, GBLUP attained an accuracy of 0.19, whereas BayesB, BayesLASSO, and BayesR scored 0.21, 0.24, and 0.25, respectively. The accuracy for GL was uniform across GBLUP, BayesB, and BayesR at 0.31, whereas BayesLASSO showed a slightly higher accuracy of 0.33. For NAIPC, GBLUP showed an accuracy of 0.24, while BayesB, BayesLASSO, and BayesR recorded 0.22, 0.27, and 0.30, respectively. The variation in genomic prediction accuracy among methods indicated Bayesian approaches slightly outperformed GBLUP. The findings suggest that Bayesian methods, notably BayesLASSO and BayesR, offer improved predictive capabilities for reproductive traits. Future research may explore more advanced genomic approaches to enhance predictive accuracy and genetic gains in Hanwoo cattle breeding programs.

Using genomic selection to improve the accuracy of genomic prediction for multi-populations in pigs

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources.

Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs.

Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.

Genomic loci involved in sensing environmental cues and metabolism affect seasonal coat shedding in Bos taurus and Bos indicus cattle.

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations between chromosomes 5 and 23. Genotype-by-environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multibreed datasets. Loci related to metabolism and light-sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight into both agriculture production and basic science.

Imputation strategies for genomic prediction using nanopore sequencing

BackgroundGenomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies’ (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance.ResultsSNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays.ConclusionsHere we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.