Genetic association studies seek to uncover the link between genotype and phenotype, and often utilize inbred reference panels as a replicable source of genetic variation. However, inbred reference panels can differ substantially from wild populations in their genotypic distribution, patterns of linkage-disequilibrium, and nucleotide diversity. As a result, associations discovered using inbred reference panels may not reflect the genetic basis of phenotypic variation in natural populations. To address this problem, we evaluated a mapping population design where dozens to hundreds of inbred lines are outbred for few generations, which we call the Hybrid Swarm. The Hybrid Swarm approach has likely remained underutilized relative to pre-sequenced inbred lines due to the costs of genome-wide genotyping. To reduce sequencing costs and make the Hybrid Swarm approach feasible, we developed a computational pipeline that reconstructs accurate whole genomes from ultra-low-coverage (0.05X) sequence data in Hybrid Swarm populations derived from ancestors with phased haplotypes. We evaluate reconstructions using genetic variation from the Drosophila Genetic Reference Panel as well as variation from neutral simulations. We compared the power and precision of Genome-Wide Association Studies using the Hybrid Swarm, inbred lines, recombinant inbred lines (RILs), and highly outbred populations across a range of allele frequencies, effect sizes, and genetic architectures. Our simulations show that these different mapping panels vary in their power and precision, largely depending on the architecture of the trait. The Hybrid Swam and RILs outperform inbred lines for quantitative traits, but not for monogenic ones. Taken together, our results demonstrate the feasibility of the Hybrid Swarm as a cost-effective method of fine-scale genetic mapping. Genetic mapping studies seek to describe the link between genotype and phenotype. For experimental crosses, mapping was traditionally conducted by scoring the phenotypes of recombinant offspring descended from a limited number of parental lines . While sTo generate higher resolution mapping populations than the traditional biparental crosses, multiparental populations (MPPs) are becoming increasingly used. By crossing together multiple inbred lines, one can produce genetically diverse mapping populations without sampling wild individuals. MPPs are commonly used for the dissection of complex traits in model organisms  . We then assesse the power and precision of association mapping using a Hybrid Swarm relative to common alternatives [8-way MPP—DSPR (l (DGRP) . GWA simDrosophila Genome Nexus  per chromosome. For simulations of scrm  founders with high proportions of ancestry (∼25%) and unambiguous haplotype paths . However, empirical evaluation of HARP output for sample chromosomes showed that many had more than four founders with high proportion of ancestry and difficult-to-resolve haplotypes .We used the software package i.e., greater than a specified quantile) for each potential founding haplotype. Potential founding lines were then ranked by the number of windows passing this threshold. We examined two measures of effectiveness for this method across a range of quantile threshold values  when selecting up to a maximum number of most likely ancestral founders. The first measure is the number of true ancestral founders excluded; the second measure is the fraction of the chromosome derived from ancestors missing from the selected subset.We developed a heuristic method to identify the set of most likely ancestors. We ranked all possible founders by their contribution to the focal chromosome. To do this, we counted the number of windows with extreme likelihoods of ancestry  using the Genome Analysis Toolkit ASEReadCounter tool . Because STITCH draws inference from haplotypes between related individuals, genotype estimate accuracy is expected to increase with greater numbers of sequenced individuals. To capture the effect of sample size on STITCH accuracy, we ran STITCH for sample sizes from 100 to 5000 sequenced individuals. We calculated genotype estimate accuracy as the fraction of variable sites with a correct diploid genotype estimate using a custom R script. Because STITCH memory requirements increase with greater values of k (founding haplotypes), it was not possible to evaluate k = 128 founders, or to use the diploid estimation model. As a result, we limited our evaluations to 32-founder populations, using the pseudoHaploid model.We estimated genotypes with alternative approach, STITCH, which is capable of imputing genotypes without reference panels . Following DNA extraction, we purified samples with Ampure XP Beads (Beckman Coulter product number A63880). We prepared 1 μl of DNA at ∼2.5 ng/μl sequencing using a modified Nextera protocol developed by We tested if RABBIT genotype estimates accurately reflected genotypes from higher coverage data for six individuals. First, we called genotypes in the high coverage samples using a heuristic based on reference and alternate allele counts produced by GATK’s ASEReadCounter to call genotypes. Sites with at least three reference and three alternate allele bearing short-reads were called as heterozygous; any site with at least six reference reads and zero alternate reads (or the converse) was called as homozygous. All other sites with fewer six reads were excluded. We calculated accuracy as the proportion of sites where the low-coverage genotype derived from reconstruction matched our higher coverage genotype call. We note that our measurement of genotype accuracy from the empirical data is conservative as we expect a moderate fraction of true heterozygous sites to be called as homozygous.We performed GWAS on simulations of various types of Hybrid Swarm designs and contrasted the signal of association using these designs to that from a simulated 8-way MPP (akin to the DSPR), and the DGRP. Simulation of GWAS proceeded in two steps.For every type of genetic architecture or mapping population explored, we simulated 500 mapping populations comprised of 5000 individuals. Full genomes for individuals were generated using the forward simulation framework described above. We used the DGRP as the founder chromosomes. One important note is that the genotypes we use for GWAS are the “true” genotypes and are not affected by genotyping error. This decision was made to reduce computation time. Because genotyping error is randomly distributed throughout the genome  curves and calculated Area Under the Curve (AUC). Briefly, we calculated the true positive rate (sensitivity) as a function of false-positive rate (1-specificty) using the We calculated the genomic inflation factor (GIF) to evaluate the role of mapping design and genetic architecture on the overall signal of association. We evaluated GIF on chromosomes linked and unlinked to causal loci. We calculated the GIF by correcting for sample size following First, we evaluated our most-likely ancestor selection algorithm, which selects a minimum representative set of most-likely ancestors (MLAs). This step required optimizing a discrimination threshold for likelihood values calculated with HARP , providing 68.5% genotype accuracy with N = 100, increasingly approximately linearly until 94.5% accuracy at N = 3000, and providing 99% accuracy with N = 4000.We contrasted the accuracy of genotype calls made with RABBIT to those made with an alternative tool, STITCH  and high (∼8) coverage were highly concordant ∼95%; . AccuracP-values below 10−5.We evaluated GWAS accuracy for several configurations of Hybrid Swarms and compared these to simulations of GWAS using an 8-way RILs (modeled after the DSPR), and an inbred reference panel (the DGRP). Examples of representative Manhattan plots for a single causal locus of large effect are shown in P = 1.101 × 10−6). Inbred reference panels consistently had the lowest AUC for multi-locus traits (GIF). See Supplemental Table S2 for comparisons between all mapping population types.To assess overall performance of these mapping populations across hundreds of simulations, we generated ROC curves and calculated AUC . In genes traits . We suspHybrid Swarm populations, composed of either 32- or 128-founders intermated for 5 generations had intermediate performance , and genp-values and can reflect population structure  allowing researchers to address questions that require heterozygotes; (2) reducing labor and the influence of block-effects; and (3) breaking down linkage-disequilibrium and population structure that arises from the sampling of individuals from the wild. These benefits are possible due to the ability to accurately reconstruct genomes from ultra-low-coverage sequencing data, We evaluated simulated Hybrid Swarms founded by 32 or 128 founders to determine the effect of founding haplotype count on reconstruction accuracy and GWAS performance. One might expect 32-founder populations to yield more accurate reconstructions than 128-founder populations, because it is simpler to make a correct inference out of a smaller pool of ancestors. Yet, 32-founder populations were less accurate in our DGRP simulations , and forThe Hybrid Swarm method is not limited to populations founded by inbred lines, as the technique can be applied to populations where phased genomes are available for all outbred founders. Phasing the outbred founders could be accomplished in two basic ways. For instance, phased genomes could be generated using a variety of long-read sequencing technologies  of random outbreeding. This approach was used by Erickson et al. (2020) and resulted in a relatively even distribution of founding haplotypes after five generations.One experimental consideration is the effects of drift and selection that operate during the generation of the Hybrid Swarm e.g., . To atteWe compared genotype calls from our low-coverage reconstruction pipeline to genotype calls from higher coverage data for six experimental Hybrid Swarm individuals . Our resOne clear difference between inbred and outbred mapping populations is the presence of heterozygotes. On the one hand, the presence of heterozygotes in outbred populations decreases power to detect association relative to inbred lines for an (semi-) additive allele with a given effect size . This baThe ability to assess heterozygous effects of alleles will provide valuable insights into several interesting aspects of biology, such as the nature of dominance and the identity of regulatory polymorphisms governing allele-specific expression (ASE) in a variety of heterogenous genomes. An increased understanding of dominance relationships and regulatory polymorphisms is important for advancing our understanding of quantitative trait variation and evolution. For instance, several theoretical models have shown that context-dependent dominance of quantitative fitness traits can underlie the stable maintenance of polymorphisms subject to seasonally variable  selectioi.e., ASE) as a driver of local adaptation in a variety of organisms.Regulatory polymorphisms are known to underlie genetic variation in expression  and thisThe Hybrid Swarm approach involves propagation of a single large outbred population via undirected crossing. This design confers benefits over alternatives of either rearing inbred lines separately or performing controlled crosses. The relative value of these benefits may vary across organisms and experimental designs.Individuals within a Hybrid Swarm are reared in a common environment. This reduces the influence of random block effects associated with rearing families or closely related individuals in separate enclosures or defined areas. Therefore, studies examining genotype-by-environment interactions can dramatically expand the number of environmental factors used because all individuals were previously reared in a common environment, enabling a Hybrid Swarm to be randomly scatted across environmental treatments. This feature is in contrast to rearing each inbred family across all environments, a practice which generally limits the number of environmental treatments used. Rearing individuals in a common environment also likely reduces genotype-environment correlation that could be exacerbated by vial effects.e.g., mice), controlled crosses may be logistically easier than a randomly mating swarm. For many other species, however, the Hybrid Swarm would be logistically advantageous. The cost of maintaining inbred reference panels also varies among taxa, most notably between those species which can be kept as seeds or in a cryogenically preserved state. For other species, such as flies, the monetary and environmental cost of maintaining hundreds to thousands of inbred lines may be prohibitive.Random outbreeding of a single population can require less labor compared to performing controlled crosses or serial propagation of inbred lines. The relative value of these benefits is likely to be species specific. For instance, for larger animals . Two or more generations of recombination were sufficient to reduce this inflation . This repository includes a containerized version of the reconstruction pipeline along with a test data-set. Supplemental material is available at figshare: https://doi.org/10.25387/g3.14096579.The raw sequence data for our six reconstructed real-life hybrid swarm individuals is available at SRA BioProject accession PRJNA691163. The most up-to-date code associated with this project is available on GitHub: CW: conceptualization, data curation, methodology, formal analysis, software, validation, visualization, writing the manuscript; ST: investigation, review of manuscript; SR: investigation; AOB: conceptualization, funding acquisition , project administration, resources, supervision, review, and editing of the manuscript.


Recent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses.Left ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses.Even within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies.Improving the precision of phenotyping is important for maximizing the yield of genome-wide association studies. From quantitative laboratory biomarkers elucidating the relationship between hypercholesterolemia and coronary artery disease5 to imaging characteristics in population cohorts4 revealing the genetic determinants of cardiovascular development 7 quantitative assessments of health provide additional signal compared to conventional binary labels of disease.Cardiovascular disease is the leading cause of death in the world, and significant work has been undertaken to understand the mechanisms of disease and develop preventive measures. By studying the human genome, insights have been obtained to understand pathways and mechanisms of function and disease risk, and in recent studies, researchers have moved beyond binary labels of disease diagnosis to quantitative phenotypes to obtain greater power in assessing the relationship between genotype and phenotype8. Different cohorts resulted in heterogenous findings in the genetic determinants of cardiomyopathy. More recent studies with measurements from cardiac magnetic resonance imaging (MRI) have been able to find additional loci of relevance and reaffirm previously suspected variants9, suggesting both larger sample sizes as well as improvements in phenotyping precision, can improve our understanding of the human disease. However, as populations increase, the scaling of phenotyping becomes more costly, and automated approaches are used for most quantitative measurements, despite only moderate correlation compared to manual measurements (R2 = 0.318 for cardiac MRI LVEF)9.Despite its relative frequency, critical public health importance, and often penetrant inheritance, heart failure has relatively few known genetic risk factors. Early classic genetic studies were not able to identify many genetic associations with measurements determined by echocardiography11, impacting downstream analyses. While cardiac MRI is frequently thought of as a reference standard, given its improved image quality and high signal-to-noise ratio, automated approaches similarly have measurement variation of between 3 – 5%9. We use LVEF, an important metric of cardiac function, as an example of an important but noisy measurement to explore the impact of measurement variability on downstream genetic association studies. We compare various methods to obtain the same phenotypic measurement as well as introduce simulated noise in the phenotype measurement to evaluate the relative impact of measurement imprecision and sample size on downstream genetic studies.While quantitative traits have more power than often binary labels of disease, quantitative traits can have noisy measurements. For example, left ventricular ejection fraction (LVEF) as measured by echocardiography can have measurement variation up to 7 - 10%13. We focused on 39,624 participants who had InlineVF measured LVEF 14, cardiac MRI, and genetic data available. Before running Genome-Wide Association Studies this cohort was passed through additional quality check filters  ranging from . We generated multiple phenotypic measurements from the same underlying imaging data, gradually incrementing Gaussian noise, and performed GWAS on each to investigate how measurement error/imprecision affects genetic associations.Additionally, we further compared results with two final approaches to assess LVEF. When visually assessing LVEF, clinicians often round the value to the nearest 5%, thus we generated a set of phenotype labels by rounding LVEF values to the nearest multiple of 5. For the final comparison, we generated binary LVEF labels by categorizing values as normal or abnormal, with normal values ranging from 52-72 for males and54-74 for females.12. We used the QC files provided by UKB to create a GWAS cohort consisting of subjects who did not withdraw, were of inferred European ancestry, and were unrelated. Subjects with a genotype call rate < 0.98 were also removed. We considered variants with a minor allele frequency (MAF) ≥ 0.01, and we required genotyped variants to have a call rate ≥ 0.95 and imputed variants to have an INFO score ≥ 0.3. Variants with a Hardy-Weinberg equilibrium P value < 1x10−20 were excluded. After variant filtering, we were left with 9774199 filtered variants. GWAS was done on a Spark 3.1.1 cluster, using the library Hail 0.2 with Python version 3.6. The GWAS was adjusted for age at MRI and sex. We used the conventional P value of 5x10−8 as the threshold for defining genome-wide significance.We used the UKB imputed genotype calls in BGEN v1.2 format. Samples were genotyped using the UK BiLEVE or UK Biobank Axiom arrays. Imputation was performed using the Haplotype Reference Consortium panel and the UK10K+1000 Genomes panelApart from noise in phenotype measurements, we also evaluate the effect of cohort decrease on GWAS results. We generated 6 different phenotype files where, starting from the original LVEF cohort , we keep 90% , 80% , 70% , 60% , 50% (19812), and 40% (15850) of the samples. Cohort decrease was performed before GWAS QC, and for each step the selection of samples to be excluded was random. Inspecting the effect of cohort decrease helps us define the relationship between the number of LVEF samples and GWAS power.−8, which is the Bonferroni corrected p-value threshold. Then, we consider significant SNPs found in both the base results and noise-modified results as true positives (TP), the SNPs found only in the noise-modified results as false positives (FP), and the SNPs not found in the noise-modified results but found in the base results as false negatives (FN). We then calculate We use an accuracy metric to determine the amount of overlap in significant SNPs between the baseline GWAS results and noise-modified GWAS results. First, we remove all non-significant SNPs by excluding SNPs with a p-value less than 5 × 10basepeaks is the number of significant loci in base GWAS, and correctpeaks is the number of significant loci that persisted in noise GWAS then basepeaks and correctpeaks.Sensitivity determines the amount of overlap in significant loci between the baseline GWAS results and noise-modified GWAS results. Specifically, given that The study cohort for all analyses consisted of 39,624 adult unrelated subjects of European ancestry . As a ba10, such measurement imprecision could contribute to the limited hits in historial echocardiography-derived GWAS8.To investigate the effect of measurement imprecision on GWAS power, we performed a series of association studies while introducing noise in the range of known clinician variation . SimulatGiven the summary statistics from 16 different GWAS, we modeled the relationship between noise and GWAS power . There i11. With so many images and patients, the released UKBB measurements were generated using a fully automated workflow (with Siemens inLineVF) without quality inspection and bias correction. When compared with manual clinician evaluation, the automated measurements of LVEF result in a mean absolute error of 3.4%, R2 of 0.348, and ICC of 0.521 for LVEF. Using a previously published deep learning segmentation model16, we independently derived LV segmentation-based calculated LVEF and found a mean absolute error of 6.1%, R2 of 0.335, and ICC of 0.431 for LVEF compared to the automated measurements from UKBB (Cardiac MRI provides clinicians and researchers with a plethora of high-resolution imaging, with even the abbreviated 20-min UK Biobank cardiac MRI protocol resulting in 9 sequences with over 30,000 images per studyrom UKBB . HoweverIn this study, we assessed the impact of measurement imprecision on genetic associations with LVEF and found substantially impaired power in downstream GWAS analysis with even slight increases in imprecision. Even modest phenotyping variation significantly impacted downstream genetic associations, often to a greater extent than changes in cohort size. As measurement variation is present in many clinical measurements and large cohorts increasingly rely on automated measurements, efforts to improve the precision of measurements can potentially be a cost-effective way to maximize the yield of genetic association studies.16. Few variability studies have been undertaken in cardiac MRI, although improved degrees of manual measurement variability have been found17. Our analysis suggests that a substantial gain in signal comes from the improvement of measurements precision, such that it’s impact is often greater in relative size than sample size. Efforts to improve phenotyping precision that can significantly affect the power and accuracy of downstream analyses.Cardiac function as measured by LVEF is an important clinical measurement that defines disease and identifies patients who are eligible for life-prolonging therapeutics. In echocardiography, human test-retest evaluation of LVEF can range between 7-10%, with slight changes in annotation as well as timing that can significantly impact calculations17, and by improving the precision of measuring LVEF, we also improve the accuracy and robustness of downstream GWAS results. The relatively large improvement in yield of genetic association with more precise phenotyping was substantial in comparison to the marginal benefit of increasing the cohort size. As more genetic analyses are undertaken with automated measurements or assessments 19, an additional evaluation must be taken to assess the variability and quality of the phenotyping. Such insights ideally will be confirmed with parallel measurements of similar phenotypes or multiple approachs to obtain the same phenotypeNoise in measurements can appear in both semi-automated and fully automated workflows20. For LVEF, even advanced imaging has variability in measurements that can substantially impact downstream association studies. The impact of such variability is even more profound than significant changes in cohort size, suggesting improvement in imaging precision and precise phenotyping in general has significant additional value in improving the power of genetic association studies.In summary, genetic association studies on imaging phenotypes allow researchers to discover many associations that help understand the underlying biology of the disease and structure1


Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows.g) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase.GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rThe accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.The online version contains supplementary material available at 10.1186/s12711-022-00749-z. The dairy industry has seen tremendous gains by selecting for milk production yield, while maintenance requirements have increased more slowly, leading to improvements in gross efficiency (defined as production per unit of intake) . HoweverOver the last 20 years, the Australian dairy industry has had a penalty on maintenance in national selection indices, initially through the inclusion of body weight, and more recently through “Feed Saved”. The Feed Saved trait accounts for maintenance requirements through body weight in addition to metabolic efficiency through residual feed intake (RFI). RFI is defined as the difference in an animal’s actual and expected dry matter intake (DMI), after adjusting for its body size, growth, and productivity. Variation in RFI is likely due to differences between animals in the metabolic processes related to feed intake level, digestion of feed, absorption of nutrients, basal metabolism, health status, rumen microbial metabolism, and physical activity –5.Bos taurus and Bos indicus cattle.Genomic selection has been widely adopted by the dairy industry and is especially useful for traits such as RFI that are very expensive and difficult-to-measure. The accuracy of genomic estimated breeding values (GEBV) depends on the size of the training population and the extent of linkage disequilibrium between single nucleotide polymorphisms (SNPs) and causal variants . Due to Combining data from international partners on feed intake may be one way to increase the quantity of data available to improve the accuracy of GEBV. Furthermore, the data could be used to increase the power and precision of mapping the causal variants for feed efficiency from imputed sequence data. However, collating a large enough training population for accurate GEBV prediction is a major challenge for feed efficiency traits due to different management systems, which can lead to the occurrence of genotype-by-environment interactions between countries and continents. Furthermore, genetic differences may exist between traits and measurements that are taken at different stages of the lactation period. Nonetheless, the study by Berry et al.  successfGenomic selection refers to selection decisions that are based on breeding values predicted using genome wide marker data such as SNPs . GenomicUntil recently, the Australian dairy industry relied mainly on the genotypes obtained with the Illumina Bovine 50k SNP panel for genomic prediction of breeding values. However, standard SNP arrays  are unlikely to include causal variants because they are composed of random markers that are preselected only to be (highly) polymorphic across breeds. This can result in less common causal variants not being in strong linkage disequilibrium (LD) with the SNPs on the array; thus, their effects may not be captured in genomic predictions. Indeed, there is now evidence in both cattle and sheep that the accuracy of genomic prediction can be increased by combining several thousands of highly predictive sequence variants using a standard SNP array –21. In cThe main objectives of our study were: (1) to improve the accuracy of genomic prediction for RFI of Australian cows by treating their RFI records as either a correlated trait with international cow and/or Australian heifers’ RFI records or a same trait as a combined dataset; and (2) to increase the accuracy of genomic prediction for feed efficiency in Australian cows by augmenting the standard 50k SNP panel with a subset of sequence variants selected from GWAS using international cow data.In this study, we used feed intake, body weight, and milk production data of the overseas cows from research groups in seven countries , United States of America (USA), Canada (CAN), Denmark (DNK), United Kingdom (GBR), Switzerland (CHE), and the Netherlands (NLD)) Table . The comThe AUSh dataset comprised a total of 824 Holstein heifers, which were measured for residual feed intake , dry matter intake , mean body weight , and change in body weight , and the phenotypes were adjusted for systematic environment effects (experiment), age at measurement, and its squared value. Trait deviations for RFI in AUS heifers were previously calculated as mean values of the difference in actual and predicted DMI that was measured over a 6 to 7-week period on about 6-month old heifers. A description of the design, measurements, and calculations for RFI is in Pryce et al. . Full deThe AUSc dataset consisted of 584 cows, of which 137 overlapped with the animals in the AUSh dataset  . RFI forThe phenotypes from USA, CAN, DNK, and CHE were downloaded from the EDGP database in December 2019. The RFI phenotypes from NLD and GBR are described in Pryce et al.  and the e et al. , which aBos taurus cattle representing multiple breeds and crosses from across the world  with an excessive heterozygosity higher than 0.5  ).The accuracies of GEBV using the GRM constructed from 50k SNP genotypes were at almost the same level as those using a GRM from HD SNP genotypes, probably because of long-range LD sharing across Holstein animals and hence, the 50k SNP density was as effective in tagging QTL as the HD SNP. In addition to the 50k SNPs, the variants from imputed sequence data selected in the discovery set  increased the accuracy of genomic prediction in the AUSc dataset. Several other research groups also reported an increase in genomic prediction accuracies by combining more predictive variants from imputed sequence data with a standard SNP chip, particularly for across-breed prediction, or prediction of animals that are weakly related to the reference animals , 47, 48.R2 > 0.4) for imputation quality and applied a MAF threshold (MAF ~ 0.01), which are important factors to reduce the level of false positives in the GWAS. In our study, the external independent data  were used to select the sequence variants, which were subsequently used in genomic prediction for the Australian cows. The genetic variation for DMI varies across lactation stages and the genetic correlations between DMI at different stages of lactation were lower than 1 [Causal sequence variants may be rare and, hence, in low LD with the SNPs on the SNP arrays. On the one hand, the study by Druet et al.  who usedr than 1 . Severalr than 1 , 51–53 rIn our study, the relative increase in accuracies obtained by using the selected sequence variants with 50k SNPs were lower than the accuracies estimated using the external phenotypes (OVE) with the AUSc dataset, but were consistently higher than when only 50k or HD SNP genotypes were used. The increase in accuracy was generally largest for the SNPs that were selected and combined from the single and multi-trait meta-GWAS, as expected, because the multi-trait GWAS increases the power to detect pleiotropic variants , while tWe used two validation approaches, i.e. one using cohort-by-birth year sets treated as different validation sets and the other using random cross-validation sets, which, whenever possible, do not allocate the same sires’ offspring to the training and validation sets. In most cases, the relative increase in accuracy of GEBV using random validations was slightly greater than using the cohort validations. This is probably because animals in the training and random validation sets are more closely-related than those in the cohort-split datasets. A strong relationship between the animals in the training and validation populations is likely to lead to a greater increase in accuracy due to the sharing of long haplotypes as observed previously in cattle and sheep , 54. AnoBacteroidetes were more efficient at feed utilization.). A few other studies [The improvement of the genomic prediction accuracy obtained for feed efficiency using sequence variants is interesting since it is an expensive and difficult-to-measure trait with limited data availability. Improving the precision of variant detection not only contributes to better understand the underlying biology, it can also be valuable to enhance and improve the accuracy of genomic prediction, as shown here. Adding AUSc data to the international dataset increased the power for identifying the putative causal sequence variants, but the main focus of our study was to implement genomic prediction using international cows as an independent discovery population and to select the top sequence variants for improving the accuracy of genomic prediction. Furthermore, integrating feed efficiency data with other phenotypic sources, such as ruminal microbes and potential intermediate phenotypes  could help identify potential physiological mechanisms to enhance feed uptake and production efficiency. In a study on a Holstein cattle population, Delgado et al.  showed t studies , 57 haveThe accuracy of genomic prediction of RFI improved when more than 3700 Australian cows and heifers and overseas cows were used by fitting a multivariate model. The results suggest that the accuracies of feed efficiency will improve if the training population is increased in size through international collaboration. The predictive sequence variants from the meta-GWAS of overseas data combined with the 50k SNP array data also provided an apparent increase in accuracy, but it was smaller than that obtained by combining the phenotypic datasets. However, this result should be interpreted with caution because of the small size of the reference and validation sets used in this study, and repeating the approach with larger datasets may be informative. However, our finding is important, because when international data are limited by sample size, sharing genetic variants from a GWAS approach is likely to be an effective alternative to sharing phenotype and genotype datasets. Since the current training population based on Australian animals remains small, international collaboration is crucial to achieve more accurate EBV for feed efficiency in Australia. The standard errors of genomic parameters, including estimates of heritability and genetic correlations, were large and hence, further studies with larger datasets are required to obtain more accurate estimates.Additional file 1: Figure S1. Allele frequency of HD SNPs in Australian cows (on the X axis) and Australian heifers or cows from each international country.Additional file 2: Table S1. Number of filtered polymorphic variants (Minimac R2 > 0.4) per chromosome in the imputed WGS for the overseas cows.Additional file 3: Figure S2. –log10 of single SNP regressions from single-trait GWAS for residual feed intake  and dry matter intake , and multi-trait meta GWAS  using the overseas (OVE) cow dataset. The orange points represent selected significant variants at P < 10–3.


In addition, we performed the first GWAS analysis looking for SNP genetic markers associated with this character in cattle, which showed five markers significantly associated with the trait located on BTA4 and BTA28. Finally, the functional analysis revealed the presence of five candidate genes located within these regions, which were previously shown to be related to fertility in cattle and mice models.Fertility is one of the most important traits for productivity in extensive beef production systems, since it has a major effect on the number of calves born and, as a result, the quantity of weaned calves produced per year. However, it is difficult to improve this trait under extensive conditions, mainly due to the lack of reliable and easy-to-obtain selection criteria. In this study, fertility was analyzed using reproductive efficiency, which was calculated as the deviation between the optimal and real parity number of females at each age. We demonstrated a high h2 estimate for this model was 0.30, while for the random regression model it increased across the parities, from 0.24 at the first calving to 0.51 at calving number 9. Additionally, we performed a preliminary genome-wide association study for this trait in a population of 252 Retinta cows genotyped using the Axiom Bovine Genotyping v3 Array. The results showed 5 SNPs significantly associated with reproductive efficiency, located in two genomic regions (BTA4 and BTA28). The functional analysis revealed the presence of 5 candidate genes located within these regions, which were previously involved in different aspects related to fertility in cattle and mice models. This new information could give us a better understanding of the genetic architecture of reproductive traits in this species, as well as allow us to accurately select more fertile cows.In this study, we analyzed the variation of reproductive efficiency, estimated as the deviation between the optimal and real parity number of females at each stage of the cow’s life, in 12,554 cows belonging to the Retinta Spanish cattle breed, using classical repeatability and random regression models. The results of the analyses using repeatability model and the random regression model suggest that reproductive efficiency is not homogeneous throughout the cow’s life. The h The productivity of extensive beef production systems relies on the sum of several traits. Of these, fertility is one of the most crucial, since it has a major effect on the number of calves born and, therefore, the quantity of weaned calves produced per year . This haAn increase in the calving interval is one of the most common causes of culling in beef cattle . HoweverDuring the last few years, the use of an indirect fertility criterion, including precocity and the calving interval, which can be estimated indirectly based on reproductive records, has been demonstrated as an interesting option for selecting more fertile females in extensively-bred livestock species ,14. ThisIn recent years, the genomic revolution has allowed us to identify single nucleotide polymorphisms (SNPs) associated with phenotypic traits, leading to a better understanding of complex traits. One of the most powerful tools for analyzing such associations is the genome-wide association study (GWAS). In beef, a large number of GWAS analyses have been reported for different traits such as longitudinal, carcass, fatty acid profiles, meat tenderness and quality, and growth traits ,24,25,26Retinta is an autochthonous breed with unique adaptive characteristics linked to an extensive regime in the Dehesa ecosystem. Nowadays, it is widely bred in the south of Spain, due to its excellent adaptation to the harsh environment characterized by marginal pasturelands and the hot, dry climate . In 2018In this study, we aimed to determine the genetic influence on the fertility of Retinta breeding cows by measuring the variance component patterns of reproductive efficiency across the calvings in the Retinta beef breed using random regression models (RRM). Additionally, we performed a preliminary genome-wide association study for reproductive efficiency traits to identify genetic variants, genomic regions, and candidate genes associated with fertility.In this study, we analyzed 63,421 calving records collected by the National Retinta Breeders’ Association. The dataset comprises information on 13,888 cows , which produced offspring with 3922 different sires. The pedigree was extended to include all the available information in the breed database, with a total of 20,178 animals. The inbreeding coefficients of the cow (Fc) and the bull (Fs) were determined according to the methodology described by Meuwissen and Luo  using thp > 0.05). First, we tested the classical repeatability model (Rep), in which it is assumed that the Re effect and residual variance are homogeneous across the Cn scale, as follows:y is the dependent variable Re.1, X2, Z1, Z2, and Z3 are incidence matrixes (0 or 1).µ is the average, Cn is the calving number (fixed effect), HYSN is the herd-year-season of birth of the cow combination (random effect), and Fc is the inbreeding value of the cow (fixed effect). XGenetic analysis was performed using 2 animal models implemented in ASreml3 , withoutres = 1,2,3,4,5, and 6 or more), using the following model:1 and b2 are the fixed regression coefficients modeled by a Legendre polynome (3 is an incidence matrix (0 or 1).In addition, we tested a random regression methodology (RA), in which (co)variance components for Re varied across the Cn, assuming the existence of 6 classes of residual variance according to the calving number  are random effect vectors for the animal and their ancestors; and the repetition of records of the same trait in the animal and contemporary groups, respectively. Residual effects were considered homogenous  of r = n order to estimate the results in a longitudinal way along the calving trajectory of the cows in RA. The parameters by RA model were estimated using two different approaches with r = 1 and r = 2.In both models, aRep modelRA modelThe variance components were estimated per model as follows:In both cases, a matrixIn the RA model, the same parameters were estimated along the Cn, using the K(Co)variance components were estimated along the Cn trajectory following the methodology proposed by Jamrozik, et al. , using t2) and repeatability (r) were estimated in Rep by the classical procedure proposed by Falconer and MCKay [2 and genetic correlations (rg) were estimated in each ith Cn point included in the respective Legendre polynomials  on all the ith points of the Cn trajectory as follows:m = [ao as aq ] is the animal genetic function estimated by the model described above.In addition, the RA model allows us to determine the EGV per individual  estimated using a PCA analysis, according to the procedure described by Menendez-Buxadera, et al. .i are the genetic values of the ith calving, expressed as standardized values, and PC1evi and PC2evi are the estimations of the PC1 and PC2 for the ith calving, respectively.In the second step, we arrived at a new EGV value based on the PCA results (IpcT) by applying the methodology described by Togashi and Lin , as foll–indep-pairwise 50 5 0.5 command. The final genomic dataset included 29,037 SNPs from the autosomal and X chromosomes.In total, 252 Retinta cows were selected for genotyping by their EGV index based on the PCA results (IpcT), applying the methodology described by Togashi and Lin  (describ−1 K), where τ−1 is the variance of the residual errors; λ is the ratio between two variance components; K is the genomic relationship matrix (estimated from the markers); and ɛ is the n-vector of errors. In addition, the model included a correction for population stratification based on the first 10 components of a principal component analysis performed in PLINK v.1.9 as covariates. The statistical significance of the SNP effect was calculated using the Wald test statistic, which determined a p-value for each SNP.GWAS analysis was performed using GEMMA software , employihttps://www.ensembl.org/Bos_taurus/Info/Index (accessed on 28 September 2022). Finally, the function of these genes and their putative relationship with fertility processes was established by performing an extensive review of the available literature in public databases, as well as in the DAVID V6.8 and Uniprot online resources.Potential candidate genes associated within ± 500 Kb of the significant SNPs were annotated using the Ensembl BioMart database with the last available cow reference genome  throughout their entire productive lives. Our results showed a wide variability in average Re  among Retinta cows .Furthermore, Re increased in value almost linearly with the calving number . This poThe same analysis revealed lower variability in the age of first calving , which can be associated with differences in precocity, as has been convincingly proven in beef breeds ,52. ThisIn this study, we employed two different approaches to estimate the genetic component affecting Re: the classical Rep model and the RRM. The former is the most common methodology employed, having been used for over 20 years . However2 values (near 0.25) were recently reported in goats and horses [The variance components obtained using the Rep model are shown in d horses ,47. In ad horses . Regardid horses .2 variations across the stage of production of the individual  [One of the drawbacks of the Rep model is the assumption of homogeneity of the variance components of Re across calvings. This hypothesis can be problematic for traits with low-moderate heritability, such as fertility-related traits. In contrast, RA models take into account the possible hur case) . In our In the RA model, heritabilities rose gradually with increased calving number, from 0.246 in the 1st calving to 0.583 in the 9th, mostly due to a slower pace of reduction observed in genetic variance in comparison with phenotypic variance . In thisFinally, the dataset employed in this study allows us to analyze the evolution of EGV for Re during the last 3 decades, since the beginning of the improvement program  in which all the available information was transformed into vectors , which account for a certain % of the (co)variances existing among the variables studied. In this procedure, originally developed by Togashi and Lin , PCs areOur analysis revealed that 99.7% of EGV (co)variance was explained by two major hodology . In our Finally, the estimated IpcT  showed a much more accurate fit to the breeding value distribution observed in all the population in comparison to that obtained by estimating the average EGV values for each individual using the RA model . This vaIn addition, the use of IpcT permits a significant reduction in the risk of the possible interaction genotype x environment interaction problems .p-value < 10−4, NRF1, SSMEM1, CPA5, RYR2, and ZP4) previously linked with known biological processes, molecular functions, and pathways related to fertility located within ± 500 Kb of the significant SNPs , which is involved in mitochondrial biogenesis, signal transduction, and protein synthesis. In mice, this gene has been related to late gestational embryonic lethality when a loss of function occurs [NRF1 on steroidogenesis and cell apoptosis in goat luteinized granulosa cells. An attenuated expression of NRF1 led to mitochondrial dysfunction, disrupted the cellular redox balance, impaired steroid synthesis, and finally resulted in granulosa cell apoptosis through the mitochondria-dependent pathway. The other two candidate genes found in this region were SSMEM1 and CPA5, both related to male fertility. Serine-rich single-pass membrane protein 1 (SSMEM1) is a conserved testis-specific gene in mammals. Nozawa, et al. [SSMEM1 is essential for male fertility in mice and found that the SSMEM1 protein is expressed during spermatogenesis but not in mature sperm. The sterility of the SSMEM1 KO mice was associated with globozoospermia and a loss of sperm motility, which is crucial for fertilization. Similarly, CPA5 (carboxypeptidase A5) is involved in spermiogenesis and in the regulation of sperm function, as it is exclusively or highly expressed in spermatogenetic cells, especially from the secondary spermatocyte to elongated spermatid stages. However, Zhou, et al. [CPA5-KO mice, suggesting that it is a dispensable factor for spermatogenesis and male fertility in mice.The candidate genes located within the significant regions of the SNP AX-124382279 have previously been described in important biological pathways. One of these genes was nuclear respiratory factor 1 , the extracellular matrix that houses mammalian oocytes and embryos. In rabbits, Lamas-Toranzo, et al. [ZP4 has a structural role in the zona pellucida and that rabbits without ZP4 were subfertile. In vitro, the loss of ZP4 did not affect ovulation, fertilization, or the early stages of the development of embryos. However, in vivo development was severely impaired in embryos covered by a ZP4-devoid zone. In addition, two recent studies in rats showed that ZP4 is not responsible for gamete-specific interaction [On BTA28, we found the regnancy , providiregnancy . Finally, et al.  reportederaction .In this study, we were able to model the fertility of cows bred in extensive conditions using a novel fertility trait, reproductive efficiency (Re), which can be estimated through the analysis of reproductive records, using two different genetic methodologies. Our results showed that Re has a significant genetic component, but also that the genetic influence on Re is not homogeneous during the length of life of the cows, and individuals were observed showing very different patterns of variability in the breeding values across parities, which cannot be detected by the Rep model. Therefore, the use of the RA model would be an option to enhance fertility in the Retinta breeding program. We also determined that the use of a composite index, based on a principal component analysis could satisfactorily integrate all the information produced by RA models in an efficient and user-friendly way for the breeding program. Finally, we demonstrated that evaluating reproductive efficiency using the RA model makes it possible to differentiate the cows’ genetic potential throughout the calving trajectory and enjoy greater flexibility in selecting female breeders. Finally, our preliminary GWAS analysis shows the existence of specific genomic regions influencing the fertility of Retinta cows. This new information could help us understand the genetic architecture of reproductive traits in the species better as well as allow us to select more fertile cows with greater accuracy. However, further analyses including large populations and different breeds would be required to validate our genomic findings.


This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits.Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions.The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.The online version contains supplementary material available at 10.1007/s13668-022-00430-3. The transition to westernized lifestyles has stimulated an increase in food availability and the opening up of food choices across seasons and countries , leadingThe increase in the prevalence of MetS has turned into one of the major chronic non-communicable diseases impacting healthcare costs worldwide . CurrentIn this regard, genome-wide association studies (GWAS) aim to identify genetic markers associated with phenotypes by comparing the frequency of millions of genetic variants, such as the substitution of an individual base of the genome sequence, called single-nucleotide polymorphisms (SNPs), in a specific population with common ancestry . The useThe increasing availability of larger population samples and new methodologies for modelling interactions in complex diseases is allowing researchers to integrate and combine datasets of different natures. These new multivariate models shed light on disease complexity and enable the development of new tools for precision medicine and nutrition approaches , 21. In With this in mind, the present review aims to analyse the current evidence on G × E interactions related to MetS and reported in the GWAS Catalog, concretely on environmental factors related mainly to lifestyle and dietary habits.https://www.ebi.ac.uk/gwas/) has been collecting information from GWAS since 2005 and provides a public database that summarizes the compiled information associated with a large number of traits, such as obesity, diabetes, cardiovascular disease or different types of cancer [The cumulative knowledge from GWAS has paved the way for the study of complex traits by considering pleiotropic effects between genetic variants for multiple complex traits . As the f cancer . Over thf cancer . For thif cancer .gwasrapid R package [The present review aims to systematically search, identify and provide a narrative synthesis of the GWAS that assessed interactions between genetic variants and environmental factor with impact on related metabolic syndrome traits. Data were searched using the  package  to queryThe information collected from the GWAS Catalog included author(s), journal and year of publication, size and ethnicity of the cohort population, information from the reported interaction, SNP and the nearest mapped gene, associated trait and the environmental factor interacting with the SNP, as well as covariates included in the model  were associated with more than three MetS traits Fig. A. To ourObesity is one of the most studied metabolic disorders because of its increasing prevalence in recent decades and its impact on life quality and overall health. Similarly, obesity has been the study subject of several GWAS aimed at shedding light on the polygenic architecture of the disease . At the FTO is one of the most representative obesity-related genes, which has been widely studied due to its association with multiple obesity phenotypes and diabetes [FTO exhibits pleiotropic effects with BMI-independent traits [FTO was the only gene associated with two different traits: obesity [FTO locus has been widely studied, displaying a consistent attenuation. A meta-analysis reported that active individuals exhibited around 20% reduction of the BMI-increasing effects of FTO-associated SNPs [FTO [In addition, some authors suggest that the difficulty in unravelling risk factors explaining the interindividual variability observed in obesity and MetS is related to the complex interactions with environmental factors , 44. Desdiabetes . Additiot traits , with int traits  and witht traits . Herein, obesity ••, 49 an obesity , and int obesity ••, 49, s obesity ••, sweet obesity •• and al obesity  , based on the addition of multiple small effects across SNPs associated with a specific trait and able to capture part of the individual susceptibility to develop a disease. These PRS aim to capture the contribution of heredity to multifactorial complex diseases by estimating a risk score based on multiple genetic variations that reflects the risk of developing a disease compared to a population with a common genetic sequence without these genetic variations . This haDespite these promising results, some authors are still conservative with the application of PRS because of the limited generalizability of GWAS results and the insufficient diversity of studied populations . This isEnvironmental and genetic factors contribute to the susceptibility of chronic complex diseases, such as MetS. The improvement of analytical and computational approaches in the past years has provided tones of information leading to a better understanding of the biological processes underlying the development of these diseases , 105. HoSupplementary file1 (PDF 584 KB)Below is the link to the electronic supplementary material.


Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits  in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios.indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25–0.88 for plant height, and − 0.29–0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects . The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset.The elite lines were found to belong to the Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.The online version contains supplementary material available at 10.1186/s12284-023-00623-6. Oryza sativa L.) is one of the most important food crops in the world and in Asia in particular. About 3.5 billion people depend on rice as their main food source. As the world's population increases, the demand for rice will be under pressure as an estimated 116 million additional tons of rice will be needed to meet demand by 2035  has arisen as a more efficient option for breeding program optimization . This panel was phenotyped in 15 environments in Asia and Africa regions from 2018 to 2020. This information from multi-environment trials  was used to characterize the level of G × E interaction and to cluster the environments. We then compared seven genomic prediction models to evaluate the impact of modeling G × E and environmental covariates on predictive abilities when new environments were predicted.The plant material consisted of 111 elite lines from the IRRI breeding program for irrigated systems  fluorometric kits and adjusted to obtain a concentration close to 10 ng/µl gDNA for the library preparation. Illumina®’s TruSeq Custom Amplicon chemistry was used to create the libraries and the sequencing was performed using the MiSeq Sequencing-by-Synthesis Technology System. A custom SNP-calling pipeline was used to align sequence data on the Nipponbare rice genome MSU7  data  and research stations from partners in Asia and Africa. The information regarding the locations of the 15 field experiments is available in Table Three agronomic traits were measured on each elite line: days to flowering (DTF), plant height (HT), and grain yield (YLD). DTF (days) were calculated as the number of days from seeding to the time of 50% of the plants flowering within a plot. The plant height (cm) was measured from the ground level to the tip of the highest panicle (awns excluded) at the maturity of five randomly selected plants for each elite line. For grain yield (tons/ha), each plot was harvested excluding border rows. From this sample, grain moisture content was measured using a moisture meter. Then, plot-level grain yield was computed as the grain weight in kilograms from each plot, normalized at 14% of moisture, and adjusted by the harvested areas to obtain the yield in tons per hectare.2eΣc(pc) ⊗ Σr(pr), where σ2e is the variance components of residual, Σc(pc) and Σr(pr) are the correlation matrices of the first-order autoregressive, pc and pr are the autocorrelation parameters for the spatial coordinates, columns, and rows of plots respectively, ⊗ is the Kronecker product from the auto-regressive process in columns and rows, respectively. The factors of fixed, random, and residual effects for statistical models of each trial were described in detail in Additional file For the statistical analysis of the trials, two linear mixed models were used to take account of the diversity of the experimental design. The general form of the base models was:mentclass2pt{minimasreml function of the R package asreml (version 4.1.0.143)  was estimated for each trait using the following formula:2g is the genotypic variance obtained from the experimental data and σ2e is the residual variance obtained from the model. H2 and the associated standard error were estimated with the function predict. The best linear unbiased predictors (BLUPs) for all genotypes were extracted for each trial and each trait and were used as adjusted phenotypes for further analysis. For the two trials without the replications (Nizmawna-Bangladesh and Hyderabad-India), we used the phenotypic data directly. The phenotypic information of the ECP is available in Additional file The analyses were performed using the  version .1.0.143 ggpairs function in the GGally R package  for genotype by environment interaction (G × E) upon the phenotypic performance of ECP was also carried out using the https://power.larc.nasa.gov/). This database was queried using the R package nasapower  of each trial. The following daily weather variable from the transplanting date to the harvesting date was obtained for all the trials: the total precipitation , the dew-point temperature at two meters , the minimum, maximum and mean temperature at two meters , the relative humidity at two meters , the all-sky surface photosynthetically active radiation total  and the clear sky surface photosynthetically active radiation total . The processWTH function from the R package EnvRtype was then used to compute the temperature range , the potential evapotranspiration  and the vapor pressure deficit  ; the reproductive phase ; and the ripening phase (from the latest date of flowering up to the latest harvest date)  in the ij-th genotype X environment combination which is denoted as ; gwij the interaction effect of the genotypic and environmental covariates in the ij-th genotype X environment combination which is modelled by the Hadamard product of Zg as an incidence matrix for the vector of additive genetic effects.Due to its stability of accuracy across different environments and traits and its ease of implementation, GBLUP (genomic best linear unbiased prediction) is the most used method on rice  experiments were designed to assess the predictive abilities (PA) in untested environments. In the first CV experiment (CV-RAN), the target environment  was predicted using four environments selected randomly among the 14 remaining environments from the training set. Random sampling was repeated 50 times. The predictive ability was computed for each of the 50 replicates and then averaged. An ANOVA and Tukey's tests were then carried out at the significance level of 5% based on z-transformed values (Z = 0.5 [ln(1 + r) − ln(1 − r)]), to identify the significant differences in predictive ability (r) among the models in each environment. Analyses were performed separately for each trait. After the confidence limits and means for Z were estimated, these were transformed back to r values.For the second CV experiment (CV-SEL), the target environment was predicted using four environments specifically selected among the remaining fourteen environments to form the training set. The selection of environments for the training set was based on Euclidean distance in terms of ECs. The closest environments were then identified  method. The target environment was predicted using the remaining fourteen environments as a training set. Each environment was predicted using the model trained based on the information (genotypic and phenotypic data as well as ECs) of the remaining fourteen environments.For the three CV experiments, the PAs were measured as the Pearson correlation coefficient between the predicted values and the adjusted phenotypes in the validation set (target environment).For this CV experiment, we used the leave-one-out method for predicting the untested lines. The models were trained using all the environment and all the lines except one. The remaining line was predicted across all environments. We repeated this process for all 33 lines evaluated in all fifteen environments. In this CV experiment, the PAs (Pearson correlation coefficient) were measured in two ways: at the line level  and at the environment level .The R scripts for the different CV experiments are provided in the Additional file The results showed that the Japonica (GJ), circum-Basmati (cB), circum-Aus (cA), and Indica (XI) subgroups from 3 K-RG were clearly separated and confirmed the clustering of the ECP into the XI subgroups Fig. A. When oA large phenotypic variability was found for the three traits across all the environments Table , Fig. 2.2) was rather high for all three traits. The H2 ranged from 0.52 (Ahero) to 0.96 (Cuttack) for DTF, from 0.27 (Chokwe) to 1.0 (Cuttack) for HT, and from 0.19 (Gazipur) to 0.89 (Cuttack and Dakawa) for YLD trait  were low to medium (− 0.31–0.53). No clear trend was identified for all environments, although HT was significantly correlated with days to flowering in nine of the environments and flowering was significantly correlated with yield in only five environments  confirmed the strong effect of the environments  ranged from 0.19 (Ahero) to 0.67 (Hyderabad-2018) for DTF, from 0.28 (Los Baños - wet season) to 0.83 (Hyderabad-2018) for HT and from − 0.06 (Nimwa) to 0.45 (Hyderabad-2018) for YLD  with only GE and GE-G × E models, to evaluate the effect of training set composition on PA. For DTF, the PA ranged from 0.19 (Ahero) to 0.67 (Hyderabad 2018) for CV-RAN, from 0.2 (Ahero) to 0.79 (Hyderabad 2018) for CV-SEL, and from 0.18 (Ahero) to 0.77 (Dakawa) for CV-LOEO. For HT, PA varied from 0.36 (Hyderabad 2019 wet season) to 0.83 (Hyderabad 2018), from 0.34 (Hyderabad 2019 wet season) to 0.81 (Hyderabad 2018) and from 0.38 (Hyderabad 2019 wet season) to 0.87 (Hyderabad 2018) for CV-RAN, CV-SEL and CV-LOEO, respectively. While YLD reached PA ranging from − 0.06 (Nizmawna) to 0.32 (Los Baños 2019 dry season), from − 0.05 (Dakawa) to 0.62 (Hyderabad 2018) and from − 0.1 (Nizmawna) to 0.48 (Hyderabad 2019 wet season) for CV-RAN, CV-SEL and, CV-LOEO, respectively , Monteverde et al.  have been used in GS studies depending on the context and the objective  were sufficient to obtain a good level of accuracy and that modelling the genotype by environment interaction (G × E or G × W) did not increase the accuracy. These results will allow more efficient use of the information generated by the IRRI breeding program and optimization of the testing strategy for updating the GS models.Additional file 1: Table S1. Composition of the elite core panel (ECP) used in this study. Table S2. Information on each field trial's locations and technical parameters. Table S3. Description of the statistical models used for phenotypic data analysis. Table S4. Environmental covariates (ECs) of each environment throughout the whole growing season for the different phases. vegetative (VE), reproductive (RE), and ripening (RI). Table S5. List of environments selected for the CV-SEL scenario. Table S6. Analysis of variance (ANOVA) for the three traits. Table S7. Genomic heritability (h²g) and the associated standard error (SD) for the three traits: days to flowering (DTF), plant height (PH) and grain yield (YLD). Table S8. The predictive abilities for the DTF, HT, and YLD traits for the CV-RAN scenario. Table S9. The predictive abilities for the DTF, HT, and YLD traits for the CV-SEL scenario. Table S10. The predictive abilities for the DTF, HT, and YLD traits for the CV-LOEO scenario. Table S11. Predictive abilities for each environment, based on the predictions of untested lines.Additional file 2: Fig. S1. Physical positions of the 882 SNPs on the 12 chromosomes of rice using Nipponbare reference genome. Fig. S2. Graphical representation of the allocation of ECP across 15 environments. Blue vertical lines represent the ECP-environment combinations that were observed, and the white lines (blanks) correspond to unobserved combinations. Fig. S3. Schematic representation of the developmental stages of ECP in each environment. Fig. S4. Unweighted neighbor-joining trees between the ECP and Oryza sativa groups from 3K-RG (A), or the indica subgroups (B). Fig. S5. The scatter plot, histogram, and correlation between the DTF, HT, and YLD traits in the single environments. Fig. S6. The scatter plot, histogram, and correlation between environments for DTF trait (A), HT trait (B) and YLD trait (C). Fig. S7. Comparison of predictive abilities between the three cross-validation scenarios. (A) models with genotypes and environmental covariates' main effects (GW) and their interactions (GW-GxW); (B) models combined all main effects (GEW) and their interactions (GEW-GxE-GxW). Additional file 3: The genotypic data of elite-breeding lines (882 SNPs).Additional file 4: The phenotypic data  of three traits of elite-breeding lines across 15 environments.Additional file 5: Environmental covariates (ECs) of each environment throughout the whole growing season for the different phases: vegetative (VE), reproductive (RE), and ripening (RI).Additional file 6: R scripts to perform the genomic prediction analysis.