Accuracy Of Genomic Prediction Research Articles

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.

Machine Learning for Prediction of Resistance Scores in Wheat (Triticum aestivum L.)

ABSTRACTMachine learning methods were shown to improve the prediction accuracies of genomic prediction of resistance scores compared to methods like RR‐BLUP, which were originally designed for metric rather than ordinal response values. We conducted a cross‐validation study with 361 wheat genotypes evaluated for five fungal diseases. Our objective was to compare the prediction accuracy and the ability to identify the most resistant genotypes of 19 genomic prediction approaches. Each approach consisted of a different combination of prediction method (RR‐BLUP, an alternative method with heterogeneous marker variances, Bayesian generalized linear regression with an ordinal response, support vector machine, gradient boosting machine and random forest), predictor (single SNP markers, LD‐based haplotype blocks, 250 variables generated with an autoencoder and SNPs identified with incremental feature selection) and response value (untransformed and logit‐transformed resistance scores). In our dataset, RR‐BLUP was consistently among the methods with the largest prediction accuracies and the best abilities to identify resistant genotypes in four of five investigated traits. However, in P. triticina, using gradient boosting machine and random forest instead of RR‐BLUP increased the prediction accuracy from 0.64 to 0.71, indicating that machine learning methods may have an advantage over linear models in genomic prediction. We also found that even though there was a positive correlation between the prediction accuracy and Cohen's , a measure to judge how well the most resistant genotypes can be identified, the correlation is not perfect and a large value for the prediction accuracy does not necessarily translate into an equally large value.

Experimental evaluation of effectiveness of genomic selection for resistance to northern corn leaf blight in maize.

Northern corn leaf blight (NLB) caused by Setosphaeria turcica (Luttrell) Leonard & Suggs is a severe foliar disease in maize. Resistance to NLB is complexly inherited and controlled by several quantitative trait loci (QTL) distributed across the genome. Phenotype and DNA marker-based selection for resistance to NLB is expected to be effective. Hence, an investigation was carried out to predict the genetic value of selection candidates for resistance to NLB and compare the accuracies of genomic prediction in two F2:3 populations of two crosses (CM212 × MAI172; CM202 × SKV50) derived from contrasting parents. Linkage analysis using 297 polymorphic SNPs in population-1 and 290 polymorphic SNPs in population-2 revealed ten linkage groups spanning 3623.88cM and 4261.92cM with an average distance of 12.40cM and 14.9cM in population-1 and population-2, respectively. Location-wise and pooled data across locations identified common QTLs on linkage groups 1 and 6 in population-1 and 3 and 8 in population-2. The prediction accuracy of the QTL mapping (9.92 and 9.10 for population-1 and population-2, respectively) was based on only a few markers, which explained higher percent phenotypic variation. The prediction accuracies of the genomic estimated breeding values in the present investigation were relatively low in population-1 (0.24 to 0.26) and population-2 (0.29-0.32) compared to the expected accuracies. This could be due to fewer polymorphic markers and a small training/population size. Though the GS prediction accuracies were relatively low, they were significantly higher than QTL mapping, which promises better genetic gain per cycle. The resistant progenies from both populations were advanced to derive inbred lines and crossed with four different testers in line × tester mating design to test for their combining ability and effectiveness of genomic selection. High overall general combining ability was exhibited by 21 inbred lines. Among F1s, 48% were assigned high overall specific combining ability status. Out of the 136 single crosses, seven recorded significant positive standard heterosis over the best check for grain yield. Twenty-five inbreds with high GEBVs were crossed with four testers to obtain 100 F1s and evaluated for their response to NLB. The majority of hybrids displayed moderate to resistant reaction to NLB either in combination with susceptible or resistant testers indicating the effectiveness of selection based on high GEBVs.

Genomic prediction and validation strategies for reproductive traits in Holstein cattle across different Chinese regions and climatic conditions

Accurate genomic predictions of breeding values for traits included in the selection indexes are paramount for optimizing genetic progress in populations under selection. The size of the reference populations is a major factor influencing the accuracy of genomic predictions, which is even more important for lowly-heritable traits such as fertility and reproduction indicators. Combining data from different geographical regions or countries can be beneficial for genomic prediction of these lowly heritable traits. Therefore, the objectives of this study were to: 1) evaluate the benefits of performing across-regional genomic evaluations for reproduction traits in Chinese Holstein cattle; and, 2) assess the feasibility of validating genomic predictions across environments based on reaction norm models (RNM) and the Linear Regression (LR) method after accounting for genotype-by-environment interactions. Phenotypic records from 194,574 cows collected across 47 farms located in 2 regions of China were used for this study. The reference and validation populations were defined based on birth year for applying the LR validation method. The traits evaluated included: interval from first to last insemination (IFL), conception rate at the first insemination (CR_f), and number of inseminations (NS) recorded in heifers and first-parity cows. The results indicated that combining data from different regions resulted in greater genomic prediction accuracies compared with using data from single regions, with increases ranging from 2.74% to 93.81%. This improvement was particularly notable for the region with the least amount of available data, where the increases ranged from 26.49% to 93.81%. Furthermore, the predictive abilities could be validated for all studied traits based on the LR method across different environments when fitting RNM. The prediction accuracies and bias of genomic breeding values based on RNM were better than regular single-trait animal models in extreme climatic conditions for IFL and NS, whereas limited increases in predictive abilities were observed for CR_f. Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.

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.

212 Accuracy of genomic prediction for indicator traits of resistance to gastrointestinal nematode parasites in grazing Arcott Rideau sheep

Abstract Gastrointestinal nematodes (GINs) parasites are a major problem in the sheep industry. To mitigate their impact, combined approaches need to be applied, which may include the implementation of genomic evaluation for indicator traits of resistance to GIN to increase genetic gain. This study aimed to investigate the impact of including genomic information in the genetic evaluation of indicator traits of resistance to GIN in grazing Arcott-Rideau sheep in Ontario. Fecal egg count (FEC) was counted using Triple Chamber (FEC-TC; n = 1,626) and McMaster (FEC-MM; n = 790) methods from samples collected from ewes and rams between 15 to 20 mo of age from 2012 to 2023. Trait values were transformed using a natural log. Two-trait analysis was performed using a repeatability model and the blupf90+ family programs to estimate variance components and predict breeding values. The models included month and year of evaluation, as fixed effects, and animal, permanent environment, and contemporary group, defined as animals of the same sex evaluated in the same year and month of evaluation, as random effects. The analysis was performed twice; 1) using a traditional model, in which pedigree information was used to calculate the relationship matrix (A), and 2) using a genomic model fitting a hybrid genomic relationship matrix (H). Quality control was performed, which included excluding SNPs with MAF < 0.05 and Call rate < 0.90 and samples with Call rate< 0.90; animals with parent-progeny conflicts were also removed. A total of 50,486 SNPs and 950 genotyped animals were included in the analysis to create the H matrix. The prediction accuracies were calculated as √1-SEPi2/(1+fi)σa2, where SEPi is the standard error of prediction of the estimated breeding value (EBV) of the ith animal; fi is the inbreeding coefficient of the ith animal; and σa2 is the population additive genetic variance. Four groups of animals were considered: i) where all animals were used; n = 14,626; ii) rams with progeny; n = 125; iii) ewes with progeny; n = 1,741; and iv) genotyped animals without phenotype information; n = 501. The average accuracy for all scenarios was greater when the genomic information was considered in the model for both traits (i = 0.77; ii = 0.82; iii = 0.79; and iv = 0.77 versus i = 0.45; ii = 0.56; iii = 0.49; and iv = 0.44 with the traditional model). The moderate Spearman rank correlation for the top 5% of animals (731) between the EBVs from the traditional and genomic models for both FEC traits (0.68) indicated a considerable re-ranking of the animals, which can be expected since the genomic information accounts for the Mendelian sampling. These results indicate that the inclusion of genomic information in the genetic evaluation of FEC-MM and FEC-TC improves the accuracy of prediction and would help in a more accurate selection of animals for breeding, resulting in an increased genetic gain for GIN resistance in sheep.

The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes

This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{h}}^{2}$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{h}}_{\ ext{S}\ ext{N}\ ext{P}}^{2}$$\\end{document}. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.

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.

Validating genomic predictions for economic traits in purebred U.S. dairy heifers

Most genotypes in the National Cooperator Database now originate from cows, but most previous studies validating genomic predictions have primarily focused on bulls. This study paired official within-breed genomic predicted transmitting ability (GPTA) and parent average (PA) for genotyped heifer calves born between 2019 and 2021 using the August 2021 database with their corresponding performance deviations (PDEV) for 17 different traits. The PDEV data became available when the heifers completed their first lactation and were extracted from the August 2023 database in which at least one PDEV value for those 17 traits existed for each genotyped heifer record. The separate breed analyses included records for 219 Ayrshires (AY), 2,715 Brown Swiss (BS), 1,055 Guernseys (GU), 949,904 Holsteins (HO), and 125,275 Jerseys (JE). These validation cows were heifer calves born between 2019 and 2021. However, due to timing or recording patterns, each trait had missing or incomplete PDEV data, leading to unbalanced distributions of records across traits. The squared accuracy of genomic prediction, or genomic reliability (r2), was divided by the corresponding heritability for each trait, as only the heritable portion of cow records could be predicted, and this reliability varied across different traits and breeds. For HO and JE, the predictive ability of GPTA outperformed PA in predicting cow PDEV for yield, productive life, somatic cell score, fertility, and health traits. The improvement ranged from 33% to 142% compared with the predictive ability of the PA. However, the results for AY, BS, and GU breeds were less consistent due to the smaller number of genotyped heifers. The r2 gains in those breeds were smaller and aligned with the published reliabilities of GPTA. Weighted and unweighted regressions of PDEV on GPTA and PA traits mostly exceeded the expected value of 2.00 when predicting the future trait PDEV using GPTA or PA. The larger number of observations and lower standard error of the weighted regression coefficient prediction in HO and JE breeds contributed to more stable and consistent regression coefficients for all traits except milk fever and heifer livability. Our study suggests that herd owners may experience greater benefits from genomics than originally forecast.

Weighed ssGBLUP enhances the genomic prediction accuracy for milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Previous studies have shown that milk citrate predicted by milk mid-infrared spectra (MIR) is strongly affected by a few genomic regions. This study aimed to explore the effect of the weighted single-step GBLUP on the accuracy of genomic prediction (GP) for MIR-predicted milk citrate in early lactation Holstein cows. A total of 134,517 test-day predicted milk citrate collected within the first 50 d in milk on 52,198 Holstein cows from the first 5 parities were used. There were 122,218 animals in the pedigree, of them 4,479 had genotypic data for 566,170 SNPs. Two data sets (partial and whole data sets) were used to verify whether the accuracy of GP is improved using the following different methods. The (genomic) estimated breeding values (EBV or GEBV) in the partial and whole data sets were estimated by pedigree-based BLUP (ABLUP), single-step GBLUP (ssGBLUP, pedigree-genomic combined using no weight for SNP), weighted ssGBLUP (WssGBLUP, pedigree-genomic combined using weighted SNP), respectively. The difference between the 2 data sets is that the phenotypic data from 2017 to 2019 in the partial data set were set as missing values. 181 youngest cows with genomic were selected as the validation population. A linear regression method was used to compare EBV (GEBV) predicted for partial and whole data sets. The accuracies of GP for ABLUP and ssGBLUP were 0.42 and 0.70, respectively. The accuracies of GP for WssGBLUP in the 5 iterations with different CT (constant) values (determines departure from normality for SNP effects) ranged from 0.70 to 0.86. This study showed that weighted SNP is beneficial in improving prediction accuracy for predicted milk citrate.

114 Accuracy of genomic predictions using single and multiple-trait machine learning methods in Canadian beef cattle population

114 Accuracy of genomic predictions using single and multiple-trait machine learning methods in Canadian beef cattle population

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.

Developing genomic tools to assist turnip rape [Brassica rapa (L.) subsp.oleifera (DC.) Metzg.] breeding

IntroductionTurnip rape is recognized as an oilseed crop contributing to environmentally sustainable agriculture via integration into crop rotation systems. Despite its various advantages, the crop’s cultivation has declined globally due to a relatively low productivity, giving way to other crops. The use of genomic tools could enhance the breeding process and accelerate genetic gains. Therefore, the present research investigated 170 turnip rape accessions representing its global gene pool to identify SNP markers associated nine phenological and agro-morphological traits and estimate the genomic breeding values (GEBVs) of the germplasm through GWAS and genomic prediction analyses, respectively.MethodsField trials were conducted at two sites in northern and southern Sweden to obtain the phenotypic data while genotyping was conducted via the genotyping-by-sequencing (GBS) method. The traits studied include days to flowering (DTF) and maturity (DTM), plant height (PH), seed yield (YLD), thousand seed weight (TSW), silique length (SL), number of siliques (NS), number of seeds per silique (SS), and pod shattering resistance (PSHR).Results and conclusionAnalysis of variance revealed substantial variation among accessions, with significant genotype-by-environment interaction for most traits. A total of 25, 17, 16, 14, 7, 5, 3, and 3 MTAs were identified for TSW, DTF, PH, PSHR, SL, YLD, SS and DTM, respectively. An 80%–20% training-test set genomic prediction analysis was conducted using the ridge regression – BLUP (RR-BLUP) model. The accuracy of genomic prediction for most traits was high, indicating that these tools may assist turnip rape breeders in accelerating genetic gains. The study highlights the potential of genomic tools to significantly advance breeding programs for turnip rape by identifying pivotal SNP markers and effectively estimating genomic breeding values. Future breeding perspectives should focus on leveraging these genomic insights to enhance agronomic traits and productivity, thereby reinstating turnip rape as a competitive and sustainable crop in Sweden and broader global agriculture.

Advancing water absorption capacity in hard winter wheat using a multivariate genomic prediction approach

AbstractThe water absorption capacity (WAC) of hard wheat (Triticum aestivum L.) flour affects end‐use quality characteristics, including loaf volume, bread yield, and shelf life. However, improving WAC through phenotypic selection is challenging. Phenotyping for WAC is time consuming and, as such, is often limited to evaluation in the latter stages of the breeding process, resulting in the retention of suboptimal lines longer than desired. This study investigates the potential of univariate and multivariate genomic predictions as an alternative to phenotypic selection for improving WAC. A total of 497 hard winter wheat genotypes were evaluated in multi‐environment advanced yield and elite trials over 8 years (2014–2021). Phenotyping for WAC was done via the solvent retention capacity (SRC) using water as a solvent (SRC‐W). Traits that exhibited a significant correlation (r ≥ 0.3) with SRC‐W and were evaluated earlier than SRC‐W were included in the multivariate genomic prediction models. Kernel hardness and diameter were obtained using the single kernel characterization system (SKCS), and break flour yield and total flour yield (T‐Flour) were included. Cross‐validation showed the mean univariate genomic prediction accuracy of SRC to be r = 0.69 ± 0.005, while bivariate and multivariate models showed an improved prediction accuracy of r = 0.82 ± 0.003. Forward validation showed a prediction accuracy up to r = 0.81 for a multivariate model that included SRC‐W + All traits (SRC‐W, Diameter, SKCS hardness and diameter, F‐Flour, and T‐Flour). These results suggest that incorporating correlated traits into genomic prediction models can improve early‐generation prediction accuracy.

A computationally feasible multi-trait single-step genomic prediction model with trait-specific marker weights

BackgroundRegions of genome-wide marker data may have differing influences on the evaluated traits. This can be reflected in the genomic models by assigning different weights to the markers, which can enhance the accuracy of genomic prediction. However, the standard multi-trait single-step genomic evaluation model can be computationally infeasible when the traits are allowed to have different marker weights.ResultsIn this study, we developed and implemented a multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model for large genomic data evaluations that allows for the use of precomputed trait-specific marker weights. The modifications to the standard single-step SNPBLUP model were minor and did not significantly increase the preprocessing workload. The model was tested using simulated data and marker weights precomputed using BayesA. Based on the results, memory requirements and computing time per iteration slightly increased compared to the standard single-step model without weights. Moreover, convergence of the model was slower when using marker weights, which resulted in longer total computing time. The use of marker weights, however, improved prediction accuracy.ConclusionsWe investigated a single-step SNPBLUP model that can be used to accommodate trait-specific marker weights. The marker-weighted single-step model improved prediction accuracy. The approach can be used for large genomic data evaluations using precomputed marker weights.

Decoding the genetic basis of ear‐related traits in maize (Zea mays L.) using linkage mapping, association mapping and genomic prediction

AbstractAs important yield components, the genetic analysis of ear‐related traits could provide a theoretical basis for maize breeding. Here, we reported the comprehensive genetic architecture of five ear‐related traits using 150 recombinant inbred lines (RIL) populations derived from the cross between Xu178 and K12. Besides, two sets of association populations were used to dissect genetic loci of five traits by genome‐wide association study (GWAS). A total of 32 QTLs of ear‐related traits were detected in the linkage mapping. Based on the mixed linear model (MLM), a total of 117 significant SNP markers of ear‐related traits were detected. Furthermore, a combined GWAS and linkage mapping analysis revealed 51 significant SNP markers fell within the confidence interval of QTLs. A total of seven co‐located significant SNP markers among different traits were found. Finally, six important candidate genes related to grain development were screened out. In addition, through haplotype analysis, two favourable haplotypes were found on chromosome 4, which could increase row number per ear (RNE), kernel number per row (KNR) and grain yield per plant (GYP) to a certain extent. Compared with the random model, the prediction accuracy of genomic prediction (GP) was improved in different degrees by considering the significant SNP markers as fixed effects. The stable genetic QTLs, candidate genes and favourable haplotypes found in this study are valuable resources, which will provide theoretical reference for high‐yield breeding of maize. Taken together, the research results also highlight the benefits of integrating GWAS with GP to further improve the accuracy of GP.

Imputation strategies for low-coverage whole-genome sequencing data and their effects on genomic prediction and genome-wide association studies in pigs

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31–1.04% compared to the chip data, and by 0.7–1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

Modeling the impact of resource allocation decisions on genomic prediction using maize multi‐environment data

AbstractIn a hybrid maize (Zea mays L.) breeding program that utilizes genomic selection, resource allocation used in phenotypic data acquisition must be balanced between population size, number of environments, and the number of testers used for generating hybrids. Plant breeders evaluate newly developed inbred lines using multi‐environment trials to account for genotype‐by‐environment interaction effects. The replication of hybrids across environments in these trials impacts the training data accuracy for developing genomic prediction models. This study examined the impact of resource allocation scenarios on genomic prediction accuracy using a multi‐environment trial dataset generated using inbred lines crossed to multiple testers. A total of 369 Stiff Stalk double haploid lines from a synthetic mapping population were testcrossed to three non‐Stiff Stalk inbred lines as testers, PHZ51, PHK76, and PHP02, and evaluated across 34 environments by the Genomes to Fields Initiative in 2020 and 2021. Resource allocation scenarios significantly impacted site‐specific genomic prediction accuracy for unobserved hybrids in unobserved environments. A training set with three to five environments that had the highest quality data produced similar prediction accuracy as data from 10 random environments for both observed and unobserved hybrids, indicating that strong prediction models can be built with a limited set of environments for both grain yield and plant height. We found that resource‐efficient prediction models that use data from one tester and three to five environments can effectively conduct selection of untested hybrids and in untested environments. Public research programs are often limited in testing capacity, and this study provides support for genomic selection in resource‐limited breeding programs.

Comparison of genomic prediction accuracy using different models for egg production traits in Taiwan country chicken

In local chickens targeted for niche markets, genotyping costs are relatively high due to the small population size and diverse breeding goals. The single-step genomic best linear unbiased prediction (ssGBLUP) model, which combines pedigree and genomic information, has been introduced to increase the accuracy of genomic estimated breeding value (GEBV). Therefore, this model may be more beneficial than the genomic BLUP (GBLUP) model for genomic selection in local chickens. Additionally, the single-step genome-wide association study (ssGWAS) can be used to extend the ssGBLUP model results to animals with available phenotypic information but without genotypic data. In this study, we compared the accuracy of (G)EBVs using the pedigree-based BLUP (PBLUP), GBLUP, and ssGBLUP models. Moreover, we conducted single-SNP GWAS (SNP-GWAS), GBLUP-GWAS, and ssGWAS methods to identify genes associated with egg production traits in the NCHU-G101 chicken to understand the feasibility of using genomic selection in a small population. The average prediction accuracy of (G)EBV for egg production traits using the PBLUP, GBLUP, and ssGBLUP models is 0.536, 0.531, and 0.555, respectively. In total, 22 suggestive- and 5% Bonferroni genome-wide significant-level SNPs for total egg number (EN), average laying rate (LR), average clutch length, and total clutch number are detected using 3 GWAS methods. These SNPs are mapped onto Gallus gallus chromosomes (GGA) 4, 6, 10, 18, and 25 in NCHU-G101 chicken. Furthermore, through SNP-GWAS and ssGWAS methods, we identify 2 genes on GGA4 associated with EN and LR: ENSGALG00000023172 and PPARGC1A. In conclusion, the ssGBLUP model demonstrates superior prediction accuracy, performing on average 3.41% than the PBLUP model. The implications of our gene results may guide future selection strategies for Taiwan Country chickens. Our results highlight the applicability of the ssGBLUP model for egg production traits selection in a small population, specifically NCHU-G101 chicken in Taiwan.

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

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).