DRD2 gene, (3) gray matter density in striatum, and (4) functional striatal activation elicited by temporarily surprising reward-predicting cues. Comparing the relative importance for the prediction of gIQ in an overlapping subsample, our results demonstrate neurobiological correlates of the malleability of gIQ and point to equal importance of genetic variance, epigenetic modification of DRD2 receptor gene, as well as functional striatal activation, known to influence dopamine neurotransmission. Peripheral epigenetic markers are in need of confirmation in the central nervous system and should be tested in longitudinal settings specifically assessing individual and environmental factors that modify epigenetic structure.Genetic and environmental factors both contribute to cognitive test performance. A substantial increase in average intelligence test results in the second half of the previous century within one generation is unlikely to be explained by genetic changes. One possible explanation for the strong malleability of cognitive performance measure is that environmental factors modify gene expression via epigenetic mechanisms. Epigenetic factors may help to understand the recent observations of an association between dopamine-dependent encoding of reward prediction errors and cognitive capacity, which was modulated by adverse life events. The possible manifestation of malleable biomarkers contributing to variance in cognitive test performance, and thus possibly contributing to the “missing heritability” between estimates from twin studies and variance explained by genetic markers, is still unclear. Here we show in 1475 healthy adolescents from the IMaging and GENetics (IMAGEN) sample that general IQ (gIQ) is associated with (1) polygenic scores for intelligence, (2) epigenetic modification of  The largest available study describes that polygenic scores  explain up to 4.8%4 of the variance of general intelligence quotient IQ (gIQ). A more recent larger but not yet peer reviewed study, shows up to 5.4% of variance explained5. On the other hand, environmental factors have a significant impact on general cognitive capacity, as indicated by the strong rise in average IQ performance following the decades after World War II7. According to Flynn et al.7 the change ranged from 5 to 25 IQ points (eg. 0.3 to 1.7 standard deviation (SD)) within one generation. This change appears to be too strong to be explained by genetic changes. While various environmental factors  might contribute to this so-called Flynn effect, those factors should act via changes in neurobiological systems relevant for cognition. Possible neurobiological factors that mediate this effect and link genotype with complex traits like cognition are (1) epigenetic markers including methylation count, (2) cortical architecture of the brain evaluated using magnetic resonance imaging (MRI), and (3) the functioning of the brain explored in vivo with functional MRI (fMRI). Those malleable markers might as well contribute to the “missing heritability” that is present between variance explained by accumulating single-nucleotide polymorphisms , estimates of genomic similarities between individuals (~20% SNP heritability5), and based on heritability estimates from twin studies (50–70%3). Here we aim to explore individual variance in gIQ that can be accounted for by neurobiological markers of cognitive performance and describe the interplay of mechanisms, including epigenetic variance that may contribute to individual malleability in cognitive capacity.Genetic variance is known to explain a substantial part of variability in cognitive capacity8 explaining up to 16% of the variance in right insula. Beyond cortical findings, the architecture9 and volume of subcortical structures have been associated with cognitive capacity explaining between 2.4% in striatum10 and up to 4.2% in caudate volume11. The importance of subcortical structures is further underpinned by the finding that training in reasoning alters resting state connectivity between subcortical and cortical brain areas, including striatum, parietal, and prefrontal areas12. This is highly plausible given the relevance of cortico-striatal networks implicated in executive function and goal-directed behavior15. In line with this, dopamine synthesis capacity in the ventral striatum has been associated with frontal cortical and striatal functional activation during goal-directed vs. habitual decision-making as well as IQ17, in accordance with the well-known role of dopamine in cognition and decision-making20. A readily available proxy for dopaminergic neurotransmission is the well-known reward anticipation signal that can be measured with fMRI22. Dopaminergic neurotransmission is partly heritable, but also substantially modulated by environmental factors27. An emerging field that could potentially link the abovementioned environmental factors and dopaminergic neurotransmission is epigenetic modulation, which can help to explain individual malleability. Finding possible links between epigenetic changes, reward signaling, and cognitive capacity in adolescents might contribute further evidence for long-lasting neurobiological correlates of environmental effects, including stress exposure, as already observed in rodents .Several lines of evidence suggest that gIQ is associated with the architecture of the brain measured as cortical volume and thickness29, however, their relative importance has not been tested in a cumulative fashion. Moreover, the interplay between genetic variance and possible neurobiological underpinnings of individual difference in cognitive capacity, including epigenetic markers is not known in detail. Data from the IMaging and GENetics (IMAGEN) consortium, offer a well-characterized sample to study these topics. With experts from a variety of fields, we aimed at contributing a broader insight into that research question.The aforementioned candidate markers for neurobiological underpinnings of cognitive capacity have been assessed before30, replicate in our sample?Do two different polygenic scores, which have previously been associated with cognitive capacityAre there epigenetic markers  of the dopaminergic system that show associations with gIQ?11 of a correlation between gray matter density in bilateral striatum and gIQ?Can we replicate previous findings31 be replicated in a large sample of adolescents?Can the previously observed association between functional activation of the ventral striatum  and IQTherefore, we measured associations between cognitive capacity (gIQ) and polygenic scores, epigenetic markers of the dopaminergic system, gray matter density in striatum, and striatal activation during reward processing, in a large sample of healthy adolescents, and we quantified their relative contribution to interindividual differences in IQ. We addressed the following research questions:In a subset of individuals for whom we have complete data, we evaluated the relative contribution of each of the aforementioned predictors for gIQ, assessed possible interactions of genetic variance with our other predictors, and performed model comparison for combinations of predictors.32) with available data from neuropsychological assessment, functional imaging, and genetic data. The study is intended to investigate the genetic and neurobiological basis of individual variability in psychological traits, and their relation to the development of frequent neuropsychiatric disorders. Recruiting took place at eight different sites . We therefore included site as a covariate in all analysis in order to account for variance introduced by center-specific variations. We excluded subjects with contraindication for MRI scans as well as serious medical conditions. Each local ethics committee approved the study. Subjects and their parents provided informed assent and consent, respectively.We used a sample of 1475 adolescents  from the large multicenter imaging and genetics study 29. On the other hand there is considerable criticism of constructing general factors33 regarding cognitive test performance and the authors have voiced similar concerns elsewhere34. PCA gains general information at the expense of specific information associated with the Wechsler’s intelligence scale (WISC) IV subscales. Calculating a general factor based on PCA  does not necessarily invalidate the nature of the original scales; instead, dimensionality reduction allows for capturing variance that is common to a variety of subscales. In this study, we therefore performed PCA in order to derive a measure of general cognitive ability from WISC IV subtests, comprising matrix reasoning, block design, digitspan backward and forward, similarities, and vocabulary35. The first principal component (gIQ) explained a large proportion of the variance (variance explained = 0.49) and was used for further analyses as a marker for gIQ  marker. After pruning we had 70 568 LD-independent SNPs for the score by Benyamin et al.30 and 86 330 for the score according to Sniekers et al.4. For both scores we performed risk profile scores (RPS) of our sample described for a range of p-value thresholds , multiplying the logistic regression  of each variant by the imputation probability for the “risk” allele in each individual. The resulting values were summed over each individual, so that each individual had a whole-genome RPS for further analysis. We aimed at replicating the association of the polygenic score with gIQ in a sample of 1388 subjects with sufficient data quality.We performed linkage disequilibrium (LD) pruning and “clumped” the summary statistics, discarding variants within 500 kb of, and in http://www.illumina.com/products/methylation_450_beadchip_kits.html), following the manufacturer’s protocol. Unlike polygenic scores based on multiple SNPs, no epigenetic score exists for intelligence. With respect to epigenome-wide data, our sample size is far too small to find effects on an epigenome-wide association study (EWAS) level. Therefore, we focused on epigenetic markers potentially affecting dopamine-dependent neural encoding of reward anticipation in the striatum and planned Bonferroni correction for multiple testing.Global blood DNA methylation levels were assessed by hybridizing DNA samples to the Infinium Human Methylation 450 Bead Chip  was used to tag candidate gene to its nearest CG site. We extracted the start coordinate for our candidate probes from this package. This start coordinate was then used to go up and down 50 kb to create a region file. We assessed methylation count in CG site from the following genes involved in dopamine metabolism and neurotransmission: tyrosine hydroxylase (TH); DOPA decarboxylase (DDC); catechol-O-methyl transferase (COMT); dopamine transporter 1 (SLC6A3); dopamine receptor D1 (DRD1); and dopamine receptor D2 (DRD2) resulting in 24 CG sites. We focused on D1 and D2 receptors because they are the most abundant dopamine receptors in the brain with expression in regions relevant for motor, limbic, and neuroendocrine functioning36. D3, D4, and D5 mRNAs are one to two orders of magnitude lower than that of the D1 or D237. We think that in addition to D1 and D2, D3, 4, or 5 only provide limited further insight for possible markers of gIQ. Nonetheless, for a more comprehensive view, we include an exploratory search for D3, D4, and D5 receptor gene and tested for association with gIQ in the Supplement. Epigenetic data with sufficient quality and corresponding data on gIQ was available for 817 subjects.The FDb.InfiniumMethylation.hg19 package for R (32). We controlled for variance accounted for by scanning site using dummy coded variables38. We used high resolution T1-weighted three-dimensional magnetization prepared rapid gradient echo sequence based on the ADNI protocol (http://www.loni.ucla.edu/ADNI/Cores/index.shtml). Gray matter density was estimated, including age, gender, and total intracranial volume as covariates of no interest. Mean striatal gray matter density was extracted from anatomical masks using the WFU-Pick atlas39 comprising bilateral striatum (http://fmri.wfubmc.edu/software/pickatlas) as an anatomic voxel mask with the individual Brain Atlases tool in SPM (IBASPM 71). Structural imaging data with sufficient quality and corresponding data on gIQ were available for 1401 subjects.Subjects were scanned in 3T-MRI-Scanners from different manufacturers . The cue was followed by a variable anticipation interval. Then the subjects were asked to push a button with their left or right index finger in order to hit an appearing target. If the subject is able to hit the target, following a reward-indicating  cue, he or she scores points  tasknts Fig.  in SI.22, we tested our research question in a region of interest (ROI). For this a literature-based mask41 of the bilateral ventral striatum was used in order to test for effects of individual signal change. We extracted the mean beta-values from the main effect in the abovementioned volume of interest of the ventral striatum from contrast images estimating the BOLD-signal change during anticipation of big and small vs. no reward  and a small effect sizes ranging from 2.4%10 in previous structural imaging studies and 4.8%4 for previous polygenic scores, according to Hulley et al.43 we would need a total sample size of 161–324 subjects. To estimate the variance explained by two different polygenic scores in our sample, we were able to calculate linear regression models with gIQ as dependent variable and polygenic scores as predictors in n = 1388 subjects. Additional covariates included age, gender, and principal components from our prior PCA, which account for population stratification and tested two different polygenic scores, therefore we chose a significance level of p = 0.025.We assumed that for a sufficient power of 1 − p = 2 × 10−3.For epigenetic markers, linear regression models were fitted for each marker in a combined sample of 817 subjects. Age, gender, and site as well as first two principle components of estimated differential cell counts and wave information were included into linear regression models as variables of no interest. We plotted a correlation matrix for all candidate markers in order to explore associations between methylation count in each CG site. Correlations between candidate CG sites revealed that most regions were independent markers Fig. . As candWe applied linear regression to estimate the correlation between gIQ and bilateral gray matter density in striatum of 1401 subjects with sufficient imaging quality. We accounted for variance from the following variables of no interest: age, gender, site, and total brain volume.p = 0.05 was chosen.To statistically evaluate associations between bilateral ventral striatal reward anticipation signal  and gIQ, we used multiple linear regression controlling for age, gender, and site in a sample of 1475 subjects. For imaging parameters, we used split-half cross-validation on two subsets. A significance level of p = 0.05) applying random field theory as implemented in statistical parametric mapping software (SPM 8) in n = 1475 subjects.For explorative analysis of gIQ and whole-brain associations with BOLD-signal during reward anticipation, we computed linear regression models at each voxel, using ordinary least squares. Due to spatial auto-correlation we used whole-brain family-wise error correction  in a multiple regression model, which assumes standardized predictors and dependent variables . The standardized parameter estimate (beta) indicates how many SDs gIQ will change, per SD change in the predictor variable. We used the lavaan packagep = 0.017 .Although we did not primarily hypothesize interaction effects, we calculated interaction terms in order to explore the interplay between our variables. We focused on possible interactions of genetic effects on epigenetic, structural MRI and BOLD-signal resulting in three interaction terms . Correcting for multiple comparisons, we considered a significance level of Finally, we formally described and compared different combinations of our predictors using model comparison. With an exhaustive search for all combinations between our predictors we wanted to find the best model explaining gIQ. We choose to compare a set of all combinations, resulting in 15 models. We performed model comparison based on difference in Bayesian information criterion (BIC), which is known to penalize for models with larger numbers of parameters more strongly, resulting in a parsimonious model.All probability values for the abovementioned tests are reported non-directional (two-tailed).If not stated differently statistical tests were performed using R version 3.2.430 at a p-threshold of 0.1 comprising 16 972 SNPs was significantly associated with gIQ  = 1376, p = 1.7 × 10−2; Fig. With respect to our first research question regarding the influence of genetics on gIQ, we observed that the polygenic score by Benyamin et al.p = 7.3 × 10−8, df = 1376, bootstrapped confidence interval (CI): 1.64–5.43%) using a score comprising 5636 SNPs significant at a p-threshold of 0.01 , which survived Bonferroni correction for multiple testing .We found a positive correlation between gray matter density in bilateral striatum with gIQ .In accordance with our third research question, we found that in our ROI, the ventral striatum, beta parameter estimates of reward anticipation  showed a significant positive association with gIQ  in a large network outside of the ventral striatum as well in frontal and temporal regions , gray matter in striatum, and striatal activation, we observed that an increase in the polygenic score of one SD leads to a beta = 0.13 change in gIQ  was associated with a decrease in gIQ . There is a positive effect of BOLD-signal change during reward anticipation predicting gIQ . In this additional analysis gray matter density in striatum showed no significant association with gIQ  and BOLD-signal during reward anticipation in the ventral striatum . We calculated an additional correlation matrix in order to explore our candidate markers association with WISC IV subscales , epigenetic finding DRD2 cg2632809, gr46. Using a polygenic score previously associated with cognitive capacity in children30 and a novel score4 tested in children and adults, we were able to replicate significant associations with gIQ, with the score based on 78 308 adults4 performing better than the one based on a sample of 17 989 children30. The striking difference between heritability estimates derived from twin and adoption studies (around 50–70%3) and variance explained by common genetic polymorphisms (around 5%5) for many traits has been labeled “the case of the missing heritability”. For example, regarding the partially heritable and polygenic human trait “body height”, polygenic scores account for 10% of the variance47, much below the high heritability estimates derived from monozygotic twin studies (heritability estimates around 85%3). So the discrepancy between variance explained by polygenic risk scores and twin studies may simply be due to the fact that polygenic risk scores only include common genetic polymorphisms and do not assess effects of rare gene variants. On the other hand, if epigenetic variation is transmitted to the offspring, as has been shown for some stress-related epigenetic effects28, a more or less substantial part of the presumably genetic background regarding IQ test results may indeed be due to epigenetic factors .Individual differences in intelligence have a substantial heritable background, while strong increases in test performance across the world in the last decades also point to strong environmental effects18 we found significant associations between methylation of DRD2 gene (cg26132809) and gIQ. Epigenetic control of gene expression is modulated by environmental factors such as stress exposure to the individual or in some cases parental generation48. Stress exposure and further environmental factors also strongly modulate dopaminergic neurotransmission, with relations to epigenetic modification unexplored. In line with previous findings11 we found gIQ to be related to gray matter density in striatum. These observations suggest a striatal contribution to the malleability of cognitive capacity49.Searching for neurobiological markers associated with dopaminergic neurotransmission in light of studies linking this system to cognitive capacity21. Unlike in studies directly quantifying the size of the reward prediction error using computational modeling50, in the MID task, the size of the temporal error in the prediction of reward-anticipatory cues cannot be individually computed. Although not limited to the ventral striatum, finding the strongest effect in this region suggests that dopamine-dependent encoding of reward-anticipatory cues and prediction errors contribute to cognitive flexibility and rapid decision-making.The association between ventral striatal activation and gIQ was found to be robust using split-half cross-validation as well as estimation of bootstrapped confidence intervals. In the MID task, temporarily unpredicted presentation of reward-associated stimuli elicit functional activation of the ventral striatum , which was previously associated with dopamine release measured indirectly by displacement of radio ligands of dopamine D2 receptors in this brain area4, epigenetic markers of the DRD2 gene and the ventral striatal BOLD-signal were significantly associated with gIQ with a similar effect size. In combined assessment in this subsample, gray matter density in striatum did not show a significant effect. Exploring the interrelatedness of our candidate markers, we found no significant association, thus pointing to rather independent sources of variance for gIQ. The abovementioned polygenic scores  did not show a significant interaction effect with our candidate markers  in our sample. In order to formally describe and compare possible models that predict gIQ, comparison of all possible combinations of our predictors resulted in a model comprising BOLD-signal during reward anticipation in the ventral striatum, methylation count in the DRD2 gene and the polygenic score. Altogether, the winning model points to a rather independent contribution of variance of dopaminergic neurotransmission to variance in gIQ on the one hand and genetic differences on the other hand.Calculating a regression model in a subsample of 755 subjects with fully available data for all predictors we found polygenic score of Sniekers et al.30. Further limitations include that our epigenetic markers are assessed in peripheral blood. They may not reflect variance in brain tissue and have to be validated in studies with methods directly accessing tissue in the central nervous system51. The cross-sectional design of the study does not allow any statement concerning causality. Further studies with a longitudinal design in possible quasi experimental settings are warranted.Limitations of our study include the sample size, which is rather large for neuroimaging studies but exceedingly small for explorative genetic and epigenetic studies. This is reflected in rather large CIs when applying bootstrap procedures in epigenetic markers. Furthermore, our sample size is too small for the detection of epigenome-wide markers. Therefore, the DNA methylation score was limited to CG sites in selected dopaminergic genes, with only a single CG site emerging as significant. It is highly likely that a more comprehensive DNA methylation analysis would have identified more epigenetic loci, which are associated with IQ score. Despite the relatively small sample size, we were able to replicate effects of polygenic score on gIQ derived from large samples10 in previous structural imaging studies and up to 4.8%4 for previous polygenic scores). Eventually, it is fundamental for the understanding of cognitive capacity that we find variable neurobiological correlates of gIQ. Variance of methylation count in our epigenetic candidate marker and individual differences in ventral striatal activation during reward anticipation seem to be independent predictors and do not show a relation with genetic correlates. Observing an association between epigenetic markers and neural signatures of gIQ should encourage further studies exploring mechanisms that mediate genetic and environmental effects on the neurobiological correlates of cognitive functions.Taken together, our findings suggest that both functional activation of the reward system, epigenetic control of dopaminergic neurotransmission, and genetic markers contribute to gIQ. Of note, the effect sizes studied are small but in the same range as previous studies (2.4%Supplemental materialSup fig 5sup fig 4sup fig 3sup fig 2sup fig 1


Doubled-haploid libraries from landraces capture native genetic diversity for a multitude of quantitative traits and make it accessible for breeding and genome-based studies.Maize landraces comprise large allelic diversity. We created doubled-haploid (DH) libraries from three European flint maize landraces and characterized them with respect to their molecular diversity, population structure, trait means, variances, and trait correlations. In total, 899 DH lines were evaluated using high-quality genotypic and multi-environment phenotypic data from up to 11 environments. The DH lines covered 95% of the molecular variation present in 35 landraces of an earlier study and represent the original three landrace populations in an unbiased manner. A comprehensive analysis of the target trait plant development at early growth stages as well as other important agronomic traits revealed large genetic variation for line per se and testcross performance. The majority of the 378 DH lines evaluated as testcrosses outperformed the commercial hybrids for early development. For total biomass yield, we observed a yield gap of 15% between mean testcross yield of the commercial hybrids and mean testcross yield of the DH lines. The DH lines also exhibited genetic variation for undesirable traits like root lodging and tillering, but correlations with target traits early development and yield were low or nonsignificant. The presented diversity atlas is a valuable, publicly available resource for genome-based studies to identify novel trait variation and evaluate the prospects of genomic prediction in landrace-derived material.The online version of this article (10.1007/s00122-019-03428-8) contains supplementary material, which is available to authorized users. Zea mays L. ssp. mays) seed banks around the world harbor thousands of landrace accessions, representing a rich resource of currently untapped native diversity that could be harnessed for plant improvement and adaptation to environmental changes  to create a publicly available diversity atlas of European flint maize by characterizing landrace-derived DH libraries genotypically and phenotypically for line per se and testcross performance, (ii) to provide a comprehensive analysis of the DH libraries in terms of population structure, performance level, trait correlations, and genetic variances for a broad range of traits, and (iii) to gain insights into potential strategies for capturing native diversity for use in germplasm improvement.The three landraces Kemater Landmais Gelb , Petkuser Ferdinand Rot , and Lalin  were chosen for the production of DH lines because they showed phenotypic variation for early development as well as low levels of linkage disequilibrium (LD) and population structure within populations. They were selected from a set of 35 European maize landraces covering a broad geographical region of Europe that was described in detail by Mayer et al. . Togethe0 plants (48 per landrace) from the landraces KE, PE, and LL were genotyped using the 600 k Affymetrix® Axiom® Maize Array . Assignment of lines to their respective landrace was performed using the ADMIXTURE software tool   and a reference panel consisting of the corresponding DH lines. Pairwise modified Rogers’ distances  were ca0 plants. The MRD matrices of DH lines and S0 plants were hierarchically clustered using the unweighted pair group method with arithmetic mean (UPGMA) implemented in the hclust function in R and are displayed as 1-MRD. In order to estimate the proportion of molecular variance explained by the three landraces under study, an analysis of molecular variance . Each c−2. Fertilization and plant protection were carried out according to standard agricultural practices in both the LP and the TC trials.The testcrosses (TC) were evaluated in four 10 × 10 lattice designs in four locations in Germany in 2018 . In the TC trials, testcrosses of lines DK105, EP1, and F2 as well as testcrosses of the two landraces KE and PE and two commercial hybrid varieties  were planted as checks. The testcrosses of landraces KE and PE were planted in one lattice only, while all other checks were planted in every lattice. In TC, plots were double rows 5 m in length at locations ROG and OLI and 6 m in length at locations KLW and EIN, in both cases with 0.75 m distance between rows. Sowing density followed local practice at the experimental stations and varied between 9 and 11 plants mIn the LP trial, a total of 25 morphological, agronomic, and early-development-related traits were measured (Table S2 provides detailed information on trait × environment combinations). The traits that were scored in ≥ 10 environments included emergence , early vigor , early plant height , final plant height , and female flowering . Root lodging at the R6 stage  was scored in six environments; tillering  and male flowering  were scored in five environments. The anthesis-silking interval  was calculated for the environments in which both MF and FF were scored. Ear height  was measured in four environments. In the Spanish environments, physiological traits like the maximum efficiency of photosystem II  were measured at stages V4 (2017 + 2018) and V6 (only 2017), and leaf greenness (SPAD) was measured by a chlorophyll content meter . Reaction to stress was scored as cold tolerance  after a very cold night with a slight frost at OLI 2017, drought/heat tolerance  at EIN 2018, and rust susceptibility (binary) at TOM 2018. Traits related to tassel architecture were measured in ROG 2018. Tassel length was measured from the lowest tassel branch to the tassel tip , spike length was measured as the length of the top spike , the number of branches was counted (NB), and the tassel angle was scored on a 1–9 scale . In the TC trial, EME, EV, PH, EH, PH_final, FF, TILL, and RL were scored as was described for LP. In addition, TC plots were harvested with a forage harvester to measure total dry matter yield  and dry matter content .i = 1, 2, 3 denotes three groups, i.e., DH lines from landraces (DHL), checks (CH), and landrace populations (LR_S0); j = 1, 2, 3 denotes the three landraces KE, PE, and LL; µ is the overall mean; i; j in group i = 1; i = 1 and j = 1, 2, 3 and k nested in group i and landrace j; o; j and environment o; k and environment o. The effects j = 1, 2, 3), assuming that DH lines across and within landraces were unrelated. Residuals were assumed to be normally distributed with mean zero and two heterogeneous variances, one for i = 1, 2 referred to DHL and CH and j = 1, 2 referred to landraces KE and PE. Restricted maximum-likelihood estimation implemented in the ASReml-R package  between original landraces and the mean of the corresponding DH library (LP and TC) and between the mean of the two check hybrids and the mean of the DH library (TC only). We calculated the predicted response from selection within DH libraries (LP and TC) according to Falconer and Mackay  and for a subset of 378 lines  that were evaluated as testcrosses in four environments. For all lines, data on almost 500,000 SNP markers are available.0 gametes derived from the same landrace clustered together except for four gametes from S0_PE, which fell outside the PE-cluster. Complementing our data with those from Mayer et al.  across landraces for 20 out of 25 traits in LP and for 5 out of 14 traits in TC, which was a result of the high-quality phenotypic data and large sample sizes of KE and PE. As expected, mean LP performance of the DH libraries was significantly (P < 0.05) lower than the respective landraces for almost all traits. The reduction was most pronounced for early development traits, final plant height, and photosynthetic efficiency  outperformed the TC mean of DH lines for TDMY by about 15% and, in contrast to the DH lines, showed no TILL or RL . Variance component estimates for LL were similar to the other two libraries, but, due to the small sample size, they were estimated with considerably larger error, resulting in nonsignificant genetic variances for PH_V3, TILL, ASI, photosynthesis-related traits, and SPAD. As expected from quantitative genetic theory, genetic variance component estimates were smaller in TC than in LP. In the statistical model, we allowed for heterogeneity of genetic variances estimated within landraces, but only a few traits  showed strong differences (> twofold) in genetic variance estimates between KE and PE in LP, which were even alleviated in TC.In LP, trait heritabilities were generally high and similar across landraces, ranging from 0.35 to 0.96. Except for PH_V3, TILL, ASI, Fv/Fm, and SPAD in LL, the heritability estimate always exceeded twice its standard error (Table S3). In TC, heritabilities were slightly lower overall than in LP (Table S4), ranging from 0.31 to 0.92, which was expected from the lower number of testing environments and the lower genetic variance compared to LP.DH libraries were evaluated in a total of 11 environments comprising seven different locations and two years. Locations covered a geographical region spanning from northern Germany to northwestern Spain at altitudes ranging from 29 to 706 m above sea level (Table S1). Average temperatures differed by 5 °C between the coldest  and the warmest  environments, and precipitation varied from 159 (KLW 2018) to 548 mm (ROG 2018) during the vegetation period. The ratio of genotype by environment and genotype variance components depended on the trait under study. In LP, values ranged from 0.11 (EH in KE) to 1.22 (ASI in PE), but varied between 0.2 and 0.7 for most traits with a mean of 0.51 (Table S3). Similar ratios were observed in TC (Table S4).Correlations between locations for traits measured in at least five environments ranged from 0.40 to 0.87 in 2017 and from 0.19 to 0.86 in 2018 (Table S6). Correlations between years of a given trait and location ranged from 0.31 to 0.83 (Table S6).In LP and TC, phenotypic correlations among early development traits measured at different growth stages were high and stable across DH libraries, ranging from 0.58 to 0.95 Fig. . The corIn LP, the early development traits showed intermediate to high positive phenotypic and genetic correlations with final plant height . In TC, only the phenotypic correlation between PH_V6 and final plant height was significant, but it was lower than in LP. Genetic correlations for EV_V4, EV_V6, PH_V4, and PH_V6 with PH_final ranged between 0.2 and 0.5. Intermediate positive correlations were found between early development traits and TDMY in TC  as well as negative correlations of early development with flowering time in LP  in KE and PE libraries. Phenotypic and genetic correlations of RL with all other traits were nonsignificant or small in LP and TC. The same was true for TILL except for TC of PE, where intermediate correlations with early and late plant height, ear height, and TDMY were observed.Phenotypic correlations between LP and TC performance were significant for all traits except EME. Genetic correlations between LP and TC were intermediate  to high  (Table S5).www.europeanmaize.net). We produced DH libraries from three landraces for obtaining reproducible genetic units for phenotyping and genotyping and characterized them comprehensively to build a publicly available, immortal genetic resource that is ready to use for pre-breeding and for investigations on functional diversity and the prospects of genomic prediction.Our study is part of a long-term research project which aims to make maize landrace diversity amenable to plant breeding  and not between them. Individuals sampled from a maize landrace are assumed to be unrelated, but pairwise comparisons share different numbers of alleles alike in state, leading to variation in similarity between them. Patterns of variation in similarity were comparable for S0 plants and DH lines . Despite locations with very different climatic conditions  and large differences in temperature and precipitation in 2017 and 2018, the ratio of genotype × environment and genetic variance  and drought susceptibility in EIN , both in 2018. Although infections with rust or severe drought may not occur frequently, it would be devastating if these susceptibilities were transferred inadvertently to elite germplasm through the introgression of landrace-derived material. If evaluating the landrace-derived material in a large number of environments is not possible, prioritized testing in environments known for high disease pressure, abiotic stress, or frequent occurrence of undesirable traits like RL is highly advisable.The DH libraries presented in this study link the large molecular diversity present in landraces to meaningful phenotypes. DH lines from landraces outperformed flint founder lines and commercial hybrids in early development, and as immortal genetic units they are directly accessible for plant breeding. Improving one or several target traits and simultaneously closing the performance gap between elite and landrace-derived genetic material for multiple traits of agronomic importance requires efficient recurrent population improvement. In this context, knowledge of trait correlations is crucial in order to broaden the narrow genetic base of the elite flint germplasm pool without introducing undesired traits from landraces into elite breeding populations. To obtain maximum selection gain per unit time, theory offers different strategies, such as multi-stage or index selection Bernardo , which nThe implementation of GP in pre-breeding of landrace-derived material is still underexploited. The comprehensive phenotypic data and derived quantitative genetic parameters presented for the three DH libraries in this study provide an excellent basis for optimizing genome-based pre-breeding schemes. Multi-environment phenotypic data are available for model training in LP and TC. Sample sizes and marker densities are large, allowing to investigate the effects of population size and required marker densities in populations with relatively low linkage disequilibrium compared to elite germplasm. In addition to investigating the prospects of genome-based prediction, our data provide a comprehensive framework for the discovery of genes controlling favorable and unfavorable traits as well as for the genetic analysis of additional relevant traits such as nutrient efficiency, photosynthesis-related traits, and additional biotic and abiotic stress tolerances.Supplementary material 1 (PDF 1613 kb)Below is the link to the electronic supplementary material.


Genome-wide association studies (GWAS) are utilized in cattle to identify regions or genetic variants associated with phenotypes of interest, and thus, to identify design strategies that allow for the increase of the frequency of favorable alleles. Visual scores are important traits of cattle production in Brazil because they are utilized as selection criteria, helping to choose more harmonious animals. Despite its importance, there are still no studies on the genome association for these traits. This study aimed to identify genome regions associated with the traits of conformation, precocity and muscling, based on a visual score measured at weaning.Bayesian approaches with BayesC and Bayesian LASSO were utilized with 2873 phenotypes of Nellore cattle for a GWAS. The animals were genotyped with Illumina BovineHD BeadChip, and a total of 309,865 SNPs were utilized after quality control. In the analyses, phenotype and deregressed breeding values were utilized as dependent variables; a threshold model was utilized for the former and a linear model for the latter. The association criterion was the percentage of genetic variance explained by SNPs found in 1 Mb-long windows. The Bayesian approach BayesC was better adjusted to the data because it could explain a larger phenotypic variance for both dependent variables.There were no large effects for the visual scores, indicating that they have a polygenic nature; however, regions in chromosomes 1, 3, 5, 7, 14, 15, 16, 19, 20 and 23 were identified and explained a large part of the genetic variance. With the advent of bovine genome sequencing , new infThe Bayesian approach differs with respect to a priori distribution for QTL (quantitative trait locus) effects. Bayesian LASSO assumes that there are few genes with large effects and many genes with small or no effects, whereas BayesC assumes that most SNPs (Single Nucleotide Polymorphisms) are not associated with phenotype, and only a small π portion has some effect on traits. The best model depends on the trait genetic architecture, which is unknown, mainly when the traits have not been extensively studied .In cattle, association studies have been carried out for most economically important traits –13. HoweIn dairy cattle, the results of some association studies for linear evaluations of type, which assess several morphological traits –23, are Due to the importance of visual scores for Brazilian cattle breeding, it is desirable that the regions involved in the genetic control of these traits have pleiotropic effects on other important economic traits and that there are large effect QTLs, resulting in greater genetic gains when utilized as selection criteria. This study aimed to identify genome regions associated with the traits of conformation (C), precocity (P) and muscling (M) visual scores, measured at the weaning of Nellore cattle, and to compare BayesC and Bayesian LASSO in a genomic association study.Data collection procedures were reviewed and approved by the Ethical Committee for Animal Care and Use (CEUA) of the São Paulo State University, UNESP - Jaboticabal, São Paulo, Brazil (protocol number: 18.340/16).Data were used from Nellore males and females, born between 2007 and 2011, belonging to two animal breeding programs, DeltaGen and Paint, and including more than 250 farms distributed across Brazil. Although the animals were from two distinct programs, they had a similar objective selection for weaning and yearling weight, and several sires were common to both programs, which were used for artificial insemination service. A previous GWAS analysis on birth weight revealed that the animals from these two breeding programs clustered in the same group (“cluster 1”) of a principal component analysis based on the genomic kinship coefficient [Phenotypes for the visual scores were evaluated at weaning. Each contemporary group was assessed by a single technician. First, the whole herd was observed and characterized. Then, the average profile for each trait was used as a basis of comparison to attribute scores that varied from 1 to 5, where 5 was the highest expression and 1 was the smallest expression. Each visual score was defined as : conformA total of 2021 females and 1416 males with records for C, P and M with genotypical information were utilized for the analyses. For genome association studies, the phenotype of visual scores and the deregressed breeding values (dEBV) were used as dependent variables for 2873 records after accounting for quality control and the smaller number of records for dEVBs  of scores was done through a multi-trait threshold animal model, as described by . Direct p-value for Hardy–Weinberg equilibrium (HWE) test less than 1 × 10− 5 . Highly co-related SNPs (r2 > 0.98) were excluded. Quality control was an interactive process that stopped when no SNP or sample were excluded, resulting in 309,865 SNPs for analyses that considered phenotype as the dependent variable, and 308,861, 308,561 and 308,481 SNPs for C, P and M, respectively, when the dependent variable was dEBV. Quality control and the input of missing genotypes were done with the snpStats package of R software [The animals were genotyped with Illumina BovineHD BeadChip , according to the manufacturer’s protocol. BovineHD BeadChip has 777,962 SNPs scattered throughout the genome with an average distance of 3.43 kb between markers. The inclusion criteria used to control genotype quality were: SNPs located in autosomes; call rate per SNP greater than 0.95; call rate per animal greater than 0.90; minor allele frequency greater than 0.05; software .Bayesian approaches BayesC and Bayesian LASSO were utilized to estimate marker effects. Both Bayesian approaches differ in their a priori distributions, which are assumed for marker effects. In general, the models can be presented in matrix notation as:Y is a vector n × 1 of phenotype or dEBVs for the visual score of C, P and M; X is an n × p matrix that relates β vectors of fixed effects with Y; Z is an n × k matrix of genotypes  of k SNPs; α is a k × 1 vector of random coefficients of regression for SNPs  and e is a residual vector with a normal distribution N ∼ , where σ2e is the residual variance, considered unknown with a scaled inverse Chi-square distribution [where: ribution .The difference between Bayesian approaches is the a priori marginal distribution assumed for α, which determines the variable selection and shrinkage in SNP effect estimates. For LASSO, the assumed distribution for effects is double-exponential. This distribution has a greater density at zero and thicker tails than a normal distribution, which causes an effect-dependent shrinkage because SNPs with small effects are regressed toward zero with greater shrinkage than the SNPs with large effects , 33. ShrAnalyses were carried out with the BGLR package of the software R , which iWith the use of a high-density chip, QTL effects can be distributed across several SNPs that are in LD with QTL, resulting in non-significant individual SNP effects , 37; the2j is the genetic variance percentage explained by window j; σ2j is the genetic variance explained by window j; σ2SNP is the genetic variance explained by SNPs; h2 is the trait heritability and h2SNP is the phenotypic variance proportion explained by markers (marker heritability). σ2j and σ2SNP were calculated as the variance of genomic breeding values (GEBV) of each window or the whole genome. GEBV for animal i in window j was calculated as:where: %σk is the number of SNPs within window j; Xik is the genotype of animal i for SNP k and αk is the allele substitution effect for SNP k. For 1 SNP windows, this method is equivalent to 2pk(1 − pk)α2k [where: − pk)α2k . The Bayhttp://www.ncbi.nlm.nih.gov/snp/), loaded with bovine genome version UMD 3.1, was utilized to identify genes within the windows. Gene classification with respect to biological function was done through the Database for Annotation, Visualization and Integrated Discovery (DAVID) [http://david.abcc.ncifcrf.gov/.The database from the National Center for Biotechnology Information (NCBI) ( (DAVID) , availabIn general, the proportion of phenotypic variance explained by SNPs was smaller than trait heritability. Moreover, BayesC explained the largest phenotypic variance and was nearest to heritability, and among the dependent variables, phenotype was superior to dEBVs. Table The identification of regions associated with visual scores was done through BayesC, utilizing phenotype as the dependent variable, because it was the Bayesian approach that best explained the proportion of phenotypic variance for all three visual scores. In Fig. TBC1D5, LPAR2, TMEM9, NDUFA13, GSTA3 and FBXL17), regulation and transcription of other genes , lipid metabolism (SORL1 and ASCF2) and genes related to growth and skeletal muscle . Furthermore, windows located in chromosomes 3 (0.4–14 Mb), 5 (118.7–119.7 Mb), 16 (20.7–21.7 Mb) and 23 (24.1–25.1 Mb) were associated with at least two scores , 3 were miRNA (microRNA), 6 were tRNA (RNA transporter) and 15 were pseudogenes. In the identification of candidate genes, only genes that codify proteins were considered. Thus, 18 candidate genes were identified and classified into four groups according to the function that they perform in the organism: basal and cellular metabolism  to be the best dependent variable for GWAS analyses  as they The great number of windows needed to explain 10% of the genetic variance Table  indicateNDUFA13  is found in this gene group and it is a functional component of mitochondrial complex I, which is also involved in apoptosis and cellular energy production processes [FBXL17, which intervenes in protein recycling processes in part of proteosoma 26S, acting on several cellular processes. This gene is expressed at different levels in the skeletal muscle of bovines from 8 to 12 months old, indicating that it is associated with muscle development at this age [The presence of genes related to basal and cellular metabolism, located in significant windows, is explained by the fact that these genes act on several tissues, affecting cell and body development as a whole and, therefore, contributing to general performance. These genes have an important pleiotropic effect because they are non-specific tissues that act on several body cells and metabolic pathways, which include: signaling among cells, protein synthesis and transportation, cell proliferation and survival, and transportation and formation of cell membrane and its receptors. The gene protein rocesses ; modificthis age .STK3, MAPK11 and MAPK12, which belong to the kinase family (serine/threonine-protein subgroup), and another that includes genes POU2F1, CREG1 and ESRRG, whose main function is to modulate genetic expression (transcription factor).Genes that regulate transcription can be divided into two subgroups: one formed by the genes STK3 (known in humans as MST2) acts as a repressor of genes involved in the fat deposition of Hanwoo bovines; the high expression levels of this gene have been associated with a low level of intramuscular fat, showing that this gene inhibits adipocyte proliferation [MAPK11 and MAPK12 are part of the metabolic pathway p38 MAPK, which is a preserved mechanism of cellular response to a broad variety of extracellular signals, and is proposed as a regulator of cellular differentiation, proliferation and development [Kinases are enzymes that catalyze protein phosphorylation through the transfer of the ATP phosphoryl group, changing protein configuration and resulting in its activation or inactivation. Kinases represent the largest protein family in eukaryotes and are involved in multiple cellular processes, including cellular signaling mechanisms and the activation of transcription factors . The genferation . The genelopment . As p38 POU2F1 and CREG1 are transcription factors that regulate the activity of multiple genes that act on several metabolic pathways; among them are genes involved in growth, proliferation and cellular differentiation processes. POU2F1 was identified by [CREG1 in the psoas major and flexor digitorium muscles of bovines, indicating that both genes act on the skeletal muscles and fat tissue. Gene ESRRG is a member of the receptor family related to estrogen (ESRR) that act as transcription activators for gene PERM1, involved in the energy metabolism of skeletal and cardiac muscle [In the second subgroup, genes ified by  as one oified by  found dic muscle , making SORL1 belongs to the low-density lipoprotein receptor family (LDLR) involved in cholesterol metabolism, whereas ACSF2 is associated with lipid metabolism and adipocyte differentiation. These two genes have been associated with intramuscular fat deposition in swine and bovines [Both genes involved in lipid metabolism were associated with the precocity score, which evaluates the animal’s capacity to deposit fat. Gene  bovines , 53.GDF8) region did not show an association with visual scores. The miostatin gene is important in muscular development and is one of the genes responsible for the presence of double musculature in breeds like the Belgian Blue and Limousin. Gene PPP1R39 , like the miostatin gene, negatively regulates muscular tissue growth once low expression levels are associated with muscular hypertrophy. Studying fast and slow growth lines in broiler chickens [SH3RF2 in the fast growth line, and it was identified as the cause of the largest growth. In bovines [Out of the four genes related to growth, three were associated with precocity and muscling, and the miostatin gene  and GDF11, involved in muscular development, leading the overexpression of this gene to produce muscular hypertrophy, as the miostatin gene negatively regulates muscular growth, as observed in rats and sheep [IGFBP6 and C1QTNF3 positively regulate muscular growth in bovines and high expression levels are associated with cellular growth in skeletal muscles. Gene C1QTNF3 is also involved in subcutaneous and intramuscular fat deposition [Gene nd sheep , 57. Genposition , 59.Windows located in chromosomes 3, 5, 16 and 23 were associated with at least two scores; this was expected since the genetic correlations among the traits were high, ranging from 0.80 to 0.92, indicating that the same genes were controlling the visual scores. The results of this study will help the selection process as the increase of favorable allele frequencies for identified genes will lead to greater genetic gains in visual scores.According to our results, visual scores have a polygenic nature because regions explaining a great percentage of the genetic variance were not found. However, DNA regions associated with visual scores were identified, containing genes that are part of important biological processes and molecular functions in body development.


GWAS) to study trait architecture in natural hybrids of two ecologically divergent Populus species.The genomic architecture of functionally important traits is key to understanding the maintenance of reproductive barriers and trait differences when divergent populations or species hybridize. We conducted a genome‐wide association study , morphological and growth traits, and used a Bayesian polygenic model for mapping.We genotyped 472 seedlings from a natural hybrid zone of MYB transcription factors and glycosyl transferases.We detected three classes of genomic architectures: traits with finite, detectable associations of genetic loci with phenotypic variation in addition to highly polygenic heritability; traits with indications for polygenic heritability only; and traits with no detectable heritability. For the first class, we identified genome regions with plausible candidate genes for phenylpropanoid biosynthesis or its regulation, including GWAS in natural, recombinant hybrids represent a promising step towards resolving the genomic architecture of phenotypic traits in long‐lived species. This facilitates the fine‐mapping and subsequent functional characterization of genes and networks causing differences in hybrid performance and fitness. At the within‐species level, interest has primarily been on understanding local adaptation in wild species and the selective forces operating during domestication of agriculturally important species , widespread in southern Eurasia and northern Africa, and Populus tremula (European aspen), found mainly in northern Eurasia. Even after > 2.8 Myr of divergence  in trees, humans and other species suggest that the architecture of adaptive traits is often polygenic, that is, they are not determined by a few genes of large effect, but rather by many loci with small effect  Sm. All seeds were collected from open‐pollinated mother trees in the Parco Lombardo della Valle del Ticino in the north of Italy , where five libraries with 95 individuals each were prepared according to their standard commercial procedure, very similar to the original RAD‐seq protocol . Fourth, we applied strict filters so as to retain only reliable sites: we removed all sites with more than segregating alleles or with an average depth above the 95% quantile to exclude potentially paralogous loci. To avoid single nucleotide variants (SNVs) originating from misalignments, we further removed indels and variant sites within 5 bp of all indels that we could identify confidently using gatk on the full data.We conducted a restriction site‐associated DNA sequencing (RAD‐seq) experiment as follows. First, we extracted DNA from silica‐dried leaf material of 472 individuals using the Qiagen DNeasy Plant Mini Kit  and standardized concentrations to 20 ng μlq) using entropy . LD is required for mapping because causal allelic variants are unlikely to be directly observed in reduced representation studies. We estimated local ancestry using P. alba and P. tremula individuals (51 each) from the Italian, Austrian and Hungarian hybrid zones  and leaf shape (LFSHAP), known to be strongly divergent between ., et al. and meas.et al., .For growth traits we included measures of height and diameter of the seedlings at 1 and 2 yr after planting . Height was quantified with a tape measure from the soil to the top of the main stem with a precision of 1 cm, whereas diameter was assessed with calipers with a precision of 1 mm at 10 cm above the soil. HEIGHT1 and DIAM1 were available for seedlings planted in 2010, seedlings planted in 2011 in Fribourg  and seedlings planted in 2014. HEIGHT2 and DIAM2 were available only for seedlings planted in 2011, in both Fribourg and Salerno.q, we quantified the proportion of phenotypic variance explained by this variable using linear regressions for each trait We then bootstrapped the data 1000 times to obtain confidence intervals.To examine how phenotypic variation relates to gemma v.0.94.1  or by the infinitesimal effects of all markers (‘random effects’ estimated from the kinship matrix). This set of parameters includes the proportion of phenotypic variance explained by the sparse effects and random effects (PVE), the proportion of PVE explained by the sparse effects only (PGE) and the putative number of sparse effect loci involved in determining the phenotype (n_gamma). The product of PVE and PGE gives the proportion of total phenotypic variance explained by sparse effects, which is commonly referred to as narrow‐sense heritability h2.To carry out GWAS by admixture mapping, we used the Bayesian sparse linear mixed model . Neighboring SNVs in a genomic region are expected to have some redundancy and exchangeability as predictors of phenotype, and therefore to have lower individual PIPs than if a SNV tagged variation in the phenotype. To appropriately aggregate information from neighboring SNVs regarding the cumulative evidence for sparse effects in an interval, we summed PIPs in nonoverlapping windows of 0.5 Mb, as we found windows of 1 or 2 Mb resulted in similar patterns but made the identification of candidate genes harder , ies Fig. . Before gemma including the covariates in the input file (‐notsnp option), rather than regressing them out. For the 12 phytochemical compounds with zero abundance in > 10% of individuals, we further coded trait values as presence (1) and absence (0) and used binomial logistic regression to obtain residuals used as phenotypic information. Additional information on gemma models and parameter settings can be found in Methods For each trait, we ran 10 independent Markov chains of 12 million iterations and discarded the first two million as burn‐in. To evaluate the robustness of our conclusions, we also ran h2 ≥ 0.01 and n_gamma > 0 with at least 95% posterior probability. For these traits we then selected windows with PIP ≥ 0.4  and retrieved genes annotated in them in the P. trichocarpa reference genome  across individuals. We found considerable variation in the genomic composition of the seedlings, spanning the full range between the parental species  followed a heterogeneous pattern .The first class included traits with strong evidence for heritability and with both the genomic background and loci with measurable effect contributing to the phenotypic variation. This class of loci with strong evidence for a genetic role in explaining phenotypes included 16 phytochemical traits with ty Table b, 5a. Th> 0 Fig. b,c. An aThe second class corresponds to traits for which only the genomic background appears to play a role in explaining the phenotype, while the actual contribution of individual loci with measurable effect on the phenotype is less clear. This group encompasses six phytochemical traits, the growth traits DIAM1 and HEIGHT1 and the morphological trait LFSHAP Table b, 5b, whh2 to approach zero. The most evident cases for this scenario were phytochemical traits C4 , three salicinoids  and 12 flavonoids . As mentioned earlier, we investigated an additional trait as part of this set , as it exhibited a genomic window with PIP ≥ 0.4 and only barely missed our threshold on h2 with a posterior probability > 93%.We identified the genomic regions with sparse effects for the 16 traits in the first class of genomic architectures. All of these were phytochemical traits, that is, secondary metabolite compounds: one chlorogenic acid ; COMT1, a methyl transferase that transforms quercetins into isorhamnetins (Potri.015G003100); and CHS, a chalcone synthase (Potri.012G138800). More information on candidate genes found in genomic windows with high PIP is provided in Notes Within the windows of interest (PIP ≥ 0.4), we identified several candidate genes potentially responsible for the significant associations between specific genomic regions and phenotypic traits. Most conspicuously, these windows contained genes encoding et al., et al., et al., et al., et al., P. alba and P. tremula) by applying GWAS to an admixed population.Early genetic mapping studies have often pointed to potentially simple, sparse genetic architectures of phenotypic traits in wild and domesticated species , in line with previous estimates . One likely reason why the genomic similarity matrix explains phenotypic variation is that the trait is heritable but highly polygenic, as was previously reported for growth‐related traits  matrix, but no localized association was detected . These windows could host enzymes acting upstream in a specific pathway branch, thus affecting several downstream steps and compounds , one of the most widespread modifications of plant secondary metabolites  should be directed to the Fig. S1 Posterior inclusion probabilities in genomic windows of different size.Fig. S2 Kinship matrix calculated by Gemma.Fig. S3 Local ancestries for all analyzed seedlings.Fig. S4 Admixture linkage disequilibrium in all chromosomes.Fig. S5 Decay of admixture linkage disequilibrium along all chromosomes.Fig. S6 Degrees of phenotypic differentiation between P. tremula, hybrids and P. alba for all traits.Fig. S7 Relationship between genome‐wide ancestry and phenotype for all traits.Fig. S8 Posterior distributions of PVE, PGE and heritability for all phenotypic traits.Methods S1 RAD‐seq data processing, reference‐mapping and variant calling.Methods S2 Inference of local and genome‐wide ancestry.Methods S3 Rationale for choice of plant traits measured in this study.Methods S4 Admixture mapping with gemma: model choice and validation.Notes S1 Genomic windows highlighted by alternative modeling approaches in gemma.Notes S2 Additional information on candidate genes.Table S1 Number of seedlings per family and common garden location.Table S2 Phenotypic data used in this admixture mapping GWAS study.Table S3 Probabilities from posterior distributions of heritability, PVE, PGE and n_gamma.Table S4 Candidate genes in genomic windows with high posterior inclusion probability.Click here for additional data file. Click here for additional data file.


O-caffeoylquinic acid and procyanidins B1, B2, and C1. Our observation that a relatively simple genetic architecture underlies the dramatic variation of key polyphenols in apples suggests that breeders may be able to improve the nutritional value of apples through marker-assisted breeding or gene editing.Apples are a nutritious food source with significant amounts of polyphenols that contribute to human health and wellbeing, primarily as dietary antioxidants. Although numerous pre- and post-harvest factors can affect the composition of polyphenols in apples, genetics is presumed to play a major role because polyphenol concentration varies dramatically among apple cultivars. Here we investigated the genetic architecture of apple polyphenols by combining high performance liquid chromatography (HPLC) data with ~100,000 single nucleotide polymorphisms (SNPs) from two diverse apple populations. We found that polyphenols can vary in concentration by up to two orders of magnitude across cultivars, and that this dramatic variation was often predictable using genetic markers and frequently controlled by a small number of large effect genetic loci. Using GWAS, we identified candidate genes for the production of quercitrin, epicatechin, catechin, chlorogenic acid, 4- Widely recognized as a nutritious food source, apples contain significant amounts of polyphenols and other bioactive compounds that contribute to human health and wellbeing. Many polyphenols  are strong antioxidants associated with reduced incidence of disease, including cardiovascular disease, metabolic syndrome, and certain cancers2. In the US, 22% of the polyphenols in the human diet originate from apples, which makes apples a primary dietary source of these antioxidant compounds3. Several epidemiological studies have reported that the consumption of apples can reduce the risk of chronic diseases, including cardiovascular diseases, asthma, various cancers, and type II diabetes6. Thus, apples represent a key source of polyphenols in the human diet that may contribute significantly to disease prevention and overall health.Apples are one of the most produced and consumed fruits in the world with worldwide production reported at 90 million tonnes in 20168. For example, phenolic acids and flavonoids in the epicarp and endocarp tissues decrease during ripening9. However, it is genetic variability that likely plays the primary role in determining polyphenol concentration because most of the variation in polyphenol concentration is captured by variation among apple cultivars10. Thus, a major determinant of an apple’s nutritional value is likely determined by its genome, which makes the genetic mapping of polyphenols a promising avenue of scientific inquiry.The concentration of polyphenols in apples varies during ripening and can be influenced by growing conditions11. Biochemical analyses have identified numerous compounds belonging to these polyphenolic groups in apples13 and genetic mapping studies have revealed genes and enzymes controlling their biosynthesis18. However, the genetic mapping of apple polyphenols to date has relied exclusively on relatively small (N < 170) bi-parental populations17, which lack the ability to reveal the genetic architecture of polyphenol production across diverse apple germplasm. It therefore remains unknown whether previously identified polyphenol QTL account for variation in diverse apple breeding material, and whether polyphenols are predictable with genome-wide markers using genomic prediction. The nutritional value of crops is increasingly being targeted using genomics-assisted breeding20, and polyphenol concentration in apple represents a possible target for apple breeders. Thus, to quantify the genetic architecture of apple polyphenols and advance genomics-assisted breeding of apple nutritional content, we conducted genome-wide association studies (GWAS) and genomic prediction using high performance liquid chromatography (HPLC) data of apple extracts in two diverse apple populations.The biosynthesis of polyphenols in plants occurs via the secondary metabolism of the phenylpropanoid pathway. This pathway leads to the production of flavonols, flavonoids, anthocyanins, and proanthocyanidinsr = 0.94, P = 4.17 × 10−34) and the lowest for total flavonols . In the main text, we report the results from the 2014 data set, while results from 2016 are found in the supplementary material.The concentrations of six major polyphenolic groups , total flavonols, total fluorescence, total anthocyanins, and total phloretin-like compounds) and 14 individual phenolic compounds in two different years are presented in Tables Polyphenols showed substantial variation among cultivars Figs.  and S2. r = 0.747, P = 2.48 × 10−24) and significant correlations among epicatechin, catechin, and the three procyanidins . The same locus on chromosome 16 was also detected in the GWAS for total fluorescence, which largely captured the sum of the concentrations of these five compounds together with unidentified compounds of similar chemical structure  and a cinnamyl alcohol dehydrogenase gene (CAD). The other SNP significantly associated with chlorogenic acid (chr15:20077193) was found just slightly more than 100 kb downstream of a 3-dehydroquinate synthase gene  was found 5 Mbp from a phenylalanine ammonia-lyase gene (PAL). The second GWAS hit for 4-O-caffeoylquinic acid (chr14:2530628) was ~2.3Mbp upstream of a 4-coumarate-CoA ligase-like gene  that regulates apple skin color22  varied widely from −0.18 for total flavonols to 0.49 for 4-id Figs.  and S10 24, however our larger sample size of 136 cultivars resulted in a far greater range of values with up to a 10-fold difference in concentration of some polyphenols across cultivars , include the production of polyphenols. Numerous polyphenols have been found at higher concentrations in scab resistant cultivars when compared to scab susceptible cultivars30. In particular, total flavanol concentration in the skin of scab resistant cultivars was found to be 3-fold higher than in susceptible cultivars33. Thus, breeders focussing on scab resistance as a breeding target may simultaneously be enhancing the nutritional value of breeding material by selecting for higher concentrations of polyphenols.Scab resistant cultivars had higher concentrations of quercitrin compared to scab susceptible cultivars Fig. . The natr < 0.5 and were thus strongly affected by either differences in location, year or sources of phenotyping noise  has been identified as a putative candidate gene for this hotspot, as it is thought to catalyze the conversion of leucocyanidin to catechin. However, this region also contains several transcription factors of different classes , which could also be affecting phenolic levels  and/or a flavonoid 3′-hydroxylase (F3′H) gene as potential candidate genes underlying this signal17. In the present study, the most strongly associated SNP with quercitrin was found approximately 44 kb upstream of a UGT gene (MD01G1148700). Plants contain large families of UGTs, and there are dozens or perhaps even hundreds of UGT genes in apples35. UGTs mediate the glycosylation of flavonoids, and quercitrin is produced by the glycosylation of the flavonoid quercetin. The glycosylation of secondary metabolites increases the solubility and stabilization of flavonoid compounds36, and specific UGTs have been identified that glycosylate flavonoids into potent antioxidants like phloridzin35. To the best of our knowledge, however, no specific UGT has been associated with the formation of quercitrin in apples. We hypothesize that the GWAS signal we observed here on chromosome 1 is the result of variation in a specific UGT gene (MD01G1148700) that regulates the glycosylation of quercetin and thus the concentration of quercitrin. To further investigate the function of this UGT gene, we plan to determine whether it in fact uses quercetin as a substrate, and whether the expression of this gene correlates with quercitrin concentration across diverse apple cultivars. Ultimately, markers at this locus could be leveraged for marker-assisted breeding, or the antioxidant content of novel cultivars may be mediated by introducing variation at this locus via genome editing.GWAS peaks for several other phenolic compounds were also observed including for the flavonol, quercitrin, on chromosome 1 Fig. . Other sO-methyltransferase , cinnamyl alcohol dehydrogenase , and 3-dehydroquinate synthase . Both CCOAMT and CAD are enzymes associated with the biosynthesis of hydroxycinnamic acids through the phenylpropanoid pathway, which also supplies intermediates for the synthesis of phytoalexins, flavonoids and tannins37. Although not directly involved in the final step of chlorogenic acid biosynthesis, CCOAMT is active upstream of its production through the conversion of caffeoyl-CoA to feruloyl-CoA38 and has been associated with chlorogenic acid accumulation in coffee39. CAD converts cinnamyl alcohol to cinnamaldehyde. A CAD gene was found to be strongly expressed in ripening receptacle tissue in strawberries40, but, to our knowledge, no CAD gene has yet been characterized in apples. Finally, DHQS is involved in the shikimate pathway by catalyzing key substrates for chlorogenic acid biosynthesis41. All three of these genes represent candidates worthy of future investigation given their previously established relationships to chlorogenic acid production.The GWAS for chlorogenic acid produced two significant hits on chromosomes 5 and 15, suggesting that variation at two independent loci affect this trait Fig. . We iden17, and have suggested shikimate/quinate O-hydroxycinnamoyl transferase (HCT/HQT) genes as potential candidates. While there are no SNPs on chromosome 17 significantly associated with chlorogenic acid in the present study, there was a suggestive GWAS signal on chromosome 17 for chlorogenic acid  or flavonoid 3′,5′-hydroxylase (F3′5′H) as potential candidate genes for the signal they discovered on chromosome 14 for hydroxycinnamic acids, but neither F3′H nor F3′5′H genes were found within the interval we identified on chromosome 14. Although nearly 2.5 Mb from the most significant SNP, our candidate gene for the signal on chromosome 14 was 4-coumarate-CoA ligase-like (4CLL), which converts 4-courmarate into 4-courmaroyl CoA. Interestingly, 4-courmaroyl CoA is a precursor of p-coumaroylquinic acid, which shares QTL intervals with 4-O-caffeoylquinic acid according to previous work16.A stable signal of association for 4-p-coumaroyl-CoA, with cinnamic acid and p-coumaric acid acting as intermediates, is catalyzed sequentially by phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H) and 4-cinnamoyl-CoA ligase (4CL). Although nearly 5 Mb from our association signal for 4-O-caffeoylquinic acid on chromosome 3  may be a candidate gene for the signal we observed here. PAL is an enzyme that catalyzes the production of cinnamic acid, a precursor to the hydroxycinnamic compounds. Because PAL is the starting enzyme of the phenylpropanoid pathway, it plays a crucial role in controlling the biosynthesis of acyl-quinic acids38. The rapid decay of LD we observed in apple GWAS populations highly similar to the one studied here44 suggests it is unlikely that causal alleles will be found megabases away from the association signals detected in the present study. However, we propose these candidates despite this observation because we were unable to accurately quantify the physical distance over which association signals potentially span in this population. With denser marker data and larger sample sizes, a more accurate delimiting of the physical intervals will be achievable in future apple GWAS.The conversion of phenylalanine to e 3 Fig. , phenyla13 and QTL for apple skin color repeatedly co-locate to this genomic region49. A SNP  used to predict skin color by apple breeders48 is located 666 kb upstream from the most significant SNP we identified. However, a recent study identified a retrotransposon insertion 1 kb upstream of the MYB1 gene  that likely causes the red-skinned phenotype49. Although this putatively causal allele was 1.8 Mb downstream from our top GWAS hit, it overlapped with the broad GWAS peak we observed for both cyanidin-3-galactoside and total anthocyanins  of the traits studied here have potential for improvement via genomic selection.Finally, genomic prediction accuracies for polyphenolic concentrations ranged from below 0 (not predictable) to 0.49 Fig. . Using t2) explained by the top SNPs ranged from 0.31 to 0.63  to 6 (severe flesh browning). Scab resistance/susceptibility was scored as a binary trait according to information obtained from breeders upon introduction of cultivars into the orchard.Apple cultivars used for this study originated from the Nova Scotia Fruit Growers’ Association (NSFGA) Cultivar Evaluation Trial (CET) based at Agriculture and Agri-Food Canada’s (AAFC) Kentville Research and Development Centre in Nova Scotia, Canada as described previously54. Next, these phenotypes were correlated with fruit skin color using Pearson’s product-moment correlation test, flesh browning using Spearman’s rank correlation test, and scab resistance presence using the Wilcoxon rank sum test. P values are reported after Bonferonni correction for multiple comparisons for all of these comparisons. All statistical analyses were performed in R55.Following measurements taken at harvest, the remaining fruit were kept at 0.8–1 °C in stacked, perforated plastic bins covered in plastic sheets. After 1 month of storage, fruit were removed for phenolic analyses. The distributions of phenolic phenotypes were visualized using the “geom_boxplot” function in the ggplot2 R packageOf the 136 cultivars evaluated in 2014, 85 were evaluated again in 2016. The 2016 phenotype data were collected from 85 cultivars from the CET that were grafted onto M.9 rootstock in the spring of 2012 and planted in an adjacent orchard in the spring of 2013. We refer to this dataset as the “2016 data”. Cultivars were planted in two different randomized blocks with a single tree in each block. The orchard was maintained to industry standards and no supplemental irrigation applied. Trees were hand thinned in mid-July to adjust crop load to commercial standards of one fruit per cluster, with 10 to 15 cm between each fruit. Fruit were harvested based on maturity assessment using a starch-iodine or starch-to-sugar conversion test, seed color, background fruit skin color, and presence of fruit drop. Following harvest, fruit were kept at 0.8–1 °C in stacked, perforated plastic bins covered in plastic sheets to retain moisture. After 1 month of storage, fruit were removed for phenolic analyses. Apple tissue with peel and flesh was frozen and ground in liquid nitrogen, and stored at −86 °C until extraction. There were 70 common cultivars phenotyped in 2014 and 2016, and we calculated the Pearson correlation between years for each phenotype using the cor.test function in R.56. Briefly, 0.5 g of ground tissue was extracted twice with 0.7 mL of extraction solvent  in micro-centrifuge tubes. The samples were mixed for 10 s, sonicated for 20 min before mixing for another 10 s, followed with centrifugation at 10,000 × g for 10 min at room temperature to pellet suspended tissue . The supernatants from the two extractions were pooled and transferred to weighed microcentrifuge tubes and dried in a vacuum centrifuge (Thermo Fisher) for 16 h, to determine extract yield. The dried extracts were re-dissolved in 1 mL 10% methanol, 0.1% formic acid and mixed via sonicating for 10–15 s then vortexing for 10 s. Extracts were centrifuged at 10,000 × g for 10 min at room temperature and supernatants were transferred to HPLC vials for injection. Meanwhile, the percent dry weight of the tissue samples was also determined at the same time that extractions were conducted, on separate duplicate ~1 g aliquots of the ground tissue, using a vacuum drying oven heated to 100 °C for 24 h.High performance liquid chromatography (HPLC) was used to analyze the phenolic compounds in apple tissue that included both peel and fleshO-caffeoylquinic acid), 4-O-caffeoylquinic acid, phloridzin, quercetin, quercitrin, rutin, isoquercetin, cyanidin-3-glucoside were purchased from Sigma-Aldrich Canada Co.  and used as standards. For phloretin xyloglucoside, the phloridzin standard was used. Standards for procyanidin B1, procyanidin B2, procyanidin C1 and avicularin were purchased from the Indofine Chemical Co. . Standards were used to calibrate the HPLC under the same conditions  to quantify the respective phenolic compounds in the extracts. Two individual extractions were conducted and results were averaged from the replicates.A HPLC system with photo diode array detector (PDA) and fluorescence detector  was used to quantify polyphenols and anthocyanins in the apple extracts (flesh with peel) with a few modifications. Liquid chromatographic separation was achieved using an Agilent Poroshell 120 SB C18 2.7 µ 3.0 × 75 mm column  at room temperature with a flow rate of 0.5 mL/min. The mobile phase consisted of 0.8% trifluoroacetic acid in water (solvent A) and 0.68 % trifluoroacetic acid in acetonitrile (solvent B) with a solvent elution gradient as follows: 0 min: 2% B, 2 min: 2% B, 22 min: 6% B, 30 min: 12% B, 60 min: 35% B, 62 min: 100% B, 64 min: 100% B, 65 min: 2% B, re-equilibrating 10 min before next injection. Injection volume was 30 µL, and detection was 200–600 nm on PDA, extracting chromatograms at 280 nm , 320 nm ), 360 nm  and 520 nm . We also quantified total fluorescence with 228 nm as excitation and 324 nm as emission, which individually detects catechin, epicatechin, procyanidin B1, procyanidin B2 and procyanidin C1. For phloretin-like compounds, we detected and extracted from the UV profiles at 280 nm for the 2014 data because numerous cultivars showed extra phloretin-like peaks not fully captured by the specific compounds quantified using standards listed below. For the 2016 data, however, only three cultivars (‘Coop29′ (11.79 µg/g), ‘Reinette Russet’ (46.16 µg/g) and ‘Britegold’ (4.66 µg/g)) showed other phloretin-related peaks and therefore no results for “total phloretin-like” are shown in Fig. 57 as described previously44 except that reads were aligned to the most recent reference genome version GDDH13 Whole Genome v1.158. Raw VCF files were filtered using VCFtools59 to include only bi-allelic SNPs with a minor allele frequency (MAF) >1%. The two VCF files were merged using a custom perl script that preferentially kept SNPs generated from the PstI-EcoT22I file because these SNPs tended to have higher coverage. SNPs were imputed using LinkImputeR60, allowing for PositionMiss(0.7), SampleMiss(0.7), and Depth(6), which resulted in 154,153 SNPs with an imputation accuracy of 0.956. Finally, two final genotype tables were produced, one for the samples phenotyped in 2014 , and one for the samples phenotyped in 2016 , by removing SNPs within each set of samples that had heterozygosity >90% and MAF < 5% using PLINK61 separately for the 2014 and 2016 data. This resulted in 98,584 SNPs across 136 samples for the 2014 data, and 97,886 SNPs across 85 samples for the 2016 data. The population genetic structure of these samples was previously described in44.Genotype data for the CET were generated via genotyping-by-sequencing (GBS)62 controlling for both population structure (Q) using principal components (PCs) and relatedness using a kinship matrix (K). If the Shapiro-Wilk test for normality produced a value <0.91 for any given phenotype, the phenotype data were transformed to improve normality using either the log or square-root transformation. The MLMM is a modified mixed linear model that uses stepwise regression to incorporate significant SNP markers as cofactors. For this study, the optimal MLMM model for each phenotype was selected using the extended Bayesian information criterion (EBIC). The percentage of variance explained by the SNPs included in the selected model was determined from the partitioning of phenotypic variance for each forward inclusion and backward elimination of the model. The significance threshold for MLMM was determined separately for the 2014 and 2016 data, using the “simpleM” package in R63, which calculates the effective number of independent tests (Meff). The significance threshold was drawn as -log10(α/Meff) where α was set to 0.05 (dashed line in GWAS plots). If a SNP crossed the Meff threshold and was deemed significantly associated with a phenotype, a 100 kbp window centered on that SNP was explored for candidate genes using the Genome Database for Rosaceae (GDR)64. Keyword searches for genes on chromosomes with significant GWAS results were also completed using GDDH13 Whole Genome v1.1 (https://www.rosaceae.org/search/genes).GWAS was conducted using the multi-locus mixed model (MLMM) R package65. The rrBLUP model was selected and 5-fold (nFold = 5) cross-validation was repeated 3 times (nFold.reps = 3) using the same SNP sets used for GWAS. All other default parameters were used. The correlation of the genomic prediction accuracy (r) between the 2014 and 2016 data sets was calculated using a Pearson’s correlation.Genomic prediction was performed using the “x.val” function in the R package PopVarAll phenotype data are available in the Supplementary material. The genotype data are available from the Dryad Digital Repository: 10.5061/dryad.8fb46m5.Supplementary materialsSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4