Heritability enrichment analysis is an important means of exploring the genetic architecture of complex traits in human genetics. Heritability enrichment is typically defined as the proportion of an SNP subset explained heritability, divided by the proportion of SNPs. Heritability enrichment enables better study of underlying complex traits, such as functional variant/gene subsets, biological networks and metabolic pathways detected through integrating explosively increased omics data. This would be beneficial for genomic prediction of disease risk in humans and genetic values estimation of important economical traits in livestock and plant species. However, in livestock, factors affecting the heritability enrichment estimation of complex traits have not been examined. Previous studies on humans reported that the frequencies, effect sizes, and levels of linkage disequilibrium (LD) of underlying causal variants (CVs) would affect the heritability enrichment estimation. Therefore, the distribution of heritability across the genome should be fully considered to obtain the unbiased estimation of heritability enrichment. To explore the performance of different heritability enrichment models in livestock populations, we used the VanRaden, GCTA and α models, assuming different α values, and the LDAK model, considering LD weight. We simulated three types of phenotypes, with CVs from various minor allele frequency (MAF) ranges: genome-wide (0.005 ≤ MAF ≤ 0.5), common (0.05 ≤ MAF ≤ 0.5), and uncommon (0.01 ≤ MAF < 0.05). The performances of the models with two different subsets (one of which contained known CVs and the other consisting of randomly selected markers) were compared to verify the accuracy of heritability enrichment estimation of functional variant sets. Our results showed that models with known CV subsets provided more robust enrichment estimation. Models with different α values tended to provide stable and accurate estimates for common and genome-wide CVs (relative deviation 0.5–2.2%), while tending to underestimate the enrichment of uncommon CVs. As the α value increased, enrichments from 15.73% higher than true value  to 48.93% lower than true value for uncommon CVs were observed. In addition, the long-range LD windows  led to large bias of the enrichment estimations for both common and uncommon CVs. Overall, heritability enrichment estimations were sensitive for the α value assumption and LD weight consideration of different models. Accuracy would be greatly improved by using a suitable model. This study would be helpful in understanding the genetic architecture of complex traits and provides a reference for genetic analysis in the livestock population. Heritability enrichment analysis is a widely used method to explore the genetic architecture of human complex traits ,2,3, andSNP-based methods have been developed to estimate heritability enrichment using measured genotypes of SNPs. This method is more likely to reflect the proportion of phenotypic variation due to causal variants (CVs) tagged by SNPs for nominally unrelated samples. The unbiased estimation of heritability enrichment relies on accurate modeling of genetic architectures. Due to heterogeneity in natural selection, artificial selection and recombination rates across genome regions, heritability enrichment generally varies with minor allele frequency (MAF) and linkage disequilibrium (LD) of SNPs . PreviouThe variance of the allelic effect is proportional to election . The Claelection . Animal election . Speed eelection  examinedelection . A valueelection , but theNumerous works have also emphasized the importance of considering LD-dependent genetic architectures in the analyses of heritability ,14. LD iThe quality of sequencing technology and imputation for common SNPs are higher than for uncommon or rare SNPs, so a majority of studies have mainly focused on heritability research for common SNPs. Recently, the effects of uncommon CVs have been studied to gain insight into the genetic architecture of traits and examine genetic networks and annotation categories using imputed SNPs ,20,21. WMost traits of interest to the livestock industry are complex traits and many CVs are likely to be of small effect and difficult to find . Heritabp-value < 0.000001) using PLINK. Finally, 340,720 SNPs were retained for continued study.The German Holstein population provided by Vereinigte Informationssysteme Tierhaltung Wirtschaftlicher Verein was used in this study , which cTo assess the performance of different models on a range of genetic architectures, we first defined a “Functional subset”, including SNPs mapped from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Briefly, we downloaded 150 KEGG pathways of dairy cattle using KEGGREST package in R  and extracted the genes involved in each pathway. According to the physical location of the genes, we mapped 33,544 SNPs located on genic region for these 150 KEGG pathways and defined these as a “Functional subset”. We also set up a “Non-functional subset”, including randomly selected SNPs with the same number of SNPs as in the “Functional subset”.We then simulated three types of phenotypes, affected by 2000 CVs but having different levels of MAF for CVs . Of these 2000 CVs, 500 CVs were selected randomly from the “Functional subset” and 1500 CVs were selected from the rest of the SNPs. The MAF of genome-wide variants ranged from 0.005 to 0.5. We defined “common” as variants with 0.05 ≤ MAF ≤ 0.5, and “uncommon” as variants with 0.01 ≤ MAF < 0.05.y is the vector of phenotypic values; g is the vector of true breeding values; e is the vector of residual errors with distribution of N  with its corresponding coefficient matrix X, e was the vector of residual errors. The additive genetic and residual values were assumed to be independent normally distributed values: g~N  constructed from SNP genotypes. G was changed in different heritability enrichment models, as detailed below.We partitioned the heritability explained by all the SNPs into the “Functional/Non-functional subset” and the rest of the SNPs  of markers.Using real genotypic data, this study simulated phenotypes with different genetic architectures to explore the performance of heritability enrichment in livestock population. The simulated phenotypes were controlled by 2000 CVs, with the heritability of 0.8. By varying simulation parameters, we obtained three types of phenotypes with different MAF CVs. We first compared the heritability enrichment results of four models with the default parameters. For comparison purposes, we used the default parameters in the LDAK model  and α value of −0.25 for the α-model. When the estimated models applied “Functional subset” as the component, there was little difference between all the four models for T1 and T2 phenotypes, and only resulted in a difference of 0.015 to 0.067 from the true values . In particular, the α-model generated the most accurate result. However, for the T3 phenotype, all the four models, except for the GCTA model, generated values that were almost two-thirds of the real results. The GCTA model, combining “Functional subset”, performed the best, overestimating the 3.000 by 0.471 A. The suncreased .For different traits, optimal α may differ, we therefore explored the effect of different α value assumptions on heritability enrichment estimation, which was defined as α-model. For ease of comparison, we did not consider the LD weights. For T1 and T2 phenotypes. The model using “Functional subset” with different α values showed no significant difference, which was consistent with previous research . Moreove2 between SNPs decayed to 0.2. LD decay distance was approximately 40 kb in this population, with the criterion of 0.5 r2. When establishing the GRM, we set six different LD windows  which indicated different SNPs weights. For example, setting 1 kb window meant that the genome was divided into some 1 kb windows, and the software would accord the average r2 of all SNP pairs to weight each SNP for each window. SNP weight tended to be higher for the SNP in a region of low LD, and, thus, the model assumed that this SNP contributed more than those in high-LD region. In this way, the model assigned a different weight to each SNP and excluded SNPs with a weight of zero. In addition, we applied the default α parameter (−0.25) of the LDAK model to explore the effect of LD.We then investigated the potential ramifications of LD-dependent architectures on heritability enrichment. As For T2 phenotype, enrichment values were widely overestimated by the LDAK model. As the LD window increased, the enrichment of the model using “Functional subset” slowly raised to 3.926. What was more, the results of the T1 phenotype showed the same trend. As for the T3 phenotype, with the LD windows enlarged, the estimated enrichment reduced . CompareAs more and more heritability enrichment models are proposed, there is a need to assess the performance of different models for the livestock industry. In this study, we used simulated phenotypes with a variety of underlying genetic architectures to evaluate four heritability enrichment models with different parameters. According to the results of this study, the estimates of heritability enrichment showed bias and depended on the model and trait genetic architecture.In this study, we explored how different models and trait genetic architectures effected the estimates. Previous livestock studies often used VanRaden and GCTA models for heritability analysis ,29. ThisVarying the value of α in the α-model would change the assumed relationship between the MAF and effect size of CVs, and, then, the heritability estimation and the heritability enrichment value would be affected. The value of α is applied to measure selection ,11 for wUnlike humans, livestock have mostly undergone natural and artificial selection. Hence, there is extensive positive and negative selection in genetic variant associated with complex traits, so the α value of a trait  is the result of the many signals of positive and negative selection on trait-associated variants cancelling each other out . In addiCommon CVs are widely distributed in the genome that are greatly affected by LD. As the LD window size increases, the LD between SNPs in each window weakens. Therefore, the LDAK model allocates higher weights to CVs among the whole genome, so then the heritability enrichment increases. Enlarging the LD window strengthens the LD of uncommon CVs and low-frequency SNPs, thus decreasing the heritability enrichment of uncommon CVs . NeverthWe used raw genotype files and chip SNP data to explore the performance of different heritability enrichment models. In reality, as more and more Genome-wide association analysis (GWAS) data from public databases are used, heritability enrichment models using summary statistics estimation have come into being ,27,36. TIn this study, we investigated the performance of different heritability enrichment models, with a series of parameters, on three different phenotypes. Models with a subset of known causal genes acquired the more accurate and stable enrichment values compared with using the “Non-functional subset”. Heritability enrichments were sensitive for the α value assumption and LD weight. This is a prospective study on livestock, which can provide a reference for heritability enrichment analysis of complex traits of livestock.


Crassostrea gigas dataset were improved by using a genome-wide association study (GWAS) to select subsets of significant SNPs. An R package, “ASGS,” which integrates the commonly used traditional and machine-learning algorithms for efficiently finding the optimal algorithm, was developed to assist the application of genomic selection breeding of aquaculture species. This work provides valuable information and a tool for optimizing algorithms for GP, aiding genetic breeding in aquaculture species.The extensive use of genomic selection (GS) in livestock and crops has led to a series of genomic-prediction (GP) algorithms despite the lack of a single algorithm that can suit all the species and traits. A systematic evaluation of available GP algorithms is thus necessary to identify the optimal GP algorithm for selective breeding in aquaculture species. In this study, a systematic comparison of ten GP algorithms, including both traditional and machine-learning algorithms, was conducted using publicly available genotype and phenotype data of eight traits, including weight and disease resistance traits, from five aquaculture species. The study aimed to provide insights into the optimal algorithm for GP in aquatic animals. Notably, no algorithm showed the best performance in all traits. However, reproducing kernel Hilbert space (RKHS) and support-vector machine (SVM) algorithms achieved relatively high prediction accuracies in most of the tested traits. Bayes A and random forest (RF) better prevented noise interference in the phenotypic data compared to the other algorithms. The prediction performances of GP algorithms in the  Nowadays, selective breeding has become essential in the aquaculture industry. The growing demand for aquaculture products has led to an increased interest in improving productive and efficiency traits ,3,4,5,6.(Larimichthys crocea) [Oncorhynchus mykiss) [Oreochromis spp.) [(Sparus aurata) [O. niloticus) [O. niloticus) [Litopenaeus vannamei) [Salmo salar) [p-value selected by GWAS [Several recent GP studies have been conducted in aquatic animals, mainly focusing on productive and efficiency traits, such as growth and disease resistance. GP potentially performs efficiently in aquatic animals because of the large number of offspring produced by a pair of parents and the use of high-density marker panels to genotype animals ,13. GP i crocea) , rainbow mykiss) , hybrid is spp.) , gilthea aurata) , and Nilloticus) . GP for loticus) , Pacificannamei) , and Atlo salar) . The afo by GWAS ,22.Numerous algorithms have been proposed for GP. Each algorithm is usually suitable for one or more scenarios . GP has p-value selected by GWAS, and (3) develop an R package embedded with the commonly used traditional and machine-learning algorithms to assist in selecting the optimal algorithm for specific datasets. In this study, ten genomic-prediction algorithms, including both traditional methods and machine-learning methods, were used to conduct a systematic comparison of publicly available genotype and phenotype data from five aquaculture species. This study aimed to (1) compare the genomic-prediction accuracy and computational time of traditional and machine-learning methods, (2) evaluate the prediction accuracy of different algorithms upon inclusion of noise from phenotypic data and using top SNPs with significant Crassostrea gigas) [S. salar) [O. mykiss) [O. niloticus) [Cyprinus carpio) [C. gigas dataset was generated from a GWAS study aimed at assessing oyster resistance to Ostreid herpesvirus (OsHV-1). The S. salar dataset was generated from a GP study aimed at assessing genomic-prediction accuracy for salmon resistance to amoebic gill disease. The O. mykiss dataset was from a GP study aimed at assessing genomic-prediction accuracy for trout resistance to Piscirickettsia salmonis. The C. carpio dataset was from a GP study aimed at assessing genomic-prediction accuracy for the resistance of carp to koi herpesvirus disease (KHVD). The O. niloticus dataset was from a GP study aimed at assessing genomic-prediction accuracy for growth-related traits, including fillet yield and harvest weight. The missing genotypes of the aforementioned datasets were randomly imputed using the R package “synbreed” (version 0.12-9) [S. salar dataset and the weight traits in the O. mykiss dataset, C. carpio dataset, and O. niloticus dataset were continuous traits. Notably, the mean gill refers to how the resistance to amoebic gill disease is defined. It is the mean of the gill-lesion score recorded for both gills. The amoebic load is the qPCR values using Neoparamoeba perurans specific primers [Publicly available datasets from GP studies in five species, including the Pacific oyster (a gigas) , Atlanti. salar) , rainbow mykiss) , Nile tiloticus) , and com carpio) , were re 0.12-9) . Table 1 primers .i is the vector of phenotypes to the ith individual, μ stands for the vector of the constant term, βj is the vector of regression coefficient to be estimated, Xij is the incidence vector to individual i and marker j , and e represents the random residual vector [Five traditional genomic-prediction algorithms, including Bayes A (BA), Bayes B (BB), Bayes C (BC), Bayesian ridge regression (BRR), and Bayesian lasso (BL), were fitted to the data, followed by an assessment of their prediction accuracy. The general formula of Bayes A and Bayes B isl vector ,40. The l vector , was prol vector  and appll vector , assuminl vector . The RR-l vector , meaningl vector ,44 by adl vector . All fivl vector . The iteFive machine-learning algorithms, including artificial neural network (ANN), reproducing kernel Hilbert space (RKHS), support-vector machine (SVM), gradient-boosting machine (GBM), and random forest (RF) were evaluated for their genomic-prediction performance. ANN belongs to neural network algorithms, RKHS and SVM belong to kernel-based algorithms, while GBM and RF belong to decision-tree algorithms ,45.ANN was implemented in the R package “brnn” (version 0.8) , fittingThe prediction accuracies of the continuous traits were evaluated using Pearson correlation coefficient between the observed phenotypes and genomic estimated breeding value (GEBV). The area under the curve (AUC) was used for survival traits . Hold-out cross-validation was applied by randomly selecting 10% of the samples as the testing population while the remaining 90% represented the training population. The cross-validation was repeated 50 times, a correlation was obtained every time and then an average value of correlation was obtained. O. niloticus was selected to conduct the study because it was the only dataset specializing in the weight trait. ANN was excluded from this analysis because it required high-quality datasets and cannot run adding noise. The cross-validation was repeated 50 times at each point, a correlation was obtained every time and then an average value of correlation was obtained.Biological experiments usually have uncontrolled environmental conditions. This, and the inherently unpredictable nature of individuals , lead to considerable amounts of noise . Noisy dC. gigas dataset, GWAS was performed through R package “GAPIT” (version 3.1.0) [p-value [p-value results in GWAS. We gradually added the percent of SNPs from the best 10% to all, with 5% at a time, to evaluate prediction accuracy under different significant marker densities. The cross-validation was repeated 50 times at each point, a correlation was obtained every time and then an average value of correlation was obtained.In order to optimize the prediction accuracy of each algorithm in the n 3.1.0) . We used[p-value . The fir[p-value . The GP O. mykiss was used to compare the computational times when fitting each algorithm because it had a considerable number of individuals and thus would achieve more accurate computational times. The dataset contained phenotype and genotype data of 2047 individuals and 26,068 SNPs. No parallelization was adopted in these comparisons. All the algorithms were benchmarked using a single core of Intel® Xeon® Platinum 8164 CPU @ 2.00 GHz.The survival trait in C. gigas was used to compare the AUC scores of tuning hyperparameters manually and automatically. The comparison was conducted under different significant marker densities.An R package “ASGS” was developed to assist the efficient use of the GP algorithms, including the seven traditional  and five machine learning  algorithms. The hyperparameters in machine-learning algorithms can significantly influence their prediction accuracies . An autoO. niloticus (0.520) and the resistance to KHVD trait of C. carpio (0.734). Bayes B performed well in the weight trait of O. niloticus (0.506), resistance to KHVD trait of C. carpio (0.731), and resistance to P. salmonis trait of O. mykiss (0.810). Both the Bayesian lasso and Bayesian ridge regression performed well in the resistance to P. salmonis trait of O. mykiss at 0.807 and 0.811, respectively. Moreover, GBM performed well in the weight trait of C. carpio (0.395), while RF performed well in the mean gill trait of S. salar (0.332). Of note, RKHS performed well in the weight trait of C. carpio (0.392), the weight trait of O. mykiss (0.483), the amoebic-load trait of S. salar (0.356), mean gill trait of S. salar (0.319), and resistance to KHVD trait of C. carpio (0.732). SVM performed well in the weight traits of C. carpio (0.406) and O. mykiss (0.489). Notably, RKHS and SVM achieved relatively high prediction accuracy in 5 out of 8 traits. Moreover, there was a significant difference between the poorly and well-performed algorithms in each trait. For instance, the highest prediction accuracy of the survival trait in C. carpio was 33% higher than the lowest prediction accuracy, while that of the amoebic-load trait in S. salar was 8%. Notably, all algorithms achieved AUC scores near 0.5 in the C. gigas dataset.All algorithms exhibited a declining trend in accuracy as the amount of noisy data increased . Well-peC. gigas dataset. The results are shown in O. mykiss dataset, as described earlier, to provide examples for users on how to run it. The package has been released to GitHub . The R package “ASGS” was developed to assist in the efficient use of the GP algorithms. The input data comprised the genotypic, phenotypic, and fraction testing data. The 50 times cross-validation was implemented to obtain the mean prediction accuracy. The output data comprised a model-fitting situation and prediction accuracy for both the training and testing populations. Auto hyperparameter adjusting through the grid-search method was implemented for machine-learning algorithms to achieve a higher prediction accuracy. The R package “ASGS” can overcome the complex hyperparameter adjusting problem in the machine-learning algorithms. The extensive use of genomic selection in both livestock and crops has led to a series of algorithms, though none of the algorithms suit all the species and traits under study . StudiesHerein, RKHS and SVM achieved relatively high prediction accuracy in five out of eight traits, suggesting that RKHS and SVM are potentially optimal algorithms for more traits. Notably, SVM required much less computational time than RKHS. These findings collectively suggest that breeding organizations should try using SVM for less-well-known traits because of its relatively high prediction accuracy and reduced computational time. However, the prediction accuracy of any algorithm is affected by many factors, for example, the population structure and the degree of non-additive effects . BreedinC. gigas dataset. This phenomenon may be attributed to the small individual number and the existence of a large number of SNPs with no effects or negative effects. Therefore, using all SNPs to conduct GP is not suitable for the C. gigas dataset. Herein, we used the top SNPs with significant p-values selected by GWAS to conduct GP in the C. gigas dataset. The highest AUC scores for most algorithms were achieved based on 4500 SNPs. By reducing SNPs with no effects with negative effects, previous studies also proved that the prediction accuracy can be improved when using the top SNPs selected by GWAS [Notably, all algorithms achieved low AUC scores (near 0.5) when using all SNPs in the  by GWAS ,22. Of note, the adjustment of the hyperparameters in machine-learning methods significantly affects the prediction accuracy and computational time , while Overfitting and underfitting are the two main causes of low prediction accuracy of a model. Overfitting is caused by the inclusion of too much useless information in the models, insufficient size of the training dataset, or the high complexity of the model. Underfitting is caused by a simplified and hence poorly fitted model to predict the multi-dimensional data sufficiently ,67,68. TNumerous factors, including the number of individuals and SNPs, the degree of dominant effects and additive effects ,14, and Notably, the adjustment of hyperparameters in machine-learning algorithms is quite time-consuming . It has C. gigas dataset were optimized by using GWAS to select subsets of significant SNPs. Furthermore, ANN and SVM outperformed the time-consuming traditional Bayes algorithms. The decision-tree algorithms and RKHS were proved to be highly time-consuming. The relationship between the complexity of the algorithms and data influences the prediction accuracy. Adapting complex algorithms to rather simple data causes overfitting, while adapting the simple algorithm to complex data causes underfitting. This work provides valuable information on the prediction efficiencies of the currently available GP algorithms and a useful tool for assisting in choosing the optimal algorithm for selective breeding of aquaculture species.In conclusion, none of the ten representative GP algorithms used to analyze datasets of eight traits in five species achieved the highest prediction accuracy in all the traits. However, RKHS and SVM achieved relatively high prediction accuracy in five out of eight traits, suggesting their suitability as the optimal algorithms for more traits. Notably, SVM required much less computational time than RKHS. The prediction accuracy of each algorithm was affected by the inclusion of noise in the phenotypic data. Bayes A and RF were the better algorithms when noise was included. The prediction accuracies of GP algorithms in


Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. Since 2007 , genome-It may be important to identify individuals who deviate from their predicted phenotype based on an assumed polygenic model of association because they may be more likely to carry rarer and more penetrant pathogenic mutations or have some other cause to their phenotype. Specific alternative causes of an extreme phenotype may require targeted clinical investigations for an individual.Using height and LDL cholesterol (LDL-C) as exemplar traits, chosen for their high heritability and clinical relevance respectively, we aimed to classify individuals who deviate from their genetically predicted phenotype, using 158,951 unrelated individuals from the UK Biobank with whole exome-sequencing data. We subsequently aimed to determine if individuals classified as misaligned to their genetically predicted height were enriched for recall of being relatively short or tall in childhood, disproportionate body stature, clinical diagnoses of syndromes associated with extreme stature, carriers for rare genetic variation relevant to height, or environmental factors that may have influenced growth. Secondly, we aimed to determine if individuals classified as misaligned to their genetically predicted LDL-C were at higher risk of heart disease, more or less likely to have type 2 diabetes, or were carriers for rare genetic variation relevant to LDL-C. Finally, we assessed the sensitivity of our results based on four methods, each with two thresholds, that have the potential to be used to identify individuals whose phenotype deviates from the expectation based on their polygenic score.P < 5 × 10−8 obtained from a meta-analysis of 1.2M individuals from European-based studies (excluding the UK Biobank) contributing to the Genetic Investigation of ANthropometric Traits (GIANT) consortium. The polygenic score explained 31.6% of the variance in height among 158,951 unrelated individuals of European genetic ancestry with exome sequencing in the UK Biobank (P < 1 × 10−128), equivalent to 5.19cm. Effects were similar in males and females .We derived a polygenic score using conditional effect estimates of 3,198 SNPs reaching  Biobank . A 1SD iUsing a simulated dataset of 158,951 individuals and 3,198 SNPs explaining 31.6% of the variance under an additive model see , we clasP = 2 × 10−42 and OR = 10.4 , P = 4 × 10−27, respectively.As a validation of our polygenic deviation classification for height, we first tested for enrichment of self-reporting being shorter or taller than average at age 10 among individuals who were shorter or taller than genetically predicted, respectively. We observed evidence of enrichment in both the short and tall deviator groups relative to the group aligned to their genetic score with OR = 10.1 , P = 1.22 × 10−10, OR = 6.39 , P = 7.85 × 10−4, respectively.As individuals at the extremes of the polygenic score distribution for height are enriched for recalling being shorter or taller at age 10, we next hypothesised that individuals classified as deviating from their genetically predicted phenotype are also more likely to have disproportionate body sizes that affect standing height and have more extreme sitting-to-standing height ratios. We observed individuals who were shorter or taller than genetically predicted were enriched for extreme values of sitting-to-standing height ratio (greater than 1SD) with OR = 2.99 , P = 2 × 10−5) of having a diagnosis of congenital malformations and deformations of the musculoskeletal system but observed a lack of enrichment among the taller group .To identify potential reasons why individuals deviate from polygenic prediction, we first tested for enrichment of clinical diagnoses of congenital malformations and deformations of the musculoskeletal system as captured by ICD9 (754–756) and ICD10 (Q75-Q69) codes from Hospital Episode Statistics and primary care data where an ICD9 or ICD10 code could be extracted. We observed an enrichment within the group of individuals with shorter stature misaligned to the genetic predictor with an odds ratio of 3.45 , We next hypothesised that individuals classified as having relatively short or tall stature given their polygenic score for height would be enriched for rare variants in dominantly inherited genes previously associated with growth disorders, including overgrowth.< 0.1%) loss-of-function (LoF) variants in those genes. We did not observe evidence (at P < 0.05) for enrichment of rare LoF variants present in people defined as relatively short for their polygenic prediction . However, we did observe a stronger enrichment for LoF carriers when limiting the analysis to a subset of 6 genes  in which variants are known to be relatively common Mendelian causes of short stature   see .P = 0.63). These results were nominally significant (P < 0.05) when limiting our analysis to 3 genes in which variants have previously been described as causal for some of the most prevalent syndromes associated with tall stature, specifically Marfan syndrome (FBN1)  and 0.424 SD  change in LDL-C in the males and females, respectively.A 1SD increase in the polygenic score increased rank-inverse normalised residualised LDL-C  by 0.408 SDs , and 123,254 individuals as aligned to their polygenic score (P>0.05).We again used the Mahalanobis metric to classify individuals who deviated from their polygenic score. Based on 134,979 individuals and 1,239,184 variants that explained 16.7% of the variance of a normally distributed outcome, we classified 159 individuals from the UK Biobank as deviating from the polygenic expectation  of which had a higher LDL-C than expected. The absolute number of males classified as misaligned to their polygenic score did not change in the sex-stratified analysis, but the relative number of individuals who had a polygenic score higher than expected increased by 12.1%. Due to these differences, we used the sex-stratified analysis as our primary results. We provide scatter plots in β = 0.695 , P = 2.87 × 10−6) and nominally higher HDL levels . All effect sizes are in sex-specific SD units. Based on the same comparison in females, individuals with a high LDL-C for their polygenic score had higher triglyceride levels , higher BMI  and higher cigarette use .Compared to individuals classified as not deviating from their genetically predicted LDL-C levels, males with high LDL-C relative to their polygenic score had higher triglyceride levels , lower HDL levels  and nominally lower diastolic blood pressure . In females, individuals with a low LDL-C for their polygenic score had lower triglyceride levels  and were nominally older  - see Compared to individuals labelled as aligned to the genetically predicted LDL-C, males whose LDL-C was low for their polygenic score had lower triglyceride levels (P = 0.019), even after adjusting for the effects of measured LDL-C, age and BMI. We did not observe an association between of higher risk of T2D in males labelled as deviating from genetically predicted LDL.Compared to individuals labelled as aligned to genetically predicted LDL-C levels, females whose LDL-C was high for their polygenic score had a nominally increased risk of T2D  and nominally higher risk of type-two diabetes . In females, individuals with a low LDL-C for their polygenic score showed no evidence of enrichment for T2D or CAD. Refer to Among males classified as misaligned to their LDL-C genetic predictor and whose LDL-C was lower than expected, we observed an enrichment for coronary artery disease  loss-of-function variants in the LDLR gene .Males and females whose LDL-C was high for their LDL-C polygenic score showed evidence of enrichment for rare , and for males in PCSK9 .Males and females whose LDL-C was low for their LDL-C polygenic score showed evidence of enrichment for rare loss-of-function variants in Refer to P = 2.09 × 10−6). We present all GRS-ranking method results in We additionally classified individuals who were misaligned to their polygenic score for LDL-C using the GRS ranking method, and based on interquartile ranges and the residual of regression of LDL-C on the polygenic score. Of the four methods, classifying deviation from a polygenic score using the results of which can be found in We have established novel, robust methods for identifying individuals whose phenotype is misaligned to their polygenic prediction, which we referred to as deviating from a polygenic score, applied to two well-known phenotypes: height, chosen for its high heritability and strongly predictive polygenic score, and LDL-C, chosen for being clinically actionable into adulthood, with a range of associated co-morbidities.Our results were broadly consistent across the methods tested and are thus likely to be applicable to a range of phenotypes. With ever-increasing sample sizes, we suspect more traits will have highly powered polygenic risk scores that increase the efficacy of this method.ACAN and SHOX for height and LDLR and PCSK9 for LDL-C. Second, individuals who deviated were also enriched for other factors known to be associated with differences in phenotype, such as differences in BMI, smoking, and socio-economic position for LDL-C. For LDL-C, these differences were also reflected in different risks of heart disease and type 2 diabetes.Several lines of evidence indicate that our approach is effective. First, we found, for both standing human height and LDL-C, individuals who deviated from their expected genetic score were enriched for rare genetic mutations in several genes known to be associated with extreme stature and LDL-C. These mutations were discovered using the whole exome sequence data in UK Biobank, and occurred in established genes, such as The number of individuals identified as deviators from their expected phenotype given their polygenic risk varied by method and statistical threshold used. For example, based on the less stringent statistical thresholds  are known to be skewed  and therThere are some limitations of this study. First, while the primary method is suited for normally distributed phenotypes and genetic scores, as observed for height, no optimal Mahalanobis distance threshold is known. We have attempted to overcome this by demonstrating the efficacy of our method on LDL-C, a skewed phenotype. We have also shown that our results remain largely consistent when changing statistical thresholds that guide inclusion of individuals to follow-up who are deviating from polygenic expectation. Second, the UK Biobank is healthier than the general population , which mIn conclusion, our results support the hypothesis that individuals who deviate from their genetically predicted phenotype, as defined by common variants and using a suite of statistical methods, are of clinical interest. These individuals are more likely to carry rare genetic variation, or be at greater risk of co-morbidities, and should be considered in future discovery studies.http://www.ukbiobank.ac.uk/ethics/) and covers the work in this study, which was performed under UK Biobank application numbers 9072. All participants included in these analyses gave written consent to participate.The UK Biobank was granted ethical approval by the North West Multi-centre Research Ethics Committee (MREC) to collect and distribute data and samples from the participants We analysed 158,951 unrelated individuals from the UK Biobank with inferredEuropean genetic ancestry as previously described . All ind> 2 standard deviations from the mean.Height (cm) was derived from the UK Biobank (field 50) and converted to standardized residuals, after adjustment for age, sex and UK Biobank assessment centre. We subsequently defined short/tall stature as a residualised height LDL cholesterol (mmol/l) was derived from the UK Biobank (field 30780) and converted to rank-inverse normalised residuals, after adjustment for medication, age, sex and UK Biobank assessment centre.P ≤ 5×10−8 from an interim meta-analysis of height performed by the Genetic Investigation of Anthropometric Traits (GIANT) consortium in up to 1,400,860 individuals  that excluded the UK Biobank.We created a genetic predictor for height  for eachWe created a genetic predictor for LDL-C  for eachPSi refers to the ith individual’s polygenic score, summed over n genetic variants each with an effect size βn, multiplied by an individual’s genotype Gn,i. The genetic predictors were subsequently corrected for the first five principal components, calculated within a broader set of unrelated European individuals from the UK Biobank [µ=0 and σ=1.We calculated the genetic predictors using the following formula: Biobank . Finallyχ2 distribution with 2 degrees of freedom to represent the probability of a data point being an outlier relative to the correlation between the genetic predictor and observed phenotype. We used P-value thresholds of <0.001 to define individuals deviating from their expected phenotype.For our primary analysis on standing height, we defined two statistical criteria for labelling individuals as deviating from their expected height given their genetic height score. First, we estimated the variance explained by the genetic predictor in the 158,951 individuals from the UK Biobank. Next, we simulated 158,951 individuals and 3,198 SNPs under the additive polygenic model whereby the phenotypic variance explained by the simulated SNP effects approximated those observed in the UK Biobank. We subsequently calculated a polygenic score for each simulated individual  prior toz| > 2, where z represents the z-score of the normalised residuals of the regression model. Individuals with |z| < 1 were defined as being consistent with the additive polygenic model.Second, to account for the possibility of outlying Mahalanobis distances being associated with individuals with both an extreme polygenic score and height measurement, consistent with the additive polygenic model, we regressed the observed standardized polygenic scores against the observed standardized heights and retained individuals reaching our P-value threshold if |Individuals classified as deviating from their expected phenotype were subsequently split into two groups dependent on whether their standardized height was below the mean (shorter) or above the mean  for follow-up analyses.We performed separate enrichment analysis of several characteristics in the shorter and taller than predicted for their genetically predicted phenotype individuals defined above.We tested whether individuals who were classified as deviating from the polygenic risk score were enriched for physical observations we may expect. This included self-reporting of bring shorter or taller at age 10 (UK Biobank field 1697), and extreme values of the ratio of their sitting-to-standing height ratio (UK Biobank data fields 20015 and 50) adjusted for age, sex and centre.To identify individuals previously clinically diagnosed as having congenital malformations affecting the musculoskeletal system we used ICD9 and ICD10 codes available from Hospital Episode Statistics (HES), and primary care data where read codes could be converted to ICD9 or ICD10 codes. We selected ICD9 codes 754–756  and ICD10 codes Q65-Q79  (and the sub-classifications of these codes).< 0.001) loss-of-function variants residing in a curated list of genes related to short and tall stature from OMIM (Online Mendelian Inheritance in Man) [SHOX, NPR2, ACAN, IGF1, IGF1R, and FGFR3) [FBN1, EZH2 and NSD1) [Using whole-exome sequence data available in the UK Biobank, we tested for enrichment of rare (MAF  in Man) . This li in Man)  (curated in Man)  with OMI in Man)  and the  in Man)  to annotd FGFR3)  and 3 gend NSD1) –10 estabIt has previously been shown that enhanced inbreeding can lead to lower height .We thus assessed whether the F-statistic for inbreeding was significantly different for those individuals classified as deviating. The F-statistic for inbreeding was calculated using PLINK (v1.9) .We tested for enrichment of socio-economic status using townsend deprivation index (UK Biobank data field 189), to determine whether individuals who were short/tall had a depleted/enriched socio-economic status respectively.< 0.05/n, where n is the number of individuals in the analysis. As a second approach, we generated standardized residuals for height by regressing the polygenic score for height on height measures and subsequently labelling individuals as deviating from genetic predictions if their ∥z-score∥ was >2 or >3 (‘Regression’ - r + 1)/10001, where r represented the number of simulated phenotypes that were as extreme as that observed at the given PS rank.  loss-of-function variants in one of three genes known to affect levels of LDL-C:LDLR, APOB and PCSK9, as in [Using whole-exome sequence data available in the UK Biobank, we tested for enrichment of rare (MAF 9, as in . As for Supplement 1Supplement 2


Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species,  The maintenance of quantitative genetic variance presents geneticists and evolutionary biologists with a formidable challenge. While models of the evolution of allele frequencies can be relatively simple, evolution of the genetic variance for phenotypic traits also depends on the effects of those alleles . A substFrom a theoretical perspective, the resulting allele frequency spectrum is a key distinguishing feature between models in which selection actively maintains polymorphisms  and models in which selection eliminates variation . Balancing selection mechanisms are predicted to maintain relatively symmetrical allele frequencies at a locus, while under MSB models, genetic variation is determined by rare alleles, where the greater the fitness effect of a locus, the rarer the minor allele at that locus . GenomicDifferent MSB models also make contrasting assumptions about the magnitude of effects of new mutations relative to segregating alleles: Gaussian models assume mutations arise frequently, but have small effects, while House-of-Cards (HoC) models assume rarer, larger effect mutations . GWAS ofn) is much lower than the number of traits measured (p). Indeed, based on genetic load and genetic variance in fitness arguments, n > 200 is predicted to be unlikely  modeling  to internogaster . SeparatD. serrata population in Brisbane, Queensland, Australia, as detailed in D. serrata lines , and a random subset of 3,385 of the 10,096 genetically variable gene expression traits. We analyzed data for males only (as in D. serrata). The previous analysis of these data indicated strong patterns of genetic covariance among expression traits  across traits, then plotted the sorted pooled observed values against the sorted pooled values from the simulated data . The obsG, is modular and of low rank (i.e. sparse). This assumption is consistent with published estimates of G from a range of taxa and traits that suggest both modularity  measures of the 3,385 gene expression traits; X is the 60 × 1 incidence matrix relating observations to the fixed effect of trait means where B is the 1 × 3,385 matrix of trait means; Z is the 60 × 30 incidence matrix relating the 2 replicates per line to their respective line; U is the 30 × 3,385 matrix such that itu is the mean effect of line i on trait t; and E is the 60 × 3,385 matrix of deviations of the replicates from their line mean, such that ijte is the effect of replicate j of line i on trait t.The BSFG analysis is founded in the classical linear mixed model that partitions observed phenotypic variation to genetic and nongenetic sources. Here, the linear mixed model, fit separately to the U and E, are assumed to follow multivariate normal distributions:G) and replicate (R) variance–covariance matrices sum to give P, the phenotypic variance–covariance matrix:The random effects of genotype (line) and replicate (reflecting microenvironment and measurement error), P as:Λ is the 3,385 × k matrix whose columns form the k predicted latent factors underlying the phenotypic variance and Ψ is the diagonal matrix containing the 3,385 trait-specific variances.In the BSFG model, common and specific components of phenotypic (co)variance are estimated by modeling k is free to vary from one iteration of the Gibbs sampler to the next, unless otherwise specified. For computational efficiency, we constrain the model to estimate at most 59 factors; this number corresponds to the maximum number of nonzero eigenvalues in an orthogonal decomposition of a covariance matrix of 60 observations per trait. BSFG analysis is not well suited to estimating the true number of independent latent traits in the data , and is instead primed to provide robust estimates of latent factors with the greatest influence on the total phenotypic variation  and specific (i.e. Ψ) components of P can each be partitioned into genetic and nongenetic components:k × k diagonal matrix of latent factor heritabilities, Ik and Ip are identity matrices of size k and p, respectively. A summary of the outlined parameters can be found in 2ij, as approximately the proportion of phenotypic variance in trait i that can be explained by factor j.The common .To determine statistical significance of the latent factors and their heritabilities, we used the local false sign rate (LFSR) approach with an average error rate of 1%, as described in Other studies investigating the relationship between allelic effect size and frequency have defined effect size quantitatively  is far greater than the number of objects measured per variable (60 samples), random correlations among variables are expected to be common e.g. . To helpAs anticipated, sampling error generated spurious genetic correlations among traits , resultiD. serrata) or 20 (D. melanogaster) times as many large pairwise correlations (i.e. |r| > 0.5) in the observed data than the randomized datasets  D. serrata heritable factors but only 4 of 42 D. melanogaster heritable factors were retained for further analysis based on this criterion  is arbitrary. To aid interpretation of the latent traits, we imposed directionality on each factor by multiplying its trait loadings and latent trait values by the sign of its mean trait loading . This allowed us to investigate whether extreme variants were more likely to be associated with an overall increase or decrease in gene expression across the variance-standardized traits significantly affected by the latent factor.Like an eigenvector, the direction of each factor  were available for 29 of the 30 lines analyzed here. Across these 29 lines, 5 known inversions were segregating, present as 1 or 2 copies across 11 lines (including one line carrying 2 inversions). Two of the 5 inversions, In(2L)t and In(3R)Mo, are associated with variation in the expression levels of hundreds of genes . Of the 3,385 probe sets analyzed, 3,119 were associated with a single FlyBase Gene ID . For D. serrata, we used FlyBase gene IDs corresponding to orthologs in D. melanogaster of the D. serrata transcript-level coding sequence, matched to D. serrata ESTs using BLASTN  using GffRead  or 2,154 (D. serrata) FlyBase Gene IDs. Limiting the background to only the genes that could have been in the gene set of interest reduces the probability of detecting significant enrichment where there is none  factors were statistically supported, collectively explaining 59% of the total phenotypic variance in the 3,385 expression traits, while in D. melanogaster 39% of the phenotypic variance was explained by 47 statistically supported factors. We assessed the effect of these latent traits on the distribution of gene expression phenotypes and, in particular, whether the observed extreme gene expression values (D. serrata (D. melanogaster). That is, latent factors contributed more than trait-specific effects did to extreme values in the data.The BSFG analysis returned evidence consistent with previous analyses of these data, namely that a substantial portion of the variation in expression was shared among traits, not trait specific. In n values  were accn values , and cenn values , top rowD. serrata and D. melanogaster, respectively, 32 and 22 of the latent factors were significantly heritable and met the additional criteria from the randomization analysis  or 94% (D. melanogaster) reduction in extreme genotypic values after model fit. This suggests that the majority of extreme genotypic values may arise via processes affecting multiple traits.In analysis . In D. s to 0.99 , with in to 0.99 . In D. m to 0.99  with bet to 0.99 . We asseD. serrata and 22 D. melanogaster latent factors for which heritability was statistically supported revealed patterns that suggested only some of the heritable factors may contribute to the extreme genotypic values. For 7 (22%) and 11 (50%) of the heritable factors in D. serrata and D. melanogaster, respectively, there were no outlier lines  across the 960 (30 lines × 32 heritable factors) latent trait values  across the 660 (30 lines × 22 heritable factors) latent trait values, with at most a single extreme value for any given line or heritable factor  or 6.7% (D. melanogaster) had at least one outlier line (D. serrata) or 19 (D. melanogaster) IQR from the median ; 127 of these traits were also associated with heritable factors with outlier line(s)  . Similarx-axis in y-axis in x-axis in y-axis in Second, we investigated whether the frequency of outlier trait values for individual lines could be predicted by the magnitude of the latent trait value. We observed a statistically significant but imperfect association . IndividD. serrata) or 20% (D. melanogaster) of the associated individual traits  that were outliers for a heritable factor were outliers for only a small proportion (<10%) of the associated individual traits  allowed us to infer positive (negative) extreme latent trait values to indicate an overall increase (decrease) in variance-standardized gene expression when summed across the traits significantly associated with the factor. In the 25 heritable factors with outliers in D. serrata, 33 of the 40 outlier values deviated above the median  had at least one extreme genotype (line), resulting in higher heritability of these multivariate traits relative to other multivariate traits lacking extreme genotypic values. While extreme latent trait values typically occurred for lines (genotypes) with a higher proportion of individual trait outliers, individual traits could be extreme without resulting in extreme latent trait values for the heritable factor that they were influenced by, and conversely, extreme latent trait values were observed when the heritable factor did not cause extreme values of any individual trait . Thus, tDrosophila populations. First, we presume that extreme trait values are caused by single loci, where for heritable factors these loci have pleiotropic effects. Trans-regulatory factors can influence expression of large numbers of genes . Applying equation (2.13) of μ is the genic mutation rate, predicts that the strength of selection required to maintain q at 1% was ∼0.01–0.1, given μ of 10−5–10−6 (q is in fact lower (higher) than our sample suggests, s must be stronger (weaker) to maintain the allele frequency, assuming q ∼ 1%. Finally, this prediction assumes that the large-effect alleles are recessive; if the observed outlier lines in fact carry alleles with additive or dominant effects, then the resultant increased visibility to selection means that selection 50–100 times weaker could maintain alleles at 1% frequency (Given the presumption of a single (pleiotropic) locus, and the observation that, for most individual and latent traits, only one extreme line was observed, we infer that alleles with (homozygous) major effects were segregating in the base population at a frequency of 0−5–10−6 . If the requency  and 2.15s ∼ 0.01–0.1) detailed above is within the range estimated for new mutations affecting fitness traits, such as viability and fecundity, based on the ratio of mutational to genetic variance  of gene expression traits in the D. serrata lines analyzed here, inferring selection within the same range for 5D multivariate axes of expression (median s = 0.032), but weaker selection on the individual gene expression traits  cause coordinated changes in expression of functionally unrelated genes via horizontal mechanisms. Second, the observed pattern  of heritable factors with outliers could reflect extreme values of pleiotropic effects. The distribution of effects of pleiotropic alleles across multiple traits is largely unknown, but might be expected to include alleles with very weak effects on some trait(s) (Heritable factors with outliers were less likely to be enriched for specific functions and exhibited patterns among lines where expression was elevated for some traits but depressed for others (i.e. unbiased direction of loadings). There are several possible nonexclusive explanations for these observations, which our data cannot further distinguish among. Heritable factors with outliers might capture gene coexpression caused by a mixture of so-called horizontal (e.g. shared chromatin status) and vertical (e.g. shared transcription factors) processes. trait(s) ; the herSaccharomyces cerevisiae found that mutations more frequently increased than decreased expression of 2 of the 10 genes studied, while a third gene exhibited the opposite bias, with more mutations decreasing expression , expression was biased upward; the question of emergent strong bias in the multivariate distribution of phenotypic effects warrants further investigation.Quantitative genetic theories of the maintenance of genetic variance that incorporate indirect (apparent) stabilizing selection assume that mutations have directionally biased effects on fitness itself (decreasing it), but that the pleiotropic effects of those mutations on other traits are unbiased, equally frequently increasing as decreasing trait values . EmpiricResolution of theory predicting the maintenance of genetic variation for quantitative phenotypes depends on better insight into the distributions of (pleiotropic) allelic effects on multiple traits and fitness. Widespread availability of data on many expression traits from the same genotypes provides a particularly powerful system for investigating shared variation among high-dimensional phenotypes, while the intermediary causal nature of expression traits, connecting genotype to more complex traits (iyac122_Supplemental_TablesClick here for additional data file.iyac122_Supplemental_FiguresClick here for additional data file.


Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.The online version contains supplementary material available at 10.1186/s12863-023-01111-y. Genome-wide association studies (GWAS) aim to find statistical associations between genetic variants and traits of interest using data from a large number of individuals , 2. TheySeveral data sources provide open access to limited amounts of summary-level GWAS, including the GWAS Atlas   homogenize the use of different ontologies that describe phenotypic observations across databases ; 2) makeDownloader module has the ability to integrate relevant GWAS sources (here we restrict to two example sources). The Transformer module transforms data into a shared format – employing the Genomic Data Model on which the output format is based. The two following modules only act on metadata, leaving genomic region data unchanged: the Mapper module is in charge of the extraction of selected information and its representation within an extended version of the Genomic Conceptual Model, where heterogeneity is addressed at the schema level; the Enricher is in charge of the integration at the value semantics level. Finally, the Flattener is reused as is to handle the conversion back to the original file-based representation for processing within the GMQL system [We employ a structured data integration process, which allows to retrieve GWAS datasets from their sources and import them in our systems. The pipeline is summarized in Fig. L system .The execution of the overall process, composed of the five mentioned steps, is driven by an XML configuration file, selecting which phase to execute, URLs of API/FTP servers through which data are downloaded, local paths to the source-specific classes, and the local path to reach the source-specific GDM schemata (a list of fields and their data types).The Downloader module connects to the endpoints of selected genomic data sources and produces files - both for the genomic data and its metadata - in original source-specific format, at the processing site hosting our repository. We build a collection of protocol-specific modules with few parameters to adapt them to new sources; tunings for each specific source may be necessary. For the scope of this project, we focused on two sources, detailed next.https://ftp.ebi.ac.uk). New versions of the repository are released monthly. In this work, we focus on the stable release of May 6th, 2021 that includes 16,854 studies, corresponding to 257,352 associations between SNPs and related traits. The Studies file contains one entry for each trait analysed in a study on PubMed; studies regarding multiple traits are split in multiple entries. The Ancestry file contains information about the cohorts of patients who participated to the studies . The Associations file contains one row for each association , equipped with statistical properties about the found correlations . The three files can be merged by means of a number of shared attributes, including the ‘STUDY ACCESSION’. Additional file The GWAS Catalog  started The FinnGen project  was borndatasets and samples; datasets are collections of samples. Samples are the basic unit of information, containing experimental data that corresponds to a given individual and preparation  that first undergoes sequencing (producing “raw data”), then alignment and calling steps (producing “processed data”). Each sample includes DNA segments or regions  and it is associated with information about the performed experiment, i.e., metadata describing the general properties of the sample. Genomic region and feature data can describe many molecular aspects, which are measured individually; the resulting variety of formats hampers their integration and comprehensive assessment. GDM provides a schema to the genomic features of DNA/RNA regions, making heterogeneous data self-describing and interoperable.The Transformer deals with the lack of agreement towards a standard data unit for genomic tertiary analysis. We propose to use the “sample” of the Genomic Data Model (GDM ), in conThe original files are translated into the GDM format, which has a fixed part – representing the genomic coordinates – that guarantees the comparability of regions produced by different kinds of processing, and a variable part, i.e., data-type-specific attributes, describing region properties, reflecting the process of feature calling that produced the regions with their features specific of the particular processing experiment. GDM represents metadata using a free arbitrary semi-structured attribute-value pairs structure.as is. Additional file Figure The Mapper module is in charge of the integration at the schema-level of a set of transformed metadata produced for each source. The method applies local-to-global mappings using a syntax inspired to Datalog . MappingItem entity; a GWAS “publication” maps to the GCM CaseStudy entity; a “trait” has a strong correspondence with the GCM BioSample’s disease attribute. Samples of typical sources integrated within the GCM are assigned to single individuals; for each biological sample we can retrieve the information about the donor(s) who provided it. However, GWASs are based on cohorts of patients, so the considered granularity is coarser with respect to already integrated datasets. The existing GCM biological view cannot capture the concept of “cohort”  and “ancestry”  that are relevant for GWAS. For each GWAS sample (corresponding to summary statistics) we only know the cohort size and limited ancestral information, while detailed information about each single component of the cohort is not available. To meet the constraints of the considered class of studies, we have extended the GCM introducing a new GWAS-specific view; the resulting schema, called Extended Genomic Conceptual Model, can be appreciated in [For supporting metadata search in a semantic-aware way, the Genomic Conceptual Model (GCM ) was preiated in .Item is interpreted as a GWAS study, i.e., a file that contains all the SNPs associated with the phenotype under consideration; each study has a corresponding Cohort, which includes the information about the groups of people from which the biological sample is collected, gathered to study a specific phenotypic trait (trait_name attribute). Specifically, the regions represented within the Item are obtained by comparing the DNA sequences of cases (people affected by the phenotype) with controls (people not showing that phenotype). An Item may represent a sample at different stages ; each study may be based upon groups of individuals or on trios. The Cohort entity stores the cardinalities of the cases, controls, individuals or trios that provide the corresponding item, both of the initial stage or replicate stage(s). A Cohort can reference many Ancestries, each containing given ancestral information about the represented partition, e.g., the country of origin, the ancestral category or the country from which the participants are selected.According to this view, the central entity Ancestries, we will include the metadata pairs: Ancestry table.This module required an ad-hoc implementation to create the computational structure for the novel GWAS view of the model. Note that each GWAS study has one cohort, but can have more than one ancestry; to indicate this, we append an ordinal number to all the attributes referring to ancestries . As an example, let us consider the metadata file of the GCST007269 study; as its cohort is linked to seven different Cohort table (extracted from the output of the Mapper) is associated with controlled terms, lists of synonyms and hyperonyms, and external links to reference ontologies. The result of this phase complements the information contained in the database of metadata. The adoption of a specific knowledge base for this attribute provides us with value normalization. Using external knowledge bases is essential in the biomedical domain, where specialized ontologies are already available and well-recognized. This process is supervised and requires a preliminary selection of the most suitable ontologies to describe the attribute .fieldList parameter can be used to specify the specific elements to be included in the output along with other formatting preferences.Ontological access to genomic data is currently well-supported by several search services, which are capable to integrate a high number of ontologies. As a broker search service to the underlying ontologies, we chose the Ontology Lookup Service (OLS ) by EMBLFor each distinct value of the relational database trait_name field, resulting from the union of all the traits from GWAS Catalog and FinnGen, we perform one call to OLS API whose results is stored in the following form: the original value ; possible parsed values deriving from a simple syntactic pre-processing of raw ; the In total, from OLS, we were able to retrieve 4,694 original trait_name raw values to be enriched, which resulted into 5,145 distinct parsed values . Out of  120K API calls performed on OLS, about one half found partial/exact matches with terms in 232 different ontologies. Such matches were used for calculating more advanced scores. An excerpt of the results is shown in Table The distance is computed using the principle of the Needleman-Wunsch algorithm ; in the Each ontology is scored from two perspectives: i) the onto_acceptance, i.e., how well-known and trusted the ontology is by the biomedical community (retrieved through Recommender Web Services 3); ii) tCoverage as the percentage of raw values that are found in each ontology; the Score as the average match_score of all the annotated attribute values weighted by the onto_acceptance; the Suitability as the measure of the adequacy of the ontology to annotate the attribute values. Since most of the times only one ontology does not provide an acceptable coverage for all the attribute values, we also compute a small set of ontologies to annotate values. Our algorithm first tries to match values only with the first (most appropriate) ontology, then tries to match only the values left unmatched with the following ontologies, until a fixed point is found for coverage. As a consequence, we compute the SetCoverage, SetScore, and SetSuitability metrics, corresponding to these small sets of ontologies.Finally, for each annotation, i.e., the mapping between a parsed value and an ontology term (ontology_id), we compute an overall annotation_score by multiplying each raw value’s match_score by a linear combination of onto_suitability and onto_acceptance. Based on the annotation_scores obtained for each parsed value using different ontologies, we informed the service evaluation phase. Specifically, we aggregate results by grouping on specific ontologies, thereby computing the Results of the transformation, mapping, and enrichment stages are fed back to the file-based representation of metadata (in GDM format), so that the pipelines that use this representation can exploit the understanding, modeling, and integration efforts that have been applied on GWAS information.The Methods have presented all the steps to reach a complete integration of the datasets of two selected data sources within the META-BASE repository. Measurable results are produced at the end of the Semantic Enrichment and of the Flattening. In this section, we provide a preliminary measurement of how the proposed steps contributed to resolving two main needs: i) the lack of interoperability between phenotypic traits among GWAS sources and cross-data-type source; ii) the lack of solutions for processing GWAS studies with other genomic signals. To address the first point, we propose a systematization of the enrichment process, allowing GWAS phenotypic traits to be connected to arbitrary ontologies, either selected through an automatic evaluation process or specified by users; to address the second point, we enable the possibility to query GWAS datasets by means of the GenoMetric Query Language (GMQL ), overcoSetCoverage, SetScore, and SetSuitability. Note that a second preferred ontology is added when the first one did not reach 0.85 coverage; in such case, indicators refer to the union of the ontologies. Additional file The results of our selection, based on the calculations presented in the Methods ‘As a final outcome, we choose the Experimental Factor Ontology (EFO) and the National Cancer Institute Thesaurus (NCIT ) to annoAfter selecting the ontology set, we proceed with the enrichment of the trait values. The process is supported by an interactive toolVocabulary, Synonym, containing alternative labels that can be used as synonyms of the preferred label, Reference, containing references to equivalent terms from other ontologies, Ontology, a table presenting details on the specialized ontologies; Relationship, containing ontological hierarchies between terms and the type of the relationships.The result of the enrichment is contained within the relational database described in the logical schema of Fig. is_a and part_of kinds). The depths of ancestors and descendants retrieved from the ontology are configurable by constant specification.The GCM attribute trait_name is equipped with a companion-attribute trait_name_tid that references the ontological term in the vocabulary table. Value enrichment is a supervised procedure: for each value associated to a trait_name, the system initially looks for a suitable term in the Knowledge Base; if a match is available the procedure is completed. Else, a match is searched on the specified ontologies (EFO and NCIT) on OLS. Once the term has been selected, we populate the tables of the Knowledge Base with all the information derived from OLS regarding the term: description, iri, synonyms, xrefs, hyperonyms and hyponyms . The remaining non-annotated values can be handled using a manual curation procedure, which supports the expert user by providing suggestions . In any case, a manual annotation can always be provided. So far, we enriched attribute values by linking them to 3,004 terms, 1,877 from EFO and 1,127 from NCIT. In addition to terms that directly annotate values, we included all terms that could be reached by traversing up to three ontology levels from the base term.By means of the semantic enrichment process, which took in input 3,276 distinct traits from the GWAS Catalog and 2,778 traits (endpoints) from FinnGen, we were able to find 90 common concepts ; see their list in Additional file Figure Genome-wide association studies inform on the correlations between many phenotypes and their corresponding mutations of DNA. The exact interpretation of that SNPs is not trivial for two reasons: 1) the outputs of GWASs are often large clusters of SNPs in linkage disequilibrium, making it difficult to distinguish causal SNPs from neutral variants in linkage; 2) even assuming that the causal variants can be identified, interpretation is limited by incomplete knowledge of non-coding regulatory elements, their mechanisms of action and the cellular states and processes in which they function. For the aforementioned reasons, it becomes important to further investigate GWAS data by merging and analyzing different genomic datasets.http://gmql.eu/gwas/) can be easily uploaded in the GMQL system private space of any user and processed together with the ones in the GMQL repository  on the e.g., in ).In the following, we propose four use cases along with their GMQL queries  gathersreference) with the GWAS dataset of SNPs . The result reports all the regions of the dataset GENE_EXP, equipped with counts of how many SNPs they overlap with .The GMQL query in the Code snippet 1 loads the studies from GWAS Catalog mapped to the trait ‘breast carcinoma’ (line 2) and the data referred to genes BRCA1 and BRCA2 from the GRCh38_TCGA_gene_expression dataset (line 4), which are merged within a single sample grouping all the regions from the TCGA samples (line 6). Then, the metadata of such sample are extended with an additional attribute that represents the third quartile of FPKMA typical row of the result materialized by line 13 has the following form: tastasis , cancer tastasis , or promtastasis ).per se are insufficient for mapping complex diseases. Variants in the intervening sequences (introns) or in the untranslated regions (UTRs) – although not changing the predicted protein sequence – may instead be pivotal in the regulation of gene expression [The GENCODE consortium  providespression . The UTRWith GMQL we can contribute to explore this issue by allowing, for arbitrary GWAS traits, to quickly map all SNPs onto UTRs, as they are annotated in GENCODE. The GMQL query reported in the Code snippet 2 selects GWAS Catalog studies mapped to the “primary biliary cirrhosis” trait (line 2) and the UTR regions from the latest release of the GENCODE dataset (line 4). The MAP operation (line 6) extracts, for each region in the UTR dataset, the overlapping SNPs ; Line 7 statement selects in the output dataset only the SNPs that occur in UTR regions. Finally line 9 extracts the UTRs only projecting their useful attributes . Figure rs2189521 mutation, occurring in gene IL21R: Qiu et al. [Note that the MAP operation at line 6 could have been used for the dual purpose of extracting SNPs falling within UTRs, instead of extracting UTRs hosting at least one SNP. This can be easily achieved by using the statement MAP  CIR UTR, where the two datasets are swapped. When executed in this way, the query extracts SNPs that deserve further analysis. Indeed, genetic variation happening in UTRs could impact regulatory elements that affect the interaction of the UTRs with proteins or microRNAs. Among the consequences on functions, there are the change of mRNA transcription, translation, and access to regulators. Alterations of these regulatory mechanisms are known to modify molecular pathways and cellular processes, potentially leading to disease processes , 55. It u et al.  reportedGenome-wide association studies can discover new loci that contribute to common human diseases. For each locus, it is currently necessary to sequence the newly discovered region to define all common and rare variants. GWASs carried on so far explained a modest fraction of all the disease risks; part of these unexplained risks are due to alleles with lower frequencies but probably larger effects. If such alleles are in genes that were already localized by GWAS, then targeted sequencing may find them. Similarly, some of the unexplained risks are due to the effects of structural variants that are not in linkage disequilibrium with common SNPs. Thus, a more complete understanding of the role of genetic variation in disease requires a deeper catalog of genetic variation.GWAS data can be usefully compared with referenced data, e.g., 1000 Genomes , a projep-value. Line 6 uses the JOIN operator to find, for each pair of samples – one from the FIN dataset and one from the OKG dataset – the closest deletion from each FinnGen SNP only if its distance is less than 1000 base pairs from the SNP. Line 7 exploits the operator PROJECT to remove superfluous region attributes, keeping only the relevant ones. The query process is visually represented in Fig. The GMQL code is reported in the Code snippet 3: line 2 selects the samples from 1000 Genomes dataset referred to the Finnish population. For those samples, it filters only regions that represent deletions. Line 4 selects the samples from FinnGen dataset referring to Schizophrenia phenotype. From the resulting sample, it filters the regions based on a reasonably low A typical row of the result materialized by line 8 has the following form: Note that if a user alternatively requested instead variations that are overlapping relevant SNPs, a different JOIN condition may be used: JOIN. In this way we would find, for each variation from 1000 Genomes dataset, the overlapping SNPs from FIN dataset. Then, only variation of SNP type should be selected: SELECT(region: OKG.mut_type == “SNP”) RES. Note that similar queries may be iterated on other populations/cohorts, such as the japanese one ; the chinese one ; or the United Kingdom one , allowing to process thousands of regions at the same time. This query demonstrates the possibility to systematically compare locations of GWAS and reference panels of variation in healthy populations. GWAS-derived SNPs have – to date – been used to impute about 2.5 million SNPs in the HapMap Project (HapMap) . HoweverIn  the authThe computational experiment can be formulated as a GMQL query that exploits GWAS mutations and enhancer regions from ENCODE. Pinoli’s experiment  focuses The GMQL code is reported in Code snippet 4: lines 2–3 hold the instructions to upload the studies mapped to schizophrenia from GWAS Catalog and FinnGen datasets. Line 5 loads the GRCh38_ENCODE_NARROW dataset, selecting DNA regions that are enriched by H3K27Ac. Line 7 allows to update the coordinates of the previously selected ENCODE regions, enlarging them by 3000 base pairs around the peaks; this operation defines the enhancer regions. Line 9 applies the operator COVER over the ENCODE samples, using the groupby option. It computes the result grouping the input dataset samples by the values of their biosample__ontology__name metadata attribute. Lines 11, 12 and 13 filter the regions that are cell type-specific enhancers. To distinguish cell type-specific enhancers from shared ones, we considered their frequency; we are looking for those peaks of H3K27Ac that occur in no more than two cell lines among all the samples that we considered. The COVER  operation considers all areas defined by a minimum of one overlapping region up to two of them; output region attributes include only region coordinates. The operation MAP  allows to retrieve the original regions with all their region attributes, adding the information of their frequency. Finally, using the SELECT operator we extract only the regions identified in line 12. Lines 15 and 16 exploit the operator PROJECT to add the new region attribute trait_name in each region (copied from the homonymous metadata attribute). Line 18 creates a dataset called UNI containing all the samples from GWAS_trait and FINN_trait datasets. Lines 20–21 contain the core operations of the query. The MAP operator adds to each region of the ENCODE dataset a counter corresponding to the number of overlapping regions of UNI dataset. The option bag adds a further region attribute with a list of values of the attribute trait_name of the mapped GWAS regions. The operator SELECT extracts only those ENCODE regions that have at least one corresponding GWAS mutation. Line 22 allows to keep in the output regions only the coordinates and two relevant columns. As last operation, the dataset P is materialized so it can be downloaded and explored. Approximately, this complex query takes about 8h running time and outputs 39008 regions distributed over 318 samples (about 4GB of memory).The GMQL query has two main purposes: i) filtering out only the truly causal variants ; ii) identifying the variants that occur in non-coding regions, in particular the enhancer regions where the H3K27Ac modification occurred. It can be repeated on all the traits in GWAS Catalog and FinnGen, allowing to study whether mutations that occur in cell-specific enhancers are related with any particular disease or trait . The lisAs genomic data continues its exponential growth , data maData integration. We designed an integration process to include GWAS within META-BASE. This strategy can be re-applied with small effort on all GWAS datasets, even when organized in a structure different from the NHGRI-EBI GWAS Catalog and FinnGen. With reference to our pipeline  is very powerful but is still mostly performed through ad hoc scripts . More sBEDTools , GORpipeBEDTools , STQL [8BEDTools ), but noGWASs bring important insights and outputs to the current genomic research. Their identified genomic regions have, however, rarely been analyzed in the context of other genomic information, including other mutations, epigenomic regions, or gene expressions. Being able to analyze GWAS data from multiple sources together with other processed genomic datasets is of high importance. We thus proposed to express GWAS datasets in the GDM format, compliant with the other sources included in the META-BASE repository. A purely model-driven integration effort achieves the inclusion GWAS datasets within a repository of other tertiary analysis processed datasets. We download, transform and map GWAS information within the integrated repository, also enriching the traits descriptions by means of several information from existing specialized biomedical ontologies, shortening the distance of different GWAS datasets; this process can be generalized to all kinds of genomic experiment descriptions that benefit from bio-ontology mapping , 85. We For fully exploiting the achieved integration result, we make GWAS datasets usable together with other processed datasets  embedding them within a multi-sample processing system called GMQL . This syAdditional file 1. GWAS Catalog source files descriptions.Additional file 2. FinnGen source files descriptions.Additional file 3. Region data representation for GWAS Catalog and FinnGen.Additional file 4. Results of ontology selection for trait_name annotation.Additional file 5. Common traits and mapping to ontologies.