A sequencing-based genomic assay such as ChIP-seq outputs a real-valued signal for each position in the genome that measures the strength of activity at that position. Most genomic signals lack the property of variance stabilization. That is, a difference between 0 and 100 reads usually has a very different statistical importance from a difference between 1000 and 1100 reads. A statistical model such as a negative binomial distribution can account for this pattern, but learning these models is computationally challenging. Therefore, many applications—including imputation and segmentation and genome annotation (SAGA)—instead use Gaussian models and use a transformation such as log or inverse hyperbolic sine (asinh) to stabilize variance.We show here that existing transformations do not fully stabilize variance in genomic datasets. To solve this issue, we propose VSS, a method that produces variance-stabilized signals for sequencing-based genomic signals. VSS learns the empirical relationship between the mean and variance of a given signal dataset and produces transformed signals that normalize for this dependence. We show that VSS successfully stabilizes variance and that doing so improves downstream applications such as SAGA. VSS will eliminate the need for downstream methods to implement complex mean–variance relationship models, and will enable genomic signals to be easily understood by eye.https://github.com/faezeh-bayat/VSSBioinformatics online. Sequencing-based assays can measure many types of genomic biochemical activity, including transcription factor (TF) binding, histone modifications and chromatin accessibility. These assays work by extracting DNA fragments from a sample that exhibit the desired type of activity, sequencing the fragments to produce sequencing reads and mapping each read to the genome. Each of these assays produces a genomic signal—i.e. a signal that has a value for each base pair in the genome. Examples include ChIP-seq measurements of TF binding or histone modification and measurements of chromatin accessibility from DNase-seq, FAIRE-seq or ATAC-seq. The natural unit of sequencing-based assays is the read count: the number of reads that mapped to a given position in the genome .Read counts of genomic assays have a nonuniform mean–variance relationship, meaning that variance of the data is a function of the read counts, resulting in higher variance for higher read counts and lower variance for lower read counts, which poses a challenge to their analysis. This property means that, e.g. the difference in read count between biosamples is a poor measure of the difference in activity. For instance, a locus having 100 reads in one replicate while 0 in the other is usually considered more significant than a locus with 1100 reads in one replicate and 1000 reads in the other one.To handle this issue, most statistical models of genomic signals—such as those used in peak calling—model the mean–variance relationship of read counts explicitly using, e.g. a negative binomial distribution  methods, such as Segway or IDEAS  or use ac  and Most Gaussian-based methods employ a variance-stabilizing transformation to handle the nonuniform mean–variance relationship. They most commonly use the log or inverse hyperbolic sine transformations (asinh), which have the formulae Variance-stabilizing transformations can also be beneficial for visualizing genomic signals. Note that Euclidean distance in a 2D plot corresponds to the log likelihood of difference in a Gaussian model, so a nonuniform mean–variance relationship complicates visualization. Although researchers sometimes visualize raw signals, doing so requires carefully choosing a maximum viewing range which is akin to a crude linear + flat transformation, as otherwise the viewing range is dominated by a few outliers. For example, the UCSC genome browser’s default built-in H3K27ac track by default has a maximum viewing range of 100, whereas the track’s maximum value is 3851.Despite the widespread use of log and asinh transformations to stabilize variance, to our knowledge, no work has evaluated whether they in fact do so. The use of these transformations assumes that the signals have a specific mean–variance relationship (Section 2). Here, we show that, for many genomic signals, this assumption is violated and thus existing transformations do not fully stabilize variance (Section 3). To solve this issue, we present VSS, a method that produces variance-stabilized genomic signals. VSS determines the empirical mean–variance relationship of a genomic signal by comparing replicates. It uses this empirical mean–variance relationship to produce a transformation function that precisely stabilizes variance.et al., 2015). Third, S3norm  measures a genomic signal as the ratio of reads of the experiment to a control (such as ChIP Input) .We acquired ChIP-seq data from the ENCODE consortium . These RNA-seq datasets were processed with a uniform pipeline that produces a TPM (Transcripts Per Million) value for each gene . To stabilize the variance of these signals, we used an asinh transformation.For use in evaluation, we acquired RNA-seq datasets for each of the cell types above from the Roadmap Epigenomics consortium , which are not zero-inflated.Most genomic signals are zero-inflated. That is, a large fraction of positions have zero signal. To account for this pattern, we defined a separate bin for zero-signal positions w are selected to μ and w and p represent smooth spline curve, weight coefficients and smoothing parameter, respectively. The variable p parameter varies between p = 0 results in a cubic spline with no smoothing, and when p approaches zero the result is a linear function.We used a smoothing spline to fit an estimated mean–variance curve spar parameter (p), first the smooth.spline function is called by activating the cross-validation in the smooth.spline (CV = TRUE). Following the cross-validation procedure, spar parameter is returned as the smoothing factor. We identified the optimal curve using the R function call smooth.spline.To find the optimum value of We examined a number of methods for identifying the mean–variance relation from multiple replicates. We discussed the details of the examined approaches in the b and β =sμ2+1 . In the  results , so we fu equals the inverse of the derivative of the transformation (Section 2.4)A transformation implicitly assumes that a dataset has a specific mean–variance relationship. The assumed variance x + 1) transformation implicitly assumes the mean–variance relationship x) transformation assumes the mean–variance relationship As noted above, a log and α) have different variance instability. Signals with unstable variance will have large values of the variance-instability metric.The As described above, SAGA algorithms are sensitive to the mean–variance relationship in the input datasets. SAGA algorithms take as input a collection of signals for a given biosample. They partition the genome and assign a label to each segment such that positions with the same label have similar patterns in the input datasets. SAGA algorithms are widely used to integrate datasets and annotate regulatory elements.To evaluate the quality of annotations produced by signals under a given transformation, we defined the following SAGA metric. Following previous work , we quank integer labels i. We defined features for each gene as follows. We divided each genic region into 20 bins by dividing the transcribed region into 10 equally spaced bins and defining five 1 kb bins upstream of the transcription start site (TSS) and downstream of the transcription termination site (TTS), respectively. We defined features b as a one-hot encoding of the majority label in each bin. That is, this process associates each gene with a vector of 20k features.Specifically, for a collection of signals from a given biosample, we used the SAGA algorithm Segway (r2) to quantify the predictive power of this regressor.We trained an Extreme Gradient Boosting (XGBoost) regression model to predict a gene’s RNA-seq expression (Section 2.8) value from this vector of features. We trained a regression model on a matrix containing all genes in a chromosome. As features, we used a one-hot encoding feature vector that indicates 1 in the corresponding position of the predicted label and 0 elsewhere. For each bin, we considered the majority feature vector as a representation of that bin’s annotation. We used the coefficient of determination . However, we found that mean–variance relationship learned by VSS had much better likelihood (average log density –1.2) than either transformation, indicating that the learned curve successfully models the mean–variance relationship of the data . This metric measures the degree to which differences between replicates vary for different magnitude of signal. In other words, this metric quantifies the consistency of the variance of mean squared between-replicate differences, among bins which divide the signal values in equally spaced groups. A lower value of the variance-instability metric indicates that the transformation has been successful in stabilizing the variance of the dataset. We found that signals transformed using VSS have better (lower variance-instability score) variance stability than either untransformed signals or signals after alternative transformations .x + 1) and asinh(x) had an average of 1.8 variance instability, whereas VSS have instability of 1.5 . However, VSS outperforms asinh when using LPPV as the base signals . Likewise, VSS outperforms a log transformation for LPPV and raw signals . This improvement likely results from the fact that VSS stabilizes variance in all cases, whereas asinh does so only when datasets happen to have a specific mean–variance relationship.We found that using variance-stabilized signals from VSS improves annotations produced by SAGA algorithms . As had In this manuscript, we proposed VSS, a method that produces units for sequencing-based genomic signals that have the desirable property of variance stability. We found that the transformations that are currently used to stabilize variance—We showed that VSS successfully stabilizes variance in genomic datasets. Further, we found that using variance-stabilized data improves the performance of Gaussian models such as SAGA.Variance-stabilized signals will aid in all downstream applications of genomic signals. In particular, they are valuable for three reasons. First, VSS allow downstream methods to use MSE loss or Gaussian likelihood distributions, which are much easier to optimize than the existing practice of implementing a model that accounts for the mean–variance relationship. This will improve tasks that currently use Gaussian models, such as chromatin state annotation and imputation.Second, VSS can be easily analyzed by eye because the viewer does not need to take the mean–variance relationship into account when visually inspecting the data. For example, when viewing genomic signals in a genome browser, variance-unstable signals often exhibit high peaks that swamp the vertical axis and flatten other variations in signal . ExistinThird, VSS overcomes the problems that other signal transformation methods like log transformation may cause. When transforming genomic signals, a trade-off must be considered. One must (i) reduce outlier spikes in signals so they do not dominate analysis while (ii) avoiding overly amplifying background noise. VSS provides a principled way to make this trade-off; it reduces signals by exactly the amount needed to stabilize variance.A key limitation of VSS is that it requires the availability of replicated data. A fruitful direction for future work might aim to remove this dependence, e.g. by training a consensus transformation to apply across non-replicated data. Another direction for resolving this issue may be to use the autocorrelation in the genome, as neighboring positions in a replicate, can be considered as pseudo-replicates of one another. Doing so would eliminate the need for multiple replicates for identifying the mean–variance relationship, as this trend can be identified from a single replicate.A related limitation is that VSS relies heavily on the comparability of its input replicates. For example, if a pair of replicates exhibits completely irreproducible peaks or there are (e.g.) batch effects between the replicates, VSS will estimate extremely high variance overall, leading to very low-magnitude signals.Moreover, it would be beneficial to investigate if VSS can stabilize the variance between different experiments. This task would be specifically useful in the downstream analyses in which selecting a region as significant depends on the between-replicates-variance. Future work may focus on figuring out if one can use the same mean–variance relationship trained on one experiment to get the VSS for different samples.Another related issue concerns standardizing dynamic range of different experiments. VSS has not been designed to address this issue itself. However, a user can use VSS as part of a pipeline with cross-experiment normalization methods such as S3norm (This was supported by a Simon Fraser University President’s Research Grant and a NSERC CREATE scholarship. Conflict of Interest: none declared.btab457_Supplementary_DataClick here for additional data file.


In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.The online version contains supplementary material available at (10.1186/s13059-021-02353-8). Over the past decade, genome-wide association studies (GWAS) have successfully identified many variants significantly associated with diseases and complex traits. Unfortunately, those variants only explain an extremely small proportion of phenotypic variation , 2 and tPrevious works have shown that many loci in the genome harbor more than one causal variant for a given disease or a trait –15. The In this paper, we propose a new model-based method for identifying the association between multiple variants in a locus and a trait we call Model-based Association test Reflecting causal Status (MARS). Our approach builds upon recent progress in fine-mapping approaches that attempt to identify causal variants in a locus. Causal variants are responsible for the association signal at a locus; however, each locus often contains tens to hundreds of variants tightly linked  to the reported associated single nucleotide polymorphism (SNP). Therefore, the LD hinders the identification of causal variants at the risk locus. CAVIAR  is a recZ-scores and the LD of variants in a locus, which can be obtained from a reference dataset such as HapMap , the summary statistics of m SNPs, S=T, is as follows: Now we consider the case with Σ is a correlation matrix, where Σ{i,j}=rij. We utilize Fisher’s polygenic model and assume that the effect sizes follow a normal distribution. Let C be a binary vector of length m that indicates the causal status of m SNPs; 0 indicates that a SNP is non-causal and 1 indicates that a SNP is causal. Given the causal status C, we assume that the true effect size is as follows: Here, Δ is a diagonal matrix, where Δ{i,i}=σ2 if the ith SNP is causal and Δ{i,j}=ε, otherwise. From Eqs. (where rom Eqs.  and 2),,Δ is a dThen the likelihood function is given as follows: γ, which is independent from other SNPs. To compute the prior of causal status, we use the same assumptions that are widely used in fine-mapping methods, and γ is set to 0.01 . We examined the power for cases with two causal or three causal SNPs implanted in the simulated data, where the causal SNPs are randomly selected for each simulation. Then, we computed the p-value of SNULL using the univariate test, UNI pNULL, and found the quantile q, where the p-value equals to the standard GWAS p-value threshold of 5×10−8 as follows: To show MARS increases statistical power over the univariate test, we compare the power between MARS and the univariate test. For a fair comparison, we utilized the standard GWAS LRTstats of SNULL as LRTstat at the quantile q as the threshold of LRTstats as We compute the p-value threshold of 5×10−8. Now, we compute the LRTstats of SALT as Here, p-value of SALT using the univariate test; UNI pALT, as follows: Similarly, the power of the univariate test is defined similarly by computing the 5 simulations and a threshold of 10−5 is used instead of 108 simulations and a threshold of 5×10−8, respectively.In the power comparison of MARS, fastBAT and DAP-G, the power estimation process is the same as that described above except for that 10m is very large, the standard re-sampling approach, S∼N, using the Cholesky decomposition [Σm×m itself often requires a few gigabytes of space. We can reduce the space and time complexity dramatically by utilizing the fact that Σm×m is a covariance matrix of X; Σm×m=XTX/n, where n is the number of samples. Instead of sampling statistics from MVN with the variance-covariance matrix of Σm×m; S∼N, we sample statistics from MVN with the variance-covariance matrix of In×n; S∗∼N. This neither takes time nor space because in general n<<m and n is not large. Then we multiply S∗ by To access the significance of associations, MARS uses a re-sampling approach that requires a lot of sampling from MVN distribution. There are two main obstructions to this standard re-sampling approach. One is that a locus may contain many SNPs; for example, many genes in the GTEx data contain >10,000 SNPs within ±1Mb of their TSS. When the number of SNPs position  is imprap-values. However, for the GWAS analysis, MARS needs to perform a lot of samplings to find a LRT threshold that corresponds to the standard GWAS p-value threshold of 5×10−8. Thus, for GWAS, we have applied importance sampling, which is an approximation method of standard sampling. The main idea of importance sampling is that it draws the sample from a distribution with thicker tails than a target distribution. Then, it uses importance weights so that the correct distribution is targeted [In×n; S∗∼N, we sample statistics from MVN with the variance-covariance matrix of f indicates the probability density function of MVN. We repeat the process of sampling statistics Simp and W, K times. We call each process as a run and after K runs we have a set of statistics W1,W2,⋯,WK}. Then, we estimate a univariate p-value from each Simp and compute the p-value threshold as the ratio of the sum of weights that have the univariate p-value <5×10−8 over the sum of all the weights as follows: The other main obstruction of the standard sampling approach is that the number of sampling required to find a proper threshold for MARS may be very large. For the GTEx data, we compared the eGenes to those reported by the GTEx consortium and performed 10,000 samplings to determine the number of samples used for computing their empirical targeted . The proi indicates the index of a run and C is a set containing the indices of runs where the univariate p-value <5×10−8. Given summary statistics of a locus, we can access the significance of the locus by computing LRTstat of the summary statistics as K number of LRTstats for the top 50 SNPs of the Simp as p-value of the locus as the ratio of the sum of weights where Here, D is a set containing indices of runs with p-value estimated from the Eq.  and the “fastBAT” option was used to run GCTA-fastBAT. The DAP-G program was downloaded from the appropriate website (https://github.com/xqwen/dap/tree/master/dap_src), and summary statistics were used from the run option.To compare MARS with the standard approach of the set-based association test, we defined a univariate test that uses a maximum summary statistic among the SNPs in the analysis locus. In addition, the widely used set-based association tests fastBAT  and DAP-https://www.ncbi.nlm.nih.gov/gap); these were used to generate all results throughout this paper. The eGene list of GTEx data version 7 was downloaded from dbGap and only used to validate eGenes that had been identified by MARS applied on GTEx data version 6. In total, 23,163 gene loci selected from the Whole Blood data were used for the analysis; these contain at least 50 SNPs in their ± Mb of TSS. We generated the null panel of LRTstats using Whole Blood data that contains the most samples, 338. The numbers of genes differ between tissues due to factors such as sample size differences; therefore, for eGene detection in 44 tissues, we used common gene regions in each tissue and the Whole Blood data.The summary statistics and genotypes of 44 tissues of GTEx data version 6 were downloaded from dbGap (http://zzz.bwh.harvard.edu/plink/index.shtml), was used to compute the statistics. For the set-based association test, the gene map of the GTEx data that contains 56,319 gene positions was used to define the loci for analysis. SNPs ±1 Mb around the transcription start site (TSS) of the genes were searched in the NFBC genotype data and 51,762 regions with >50 SNPs were used for the analysis. 104 importance samplings were performed to generate the null panel to estimate the p-values of MARS and the univariate test.The genotypes and 10 phenotype values of triglycerides (TG), high-density lipoproteins (HDL), low-density lipoproteins (LDL), glucose (GLU), insulin (INS), body mass index (BMI), C-reactive protein (CRP) as a measure of inflammation, systolic blood pressure (SBP), diastolic blood pressure (DBP), and height of 5326 samples from the Northern Finland Birth Cohort (NFBC) dataset were downloaded from dbGap. PLINK, a whole-genome association analysis toolset . We compared those extra eGenes with a newer version of data recently published by the GTEx consortium, GTEx v7, as the newer version of the data is expected to contain more eGenes with a bigger sample size. In addition, we compared those extra eGenes with the eGenes reported by Framingham Heart Study (FHS) data that has a large number of samples, 5257 samples, using the Whole Blood data. In each column, ‘0’ indicates the gene is not identified as an eGene and ‘1’ indicates the gene is identified as an eGene.Additional file 3 List of eGenes identified by MARS, the univariate test, and GTEx 23,163 genes from the Whole Blood data were used for the analysis, which contain at least 50 SNPs in their +/- 1Mb of TSS. We generate null distribution of LRTstats using the Whole Blood data as which contains the maximum number of samples,338. The number of genes is different between tissues due to the sample size differences and etc, thus, for the eGene detection in 44 tissues, we use genes common in each tissue and the Whole Blood data. The first column shows the gene ID, the second column shows eGenes identified by MARS, the third column shows eGenes identified by the univariate test and last column shows the eGenes reported by GTEx consortium. In each column ‘0 ‘indicates the gene is not identified as an eGene and ‘1’ indicates the gene is identified as an eGene.Additional file 4 List of genes significantly associated with traits in NFBC data detected by MARS and the univariate test. The first column shows the geneID. Only genes at least one of the method, MARS or the univariate test, is significantly associated with the trait is shown. The second column and the third column show whether SNPs +/- 1Mb of TSS of the gene is significantly associated with the trait or not for MARS and the univariate test. Here, ‘1’ indicates the gene is significantly associated with the trait and ‘0’ indicates the gene is not significantly associated with the trait.Additional file 5 List of loci found by previous studies among the loci identified by MARS but not by the univariate testAdditional file 6 Review history


Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the The online version contains supplementary material available at 10.1007/s00122-021-03815-0. Hordeum vulgare L.) is the fourth most important cereal crop worldwide. Sustainable barley production depends on the efficient use of valuable diversity in breeding. In this direction, genebanks have collected and preserved about half a million of barley accessions covering a large genetic diversity to improve crops  and Barley mild mosaic virus (BaMMV), transmitted by the vector Polymyxa graminis, cause the disease. Phenotyping of entire genebank collections for resistance to BaYMV and BaMMV in field trials is laborious and time-consuming. As an interesting alternative, allele mining has been successfully implemented in populations of ~ 2000 and 1090 barley accessions to identify novel donors of resistances for the cloned resistance genes rym11 and rym4, respectively  belonging to the ex situ collection of the IPK Genebank. In an attempt of mining new sources of resistance against key barley pathogens in Germany, the Federal Research Centre for Cultivated Plants  and its predecessor organizations received each year a different set of accessions from the IPK Genebank for disease resistance screening. Over the years, the accumulated data formed a historical phenotypic data set. In more detail, we made use here of 15 and 18 years of historical screening data accumulated during the 1985–2016 period for susceptibility to the barley mosaic viruses: BaYMV and BaMMV, respectively. The data for BaYMV and BaMMV susceptibility were published and used in a previous study to show the potential use of genome-wide association analysis in genebank collections  -entry is given by Pairwise Rogers’ distances Rogers  were calr2) between markers  analysis Gower  was appla effect was significantly different from 0. Genome-wide multiple testing was assessed by adjusting R2).Genome-wide association analysis (GWAS) was performed using the following linear mixed model  the first, (ii) first 5, (iii) first 10, and (iv) first 20 SNPs having the lowest P-values for associations during GWAS, as well as all SNPs whose associations were significant at (v) p markers, the effective number of independent associations , we have estimated the prediction ability of GBLUP not only for the total data set, but also within locations across years, as well as for each year–times–location combination separately. The prediction abilities were high for the total data set amounting to 0.62 for BaYMV and 0.64 for BaMMV susceptibility Fig. . SeparatGWAS was performed for the entire data set and revealed 52 significant  first 5, (iii) first 10, and (iv) first 20 most significant SNPs from GWAS scans. The most relaxed thresholds of R2 values of markers approached zero by using more liberal significant thresholds . Nevertheless, the highest average R2 values observed with the most conservative threshold, i.e., only considering the most significant marker, were never higher than 1.6% and 1.4% for BaYMV and BaMMV, respectively. These very low average R2 values observed in general were a consequence of the high number of spurious associations detected during cross-validations at any used significant threshold. For both BaYMV and BaMMV, average MAF values increased slightly with more liberal significant thresholds, while the average of the detection rate increased first progressively by relaxing the significance thresholds, peaking at the 20 most significant markers, but dropped afterward using the two most liberal thresholds of R2, and detection rate in cross-validations . While the detection rate was not correlated with MAF, low positive correlations were observed between MAF and R2 (r = 0.12* for BaYMV and BaMMV), and among detection rate and R2 (r = 0.35*** for BaYMV and r = 0.29*** for BaMMV). Accordingly, the most stable association for BaYMV was observed at marker 3:690,855,738, mapping on chromosome 3H, having a MAF of 0.14, a detection rate of 90.7%, and an R2 value of 7.5%. In addition, marker 3:691,483,734 mapped 0.62 Mb away from 3:690,855,738 and carried the second most stable association for BaYMV and the most stable one for BaMMV. This marker has a MAF of 0.21 and presented detection rates of 93.8% and 85% as well as R2 values of 8.8% and 6.4% for BaMMV and BaYMV, respectively.In cross-validations, average We contrasted the prediction ability of GBLUP with that of W-BLUP, which gives special weight to important diagnostic markers Fig. . The W-BWe compared the usefulness of MAS with that of genome-wide prediction from a breeder’s viewpoint. For this, we considered the most significant marker of chromosome 3H for both traits and on chromosome 4H for BaMMV and performed predictions using MAS and W-BLUP Fig. . PredictQuadrant (I) represents susceptible genotypes for both genomic prediction and MAS. It includes 81.4% of accessions for BaYMV, and 81.5% and 80.3% of accessions for BaMMV. Quadrant (II) stands for susceptible accessions according to MAS but classified in the top 10% as less susceptible according to genomic prediction. This quadrant comprehends 5.3% of accessions for BaYMV, and 4.7% and 8.9% of accessions for BaMMV. Quadrant (III) represents accessions that would be selected by both MAS and genomic selection, including 4.7% of the accessions for BaYMV, and 5.3% and 1.1% of accessions for BaMMV. Quadrant (IV) includes accessions that would be selected by MAS but fall outside of the selection criteria for genomic prediction. This involved 8.6% of accessions for BaYMV, and 8.5% and 9.7% of accessions for BaMMV.The agronomical value of crop varieties comes from the combination of several traits into the same single cultivar. In order to have enough variation to breed for multiple traits using methods such as tandem selection or independent culling levels, plant breeders tend to relax their selection criteria for single traits Bernardo . It is vPast studies have demonstrated the great value of historical data to populate a bio-digital resource center  and rym13, respectively  were modeled as fixed effects in a GS model.One long-term goal of genebanks is to characterize entire collections for important agronomic and quality traits. This information can then be used in a first step to identify donors of novel beneficial genes and alleles, which are potentially lost in the course of intensive breeding. However, due to the large population sizes and the phenotyping bottleneck, the characterization of entire collections is often limited to smaller subpopulations. We followed the strategy proposed by Yu et al.  and studBernardo  studied 2 values from past linkage mapping studies are usually inflated, which make them difficult to compare with our less biased cross-validated prediction ability results.Most modern breeding approaches targeting resistance to yellow mosaic virus disease have been based on resistance screenings for the identification of donors and genetic mapping  algorithm (Mackay and Street +) by including besides the marker-based additive relationship matrix also a relationship matrix established using bioclimatic variables of the collection site. This FIGS+ approach lead to slight but non-statistically significant improvement in a data set of 789 bread wheat landraces that were fingerprinted with 12 K DartSeq SNP markers and scanned for seed morphometric traits Below is the link to the electronic supplementary material.


In the Mediterranean basin countries, the dairy sheep production is usually based on local breeds, which are very well-adapted to their production systems and environments and can indeed guarantee income, employment, and economic viability in areas where production alternatives are scarce or non-existent. Mastitis is still one of the greatest problems affecting commercial milk production. However, genetic evaluation of mastitis is particularly difficult because of its low heritability and the categorical nature of the trait. The aim of this study was to identify genomic regions putatively associated with somatic cells count (SCC) in the local economically important Valle del Belice sheep breed using of deregressed breeding values (DEBV) as response variables. All the samples were genotyped using the Illumina OvineSNP50K BeadChip. Genome-wide association analysis was carried out based on regression of DEBV. A total of eight markers were found to be significantly associated with log-transformed SCC. Several candidate genes associated with SCC were identified related to immunity system and udder conformation. The results can help improving the competitiveness of the local Valle del Belìce breed. Further studies considering a higher sample size or independent population will be needed to confirm our results. The negative effects of mastitis are provoked by a combination of animal characteristics , genetic  and environmental factors   that are present on and around the udder skin  with a dt, etc.) . Therefot, etc.) , but no The emergence of high-throughput genotyping technologies allowed routine genome-wide association studies (GWAS) to be performed in livestock populations. GWAS allows screening of the genome utilizing a large number of genetic markers spread across the entire genome to detect genetic variants associated with a particular disease or trait. The estimated breeding values (EBVs) were generally used to perform the GWAS. As an alternative, the EBVs can be “deregressed”  to standIn Sicily, dairy sheep production represents an important resource for the local economy, and the Valle del Belice is the main local breed reared on the island for the production of traditional raw milk cheeses, at farm level by small local dairies. The breed is subjected to limited breeding selection programs for milk production traits, but shows excellent adaptability to local environments, sometimes with harsh conditions . TherefoBetween 2006 and 2016 the University of Palermo collected phenotypic data from 15 Valle del Belìce flocks, for a total of 1,813 individuals. The milk samples were collected aseptically from each individual from the two udder halves in sterile containers following an A4 recording procedure , stored Preliminary analyses using the general linear model of ASReml R  were perwhere y is the observation vector for SCS TD; β is the vector of fixed effects that includes order of parity (op: 4 classes), age at first lambing ; birth season (bs: 3 classes), interaction between herd and birth season (hbs: 74 classes) and days of lactation (dim) modeled with a Legendre polynomial of order three. Htd is the vector of interaction between herd and test day random effect; a is the vector of direct additive genetic effects ; pe is the vectors of permanent environmental effect between lactations; e is the residual vector. X and Z are the corresponding incidence matrices relating records to fixed, animal, and permanent environmental between lactations effects, respectively. The pedigree file included 5,534 animals with 178 sires and 2,548 dams. The assumptions regarding the components of the model were:and ASReml R . In addi2 is the reliability of that EBV.where, EBV is the estimated breeding value and rA total of 476 sheep of Valle del Belice breed were sampled. About 10 mL of blood was collected from the jugular vein using vacutainer tubes containing EDTA as anticoagulant. The procedures involving animal sample collection followed the recommendation of directive 2010/63/EU. Sampling was carried out by trained veterinarians within the frame of vaccination Campaigns, hence no permission from the animal research ethics committee was necessary. Veterinarians adhered to standard procedures and relevant national guidelines to ensure appropriate animal care. Genomic DNA was extracted from each blood sample with a salting-out method . The DNA1. Quality control and association analyses were performed using GenABEL package  call rate per individuals and per SNPs > 95%; (ii) minor allele frequency > 2%; (iii) no extreme deviation from Hardy-Weinberg equilibrium (P < 10–6).All the samples were genotyped using the Illumina OvineSNP50K BeadChip v2. Position and chromosomal coordinates for each SNP were obtained from the ovine genome sequence assembly (Oar 4.0) package  in R envP < 1.34 × 10–6 for genome-wide (P < 0.05) and P < 2.69 × 10–5 for suggestive (P < 0.10) , corresponding to −log10(P) equal to 5.87 and 4.56, respectively. Quantile-quantile (Q-Q) plots were used to analyze the extent to which the observed distribution of the statistic test followed the expected (null) distribution, in order to assess potential systematic bias due to population structure or analytical approach. Population substructure was explored using classical multidimensional scaling (MDS) in order to verify the genetic homogeneity of the sample before analysis using PLINK v1.9 4 related with the considered trait was also checked. Finally, to investigate the biological function and the phenotypes that are known to be affected by each annotated gene, we conducted a comprehensive literature search, including information from other species.Genomic regions showing significant results were further explored to identify candidate genes underlying the loci. In particular, the gene contents located at ±250 kb distances from the significant SNP were annotated using Genome Data Viewer genome browser at the National Center for Biotechnology Information DatabaseDescriptive statistics and genetic merit for SCS in the sampled animals are presented in P < 10–6). Moreover, 12 individuals were also excluded for a low (<95%) call rate. Then, after quality control, we considered a total of 37,140 SNPs and 464 individuals for further analyses.Among the 54,241 SNPs, 7,414 SNPs are located on sex chromosomes and thus were withdrawn from the analysis. A total of 3,999 SNPs were removed due to genotype rate <0.05, 2,037 SNPs due to minor allele frequency < 0.02 and 3,651 SNP due to Hardy-Weinberg disequilibrium (P < 4.72 × 10–7). The details of these SNPs including P-values, the positions on Ovis aries v4.0 genome assembly, the chromosomes and the closest known genes are given in P-values [in terms of –log(P)] of all tested SNPs, is showed in P-values of the GWAS for SCS. The genomic inflation factor (lambda) was lower than one indicating some population stratification. However, departure from this line is also expected for a really polygenic trait, as many causal SNPs may not yet have reached genome-wide significance owing to a lack of power . Individuals with homozygous genotypes GG for rs401598547, CC for rs161717499 and AA for rs403091159, rs422960374, and rs426621433, showed lower somatic cells content among all three genotypes  were generally used (like as pseudo-phenotypes) to perform the GWAS. Although EBVs have been used as dependent variables in GWAS , this apA total of eight SNPs were found to be significantly associated with SCS in Valle del Belice sheep. For five significant SNPs, results suggested that individuals with the GG genotypes at rs401598547, CC at rs161717499, and AA at rs403091159, rs422960374, and rs426621433, could be selected to reduce the somatic cells content in milk, although these genotypes had a low frequency in the breed. The lack of selection pressure in Valle del Belice dairy sheep may also contribute to the low frequency of the favorable alleles and genotypes. Therefore, the effect of these alleles for somatic cells content trait should be verified in a larger population or by testing them in an independent sample.SERP1. This gene encodes the stress-associated endoplasmic reticulum protein 1 and was associated with immune system  in Yak  (FGF7)  in Yak , LRRIQ3 athogens  and FPGTrocesses . As abovn cattle . The genFAM227B)  and with) (FGF7) , suggests uberis . Anothers uberis , and neathy ewes . MitterhIn this study, we estimated the breeding value for SCS in Valle del Belice sheep. DEBVs of the SCS were used as trait scores for the association analysis. Several candidate genes associated with SCS were identified related to immunity system and udder conformation. These candidate genes provide valuable information for future functional characterization. Therefore, our results may contribute to increase knowledge on the role the genes play in the genetically determined mechanisms involved in mastitis in sheep. The results can help improving the competitiveness of the local Valle del Belìce breed, through the development of genetic improvement programs directed toward reducing the incidence of mastitis, also considering the udder conformation into selection objectives, with planned mating between subjects carrying favorable alleles. Anyway, further studies considering a higher sample size or independent population will be needed to confirm our results.https://figshare.com/articles/dataset/GWAS_for_somatic_cell_counts_in_sheep/13797851.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Ethical review and approval was not required for the animal study because the procedures involving animal sample collection followed the recommendation of directive 2010/63/EU. Sampling was carried out by trained veterinarians within the frame of vaccination Campaigns, hence no permission from the animal research ethics committee was necessary.MT and AS: conception of the work. AM, RD, and MT: contributed to the data acquisition. MT, AS, and AM: data analysis. AS, MT, SM, and BP: results interpretation. MT, SM, and AM: drafting the article. MT, SM, MS, and AS: critical revision of the article. AS, AM, SM, MS, RD, BP, and MT: final approval of the version to be published. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The management of farm animal genetic resources and the adaptation of animals to climate change will probably have major effects on the long-term sustainability of the livestock sector. Genomic data harbour useful relevant information that needs to be harnessed for effectively managing genetic resources. In this paper, we report the genome characterization of the highly productive Mediterranean Chios dairy sheep and focus on genetic diversity measures related with local adaptation and selection and the genetic architecture of animal resilience to weather fluctuations as a novel adaptative trait linked to climate change.2 = 0.26) and genetically antagonistic to milk production. Genome-wide association and regional heritability mapping analyses revealed novel genomic markers and regions on chromosome 5 that were significantly associated with sheep resilience to climate change. Subsequently, an annotation analysis detected a set of genes on chromosome 5 that were associated with olfactory receptor complexes that could participate in heat stress mitigation through changes in respiration rate and respiratory evaporation. Other genes were grouped in previously reported biological processes relevant to livestock heat dissipation, including stress and immune response.We detected runs of homozygosity (ROH) and heterozygosity (ROHet) that revealed multiple highly homozygous and heterozygous hotspots across the Chios sheep genome. A particularly highly homozygous region was identified on chromosome 13 as a candidate of directional genetic selection associated with milk traits, which includes annotated genes that were previously shown to be linked to local adaptation to harsh environmental conditions. Favourable heterozygosity related with a potentially protective role against livestock diseases and enhanced overall fitness was revealed in heterozygous-rich regions on sheep chromosomes 3, 10, 13 and 19. Furthermore, genomic analyses were conducted on sheep resilience phenotypes that display changes in milk production in response to weather variation. Sheep resilience to heat stress was a significantly heritable trait (hOur results may contribute to the optimal management of sheep genetic resources and inform modern selective breeding programmes that aim at mitigating future environmental challenges towards sustainable farming, while better balancing animal adaptation and productivity. Our results are directly relevant to the studied breed and the respective environmental conditions; however, the methodology may be extended to other livestock species of interest.The online version contains supplementary material available at 10.1186/s12711-021-00682-7. Intensive genetic selection of farm animals combined with environmental changes raises concerns about the preservation of livestock genetic diversity. Modern programmes of selective breeding must integrate the effective management of genetic resources and the adaptation of animals to present and future challenges . The intAssessment of genome diversity, by estimating homozygosity and heterozygosity levels and genomic inbreeding, is of paramount importance for the management and conservation of genetic resources . The avaThe study of the genetic architecture of adaptive traits that are relevant to animal performance in response to changing environmental conditions, such as weather fluctuations, could also contribute to the characterization of animal genetic resources , and at The present study focuses on the genomic characterization of the Chios dairy sheep, which is one of the most productive sheep breeds worldwide. Our specific objectives were to: (i) derive and assess multiple genetic diversity indicators including levels of homozygosity and heterozygosity, and genomic inbreeding estimates, (ii) detect genomic regions associated with local adaptation and selection based on ROH and ROHet hotspots, and (iii) characterize an adaptive trait relevant to climate change defined as the performance resilience of animals to weather volatility.Total genomic DNA was extracted from blood samples of 600 Chios female sheep (milking ewes) from three farms in Northern Greece, according to the standard protocol of the QIAamp DNA Mini and Blood Mini kit , following the manufacturer’s recommendations with minor modifications. DNA samples were quantified using a Nanodrop spectrophotometer and stored at − 20 °C until use. Subsequently, DNA samples were oven-dried and 500 ng per sample were transferred to 96-well plates. All DNA samples were genotyped with the OvineSNP50 Genotyping BeadChip that contains 54,124 single nucleotide polymorphisms (SNPs)  and was developed in collaboration with the International Sheep Genomics Consortium  which inQuality control of genotypes was performed by setting sample and marker call rate thresholds at 90% using the PLINK 1.07 software . In addio) and expected (He) heterozygosity levels, and the inbreeding coefficient (F) based on the difference between the observed and expected number of homozygous genotypes under Hardy–Weinberg equilibrium, which were calculated from the allele frequencies in the sample and the total number of markers. These analyses were conducted with the PLINK 1.07 software [First, genotypes were analysed to derive observed  for each ROHet, a minimum number of 15 consecutive SNPs and a minimum length of 250 kb; (ii) a maximum gap of 250 kb between consecutive homozygous SNPs; and (iii) a maximum number of two missing calls and three homozygous genotypes. Highly heterozygous genomic regions, defined as ROHet islands, were identified based on the frequency distribution of SNPs within a ROHet, as described for ROH islands above.For the subsequent analyses, an additional edit was applied to the genotyping data, where SNPs with a minor allele frequency lower that 0.01 were removed. Thus, the remaining analyses were based on the same 538 individuals and the remaining 47,605 SNPs across the 26 ovine autosomes.A principal component analysis was performed on the animal genotypes to explore the potential population structure that should be accounted for in the following downstream genomic analyses to avoid possible inflation effects. The GEMMA software  was usedMilk yield records were available for all genotyped animals during the 2003–2018 period, during which animals had multiple lambings (births of lambs) followed by lactation periods (milking periods) lasting on average five months. Milk yield was recorded on each individual on a specific date once per month within a lactation. Thus, multiple milk yield records were available for each genotyped animal. This dataset comprised 10,348 records on 538 individuals.Corresponding weather data were obtained from the Hellenic National Meteorological Service pertaining to the weather stations that were closest to the three farms on which animals were sampled. Average daily air temperature data were extracted for the dates corresponding to the available milk yield records.Individual resilience phenotypes, reflecting a change in milk production in response to temperature variability, were derived based on a methodology that is described in detail in . BrieflyThe newly derived resilience phenotypes were studied together with animal lifetime milk production, estimated from daily milk yield records according to previously described formula , and lenVariance components of animal resilience to temperature variability in cold and hot weather conditions were estimated to implement the following mixed linear model:In separate analyses, variance components were also estimated for lifetime milk production and length of productive life using Model (1), with the addition of the latter as a covariate in the analysis of the former, and vice versa. Thus, results pertained to lifetime milk production adjusted for length of productive life, which reveals the true producing capacity of the animals. Furthermore, length of productive life was adjusted for the amount of milk produced, which then represents a functional longevity proxy. This trait reflects the capacity of an animal to remain on the farm independently of its production level, implying good health and fitness.Genomic relationships between animals were estimated based on their SNP genotypes using the Regional Heritability Advanced Complex Trait Analysis (REACTA) software  and werePhenotypic and genomic correlations between the studied traits were subsequently estimated in a series of bivariate analyses with the above-described models. Statistical significance of all estimates was assessed using the two-tailed Student’s t-distribution.The same model was also used to derive the genomic estimated breeding values (GEBV) of each individual for the studied traits. For resilience traits, these values represent the inherent propensity of animals to maintain milk production levels regardless of changing weather conditions. The respective GEBV accuracy was estimated as:software .Animal phenotypes and genotypes were jointly analysed to identify SNPs associated with animal resilience to hot and cold weather, lifetime milk yield, and length of productive life. The same effects as in the above-described Model (1) were fitted, with the addition of the term software . A Bonfesoftware .Regional heritability mapping (RHM) was implemented to identify potential genomic regions associated with animal resilience and the other traits by scanning windows across the whole genome. A sliding-window approach was used with genomic regions of 100 consecutive SNPs  and overlaps of 30 SNPs defined along each autosome. The REACTA software  was usedThe likelihood ratio test (LRT) was used to assess the significance of the effect of each genomic region. In total, 681 regions were tested across the genome resulting in a genome-wide significance threshold (P < 0.05) after Bonferroni correction for multiple testing corresponding to P < 7.34E−05 with −log10 (P) of 4.13 and a suggestive significance threshold  defined at P < 1.47E−03 with −log10 (P) of 2.83.2) and visualization were carried out using the PLINK v1.07 [The extent of linkage disequilibrium (LD) was determined in regions where (i) ROH or ROHet islands were detected, and (ii) genome-wide and suggestive significant SNPs were identified in genomic and RHM analyses. Pairwise LD estimation (rNK v1.07  and HaplNK v1.07  softwareNK v1.07  within dNK v1.07 . FunctioNK v1.07  and the NK v1.07  to deteco) and expected (He) heterozygosity estimates were 0.314 ± 0.167 and 0.355 ± 0.201, respectively, revealing quite high levels of polymorphism within Chios sheep breed. Average ROH-based inbreeding (F) based on the difference between the observed and expected number of homozygous genotypes ranged from − 0.1123 to 0.2465, with an average of 0.005. Inbreeding levels were also estimated for different lengths of ROH segments and were lower for longer ROH  was detected on chromosome 14 and the longest (97.57 Mb) on chromosome 2. The highest individual sum of ROH reached 737.52 Mb with the corresponding maximum Furthermore, 15,218 ROHet were identified in the studied sample of animals. The average ROHet length detected across all the autosomes was 719.92 kb; the shortest was detected on chromosome 1 (376.04 kb) and the longest on chromosome 10 (1748.15 kb). The numbers of ROHet by size class (length of ROHet) are in Table Figure 2) and average distance between the SNPs detected in the ROH and ROHet islands, a SNP annotation analysis was performed in 200–250 kb-regions upstream and downstream of the SNPs. Ninety-nine genes were located in the annotated regions of the respective ROH and ROHet islands  are known to be associated with animal traits, such as litter size in sheep [CTCFL, PCK1, RAE1, and BMP7) are reported to be involved in local adaptation processes [ROMO1), protection against ruminant disorders , and cheese making traits (ITPR1) [ITPR1 and SUMF1) [Based on the average pairwise LD r and averin sheep  and milkin sheep , 47, 48,rocesses . Moreove (ITPR1) . In addid SUMF1)  were fouThe results of the principal component analysis that was carried out on the genomic relatedness matrix between individuals are illustrated in Fig. Table Indicative resilience phenotypes to cold (10 °C) and hot (25 °C) conditions were selected for further study. Positive or negative values of individual slopes represent milk yield changes in response to ambient temperature change, whereas zero (flat) slopes represent individuals whose milk production remains unaffected by temperature fluctuations.2 = 0.20 and 0.26, SE = 0.095 and 0.009, respectively). Resilience to cold temperature and length of productive life were lowly heritable with estimates that did not reach statistical significance. The two animal resilience traits were strongly positively correlated at both the phenotypic and genetic levels. Phenotypic and genetic correlations of animal resilience under hot conditions with lifetime milk yield were negative, implying an antagonistic relationship between sheep production and fitness, while those between resilience to hot weather and length of productive life were positive as they both represent fitness traits. All other correlations were not statistically different from 0.Trait heritability and correlation estimates are in Table The comparison between individual GEBV estimated for the different studied traits is illustrated in Fig. Average GEBV accuracy estimates were 0.47, 0.20, 0.56 and 0.34 for animal resilience to hot and cold weather, lifetime milk yield, and length of productive life, respectively. The magnitude of these parameters reflects the corresponding trait heritability estimates summarized in Table Results from the GWAS of animal resilience under hot and cold conditions, lifetime milk yield, and length of productive life are illustrated in Fig. One SNP approached the genome-wide significance threshold P = 1.15E−06) and five genome-wide suggestive significant SNPs were also detected for resilience to hot weather. Interestingly, one of the latter on chromosome 19 also had a genome-wide significant association with lifetime milk yield with a substitution effect of opposite sign compared to resilience , and immune response  [The LD structure of the region on chromosome 5 where the significant SNPs were identified in the genomic analysis of animal resilience to temperature change in hot weather was estimated  were consistent with previously reported estimates for domestic sheep breeds worldwide [e = 0.38–0.39) [F) shown in Table FROH is an identity by descent probability ranging from 0 to 1, whereas, F is estimated based on allele frequencies resulting in both positive and negative values; this may explain the near zero mean value of the latter in our data (Table We derived multiple genome-wide indicators of the Chios genome diversity. Average observed and expected heterozygosity levels (Horldwide  and part38–0.39) , 58. By mentclasspt{minimaInbreeding levels in farmed livestock are often attributed to selective pressure towards enhanced productivity resulting in the likely selection of and mating between genetically-related individuals. Long-term unmonitored selection towards the same direction, for example increased productivity, may result in increased homozygosity and compromise animal fitness . In the Patterns of homozygosity were observed according to different clusters of ROH by size class Table a. Short We detected a genomic region with increased homozygosity, defined as a ROH island, spanning 2.89 Mb on chromosome 13 of the Chios sheep genome , which may be associated with balancing selection favouring animal fitness, are poorly characterized in livestock and, to our knowledge, have not been previously examined in Mediterranean sheep . In thisWe detected four ROHet islands on ovine chromosomes 3, 10, 13 and 19, providing signals of favoured heterozygosity related to protection against livestock diseases, immune response, and local adaptation. Particularly, the heterozygous-rich segment on chromosome 10 489.35 kb) is mapped to a selection sweep region that was previously identified in Egyptian sheep and includes a region of conserved synteny between Egyptian sheep and goat breeds adapted to hot arid environments . Adaptat9.35 kb iCTCFL, PCK1, RAE1, BMP7, and SPO11) in sheep [NFS1 and TFAP2C) [BMP7 and TFAP2C genes are also associated with lifetime milk production in goats [LOC101117181 and CEP250), metabolic pathways (EIF6), and milk-related traits , which suggest a multiple protective role. Highly heterozygous regions may also be associated with natural selection contributing to overall fitness in contrast with the ROH islands where directional genetic selection possibly leads to high levels of homozygosity.Functional annotation analysis of the detected ROH island on chromosome 13 revealed a set of genes previously characterized as signatures of selection for local adaptation to harsh environmental conditions  , 67. Intin goats . This hiin goats  and boviin goats  are locaTo further investigate Chios sheep adaptiveness to changing environmental conditions, we evaluated the performance resilience of animals to weather fluctuations Fig.  with a vOur genome-wide association (GWAS) and regional heritability mapping (RHM) analyses identified several SNPs, regions, genes, and molecular pathways associated with Chios sheep resilience under hot weather conditions Figs.  and 6. T2 = 0.05–0.07 for resilience under hot weather, h2 = 0.05 for lifetime milk production and h2 = 0.06 for length of productive life). Nevertheless, the majority of the trait variance was always accounted for by the polygenic effect. These results are consistent with previous studies, which suggest that fitness-related traits, such as resilience, are usually complex polygenic traits [Our genomic analyses revealed regions accounting for a detectable variation in the studied traits , whole-genome ROH-based inbreeding (FROH), and FROH by the respective size classes (ROH segment length) 2 to 4 Mb (FROH2_4), 4 to 8 Mb (FROH4_8), 8 to 16 Mb (FROH8_16), >16 Mb (FROH16).Additional file 2: Table S1. Islands of runs of homozygosity (ROH) and heterozygosity (ROHet), and associated genes and animal traits of the studied sheep. The respective chromosomal locations are attributed to Oar v4.0 genome assembly.Additional file 3: Table S2. Functional characterization of annotated genes in detected islands of runs of homozygosity (ROH) and heterozygosity (ROHet).Additional file 4: Figure S2. Examples of individual animal resilience phenotypes manifested in reaction norm curves. Each curve represents the individual animal change in daily milk yield  in response to average air temperature variation on the day of milk record .Additional file 5: Figure S3. Quantile-Quantile plots for genome-wide association. Figures show the Q-Q plot before correction by the inflation factor for animal resilience to temperature change in hot weather (a), lifetime milk yield (b), length of productive life (c) and cold weather (d).Additional file 6: Figure S4. Linkage disequilibrium (r2) heatmap of chromosome 5 region associated with Chios sheep resilience to hot weather conditions. This region extends between the furthest SNPs above the genome-wide and suggestive significance thresholds spanning a region of 2.41 Mb for animal resilience to temperature change in hot weather. The pairwise LD between these two SNPs is illustrated at the bottom right part of the figure.Additional file 7: Table S3. List of the genes identified in the genomic analysis of resilience to temperature change under hot weather conditions. The respective genes were detected 1 Mb upstream and downstream of the genome-wide and suggestive significant SNPs.Additional file 8:Figure S5. Protein interaction network. The present network represents the candidate identified genes for sheep resilience to temperature fluctuations under hot weather.Additional file 9:Figure S6. g:GOSt multi-query Manhattan plot of functional enrichment analysis for sheep resilience to temperature fluctuations under hot weather. The X-axis represents the functional terms colour-separated by data source. The inset table summarizes the biological processes of the genes.Additional file 10:Table S4. Significantly enriched annotation clusters and functional terms.


Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.Using eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered.Overall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.The online version contains supplementary material available at 10.1186/s40246-021-00357-w. Although the costs of genome sequencing have been reduced over the past decade , the expParkinson’s disease (PD) is one of the common neurodegenerative disorders and exerts a significant influence on the world in terms of both healthcare and economy . AlthougPolygenic risk score (PRS) has been widely used for predicting the risk of many complex diseases and traits based on summation of risk alleles and weighted by their effect sizes derived from GWAS results, and it becomes an important factor in the field of precision medicine . RegardiR2) and non-reference discordance rates (NDR) between high-coverage and imputed genotypes. We prepared simulated LPS data by downsampling high-coverage WGS to 0.5 × , 1.0 × , 2.0 × , 3.0 × , 4.0 × , 5.0 × , 6.0 × and 7.0 × . Aligned read distribution for each LPS data was presented  of low-coverage data need to be updated using the reference panel for more accurate genotype imputation , 17. RecR2 and inversely proportional to NDR when genotype concordances were measured between non-imputed LPS data and high-coverage genotypes  when imputation was conducted using Minimac4. We also calculated genotype concordance using the 1KGP3 panel only, and the results demonstrated a similar pattern with imputed genotypes using NARD1  at rare and low frequency (AF < 5%) were lower than those of LPS with > 3.0 × . The differences in imputation accuracy between each LPS were diminished as AF increases (from AF > 5%), and subtle differences were observed for SNPs with AF > 50%  of > 0.8 were used in GWAS [R2 of variants at rare frequency bins were highly variable  that were more accurately imputed among the overlapping imputed SNPs between GSA and LPS. The quality of most SNPs (> 0.6) were more decent in LPS, and from 3.0 × , more than 90% of SNPs were more accurate across every allele frequency bins. Also, the fractions in these LPS were diminished as allele frequencies are increasing at ultra-low coverage (0.5 × and 1.0 ×)  in a meta-GWAS, (2) the 9 previously identified to be associated with PD in European populations that were replicated in this meta-GWAS  and (3) combined EAS and EUR replicated SNPs (Combined set). For the Combined set, we excluded less significant SNPs within the same linkage disequilibrium blocks (R2 ≥ 0.5) using LDmatrix [R2 = 0.998 and 0.992 for NARD1 and 1KGP3 panel, respectively; Additional file The GWAS summary statistics of 23 million SNPs for PD from the UK Biobank study of 1239 PD cases and 451,025 controls of European ancestry (UK Biobank G20) . To avoiLDmatrix . As prevP-value as parameters. In addition to P + T approach, several Bayesian approaches for PRS calculation have been continuously developed. We therefore used P + T method and Bayesian approaches; PRScs [P value thresholds  and genotype array, and deep sequencing data ~ 30 ×) were highly correlated, and several studies have continuously demonstrated potentials of LPS for precision medicine , 6. Addi × were hR2 and direct comparison of overlapping SNPs between LPS and GSA demonstrated that higher imputation accuracies were obtained from LPS data, especially at rare frequency bins  and low (< 5%) variants still had relatively lower accuracies than common variants, but outperformed when simulated GSA data was imputed. The aggregate ins Fig. a. Since We selected a cohort of Parkinson’s disease (PD) because it is one of the most common neurodegenerative diseases with complex genetic characteristics. Even though substantial efforts have been devoted to elucidate the complex genetic architecture of PD, predicting early diagnosis of PD is still challenging due to missing heritability of this disease. To evaluate the performance of LPS for predicting a complex disease, we prepared a cohort of 188 Korean individuals including 87 PD cases and 101 controls and generated genotyping array and WGS data to an average depth of 5.0 × , which is known to be the minimum coverage for accurately detecting genome variation , and shoWhether using genome-wide SNPs around 100 k or population-specific SNPs, correlation of individual scores between LPS and array were generally high, particularly for East Asian SNPs Fig. . Along wWe demonstrated the potential of LPS with coverage less than 1.0 × to be used for predicting PD, and suggested cost-efficient LPS to replace GSA data which have been widely and popularly used in this field. Therefore, we believe that utilization of LPS could become useful in precision medicine with financial and technical advantages over genotype array.A total of 188 Korean individuals, consisting of 87 individuals diagnosed with PD and 101 controls, were collected at Asan Medical Center . High-depth WGS were generated using additional eight Koreans without PD diagnosis from the cohort for evaluating imputation performance of downsampled data. Genomic DNA from the blood of collected individuals were extracted and prepared. All quality control passed blood genomic DNA samples were subjected to library preparation with the Illumina Nextera DNA Flex kit  following manufacturer’s instruction. Briefly, input genomic DNA was treated with bead-linked transposomes. After tagmentation stop reaction and purification, tagmented genomic DNA was amplified by PCR reaction with recommended cycles described in the manufacturer's instruction. Library was quantified both with the quantitative PCR method  and fluorescent method . Each constructed and measured library was normalized by diluting with the calculated amount of nuclease-free water, and all normalized libraries were pooled and then sequenced with the Illumina NovaSeq 6000 platform  based on the manufacturer’s instruction.Obtained DNA was genotyped on the customized Global Screening Array  which captured multiethnic genetic variation. Genotypes were processed to variant call format (VCF) files according to the manufacturer’s guide using Illumina’s GenomeStudio and the in-house processing method. Produced individual VCFs were called and merged using GATK v4.1.2 , then vaTo evaluate the efficiency of LPS coverage, we downsampled eight high-coverage (average depth of 27.2 ×) WGS to 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 and 7.0 × using SAMtools view  based onWe performed genotype phasing using Eagle v2.4  and impuR2) and non-reference discordance rates were computed using bcftools stats. We extracted a total of 1,373,903 overlapping variants between different depths of non-imputed downsampled LPS and high-coverage WGS, to compare genotype concordances. For assessing two different imputation approaches, 5,371,175, 5,465,923, 5,468,916, 5,469,108, 5,469,158, 5,469,208 and 5,469,213 overlapping variants between approaches were used for 0.5 × , 1.0 × , 2.0 × , 3.0 × , 4.0 × , 5.0 × , 6.0 × and 7.0 × , respectively.We compared raw and imputed downsampled to high-coverage WGS to assess genotype concordance for evaluating imputation performance. We extracted overlapping variants between two sets of WGS. Pearson’s correlation coefficient . Calculated scores were normalized to have mean zero using PLINK. Area under the curve (AUC) of PRS for each variant set was estimated using scikit-learn libraries [Calculating PRS requires two types of data; GWAS summary statistics including known risk allele with their effect sizes are called base data, and individual-level genotype data with their phenotypes are called target data . We convng PRScs  and EB-Png PRScs  were conng PRScs . Individibraries .Additional file 1: Figure S1. Distribution of aligned read per genotype of downsampled WGS of eight individuals. The x-axis represents the number of read counts aligned to the genotypes. The y-axis represents a fraction of genotypes from downsampled WGS of eight individuals.Additional file 2: Figure S2. Genotype concordance between high-coverage genotypes and non-imputed genotypes. The x-axis represents each downsampled depths, and the y-axis represents a, Pearson’s correlation coefficient (R2), and b, Non-reference discordance rate.Additional file 3: Figure S3. Number of typed and imputed variants in million from simulated array (GSA) and downsampled LPS from 0.5 × to 7.0 × .Additional file 4: Figure S4. Number of variant with R2 > 0.8 from simulated array (GSA) and downsampled LPS from 0.5 × to 7.0 × across each frequency bins. The x-axis represents non-reference allele frequency (AF) of East Asian population derived from the gnomAD v3.1. The y-axis is the number of variants in log scale.Additional file 5: Figure S5. Comparison of imputation approach using 20 PD-associated SNPs. Red indicates imputation using GLIMPSE, and green indicates haplotype phasing using Eagle v2.4 and Minimac4 for imputation. a, Pearson’s correlation coefficient (R2) and b, Non-reference discordance rate.Additional file 6: Figure S6. Distribution of aligned read per genotype of downsampled WGS of 188 individuals. The x-axis represents the number of read counts aligned to the genotypes. The y-axis represents a fraction of genotypes from downsampled WGS of 188 individuals.Additional file 7: Figure S7. AUC of 5 different PRS sets at each P value threshold. After LD clump, multiple P value thresholds were set to assess AUC values. The x-axis represents significance thresholds from 5.00 × 10–2 to 1.00 × 10–20, and the y-axis represent AUC.Additional file 8: Figure S8. Assessment of PRS models based on SNP sets from GWAS in European and East Asian populations using LPS. Evaluation of 5 different PRS models using 4 different PRS approaches based on data of GSA followed by imputation. The x-axis presents PRS models: UK Biobank G20; GWAS summary statistics of PD from the UK Biobank study of European populations, EUR total; 74 previously identified PD-associated SNPs in European populations, EAS; 11 genome-wide significant SNPs in a meta-GWAS of East Asians (P < 5.00 × 10–8), EUR replicated; 9 SNPs in EUR total that were replicated in a meta-GWAS (P < 1.00 × 10–5), and Combined; 16 SNPs of EAS and EUR replicated that were LD clumped. A total of 4 different approaches for PRS calculations were used: unadjusted, P + T, PRScs, and EB-PRS. The area under curve (AUC) with 95% confidence intervals is shown in the y-axis.Additional file 9: Figure S9. Density plots using 16 PD-associated SNPs from LPS data. Green color represents density for cases, and pink color represents for control. The x-axis represents polygenic risk score, and the y-axis represents density of samples.Additional file 10: Figure S10. AUC of PRS analysis. Genotype imputation was conducted using the NARD reference panel by GLIMPSE. a PRS calculated based on 11 Asian SNPs, b PRS calculated based on nine European SNPs that were replicated in East Asian cohorts.Additional file 11: Figure S11. Correlation of PRS between GSA and LPS. a using 11 Asian SNPs, b using nine European SNPs that were replicated in East Asian cohorts.Additional file 12:  Supplementary Tables.


The results revealed that water-deficit stress substantially declined days to heading, plant height, chlorophyll content, relative water content, grain yield, and yield attributes. Contrarily, leaf rolling and the sterility percentage were considerably increased compared to well-watered conditions. Genotypes differed significantly for all the studied characteristics under water-deficit and well-watered conditions. Both additive and non-additive gene actions were involved in governing the inheritance of all the studied traits; however, additive gene action was predominant for most traits. The parental genotypes P1 and P2 were identified as excellent combiners for earliness and the breeding of short stature genotypes. Moreover, P3, P4, and P6 were identified as excellent combiners to increase grain yield and its attributes under water-deficit conditions. The hybrid combinations; P1 × P4, P2 × P5, P3 × P4, and P4 × P6 were found to be good specific combiners for grain yield and its contributed traits under water-deficit conditions. The parental genetic distance (GD) ranged from 0.38 to 0.89, with an average of 0.70. It showed lower association with hybrid performance, heterosis, and combining ability effects for all the studied traits. Nevertheless, SCA revealed a significant association with hybrid performance and heterosis, which suggests that SCA is a good predictor for hybrid performance and heterosis under water-deficit conditions. Strong positive relationships were identified between grain yield and each of relative water content, chlorophyll content, number of panicles/plant, number of filled grains/panicle, and 1000-grain weight. This suggests that these traits could be exploited as important indirect selection criteria for improving rice grain yield under water-deficit conditions. Water deficit is a pivotal abiotic stress that detrimentally constrains rice growth and production. Thereupon, the development of high-yielding and drought-tolerant rice genotypes is imperative in order to sustain rice production and ensure global food security. The present study aimed to evaluate diverse exotic and local parental rice genotypes and their corresponding cross combinations under water-deficit versus well-watered conditions, determining general and specific combining ability effects, heterosis, and the gene action controlling important traits through half-diallel analysis. In addition, the research aimed to assess parental genetic distance (GD) employing simple sequence repeat (SSR) markers, and to determine its association with hybrid performance, heterosis, and specific combining ability (SCA) effects. Six diverse rice genotypes  and their 15 F Oryza sativa L.) is a vital cereal food crop for most parts of the world [Rice  to explore the performance of six diverse rice genotypes and their 15 F1 crosses for some physiological and agronomic traits under well-watered and water-deficit conditions; (ii) to assess the combining ability, heterosis, and gene action type that regulates the inheritance of the studied traits; (iii) to investigate parental genetic distance and its association with hybrid performance, heterosis, and SCA; and (iv) to study the associations among evaluated traits under well-watered and water-deficit conditions. Assessing genetic diversity is an integral aspect of the development of new genotypes with a desired combination of traits ,23,24. Meterosis ,32,33, aeterosis ,26,34,35Oryza sativa L.) genotypes were selected according to their distinct origin and tolerance to water deficit. The selected parents included tolerant exotic genotypes and adopted local cultivars that are sensitive to drought stress. Seeds were obtained from genetic stock of the Rice Research and Training Center (RRTC). The pedigree of utilized parents is displayed in 1 hybrids during the summer season of 2019. The hot-water emasculation method was utilized according to Jodon [Six diverse rice . The experimental site is hot, with no precipitation events during rice growing seasons, which is representative of the summer season in Egypt. The meteorological data are shown in 1 hybrid combinations were assessed under two irrigation regimes; well-watered and water-deficit conditions in separated experiments. A Randomized Complete Block Design (RCBD) with three replications was used in each experiment. The well-watered condition (W) was performed using continuous flooding every 4 days with an adequate depth of submersion that ensured all surface areas were covered by water in each irrigation incident. The water-deficit treatment (S) was performed using irrigation every 12 days without standing water. The stress condition was applied after 15 days from the transplantation date until maturity. The applied irrigation quantities for each treatment were measured using a flow meter, and were 13,100 and 8360 m3/ha under well-watered and water-deficit conditions, respectively. The seeds of each genotype (parents and their corresponding F1 crosses) were sown in the nursery on 7 and 5 of May in the 2020 and 2021 seasons, respectively, then transplanted to the field after 30 days. The seedlings of each genotype (the parents and F1s) were individually transplanted in three rows per replicate. Each row was 4.0 m long with a spacing of 20 × 20 cm among rows and hills. Nitrogen fertilizer at a rate of 165 kg N ha−1 was applied in three splits in the form of urea (46.0% N). Phosphorous was applied at a rate of 37 kg P2O5 ha−1 as super-phosphate (15% P2O5), and potassium at a rate of 50 kg K2O kg/ha as potassium sulfate (48% K2O). Zinc fertilizer was applied at a rate of 24 kg/ha ZnSO4. Other standard agricultural practices such as weed control and disease protection were applied.The parental genotypes and generated FThe value of days to heading (DTH) was measured as the number of days from transplanting to the date when 50% of panicles were fully exerted in each plot. The plant height  was recorded as the distance from the soil surface to the tip of the main panicle at maturity. Chlorophyll content  was recorded utilizing a SPAD meter  from the topmost completely expanded leaves on the main panicle during the flowering period. Leaf rolling (LR) was recorded using visual assessment following the method outlined by De Datta et al. . RelativAt harvest, the number of panicles per plant (NP) was measured by counting the number of panicles/plant of 10 randomly selected plants in each plot. The number of filled grains per panicle (NFG) was recorded by separating and counting the filled grains of 10 main panicles selected randomly from each plot. The 1000-grain weight  was calculated based on the weight of 1000 grains sampled from each plot. The sterility percentage  was recorded by dividing the number of unfilled grains by the total grains from 10 panicles/plot. Grain yield/plant  was recorded as the weight of the individual plant grain yield and adjusted to 14% grain moisture content.http://www.gramene.org, accessed on 26 January 2022). PCR analysis was performed utilizing a 10 μL reaction volume including 1 Taq DNA polymerase unit, 0.2 mM dNTPs, 2 mM MgCl2, 0.5 μM reverse and forward primers, and 1 μL of 20 ng/μL genomic DNA template. The PCR reaction was initially started by denaturation at 94 °C for 2 min followed by 94 °C for 30 s, annealing at 55–64 °C (based on primer Tm) for 30 s, extension at 72 °C for 30 s for thirty-five cycles, and the program was completed with a final extension step at 72 °C for 3 min. Amplification products were separated using a gel electrophoresis unit incorporating 2% agarose gel, stained with ethidium bromide and visualized under a UV-Gel documentation system. Allele number, major allele frequency, gene diversity, and the polymorphic information content (PIC) were identified for all markers utilizing Power-Marker version 3.25. Genetic distances were estimated using the PAST program. The dendrogram tree was constructed with the unweighted pair group method utilizing arithmetic averages (UPGMA) within the computational package MVSP version 3.1.Genomic DNA was extracted utilizing the CTAB method  from a h1 − MP)/MP × 100 and better-parent (BP) heterosis = (F1 − BP)/BP × 100. The analysis of variance (ANOVA) was performed for all measured traits using R statistical software version 4.1.1 The combined analysis was applied whenever the homogeneity test was non-significant using Bartlett’s test. Combining ability analysis was applied as outlined by Griffing’s method 2, model 1 . HeterosFour tolerance indices were estimated to determine potentially drought-tolerant genotypes as follows: Mean productivity (MP)Ys+ Yp2, Geometric mean productivity (GMP) =Ys×Yp, Stress tolerance index (STI) p(Y-p)2, p is yield of each genotype under well-watered conditions, Ys is yield of each genotype under water-deficit conditions, and Ȳp and Ȳs are the means of all genotypes under well-watered and stress conditions, respectively. The hierarchical cluster analysis was applied based on tolerance indices to differentiate the evaluated genotypes based on their drought tolerance following the method of Ward Jr [Yield index (YI) =YsY-s, where Yp Ward Jr . PrincipThe analysis of variance results indicated significant impacts of irrigation (I), genotype (G), and their interactions (G × I) on all of the studied traits . Dividin1 hybrids manifested a wide variation for all measured traits under both well-watered and stress conditions. Water-deficit stress caused early heading in all tested genotypes compared with normal irrigation. The mean values of days to heading (DTH) reduced from 103.1 to 99.3 days due to water deficiency  significantly decreased as irrigation decreased, falling from 131.95 to 104.77  from 11.83% to 21.04%  to five (RM20A), with an average of 3.31 alleles/locus . The eff1 and P2, whereas the uppermost genetic distance was observed between P1 and P5 (0.89), followed by P1 and P4 (0.88). The dendrogram constructed based on GD divided the parental genotypes into two major clusters with internal sub-clusters within both groups . Accordingly, the two PCAs were employed to perform the PC-biplot . A stronDeveloping high-yielding and drought-tolerant rice genotypes has come to be more imperative to sustain rice production in the face of ongoing population growth and the threats of climate change. The significant genotypic difference detected for all measured traits indicated the presence of adequate genetic variability among the evaluated genotypes. Accordingly, the genetic variations that were discovered could be exploited in establishing a successful rice program for breeding high-yielding and drought-tolerant genotypes. In keeping with these findings, high genetic variations have also been observed for different agronomic traits in rice under well-watered and water-deficit conditions by Venuprasad et al. , Monkham1, P2, and P3 were recognized as good combiners for earliness and shortened plant height under both conditions. This indicates that these parents could be useful in developing early and dwarf genotypes, which are preferred to escape from terminal drought stress [1 and P6 were recognized as good combiners for increasing chlorophyll content. Meanwhile, improvements in RWC could be achieved by using the parents P3 and P4, which had steadily positive and significant GCA estimates under stress conditions. The local parental genotype P3 and the two exotic genotypes P4 and P6 could be favorable combiners for grain yield and some of its contributed traits. Consequently, these parents could transfer their promising alleles to progenies and enhance grain yield under water-stress conditions. Previous studies have also emphasized the significance of using exotic and local parents with positive and high GCA effects for boosting grain yield and related agronomic traits under normal and water-deficit conditions [An efficient breeding program relies on the careful choice of parents. The GCA effects are of crucial importance in selecting potential parents that could be exploited in developing improved populations , as paret stress . The parnditions ,21,72.1 × P4, P2 × P5, P3 × P4, and P4 × P6 can be considered excellent specific combiners for developing high-yielding hybrids under water-deficit conditions. These hybrids were generated from parents with good × good or good × poor general combiners. These results reveal the role of accumulative impacts of additive × additive interactions of positive alleles [3 × P4 was a good specific combiner for CHLC, RWC, NP, NFG, SP, and GYPP under both conditions. Moreover, P4 × P6 combined well for DTH, RWC, NP, NFG, SP, and GYPP under both conditions. Remarkably, the crosses possessed significant and positive SCA effects and better parent heterotic impacts for grain yield and contributing traits. Consequently, desirable segregants could be projected from these cross combinations. Thus, these crosses could be efficiently employed in rice programs to improve these traits under well-watered and drought stress conditions. SCA elucidates the deviation of hybrids’ performance from that of the parents utilized, and allows for estimation of non-additive gene effects. Hybrids with significant SCA effects are great choices for the selection of transgressive segregates. SCA effects revealed that all of the developed hybrids displayed significant SCA in a preferable direction for at least one trait. On the basis of these results, the hybrid combinations P alleles . SimilarThe significant GCA and SCA effects for grain yield and other evaluated traits indicate that both additive and non-additive gene actions play integral roles in regulating the inheritance of all the assessed traits. However, the variance due to GCA demonstrated a higher magnitude than SCA for most of the studied traits. This implies a greater role for additive gene effects in regulating the inheritance of these traits, and consequently, the recurrent selection method could be efficacious for improving these traits. The obtained results coincide with those of Suvi et al. , Zewdu , Gramaje3, P4, P1 × P4, P3 × P4, P3 × P6, and P4 × P6 were determined to be drought-tolerant , varying from tolerant to sensitive genotypes. The genotypes Ptolerant . Consequ1 and P2, which are japonica-type and drought-susceptible genotypes. On the other hand, the greatest genetic distance was observed between the local parental cultivar P1, which is drought-susceptible and japonica-type, and the exotic genotype P5, which is an indica type and drought tolerant. Similarly, Chakravarthi and Naravaneni [3 × P4 gave higher grain yield under well-watered and drought stress conditions. It was derived from crossing between group 1 (exotic genotypes) and group 2 . This proves there is the potential to attain more efficient hybrids by crossing diverse genotypes from distinct groups. Thus, parental selection using DNA markers could help in developing superior rice hybrids and population improvements [The SSR markers utilized in the current study reflected the degree of genetic diversity among the assessed parental genotypes. The results indicate that the number of alleles per locus varied from 2 to 5 with an average of 3.31 alleles per locus, a finding that is comparable to Khan et al. , and Sinravaneni  discoverravaneni  determinravaneni ,87. The ovements , Hua et ovements , Gramajeovements , and Zhaovements . Howeverovements  reportedovements , who repInformation on the interrelationships between grain yield and other characteristics could reinforce the efficacy of breeding programs by allowing the exploitation of suitable traits as selection criteria under stress conditions ,90,91. T1 hybrids for all the studied traits under well-watered and water-deficit conditions. The parental genotypes P3, P4, and P6 were identified as excellent general combiners for developing high-yielding and drought-tolerant rice genotypes. Moreover, the crosses P1 × P4, P2 × P4, P3 × P4, and P4 × P6 were the most favorable combinations for improving grain yield, particularly under water-deficit conditions. These are the best hybrid combinations with the highest better-parent and mid-parent heterosis for grain yield and other agronomic traits. Additive and non-additive gene effects are implicated in regulating the inheritance of all traits, with a prevalence of the additive gene action for most traits. Different plant traits comprising chlorophyll content, relative water content, number of panicles/plant, number of filled grains/panicle, and 1000-grain weight were recognized as indirect selection criteria for breeding drought-tolerant genotypes due to their positive association with grain yield. SSR based-genetic distance failed to predict SCA effects, heterosis, and hybrid performance. However, SCA could be employed in order to predict the heterosis and performance of hybrids under water-stress conditions.Noticeable genetic variability was detected among the tested parents and their F