Food legumes are at the crossroads of many societal challenges that involve agriculture, such as climate change and food sustainability and security. In this context, pulses have a crucial role in the development of plant-based diets, as they represent a very good source of nutritional components and improve soil fertility, such as by nitrogen fixation through symbiosis with rhizobia. The main contribution to promotion of food legumes in agroecosystems will come from plant breeding, which is guaranteed by the availability of well-characterized genetic resources. Here, we analyze seeds of 25 American and European common bean purified accessions  for different morphological and compositional quality traits. Significant differences among the accessions and superior genotypes for important nutritional traits are identified, with some lines showing extreme values for more than one trait. Heritability estimates indicate the importance of considering the effects of environmental growth conditions on seed compositional traits. They suggest the need for more phenotypic characterization in different environments over different years to better characterize combined effects of environment and genotype on nutritional trait variations. Finally, adaptation following the introduction and spread of common bean in Europe seems to have affected its nutritional profile. This finding further suggests the relevance of evolutionary studies to guide breeders in the choice of plant genetic resources. Phaseolus vulgaris L.) is the most grown food legume crop worldwide. It is largely grown as a grain crop  and as a fresh vegetable . In 2018, 30.4 million tons of dry seeds and 24.7 million tons of fresh pods were produced worldwide, with a total of 34.5 and 1.6 million hectares destinated for its cultivation, respectively [Common bean . The maP. vulgaris has its origins in Mesoamerica [Genetic improvement is one of the most viable strategies to obtain varieties that in addition to having improved agronomic traits, such as yield and resistance to biotic and abiotic stress, also have the best nutritional characteristics to satisfying consumer needs . Exploitoamerica , from whoamerica ).Phaseolus vulgaris was introduced into Europe in the 16th century, after the discovery of the Americas. It then spread throughout Europe, following complex routes that involved multiple exchanges between different European countries, with continued introduction of new varieties from the Americas [Americas ,19,20,21Seed compositional traits in common bean genetic resources have been evaluated in the literature ,32,33,34In the present study, we aimed to characterize and describe nutritional quality traits of 25 different American and European domesticated genotypes of common bean to identify potential sources of improved seed quality traits that can be used in future breeding programs. We also investigated whether the process of adaptation to European environments had any effects on seed compositional traits.Pv_all, Pv_core1, Pv_Core2) that were developed to be representative of both American and European germplasm, and of the two main gene pools of the species: Mesoamerican and Andean.A total of 25 common bean domesticated accessions were used in this study , 13 fromPv_all, Pv_core1, and Pv_core2 comprise ~10,000, 500, and 200 domesticated lines of P. vulgaris, respectively. The 25 accessions included in the present study were part of Pv_core1. Within the activities planned for the BEAN_ADAPT project, genotyping-by-sequencing of Pv_core1 provided the data for the population structure analysis that highlighted the main structure as two genetic clusters, which represent the Mesoamerican and Andean gene pools .All of the materials are lines of single seed descent that were purified by one or more cycles of selfing under insect-free conditions. This allows association of phenotypes to reliable genotypic information and represents strong potential to promote genetic resources used in pre-breeding and breeding programs. q) >50% used to assign the genotypes to one of the two gene pools, 20 were Mesoamerican, and five were Andean  to the Mesoamerican gene pool, while five Mesoamerican accessions showed introgression from the Andean gene pool . One accession was completely assigned to the Andean gene pool , while the remaining four Andean accessions showed introgression from the Mesoamerican gene pool .In the present study, with a threshold of memberships . This is an internal intensive horticultural area, characterized by a temperate Mediterranean climate with cool winters and warm dry summers. The data of maximum and minimum temperature and rainfall for the growing season (May to October) were obtained from the nearest weather station located, as shown in 2O5) fertilizer was applied as basal fertilization before sowing. Standard conventional agronomic management and irrigation practices were applied to the experimental fields. Plastic cover was used for weed control. Pest and disease management was done by spraying the plants with Ridomil Gold (fungicide) and Klartan 20 Ew (against aphids).The morphology and nutritional quality traits of these 25 accessions were analyzed using seeds produced during a field experiment involving all of the 500 Ten pods for each plot and replicate were harvested at fully maturity  and threshed. Seed weight , cotyledon and embryo weights, and seed coat weight and thickness were determined. For cotyledon, embryo, and seed coat weights, seeds were soaked in distilled water for 24 h. When the seeds were fully hydrated, the cotyledons, seed coat, and embryo were removed and dried in silica gel. When the components of the seeds were completely dry, the weights of the cotyledons, embryos and seed coat were determined using a precision scale, and the thickness of the seed coat was measured with a digital micrometer . Cotyledon, embryo, and seed coat weights for each accession are reported as mg/g seeds.The seeds were lyophilized and milled to a fine powder using a planetary mill with an agate jar and balls . The following seed nutritional traits were measured.Protein content was determined using Dumas combustion. The N content was measured using a N/protein analyzer , as described in the Official Methods of Analysis, Dumas method, 990.03 .v/v) for extraction of the lipid matter, at room temperature and with magnetic stirring. After 3 h, the content was divided in two tubes and centrifuged at 5000× g for 15 min. The lipid-free powder pellet was discarded. The solvent was transferred to a new tube and removed in a rotary evaporator . The remaining oil was recovered with acetonitrile and transferred into a 2 mL vial. The total recovered lipids for each accession was weighed. The lipid content was expressed as g/100 g of dry seed.For the tocopherol analysis, 5 g to 10 g of powdered seeds obtained by lyophilizing and grinding was dissolved in chloroform:methanol , at a flow rate of 0.45 mL/min. Fluorescence detection was at 296 nm excitation and 325 nm emission. For quantification of tocopherols, seven standard stock solutions of γ-tocopherol in acetonitrile were prepared and analyzed .The different tocopherol components were identified according to Giardinieri et al. . BrieflyThe antioxidant activity was measured according to the α, α-diphenyl-β-picrylhydrazyl (DPPH) method . The TroThe percentage of inhibition for the samples and Trolox was calculated according to Equation (1):The TEAC of each sample was as given by Equation (2):Total carotenoid content was reported as yellow pigment content (YPC), according to the micro-method described by Beleggia et al. ,39 and e3 (69.5%)/2 mL H2O2 (30%) using a microwave oven , and then diluted to 50 mL with high purity deionized water acidified with 3% HNO3. The mineral contents were then determined using inductively coupled plasma mass spectrometry  with an auto-sampler , as described by Beleggia et al. [To determine the mineral contents , 20 mg of each sample was dissolved in 8 mL HNOa et al. . BrieflyData for the following 23 traits were used for the analysis : (i) morcorrplot [The statistical analysis was performed using RStudio v1.2.5001 , with thcorrplot .p < 0.05, using the R package agricolae v 1.3-3 [minque v 1.1 [2) was computed as the ratio between genotypic variance and phenotypic variance [Differences among accessions for all of the quality traits considered were tested using one-way analysis of variance (ANOVA). Mean discrimination was performed, by applying Tukey’s tests, and statistically significant differences were determined at the probability level of  v 1.3-3 . To calcue v 1.1  was usedvariance . Wilcoxovariance  was usedFactoMiner v1.42 [factoextra v1.0.5 [ggplot2 v3.2.1 package [The differences between the American and European accessions were tested by an analysis of covariance (ANCOVA), including population structure information as covariate in the model. The multivariate analysis, principal component analysis (PCA), was performed with the packages er v1.42  and facta v1.0.5 . PCA was package .Means (±standard deviation) were computed for all of the traits analyzed in each accession , with thVariability among the different genotypes was seen for all of the traits considered . ANOVA aVariation was also detected for the different parts of the seeds, as cotyledon, embryo and seed coat weights, which ranged from 879.1 mg/g to 924.3 mg/g, from 9.1 mg/g to 16.1 mg/g, and from 62.7 mg/g to 106.2 mg/g, respectively . In partEven though wide ranges were seen for lipid content (1.8–3.1 g/100 g of dry seed), γ-tocopherol (43.3–127.2 mg/100 g of lipids), and the ratio between γ-tocopherol and δ-tocopherol (7.7–19.8), significant differences among the accessions were only seen for δ-tocopherol . Here, tp = 0.001). The carotenoids contents ranged from 2.2 to 9.3 mg β-carotene/kg dry seed  were seed weight, antioxidant activity, carotenoids content, Na, Mo, and Se contents. Intermediate heritabilities (0.30 < h2 < 0.60) were detected for embryo, cotyledon and seed coat weights, δ-tocopherol, protein, and Ca, Cu, Fe, Mn, Ni, and Zn contents. Very low heritability estimates (h2 < 0.30) were seen for the remaining traits, seed coat thickness, lipids, γ-tocopherol, and Mg, P and K contents.Heritability was estimated for all the traits analyzed . The tra2 estimate was for seed weight , followed by embryo weight ; seed coat and cotyledon weights, and seed coat thickness showed low h2 estimates . Among the nutritional quality traits, high h2 estimates were seen for antioxidant activity and carotenoid content . The heritabilities for δ-tocopherol and protein content were 31.5% and 30.5%, respectively. Very low h2 estimates were detected for γ-tocopherol  and lipid content , revealing the strong environmental influences on these traits and/or no differences within these specific materials. Finally, high h2 was seen for Na, Se, Mo, Zn, and Ni . The Ca macro-element and the Mn, Cu and Fe micro-elements showed moderate heritability estimates . Low heritability estimates were seen for the remaining elements (h2 ≤ 22.4%).Considering the morphological seed traits, the highest hANCOVA analysis was carried out to test for significant differences among the American and European accessions for the morphological and compositional traits of the seeds. For the morphological traits, compared to the American accessions, the European accessions showed significantly higher seed weights and lower embryo weights . For theStrong differences were detected for the antioxidant activities and carotenoids content. In particular, these were both higher for the American accessions . The TEAAmerican and European accessions differed significantly for three macro-elements. In particular, compared to those American, the European accessions showed higher Na and Ca contents, and lower Mg content . Within Principal component analysis was performed to investigate the relationships among the accessions, and to determine which seed traits define the basis of their differentiation. Considering PCA analysis based on all of the morphological traits, nutritional traits, and the macro-elements and micro-elements , principHere, PC1 was significantly and positively correlated with seed weight, γ-tocopherol, cotyledon weight, δ-tocopherol, and protein content, and negatively correlated with seed coat weight. embryo weight, YPC, and TEAC , as alsoPC1 tends to separate the American and European accessions, whereby those from Europe have higher mean seed weight and protein content, and higher mean content of both tocopherol congeners, while the American accessions showed higher TEAC and YPC activities , in agreNo clear distinction was detected in these analyses among the accessions, even if the macro-elements and micro-elements also showed wider variability for the European accessions . Europeap < 0.05), and negatively correlated with embryo weight . A strong negative and significant correlation was seen between cotyledon and both seed coat weight and embryo weights . For lipids and γ-tocopherol and δ-tocopherol contents, these were significantly and positively correlated , while γ-tocopherol content was negatively correlated with lipid content  and δ-tocopherol was positively correlated with protein content , but negative correlated with TEAC  and YPC . TEAC and YPC were positively correlated . Significant correlations were also seen between the morphological and nutritional traits; in particular, seed weight was positively correlated with γ-tocopherol content  and protein content , and strongly negatively correlated with TEAC and YPC . TEAC was also positively correlated with embryo weight  and seed coat weight  and negatively correlated with cotyledon weight .Correlations between traits were also investigated . For morp < 0.05). Among the macro-elements and micro-elements, the positive correlations that showed significance (p < 0.001) were those between Ca and Na (r = 0.34), Mn and K (r = 0.43), Mn and Ca (r = 0.41), Mg and Mo (r = 0.34), Mg and K (r = 0.64), and Fe with Ca (r = 0.52), Na (r = 0.42) and Mn (r = 0.33). K and Mg were strongly correlated with the total amount of macro-elements , and Fe content with the total micro-elements content .All of the significant correlations between the different macro-elements and micro-elements were positive, except for the macro-element P, which was negatively correlated with Na, Ca, Mn, and Se , Zn , and Ni . Seed weight was significantly negatively correlated to Mn, Se, and Mo . Significant negative correlation was seen between embryo weight and Na content ; cotyledon weight and Mg content were negatively correlated , while seed coat weight was positively correlated with Mg and Zn .Considering seed weights and macro-elements and micro-elements, significant positive correlations were detected between seed weight and Na  and YPC . YPC and Mn content were strongly correlated .Focusing on the nutritional traits, the macro-elements and micro-elements showed different significant correlations; among the most significant, Na and Zn were negatively correlated with TEAC  the seeds of the different accessions were derived by single seed descent, and are thus identical at the genome level; (ii) the majority of the accessions were landraces, thus potentially including variability that is not present in modern varieties; (iii) the seeds included accessions that were representative of all of the different growth habit types, as a character that is associated with several agronomic traits and with common bean races ,51,52,53Large variability was seen for all the traits analyzed, with the exception of lipid content, for which the heritability estimate was zero, and for seed coat thickness, γ-tocopherol, and Mg, and K contents, for which no significant differences and low heritabilities were seen. Thus, the discussion focuses mainly on the traits that showed significant differences among the accessions and intermediate-to-high heritability estimates.2, 91.4%) in the present study. This was expected, as the literature is full of studies that have reported such observations for common bean [Seed weight was the trait that showed the greatest estimate of heritability . TheIt is well reported in the literature that the antioxidant activity is higher in the seed coat compared to the cotyledons, due to the higher content of phenolic compounds in this part of the seed ,84,85,86The content of total carotenoids (reported as YPC) was also determined here. They are important in human nutrition as a precursor of Vitamin A, as well as antioxidant, with the demonstration that carotenoid-rich diets are associated with decreased incidence of diverse diseases . InteresAntioxidant activity significantly and positively correlated with YPC. A similar result was reported by , who anaThe total tocopherol content was not correlated with either antioxidant activity or YPC, which were instead negatively and significantly correlated with δ-tocopherol. Significant correlation between tocopherols and antioxidant activity has been shown in other species, which suggests that the phenotypic architecture of the antioxidant activity might be highly variable among species. Zhang et al.  reportedAntioxidant activity and carotenoid content were here significantly and negatively correlated to seed weight. This was also reported by Dong-Yun et al.  in an anRecently, there has been an ever-increasing demand by consumers for legumes as sources of protein alternative to meat. This interest arises not only because food legume crops represent valuable resources for diet change, but also because of the relative reduction in environmental impact of such protein production . In thesLegumes are good sources of essential minerals for human health ,95, and Iron is an essential element for human health. It is involved in important metabolic and cellular processes such as deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) synthesis and it is required for synthesis of proteins involved in the oxygen transport, hemoglobin and myoglobin, and other iron-containing enzymes involved in electron transfer . High vZinc is the second most important trace element for human after iron as it is involved in many metabolic processes . It is aHowever, other macro-elements and micro-elements that are important for human nutrition and health are also present in common bean seeds, such as Ca, Cu, Mg, Mn, K, P, and Se . In the Sodium showed the highest heritability, and also significant differences among the accessions. Na is needed for humans, as it is involved in several physiological functions; however, it is well documented that the Na consumption greatly exceeds the minimum daily required intake. Such excess of Na in the human diet can have several negative consequences on health , and thus beneficial effects can be obtained by reducing Na intake . IntereCalcium is a key nutrient for humans, as it is essential for many functions around human health, such as for bone health as it provides rigidity to the skeleton and calcium ions play a role in many metabolic processes. To reach proper Ca intake levels is essential to prevent skeleton system diseases such as osteoporosis and to reduce the risk of bones fractures . Ca intaAmong the micro-elements, very high heritability was seen for Se content, along with wide variability. Of interest here, for 16 of the 25 accessions no Se was detected, while the remaining accessions showed high Se content. Accessions ECa096 (landrace from Costa Rica) and ECe122 (cultivar from Germany) showed the highest Se contents . Four other accessions were characterized by high Se content . Celmeli et al.  reportedThe other micro-elements of Cu, Mn, Mo and Ni showed intermediate to high heritabilities and significant differences among the materials analyzed. The highest contents of each of these four microelements were seen respectively for accessions ECe092, ECa052, ECe234, and ECa095. Determination of the contents of such micro-elements is largely lacking in the literature. The highest Cu content of 16.5 mg/kg and Mn content of 34.8 mg/kg in the present study are similar to the contents reported by Brigide et al.  and PinhSeveral significant correlations were defined here for the nutritional elements. Considering those with significance level < 0.001, the positive correlations that involved the macro-elements and micro-elements were between: Mg and K; Mn and K; Mn and Ca and Fe and Mn ; Mo andThe data obtained in the present study can also be seen from another point of view: i.e., as the evolution of the common bean. One of the main evolutionary processes for common bean was its introduction, spread, and adaptation to European agro-ecosystems.Even if our sample is too small to have a complete picture of the effects of such process in the quality traits of common bean seeds, our findings (both the PCA and ANCOVA) represent a first observation that these events strongly affected not only common bean genetic diversity ,21, but Principal component analysis showed that the European accessions were more widespread compared to the American accessions, which suggests a higher diversity for seed morphology and chemical composition in the European common bean. This major diversity might have arisen in Europe because the frequency of hybrids between the Mesoamerican and Andean gene pool was more than four-fold that in the Americas, which appears to be due to breakdown of geographic barriers and co-cultivation of the Mesoamerican and Andean forms in close sympatry by farmers ,21. The The characterization of plant genetic resources is crucial for breeders, so that they can exploit the diversity to implement appropriate breeding programs. This was the main aim of the present study, where 25 common bean genotypes were analyzed for nutritional traits.One of the main outcomes of the present study is the high variability across these samples for almost all of the traits, and the identification of some very interesting genotypes when considering multiple traits. The Greek landrace ECe092 was characterized by the highest lipid, δ-tocopherol, protein, and Cu contents and very high γ-tocopherol, P, and Ni contents. The Spanish landrace ECe312 showed high γ-tocopherol, δ-tocopherol, protein, K, and Cu contents. The Mexican cultivar ECa095 showed high antioxidant activity and carotenoids, Ca, Mn, and Se contents, the highest content of Ni and low Na content. Focusing on the minerals, three accessions are particularly interesting for future breeding: (i) the Spanish landrace ECe234 showed the highest K and Mo contents and high contents of Mg, P, Fe, and Mn were observed for this accession too; (ii) the German cultivar ECe122 showed the highest Ca and Fe content and was among the highest for Mn, Se, and Mo contents; and (iii) the Italian landrace ECe177 was characterized by the highest P content and particularly high Ca and Mo contents and low Na content.The use of pure lines  and the evaluation of seeds obtained in a designed field trial with replicates allowed estimation of the heritabilities of the different nutritional traits. This parameter is important, inasmuch as it provides information about the breeding value of the sample. Not all of the traits showed intermediate to high heritability estimates, which indicates a relevant influence of environmental conditions for these nutritional compounds. This suggests the need to conduct more studies on the influence of the environment  on the expression of common bean nutritional traits, to identify the best agro-ecological conditions that can maximize the nutritional quality of the promising genotypes.Finally, this study points out the importance to take into account the evolutionary history of crops; indeed, even if more studies are needed in this regard, our findings suggest significant effects on diverse quality traits related to adaptation of the common bean in Europe, out of its centers of origin. Acquiring more knowledge of this evolutionary process can indeed guide geneticists and breeders in their choice of samples to be characterized for different traits.


Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels.This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.The online version contains supplementary material available at 10.1186/s13059-021-02398-9. Aging is associated with an increased risk of physical, cognitive, and degenerative disorders . While tThe first generation of epigenetic aging clocks used penalized regression models to predict chronological age on the basis of DNA methylation data, e.g., the widely used clocks from Hannum 2013) and Horvath (2013) apply to blood and 51 human tissues/cell types, respectively –14. A de013 and Hn = 13,493) revealed that metabolic and immune pathways share genetic underpinnings with epigenetic clocks . I. I44]. IP < 0.05) pairwise genetic correlation coefficients ranging from r = 0.28 (GrimAge acceleration and IEAA) to r = 0.66  Score regression . The GWAP < 0.05/693 = 7.22 × 10−5) is presented in Fig. For each epigenetic biomarker, large-scale genetic correlation analyses were conducted with 693 different traits. A selection of significant associations after Bonferroni correction for multiple testing  . Independent lead SNPs had P < 5 × 10−8 and were independent of each other at r2 < 0.6; lead SNPs within this subset were required to have r2 < 0.1. A locus was defined by considering lead SNPs in a 250-kb range and all SNPs in LD (r2 ≥ 0.6) with at least one independent SNP.MR-MEGA summary statistics were uploaded to the Functional Annotation of Meta-Analysis Summary Statistics (FUMA) (http://fuma.ctglab.nl) for further annotation and functional analysis [P < 2.69 × 10−6 was set for the gene-based GWAS. Genes annotated to significant GWAS loci were further investigated using the “GENE2FUNC” tool in FUMA for enrichment of GWAS catalog gene sets, omitting the MHC region [P value thresholds were applied.The European ancestry and African American meta-analysis summary statistics were uploaded to FUMA  then annotated to categories . Statistical enrichment was then carried out for SNPs at two P value thresholds (P < 1 × 10−5 and P < 1 × 10−8) while accounting for MAF, distance to the nearest TSS, and number of LD proxies.To test if the GWAS meta-analysis findings were associated with regulatory and functional features of interest, enrichment analyses were conducted using GARFIELD . SNPs wehttps://mrcieu.github.io/gwasglue/) to extract SNPs that were +/− 1 Mb of the lead SNP and to harmonize the datasets. For each epigenetic biomarker–molecular trait pair (or pair of epigenetic biomarkers), we then performed colocalization analysis using the coloc.abf function in the R/coloc package [HLA region from the eQTL colocalization analysis.We hypothesized that some of the lead loci from the meta-analyses will have shared variants (1) across the Epigenetic biomarkers, (2) with DNAm sites in blood, and (3) with gene expression levels in blood. We used GoDMC summary statistics on 190,102 DNAm sites  to exami package , using dP values were corrected for multiple testing with two rounds of permutation in INRICH.The potential role of Mendelian disease genes and associated pathways in influencing the epigenetic biomarkers was investigated with MendelVar , indepenhttps://data.broadinstitute.org/alkesgroup/LDSCORE/), was used to assess genetic correlations between the six epigenetic biomarkers. Genetic correlations were further assessed between the six epigenetic biomarkers and publicly available GWAS summary statistics using the LDHub web interface (http://ldsc.broadinstitute.org/ldhub/) [P < 0.05/693 = 7.21 × 10−5 was applied.LD score regression, using LD scores and weights estimated from European ancestry populations (downloaded from /ldhub/) . Meta-anp = 1; clump-r2 = 0.25) [P value thresholds were set at < 5 × 10−8, < 0.01, < 0.05, < 0.1, < 0.5, and 1. Linear regression models were built to calculate the incremental R2 between the null model (epigenetic biomarker~ sex) and full model (epigenetic biomarker ~ sex + polygenic risk score) in the test cohort. The procedure was iterated after excluding different test cohorts one by one  from the meta-analysis. Finally, these steps were repeated, using the full meta-analysis summary statistic output to generate polygenic risk scores in a completely independent cohort .To determine the proportion of variance in the six epigenetic biomarkers that can be explained by common genetic variants, we carried out a polygenic risk score analysis using results from the European ancestry meta-analyses. Weights for the additive genetic scores were created by re-running the meta-analyses excluding one cohort (test cohort) at a time. Six weighted PGR scores (one for each epigenetic biomarker) were generated using default settings of the PRSice software  . P valueFor Born in Bradford, ARIES, and SABRE, best-guess genotype files with a MAF cut-off of 1% and info score > 0.8 were generated and the polygenic risk score analyses were corrected for 20 genetic PCs.P < 5 × 10−8 or P < 1 from each of the six GWAS meta-analyses (http://mrcieu.mrsoftware.org/PRS_atlas/) [P value thresholds were based on the leave-one-out cohort PRS analyses described above . An FDR-corrected P value (PFDR < 0.05) was applied separately to each set of PheWAS results.A phenome-wide association study of 581 heritable traits from the UK Biobank study was then carried out for polygenic risk scores based on independent SNPs with _atlas/) . The P v2 < 0.001. Harmonization of the SNP effects with the exposure and outcomes were performed so that palindromic SNPs were aligned when minor allele frequencies (MAFs) were < 0.3 or were otherwise excluded.To investigate if the epigenetic biomarkers were (i) causally influenced by lifestyle factors and (ii) had a causal effect on aging and disease outcomes, Mendelian randomization (MR) was performed in MR-Base . The epiP value < 0.05. Where there was evidence for a causal effect based of the IVW model, we also assessed the potential for horizontal pleiotropy by means of heterogeneity assessment (Cochran’s Q-statistic) of individual SNP effects in both IVW and MR-Egger analyses and the Egger intercept test for directional pleiotropy [Inverse variance weighted (IVW) MR was carried out as the main analysis, with pleiotropy-robust sensitivity analyses featuring MR-Egger , weighteeiotropy .Additional file 1. Individual cohort descriptions and acknowledgements.Additional file 2. Supplementary Tables -Tables S1-S31.Additional file 3. Supplementary Figures - Figures S1-S31.Additional file 4. Assessment of genomic inflation and heterogeneity.Additional file 5. Colocalization plots.Additional file 6. Review history.


The interactions between SNPs, which are known as epistasis, can strongly influence the phenotype. Their detection is still a challenge, which is made even more difficult through the existence of background associations that can hide correct epistatic interactions. To address the limitations of existing methods, we present in this study our novel method MIDESP for the detection of epistatic SNP pairs. It is the first mutual information-based method that can be applied to both qualitative and quantitative phenotypes and which explicitly accounts for background associations in the dataset.The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens. The development of high-density arrays for genotyping in recent years has allowed genome-wide association studies (GWAS) to become powerful tools for the detection of single-nucleotide polymorphisms (SNPs) that are associated with traits of interest. However, GWAS methods are usually based on the analysis of single loci, ignoring the potential interaction between genes, and are therefore of limited applicability for traits that are controlled by multiple genes with possibly complex interactions ,2,3. TheDue to the large number of possible combinations of SNPs even if only pairwise interactions are considered, the detection of epistasis is a computational challenge, for which a large number of algorithms have been proposed. These methods can be divided into different categories depending on their search strategy. Exhaustive search strategies test every possible combination of SNPs for significance, which often results in a long execution time and can become infeasible for large datasets. This strategy has been used by partitioning methods such as the Combinatorial Partitioning Method (CPM)  and the Several of these methods use information-theory-based measures such as mutual information to quantify epistatic interactions ,44,45,46Furthermore, the methods mentioned above do not take into account different types of obstacles resulting from sample structure, relatedness between the genotyped individuals or marginal effects of single SNPs on the phenotype ,50,51. Skth-nearest neighbor-based approach. Moreover, to deal with the possible obstacles inside a genotype–phenotype dataset (as mentioned above), our method incorporates an additional step using the average product correction (APC) theorem [In this paper, we propose a novel method called Mutual Information-based Detection of Epistatic SNP Pairs (MIDESP) for the detection of pairwise epistatic interactions, which extends the previously mentioned mutual information-based approaches by additionally enabling the identification of epistatic interactions between SNP pairs and quantitative phenotypes. For this purpose we adopt, in the context of epistasis for the first time, the mutual information estimator developed by Ross , which a theorem  to estimIn order to demonstrate the performance and functionality of MIDESP, we applied it on two different types of genotype–phenotype datasets. The first type contains several hundred simulated datasets, which we analyzed to optimize the parameters used in the mutual information estimator. On the other hand, the second type contains two further datasets with a qualitative and a quantitative phenotype, respectively. While the dataset with the qualitative phenotype is related to bovine tuberculosis, the other one contains the egg weight of chicken eggs. Our findings show that we are able to successfully reduce the influence of background associations in the prediction of epistatic interactions, which leads to the identification of novel markers/genes that are important to the phenotype of interest.p–value We analyzed two different datasets, one of which had a qualitative (discrete) case–control phenotype, and the other one had a quantitative (continuous) phenotype. To ensure the data quality, we applied several filters to the datasets following Ramzan et al. ,55. We rrough LD . Using Prough LD , we remoIn the following section, we briefly describe the datasets. Researchers interested in more details about the bovine tuberculosis data are referred to  and abouThis dataset was published by Bermingham et al.  and cons® 600 K Chicken Genotyping Array, resulting in an initial set of 580,961 SNPs, which were then filtered. The dataset which was provided by the authors only consists of the 294,705 SNPs that passed their quality filters. We could not remove any further SNPs using our filters.The dataset relates to the egg weight (EW) in 36-week-old chickens belonging to a line of Rhode Island Red chicken . While tM at position X and Y as placeholders for any of the SNPs or phenotypes, respectively.Based on the number of samples, An overview of the MIDESP pipeline is shown in X, with alphabet X and Y with alphabets X and Y is defined asIn information theory, the entropy, ariables . The mutariables .X and a continuous variable Y the kth-nearest neighbor-based method developed by Ross [x is the same as the genotype d is the distance between sample kth-nearest neighbor d, irrespective of the genotypes.However, Equation  is not a by Ross , which hThe identification of these values is shown for a small exemplary dataset in Y is a continuous variable, hence in general, we can reuse the sorted tables for every SNP by only changing the values of X. This allows for an efficient calculation of As shown in Y, has been successfully identified by employing the MI metric for which Equation γ^=nugligible ,67. Thesgligible .X is deemed as significant if its p–value is less or equal to Finally, an SNP Equation .(7)FDR^For the detection of epistatic interactions using the As shown in previous studies ,50,51, aTo eliminate these issues to some extent, in our study, we applied the average product correction (APC) introduced by Dunn et al. . The APCh is a sufficiently large number  . The datase 103) . The numested in .To elucidate the biological functions of these genes, we followed previous studies ,72 and uhttps://github.com/FelixHeinrich/MIDESP (accessed on 14 September 2021), allowing for easy usage. The calculations were completely parallelized, allowing for an efficient detection of significant epistatic interactions with a multi-core CPU. Genotype and phenotype information in the form of tped and tfam files were required as input.The MIDESP pipeline was implemented in Java and is available as a JAR file from In this paper, we introduce a novel information-theory-based method, MIDESP, for the detection of epistatic interactions using genotype–phenotype datasets. MIDESP is able to analyze both qualitative as well as quantitative phenotypes, unlike previous information theoretical methods ,44,45,46k used in Equation  phenotypes ,75,76,77quations  and 4)))k, whichite Ross  and Krasite Ross  both rec>0.1 see . FurtherY. Additionally, based on the chain rule of information [Y, thus increasing the MI between Y. However, if the introduction of In information theory, mutual information (MI) is typically measured between two variables, ormation , it is waffected .Y should be taken into account for the detection of epistatic interactions. The reason for this is exemplified in Y have the maximum MI value of 1, indicating their perfect association. On the other hand, Y. Applying Equation  theorem [In case of SNP×phenotype associations, this property of the MI needs to be considered since only the introduction of an additional  theorem , which eY  are powerful approaches for the detection of epistatic interactions ,44,45,46lity see , althoug purpose ,79.The progress over the last decade in the field of genome sequencing and genotyping arrays has increased the amount of available genotype data tremendously. With the ever-increasing amount of data, however, comes the challenge to provide tools that can handle such datasets in a feasible computation time. To address this challenge, redundant SNPs can be removed through LD pruning with a high threshold  see Sec but therHowever, the sole consideration of SNPs with strong association signals could lead to a wrong interpretation in epistasis analysis since the The results we present in this study for the two different genotype–phenotype datasets show that the functional analysis of the detected genes provides essential information to decipher the genetic background of the traits under consideration. Surprisingly, we were able to clearly identify higher numbers of associated genes for the bovine tuberculosis dataset with a qualitative trait than for the egg weight dataset with a quantitative trait. The reason for this can be explained due to the large difference in the initial numbers of SNPs in both datasets see . In compTo further investigate the impact of the APC theorem in the epistasis analysis and to gain more insight into its influence on the detection of genes, we analyzed both datasets with and without the application of the APC see . It can kth-nearest neighbor-based estimation technique. Another important challenge for the detection of epistatic interactions is the control of the effect of background associations in the genotype–phenotype datasets, which lead to false interpretation and thus the overestimation of the role of some SNPs in the epistasis. To deal with this issue, in our proposed method, MIDESP, we additionally modeled these background associations by adopting the APC theorem, which we extended for the multivariate mutual information. Our findings show that the MIDESP algorithm is applicable to genotype–phenotype datasets with qualitative as well as quantitative phenotypes in a tractable computational time. For example, the analysis of the BT dataset took only 36 minutes, while the analysis of the EW dataset was completed in 105 minutes. These runtimes were achieved on a dual Intel® Xeon® Gold 6138 Processor using 70 threads. Our results further indicate that the biological processes of the identified genes in the BT and EW datasets are strongly related to both bovine tuberculosis and the egg weight of chickens, respectively. To the best of our knowledge, MIDESP is the first method that models epistatic interactions using the MI metric for both qualitative and quantitative phenotypes and explicitly corrects for background associations. The program is written in Java and is freely available as a JAR file from https://github.com/FelixHeinrich/MIDESP, accessed on 14 September 2021.Today, it is well established that MI-based methods are suitable and effective approaches for the detection of epistatic interactions for qualitative phenotypes. However, these approaches are not directly applicable for quantitative phenotypes, although epistatic interactions for quantitative traits are of great interest in life sciences. To address this limitation of the existing MI-based methods, we extend their applicability for the first time in this regard to quantitative phenotypes using a


Knowledge of genetic parameters is essential to obtain breeding values in order to increase the response to selection and incorporate novel traits in designing a breeding program. There is a growing demand for the genetic improvement of carcass traits in the Korean beef industry. The use of yearling ultrasound measurements as indicator traits can be an efficient way to evaluate carcass traits. To date, the assessment of genetic parameters for ultrasound measurements in Hanwoo cattle is still limited. Therefore, this study was conducted to estimate the heritability, and the genetic and phenotypic correlations of yearling ultrasonic and carcass traits in Hanwoo cattle. The results revealed moderate to high heritability estimates for the traits of interest, which indicate a probable increase in the response to selection for these traits. Moreover, high and favorable genetic correlations were observed between carcass traits and their corresponding ultrasound measurements. Our findings suggest that the inclusion of yearling ultrasound data on potential replacements would be suitable as a selection tool for genetic improvement of carcass traits in Hanwoo breeding programs.Genetic parameters have a significant role in designing a breeding program and are required to evaluate economically important traits. The objective of this study was to estimate heritability and genetic correlation between yearling ultrasound measurements, such as backfat thickness (UBFT), eye muscle area (UEMA), intramuscular fat content (UIMF), and carcass traits, such as backfat thickness (BFT), carcass weight (CW), eye muscle area (EMA), marbling score (MS) at approximately 24 months of age, as well as yearling weight (YW) in Hanwoo bulls  and steers (5682). The (co) variance components were estimated using a multi-trait animal model. Moderate to high heritability estimates were obtained and were 0.42, 0.50, 0.56, and 0.59 for CW, EMA, BFT, and MS, respectively. Heritability estimates for yearling measurements of YW, UEMA, UBFT, and UIMF were 0.31, 0.32, 0.30, and 0.19, respectively. Favorable and strong genetic correlations were observed between UIMF and MS (0.78), UBFT and BFT (0.63), and UEMA and EMA (0.65). Moreover, the estimated genetic correlation between YW and CW was high (0.84) and relatively moderate between YW and EMA (0.43). These results suggest that genetic improvement can be achieved for carcass traits when using yearling ultrasound measurements as selection criteria in ongoing Hanwoo breeding programs. Carcass and meat quality traits are a comprehensive term used to describe consumer perceptions of meat and are important for determining the economic profits of the beef production system. Genetic improvement of carcass traits has been a great concern in the beef cattle industry as they are expensive and difficult to measure, and can only be measured postmortem. An efficient way to evaluate carcass traits is the use of ultrasound measurements as an indicator trait in live animals. Since 1990, with the development of ultrasound technology, it has become possible to collect carcass measurements without the need to slaughter animals for recording their phenotypes  stated tIn recent years, beef cattle have been marketed based on carcass characteristics to meet consumer demands. In the Korean beef market, demand for high-quality animal protein is increasing, bringing meat quality into the spotlight of this industry. The Hanwoo, which is indigenous and unique to Korea, is very popular due to its rapid growth, characteristic bountiful marbling of meat, and quality attributes such as tenderness, juiciness, and good flavor . The Hanhttps://www.cuplab.com; accessed on 12 July 2007) provided the UBFT, UEMA, and UIMF phenotypes. In addition, YW for each animal was obtained from the weight (Wt) at the termination (t) of the test (body weight at ~12 months) and the previous weight (Wt−1) recorded at a time point (t−1) before (body weight at ~6 months) the termination, according to the equation described by Park et al. [Phenotypic records, including carcass traits, yearling body weight, and yearling ultrasound measurements, were selected for analyses. The dataset used in this study was provided by the Hanwoo Experiment Station, National Institute of Animal Science (NIAS), Rural Development Administration, South Korea, and included 15,796 for YW , 8945 for ultrasound data (3627 steers and 5318 bulls), and 5622 Hanwoo steers, which were born between 1997 and 2017. The pedigree data were obtained from 54,284 animals. Descriptive statistics for the phenotypic dataset used for the analyses are shown in The variance components and heritability of the traits were estimated through the classical Bayesian inference under a pedigree-based multi-trait animal model in the gibbsf90test software  as folloIn this study, it was assumed that random effects of u and e followed the multivariate normal distribution with the mean of zero and Var (u) = G⊗A and Var(e) = R⊗I, in which A, G, R, and I are the matrices of the numerator relationship, (co) variances of additive genetic effects, residual, and identity, respectively, as well as ⊗ is the Kronecker product. Moreover, prior distributions of G and R were defined as a flat prior according to the software default. In addition, genetic and phenotypic correlations and their corresponding standard deviations were obtained from multi-trait analyses.The Markov Chain Monte Carlo (MCMC) method was used to estimate variance components and heritability with 50,000 iterations as burn-in with a thinning interval of 50 in 550,000 cycles. In addition to these criteria, the convergence of the chain was evaluated by visual inspection of the trace plots. After verification of the convergence of the Gibbs chain, the variance components and correlations were estimated as the posterior means of the corresponding sampled values. This study was conducted to estimate the genetic parameters of yearling ultrasound measurements and their correlations with carcass traits in Hanwoo beef cattle. The descriptive statistics for the carcass, yearling body weight, and ultrasound traits are shown in The genetic and phenotypic correlations between yearling body weight, ultrasound, and carcass traits are presented in The estimated genetic correlations among the carcass traits were of low (close to zero) to moderate magnitude, ranging from 0.03 ± 0.07 to 0.56 ± 0.05. As shown in Analysis of the estimated correlations among YW and carcass traits revealed that the highest genetic and phenotypic correlation occurred between YW and CW, at 0.84 ± 0.02 and 0.78 ± 0.01, respectively. Consequently, the selection for increased YW will result in favorable correlated responses in CW. Similar results have been reported by Choi et al.  and ElzoThe estimates for the genetic correlation between yearling ultrasound and carcass traits were very low or near zero to strong and positive, and varied from −0.01 ± 0.07 to 0.78 ± 0.04. Concerning the correlations between corresponding ultrasound measures and carcass traits, the highest genetic correlation was observed between UIMF and MS (0.78 ± 0.04), followed by the correlation between UEMA and EMA 0.65 ± 0.05), and between UBFT and BFT (0.63 ± 0.05). The strong and positive genetic correlations between carcass traits and their corresponding ultrasound measurements reaffirm the usefulness of ultrasound measurements as indicators of carcass traits for informing genetic evaluation programs of beef cattle. Similar estimates of the genetic correlation between UIMF and MS were found by Wilson et al.  for Angu ± 0.05, The phenotypic correlations between ultrasound and carcass traits generally showed the same tendency as the genetic correlations but were of lower magnitude, ranging from almost zero to moderate. The largest phenotypic correlations were observed between UEMA and YW 0.51 ± 0.01), UEMA and EMA 0.48 ± 0.01), UEMA and CW (0.42 ± 0.01), UIMF and MS (0.42 ± 0.01), and between UBFT and BFT (0.40 ± 0.01). The phenotypic correlations in this study were within the range of values estimated previously in multibreed Angus-Brahman , Angus ,8, Brang ± 0.01, 1 ± 0.01,The results of this study demonstrate that carcass and yearling ultrasound traits are heritable, and the sufficient additive genetic variability observed for these traits indicates that the selection for these traits can be effective. In addition, high and favorable genetic correlations between carcass traits and their corresponding ultrasound measurements suggest that the inclusion of yearling ultrasound data as a selection tool can provide a good opportunity for genetic improvement of carcass traits in Hanwoo breeding programs. Therefore, knowledge of the genetic parameters of the ultrasound-measured traits can be effective to obtain more accurate breeding values to increase the response to the selection and reduce the expense and generation interval, which in turn could lead to considerable profitability in the Hanwoo beef industry.


Observational studies on the general population have suggested that airflow obstruction associates with left ventricular (LV) filling. To limit the influence of environmental risk factors/exposures, we used a Mendelian randomisation (MR) approach based on common genetic variations and tested whether a causative relation between airflow obstruction and LV filling can be detected.We used summary statistics from large genome-wide association studies (GWAS) on the ratio of forced expiratory volume in 1 s to forced vital capacity (FEV1/FVC) measured by spirometry and the LV end-diastolic volume (LVEDV) as assessed by cardiac magnetic resonance imaging. The primary MR was based on an inverse variance weighted regression. Various complementary MR methods and subsets of the instrument variables were used to assess the plausibility of the findings.We obtained consistent evidence in our primary MR analysis and subsequent sensitivity analyses that reducing airflow obstruction leads to increased inflow to the LV . Sensitivity analyses indicated a certain extent of negative horizontal pleiotropy and the estimate from biased-corrected MR-Egger was adjusted upward . Prioritisation of single genetic variants revealed rs995758, rs2070600 and rs7733410 as major contributors to the MR result.Our findings indicate a causal relationship between airflow obstruction and LV filling in the general population providing genetic context to observational associations. The results suggest that targeting  airflow obstruction can lead to direct cardiac improvements, demonstrated by an increase in LVEDV. Functional annotation of single genetic variants contributing most to the causal effect estimate could help to prioritise biological underpinnings.The online version contains supplementary material available at 10.1186/s12931-021-01795-9. Observational studies based on the general population have consistently demonstrated that increasing airflow obstruction can be associated with reduced left ventricular (LV) filling –3. The rPulmonary and cardiac function variables are complex traits and large, population-based genome-wide association studies (GWAS) have provided multiple independent associations between genetic variation (mostly single-nucleotide polymorphism [SNP]) and quantitative measurements across the genome –8. The pUsing MR, we tested whether airflow obstruction may be casually linked to LV filling in large general population datasets.–9)  score of 3.5) but influences protein abundance [HTR4 gene encoding the 5-hydroxytryptamine (5-HT)/serotonin receptor 4 (HTR4). Mice deficient for this protein (HTR4−/−) were found to have increased airway resistance and exhibit increased methacholine-induced airway hyperresponsiveness [Assessing the individual SNPs in relation to the overall estimate could elucidate molecular underpinnings of the observed causal relationship between FEV1/FVC and LVEDV. Nineteen SNPs exhibited a significant effect in single SNP MR and shared the effect direction of the overall result. The top three SNPs with major contribution to the overall effect were rs995758 , rs2070600  and rs7733410 . When leaving one of these three SNPs out, the effect of the overall MR-IVW was reduced by 17%, 13% and 9%, respectively. These loci have not only been linked to pulmonary function parameters such as FEV1/FVC  and airwbundance . Interesbundance . The solbundance . The varsiveness . Since Hsiveness .We acknowledge as limitation that we cannot fully exclude residual confounders. Common diseases or risk factors potentially affecting FEV1/FVC and/or LVEDV could be present in a subgroup of individuals from the general population and could provoke a systematic bias, particularly in settings when pleiotropic effects of multiple genetic variants act through the same confounder . By excluding all variants with known GWAS signals in a sensitvity analysis, we sought to control for this scenario. In addition, overlapping datasets for the exposure and outcome variable could cause potential overfitting. In a sensitivity analysis, we therefore selected variants associated with FEV1/FVC in the smaller subpopulation of the GWAS meta-analysis (SpiroMeta consortium population) .In summary, our present study provides genetic evidence for a causal link between airflow obstruction and LV filling by investigating the largest-to-date GWAS on FEV1/FVC and LVEDV in the general population. Our results suggest that even subclinical reduction in airflow impairs LV function, and that targeting airflow obstruction can lead to direct cardiac improvements, demonstrated by an increase in LVEDV. Functional annotation of single SNPs contributing most to the causal effect estimate could help to prioritise biological pathways for biomarker-discovery and therapeutic intervention.Additional file 1: Fig. S1. Variants filtering steps applied. Blue boxes indicate the set of IV used to estimate the causal effect. Fig. S2. Funnel plots on the three different IVs used to estimate the causal effect. Point estimate from IVW and MR-Egger displayed.Additional file 2: Table S1. Detailed information on the IV used in the MR analyses. Table S2. Intercept from Egger-regression analyses. Table S3. Results from single SNP MR based on the ratio estimate and from leave-one-out MR based on the IVW method. Table S4. Association of the IV with potential covariates retrieved from a curated database of human genotype-phenotype associations. P-value of association displayed.


Gene–environment interaction (GEI) studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases and traits. However, commonly used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples.Here, we develop a new software program, GEM , which supports the inclusion of multiple GEI terms, adjustment for GEI covariates and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic main and interaction effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352 768 unrelated individuals from the UK Biobank, identifying 24 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of the genetic architecture of complex diseases and traits.https://github.com/large-scale-gxe-methods/GEM.GEM is freely available as an open source project at Bioinformatics online. Genome-wide association studies (GWAS) have successfully led to many discoveries that associate genetic alleles with complex human diseases. However, many complex diseases are also influenced by non-genetic risk factors, such as lifestyle habits (e.g. cigarette smoking), environmental exposures (e.g. toxin), physiological effects (e.g. obesity) or treatment interventions (e.g. daily aspirin therapy). Most GWAS do not address the scientific question regarding how genetic effects on complex diseases may depend on these non-genetic factors. Gene–environment interaction (GEI) studies characterize the interplay of genetic and non-genetic effects on complex diseases. GEI models can further identify genetic variants with heterogeneous effects in different age, sex/gender or race/ethnic groups, improving our understanding of the genetic architecture of complex diseases and traits in the context of non-genetic risk factors that contribute to health disparities.All of Us Research Program (anticipated N ∼ 1 000 000), the U.K. Biobank (N ∼ 500 000) and the Million Veteran Program . While such big genomic data provide excellent opportunities for GEI studies, tools for GEI analyses have remained static and have not adapted to the needs of modern epidemiological investigations. Several challenges to GEI studies were articulated in a recent review . Our tool leverages a matrix projection algorithm to conduct approximate inference while accommodating important additional capabilities including robust standard errors and multiple GEI terms. The aims of this paper are to introduce the GEM methodology, benchmark GEM against existing software tools for GEI and demonstrate gains in computational efficiency from both the GEM method and our multi-threading software implementation, and finally demonstrate the use of GEM in a biobank-scale, genome-wide interaction study of waist-hip ratio, an anthropometric trait with known differences in genetic architecture between men and women  are distributed on github.com/large-scale-gxe-methods and the Dockstore platform . Benchmarking runs all used a single thread for computation. Runs that took longer than 7 days or used more than 100 GB of memory were terminated and noted in the results and figures.P-values and main and interaction joint test P-values were compared.GEM results were compared to those of ProbABEL to assess whether there were meaningful differences in their statistical output, given that ProbABEL is a commonly used program that does not use a matrix projection approach. Summary statistics were retrieved for a random subset of 5000 variants  for both GEM and ProbABEL, and both interaction test UK Biobank is a cohort study containing deep phenotyping and genome-wide genetic data for approximately 500 000 participants living in the UK  to allow us to evaluate empirical type I error rates at genome-wide significance. Each phenotype was simulated to have 10% variance explained by the associated exposure and 10% variance explained by a random 100 variants from chromosome 22. In order to simulate exposure mis-specification, a fourth set of phenotypes was generated with the same amount of variance explained by a quadratic (rather than linear) effect of the continuous exposure. Genome-wide scans on ∼11.2 million variants for chromosomes 1–22 (MAF > 0.005) were run for each phenotype  using both model-based and robust standard errors. Type I error was then evaluated at genome-wide significance for chromosomes 1–20 (for which no genetic effect of any kind was present). In addition, potential inflation was visualized via quantile-quantile plots for chromosome 21 (for which a gene–environment correlation was present for a subset of variants), and chromosome 22 (for which a genetic main effect was present for a subset of variants).Phenotype simulations to evaluate power and type I error of the GEM method were carried out using R version 3.6.0 . A serieK) {1, 2, 5, 10} , microarray used for genotyping (array) and body mass index (BMI):To illustrate the performance of GEM for use in a biobank-scale, genome-wide interaction study, we conducted a gene-by-environment interaction study of the differences in waist-hip ratio (WHR) between men and women in unrelated, British-ancestry UK Biobank participants. We used sex (confirmed by genotype) as our exposure of interest. WHR was inverse-normal transformed prior to analysis. Additional covariates included age, ageBMI in this model. An additional analysis was performed in which age, coded as a binary variable of less than or greater than 50 years old, was added as a covariate and additional The significance of the interaction effect was derived from the P < 5 −8 were used as index variants, and additional variants with P < 0.05, within 250 kb of the index variant and having LD r2 > 0.2 with the index variant were merged into a single locus. We used this LD-based procedure to obtain the list of significant loci from the https://github.com/lindgrengroup/fatdistnGWAS; accessed April 13, 2020) for genome-wide significant marginal genetic effects in the entire sample, the female sample and the male sample. A subset of these variants were noted as having effect differences between males and females at a significance threshold adjusting for the number of index variants tested for interaction  We re-derived these genome-wide tests for sex dimorphism using the EasyStrata R package  in either the P-values at index variants for each of these loci were compared to those from the primary analysis.To better understand the influence of including the To address concerns about genomic inflation, and considering prior conclusions that GEI analyses require approximately four times the sample size for equivalent power to a marginal genetic analysis of equal magnitude , the UK −8) for genetic variants on chromosomes 1-20 for which no genetic effects were simulated . We also show the results of equivalent runs in terms of run time  standard errors . We alsorun time  and memorun time  using dirun time . Though run time . Memory run time . We furt−8, 257 independent loci were significant using the joint test (Supplementary P = 2.4×10−9), in an intron of PDE5A. When applying a more stringent multiple-testing threshold as in the meta-analysis (P < 5×10−9), 11 of these novel signals remained. Many variants showed a much lower association P-value from the joint test compared to that from the marginal test of genetic effect , and 6 were not previously reported as sex dimorphic in P-value was found on chromosome 2 at rs13389219 (P = 2.6×10−71), near the SLC38A11 gene. As shown in Supplementary Figure S10 and Supplementary In addition, 54 independent loci were uncovered using the P-values of the We also evaluated the impact of the interaction covariate  (One major advantage of GEM is its ability to incorporate multiple interaction terms. This enables proper control for genotype-covariate effects  as well s’ test) . This suIn benchmarking tests using simulated data, GEM demonstrated the feasibility of genome-wide GEI scans in large-scale cohorts of up to millions of samples. For example, it took less than one hour to conduct a robust analysis of a continuous phenotype for 100 000 variants in 1 million samples using a single thread, while requiring approximately 1 GB of memory. Based on the benchmarking results and current program specifications , we founSLC38A11 gene which has been previously linked to impedance-based body fat distribution in the UK Biobank  and workflows  available in publicly available repositories. GEM facilitates the next generation of large-scale and consortium-based GEI studies and thus enables important discoveries for genomic understanding and personalized medicine.In summary, we have described a new software program, GEM, for conducting genome-wide GEI testing in datasets of up to millions of individuals, while allowing for multiple exposures and robust standard errors. We have made the software  to provide a cloud data management and computing environment for large genomic data projects.D.T.P., L.H., H.C. and A.K.M. developed the GEM algorithm. D.T.P., L.H. and H.C. implemented the GEM software program. K.E.W., D.T.P., Y.C., M.S.-G and A.K.M. implemented software programs as cloud workflows. K.E.W., H.C. and A.K.M. designed the simulations and experiments. K.E.W. and D.T.P. carried out the simulations and data analyses. Y.J.S., Y.V.S. and A.C.M. provided guidance on experimental design and data analyses. K.E.W., H.C. and A.K.M. wrote the manuscript. All authors critically read the manuscript.This work was supported by National Institutes of Health (NIH) [grant number R01 HL145025]. The Analysis Commons was funded by NIH [grant number R01 HL131136].Conflict of Interest: none declared.btab223_Supplementary_DataClick here for additional data file.


Prunus salicina L.) accumulate more than 40 phenolic compounds, with a remarkable diversity in their profiles, depending on the variety and environmental conditions. Although candidate genes have been indicated to control this trait, the loci controlling its phenotypic variation have not yet been defined in this species. The aim of this work was to identify the quantitative trait Loci (QTL) controlling the phenolic compounds content in the Asian plum skin and flesh. Using UHPLC-DAD-Orbitrap-MS, we determined that cyanidin-3-glucoside and cyanidin-3-rutinoside are the main anthocyanins in Asian plums. Other anthocyanins found to a lesser extent were tentatively identified as cyanidin bound to different sugar and procyanidin moieties. Then we phenotyped fruits of 92 and 80 F1 seedlings from the cross < “98.99” × “Angeleno”> (98 Ang) for two harvest seasons. We used HPLC-DAD to quantify single anthocyanin and spectrophotometric techniques to determine the total content of phenols, flavonoids, procyanidins, and antioxidant activity (DPPH and FRAP). To determine the phenotype-genotype association of phenolic compounds content, phenotypic values (adjusted by linear mixed-effects models), genotypic data and linkage maps were analyzed with the multiple QTL model (MQM) approach. We found a total of 21 significant trait-marker associations: 13 QTLs segregating from “98.99” and 8 QTLs from “Angeleno.” From these associations, 8 corresponded to phenolic compound content in the flesh and 13 in the skin. Phenotype variance was explained by the detected loci, ranging from 12.4 to 27.1%. The identified loci are related to the content of cyanidin-3-glucoside (LG4), cyanidin-3-rutinoside (LG4), total flavonoids and procyanidins (LG5 and LG8), and minor anthocyanin compounds (LG3 and LG4). These results will help improve the efficiency of breeding programs for the generation of Asian plum varieties with high phenolic compound content.Consumption of fresh fruit is known to protect against non-communicable diseases due to the fruit's content in compounds with an antioxidant capacity, among them is polyphenols. Asian plums ( Consumption of fresh fruits and vegetables is essential for healthy nutrition since they constitute notable sources of phytonutrients and bioactive compounds, in addition to the energy and fiber intake  is an economically important fruit tree species worldwide. According to FAOSTAT Database  and genomic dissection of several quantitative traits, including traits related to the synthesis of health-promoting compounds . QTLs for total phenols, flavonoids, anthocyanins, and antioxidant activity were reported for the peach cross < “Venus” × “Big Top”>  was a candidate for the control of (+)-catechin, (–)-epicatechin, and procyanidin oligomers biosynthesis, and hydroxycinnamate/quinate transferase (HCT/HQT) for the variation content of chlorogenic acid.The content of phenolic compounds in the Asian plums (as in other species) is a quantitative trait since it depends on the variety (genetic component) and the environmental conditions  is associated with loci of LG3 and LG4, explaining together around a 70% of the trait phenotypic variance  in the < “98.99” × “Angeleno”> progeny, using genotypic data and linkage maps already available , Trolox and Folin-Ciocalteu reagent were obtained from Sigma-Aldrich .Cyanidin-3-glucoside (C3G) and cyanidin-3-rutinoside (C3R) were purchased from Chengdu Biopurify Phytochemicals (China) and Extrasynthese (France), respectively. Merck  supplied sodium hydroxide, sulfuric acid, high-performance liquid chromatography (HPLC)-grade acetonitrile, acetic acid, formic acid, and methanol. All reagents were of an analytical grade or higher. Polyethylene membranes of 0.22-μm pore size were acquired from EMD Millipore . Gallic acid, catechin, dimethyl-amino-cinnamaldehyde (DMAC), FeSOTwo Asian plum cultivars (“98.99” and “Angeleno”) and 92 seedlings of the F1 progeny from their cross  were planted at the Rinconada de Maipú Experimental Station  in 2011. The maternal parent, “98.99,” is an early-medium maturing selection with red skin and yellow flesh of high organoleptic quality, while “Angeleno,” the paternal parent, has a late-medium maturity genotype with purple skin, yellow flesh, and excellent postharvest performance . Chroma (C* = [a*2 + b*2]5) and hue angle (H° = arctan [b*/a*]) were also measured with chroma-meter.The samples employed in this work corresponded to the same fruits used for the studies of phenological and quality traits, reported in Salazar et al. , 2020. THarvest time was determined when the fruits reached IAD values between 1 and 1.4 units and a texture close to 40 N  and flesh (10 g) were grounded in a laboratory mill  in liquid nitrogen (−196°C) until obtaining a homogeneous powder. Phenolic compound extraction was carried out by macerating samples in 10 ml of 80% methanol for 20 min on ice, followed by sonication for 20 min, and, finally, centrifugation at 8,000 g for 10 min at 4°C. Half of the obtained supernatant was concentrated in a 1:2 ratio to quantify phenolic compound families content, antioxidant activity, and single anthocyanins content. After concentration, the solutions were filtered (0.22 μm) and stored in amber vials at 4°C in darkness until analysis.Quantifications of phenolic compound families content and antioxidant activity were performed in a 96-well plate format. Three technical replicates were considered for each fruit replicate. Calibration curves included 30 technical replicates per point. In total, on each season, 18 determinations were made per individual . An Infinite M200 Pro NanoQuant 96-well plate spectrophotometer was used . Mother and working solutions were kept at 4°C in darkness.The total phenols (Phe) quantification was carried out with the Folin-Ciocalteu method adapted from Mubarak et al.  for 15 μThe total flavonoid (Flv) content was determined, using the method adapted from Alvarez-Parrilla et al.  to 15 μlTotal procyanidins (Pca) determination was performed according to the DMACA method  and DPPH approaches to consider two different chemical mechanisms of radical reduction/scavenging by fruit tissue extracts. The FRAP method was carried out, following the protocol adapted from Yang et al.  to a 2-μ6, 50 ms, 50 K, 30 and 80 V, N2, respectively. The MS data were processed, using Xcalibur software (Thermo Scientific). The peak identity was ascertained by evaluating the accurate mass, the fragments obtained in the collision cell, and the on-line UV spectra (200–600 nm).To identify the anthocyanins composition representative of the 98 Ang progeny, we prepared one mix of samples containing skin and flesh extracts from the parents and F1 individuals with contrasting color intensities. The analysis of the mixed sample was performed on an Acquity UHPLC system , coupled with an eLambda DAD (waters) and a High-Resolution Fourier Transform Orbitrap mass spectrometer, the/using the Exactive model , equipped with a HESI-II probe for ESI, operating in a positive mode and a collision cell (HCD). The operative conditions were as follows: spray voltage +4.0 kV; sheath gas flow-rate 55 (arbitrary units); auxiliary gas flow-rate 20 (arbitrary units); capillary temperature, 350°C; capillary voltage, 30 V; tube lens, 180 V; skimmer, 26 V; and heater temperature, 275°C. A BEH Shield C18 column  maintained at 50°C was used for the separation. The solution was centrifuged at 1,000 × g for 2 min, and 5 μL were injected into the UHPLC system. The flow rate was 0.5 ml/min, and the eluents were 0.01% trifluoroacetic acid in water (A) and acetonitrile (B). The UHPLC separation was achieved by the following linear elution gradient: 5–10% of B in 20 min, 10–20% B in 10 min, 20–45% B in 5 min, 45–90% B in 5 min, then 90–5% B in 1 min. The acquisition was made in the full-scan mode in the range (m/z)- 200–2,000 u, using an isolation window of ± 2 ppm. The AGC target, injection time, mass resolution, energy, and gas in the collision cell were 1 × 10After identifying the anthocyanins by UHPLC-DAD-Orbitrap-MS, we used cyanidin-3-glucoside (C3G) and cyanidin-3-rutinoside (C3R) as reference standards to determine the amount of anthocyanin in the extracts, using the HPLC-DAD technique, as reported by Peña-Neira et al. .To identify the peaks obtained in HPLC-DAD according to UHPLC-DAD-Orbitrap-MS results, we compared the profiles of the mixed sample (see section Anthocyanin Identification by UHPLC-DAD-Orbitrap-MS) obtained with both techniques and assumed a similar elution order of the different compounds among both chromatographic systems. The amounts of the anthocyanins that were not available as a reference standard were estimated, using the C3G calibration curve equation, and the results were adjusted by using a molecular weights ratio.U-test  was employed to compare progeny and phenotypic means of parents  and individual anthocyanins from HPLC. Previously available data for fruit quality traits of 98 Ang , year (seasons T1 and T2), and the fruit sample replicate as independent factors. We also included every non-ontologically related trait that significantly correlated with the trait being studied as a candidate covariate. The ANOVA was given by the general equation:yijk is the phenotypic value in the ith seedling in the jth year in the kh fruit replicate, u is the overall average phenotype, Gi is the effect of the ith genotype (seedling), Yj is the effect of the jth year, Rk is the effect of the kh fruit replicate, CovX is the effect of a candidate covariate, and εijk is the error term. The generic formula used in R was “aov(Trait~ Accesion + Year + RB),” adding the corresponding covariate. The broad-sense heritability (H2) of each trait was determined by dividing the genetic variance value of ANOVA by the total variance  were determined for every individual and trait through mixed linear models. The construction of these models began by including the fruit tissue replicate as a fixed variable and the “accession” (seedling) and “season” as random variables. We then iteratively included covariates that could increase the fit and improve the residual distributions. The determination of these covariates was based on the existence of significant correlations with the trait being studied. Each trait was analyzed, using mixed-effects linear models (MLM) with the R package “lme4”  was determined with the Permutation Test (PT), considering the genome-wide significance after 1,000 permutations, as implemented in the software. We used the Kruskal-Wallis non-parametric test and interval mapping (IM) to determine significant marker-trait correlations . Subsequently, we determined marker cofactors with “automatic cofactor selection” to finally perform multiple QTL mapping analysis (MQM) to reduce the linkage-derived residual variance of QTLs and to detect possible QTL interactions. Linkage groups and QTLs were plotted, using MapChart 2.32 software  and the genotypic data of the 98 Ang progeny  were found between phenol families and between C3G and C3R in both tissues and seasons.To investigate whether there were correlations among PCC traits, per season and tissue, we used the non-parametric Spearman method . The higThe correlations between flesh phenolic compound content in both seasons  were higAs for the flesh anthocyanins from season T1, the highest correlations (ρ > 0.7) were observed among C3G, C3R, and compounds D, E, R, and M. The correlation values among anthocyanin contents in T2 were lower than T1, where those with the highest correlations (ρ > 0.7) were C3G and C3R. Correlations between C3G and C3R were equal to 1 in both seasons. Compounds E and M correlated with a ρ = 0.8 in T1.p < 0.001) between anthocyanins and the other families of phenols and antioxidant activities, although lower than 0.6. The correlations between phenolic compound families presented values ρ ≥ 0.8, with some exceptions, for example, between the DPPH and the rest of the compound families (ρ > 0.6) in both seasons. Among the T1 skin anthocyanins, those with the highest correlations (ρ = 0.8) were C3G, C3R, and compounds D, E, and M. In contrast, in T2 skin, C3G and compounds E, T, and M correlated strongly. The correlation value between C3G and C3R almost reached a maximum (ρ = 0.9). Finally, both anthocyanins in T1 correlated with compounds D and E with a value ρ = 0.9, and, in T2, they correlated with compounds E with a value ρ = 0.8.Regarding the skin, in both seasons, there were higher correlations (ρ > 0.8) than those observed in the flesh, as well as significant correlations  but low correlation coefficients between data obtained in different seasons (ρ < 0.46). The highest correlations between seasons are found in flesh tissue and correspond to C3G, C3R, and total anthocyanins with values ρ ≥ 0.46. These data indicate moderate correlations between seasons for the analyzed single compounds and families, suggesting a medium and significant genetic effect, but a more substantial seasonal influence in the phenolic content in Asian plums of the 98Ang progeny.The correlation for each tissue among seasons was examined to estimate stability of PCC traits across different seasons, and thus understanding the influence of environmental variations . SpearmaTo determine if fruit quality traits influence PCC traits in the 98Ang progeny, we performed correlation analyses between previously available fruit quality traits  and fruit color parameters in both seasons and tissues were observed. Regarding the phenol families, these showed few correlations with the agronomic data for both tissues. Interestingly, in the flesh samples, we detected a negative correlation between total flavonoids and fruit weight (T1 and T2), suggesting that the flavonoid concentration is higher in seedlings with smaller fruits in this family. In skin samples of the T1 season, total phenols and FRAP were negatively correlated with color traits, and, together with DPPH, they correlated positively with the degree of color coverage in the fruit. With regard to the T2 season, only FRAP correlated negatively with color L and positively with coverage.The low inter-season correlations observed in Broad-sense heritability for the analyzed traits in the flesh reached a maximum of 36.3% in the case of total flavonoid content. On the other hand, the lowest heritability (2.5%) corresponded to the total procyanidins .In the skin, the highest heritability value corresponded (32.04%) to the C3R, while the lowest (2.4%) corresponded to FRAP antioxidant activity .The low heritability values observed in both tissues confirm a significant influence of environmental variables on the phenolic compound traits. Such an effect is higher in the skin than in the flesh, probably due to the former tissue is subjected to slight intraplant or intra-fruit environmental variations. In contrast, the moderate values in the traits Flv-fl, C3G-sk, C3R-sk, and compD-fl indicate that significant genetic components could be found in the QTL analysis.The corrected F1-individual phenotypic values (BLUPs) for each trait were used to analyze QTLs, jointly with the genotypic data and the genetic maps of each parent of 98Ang, generated by Salazar et al. . QTLs asIn the genetic map of “98.99,” 13 QTLs were detected, most of them corresponding to single anthocyanin content in skin, concentrating mainly on LG4 . QTLs frThe LOD value of QTLs segregating from “98.99” varied between 2.77 and 5.12, while QTLs from “Angeleno” varied in a range between 2.79 and 5.69. In both parents, the reported LOD value was higher than the LOD value limit determined by the Permutation Test (PT) in analyzing QTLs for each trait evaluated. The minimum LOD values ranged from 2.3 to 2.8, depending on the trait . Despite*) was located in the LG3, which reached a LOD value of 8.22 in the “Angeleno” parent, collocating with the SNP 3_17781392 . The procedures for organic extraction and analyses were chosen to satisfy the need to process a high sample quantity quickly and with good precision. For total phenols, the progeny mean values ranged from 27.68 to 126.33 and 125.42 to 449.66 mg GAE/100 g FW in flesh and skin tissues, respectively. According to Diaz-Mula et al.  and Gil The results obtained in this study were also in agreement with Speisky's et al. , where tstimuli that modify the phenolic compound content in fruits , similar to Salazar et al. , where QPrunus in different linkage groups, such as the peach (Prunus persica [L.] Batsch) in LGs 3, 4, 5, 6, and 7 , QTLs were found for red skin color in linkage groups 3, 6, and 7 , QTLs were identified in LGs 2, 3, and 6  derived from the cross between “Venus” and “Big Top,” where a QTL associated with total phenols in both parents was found in LG2 and 5 QTLs in the linkage groups 2, 3, 4, and 7 associated with the accumulation of flavonoids. In this regard, Abdelghafar et al. , we located a candidate gene in peach genome  coding for an AP2/ERF domain protein. The function and regulation networks of these transcription factors have been associated to the control of the abiotic stress response in apple by regulating the biosynthesis of procyanidins  Batsch and Prunus salicina L.Regarding single anthocyanins, QTLs were identified in linkage groups 1, 3, 4, and 7, concentrating mainly on LG3 and LG4, where the main anthocyanins, C3G and C3R, were found in the latter QTL. These results differ from previous reports , which could be important for assisted selection regarding fruit color. The regions found in this study provide a first step in unveiling the mechanisms involved in the regulation, biosynthesis, or transport pathways of phenolic compounds with antioxidant capacity. However, for a more precise dissection of the PCC traits, it will be useful for further studies to characterize the environmental sources of their variation, for example, including additional seasons or locations with available climatic data; also, to consider more crosses could possibly show the involvement of new loci-carrying variants with an impact on the phenotypic value for PCC traits, allowing to characterize the genetic architecture of phytochemical content in the Asian plum. Finally, after a transferability validation, these QTLs and linked markers could be considered as selection tools for managing phenolic compound content in new cultivars.In this work, we obtained results that indicate a high complexity in the genetic dissection of PCC traits in Asian plum due to a strong environmental influence on these traits. Despite these difficulties, we could define regions of the Asian plum genome that control the phenolic profile in the fruit. These QTLs explained between 12.4 and 27.1% of trait variance in a group of traits highly influenced by the environment. We also found QTLs in LG3 from “Angeleno” for external fruit color with a PEV of 32% (CThe original contributions presented in the study are included in the article/DV-S performed data analysis and wrote the manuscript. IP developed the concept of the work, applied for funding, coordinated experimental work, and supervised manuscript writing. RI developed the crosses and F1 populations and maintained them on the field in good condition for this study. JS coordinated harvest, quality analysis, and fruit sampling. DV-S, AS-G, CS-A, ÁP-N, and HM participated in the quantification and analyses of the compounds. CG performed MS compound identification. RI, PJ-M, BB, MG, and JS participated in editing and preparation of the final version of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genome-assisted prediction of complex  traits is an ingredient of “Genomic Selection,” a paradigm adopted successfully in animals and plants of agricultural importance. The approach has impacted the timing of selection decisions, and it has delivered improvements in the quality of predictions (“accuracy”) relative to what can be attained by the use of pedigrees and phenotypes. It has enhanced the rate of response to genetic selection and spectacularly so in dairy cattle, at least as suggested by genome-based estimates of genetic change. Researchers have spent much effort in developing and adapting prediction machines, and the author will focus on this matter, with mild excursions into tangential issues. The material is organized into nine sections and, since the author was to opine, it represents a set of personal views, rather than a review of literature, made retrospectively.1. Deconstruction of “genetic architecture”:Molecular genetics and biochemistry confirm that the theory of quantitative genetics provides just a linear  approximation to complexity with little (if any) mechanistic value. The intricate interactions and feedbacks inherent in biological systems cannot be captured by simple linear regressions, even if highly-dimensional regression models are fitted to the data. The effective dimension of a model cannot exceed the sample size. For example a model with 5 million parameters run with a sample size of 500 does not provide meaningful estimates of more than 500 distinct estimable functions of parameters: individual site effects are not likelihood-identified. The view that quantitative genomics can unravel the “genetic architecture” of complex traits by providing an inventory of allelic frequencies and allelic substitution effects, or by a decomposition of variance  is equivalent to stating that tons of bricks, steel, and glass can represent Zaha Hadid's new Beijing airport or Frank's Gehry's Guggenheim Museum at Bilbao. The author often refraines from using the buzz term “genetic architecture” and favores “statistical architecture” instead.2. Crumbs are not bread:The QTL paradigm [superseded by zillions of genome-wide association study (GWAS) in human genetics] has had a minor impact on agricultural practices , with few exceptions. GWAS with single-marker regression is also insufficient because it accounts for little genetic variation , apart from ignoring interactions as stated above. Although a more complex model may improve learning, the author has not seen reports where variable selection methods and members of the Bayesian alphabet capture signals much more effectively than a simple GWAS run with large samples, as in human genetics consortia. Shrinkage methods are typically “vector optimized” (with ridge regression notoriously so), and the borrowing of information facilitated by proper priors tends to make signals similar to each other. Bayesian variable selection (BVS) with spike-slab distributions may be more powerful, but signals from large-effect variants are strengthened at the expense of mitigating small effects. In BVS or LASSO, the “richer get richer and the poorer get poorer” whereas ridge regression is more “social democrat,” making effect-size estimates similar to each other. If a pizza for 500 persons is divided into 5 million unexpected guests, each will end up getting a crumb. The perception of the author is that advances in the resolution and causality of small-effect variants via GWAS and genome-enabled prediction have been marginal, at least in agriculture.3. Corroboration vs. induction:The main contributions of quantitative genetics (genomics) have been in description, prediction, and decision, e.g., selection choices, inbreeding management, and optimum contribution theory, as opposed to inference. In predictive approaches, genomic heritability or correlations take the role of “regularization knobs”  for constructing prediction machines. The objective is to make statements about yet-to-be-observed phenotypes based on some training data. Predictions can be calibrated empirically, but inferences cannot. How can one say that an estimate of an entelechy, such as heritability is bad or good? Following Descartes: “I cannot be observed, therefore, I do not exist.” According to Encyclopedia Britannica, for Descartes to prove that heritability exists, one must assume it does. For prediction, “there is no need for that hypothesis” as often attributed to Laplace.4. Occam's razor resurrected:A less recognized but important ingredient of the study on genomic selection of Meuwissen et al. .r et al. , are user et al.  in assesIt is futile to have information-rich training samples but unrepresentative and ridiculously small testing sets, as large variation among outcomes of similar prediction exercises is to be expected. Small testing sets and failure to replicate cross-validation in some studies have produced results where models accommodating dominance and epistasis appear as delivering a somewhat better performance than additive prediction models. Such results may be “false positives” reflecting chance, rather than signal.6. And the Oscar goes to…:A simple method such as genomic best linear unbiased predictor (GBLUP) may tell something about the state of nature and perform adequately. An involved procedure, such as a deep neural network (DNN), may tell nothing and yet produce spectacular results, although it has failed miserably in some studies. Like all neural networks, a DNN is regarded as “universal approximator.” An ongoing meta-analysis of hundreds of studies made in INIA, Spain (disclaimer: I will be a coauthor) places reproducing kernel Hilbert spaces regression (RKHS); e.g., Wahba  for routine use, but there can be exceptions. GBLUP is a “good thing” as pointed out in the early '90s, and we have known for a while that it is not only a special case of RKHS, but also a maximum penalized likelihood estimator, a linear neural network, and that it has a Bayesian interpretation. It has been extended to cross-sectional, multi-trait, longitudinal situations and has been “robustified.” Importantly, software developed mainly at the University of Georgia by Misztal allows crunching millions of predicted genomic breeding values. GBLUP with mild tweeks will probably remain the technology (term used deliberately) of choice for genetic evaluation of selection candidates. The science of genome-enabled prediction has arrived at a reasonable destination, but the voyage will continue, and new data will bring challenges.7. Help needed:Despite an abundance of chips enabling large-scale genotyping, training samples are seldom drawn at random, thus unrepresentative. This situation constitutes a selection process that is often not considered in predictive models. Animal breeders have been widely influenced by the “selection bias” study of Henderson  to be an unbiased predictor, but it is identical to ridge regression in some settings. However, ridge regression is a biased estimator. Is this a Dr. Jekyll Mr. Hide issue or some statistical bipolarity? The answer is that prediction and estimation unbiasedness have different definitions! Say you own a plant or a bull called “Charlie,” a fixed entity with identity . You are not interested in learning the average of a (very) large sample of potential Charlies; rather, you seek the breeding value of the Charlie you have. If ridge regression is used to estimate the breeding value of Charlie, Dr. Jekyll says there is estimation bias, but Mr. Hide states that there would be none. The latter is wrong (in the bias sense) with respect to Charlie, but not with respect to an average of potential Charlies, some of which will be AA, some Aa, and some aa. All good members of the Bayesian alphabet including GBLUP, with its appealing Bayesian interpretation  continues being crucial in practice, but it has become academically non glamorous at these times of massive genotyping, epigenotyping, proteomics, metabolomics, and (fine) phenotyping. It is important not to lose perspective as, otherwise, breeders will get inebriated with a cocktail “on apps.” Another issue of (some) concern is that the current emphasis on “big data,” “massive computing,” and “visualization” may diminish basic science education, as it appears that current thesis students start crunching numbers before they know genomics, or the meaning of a probability distribution, or attain an elementary knowledge of randomization or causality. Foundational theory and concepts should continue being taught. Otherwise, the field may drown in a technology-induced maelstrom, and critical or even visionary perspectives may end up playing a role that becomes secondary to that of a beautiful visualization or, even worse, to a machine.W. G. Hill (“Bill”) noted in a 2010 study discussing from Lush to Genomics: “Opinions we can debate.” I look forward to that conversation (Hill, DG prepared the first draft of the opinion, provided critical comments, typed the definitive version, and checked the spelling. The author contributed to the article and approved the submitted version.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past collaboration with the author DG.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.