Hundreds of thousands of loci have been associated with complex traits via genome-wide association studies (GWAS), but an understanding of the mechanistic connection between GWAS loci and disease remains elusive. Genetic predictors of molecular traits are useful for identifying the mediating roles of molecular traits and prioritizing actionable targets for intervention, as demonstrated in transcriptome-wide association studies (TWAS) and related studies. Given the widespread polygenicity of complex traits, it is imperative to understand the effect of polygenicity on the validity of these mediator-trait association tests. We found that for highly polygenic target traits, the standard test based on linear regression is inflated  To explain the mechanism behind the hundreds of thousands of loci discovered via genome-wide association studies (GWAS), researchers have studied the role of molecular traits as mediators, leading to development of transcriptome-wide association studies (TWAS) and related methods for other molecular traits . These mLD) score regression and related approaches  by testing the effect of the mediating trait To examine the effect of the polygenicity of the target trait To quantify the effect of polygenicity and prediction error, we examined the asymptotic distribution of the The precision terature .Under the null, One immediate implication of this result is that the standard TWAS test—which assumes that The inflation in TWAS is determined by the noncentrality parameter, which under the null is For better understanding of how We also show that The lower bound is attained when rrelated . When thThe factor Next, we examined the effect of the precision of the prediction of the mediator on the inflation. Under the null, both Under the alternative, we find that Consistent with established literature , our resTo assess how well this approximation works under the null, we simulated both the target trait For each combination of This estimated value was well approximated by our theoretical expression under the independent SNP assumption as shown in To examine the effect of the prediction error on the test under the alternative, we used the same simulation setup we used for the null hypothesis with Similarly to the null case, the estimated To assess the practical relevance of this inflation in a traditional TWAS, we computed the association statistic between actual predicted expression levels and null polygenic target traits. We used genotype data from unrelated White British individuals in the UK Biobank with sample sizes of 1,000, 5,000 and 10,000. We predicted expression levels of 7,131 genes in whole blood using the GTEx v8 prediction models . To genefeatures  and S8, Next, we used the average −4) for gene expression, 0.994 (s.e. 3 × 10−4) for metabolites, and 0.982 (s.e. 5 × 10−4) for brain features.Reassuringly, the intercepts for each class of mediator traits were all very close to one: 0.995 , and, finally, brain features as shown in −5 – 6.5 × 10−5, median of 5.3 × 10−5) are more polygenic than gene expression. The majority  of the mediating traits had −5) range. We obtained negative estimates of The estimates of To account for the inflation in the PrediXcan software, the p-values will be calculated using the noncentral To help users of the PrediXcan framework implement the correction, we will make the corrected values the default output from the software, and we will share the estimated We quantified the effect of polygenicity and prediction error of mediating traits in TWAS and related methods. To accomplish this, we derived a general closed-form asymptotic expression for the inflation, the validity of which was confirmed by simulations and TWAS associations based on real genetically predicted mediators of varying genetic architecture.We found that the inflation is driven by the polygenicity of the target trait and not the polygenicity of the mediator trait. This is important to note because cis gene expression levels are mostly sparse  . We alsoThe closed-form solution for independent SNPs provided us with useful insights of the cause of the inflation: as the number of independent causal SNPs increases, the factor We also show how to compute the correct p-values by estimating the parameters of the noncentral The fact that the uncertainty in prediction does not increase type I error means that when we leave out mediators based on their prediction performance—a common practice in TWAS—we can be less stringent with the threshold. Adding more noisy predictors reduces the power due to multiple testing but will not contribute to inflation. The decision on which parameters to prioritize will depend on the goal of the study. For example, mediators with suspected functional roles but low heritability, and therefore predicted with larger uncertainty, need not be excluded from the TWAS analysis for fear of increasing the false positive rate. This could increase the discovery yield of TWAS and related methods without increasing the false positives.In summary, this study demonstrates the following key conclusions: 1) Polygenicity of the target trait induces inflation in the test statistics regardless of the genetic architecture of the mediating trait. 2) Uncertainty in the prediction of the mediator does not cause inflation. 3) Uncertainty in the prediction of the mediator reduces the power of the test. 4) LD is not necessary for the inflation to occur (our simulations were done using independent SNPs). 5) The inflation can be corrected by using the noncentral We have attempted to use realistic assumptions and real genotype data and prediction models for mediators to illustrate the validity of our derivations. However, our study has several limitations: We made several assumptions to streamline the theoretical derivation. These are commonly used assumptions, but they may not hold in practice . However, the results based on actual genotype data and prediction models for gene expression, metabolites, and brain features suggest that prediction results are likely to be robust to these assumptions. We assume an additive infinitesimal model for the target trait to simplify the theoretical derivation, but traits may deviate from this model in practice. Improvements on the estimation of the inflation factor Here we derive the formula We also restate the definitions of the variables used in the formula. To simplify notation and derivation, we use the vector form of the model.We define the sample Without loss of generality (WLOG), we assume We assume that we know We considered the case where the sample size is larger than the number of independent causal SNPs for We list here additional assumptions and facts used in the derivation.We defined Furthermore,The estimatedin a TWAS is ( TWAS is (15)βˆUsingThe expected value ofisNext, we show thattends tofor largeWe want to calculate the  results , the vard by N-1 .RSS=(YThe variance of This variance, Hence, The Now, we take expectation of the Rearranging terms, we getWhen we expand the square of the terms between the brackets, all the cross terms have expectation 0 as shown below.So the expectation is given by the expected value of the squared termsAssuming a polygenic mediator,Multiplying both sides by Using Note We calculate ribution . The traby M see . We use by M see , which sIn the case of Therefore, Since these derivations are based on asymptotic approximations with By “fully polygenic Since the eigenvalues of FurthermoreSimilarly, we getProof thatProof thatProof that Analogous derivation for Proof thatNext, we show that the formula for the TWAS Instead, to include a sparse mediator into the scope, we can keep Similar to the derivation for the polygenic case,The last approximation, any mediators, our main result still holdsSo, for We can further simplify the expression of  theorem . We use From This motivates the following definition of any predictor So, for We note that the result on the scenario when variants are independent  can be sProof thatRecallWe will use To find an extreme of Since the eigenvalues of Now, we rewrite the function We can show that this is indeed a minimum of the function The minimum of the function corresponds to the case where all the eigenvalues are equal, therefore If we plug in the identity matrix in the equation for Proof thatRecallUsing the eigenvalue decomposition of Since the trace of Hence using This upper bound is attained when the first eigenvalue equals Since variance of Decomposition ofProof that(Note that we are using From Theorem 3.1 in , for a rRecall that We downloaded prediction weights for gene expression in whole blood, metabolites in plasma, and brain features from the PredictDB website . We usedSupplement 1


BMI) in the Electronic Medical Records and Genomics (eMERGE) network . Analyses were conducted in two ancestry groups (European and African) and three age ranges . For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics . The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses – we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRS Polygenic risk scores (PRS) provide individualized genetic estimates of a phenotype by aggregating genetic effects across hundreds or thousands of loci, typically from genome-wide association studies (GWAS). PRS are potentially a powerful source of increased prediction performance, even when combined with family history ,2. HowevSeveral outstanding questions surrounding PRS, especially within the context of adiposity traits and BMI, warrant further investigation. For instance, when cross-ancestry summary statistics  are available, can they be used to improve prediction performance in individuals from one or more different ancestry groups? We need a more thorough evaluation of the potential prediction performance gain (or loss) in African ancestry individuals when cross-ancestry GWAS summary statistics are used to estimate the SNP weights. In addition, we need to improve our understanding of the impact of the composition of the linkage disequilibrium (LD) reference panel in combination with cross-ancestry GWAS summary statistics on PRS prediction performance. For prediction of BMI specifically, how does prediction performance differ for individuals in different age groups, especially those who are not adults ? Additionally, how much these different variables impact the PRS performance when considered together is important to explore. Developing a deeper understanding of which features  have the greatest impact of PRS performance will help the field develop future studies and strategies around clinical risk prediction with PRS. The degree to which increased GWAS sample size increases prediction performance regardless of these other factors is also important to determine. Finally, there is potential for using a PRS generated for one trait to predict risk for comorbid traits. Understanding how much the different elements of PRS generation affects associations with clinical comorbidities of obesity is of great importance for precision medicine.BMI) across 90 different combinations of analyses . Investigation of these variables elucidates our understanding of the factors that affect PRS performance and transferability across ancestries and populations, especially within the context of BMI, as well as the potential of using PRSBMI to predict risk for comorbid disease.We comprehensively investigated the influence of these factors on the performance of PRS using the Electronic Medical Records and Genomics (eMERGE) Network dataset. eMERGE is an NIH funded consortium that combines participants from multiple electronic health record (EHR) linked biobanks . In the BMI for all individuals within each combination where the following elements of the model varied: 1) LD panels that differed in ancestry, 2) GWAS summary statistics with variable ancestry composition, and 3) GWAS summary statistics for two different sample sizes. The details for each of these are provided more below. The data was also split by ancestry and age group, and we statistically compared PRSBMI performance between all the different groups – in total, 90 sets of PRSBMI were calculated separately and then compared. We first estimated the effect and significance of each variable  on PRS performance. Next, we estimated how much each variable affects PRSBMI performance when all are modeled together, and finally we analyzed the potential clinical associations by testing the PRSBMI for association with common comorbid conditions from the EHR. For the primary results related to LD panel or ancestry of summary statistics and test data, we restricted analyses to adults as the other age groups were limited in sample size. In the following sections, we describe all these elements in more detail.The electronic Medical Records and Genomics (eMERGE) network dataset is an NIH funded consortium that combines participants from multiple electronic health record (EHR) linked biobanks. In this study, we included 75,661 individuals with available genetic and phenotypic data. The individuals in the eMERGE dataset include multiple ancestry groups – genetically inferred ancestry was assigned by the eMERGE consortium  – and a We obtained published GWAS summary statistics from the GIANT consortium  to use and degree relatives or higher), and within the White British ancestry subset   included as covariates. BMI, defined as weight in kilograms divided by squared height in meters, was first inverse-rank normal transformed.For the second set of summary statistics, we performed a GWAS in the individuals of European ancestry from the UK Biobank (UKBB). Individuals were first filtered by low quality samples , relatedness (no 2ncestry) ; a totalncestry) ) > 0.30,After calculation of BMI GWAS summary statistics in each of the two datasets of UKBB individuals of European ancestry, we harmonized variants across all datasets used . For the remainder of downstream analyses, we kept only those variants that were present in all datasets, and additionally excluded any strand-ambiguous SNPs , and retained only biallelic variants; in total, 2,014,457 variants were retained for analyses.BMI calculations: 1) all of 1000 Genomes (1KGAll) , 2) 1000 Genomes European ancestry (1KGEUR) (N=503), 3) 5,000 randomly selected European ancestry individuals from the UK Biobank (UKBBEUR), 4) 5,000 randomly selected individuals from all of UK Biobank (UKBBAll), and 5) up to 5,000 randomly selected individuals from the dataset for which PRSBMI were being calculated for in the eMERGE dataset (referred to as test data henceforward). These panels were chosen to test for differences in ancestry distribution and sample size on PRS performance.Five different LD reference panels were used for each set of PRSBMI were calculated using pruning and thresholding method via PRSice v2.1.9 .For each comparison set, PRSe v2.1.9 . We chos2 for PRSBMI was calculated by subtracting the R2 using a model with only the covariates from the R2 of the model using the covariates and the PRSBMI (the default option in PRSice). Statistical differences between model performances from different iterations were determined using the Wilcoxon rank-sum test to compare the distributions of the squared residuals generated from the model for all individuals in the iteration; for comparisons between the same set of individuals, the paired Wilcoxon rank-sum test was used. When testing which of the five LD panels performed the best, we used a Bonferroni-corrected threshold of 0.05/10 = 0.005 (ten comparisons between five LD panels). When comparing the best performing PRSBMI across ancestries and summary statistics using their best LD panel, we used a Bonferroni threshold of 0.05/25 = 0.002 (25 comparisons between the five LD panels used).Incremental RWe modeled all evaluated features together in the following linear regression model:Sumstat subscript is defined as a set of GWAS summary statistics, and the Test subscript is defined as a set of test individuals that PRS prediction is being assessed in. We quantified the variance in R2 that could be explained by each of these different variables using type II sum of squares from ANOVA. The sum of squares of variables involving ancestry were summed together; an interaction term between summary statistics ancestry and test data ancestry was included to identify whether the ancestry of summary statistics and test data matched.Where the BMI. For each Phecode, individuals were classified as a case for the condition if there was at least one occurrence of the respective Phecode in their EHR record; individuals were classified as a control for that condition if there was no occurrence of the Phecode. This classification is a rule-of-one instance of a Phecode to define case status. For each eMERGE ancestry subgroup, we selected the best performing PRSBMI i.e., the PRSBMI with the highest R2, and tested the association of the PRSBMI with these ten clinical conditions using a logistic regression model. PRSBMI was first mean-centered and standard deviation was set to 1. Sex, age, age2, and the first five genetic PCs were included as covariates.We selected the ten most frequent Phecodes  from theThe ‘ggplot2’ R package was used for plotting, with the ‘geom_signif’ package used to include significance bars. The association results were plotted using PheWAS-View .BMI performance compared to European ancestry LD panels  had an R2 of 0.0109 (p = 4.94×10−20). When using the GIANT summary statistics, the top PRSBMI R2 was 0.0149 using 1KGAll as the reference panel. The PRSBMI calculated using the best European ancestry panel (1KGEUR) resulted in a R2 of 0.0141, but this difference between these two reference panels was not Bonferroni significant (p = 0.037). However, the 1KGall LD panel performed significantly better than the two UKBB LD panels . The test data LD panel performed the second-best with an R2 of 0.0142, and significantly outperformed the UKBB European LD panel (p = 4.78×10−5). For adults of European ancestry, we observed more significant differences in performance when using the GIANT summary statistics compared to the down-sampled UKBB summary statistics. The 1KGAll LD panel performed the best with a R2 of 0.0612. It also significantly outperformed all other LD panels . We note that this increase was larger when using the GIANT summary statistics but was still present when using the UKBB summary statistics. When using the UKBB summary statistics, the choice of LD panel had a much smaller impact on prediction performance. While the 1KGAll LD panel performed the best, the difference in performance was much less significant between the next best performing LD panel . The difference between the best and worst performing scores – LD panel using 1KG all versus 1KG European – was also much less significant (p= 1.15×10−12). These results suggest that the choice of LD panel particularly matters when calculating PRSBMI using cross-ancestry GWAS, or for African ancestry individuals when the GWAS summary statistics are derived from European ancestry individuals.For adults of African ancestry, when using the down-sampled UKBB GWAS summary statistics, using either cross-ancestry or African ancestry test data LD panels significantly improved PRSD panels . When usEUR LD panel performing best . The 1KGAll LD panel was the worst performing LD panel with an R2 of 0.0185, and this difference between the 1KGEUR LD panel was significant after multiple hypothesis correction (p = 5.08×10−7).However, we did observe a slight decrease in the impact of the choice of LD panel when using the full UKBB summary statistics for adults; again, the largest differences were observed in adults of African ancestry, but differences in performance across LD panels were not as significant. The test LD panel performed second best with the 1KG2 values of the PRSBMI were significantly higher when calculated for European ancestry adults than adults of African ancestry, even when using the cross-ancestry GIANT summary statistics .As expected, the Ratistics . When us2 when using the GIANT summary statistics was higher than the R2 when using the down-sampled UKBB summary statistics with their respective best LD panel (GIANT (1KGAll LD panel): R2 = 0.0149, UKBB (test data LD panel): R2 = 0.0140; p = 0.038). This difference was not statistically significant after multiple hypothesis correction. However, the GIANT summary statistics with the 1KGAll LD panel did significantly outperform the UKBB summary statistics with all other LD panels. When keeping the LD panel constant, the PRSBMI calculated using the GIANT summary statistics resulted in higher R2 than using the UKBB summary statistics for all LD panels except for the test data LD panel, and this difference was statistically significant for the 1KGAll (p = 1.55×10−33), 1KGEUR (p = 6.78×10−18), and UKBBAll (p = 1.28×10−15) LD panels. Somewhat surprisingly, we observed higher R2 values for European ancestry adults when using the cross-ancestry GIANT summary statistics versus the down-sampled European UKBB summary statistics (R2GIANT = 0.0612 versus R2UKBB = 0.0590), with this difference being statistically significant (p = 6.26×10−4); the best performing LD panel for both set of summary statistics was 1KGAll.In African ancestry adults, the R2.We also compared prediction performance in all individuals using the full  European UKBB GWAS versus the European UKBB GWAS down-sampled to GIANT’s sample size  . For con2 values for adults and teenagers, with substantially reduced performance in children . Somewhat surprisingly, teenagers consistently had higher R2 than adults across all analyses, although these differences were much less significant than those compared with children.Across different ancestries and summary statistics, we broadly observed similar Rchildren . R2 valuBMI performance when considered together , while the number of individuals used to calculate the GWAS summary statistics only accounted for 1.9% of variance explained in PRSBMI R2. Per previous sections, while number of individuals used for summary statistics resulted in significant differences in PRSBMI performance, its overall impact when modeled jointly with all the other factors in the context of these analyses seemed to be small.While we observed significant differences due to ancestry, age, and number of individuals used to calculate summary statistics, we aimed to quantify the effect of these different variables on PRStogether . We obseBMI was associated with clinical comorbidities, we performed a Phenome-Wide Association Study for ten clinical conditions (BMI was tested for association with diagnosis codes (Phecodes) to evaluate whether the polygenic background for BMI associates with these clinical diagnoses. The PRSBMI was significantly associated with several of the most frequent Phecodes in eMERGE, particularly in European adults ; this was a positive control. In European ancestry individuals, the best performing PRSBMI was also significantly positively associated with type 2 diabetes (pEUR = 1.04×10−102), essential hypertension (pEUR = 7.12×10−56), coronary atherosclerosis (pEUR = 3.61×10−26), hyperlipidemia (pEUR = 4.38×10−16), depression (pEUR = 1.95×10−13), hypercholesteremia (pEUR = 3.64×10−15), asthma (pEUR = 3.13×10−13), and diverticulosis (pEUR = 0.0017). These associations were less statistically significant in African ancestry individuals, which had much lower sample size, and many associations were no longer significant after Bonferroni correction. Only type 2 diabetes (pAFR = 1.2×10−5) and coronary atherosclerosis (pAFR = 0.001) were significantly associated with the PRSBMI in African ancestry adults. We also looked at the prevalence of each condition per PRS quintile for the most significantly associated conditions . The much smaller sample sizes of the Phecodes in these age groups may have also contributed to the lack of statistically significant results – most of these diagnoses are adult-onset conditions.To determine whether the PRSnditions . Here, tn adults . As expenditions . The casSomewhat unintuitively, African ancestry LD panels performed best for African ancestry individuals, regardless of whether European ancestry or cross-ancestry GWAS summary statistics were used. We observed minimal impact of the choice of LD panel when both test data and summary statistics were of European ancestry. These results suggest that as long as either the test data or GWAS summary statistics are of similar ancestry, or the test data and LD panel are of similar ancestry, the difference in PRS performance may be minimal as compared to if all the GWAS summary statistics, test data, and LD panel are all of the same ancestry. We also observed significantly decreased PRS performance in children compared to adults and teens, with the GWAS used in this study being conducted on adult populations.While the findings in this study highlight many important strategies for performing PRS in different ancestry and age groups, there are limitations that should be addressed in future studies.BMI performance. Finally, repeating these types of analyses with different PRS methods would be useful as novel PRS methods are being developed on a regular basis, many of which incorporate ancestry in different ways.First, inclusion of analyses that evaluate how different proportions of non-European ancestry individuals affect the prediction performance of PRS would be useful. The GIANT summary statistics we used in this study are only about 6% non-European ancestry. It may be useful to see how the PRS prediction performance changes in both non-European and European ancestry datasets as a function of the proportion of non-European ancestry samples included in the GWAS. Such analyses may be possible by combining African ancestry individuals from these different datasets. These analyses will be possible once larger datasets that include non-European ancestry cohorts are publicly available or could be tested by analyzing other traits with larger African ancestry GWAS. Future analyses could also include sex-stratified GWAS and comparison sets to evaluate the influence of sex on PRSOverall, this study demonstrates the importance of expanding non-European ancestry data resources for PRS, specifically in the generation of GWAS summary statistics and LD reference panels. Failure to do so reduces the impact of PRS in diverse populations and increases the potential for continued health disparities, especially in precision medicine where genetics is being integrated into clinical care.Supplementary-9789811270611_0040


Holstein has been the most widely used dairy cattle breed in the Korean Peninsula since its introduction in 1885. Since the formal dairy herd improvement program was initiated in 1979, Holsteins have been extensively selected for Korean environments. While body conformation traits in dairy cattle are not typically considered direct economic traits for animal breeders, they are associated with various production traits, including health status, milking ability, and overall cow longevity. Consequently, these traits are recognized as significant factors in dairy cattle breeding programs worldwide. The dairy industry relies on consistent milk and calf production from cows, which presents challenges in today’s context. Conducting a genome-wide association study (GWAS) for body conformation traits in Korean Holstein cattle, using a large population, may enhance the power of detecting and mapping novel genes associated with these traits. To the best of our knowledge, this study represents the first-ever attempt to conduct a GWAS for body conformation traits in the Korean Holstein population.KCNA1, RYBP, PTH1R, TMIE, and GNAI3 for body traits; ANGPT1 for rump traits; MALRD1, INHBA, and HOXA13 for feet and leg traits; and CDK1, RHOBTB1, and SLC17A1 for udder traits, respectively. These findings contribute to our understanding of the genetic basis of body conformation traits in this population and pave the way for future breeding strategies aimed at enhancing desirable traits in dairy cattle.The objective of this study was to identify quantitative trait loci (QTL) and nearby candidate genes that influence body conformation traits. Phenotypic data for 24 body conformation traits were collected from a population of 2329 Korean Holstein cattle, and all animals were genotyped using the 50 K Illumina bovine SNP chip. A total of 24 genome-wide significant SNPs associated with 24 body conformation traits were identified by genome-wide association analysis. The selection of the most promising candidate genes was based on gene ontology (GO) terms and the previously identified functions that influence various body conformation traits as determined in our study. These genes include  Cattle represent a unique and singular species on earth, holding a remarkable position due to their ability to fulfill a wide range of essential human needs. These needs encompass food, clothing, draught power, medicine, soil improvement fertilizer, fuel energy, and more. Additionally, cows play important roles in religious and cultural practices, particularly in India, where the largest cattle population is found. In fact, according to the US Department of Agriculture and the Food and Agriculture Organization (FAO), the global cattle population reached approximately 1.5 billion heads in 2022. The genetic improvement of high-yielding animals reduces the strain on natural resources and contributes to mitigating global warming. These areas of study have captured the interest of geneticists and other scientific communities. In 2009, the sequencing of the bovine genome coupled with advancements in information technology facilitated the development of modern, scientifically designed breeding schemes for enhancing economically important traits. These efforts led to a remarkable increase in both the quality and quantity of milk and meat production per animal . It was Holsteins, renowned for their exceptional milk production capacity, represent the most popular dairy cattle breed worldwide. In Korea, this breed is also a major source of milk and milk products. The dairy cattle industry in Korea focuses on developing advanced herd management programs aimed at maximizing production and profitability and minimizing methane emissions. However, the sudden culling of cows from the herd often hampers these efforts. To address this, the Korea Animal Improvement Association (KAIA) has set general appearance and linear examination procedures for scoring cows and ranking them based on their obtained scores, with the objective of selecting the best dairy cows. This study considers 24 body conformation traits screened by KAIA, which were subjected to GWAS analysis. The evaluation of dairy cattle primarily relies on the observation of certain physical characteristics associated with longevity and production traits. These traits, collectively known as body conformation traits, have been utilized in dairy cattle judging in many countries since the 1990s . In the Recent years have seen a significant focus on conducting GWAS to investigate economic traits in dairy cattle, including fertility traits ,12,13, pThe care and management of all animals used in this study were approved by the Animal Care and Use Committee of the National Institute of Animal Science (NIAS), Rural Development Administrations (RDA), South Korea . All animal health and welfare practices followed the approved guidelines.The first parity phenotypic data were collected from 2329 Holstein dairy cattle located in Nonghyup livestock farms in different regions of Korea. The data were recorded between 2017 and 2018. There were 24 traits measured, including stature, height at front end, chest width, body depth, angularity, body condition score, locomotion, rump angle, rump width, loin strength, rear leg set, rear leg rear view, foot angle, heel depth, bone quality, udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement. The phenotypic information was recorded for these 24 body conformation scores based on the guidelines provided by the Korean Animal Improvement Association (KAIA). The body conformation traits included 24 linear descriptive traits, which were scored on a scale from 1 (indicating the poorest body conformation) to 9 (indicating the best body conformation). The details of phenotypic distribution information for the studied animals are presented in p < 0.000001) from Hardy–Weinberg Equilibrium (HWE). The identity-by-state (IBS) test was performed to determine if there were similar individuals or genotyping errors in the datasets. Pairs of individuals showing a similarity rate greater than 99% were considered either identical animals or indicative of genotyping errors. The entire QC process and IBS test were performed through the PLINK v1.9 toolset [A total of 2329 Holstein dairy cows were genotyped using the Illumina Bovine SNP 50 K v.3 BeadChip , which contained a total of 49,203 embedded SNPs. SNPs located on sex chromosomes and with unknown and duplicate positions were removed for further quality control (QC) procedures. Several QC thresholds were set to remove poor-quality SNPs for further analysis. SNPs were discarded from the analysis when the SNP call rate was less than 90%, for individuals with a genotyping call rate less than 90%, and when minor allele frequency (MAF) was less than 1% (monomorphic). The genotype frequency significantly deviated  plot algorithm in the PLINK v1.9 toolset . The resThe statistical significance of the fixed factors and covariates were tested using ASReml-SA v4.2  for fittThe genomic relationship matrix (G) was built using the genome-wide complex trait analysis (GCTA) tools developed by Yang et al. , which eFor GWAS analysis according to Aulchenko et al. , the resThe incorporation of categorical data within the GRAMMAR-based GWAS  frameworp-value for the regression coefficient was estimated. If the SNP marker has a significant effect on the trait, then it can be assumed that the SNP was in a linkage disequilibrium (LD) state with an unobserved QTL. For the statistical significance test of the effect of the SNP marker (QTL) on the trait, the null hypothesis (H0) assumes no QTL effect, while the alternative hypothesis assumes that QTL has an effect on the trait. The significance of the test’s statistic p-value (genomic control) threshold was set at α = 0.001  were regarded as significant SNPs). The PLINK v1.9 toolset  was used−5) or 4.59 (−log10P), in order to identify genome-wide significant SNPs influencing the body conformation traits in Korean Holstein, according to Guo et al. [−6) or 5.89 (−log10P).We set a significant threshold above the suggestive level (1/number of variants), that is, , BovineMine V1.6 , and BGVD .Putative candidate genes within the QTL regions and in the nearest upstream and downstream regions (500 kb) of our mapped significant SNPs were identified based on the bovine (Bos_taurus_UMD_3.1.1) genome assembly, using online resources such as Genome Data Viewer  . The purThe summary statistics for the 24 body conformation traits of the Korean Holstein population are presented in Bos taurus autosomes (BTA). However, the distribution was uneven, with certain chromosomes showing a higher number of SNPs associated with different traits. Among the chromosomal regions, BTA22 had the highest number of identified SNPs, with a total of five. Additionally, BTA3, BTA4, BTA13, and BTA14 exhibited two, two, two, and three significant SNPs, respectively. The genome-wide association study for body conformation traits identified a total of 24 significant SNPs distributed across all 29 10) of each SNP’s p-value. The statistical significance was performed on the two levels, 2.58 × 10−5 (4.59) for the suggestive significance threshold and 1.29 × 10−6 (5.89) for the genome-wide Bonferroni correction significance threshold. After the Bonferroni correction, no SNPs exhibited statistically significant associations with the body conformation traits. To assess population stratification, we calculated the genomic inflation factor, Lambda (λ), by comparing the median chi-squared test statistics obtained from a GWAS to the expected median of the chi-squared distribution. The approach for calculating lambda may vary based on the association analysis output [p-values from the GWAS results of all traits and computed λ using the qchisq function in R [p-values (−log10P) for each trait. The genomic inflation factor should be close to 1 after correcting for population stratification [p-values, and nearby candidate genes is provided as follows.To visualize the genome-wide distribution of significant SNPs, we generated Manhattan plots for body traits, rump traits, feet and leg traits, and udder traits. The level of significance was represented as the negative logarithm base 10  for body condition score and locomotion traits. The significant markers identified through our GWAS analysis are presented in ociation .p-value of 1.18 × 10−5. This SNP is situated between the NDUFA9, KCNA1, and KCNA6 genes on BTA5, serving as an intergenic variant. Secondly, another significant SNP, Hapmap50195-BTA-105299, showed a p-value of 6.34 × 10−6. This particular SNP was found within the LOC107132946 gene, and also as an intergenic variant on BTA11  was identified as intergenic variants of the RYBP gene on BTA22  on BTA22. One particularly noteworthy gene is the PTH1R gene, which has been identified as a candidate gene, both functionally and positionally, in relation to traits associated with leg weakness in pigs [PTH1R gene is responsible for mediating the effects of parathyroid hormone, and in turn plays a crucial role in the endochondral bone formation process [PTH1R gene is its role in regulating the growth of cartilage and the apoptosis of chondrocytes [PTH1R gene plays a significant role in the development of the skeletal system and is closely associated with the occurrence of osteoarthritis in mice [For chest width, we identified a total of two genes, namely  in pigs . The PTH process . One of drocytes . Further in mice ,50.p-value of 1.99 × 10−5, Hapmap43881-BTA-54837 with a p-value of 2.02 × 10−5, and ARS-BFGL-NGS-117960 with a p-value of 3.20 × 10−6). Interestingly, the locus BTB-00853109 was identified at the 53.16 Mb genomic position on BTA22, showcasing an intriguing association with both chest width and body depth traits in Korean Holstein cattle. This overlap of genetic influence on multiple traits is referred to as pleiotropy, a phenomenon in line with the strong positive genetic correlation observed between these two traits. Furthermore, the common locus influencing both chest width and body depth suggests a potential shared genetic architecture for these traits. This finding opens up an intriguing avenue for future research. It necessitates the exploration of these genomic regions on a larger scale for validation. Moreover, there is a need to delve into the further implications of these SNPs in marker-assisted animal breeding. We identified a total of five putative candidate genes, namely CCDC12, PTH1R, PRSS45, PRSS46, and TMIE, surrounding our spotted significant marker loci for body depth. Among them, the TMIE gene, which is located 53.35 Mb immediate next to our mapped SNP locus (ARS-BFGL-NGS-117960) on BTA22, has been identified as associated with sensory cell development across vertebrate species [TMIE exhibits expression not only in the developing inner ear but also in other tissues, including the brain, liver, kidney, and lung [In the analysis of body depth, a total of three SNPs were identified on the BTA22  is a gene with the potential to play a significant role in goats’ heat tolerance mechanisms [GNAI3 gene in sclerotomal derivatives is essential for establishing the normal pattern of the axial skeleton [GNAI3 knockout mice, with apparently normal development of the bony proximal ribs, suggests that the primaxial/abaxial classification system in somitic development could shed light on the function of Gαi [GNAI3 in various cellular processes, including proliferation, apoptosis, cytokinesis, and differentiation [GNAI3 has been implicated in the regulation of craniofacial growth and development. Additionally, alterations in the human GNAI3 gene can result in auriculo-condylar syndrome (ACS), a rare craniofacial congenital disorder characterized by mandible hypoplasia [Additionally, three SNPs significantly associated with the angularity trait were located on BTA3 and BTA10, within the chanisms . The expskeleton . The obsn of Gαi ,56. Incrntiation ,58,59. Fpoplasia . p-values from expected p-values, assuming no association  was detected within intergenic variants of MALRD1 gene on BTA13. The MALRD1 gene plays an essential role in intestinal cellular proliferation and growth that offers a potential target for future research [Regarding rear leg rear view, one significant SNP  was mapped on BTA4 and was found within genes such as INHBA and LOC104972162. Inhibin β A (INHBA) plays a role in regulating pituitary follicle-stimulating hormone levels. Researchers have identified the genes responsible for encoding inhibins α and β  as potential candidates for analyzing their association with fertility in Hanoverian horses. Consequently, in Hanoverian horses, single-nucleotide polymorphisms (SNPs) and haplotypes within the INHBA gene have been reliably linked to the pregnancy rate per estrus and the embryonic and paternal components of breeding values [INHBA gene serves as a marker for granulosa cell development and exhibits expression in various ovarian cell types [INHBA expression is detected in cumulus and mural granulosa cells within sinus follicles. Li et al. [INHBA displays varying expression levels across different follicular stages and exhibits particularly high expression during the granulosa cell growth phase in multiparous lamb goat populations. Notably, mutations in the INHBA gene have been found to significantly impact litter size in sheep. This further confirms its potential to influence the lambing performance of goats by regulating follicle development [In the case of heel depth, no SNPs were located within intron regions of known genes. Only one significant SNP  were detected on BTA4 and BTA16. These SNPs were positioned within genes including EVX1, HOXA13, and VAMP4. The transcription factor HOXA13 is crucial for the morphogenesis and patterning of the vertebrate skeleton during embryonic development in animals [HOXA13 gene plays a pivotal role in numerous functions. It regulates the formation of digits, phalangeal joints, and carpal/tarsal elements during limb development [HOXA13. Mutations in this gene simultaneously impact the development of both the urogenital system and the limbs [HOXA13 leads to hypodactyly [As bone quality is an essential trait in lactating cows, two significant SNPs , BTB-01584048 , ARS-BFGL-NGS-118699 , Hapmap46979-BTA-32175 , and BTA-11097-rs29016861 . Notably, another SNP, Hapmap29824-BTA-137304 , was found downstream of known gene variants. Manhattan plots showing the genome-wide distribution of the significant SNPs underlying our selected udder traits are provided in p-values differ from expected p-values under the null hypothesis, assuming no association  across hepatic basolateral membranes. Intriguingly, its expression exhibited a notably higher level in liver and lung tissues compared to its relatively lower expression in the kidney. This observation aligns with earlier research findings that also documented the expression of the SLC17A1 gene across various tissues including the heart, muscles, kidneys, liver, and lungs. Furthermore, this gene is involved in multiple transport functions such as chloride-dependent anion transport, sodium-dependent phosphate uptake, and poly-specific organic anion transport, all of which contribute to the circulation of urate.Two significant SNPs were identified on BTA13 and BTA28 associated with rear udder height in the Holstein population. Both SNPs were located within the intergenic variants of genes:  mammals . Converspressure . A signii et al.  have repThe incorporation of categorical data, specifically Holstein linear body conformation traits, into GRAMMAR-based GWAS  holds thp < 0.05) within the dataset  windows. The purpose of these analyses was to explore the functional significance of the genes related to the studied traits. Notably, a total of 37 gene ontology (GO) terms exhibited significant enrichment , cell surface (GO:0009986), integral component of plasma membrane (GO:0005887), cytoplasm (GO:0005737), and cytoplasmic vesicle membrane (GO:0030659). Voltage-gated potassium channels facilitate ion transport across cell membranes, regulating cellular electrical activity and maintaining membrane potential . The intFurthermore, significant enrichments were observed for molecular function terms, including structural constituent of eye lens (GO:0005212), identical protein binding (GO:0042802), and phosphotyrosine binding (GO:0001784). p ≤ 0.05) within the dataset. These pathways include the insulin signaling pathway (bta04910), involving three genes; the phosphatidylinositol signaling system (bta04070), consisting of two genes; and the Ras signaling pathway (bta04014), with five genes (Furthermore, the KEGG pathways analysis revealed the enrichment of three specific pathways (ve genes . The enrCCDC12 and PTH1R, showed influence on more than one trait, indicating a pleiotropic effect that impacts multiple traits simultaneously. For example, the CCDC12 gene was associated with chest width and body depth. The results of the gene ontology enrichment analysis and the KEGG pathways analysis provide valuable evidence for the functional relevance and potential roles of specific biological processes and pathways in the dataset. These findings contribute to a deeper understanding of the genetic and molecular mechanisms governing the studied system, guiding further investigations into the functional relationships and interactions of the analyzed genes.Within our studied Holstein population, certain candidate genes, such as KCNA1, RYBP, PTH1R, TMIE, GNAI3, ANGPT1, MALRD1, INHBA, HOXA13, CDK1, RHOBTB1, and SLC17A1 as the most promising candidate genes for stature, height at the front end, chest width, body depth, angularity, rump width, rear leg rear view, heel depth, bone quality, rear udder height, and rear udder width, respectively. These findings hold tremendous potential for future marker-assisted selection in Holstein cattle breeding programs, offering a pathway towards improved trait selection and genetic enhancement in this valuable breed. In conclusion, our pioneering GWAS analysis has illuminated the path forward in unlocking the genetic secrets behind Korean Holstein cattle’s conformation traits, paving the way for innovative advancements in cattle breeding practices.In this GWAS analysis, we performed an association study for conformation traits in Korean Holstein cattle. The current study is the first-ever attempt at a GWAS in Korean Holstein for body conformation traits according to the published reports. A GWAS with a larger sample size is considered as a potential tool to identify novel genomic regions underlying our traits of interest. It is important to note that conformation traits are likely controlled by multiple genes, each contributing a small proportion of the variation. Furthermore, mapping genes to SNP loci may not always result in locating the genes within or in close proximity to the identified SNPs. As there were no previous report of GWAS in Korean Holstein for conformation traits, the findings of the current study serve as a baseline and would certainly require further research to be validated. Other concerns might be raised as to the effect of breed composition and population structure on the results of the current GWAS analysis. The present GWAS for body conformation traits in Korean Holstein permitted the identification of chromosomal regions, contributing to a better understanding of the genetic and physiologic mechanisms regulating the traits, and identified candidate genes for the investigation of causal mutations. Remarkably, we have identified


Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data. Complex traits, including common diseases, result from the combined effects of multiple genetic variations together with environmental factors. Genome-wide association studies (GWAS) have revealed that most complex traits are highly heterogeneous and can involve contributions from hundreds of genetic variants, each explaining a tiny fraction of susceptibility. Although GWAS have been very successful in identifying thousands of causal genetic loci, the underlying mechanisms and interactions remain, in most cases, poorly understood. The regions identified in GWAS usually contain blocks of credible SNPs, and one challenge is to pinpoint the causal gene in that locus of interest. A further challenge is that the majority of fine-mapped SNPs are found in non-coding regions of the genome with no obvious link to protein function. It is often assumed that such SNPs will regulate transcription of a proximal gene but this can be difficult to prove and, in some cases, such as FTO , has proSystems genetics is an approach that seeks to understand the molecular and physiological phenotypes linking DNA sequence variation to complex clinical and physiological traits. It does this by using high-throughput ‘omics’ technologies to examine how intermediate molecular phenotypes, such as RNA, protein, and metabolite levels, are perturbed by natural genetic variation among individuals in the population and then attempting to relate these variations, using statistical methods, to physiological or clinical traits . SystemsThe papers in this issue of eLife provide examples of its many applications across a broad spectrum of model systems. Vast amounts of systems genetics data  are now compiled in publicly accessible databases or are available through collaborations. The primary purpose of this review is to describe some of the resources and tools of systems genetics, so that researchers in all areas of biology and medicine can make use of the data. Our focus is limited to studies in mammals although we note that many of the most important studies utilized yeast and flies. We begin with a brief overview, followed by descriptions of human and rodent tools and resources and end with thoughts about future directions.A typical systems genetics study involves the following steps: (1) Identification of an important question, or set of questions, that could be addressed with a systems genetics study. (2) Selection of an appropriate cohort exhibiting variation in the traits of interest and of sufficient statistical power. (3) Phenotyping of the population for physiological, clinical, and omics traits of interest. (4) Integration of the resulting data through genetic mapping and statistical methods . (5) ValComplex traits such as common diseases typically involve not only many genes and environmental factors, but also complications such as lifestyle factors, aging-related changes, ethnic differences, sex differences, and in some cases gut microbiome interactions. It is important to design the study with these factors in mind. Several large human biomedical data and research resources have recently emerged, most notably UK Biobank. While such databases will play an invaluable role in future biomedical discoveries, they still currently possess at least two major caveats that are noteworthy. First, access to relevant tissues or organs may be limited and this limits systems genetics analyses. Second, the function of many genes is dramatically influenced by the environment but this is almost impossible to control and/or quantify in humans. For these and other reasons, studies of model systems such as genetically diverse cohorts of mice or rats have proven especially valuable for systems genetics. Notably, the ability to control the environment, age and sex of the animals, and other variables greatly reduce the number of individuals required for the study. As discussed below, a variety of useful mouse and rat population resources have been developed.Systems genetics can be used to follow the flow of information from DNA variation to intermediate phenotypes for the disease of interest. Thus, it is in some cases useful to interrogate the relevant tissues with different omics platforms (multi-omics) such as transcriptomics, proteomics, and metabolomics. Connecting the dots in this way, from DNA variation to molecular traits to the clinical trait, has been invaluable in identifying causal genes and relevant biological pathways. Information can, of course, also flow in reverse, and statistical methods can be useful in determining the direction of information flow . Epigenomics is the study of such modifications and how they regulate gene expression . The mosAnalogous to microarrays or SNP arrays, high-throughput methylation arrays have been developed that allow the simultaneous quantitative evaluation of >800,000 methylation sites throughout the genome. While such arrays do not cover all potential methylation sites, they have been designed to capture the proximal promoters of genes and other known important regulatory regions. Coupled with their relatively low cost, methylation arrays have allowed epigenome-wide association studies (EWAS) to be carried out on a large-scale . In thisMtrr), was regulated in cis and its expression was highly correlated with methylation levels of CpGs mapping to the locus. A causal role of Mtrr in the methylation was demonstrated using genetically engineered mice  . Additiocis-eQTL) and distant . Local eQTL are loci located in close proximity to the gene they regulate, whereas distant eQTL, as their name implies, are generally defined as being >~1 Mbp from the gene they regulate. Local eQTLs have been widely used to connect variants to genes in genetic studies. Another approach is transcriptome-wide association studies (TWASs)  . eQTLs a (TWASs) . TWASs uDrosophila model which causes hyper-invasive multilayering of the follicular epithelia . This has proven to be a powerful approach for quantifying expression at the level of individual genes. However, alternative splicing is a major gene regulatory mechanism that underlies disease states, including variation in disease-associated complex traits . While eAdvances over the past decade in sample preparation, fractionation methods, and mass spectrometry have led to the large-scale implementation of proteomics methods. Many studies now report quantitative tissue or cellular proteome measurements for >5000 proteins using minimalist sample preparation methods. Further, by using much deeper fractionation approaches, researchers can measure even more complete proteomes of 10,000–13,000 proteins in a single sample . These lOther technologies capable of broadly surveying protein levels include the SOMAscan aptamer-based multiplexed platform based on the development of single-stranded DNA aptamers that show high affinity for protein targets  and the trans pQTL. The correlation between transcript and protein levels in genetic studies tends to be relatively modest  and presumably act in cis, affecting only the expression of the gene on the contiguous DNA strand. The latter are termed y modest .Thus, thy modest , for sevy modest . Moreovey modest . Third, y modest . In addiy modest .Similar to proteomics, high-throughput technologies have been applied to the characterization of the metabolome, often referred to as the pool of metabolites in biological systems, such as cells or tissues . Two of Aside from characterizing the repertoire of small molecules, the coupling of metabolomics with genetic data has also allowed large-scale GWAS to be carried out simultaneously for thousands of metabolites, analogous to eQTL and pQTL analyses . Such meRoseburia intestinalis was found to be associated with resistance to atherosclerosis. Further studies with germ-free mice confirmed this and identified butyrate, a metabolite produced by Roseburia, as the intermediate  and outbred stocks. GRPs can be recombinant inbred lines derived from two or more inbred strains  or colleThe two different populations have unique advantages and disadvantages . The maiBy comparison, outbred stocks are not limited by sample size, enabling the detection of more subtle genetic effects and relationships among traits. This additional power can be important when exploring the polygenicity of complex traits. Additionally, outbred stocks accumulate recombinations at every generation, increasing mapping resolution over time, as compared to GRPs which have a fixed set of recombinations and, thus, fixed mapping precision. The main disadvantage of outbred stocks is the uniqueness of each animal, which requires de novo genotyping. This limits the number of disease phenotypes that can be captured on the same set of mice. Also, while GXE interactions can be studied in outbred stocks, it is much more straightforward with GRPs. Below, we discuss the most popular GRPs and outbred stocks used in both mice and rats. We note that, while not reviewed here, advanced intercross lines have also been extensively used to map complex traits. For example, a recent intercross between inbred QSi5 and 129T2/SvEms mouse lines identified 37 new QTLs which associated with cardiac interatrial septation .Mutyh, a known driver of colorectal cancer, that modulate specific germline mutagenesis signatures  and can be used for interactive systems genetics studies.The BXD GRP is a set of recombinant inbred strains derived from crosses between the C57BL/6J and DBA/2J founders. The initial set of BXD strains was developed in the early 1970s by Dr. Benjamin Taylor at the Jackson Laboratory . This segnatures . The BXD actions . All of https://systems.genetics.ucla.edu/HMDP/.The HMDP is a GRP comprised of a set of commercially available recombinant inbred strains (including the BXD strains discussed above) and classical laboratory strains . Most exThe collaborative cross (CC) and diversity outbred (DO) mice are multi-parental populations created by ‘combining’ eight genetically diverse inbred founders: A/J, C57BL/6J, 129S1/SvImJ, NZO/H1LtJ, NOD/LtJ, WSB/EiJ, PWK/PhJ, and CAST/EiJ . After tThe original goal for the CC was the generation of 1000 recombinant inbred strains ; howeverThe DO has all of the advantages and disadvantages of outbred stocks that are described above. A consideration when designing systems genetics studies using the DO is the increased sample size required over crosses between classical strains to identify QTL in the DO due to the increased genetic variation . DespiteThe HXB/BXH GRP is a set of 30 RILs generated by intercrossing the spontaneously hypertensive rat with the normotensive Brown Norway rat strain . The HXBThe HRDP is a rat GRP similar to the HMDP. Like the HMDP, the HRDP is a panel consisting of both RILs and classical inbred strains, with the goal of maximizing power and mapping resolution using existing resources. Specifically, the HRDP is comprised of 35 inbred strains, 30 strains from the HXB/BXH RIL, and 34 strains from the LEXF/FXLE RIL . Simulathttps://www.globalbiobankmeta.org/; Large-scale GWAS, since their first implementation 20 years ago, have been instrumental in elucidating the genetic architecture of complex traits. More recently, this approach has been expanded to include meta-analyses using data from multiple datasets and measurement types . This has led the international scientific community to build on this success and develop multi-ancestry biobanks with tens and hundreds of thousands of subjects for systems genetics studies that leverage the vast amount of clinical data and archived blood and tissue samples available in healthcare systems around the world. Some of the datasets provide the advantage of including individuals of non-European ancestry and can be used to determine whether epidemiological and genetic findings are applicable across ethnicities. Importantly, this addresses one criticism of human genetic studies that subjects of European ancestry have been overrepresented in GWAS. Furthermore, a collaborative network comprised of 24 international biobanks has been formed, termed the Global Biobank Meta-analysis Initiative (GBMI), with the express goal of facilitating large-scale genetic analyses of selected common diseases  areas of China from 2004 to 2008 . ExtensiThe Trans-Omics for Precision Medicine (TOPMed) program is an NIH-sponsored precision medicine initiative consisting of over 200,000 participants drawn from over 85 cohorts. The primary goal is to integrate clinical, genetic (whole-genome sequencing), and other omics data  to gain a better understanding of heart, lung, blood, and sleep disorders . One nothttps://emerge-network.org/), which is organized and funded by the NIH to carry out large-scale, high-throughput genetic research with EHR-based data.BioVU is an electronic health record (EHR)-based biobank in which over 300,000 participants have been recruited at Vanderbilt University . It is oThe Million Veteran Program (MVP) is a national research program organized and sponsored by the Veterans Administration Office of Research and Development. The goals of the MVP are to better understand the interrelationships between health outcomes, genetic determinants, behavioral traits, and environmental factors, with a particular focus on improving the care of US Veterans . It is oThe Geisinger MyCode Initiative is another EMR-based biobank that is enrolling participants from a rural, integrated health system serving central and northeastern Pennsylvania . SNP genCorrelation is a simple procedure for relating molecular and clinical/physiological traits. Of course, the traits can be correlated for multiple reasons. However, since information from DNA is unidirectional, causal pathways can be modeled. For example, if a clinical trait and the levels of a transcript are correlated and map to the same locus, researchers can condition on the transcript levels and ask whether a significant association between the locus and the clinical trait remains. If so, the results suggest that the effect on the clinical trait is not mediated by the transcript. Furthermore, SNPs are often associated with multiple clinical traits, which can be due to either pleiotropy, direct or indirect genetic effects, or environmental/non-genetic factors. Mediation analysis can therefore be used to quantify the proportion of direct and indirect effects of the exposure on the outcome of interest via the intermediate biomarker as the mediator. One straightforward application of this approach would be to use causal mediation analysis to determine whether association of a variant with insulin resistance is mediated through an effect on BMI. MR (see below) is another form of mediation analysis that has especially strict criteria in that the mediator (such as the levels of a protein) is required to explain all of the association between an SNP and a complex trait. Various forms of causal inference tests have been developed for mediation analysis .SLC6A12 correlating with serum creatinine through modulation of the levels of the renal osmolyte betaine.The gold standard for determining causality with biomarkers has relied on randomized clinical interventions. However, MR has emerged as an alternative and highly efficient strategy for leveraging genetic data to make causal inferences . In thisColocalization approaches have become instrumental in deciphering the genetic basis of GWAS associations by integrating eQTL, meQTL, and pQTL data . The preSeveral colocalization methods have been developed to facilitate the integration of molecular QTL data and GWAS, each with their strengths and limitations. Bayesian colocalization methods, such as COLOC , estimatSystems genetics data from multiple tissues can be used to identify potential endocrine circuits mediating crosstalk. The method, first developed by ., kinase phosphorylates protein), systems genetics seeks to understand how genetic variation alters network homeostasis and ultimately disease  do not act in isolation, but rather participate in complex networks that operate at the cellular, tissue, and organismal levels . While r disease . As a reCo-expression networks are one of the most popular types of networks used in systems genetics studies . In a coAn important aspect of co-expression networks is that, compared to other biological networks, such as protein-protein interaction networks, they retain tissue or cell-type identity. Additionally, because these networks are generated across a set of samples, it is possible to relate the behavior of modules to other characteristics of the sample . For exaMARK3 regulated osteoblast activity and BMD  is Mergeomics , which bSESN1 (Sestrin1) as a gene associated with cholesterol levels (While GWAS approaches have proven powerful in humans or model organisms, identification of conserved association signals at syntenic loci has not been widely observed. However, several notable successful examples of cross-species integration have been recently published. In one study, 60 loci were identified for ex vivo secretagogue-induced insulin secretion from isolated pancreatic islets of several hundred DO mice. Genes at these loci were significantly enriched for genes that localized to loci identified in human GWAS for diabetes-related traits . In a sel levels . Prior tl levels . HoweverThere will undoubtedly continue to be technical and computational innovations in multi-omics endeavors and this will speed progress in the systems genetics field. For example, above we discuss the value of incorporating long-read RNA-seq into systems genetics analyses . A comprOver the past decade, researchers have significantly refined mass-spectrometry-based methods to detect utilization of specific substrates using stable isotope tracing . KnowledFusobacterium nucleatum, highlighting the complexities of GXE.Systems genetics data also present a unique appeal to quantify complex interactions such as gene-by-diet responses . Withoutalliancegenome.org (An important challenge will be to develop better methods for curating, storing, sharing, and integrating systems genetics data. We feel that a particularly useful goal would be to combine data from the various systems genetics groups into one searchable database that is accessible to biologists. One mechanism in which this type of database is implemented is available through the Alliance of Genome resources nome.org . This wenome.org .Major hurdles in applying unified analyses to common datatypes is differences in data structure, available pre-processing steps, and associated meta-data. Efforts have been proposed to reconcile these, such as the implementation of FAIR data principles . These pOne potentially fertile area of investigation is drug action using systems genetics data. An elegant example of this application was provided by Masson and colleagues who surveyed muscle proteomics from a diverse outbred population of mice exhibiting substantial variation in glucose metabolism profiles . The autSystems genetics approaches are, of course, limited by that natural variation that is captured in the cohorts that are studied. In the case of human studies, certain ethnic groups have been disproportionately studied and, thus, some results may not apply the less studied groups. Clear racial, ethnic, and gender disparities have been described for most common diseases, emphasizing the need to incorporate more diverse populations in current research gold standards.


In marginal, arid, and semi-arid areas of Morocco, crops are often exposed to multiple abiotic and biotic stresses that have a major impact on yield. Farmer-maintained Moroccan landraces have been shaped by the impact of very strong selection pressures, gradually adapting to the local ecosystem and obsolete low-input agricultural practices without improvement towards high yield and quality. Considering the increasing threat of drought in Poland, it is necessary to introduce germplasm with tolerance to water deficit into barley breeding programs. The aim of this research was a DArTseq-based genetic characterization of a collection of germplasm of Moroccan origin, conserved in the Polish genebank. The results showed that all conserved landraces have a high level of heterogeneity and their gene pool is different from the material developed by Polish breeders. Based on the analysis of eco-geographical data, locations with extremely different intensities of drought stress were selected. A total of 129 SNPs unique to accessions from these locations were identified. In the neighborhood of the clusters of unique SNPs on chromosomes 5H and 6H, genes that may be associated with plant response to drought stress were identified. The results obtained may provide a roadmap for further research to support Polish barley breeding for increased drought tolerance. Currenthe world . Within he world .Morocco is the richest and one of the most important reservoirs of plant diversity and speciation in the Mediterranean . Some 39Morocco’s geographical location, which has almost 3500 km of coastline along the Mediterranean and Atlantic coasts and four mountain ranges in the High Atlas, the Antarctic Atlas, the Middle Atlas, and the Rif, results in the division of Morocco into at least four climate zones with significant variations in total rainfall and its seasonal distribution. Regions along the coast receive most precipitation from November to March, with amounts ranging from 300 to 700 mm. In the mountainous regions, winter precipitation can reach up to 2500 mm, and snow cover persists. In contrast, in the Saharan borderlands, annual rainfall does not exceed 200 mm, and the rainy season falls in autumn and spring . Due to In Morocco, traditional agricultural ecosystems belong to the agroforestry-pastoral type and are characterized by the cultivation of various traditional crops. Most farms, especially in the mountainous zones, are small and have a reduced usable agricultural area . TraditiHordeum vulgare L.) is a cereal crop from the Poaceae family. It is one of the earliest domesticated species. Archaeological data indicate that this happened around 8000 BC in the Fertile Crescent region [Hordeum spontaneum was found in Morocco [Barley , i.e., the Polish Genebank, currently preserves a total of approximately 7330 accessions of the  species . Among tThe aim of the study was to characterize the Polish collection of Moroccan origin accessions. Single nucleotide polymorphism markers (SNP) derived from DArTseq analysis were used to assess the diversity of the collection, as well as to determine the heterogeneity of the stored accessions and population structure. The results of the genetic analyses were cross referenced with eco-geographical data to investigate the influence of environmental parameters on genetic variation and to identify unique SNPs for accessions originating from locations with extreme values of eco-geographical parameters. By mapping to a reference genome, regions of allelic variation associated with extreme environmental conditions were identified and some genes within them were identified. This is the first step towards selecting pure lines that can be used in breeding programs for drought tolerance which is a rising problem in Poland.Eco-geographical data analysis was performed based on passport data containing information on the collection site of landraces. The landraces originated from seven regions of Morocco. In the north axis, the greatest distance of collection sites was 706 km and concerned PL 43350 and PL 42745, while in the east-west axis, the most distant accessions were PL 43350 and PL 43340, with 566 km between these sites. Landraces were collected at different altitudes above sea level. Accession PL 43340 was found at the lowest altitude, i.e., 100 m above sea level (a.s.l.) Three accessions came from locations above 1600 m a.s.l. . The sites from which the landraces originated also differed in terms of 11 climatic parameters. The extreme values along with the accession number are given in post hoc test can be found in Analysis of variance ANOVA of both annual and growing season climatic conditions at the collecting sites showed no significant differences for the parameters, maximum temperature (tmax), climate water deficit (def), downward solar radiation flux at the surface (srad), and grass reference evapotranspiration (pet) . TherefoCorrelation analysis of eight eco-geographical parameters  showed aPCA analysis performed based on eco-geographical parameters showed that the first three principal components explained 83.34% of the total variability. With the first principal component, four parameters showed a very high and high positive correlation, i.e., ppt (0.99), aet (0.95), q (0.95), and soil (9.0). With the second PC, tmin (0.93) and alt (0.93) were very highly correlated. The 3D plot based on the first three PCs  clearly The study also considered the grain morphology of the studied objects. Parameters related to grain shape and color were compared. Variability among the seven parameters studied ranged from 3% to 8%. The variability within the breeding materials was higher than for the landraces. A summary for all parameters can be found in Correlation analysis showed  a very hPCA analysis showed that the first three components explained as much as 97.57% of the total variability. All parameters were positively correlated with the first principal component. A strong correlation was found for area, grain ch1, and ch2. A perimeter was strongly positively correlated with the second principal component. Grain width and all parameters describing color were negatively correlated with PC2. While grain width was strongly positively correlated with the third principal component. In the 3D plot, it can apparently be seen that the groups related to biological status partially intermingle . Three aNext-generation sequencing (NGS) provided reads for more than 77,000 loci. After filtering, 9737 loci for which the chromosomal location was known remained for further analysis . On averAbout 60% of the identified mutations were transitions and 40% were transversions; therefore, the Ts/Tv ratio was 1.48 . A>G andThe analysis of genetic differentiation was based on determining the values of three coefficients, i.e., observed heterozygosity (Ho), unbiased expected heterozygosity (uHe), and the fixation index (F). In the entire group of accessions studied, the aforementioned coefficients had values of 0.242, 0.443, and 0.455, respectively. Landraces had a significantly higher Ho coefficient value than the breeding materials. On the other hand, both uHe and F coefficient values were significantly higher in the breeding/research material group . The levDiversity analysis of landraces according to the region of origin showed that the least heterogeneous accessions were those from the Tangier-Tétouan-Al Hoceïma region (0.144) and the most heterogeneous were those from the Fès-Meknès region. However, these differences were not statistically significant. In the case of uHe, all the regions studied had very similar values, ranging from 0.370 to 0.379, and of course the differences were not statistically significant. Among breeding materials, the highest Ho value was detected in accession PL 40979 (0.361) obtained at the genebank from the Plant Breeding Station Bąków. The lowest Ho value was found in PL 42694 obtained from the same source. Among the landraces/traditional cultivars, this coefficient ranged from 0.010 to 0.438 in PL 42735 and PL 42761.The average genetic distance in the set of accessions studied was 0.474 and ranged from 0.021 (PL 42379—PL 42380 and PL 42378—PL 42378—PL 42380) to 0.742 (PL 42735—PL 42751). Among breeding materials, the average genetic distance was 0.558 and among landraces 0.395, while among those with a known collection site location it was 0.384. Among accessions from the same location, i.e., collected in close proximity, the mean distance ranged from 0.309 to 0.699. In the landraces/traditional cultivars collection regions, the mean genetic distance was lowest for the Marrakech-Safi region (0.336) and highest for Rabat-Salé-Kénitra (0.642). The rest of the data can be found in Analysis using Bayesian clustering showed that there was a distinct population structure in the surveyed set of accessions . AccordiThe ANOVA showed essentially no significant differences in the values of the variation parameters in the landrace groups separated based on the eco-geographical data. Significant differences were only found for the uHe value at 5H for ws. The highest variation was in the group with the lowest mean ws during the growing season, and the lowest in the group where ws was between 3.16 and 3.62 m/s.A comparison of the genetic and geographical distance matrices as well as absolute differences in eco-geographical parameter value matrices, using the Mantel test showed a negligible, i.e., <0.3, or non-significant correlation regardless of whether the genetic data were analyzed as a whole or by chromosome.The investigated landraces were also subjected to analysis of loci uniqueness under extreme and significantly different environmental conditions . A totalCombining the results of the seed morphometric evaluation and SNP analysis was carried out using Generalized Procrustes Analysis (GPA). Based on the results of the Procrustes Analysis of Variance (PANOVA), it was determined that scaling and rotation significantly contributed to reducing the discrepancy between the results of the two analyses. Very similar residual values indicate a high consensus in the landraces group. In contrast, the level of consensus was much lower in the breeding material group . For botAs a crop of major economic importance worldwide, barley is at the center of breeders’ attention. The new challenges posed to agriculture worldwide by global climate change, increasing pathogen pressure as a result of globalization, and the growing need to ensure food security, are leading to an intensification of breeding programs and the search for new sources of variability. There is ample evidence that intensive breeding programs conducted worldwide have led to genetic erosion of crop gene pools, resulting in a significant reduction in the diversity of cultivated genotypes ,59,60,61Genebanks established for the collection, conservation, and use of dwindling biodiversity, including genetic variation in crop plants, are a source of novel, i.e., not used in modern breeding, genetic variants that confer increased tolerance to abiotic stresses and resistance to pathogens. The intensive development of high-throughput phenotyping and genotyping technologies over the last decade is providing opportunities to characterize germplasm on an unprecedented scale and with significantly improved resolution of results. This facilitates the introduction of desirable alleles from landraces and crop wild relatives (CWRs) into elite germplasm free of other genes that negatively affect their functional value. The global germplasm stock preserved in genebanks contains about 375,000 accessions, of which 34% are landraces and less than 3% are wild relatives [Barley germplasm resources have been characterized for phenotypic and genotypic traits for many years at multiple sites worldwide, using a variety of techniques ,67,68,69In the research presented here, a collection of accessions from Morocco, preserved in the Polish Genebank, was characterized. As described in the introduction, Morocco is located within a biodiversity hotspot, and diverse eco-geographical conditions and traditional agriculture have facilitated the emergence and survival of landraces adapted to multiple stresses . High-thThe total variation of accessions of Moroccan origin was high (0.443). The total variation of the landraces was slightly lower (0.439). Compared to the previously analyzed accession groups, the differentiation of landraces was almost twice as high as that of Polish origin (0.226) and almost three times as high as that of Lithuanian origin (0.158). Moreover, the total differentiation of all previously studied landraces from several countries was significantly lower than that found in Moroccan landraces . Also, tA comparison of the level of genetic variability of landraces between different regions in Morocco showed no significant differences. This result could be due to the occurrence of different landraces in different regions with overlapping gene pools, or to the occurrence of the same alleles but at different frequencies. However, a more likely reason was the use of grouping based on the administrative division of the country rather than geographical regions or climatic zones. This division was not possible due to the lack of exact locations of the collection sites. Geographical coordinates are available in the database with minute accuracy; therefore, the location of the collection site was determined with an accuracy of up to 1 km. In addition, accurate climate maps and meteorological data for the collection period were not available. The analyses used monthly averages from 1958 to 2020 approximated with a spatial resolution of about 4 km. However, the result of the PCoA analysis clearly indicated differences at the genetic level related to the region of origin of the landraces. There was a clear distinction between landraces from the north-facing, coldest Mediterranean coastal region of Tanger-Tétouan-Al Hoceïma and those collected further south in one of the hottest regions of Morocco, Souss-Massa, located in the Sahara foreland.The results indicated that there was a strong genetic structure in the accessions studied. Two gene pools were clearly distinguished. The first was characteristic of the landrace and the second of the five breeding materials. Due to the dominance of the first gene pool in as many as nine accessions with breeding material status, it can be assumed that they originated from landraces from Morocco. The degree of internal variation varied widely from 0.015 to 0.362, indicating that these materials had undergone different degrees of selection. Accessions PL 40414 and PL 42593 had a level of heterogeneity indicating that a single genotype was present in the material studied. Analysis of the passport data showed that PL 40414 was named Rabat and came to Plant Breeding Station Strzelce (POL054), from the National Plant Germplasm System (NPGS) US Genebank (no. CIho4979). According to passport data, the biological status of this accession is uncertain, and it has been in the collection since 1927. It is not possible to determine whether the homogeneity of this accession is due to a deliberate effort by the breeders at the Institut National de la Recherche Agronomique (INRA) Morocco or whether the differentiation was lost at a later date, since this accession was acquired at the NCPGR in 1972. The origin of PL 42593 is unknown. The Plant Breeding Station Bąków (POL020) did not provide information to the database on how the seeds were obtained.Accession PL 40672 showed the presence of both gene pools in almost equal proportions. Interestingly, a very similar structure was also identified in PL 42736. PL 40672 was acquired by the NCPGR from Leibniz Institute of Plant Genetics and Crop Plant Research (DEU146) and appears in the local database under accession number HOR 3877. According to the passport data, it is an advanced cultivar bred at INRA in Morocco. Analysis of the passport data in several genebanks shows that this cultivar was definitely bred before 1964. It was acquired by the Polish Genebank in 1973, while it has been in the collections of the IPK Genebank, the USDA Plant Industry Station in Beltsville, and the John Innes Center since 1964. Interestingly, the similarity between the two accessions was also shown by PCoA analysis, i.e., the corresponding points were very close to each other. It should be noted that although these accessions show great genetic similarity, they differ at the level of seed morphology. PL 42736 was collected during the NCPGR expedition to Morocco. Since the collectors relied heavily on information provided by farmers on the farms where the seed was collected, there is a possibility of misinformation. When old cultivars have been grown on farms for several decades, information about their origin and name is lost in people’s memory. Similar examples were found for landraces of oats in Poland and landraces of barley in Lithuania .An interesting result was also obtained for accession PL 40777, which was transferred to the NCPGR from POL020 in 1972. According to the EGISET database, it is a breeding material with the name Rabat 071. The database also indicates that the accession may have been obtained from the NPGS, as the accession number CI 9776 also appears in the passport data. However, based on DArTseq analysis, this accession is characterized by a high level of heterogeneity, while passport data from the NPGS indicate that it is currently a pure line. However, it is not known when the pure line was derived from the originally acquired landrace. Unfortunately, it is not possible to verify the degree of similarity between the two accessions without comparison with the original sample obtained from the NPGS. However, it can be assumed that the accession stored at NCPGR was derived from crosses with other genotypes in the breeding program conducted by POL020. This is indicated by its considerable similarity to some of the accessions from this donor. This similarity was evident in both the PCoA and population structure plots, especially when analyzing the secondary structure.Doubts about its origin were raised by accession PL 42735. The EGISET database shows that it originated in Morocco. However, further information from the expedition to Morocco is missing. Based on DArTseq analysis, it is clearly genetically divergent from Moroccan landraces, as shown in the PCoA plot, and represents a gene pool separate from Moroccan landraces. The name of accession E 1012 in the passport data, i.e., sample number of the expedition, indicates that it may have originated from the 1984 expedition to eastern Poland, since according to the original notebook of the 1984 expedition to Morocco, the expedition numbers start with E 1025. Furthermore, this accession is characterized by a very high homogeneity, which may indicate that it is either a breeding material or that the accession was isolated during the expedition from another collected sample in which it was an admixture. Unfortunately, the original notes from the Polish expedition have not been preserved in the NCPGR archives. Therefore, it seems necessary in the future to compare the genetic data of this accession with material representing the Polish gene pool from the Lublin area and cultivars grown at that time.Based on the genetic data, it could be assumed that at least three independent crossbreeding programs were carried out in POL020 using the Moroccan gene pool. The first program is represented by accessions PL 40414, PL 40983, PL 42593, and PL 42594. The probable Moroccan donor could have been the cultivars ‘Rabat’ and/or ‘Marzaga’. The second program is represented by accessions PL 40777—PL 40982. Here, it can be assumed that the probable Moroccan donor was the original pure line, or the landrace Rabat 071 obtained from the USA. The third program is represented by accessions PL 42378—PL 42380, PL 42595, PL 42694. Unfortunately, from the passport and genetic data, it is not possible to determine which Moroccan accession was used to cross and select the most likely breeding lines acquired by the Polish Genebank, since all these accessions are characterized by a very high homogeneity. The donor of the material to the genebank did not provide any information on their pedigrees.Hordeum vulgare L. ssp. agriocrithon) [Barley shows considerable adaptation to soil stresses such as drought and salinity, which limit growth and yield . Farmer-crithon) .Due to the fact that the research presented in this article was conducted on a small number of accessions, most of which were heterogeneous, and the lack of phenotypic observations, it was not possible to perform association mapping for traits related to drought tolerance. By comparing accessions from locations with extremely different PDSI coefficient values, unique alleles were selected. The PDSI coefficient is commonly used to monitor the extent and severity of drought . It is cA total of 11 genes located near groups of SNPs of interest, at least some of which may be related to the plant’s response to drought stress, were identified based on the annotation of the reference genome.Arabidopsis, their role has been well documented in the initiation of trichomes [A gene encoding a Trichome birefringence-like N-terminal domain-containing protein has been located on chromosome 5H. Genes encoding these proteins are associated in plants with tolerance to abiotic stresses and defense against pathogens . In Arabrichomes . The denrichomes ,111.Arabidopsis thaliana L. are involved in the deposition of free phytol and free fatty acids as phytyl esters in chloroplasts. This process is associated with the maintenance of photosynthetic membrane integrity during drought stress and ageing [Brassica juncea L., differential hypomethylation of esterase/lipase/thioesterase family gene promotors in response to drought was detected. The overall hypomethylation of promoters of genes of this family may counteract drought stress by maintaining an active photosynthetic machinery [A gene encoding a putative esterase/lipase/thioesterase family protein is also identified on the same chromosome. The acyltransferase esterase/lipase/thioesterase family from d ageing . In Brasachinery .DHN12) was located in a region of unique SNPs. Dehydrins are expressed in cells during periods of low water content and are able to improve desiccation tolerance by protecting membranes, proteins, and DNA [DHN12 is the only gene in this family that is not expressed in vegetative tissues under dehydration, salt, or cold treatment. DHN12 is expressed only in developing grains [On chromosome 6H, the gene encoding Dehydrin 12 , a key enzyme in lignin biosynthesis . StudiesThe genes encoding histone H2B and monogalactosyldiacylglycerol synthase were also located in the region of unique SNPs on 6H associated with extreme PSDI values. In eukaryotic cells, H2B is one of the four major histone proteins involved in chromatin nucleosome structure . In wheaIt can therefore be assumed that the diversity in the above regions is related to adaptation to drought stress. However, the results obtained can only provide a roadmap for further research. The drought stress tolerance of landraces from Morocco coupled with yield potential should be evaluated and compared under controlled conditions. The level of heterogeneity of individual accessions estimated in this study should facilitate further research and indicate that the analysis of a single plant or SSD line may have limited effectiveness due to the large number of genotypes present in a single accession. The general evaluation of accessions, as commonly used by genebanks, may also be of limited effectiveness, as a heterogeneous accession with poor overall tolerance to biotic or abiotic stresses may contain individuals with an exceptional combination of genetic variants, but their unique value is masked by the presence of other genotypes . The resThe analysis included 63 accessions from Morocco stored in the collection of the National Center for Plant Gene Resources (NCPGR), i.e., the Polish Genebank . Forty eA leaf was taken from four-day-old, etiolated seedlings. Each accession was represented by eight randomly selected seedlings. DNA was isolated from the pooled tissue using a modified CTAB assay ,128. Quahttp://www. diversityarrays.com, accessed on 1 September 2022). The resulting reads were mapped to the Morex barley genome assembly [https://osf.io/725nb/ (accessed on 10 October 2023).Analysis of the genome representation using the next-generation sequencing (NGS)-based DArTseq technique was outsourced to the commercial laboratory Diversity Arrays Technology Pty Ltd. (assembly . The mat2), perimeter (mm), length (mm), width (mm), and color (described in the device-independent 3D CIELAB color space as 3 RGB channels).About 400 grains per accession were spilled on a Canon CanoScan LiDE 700 F desktop flatbed scanner for imaging at 300 dpi and saved as JPEG files. The files were subsequently analyzed using CSIRO GrainScan software . This sohttps://climatetoolbox.org/ accessed on 4 February 2023). The following data were downloaded: maximum temperature , minimum temperature , actual evapotranspiration , climate water deficit , grass reference evapotranspiration , precipitation , Palmer drought severity index , downward solar radiation flux at the surface , water runoff , soil moisture , and wind speed .For accessions with a known place of origin, data on climatic conditions at the collection site were downloaded. Data collected in the TerraClimate  databaseThe geographical distance was calculated using the geographical distance matrix generator v 1.2.3  softwareThe variability of environmental parameters was assessed using raw meteorological data from 1958 to 2022. The significance of differences in climatic conditions at the collection sites was evaluated using the analysis of variance (ANOVA). The strength of the relationship between parameters was assessed using the Pearson correlation coefficient. A dissimilarity matrix was calculated using the Gower coefficient. Principal Component Analysis (PCA) was then performed. The statistical tests were performed using XLSTAT Ecology . Subsequently, multi-year averages for individual months and annual averages or totals were calculated for selected parameters.Data of seven grain morphometric parameters were averaged across accessions. The minimum, maximum, mean, and standard deviation were then determined for each parameter within the study set. Averages within groups were compared using ANOVA and Tukey’s HSD post hoc test. The averaged data were normalized using unbiased standard deviation. As abovementioned, the dissimilarity matrix was calculated using the Gower coefficient and PCA was then performed. The statistical tests were performed using XLSTAT Ecology .post hoc test. As above, statistical tests were performed using XLSTAT Ecology . Population structure was assessed using an analysis of molecular variance (AMOVA) [4 burn-ins and 15 × 104 MCMCs in each run for five replications of K ranging from 1 to 15. To improve Bayesian clustering, the data were additionally filtered to meet the following criteria: RepAvg = 1.0, CallRate = 1.0, and MAF > 0.01. The number of clusters was determined based on the posteriori data probability for a given K and ΔK [The results, in the form of a table listing SNPs that were detected in the accessions analyzed, were transformed to a binary matrix with their codominant character preserved as described by Dziurdziak et al. . The res (AMOVA)  and Baye (AMOVA)  with 5 ×K and ΔK  and the K and ΔK  was used5 permutations was used to verify the correlation between genetic distance and all other dissimilarity matrices. A consensus configuration of genetic and grain morphometry data for accessions was obtained using Generalized Procrustes Analysis (GPA) [Using passport data matrices of geographical distance (in km) and absolute elevation difference between intake sites were developed. The Mantel test with 10is (GPA)  implemenA characteristic feature of Moroccan landraces preserved in the Polish Genebank is high internal heterogeneity. Thus, a single accession contains a significant number of differentiated genotypes. This positive feature from the point of view of yield under unfavorable conditions, which is also beneficial for the conservation of biodiversity, is a major obstacle to the use of this germplasm in breeding programs. In order to make this unique material suitable for breeding, further research and selection of individuals with the most favorable combination of traits, supported by the most up-to-date technologies, will be necessary. The random selection of pure lines, as well as the general evaluation of accessions, will be affected by significant errors and may not provide satisfactory results, which will be reflected in the underutilization of this gene pool in Polish breeding. Considering the acute problem of drought in Poland and its impact on cereal yields, it is essential to support breeding with selected genotypes that have increased tolerance to water shortage. Moroccan landraces preserved in the Polish Genebank can be a valuable source of drought-adapted genotypes. The results presented here provide a roadmap for further research and implementation.