Quercus spp.) are widespread hardwood trees in the Northern Hemisphere and of high ecological, economic, and social values. Optimal experimental design of genetic trials is essential for accurate estimates of genetic parameters and improving the genetic merit of breeding stock. Here, we evaluate the use of post hoc row-column factors combined with spatial adjustment to improve genetic analyses of parents and individual trees in field progeny tests of plantation hardwoods, using cherrybark oak (Quercus pagoda Raf.) as an example. For tree height, post hoc incomplete blocking reduced ~14% more of the within-block environmental variance compared to the randomized complete block design (RCBD) model. Incomplete blocking also improved the heritability estimates for height by 7% to 14% compared to the original RCBD model. No clinal trend for growth breeding values was identified due to provenances. Our approach warrants the initial selection for height as early as age ~10 based on its moderate narrow-sense heritability of 0.2; however, diameter and volume need longer evaluation times. The post hoc incomplete blocking is more efficient and promising to improve the genetic analysis of Q. pagoda to minimize the environmental heterogeneity influences. Adjusting competition and spatial effects, including the distance principal components and autoregressive residual structure notably improves the model fit based on the observed reductions in AICs and BICs. Employing our approach is promising for hardwood genetic improvement in the southern USA.Oaks ( Quercus spp.) are critical forest resources for the natural environment, society, and cultural heritage of human beings throughout Euro-Asia and America [Oaks , and volume growth (h2 = 0.2–0.4) at ages 10–15, with family heritability as high as 0.5–0.7 [Previous small scale studies demonstrated some information of the genetic architecture of the species to date. The local population showed better adaptation and growth in a regional study while seedling survival and early growth showed less intraspecific variation in the main habitat range in Mississippi , 11, 18. 0.5–0.7 . The impThe traditional randomized complete block design (RCBD) and incomplete block design (ICBD) have been frequently employed as the standard designs in progeny testing due to their simplicity and robustness for exploiting genetic variability –21. For The spatial related within-site environmental variations can be continuous  or discopost hoc blocking in both forestry and crop sciences [post hoc adjustment method uses the existing design information more efficiently to improve the accuracy and reliability of genetic parameters of tested materials [To improve the genetic parameter estimates by reducing the spatial and environmental variation on micro-site and within replicates, various approaches have been developed at both the design and statistical analytics stages including, row-column , spatialsciences , 27, 28.aterials , 27, by post hoc experiments utilized the within-block effect to adjust the competition from neighboring trees [Post hoc analyses and improved experimental designs offer advanced analytical tools that better control inter and intra block environment variations in the genetic trials and improve the breeding and selection results [Competition as a typical ‘noise’ in the genetic trials causes the within-site variation , 29, along trees . Post ho results , 34.Quercus pagoda Raf. require greater spacing for crown development and radial growth, allowing for greater environmental variation; thus, modeling with spatial effect, row-column, and incomplete blocking are promising for improving the genetic parameter estimates in this species.In forest genetic trials, the spatial adjustment has been applied in the RCBD and ICBD designs to dissect the spatially correlated variation out of the random environment residual , 36. In Q. pagoda, focusing its efforts on improving volume production. In this study, we used WGFTIP Quercus pagoda Raf. progeny tests to evaluate the post hoc adjustment of row-column factors in field progeny tests originally established utilizing RCBD. Here we tested the post hoc treatments incorporated with spatial analyses to account for a more heterogeneous variance structure compared to traditional RCBD testing. We hypothesis that the post hoc method increases the signal-to-noise ratio for the genetic variances and improves genetic analyses of RCBD tests. Furthermore, we will assess the genotype-by-environment (GXE) interaction for growth in the region.The Western Gulf Forest Tree Improvement Program (WGFTIP) of the Texas A&M Forest Service has the only region-wide breeding program for Q. pagoda since the late 1970s. The first-generation population consisted of 305 selections made across 51 counties in four states: Texas (TX), Arkansas (AR), Louisiana (LA), and Mississippi (MS). These selections were evaluated for survival and growth in region-wide progeny tests from which 62 half-sib second-generation selections were made in the top 20% of the families  based on age-15 growth evaluations. The second-generation open-pollinated progenies were tested in two test series by the Texas A&M Forest Service (TFS), the Arkansas Forestry Commission (AFC), and the Mississippi Forestry Commission (MFC). (3) was calculated as 0.02618*DBH2*Height (The Western Gulf Forest Tree Improvement Program (WGFTIP) has operated an improvement program for n (MFC). . Six pro2*Height . In our post hoc blocking methods. Test layouts generally consisted of between four and six tiers of blocks running north to south and four to 10 blocks in the east-west direction. Continuous row and column coordinates were created based on the individual site map in each trial. We kept the rows in the direction of latitude (South->North) and the columns are from west to east or the reverse as east to west . All missing and filler trees were counted to create the complete trial map. From previous studies, complete individual tree grids benefit computational speed [post hoc blocking methods and example cases are demonstrated in [We used modified-complete and incomplete (sub) al speed , 40. Forrated in .dist function in R based on the distances between each pair of individual trees. A distance matrix was made based on the spatial coordinates of each tree, of which the dimension was #row x # column per site. We created a principal component analysis (PCA) derived distance index from the distance matrix by extracting the first three principal components as three neighboring distances to capture the variability due to spatial location and distance with the prcomp function in R. The first to the third principal components  were extracted for each tree based on the distance matrix. They summarized the overall spatial distances of individual trees. Based on the first three distance principal components, 98%-100% of the total environmental variance was explained.The distance matrix for individual trees was calculated with the The nearest neighbor competition index was categorized from 0 to 4 based on the radius of each individual  tree to the most adjacent tree which was an approximation of neighbor interaction due to the spacing and location. We employed the distance matrix to identify the nearest neighbors at the one-unit radius due to the same spacing distance in the row and column direction of the trial as 3.05m. The number of adjacent trees within the radius was recorded for individual trees as the competition index: zero meant no close tree—the lowest competition level; and four indicated the highest competition with four trees nearby. The competition index was treated as a semi-categorical index. The competition condition varied by site and lower spatial heterogeneity was expected to enable the less biased estimation of competition , 33.Acronyms for blocking, spatial and model factors are listed in post hoc treatment statistical analyses for predicting the breeding values of female families were carried out: (1) single-site analyses to estimate the breeding values which is shown in the appendix; (2) analyses of each series; (3) combined analyses of two series (MET). Single series analyses were not reported here due to their similar trends as in the MET and relatively higher standard errors of variance components for the same population tested at the same breeding regions.Three β including the trial effect, row effect and column effect nested within the trial respectively; series and provenance effect were not fitted here but considered in the original randomized block design models  without the post hoc information; a denotes the random vector of additive genetic effect, with a A is the pedigree kinship matrix; ae is the random additive genetic-by-environment interaction effect with ae~ th progeny test; row and column are the nested random row/column effect in each test with row~1 to Z5 denote the incidence design matrices relating following random effects a, ae, r, row, and column, respectively, to observations Y. Post hoc factors were listed in the The linear model of a multi-environment trial (MET) of growth traits isβ) and best linear unbiased prediction (BLUP) of random effects  are solutions to the following mixed model equations,The best linear unbiased estimation (BLUE) of fixed effect , ρ is the correlation coefficient at i-direction (row) or j direction (column). R = Σrow ⊗ Σcolumn containing the correlation coefficients ρ, where Σrow and Σcolumn are the row and column correlation matrices [th index of trial, the total number of trials; the simple residual variance-covariance is as MVN.For MET analyses, except the models with simple residual variance, all other models  had two dimensional first-order autoregressive  variance of residual given 1+σe2i⊗I , where σmatrices ; for thepost hoc adjustments. The variance structure and details of predictors are listed in After the pre-assessment of genetic parameters of single sites, the AFC series 1 and TFS series 2 sites were dropped from the MET analysis due to the low genetic control for selection potential in the MET. Thus, only four tests were reported in the MET results, with two series and three states still covered by the four trials. The single-site analyses covered all six trials with the 1. Simple residual variance (SUBBLOCKING) model applied the sub-blocking (random effects) with the 1st order autoregressive variance of residual;2. Heterogeneous residual (SUBHETERO) model used the sub-blocking (random effects) and the 1st order autoregressive variance of residual plus the heterogeneous variance-covariance structure for the spatial independent residual;3. Nearest neighbor residual covariance with simple residual variance (SUBNN) model employed sub-blocking (random effects), competition index based on the nearest number of trees (fixed effects), and the 1st order autoregressive residual variance;4. Nearest neighbor distance with heterogeneous residual variance (SUBNNHETERO) model used the sub-blocking (random effects), competition index based on the nearest number of trees (fixed effects), and the 1st order autoregressive variance of residual plus the heterogeneous variance-covariance structure for the spatial independent residual;5. Nearest neighbor distance covariate with heterogeneous residual variance (SUBNNPCA1) model employed sub-blocking (fixed effects), principal components 1–3 based on the distance matrix (fixed effects), and the 1st order autoregressive variance of residual;6. Combined nearest neighbor distance covariate with simple residual variance (SUBNNPCA2) model used sub-blocking (fixed effects), competition index based on the nearest number of trees (fixed effects), principal components 1–3 based on the distance matrix (fixed effects), and the simple variance of residual;7. Simple residual variance (BLOCKING) model uses the complete blocking (fixed effects) with the 1st order autoregressive variance of residual;8. Heterogeneous residual variance (BLKHETERO): the complete blocking (fixed effects) and the 1st order autoregressive variance of residual plus the heterogeneous variance-covariance structure for the spatial independent residual;9. Nearest neighbor covariate with simple residual variance (BLKNN): the complete blocking (fixed effects), competition index based on the nearest number of trees (fixed effects), and the 1st order autoregressive variance of residual;10. Nearest neighbor distance covariate with heterogeneous residual variance (BLKNNHETERO): the complete blocking (random effects), competition index based on the nearest number of trees (fixed effects), and the 1st order autoregressive variance of residual plus the heterogeneous variance-covariance structure for the spatial independent residual;11. Nearest neighbor combined covariate with heterogeneous residual variance (NNPCA1): the complete blocking (fixed effects), principal components 1–3 based on the distance matrix (fixed effects), and the 1st order autoregressive variance of residual;12. Combined nearest neighbor and distance covariates with simple residual variance (NNPCA2): the complete blocking (fixed effects) competition index based on the nearest number of trees (fixed effects), principal components 1–3 based on the distance matrix (fixed effects), and the simple variance of residual; the trial was all fitted as the fixed effect in all the twelve models for the multi-environment tests and details of the model structure were listed previously.We estimated the narrow-sense heritability for multiple site analyses asee model , 4; σAE2A, phenotypic CVP, and random environment CVE,Type-B genetic correlation was estimated as follows2 and fi were the prediction error variance and the inbreeding coefficient of ith individual tree, respectively. We used ASReml-R v3.0 [chart.correlation of the R package PerformanceAnalytics. Other bar charts were constructed with the ggplot2 package in R. Map data were provided from the urbnmapr package in R.Accuracies of breeding values were calculated as followsl-R v3.0  to fit tl-R v3.0 . The corB) of height were 0.9±0.2~1.0±<0.01 demonstrating little to no additive genetic-by-environment interaction (GxE) effect  was not significantly different from zero or estimable for most of the traits . Type-B ) effect , Table 3r height .The AFC1 and TFS2 tests showed negligible narrow-sense heritability and additive genetic variance and were dropped from the combined MET that included 2,776 trees from 51 families across three breeding regions. Narrow-sense heritabilities and standard errors for height, DBH, volume, and survival were 0.17±0.05, 0.08±0.04, 0.07±0.04, and 0.10±0.03 respectively in the MET of the four tests on average of all models evaluated here .A) for height and DBH was 6% and 9% and dropped to 0.4% for volume and 0.1% for survival. For the phenotypic coefficients of variation (CVP), the estimates were ~32%±1%, 114%±4%, 6%±0.2%, and 0.9%±<0.1% for height, DBH, volume, and survival  and Bayesian Information Criterion (BIC). AIC was calculated as  freedom . The mod freedom .We took the complete blocking (BLK) as the benchmark for AIC  and BIC Heritability estimates were improved by 13–14% by using incomplete blocking methods for height, DBH, and volume than the original RCBD method . HeritabE) by 14% for HT  of complete blocking was approximately 10% higher than the counterparts of incomplete blocking methods for HT, DBH, and volume. DBH and volume had a smaller difference in VCOL and VROW between the complete and incomplete blocking methods.  ranged from 0.05 to 0.08 (m2) for HT and block  of the complete blocking models ranged from 0.10 to 0.15; both variances were lower than corresponding row/column variances of the incomplete blocking models.  ranged from 0.2 to 0.6 (m2) and random residual (E) ranged from 2.0 to 2.3 (m2) for HT. Variances from complete blocking were higher than from the incomplete blocking models . Thus, no quadratic clinal variation along the latitude or longitude for HT was identified.We found insignificant correlation between the geographic gradients and the breeding values of the 2d volume . Those wPost hoc blocking with incomplete (SUB-) and complete blocking (BLK-) both improved the genetic parameter estimates of Q. pagoda compared to the original RCBD. Noteworthily, the SUB models outperformed the counterparts of BLK methods. Neighboring effects  were necessary to improve the model fit in both groups of post hoc blocking methods. Incomplete blocking has not been frequently tested or applied in genetic tests of Quercus species and this study provides variance comparison of multiple model methods grouped into two blocking types in terms of model fitting and genetic parameter estimates.E reduction of height by 14% was slightly lower than previous findings of Pseudotsuga menziesii after using the spatial effect adjustment [E than height. When the site preparation and planning are cautious and effective, the spatial effect residing in the VE of the original block tends to decline [Our results show that the spatial effect was explicitly partitioned out from the residual variance so that the model fitting and heritability estimates were improved by 1–7% accordingly on average for all growth traits. The Vjustment . DBH and decline .Q. pagoda trial are partially due to the root shoot ratio, crown growth characteristics, and shade sensitivity [The biological reasons for such spatial effect in the typical sitivity . Thus, ssitivity . At the sitivity . After tThe competition effect demonstrated by the nearest neighbor models using a competition index was associated with the tree spacing and was observed in traits such as survival, diameter, and volume. If the neighboring distance partially contributes to the magnitude of the micro-environment residual, spatial adjustment is necessary to account for the biological characteristics in BLUP.post hoc adjustment factors examined in this study, were determined by the local trial layout. Thus, the differences of row and column were partially due to the arbitrary mapping and coordinates of trees per block. Other studies following the same manner may have different trends of row-column variance that are dependent on the ordination and geometry of the layout. If the two directions are orthogonal regardless of the geographic ordination, the post hoc row-column method can be applied [COL and VROW were slightly lower than the VREP of the original RCBD and such trends were associated with incorporated spatial predictors, the fixed effects in the linear mixed models.Column and row, two  applied . The sumQ. pagoda will inform the tree improvement and afforestation program for greater volume and survival for nursery seedling stock and timber production [Our region-wide investigation of genetic parameters of oduction , as welloduction , which w2 compared to the original RCBD. Low heritability brought an obstacle for breeding specific commercial traits such as DBH and volume. Long-term METs are required for further assessing the genetic parameters before the rotation age. DBH evaluation is promising at a later growing stage while age-10 is sufficient for height selection at high performing sites where crown and floor vegetation are well managed. It is necessary to update the experimental design scheme and layout with more gap size that addresses the horizontal competition of adjacent tested trees in the future, especially for the DBH and volume assessment of Q. pagoda.We could not demonstrate the specific cap value of heritability for the second-generation or earlier selections (< age 10); however, multiple methods resemble similar levels of genetic parameters which were improved by 1%~14% of hQ. pagoda as previous research suggests efficient genetic evaluation may not occur until as late as age 15 due to the following factors: 1) required crown growth and canopy structure for diameter selection compared to commercial coniferous species [Pinus taeda) and Douglas-fir (Pseudotsuga menziesii) in the genetic test setting, 2) soil requirements, and 3) root growth and competition due to spacing. Maintaining adequate survival may benefit selection as early as age 10 even with larger spacing [Earlier screening of families in genetic trials advances the genetic gain per time unit or breeding cycle . However species , such as spacing . The impPoor parameter estimation in genetic trials will limit the full expression of genetic variation and genetic controls for growth under the age 15, or even less than 10 , which ipost hoc methods generated models with improved AIC and BIC, resulting in moderately improved heritabilities compared with results using the original RCBD design, especially for height. The incomplete subblocking with spatial distance adjustment (SUBNNPC1) is preferred due to smaller blocking size and adjustment of neighboring distance, followed by the SUBNNPC2 method which introduced neighboring competition predictors with less payback on AIC/BIC improvement. Subblocking (SUBB) provided greater estimates of heritability. Computation time can be a concern. However, in this study both NNPCA1 and NNPCA2 models converged quickly, probably due to the moderate size of the samples and the number of sites. The converge time for both models were similar, although NNPCA2 took a little bit longer.Our results suggested the incomplete blocking with the The within-blocks sub-blocking method explained untapped environment heterogeneity because the original RCBD blocks potentially violate the homogeneity variance assumption of the linear model , 28 and post hoc row-column is preferred to adjust the global gradients of the field test [A previous study also reported that complete blocking with eld test ; howeverQ. pagoda should be 15 years rather than at age 10, especially in northern sites. This current operational standard confirms with other studies when survival declines from age 10 to 15 years [Standard operating procedures within the Western Gulf Forest Tree Improvement Program (WGFTIP) include progeny test evaluations at ages 5, 10, 15 and 20, with selections made at the earliest ages (5 and 10) to advance gains into the breeding and deployment populations as rapidly as possible. Based on our results the recommended selection age for volume and DBH in 15 years . After tSeed zones consolidation for volume improvement could be considered due to limited GxE interactions. Although the current breeding region does not address multiple climatic variables, the augmented zone still allows the evaluation of the best families with stable performance. East Louisiana and West Mississippi families are elite candidate pools for volume and height growth which agrees with the previous findings in a smaller regional study . Those fa priori in the establishment of future tests may allow for earlier selections through the resulting higher heritabilities for selection traits and through the reduction of residual environmental variances responsible for the analytical noise complicating selection decisions.While the use of blocking and nearest neighbor distance measurements had limited impacts on the current selection strategies employed by the WGFTIP in its cherrybark oak program over the whole breeding regions, our results do show that use of these techniques S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.S5 Fig(DOCX)Click here for additional data file.S6 Fig(DOCX)Click here for additional data file.S7 Fig(DOCX)Click here for additional data file.S8 Fig(DOCX)Click here for additional data file.S9 Fig(DOCX)Click here for additional data file.S10 Fig(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S1 DatasetA data table in comma separated values (CSV) format containing all the measurement data for individual trees at six trials. The first 11 rows describe the variables of the table.(CSV)Click here for additional data file.


PCSK9 loss of function prevented the development of AAA in a preclinical mouse model.Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor β signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that  Genome-wide association meta-analysis of AAA identifies 121 independent risk loci and highlights potential therapeutic targets such as proprotein convertase, subtilisin/kexin-type 9 (PCSK9). Approximately 4% of the US population over 65 years of age is affected by AAA, resulting in ~41,000 deaths annually3, because men develop AAA at three to four times the rate of women4 and smoking is a key risk factor5. The mainstay of management is longitudinal surveillance until the aneurysm size reaches the point at which the risk of rupture exceeds the risk of repair6. This disease surveillance period, which may last several years, represents an ideal opportunity to intervene and prevent disease progression. Unfortunately, there are currently no approved pharmacological therapies for the prevention and treatment of AAA. Although multiple pharmacological therapies have been previously proposed, based on compelling biology and promising evidence from preclinical model systems, including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, matrix metalloproteinase inhibitors and statins, to date, none have been shown to affect aneurysm growth or rupture in human trials6.AAA is often discovered incidentally or as a result of screening programs in certain demographic groups. Current US Preventive Services Task Force guidelines recommend screening via duplex ultrasonography in men aged 65–75 years who have ever smoked8. Previous genome-wide association studies (GWAS) of AAA have revealed 24 genomic risk loci for AAA13, but a substantial portion of AAA heritability remains unexplained. Here we leveraged genetic data across 17 studies to (1) perform a genetic discovery analysis for AAA with substantially higher numbers of participants with AAA than previous studies (fivefold increase); (2) create and test the predictive power of polygenic risk score (PRS) derived from this analysis; (3) prioritize causal genes and pathways leading to disease; (4) explore the spectrum of phenotypic consequences associated with AAA risk variants and (5) identify potential therapeutic targets that may help prevent and treat AAA.Over the last two decades, large-scale genetic analyses have been instrumental in revealing new targets and promising therapies for atherosclerotic conditionsP < 5 × 10−8; Supplementary Fig. P > 0.05/126) of effect estimates among the contributing GWAS (Supplementary Table P < 0.05) in both datasets as sensitivity analysis. Among 126 index variants, three rare variants  < 0.01) failed to meet this threshold  that were not included in the meta-analysis. Of 121 genome-wide significant loci, 97 were not previously reported  and the rest common (MAF > 0.05). As expected, by substantially (~5-fold) increasing the number of participants with AAA compared to previous reports13, we were able to identify variants with lower effect estimates , the largest GWAS of AAA before this study. These two sets of weights were then used to calculate PRS in three external validation cohorts, two with control groups representative of the general population (UK Aneurysm Growth Study-UK Biobank (UKAGS-UKB) and Aneurysm Consortium-Wellcome Trust Case Control Consortium (AC-WTCCC)) and one representative of populations with cardiovascular disease ). The AAAgen PRS (area under the curve (AUC); UKAGS-UKB: 0.69, AC-WTCCC: 0.66) significantly (P < 0.0001) outperformed that of Klarin et al.13 PRS  in the population-based validation cohorts, explaining an additional 13–14% of disease-associated variance in these cohorts. AAAgen PRS performed less well in the SMART cohort, in which the controls also had prevalent  cardiovascular disease, but still outperformed the PRS from Ref. 13 . This also indicates the utility of PRS in distinguishing aneurysmal risk from broad cardiovascular risks for clinical applications ons Fig. .Δ = 0.054). This model additionally showed superiority over the model with clinical factors only when comparing the model fit . We did not notice any notable optimism bias in the C-indexes when bootstrapping the statistics.To further evaluate the predictive power of the current PRS on incident AAA, we conducted analyses in data from the UK Biobank  < 0.01 and P < 10−5  heritability in H3K27ac (P = 1.8 × 10−5), H3K4me1 (P = 2.6 × 10−5) and H3K4me3 (P = 7.8 × 10−5) marks in aorta rta Fig. . This in17 was analyzed using RolyPoly18. This regression-based polygenic model uses GWAS summary statistics and scRNA data to learn a regression coefficient that captures each cell type’s influence on the variance of GWAS effect estimates. Using regression coefficients, 4 of 8 cell types were found to be enriched (P < 0.05/8), with endothelial (P = 1.1 × 10−10) and smooth muscle (P = 1.4 × 10−9) cells being most strongly associated with AAA , transforming growth factor beta (TGF-β) signaling , cytokine activity/receptor binding  and growth factor activity/receptor binding . The most substantially enriched GO cellular component term was the collagen-containing extracellular matrix  with transcriptomic analysis of aortic tissue from mouse models of AAA . Whereas all five genes were upregulated in both the TWAS prediction and in the mouse data, four genes  were upregulated while 1 (MFAP2) was downregulated, when comparing expression in aneurysmal to nonaneurysmal abdominal aortic tissue. Two of these five genes (LIPA and PLTP) likely act through lipids and atherosclerosis. Of the remaining gene products, MRC2 functions via extracellular matrix remodeling in conjunction with urokinase plasminogen activator and its receptor22 ; a microfibrillar glycoprotein (MFAP2) known to bind to FBN1 and FBN2 (ref. 23) and a serine/threonine protein kinase (PRKD2) involved in regulation of cell proliferation via inhibition of HDAC7 (ref. 24), encoded by a gene also prioritized by the GWAS.We further validated the 11 genes using qPCRnes Fig.  were dif25, which is characterized by dilation of the aortic root or the ascending/descending aorta nearest the heart itself, this GWAS provides human genetic evidence of the involvement of this pathway in AAA as well and suggests shared biology between the two diseases. To further investigate the overlap between AAA and TAA genes, we queried the association of AAA index variants in a recent TAA GWAS 26 and observed 24 AAA index variants associated with TAA at P < 0.05. These variants are generally associated with AAA with weaker effect estimates compared to TAA  and TCF7L2  are also associated with lipid traits, the effect estimates for lipid traits are relatively weaker than either type of aneurysm for these variants  involved in vascular development and/or extracellular matrix organization. Among Renard’s 53 genes, we observed a genome-wide significant variant for AAA within 1 Mb of 14 . Of note, 12 of the 14 genes were supported by at least three indicators in the gene prioritization step and nine  were prioritized as the likely causal genes. Although the contribution of the abovementioned pathways is evident in both AAA and TAA, stronger effect estimates or monogenic mechanisms likely indicate a stronger contribution to TAA compared to AAA. In contrast, differences in overlap with lipid metabolism genes likely indicate larger involvement of lipid levels as a risk factor for AAA compared to TAA. Consistent with this, we observed a significant genetic correlation between lipids and AAA, but not TAA  that were genome-wide significant (P < 5 × 10−8) in at least one of these traits  and 9 loci with blood pressure  also potentially contributed to AAA .Given the strong contribution of lipid biology to the pathogenesis of AAA identified in our gene prioritization and PheWAS analyses, we next sought to prioritize the role of circulating lipoproteins on AAA risk. We first performed conventional inverse-variance weighted MR, finding associations between each major lipoprotein-related trait (nonhigh-density lipoprotein cholesterol (non-HDL-C), LDL cholesterol (LDL-C), HDL-C, triglycerides, ApoA1, and ApoB) and AAA . In addition, higher genetically predicted circulating apolipoprotein A5 (APOA5) was associated with decreased AAA risk. Hyperlipidemic mice overexpressing Apoa5 have been shown to have markedly decreased circulating remnant lipoprotein particles37, and individuals with rare protein-altering variants in APOA5 were observed to have increased levels of remnant cholesterol38, providing further evidence of the effects of remnant cholesterol on AAA risk. These findings further support the role of lipid biology in AAA pathogenesis and the concept of lipid manipulation to prevent and treat AAA.We next sought to leverage our genetic data to screen for possible new therapeutic targets for AAA. Leveraging MR to test the association of genetically predicted circulating protein levels on genetic liability to AAA, we identified 23 putatively causal protein–AAA association Fig. . Notably39. Using a porcine pancreatic elastase (PPE) infusion model of AAA in C57BL/6J mice  from day 10 until day 28 for Pcsk9 null mice compared with wild-type mice with brightness modulation (B-mode) ultrasound imaging performed 7, 14, 21 and 28 d after PPE infusion . As with previous studies43, Pcsk9−/− mice had lower plasma cholesterol, LDL and HDL levels compared to wild-type C57BL/6J mice  are emerging therapies for cardiovascular disease but remain unstudied in patients with aneurysm. More broadly, our findings suggest that AAA shares important pathobiology with other forms of atherosclerotic vascular diseases and should be considered on this disease spectrum.Second, the genetic results highlight the critical role of lipids and lipid metabolism in AAA pathogenesis. Previous work has demonstrated the likely causal relationship between lipids and AAA49, human genetic evidence has overwhelmingly suggested that LDL-C reduction is likely to reduce AAA risk. PCSK9 inhibitors have been shown to dramatically reduce LDL-C [39] to levels well below those seen with other lipid lowering therapies.39 In addition, our Pcsk9 null mouse model demonstrated reduced AAA growth following elastase infusion in the absence of a hyperlipidemic background, although small statistical differences in lipid fractions were observed between mice strata. Taken together, these data suggest that the relationship between circulating PCSK9 protein and AAA warrants further investigation.Finally, our results lend human genetic support to PCSK9 inhibition as a potential therapeutic strategy for AAA. Although current data on the effects of statins on aortic expansion are conflictedCELSR2 or PLTP instead of SORT1 or MMP9, respectively. We acknowledge that our gene prioritization scheme is imperfect, as are all current methods for gene prioritization. Fourth, MR methods examining the effects of PCSK9 inhibition on AAA reflect lifelong exposures to reduced LDL-C levels and may not represent the more acute lipid changes associated with drug administration later in life. Finally, although our analysis suggests that LDL-C reduction through PCSK9 inhibition is likely to reduce AAA risk, it is unclear whether this treatment is likely to mitigate the progression of the disease once diagnosed. Although the results from our mouse model suggest this to be the case, further investigation into the effects of PCSK9 inhibition on AAA growth and rupture is warranted.Our study should be interpreted within the context of its limitations. While this stands as the largest genetic analysis to date for AAA, the demographics of the cases in our study were overwhelmingly male and EUR ancestry, thus our ability to detect sex-specific or ancestry-specific genetic associations was limited. Second, our AAA phenotype for many of the cohorts is based on electronic health record data that may have resulted in some misclassification of case status. Such misclassification should, however, reduce statistical power for discovery and, on average, bias results toward the null. Third, for a small number of loci our strategy of gene prioritization identified genes that are not well supported by literature. For example, PCSK9. These results are demonstrative of how large-scale analyses of human genetic variation coupled with clinical data can be leveraged and used for the treatment of understudied diseases.In summary, we identified 141 independent variants associated with AAA risk, prioritized candidate functional genes at these loci that implicated biological pathways, developed an improved and effective PRS, explored the phenotypic consequences of AAA risk variants through PheWAS, identified candidate causal AAA genes and multiple lipid pathways and genes that may be targeted for AAA risk reduction, including 12. Although we observed some heterogeneity, the effect estimates were generally consistent among well-powered studies  as described below.Independent loci were defined as variants >1 Mb and >0.25 cM apart with at least one genetic variant associated with AAA at a genome-wide significance threshold of https://r7.finngen.fi/pheno/I9_ABAORTANEUR). Additionally, we used two PRS validation sets  from Finngen . Iterations were performed at each locus until a minimum number of independently, genome-wide significant SNPs were identified. The maximum number of iterations performed was 4 at the rs10455872 locus. Next, we performed a joint analysis of variants using cojo-joint with the same reference panel and excluded variants that were not genome-wide significant in the joint analysis.To perform conditional analysis, we first defined the loci as ±1 Mb from each of the 121 index SNPs. GCTA COJO14 that calculates posterior SNP effect estimates or weights from original GWAS effect estimates using a Bayesian approach. We used default parameters of PRScs, allowing the method to generate the global shrinkage parameter phi through a Bayesian approach. PRScs use information from an external LD reference panel for this calculation. PRScs calculated posterior SNP effect sizes using a precomputed LD reference panel from EUR ancestry (ldblk_1kg_eur.tar.gz). These effect estimates were used to calculate AAA PRS of individuals in validation cohorts using PLINK2 --score command52. Validation cohorts for PRS are described in the A PRS represents an individual’s risk of a given disease conferred by the sum of the effects of many common DNA sequence variants. A weight is assigned to each genetic variant based on its strength of association with disease risk. Individuals are then additively scored in a weighted fashion based on the number of risk alleles they carry for each variant in the PRS. To calculate PRS, we used PRScs50, followed by the calculation of PRS weights by PRScs14, as described above. The UKBB hospital registry data and cause of death data up to March 2020 were used to test the predictive performance of the AAAgen PRS using Cox proportional hazards models. The PRS was adjusted with four first PCs and inverse normalized for the analysis. The analysis only included the white British subset due to the low number of AAA cases in the other ancestries. All variables used in the different models are listed in the Supplementary Table n = 213) were excluded from the analyses and individuals who deceased during the follow-up for other causes than AAA were censored. The final analysis included 838 incident AAA cases and 329,983 noncases with median follow-up time of 5.04 and 10.0, respectively. All Cox models were fitted using the R function coxph. We further tested the models for optimism, and optimism-corrected C-indexes were calculated using R package rms function validate with method=‘boot’ and 100 repetitions. The model fit of the nested Cox models was compared using likelihood ratio test implemented in R with function anova.We performed another meta-analysis without UKBB by METAL15. DEPICT uses reconstituted gene sets, consisting of 14,462 gene sets obtained from multiple sources and reconstituted using 77,840 publicly available microarray expression datasets. In the reconstituted gene sets, every gene in the human genome is assigned a z score for membership in the set. Using genes from GWAS loci, DEPICT calculates an enrichment P value for reconstituted gene sets. We used AAA summary statistics and clumping with a threshold of 5 × 10−8 as input to DEPICT. From the output, gene sets with FDR < 0.01 and P value < 10−5 are listed in Supplementary Table 53. These ‘exemplar’ gene sets are highlighted in Fig. We performed gene-set enrichment analysis using DEPICT16, a method for partitioning heritability. Using GWAS summary statistics and tissue-specific chromatin marks, this method calculates the enrichment of per-SNP heritability. First, hapmap3 variants with MAF ≥ 0.01 were obtained from the AAA summary statistics using munge_sumstats.py. Then, the analysis was performed using the–-h2-cts option in LDSC. We reported P values of enrichment of per-SNP heritability in various chromatin marks from ENCODE and Roadmap Epigenome.Calculation of enrichment in specific tissue types was done by stratified LDSC17. Calculation of enrichment in specific cell types using scRNA-seq data was performed using RolyPoly18. RolyPoly uses a regression-based polygenic model to prioritize relevant cell types from GWAS summary statistics and scRNA-seq data. Average expression per cell type was obtained by using Seurat54 AverageExpression function. This generates a matrix with genes as rows and cell types as columns. Columns were normalized using normalize.quantiles, and rows were normalized using scale function in R. Because RolyPoly does not work with negative values, we used the absolute values for each entry. To link gene expression with GWAS variants, we used block annotation of 25 kb around the gene’s start. For LD statistics, we used 1000 Genomes Phase 3 data precomputed by RolyPoly. We ran the function rolypoly_roll with 200 bootstrap iterations. P values from the bootstrap analyses were reported.Processing of scRNA sequencing data from the aorta is described in ref. 55 was used to predict the molecular consequences of these variants. Genes with at least one variant (index or in LD > 0.8) altering amino acid (missense variant) from VEP output were reported. We observed 21 loci with such genes  with index variants using 42,119 unrelated EUR individuals from CHIP + MGI. Variant effect predictor (VEP)28 that potentially causes monogenic form of heritable TAA and dissection was incorporated. For a range of related/risk factor phenotypes and disorders  that are within 1 Mb of index variant were incorporated. We found 48 loci with at least one gene from these two datasets.A list of 53 genes curated in ref. 57, a similarity-based gene prioritization method that uses a large set of publicly available RNA sequencing, curated pathway annotation and predicted protein–protein interaction datasets. PoPS was developed with the assumption that causal genes share similar functional characteristics. First, PoPS calculates gene level association statistics from GWAS variant level association statistics and MAGMA gene annotations. Second, features are selected based on precomputed statistics from public resources. Finally, PoPS computes a score for each gene. Based on these scores, for each genome-wide significant locus, we ranked the genes within 1 Mb (either direction) of the index variant and reported the gene with the highest score as the gene prioritized by PoPS. Of 121 loci, 111 loci had at least one gene with a score in the top 10% of PoPS  no association with either trait; (H1/H2) association with one of the two tested traits; (H3) association for both traits but different causal variants and (H4) association for both traits with the same causal variant. A high posterior probability for H4 (PP4) indicates colocalization of the two trait associations. Colocalization was performed with eQTLs in aorta, liver, whole blood, adipose subcutaneous and visceral omentum from The Genotype-Tissue Expression (GTEx) project v8 (ref. 20). Variants within 500 kb in either direction of GWAS index variants were extracted for the analyses. A threshold of PP4 > 0.5 was used as evidence of colocalization for identifying candidate genes at stage 1 of gene prioritization. This analysis identified 82 loci with at least one gene  of the aorta, liver, whole blood, adipose subcutaneous and visceral omentum from GTEx v8 (ref. 20). For each tissue, significance threshold was decided with correction for multiple testing (0.05/number of genes). This analysis identified 75 loci with at least one gene by TWAS  examining gene transcriptional profiling in two mouse models of AAA (AngII infusion in ApoE−/− mice and aortic PPE infusion in C57BL/6J mice), specifically those comparing sham/control aortic gene expression versus expression in AAA aortic tissue in experiments featuring 10-week-old male and female mice. Of the 523 candidate genes, 214 demonstrated differential expression in the mouse AAA gene set  We used a trio of datasets21. Tissues were homogenized in the FastPrep-24 homogenizer and Lysing Matrix D tubes . RNA was extracted using Trizol (Invitrogen) following the manufacturer’s instructions. cDNA was prepared by reverse transcribing 1 µg RNA with a High-Capacity cDNA Archive Kit with random hexamers (Applied Biosystems). mRNA expression of the selected genes was studied by real-time PCR in an ABI Prism 7900HT using predesigned validated assays  and universal thermal cycling parameters. Relative expression was expressed as transcript/β-actin ratios.The study was approved by the Ethical Committee of Investigación Clínica del Hospital Santa Creu i Sant Pau. Written informed consent was obtained from all patients. The study conformed to the principles outlined in the Declaration of Helsinki. All patients underwent surgery at Hospital de la Santa Creu i Sant Pau. Samples were obtained from the remaining mid-infrarenal aortic wall after exclusion and prosthetic replacement of AAA. Normal aortas were obtained from healthy aorta from multiorgan donors, and samples were also taken from the midportion of the infrarenal abdominal aorta during organ collection. When a luminal thrombus was present, it was separated before the aorta biopsy was taken and aortic tissue was washed twice with cold PBS. A portion of each sample was placed in an RNAlater solution (Qiagen GmbH) and stored at 4 °C for 24 h before long-term storage at −80 °C until further processing for RNA isolation. Further information can be found in ref. 63. This method relies on GWAS summary statistics to estimate the genetic correlation between pairs of traits. The GWAS summary statistics were filtered to identify genetic variants included in the HapMap3 reference panel. Cross-trait LDSC was then performed using ldsc v.1.0.1 using the default EUR ancestry LD panel.Genetic correlations between AAA and other traits were calculated using cross-trait LDSC29. The full summary statistics were then filtered to exclude the following phenotypes: methylation quantitative trait locus (mQTLs), pQTLs, eQTLs, sex-specific GWAS and non-EUR and nonmixed ancestry GWAS. Furthermore, the full summary statistics were filtered to GWAS studies that contained 50 or more cases. When the trait was measured at multiple locations , only results from one side were queried. For replicate phenotypes, the largest study of that phenotype was chosen. PheWAS results for each independent variant were considered significant if they passed a Bonferroni-corrected threshold for 161,535  tests conducted. After calculating the node and edge maps using NETMAGE64, only connections that represent a weight of >3 SNPs were used to make the graph more interpretable. Raw network statistics were calculated in Gephi65 including modularity or cluster ID using details in ref. 66. This method works to create modules that minimize the number of edges that enter and leave the module. The granularity of the definition of the modules or resolution can be adjusted, with lower resolution generating a higher number of modules. Our optimization efforts led us to set the resolution parameter as 0.561 generating seven distinct modules. Colocalization analysis with the largest available GWAS of lipid31, blood pressure32 and smoking33 traits was performed by coloc58, as described in the eQTL colocalization section.We performed a logistic-regression-based PheWAS of 121 independent variants against the entirety of the MRC-IEU open GWAS project34. MR-BMA is an extension of multivariable MR using a Bayesian variable selection method in an effort to identify likely causal risk factors among correlated exposures. In the primary analysis, the instrumental variables consisted of independent genetic variants (r2 < 0.001 based on 1000 Genomes67 EUR ancestry Reference Panel) associated with any major lipoprotein-related traits  at genome-wide significance in the UKBB based on 361,194 EUR-ancestry participants as previously described68 (Supplementary Table r2 > 0.9), the following two separate models were used for analysis: one containing LDL-C, HDL-C, ApoA1, ApoB and triglycerides (n = 519 independent genetic variants), and another substituting non-HDL-C for LDL-C  and AAA outcome were tested initially using inverse-variance weighted MR for a single lipid exposure and then using the MR-BMA methodology for multivariable modelscis-acting genomic instruments for circulating plasma proteins passing pleiotropy and consistency filters as previously described70. In brief, variants were selected that were associated with any protein at genome-wide significance (P < 5 × 10−8). For this analysis, we focused only on cis-pQTLs previously classified as having the highest relative level of reliability (‘Tier 1’ as defined in ref. 70). After excluding the major histocompatibility complex region, LD clumping to identify instruments composed of independent pQTLs was performed using the TwoSampleMR package71 with an r2 threshold of <0.001. After restricting to 717 circulating proteins with overlapping proteomic and genomic data  and the VA Palo Alto Health Care System Institutional Animal Care and Use Committee and followed the National Institutes of Health and US Department of Agriculture Guidelines for Care and Use of Animals in Research. Male mice (wild-type and Pcsk9−/− mice on a C57BL/6J background) were purchased from the Jackson Laboratory.All animal protocols were approved by the Administrative Panel on Laboratory Animal Care at Stanford University , and the aortotomy was then repaired. The induced AAA aortic segment  was collected at 28-d postsurgery.The PPE infusion model to induce mouse AAA was performed as previously described at 10 weeks of agePlasma cholesterol, LDL and HDL levels were measured at Stanford Animal Diagnostics Laboratory on the Siemens Dimension EXL200/LOCI analyzer.39.At baseline and 3, 7, 14, 21 and 28 d after aneurysm induction, B-mode ultrasound imaging was performed on the operated mice to assess the AAD as previously describedP value < 0.05 was considered statistically significant.Data are presented as means ± s.e.m. Level of significance was determined using the Mann–Whitney test. Shapiro–Wilk test was performed to test normality. A Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-023-01510-y.Supplementary InformationSupplementary Figs. 1–17, Supplementary Note, cohort acknowledgments and consortium authors.Reporting SummaryPeer Review FileSupplementary TablesSupplementary Tables 1–27.


Polygenic profiles comprised 39 single nucleotide polymorphisms and were analysed using unweighted and weighted total genotype scores . There were significant differences in polygenic profiles between groups, whereby U17–23 players had more genetic variants associated with later maturity compared to U12–16 players  and controls . More specifically, U17–23 players had over two-times the odds of possessing >36 later-maturing alleles than <30 compared to U12–16 players (odds ratio (OR) = 2.84) and controls (OR = 2.08). These results suggest there was a greater proportion of relatively later-maturing players as maturation plateaus towards adulthood, which may be explained by the ‘underdog hypothesis’. This study provides the first known molecular evidence that supports the notion that a maturity selection bias exists within male academy football.The purpose of this study was to examine polygenic profiles previously associated with maturity timing in male academy football players across different age phases. Thus, 159 male football players from four English academies (U12–16,  Biological maturation is an important variable to consider during youth athletic development in male football , as it has been shown to influence player performance capacities and selection/deselection decisions within academy systems ,2. MaturThe variation in maturation is problematic for practitioners given the changes in anthropometric  and physiological  characteristics that accompany earlier maturation are considered important factors during identification and development decision-making processes ,9,10. AsWhen considering who achieves expertise at adulthood, however, later-maturing players retained in academy systems may be proportionally more likely to succeed in adulthood , possiblThe variation in maturation between individuals is due to a combination of genetic and environmental factors ,17. For In males, voice-breaking age is commonly used as a similar maturation marker, with a GWAS in 55,871 males identifying 14 independent genetic variants associated with age at voice breaking and a high (rg = 0.74) genetic correlation between sexes in menarche and voice-breaking age . Whilst The influence of genetics is a largely under-researched component of athletic development and performance in football but has been growing rapidly ,26. GeneThe purpose of this study is to examine polygenic profiles  associated with maturity timing in male academy football across different age phases. In light of the current genetic and football research on maturity, we hypothesized that younger age groups would have more genetic variants associated with earlier maturity due to the typical onset of puberty and PHV, alongside the apparent selection bias towards players who have undergone earlier maturity within academy football. In contrast, we predicted that older age groups would have more genetic variants associated with later maturity due to the typical cessation of puberty and PHV, as well as the ‘underdog hypothesis’, whereby later-maturing players are suggested to be proportionally more likely to succeed towards the adult level.n = 86, aged 13.58 ± 1.58 years; U17–23, n = 73, aged 18.07 ± 1.69 years), and data from 240 male European controls from the 1000 Genomes database were examined. Informed assent and consent from all players, parents/guardians, and each academy were obtained prior to the commencement of the study. All experimental procedures were conducted in accordance with the guidelines in the Declaration of Helsinki, and ethical approval was granted by the corresponding author’s institutional Ethics Committee.We examined 159 male football players from four English academies  were calculated. Each genotype was assigned a score between 0 and 2 based on its association with maturity timing according to Hollis et al.  using a The TGS was calculated following the original Williams and Folland  procedurThe TWGS was calculated by multiplying each genotype score by the corresponding SNP effect size  estimates derived from the summary statistics reported by Hollis et al.  to creatp < 0.05. Jamovi version 2.8.23 was used to analyse data.Hardy–Weinberg equilibrium (HWE) was assessed using Fisher’s exact tests. Differences between players and controls in polygenic profiles were assessed using linear regression. To estimate effect size, polygenic profiles were split into equal thirds using tertiles, and comparisons between groups were made using odds ratios (ORs) and 95% confidence intervals (CIs). Statistical significance was set at MIR193B  and MKLN1  in controls. The TGS of groups ranged from 35 to 58 in U12–16 players, 33 to 54 in U17–23 players, and 32 to 58 in controls  = 3.48, p = 0.032). The mean TGS of U17–23 players (42.5 ± 4.82) was significantly lower than U12–16 players  = 2.58, p = 0.010) and controls  = 2.08, p = 0.038). Compared to U17–23 players, U12–16 players and controls had 2.84- and 2.08-times the odds of having a TGS in the higher third (47–58) than in the lower third (32–40), respectively .All SNPs were in HWE, except for rols see . There wF  = 3.22, p = 0.041). The mean TWGS of U17–23 players (41.9 ± 5.09) was significantly lower than U12–16 players  = 2.26, p = 0.024) and controls  = 2.34, p = 0.020). Compared to U17–23 players, controls had 2.03-times the odds of having a TWGS in the higher third (46–57) than in the lower third .The TWGS of players ranged from 34 to 55 in U12–16 players, 32 to 54 in U17–23 players, and 31 to 57 in controls see . There wThis study examined collective differences in the genotype frequency distribution of 39 SNPs associated with maturity timing between U12–16 and U17–23 male English academy football players. To our knowledge, this is the first assessment of a polygenic profile associated with male maturation within a football-specific context. As such, these findings provide initial molecular evidence that supports the suggestion that there may be a maturity selection bias within male academy football, as well as a methodological foundation for future replication studies in this area. Consistent with previous sports genomic research, the findings suggest there is significant genetic variation between male academy football players within the early- to middle-adolescent years and late-adolescent to early-adulthood years. Confirming our hypotheses, the younger age groups had more genetic variants orientated towards earlier maturity, whereas the older age groups had comparatively more genetic variants orientated towards later maturity. Corresponding with non-genetic research in this area, these results suggest that whilst earlier-maturing players are likely more represented during the typical onset of puberty and PHV span, as maturation plateaus towards adulthood, a greater proportion of relatively later-maturing players become better represented.ACTN3 rs1815739, AGT rs699, PPARA rs4253778, and NOS3 rs2070744 was significantly different in pre- (aged 10.6 ± 1.4 years) and post- (aged 16.8 ± 2.3 years) peak-height velocity players. Similarly, Hall et al. [EMILIN1 (rs2289360) was significantly different between pre- (aged 11.5 ± 1.1 years) and post- (aged 17.5 ± 2.1 years) peak-height velocity players. More recently, McAuley et al. [IL6 (rs1800795) and 32 other genetic variants was significantly different between U12–16 (aged 13.84 ± 1.63 years) and U17–23 (aged 18.09 ± 1.51 years) players. This genetic variation between players of different chronological and biological ages may be explained by the importance of specific characteristics  and injury profiles  c factors ,32,33,38The specific genetic variation observed in this study provides further support for the influence of heritable factors on athlete development and offers molecular evidence that enhances previous maturation research in football. Indeed, this research corresponds with the well-documented disproportionate overrepresentation of relatively earlier-maturing players within male academy football spanning early- to middle-adolescent age groups ,12,13. MThe TGS and TWGS were both effective in differentiating U12–16 and U17–23 players, which corresponds with previous research suggesting that each SNP will have a small but additive effect on a given phenotype, as well as each allele having a differing degree of influence ,29. WhilImportantly, this research expands the knowledge on how genetic variants may affect phenotypes in football-specific contexts. In the future, if genetic information is integrated into athlete development programs, this study will have contributed to a better understanding of the number of phenotypes affected by specific variants. These findings may also have important implications for further studies examining genetic associations in youth and senior football over time due to possible ‘survivor effects’, whereby it is assumed the characteristics of those who prevail in a system reflect the true qualities needed for success . For insAt present, the practical implementation of genetic information in football requires the identification of many additional genetic variants and more accurate weightings applied to specific phenotypes. However, recent research indicates that once samples become large enough, polygenic profiles may assist with more accurate estimations of a number of phenotypes applicable to football. For instance, over the past five years, the average sample size of published GWASs has more than tripled, which has led to a substantial increase in the number of significant associations identified and the accuracy of polygenic scores . Using hThe present study is not without some limitations that should be taken into account when interpreting the results. As with previous football genomic studies, despite recruiting players from four academies, as well as comprising a cohort larger than the average in this area (see  for a reMIR193B (rs1659127) and MKLN1 (rs11763660) deviated from HWE, which may be due to not correcting for family-wise error rates that were inflated due to the 39 comparisons. However, genotyping error may also have occurred and is an important consideration when interpretating the findings.It is also important to acknowledge that these findings are only in relation to male players and most likely would not transfer well to a female context, as there are still many variations in genetic associations with maturation between sexes . Future This study presents novel molecular evidence with regards to the collective genotype frequency distribution of 39 SNPs associated with the maturity timing of male academy football players in England. The key findings showed that there was significant genetic variation between U12–16 and U17–23 players, whereby U17–23 players had a polygenic profile comprising more alleles associated with relatively later maturity compared to U12–16 players. These results support previous research that suggested there may be a maturity selection bias within male academy football that favours earlier-maturing players during early to middle adolescence, but as maturation plateaus towards adulthood, a greater proportion of relatively later-maturing players become better represented. It is important that these findings are replicated in larger independent samples of youth football players, as well as the identification of many additional genetic associations with maturity before practical applications are contemplated.


Pinus halepensis Mill., by linking individual variation in serotiny presence and level to fire frequency and to genetic polymorphism in natural populations. After filtering steps, 885 single nucleotide polymorphisms (SNPs) out of 8000 SNPs used for genotyping were implemented to perform an in situ association study between genotypes and serotiny presence and level. To identify serotiny‐associated loci, we performed random forest analyses of the effect of SNPs on serotiny levels, while controlling for tree size, frequency of wildfires, and background environmental parameters. Serotiny showed a bimodal distribution, with serotinous trees more frequent in populations exposed to fire in their recent history. Twenty‐two SNPs found in genes involved in stress tolerance were associated with the presence‐absence of serotiny while 37 found in genes controlling for flowering were associated with continuous serotiny variation. This study shows the high potential of P. halepensis to adapt to changing fire regimes, benefiting from a large and flexible genetic basis of trait variation.Many plants undergo adaptation to fire. Yet, as global change is increasing fire frequency worldwide, our understanding of the genetics of adaptation to fire is still limited. We studied the genetic basis of serotiny (the ability to disseminate seeds exclusively after fire) in the widespread, pioneer Mediterranean conifer  Pinus halepensis for adaptation to changing fire regimes.While our results support a polygenic control of serotiny, they also show, for the first time, separate genetic bases for the presence/absence of serotinous cones , on the one hand, and variation of serotiny level , on the other hand. This large and flexible genetic basis of trait variation may explain the high potential of  Fire‐tolerant species are adapted to fire regimes characterized by frequent low‐ to medium‐intensity surface fires  possess traits that mitigate fire impacts , enabling them to survive after fire . This functional trait is observed in fire‐embracer species and allows the plant to create a canopy seed bank, storing seeds in serotinous cones that can remain closed for several years until the occurrence of a fire event  was following a normal distribution.The proportion of serotinous vs nonserotinous cones  can strongly vary among but also within species  because changes appeared within solely a few generations , with a strong spatial variability. In the study area, as in other Mediterranean regions, fire has shaped most ecosystems but assessing the historical number of recurrent fires on a location is difficult due to a long fire history. Fire databases, when available, can provide reliable information on fire frequency during the last six decades, at most. In the current work, the recent fire history (60 years) was reconstructed using the regional fire database Prométhée (www.promethee.com) that has recorded fires in southeastern France since 1973 and the georeferenced fire perimeters recorded in the database of the Direction Départementale des Territoires et de la Mer (DDTM Bouches du Rhône) available from 1961. When the fire perimeters were not recorded in the database, we used satellite images and aerial photos to outline fire scars .Sampling sites were located in southeastern France Figure  and are Quercus ilex, Quercus pubescens, and Pinus halepensis  and the other half in areas having suffered at least one fire  during the same time period. The pairs “Fire”–“No‐Fire” were located between 5 and 25 km apart in order to minimize genetic divergence caused solely by drift  data set.Serotiny level data were recorded as “0” for trees without serotinous cones, and as “1” otherwise; this allowed the analysis of the genetic bases of the presence/absence of serotiny. This is the Inclusive Quantitative” (“IQ”) data set.Serotiny level data were log‐transformed using the LogSt function in the DescTools R package  data set.Trees with serotiny = 0 were removed, and then transformation proceeded as in (ii). This is the “Because of the apparent bimodal distribution of trait values see , serotinThe variation of serotiny level among populations and according to fire modality was assessed using an ANOVA, followed by a Fisher Least Significant Difference (LSD) test. As the data distribution was not normal, data were either log‐transformed or tested using the Kruskal–Wallis test followed by Bonferroni post hoc tests.x = μ + cofactor1 + cofactor2 + … cofactorN + ε; the best model was identified using variable selection as implemented in the step function of the R package “stats” all cofactors were used to perform a Principal Component Analysis using the FactoExtra R package  based on 8000 SNPs, genotyped using the SPET technology  We removed variants with more than 20% missing data; (ii) we created subsets with the remaining variants so that no pair with linkage disequilibrium (LD) higher than 0.5 was left in each contig. To do this, we first computed LD using vcftools v. 0.1.13  Goudet, .Beagle 4.1  lower than 0.1 and we imputed missing genotypes using 2.4snpgdsGRM function in the R package “SNPrelate” v. 1.22.0 , we computed Ritland's heritability  and from the pairs of values  and .from Ritland's kinship and the value of the actual variance of kinship Ritland, , as estiRitland, . We usedh2.jags of the R package “gap” v. 1.2.2  and traits  or as residuals of multiple regressions, see above), Bayesian estimates of heritability  from generalized linear mixed models were computed using the function .2 Zhao, . When usWe used relatedness matrices and trait data to estimate heritability in two ways .2.5Boruta  were used for running “random forest” analyses  and a threshold of .05.Genes containing serotiny‐associated SNPs were annotated first by comparing them with 33.1p‐value <.0001 with 33 significant comparisons according to the 95% Bonferroni test) and log‐transformed data  test). Taking into account only the data with serotiny level >0, the effect of the fire modality on the serotiny level was not significant . Furthermore, the number of trees expressing serotiny (i.e. with serotiny level > 0) was significantly higher in the “Fire” populations compared with the “No‐Fire” populations  ranged from 0 to 60% overall and was highly variable among populations regardless of the fire modality Figure , for bot3.2FIS within‐population: −0.44, standard deviation = 0.43).Due to relatively high proportions of missing data and high levels of linkage disequilibrium among variants, we removed the vast majority of loci at the missing data and LD filtering steps. The final data set, prior to filtering for low‐frequency variants (MAF < 0.1), contained 904 biallelic SNP variants, and after the MAF filter, only 885 SNPs were left. While this reduced considerably genomic coverage, the remaining loci are fully independent and high‐quality, thus making the GWAS exercise more reliable, albeit very stringent. Average Nei's heterozygosity per SNP was 0.41 (standard deviation = 0.11) and there was a marked heterozygote excess  also showed a very shallow structure, with very small and similar values in all pairs , with a long, thin tail of excess values . The presence of unequally distributed relatedness values suggests that the kinship structure should be taken into account in GWAS analyses; however, the absolute numbers of pairs of individuals with very high kinship levels were small, suggesting that the bias may be relatively small. Heritability estimates were generally higher for the B and IQ type data sets and with the Ritland method, and lower in the EQ data set and with Bayesian estimations  for likelihoods; Table SNPs identified by analyzing PCA‐based residuals  and GLM‐based residuals  only partially overlapped, regardless of the algorithm used . Twenty‐two SNPs in the B data set were associated with the presence/absence of serotiny. Only one SNP was significant in both IQ and B data set analyses  and another one was significant in all the EQ data set analyses .Globally, taking into account the number of SNPs in both types of correction (GLM‐ and PCA‐transformed), 24 significant SNPs in the Twenty genes containing significant SNPs were associated with biological processes having specific functions possibly involved in serotiny. They were divided into three classes: “Flowering,” “Stress tolerance,” and/or “Reproduction.” The list of loci driving the presence/absence of serotiny included genes involved in stress tolerance, while loci linked to the continuous variation of serotiny included flowering control genes Table .4Pinus halepensis. Using an in situ genomic association study, we identified several SNPs and a variety of candidate genes that could underlie fire phenotypes associated with the variation of serotiny level observed in the field, as loci associated with serotiny might be associated with other correlated fire adaptive traits. However, our results differed from previous ones from the point of view of population structure. Indeed, population kinship structure can be affected by the population dynamics driven by fire events  values of serotiny. This could be explained by the small difference between our fire modalities , and by the fact that fire events have affected the populations only over the last few generations. Indeed, given the small number of generations since the start of the selection and the weak difference between fire regimes, selection could have had a stronger effect on population divergence at a threshold trait (such as presence/absence of serotiny) than at a continuous trait (such as level of serotiny).B and IQ models) were associated with biotic and/or abiotic stress tolerance, such as temperature, drought, light, or pest attack, which were already identified in several previous studies . Seeds from serotinous cones have indeed been shown to differ from those of nonserotinous cones for insulation against high temperatures, which stimulates their germination compared with those in nonserotinous cones and is linked to a different enzymatic activity . While it seems counter‐intuitive that a binary component of a trait has a polygenic control, this could be explained by the fact that the action of multiple genes is needed to trigger the development of serotiny, perhaps in a redundant way and through threshold effects. Notice, though, that serotiny is a complex, derived trait that may be driven by many other parameters, themselves under polygenic control, which may explain this result. Six significant genes for binary variation (EQ models) was also controlled by loci determining responses to water deprivation or high temperature  but was also driven by genes controlling flowering , already identified in previous studies , which are easily flammable compounds, and the building blocks of terpenes occurring in resin are also involved in serotinous cone formation.Contig2340_2217 was the only significant locus shared by the IQ and B models. This SNP is located in gene CER9, which could be involved in several important functions for the serotinous cone formation. One of these functions is cuticle development, affecting seed permeability ; data curation (lead); formal analysis ; investigation (lead); methodology (supporting); visualization ; writing – original draft (lead); writing – review and editing . Ivan Scotti: Formal analysis (lead); funding acquisition ; investigation ; methodology ; supervision ; validation (lead); visualization ; writing – original draft ; writing – review and editing (lead). Bruno Fady: Conceptualization ; funding acquisition (supporting); investigation ; supervision ; validation (lead); writing – original draft (supporting); writing – review and editing (supporting). Anne Ganteaume: Conceptualization ; data curation (supporting); funding acquisition ; investigation (supporting); project administration (lead); resources (supporting); supervision ; validation (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing .The authors know of no conflicts of interest associated with this publication.Appendix S1Click here for additional data file.


Their induction ability, usually expressed as haploid induction rate (HIR), is known to be under polygenic control. Although two major genes (MTL and ZmDMP) affecting this trait were recently described, many others remain unknown. To identify them, we designed and performed a SNP based (~9007) genome-wide association study using a large and diverse panel of 159 maternal haploid inducers. Our analyses identified a major gene near MTL, which is present in all inducers and necessary to disrupt haploid induction. We also found a significant quantitative trait loci (QTL) on chromosome 10 using a case-control mapping approach, in which 793 noninducers were used as controls. This QTL harbors a kokopelli ortholog, whose role in maternal haploid induction was recently described in Arabidopsis. QTL with smaller effects were identified on six of the ten maize chromosomes, confirming the polygenic nature of this trait. These QTL could be incorporated into inducer breeding programs through marker-assisted selection approaches. Further improving HIR is important to reduce the cost of DH line production.In maize, doubled haploid (DH) lines are created  In matee1 (ig1) , HIR is e1 (ig1) . The fire1 (ig1)  and vari3 population derived from Stock 6, jointly explaining 17.9% of the phenotypic variance (qhir1) to bin 1.04 and found additional QTL on chromosomes 3 (qhir2 and qhir3), 4 (qhir4), 5 (qhir5 and qhir6), 7 (qhir7) and 9 (qhir8). qhir8 explained more than 20% of the genetic variance in all filial generations of the cross between inducers CAUHOI and UH400  test designed to detect selective sweeps under the assumption of confounding trait expression and population structure, identified two distinct loci (qhir11 and qhir12) within qhir1 , located on chromosomes 1 and 2, were identified in a Fvariance . Studiesnd UH400 . Dong etin qhir1 . Subsequqhir11, and coding for a patatin-like phospholipase, triggers the formation of haploid seeds (MATRILINEAL (MTL) (Zea mays Phospholipase A1 (ZmPLA1) (NOT LIKE DAD (NLD) . This was an interesting observation because it indicated that mutations in different genes can promote and/or have synergetic effect on maternal haploid induction  , Zea may(ZmPLA1)  and NOT AD (NLD) . By indunduction .R1-nj color marker . This hybrid was chosen as a donor because it possesses good inducibility and allows clear expression of the R1-nj marker. Inducibility is the ability of the donor parent to generate haploid seeds, and multiple studies indicated that the source germplasm impacts HIR , BGEM (Iowa State University/USDA-ARS Germplasm Enhancement Maize project) and Ex-PVP lines. No haploid plants were observed when these genotypes were grown in the field, which is in accordance with A panel of 793 non-inducer genotypes was considered for a case-control GWAS (cc-GWAS) Table S1th, and the first block of inducers seeds was sown on May 21st. The second block of donor seeds was sown along with the first block of inducers seeds, while the second block of inducers seeds was sown on May 31st. Sowing of inducer blocks was delayed because most inducers have a significantly earlier maturity than the donor.The inducers and donor used for this experiment were sown at the Iowa State University, Agronomy and Agricultural Engineering Farm, located in Boone (Iowa), during the summer of 2018. The trial was grown under rainfed conditions, following the recommended practices for maize production in Central Iowa. Pre- and post-emergence herbicides along with manual weeding were used to control invasive species. Urea ammonium nitrate was applied in the area before sowing. Two blocks of inducers and donor were sown at different planting dates to ensure that enough seeds would be generated to obtain reliable estimates of HIR. The first block of donor seeds was sown on May 8With the delayed planting of the haploid inducers, it was possible to pollinate the first donor block exclusively with the first inducer block, and the second donor block exclusively with the second inducer block. Each inducer and donor block was composed of subblocks containing 16 plots. Inducers were not randomized among subblocks because the great difference in vigor among them would adversely affect other traits for which data were collected for a companion study . This raR1-nj marker, and the number of putative haploid and diploid seeds of each ear was recorded. Embryo and endosperm abortion, which are correlated with HIR  with a Where Where logit link function, expressed by the following linear predictor:The second strategy consisted of fitting a generalized linear mixed model (GLMM) with a Where Where hnp (emmeans (lme4 package (Three criteria were used to select the best-fit model: (a) the goodness-of-fit via the Akaike Information Criterion AIC; , (b) thehnp . Plots o emmeans . All sta package  in the R package .https://www.diversityarrays.com) and the version 4 of B73’s reference genome (AGPv4) as a reference. A total of 32,929 SNP markers were initially identified, and 9,007 remained after filtering for a call rate of at least 70% and minor allele frequency (MAF) of 1% (https://dr.lib.iastate.edu/home). The software Beagle 5.0 , where they were genotyped with the Diversity Arrays Technology (DArTSeq) platform . SNP calagle 5.0  was subsbestNormalize package in R (milorGWAS (We performed marker-trait association using quantitative and binary response variables. The quantitative GWAS was implemented in GAPIT  with theage in R . The binage in R Table S1ilorGWAS . The reaprcomp) of the numerical matrix of SNP markers, calculated via singular value decomposition  was used as a reference threshold. In addition to the permutation analysis for the cc-GWAS, SNPs were declared statistically significant for both GWAS analyses at an FDR-adjusted p-value of less than 0.05.For the quantitative GWAS, population/family structure was accounted by the additive genomic relationship G , and theLD.decay from the sommer package ]. The search browser in MaizeGDB (https://www.maizegdb.org/gbrowse) was used to identify the putative candidate genes in the interval of significant SNPs. Maize B73 RefGen_v4 version was used to locate the candidate genes.Lastly, we calculated the genome-wide  pairwise linkage disequilibrium (LD) using the function  package . The LD Estimates of HIR with asymptotic confidence intervals were obtained with Model 4 Table S2-log10 of the statistically significant SNP marker on chromosome 10 was of 5.30, with an adjusted FDR value of 0.04  is located at position 220.3 Mb and does not overlap with the QTL detected by The average rate of LD decay at the https://www.maizegdb.org/gbrowse). A total of 41 candidate genes with descriptions were found, out of which, 37 were protein-coding, and the remaining 4 were not annotated. The chromosomal distribution of these candidate genes included 12 located on chromosome 1, 8 on chromosome 2, 5 on chromosome 3, 5 on chromosome 7, 3 on chromosome 8, and 8 on chromosome 10. The functional domain of each of the candidate genes is provided in The physical locations of the nine significant SNPs identified were recorded using the B73 RefGen_v4 based on the LD decay distance. The candidate genes were identified by the genome browser search available at Maizegdb (ZmC1 and ZmR2) in novel haploid inducer line called ‘Maize Anthocyanin Gene InduCer 1 (MAGIC1)’. Shortly after, RUBY enabled 100% accuracy of haploid identification based on deep betalain pigmentation. The development of a group of inducers with numerous anthocyanin markers and HIRs above 12.0% allowed for effective haploid generation and screening . Similarly, marker S1_76160596 (position 76.1 Mb), is located inside the qhir12 region which qhir12 than for qhir11. Interestingly, qhir12 haplotype was exclusively found in inducers, whereas the qhir11 haplotype could be found in 2.7% of the non-inducers. qhir11 region compared qhir12 for inducing haploid embryo in maize. At the same time, it was discovered that qhir11 harbours the MTL/ZmPLA1/NLD allele that possesses major effect on haploid induction in maize. The differences in germplasm, genotyping method, statistical models and sample size might explain the divergence between earlier studies and our results. The inbred panel used in this study essentially carried the mutant MTL/ZmPLA1/NLD allele and therefore it is obvious that the gene would not be detected in our quantitative GWAS.In this study, we used four different GWAS methods, including BLINK, FarmCPU, MLMM, and cc-GWAS, to identify potential associations with single nucleotide polymorphisms (SNPs). We identified a total of nine SNPs that were significantly associated with the trait. They correspond to different chromosomal origins, validating the polygenic control of HIR in maize, as previously reported . For insMTL/ZmPLA1/NLD gene), S8_174792234, located at position 174.7 Mb on chromosome 8 and S7_131226237, located at position 131.2 Mb on chromosome 7, with higher PVE values of 37%, 25% and 21%, respectively, are likely to be more important for the trait and are more likely to have a direct impact on the HIR. Notably, detection of S1_66636144 at position 66.6 Mb on chromosome 1 by BLINK and MLMM models makes it as potential region for investigation, in addition to SNP S8_174792234, at position 174.7 Mb on chromosome 8 which is reported first time for the HIR. A smaller p-value suggests that the association is less likely to be due to chance and more likely to be real. Apart from S1_66636144 at 66.6 MB on chromosome 1, the marker S8_174792234 at position 174.7 Mb on chromosome 8 possessed the lowest p-value followed by S1_69321282 on chromosome 1 at 69.3 Mb (tightly linked to MTL/ZmPLA1/NLD gene) compared to the two other markers  detected on chromosome 1. Given their high significance, it is interesting that they have not been detected in other mapping studies , display an HIR< 1.0% at all planting dates. The population structure for the cc-GWAS analysis was only controlled with the additive genomic relationship matrix due to the confounding between the cases and controls grouping of genotypes with the first two principal components. Thus, further research is needed to ensure that marker S10_141729953 was not a spurious association.In a cc-GWAS, we compared inducers (cases) to noninducers (controls) to identify genetic variants that are associated with the haploid induction. As a result, we identified a significant novel genetic region, close to marker S10_141729953, which is located at position 141.7 Mb of chromosome 10. Therefore, the identification of S10_141729953 as associated with HIR can help improve our understanding of the genetic factors involved in this trait. The failure of detection of this region might have been due to the absence of genetic variation at these QTL in the germplasm used in other studies . For marWe searched the putative candidate genes in the interval of 56kb on both sides of nine significant markers identified in the study. The aldose 1-epimerase enzyme-coding gene Zm00001d029341 has been shown to be expressed in meiotic tassel . This coSorghum bicolor’s and maize’s kokopelli (KPL) mRNAs, respectively. Kokopelli was first described in Arabidopsis thaliana, where it was named after the Native American deity of male fertility (kokopelli is located on chromosome 2 (NCBI’s sequence ID: XP_008671112.1), whereas in sorghum it is located on chromosome 6 (NCBI’s sequence ID: XP_021319809.1). Kokopelli encodes for a natural cis-antisense siRNA (cis-nat-siRNA), which is a class of small regulatory RNAs found in various eukaryotic species. Even though two apparently normal sperms are delivered to embryos sacs by the pollen tubes of the Arabidopsis kpl mutant, single fertilization was observed in approximately 40% of the ovules fertilized (KPL forms a sperm-specific nat-siRNA pair with the inversely transcribed gene ARIADNE14 (ARI14), which encodes for a putative ubiquitin E3 ligase, and whose overexpression decreases seed set  mutant for triggering in planta maternal haploid induction.The marker S10_141729953 is in the intragenic region of gene Zm00001d026242, which was reported to be either exclusively or highly expressed in pollen grains . Zm00001ertility . In maizrtilized . In Arabseed set . Proteinseed set . AltogetMTL/ZmPLA1/NLD, ZmDMP, ZmPLD3, ZmPOD65 and the QTL identified in this study on chromosomes 1, 8 and 10, suggest that some form of MAS, such as F2-enrichment or marker-assisted backcrossing, might be efficient breeding strategies for inducer development. Employing genomic selection or predicted cross value  inducers allows automated haploid selection, which greatly facilitates accurate estimation of HIRs. However, OC is a quantitatively inherited character , as are https://doi.org/10.25380/iastate.24185784.v2. Further inquiries can be directed to the corresponding author.The original contributions presented in the study are included in the article/HT, UF, and TL designed this project; HT, UF, VR, and TL developed the inducer panel used in this study; HT, VA, and EP performed most of the HIR phenotypic selection; MK and WB analyzed the phenotypic and genomic data; HT and RZ executed the candidate gene analysis; HT, MK, RZ and TL wrote this manuscript; TL supervised the research work; All authors contributed to the article and approved the submitted version.