Eucalyptus was used to genotype a breeding population of Eucalyptus cladocalyx, yielding only ~3.9 K informative SNPs. Traditional Bayesian genomic models were investigated to predict flowering, stem quality and growth traits by considering the following effects: (i) polygenic background and all informative markers (GS model) and (ii) polygenic background, QTL-genotype effects (determined by GWAS) and SNP markers that were not associated with any trait (GSq model). The estimates of pedigree-based heritability and genomic heritability varied from 0.08 to 0.34 and 0.002 to 0.5, respectively, whereas the predictive ability varied from 0.19 (GS) and 0.45 (GSq). The GSq approach outperformed GS models in terms of predictive ability when the proportion of the variance explained by the significant marker-trait associations was higher than those explained by the polygenic background and non-significant markers. This approach can be particularly useful for plant/tree species poorly represented in the high-density SNP arrays, developed for economically important species, or when high-density marker panels are not available.High-throughput genotyping techniques have enabled large-scale genomic analysis to precisely predict complex traits in many plant species. However, not all species can be well represented in commercial SNP (single nucleotide polymorphism) arrays. In this study, a high-density SNP array (60 K) developed for commercial  Eucalyptus, which has been effective for genomic studies in a wide variety of economically important eucalypt species and their hybrids, including Eucalyptus grandis, Eucalyptus urophylla, Eucalyptus nitens and Eucalyptus globulus [A major focus of modern quantitative genetics is on assessing the association between polymorphic markers with phenotypic variations of complex traits. In this sense, genotype–phenotype studies for quantitative traits at the genome level usually require high-density genetic marker panels, i.e., a large number of markers throughout the genome and large population sizes to obtain sufficient power and prediction resolution ,2. The dglobulus ,10,11,12globulus .Eucalyptus spp., however, they concluded that it is not yet clear whether the use of smaller SNP subsets is warranted for the long-term implementation of genomic selection in Eucalyptus, an aspect that remains still unknown. Marker panels could be considered as either low- or high-density depending on the genome size, the extent of linkage disequilibrium (LD) and the traits of interest. Larger genomes and rapid breakdown of LD would imply that a higher density of SNP loci would be needed to detect QTL. However, many authors quite arbitrarily describe panels as high or low density such that comparisons among organisms should be carefully made. In cattle (genome size 3 Gb), for instance, a DNA array of 50 K SNPs is considered a low-density panel [Eucalyptus nitens (640 Mb) the 60 K SNP chip was considered as a high-density marker panel by Suontama et al. [Eucalyptus benthamii and Eucalyptus pellita. Notably, low-density SNP chips are considered as a way to reduce the cost of high-density SNP panels in animal breeding and would enable cost-effective implementation of genomic studies [E. cladocalyx using low-density SNP markers, and (ii) to assess the efficiency of Bayesian whole-genome regression models  in terms of predictive ability of complex traits and goodness-of-fit measures in the presence of QTL-genotype effects obtained from a marker-trait association analysis.According to Pryce et al. , the usety panel , while ia et al. . Howevera et al. , is morea et al.  consider studies ,18. In a studies  used lin studies . Based o studies  determinE. cladocalyx using the 60 K SNP chip, yielding a subset of only ~3.9 K informative SNPs, due to the low number of polymorphic loci found at locations where they might have been expected. The ~3.9 K SNPs  that were retained after filtering for minor allele frequency (MAF) and missing data were located in all eleven chromosomes of Eucalyptus, with an average of ~353 SNPs per chromosome, a density of 6 SNPs per 1 Mb and distributed with an average distance between SNPs of 11,600 bp , which were also distributed across all 11 chromosomes, of which 11, 16, 5, 26, 10, 5 and 17 MTAs were identified for total tree height (HT), diameter at breast height (DBH), stem straightness (STR), slenderness index (SLD), wood density (WD), flowering intensity (FI) and first bifurcation height (BHT), respectively . The MTAThe prediction models that include the MTA information (GSq) and traditional genomic prediction models (GS) were compared in terms of goodness of fit through the Deviance Information Criterion (DIC) and predictive ability (PA). The DIC and PA values of all fitted models are shown in The heritability estimates of the studied traits based on all genomic prediction models are shown in Eucalyptus cladocalyx is not a close relative of other eucalypts. Brooker [E. cladocalyx in the monophyletic section Sejunctae [Maidenaria, Exsertaria and Latoangulatae, a lower rate of cross-species amplification of SSR (Simple Sequence Repeats) can be expected in E. cladocalyx [E. cladocalyx [ Brooker  placed Eejunctae . As the adocalyx  due to dadocalyx ,25. The adocalyx ,25,26.E. cladocalyx  were detected for the seven target traits, which were subsequently used for the GSq approach. In accordance with this, several studies have previously identified genomic regions explaining part of the phenotypic variation of growth-related traits in a et al. , Arriagaa et al. , Maldonaa et al. , Valenzua et al. ). For HTa et al.  detecteda et al.  reportedEucalyptus, as the flowers provide a reliable source for honey production [E. cladocalyx and other eucalypts is particularly advantageous for the supply of honey [Eef1) controlling the early flowering in Eucalyptus grandis. In E. cladocalyx, previous studies have reported that the flowering intensity and early flowering have a positive genetic correlation and common QTLs controlling the phenotypic variation of both traits [E. cladocalyx.Flowering components are target traits in breeding programs of some species of oduction ,29. In dof honey . In the of honey , who reph traits . Interesh traits  detectedE. cladocalyx [Several studies have explored the potential of the selection based on genomic tools in forest species ,34,35,36adocalyx ,37,38. Aadocalyx . For insAccording to De Los Campos et al. , Bayes AAlthough the analytical assumptions differ among studied Bayesian genomic models, the predictive ability of the studied traits was not severely different among them. In accordance with our findings, several studies have reported that the predictive ability did not differ between methods in forest tree species, especially for growth and wood quality related traits ,46,47,482 = 0.08–0.34), which are in accordance with the range usually expected for forest tree growth, flowering and stem quality related traits, including E. cladocalyx [According to the goodness-of-fit measures, the GSq model outperformed the GS model for BHT, DBH, SLD and WD, whereas GS offered better model fit compared with GSq for STR and FI. Additionally, for HT, DBH, SLD, WD and BHT, the genomic heritability estimates based on MTAs were higher than the estimates based on SNPs (not significantly associated with a trait). On the other hand, the genomic heritability estimates based on SNP markers (not associated) considering BB and BC method, were higher than those based on BA and BRR methods . BB and BC models involve variable selection procedures, which favor the selection based on major effect markers/genes . Notablyadocalyx ,52,53,54E. cladocalyx. In a meta-analysis, Hall et al. [Eef1) controlling the early flowering in E. grandis. In this context, low-density SNP panels could limit the probability to detect key regions explaining the phenotypic variation of these traits. In addition, these results are supported by the fact that the GS model outperformed the GSq model for STR and FI (in terms of goodness-of-fit), which means that the total variation of STR and FI is better explained by the classic prediction model . The climate is classified as predominantly arid, according to the De Martonne aridity index [−1). The following quantitative traits were measured in 17 years-old trees: diameter at breast height (DBH), total tree height (HT), first bifurcation height (BHT), wood density (WD), stem straightness (STR) and slenderness index (SLD). The BHT was rated on a scale of five levels, in which a value of 1 is assigned to trees with a loss of the central axis in the first fifth of the tree’s height, a value of 2 indicates that a loss of the central axis occurs in the second fifth of the tree, a value of 3 indicates that a loss of the central axis occurs in the third fifth of the tree, a value of 4 implies a loss of the central axis in the fourth fifth of the tree’s height, and a value of 5 implies that a loss of the central axis in the last fifth of the tree height or does not show loss of the apical axis , and Z is the incidence matrix related to polygenic effects. The coefficients of relationship matrix were adjusted according to Bush et al. [N. The genomic predictions were performed using BGLR package in R [The following four Bayesian whole-genome regression models were used for the estimation of SNP marker effects, variance components and genomic heritability: Bayes A , Batructure ,63. Resph et al. . Finallyage in R . Specifiy* is a vector of adjusted phenotypic observations (by the block effect). S, Q and Z correspond to the incidence matrices for a, v and u, respectively. a and v are the vectors of SNP effects (fixed) and population structure effects (fixed). u corresponds to a vector of polygenic effects (random), and u and K is the kinship coefficient matrix, which was estimated using the program TASSEL [The GSq model consisted of two steps, in which the first step involves a marker-trait association (MTA) analysis for QTL detection according to Silva et al. . The MTAm TASSEL . For thim TASSEL . The GWAm TASSEL . The popm TASSEL . In addim TASSEL .q) from the association analysis, which is expressed as:1 and i-th marker that were not found to be significantly associated with a trait. Z is the incidence matrix of polygenic (a) and QTL-genotype (q) effects assuming a~N and q~N, respectively. The Q is a covariance matrix, in which the elements are the probabilities that individuals are identical by descent based on significant SNP markers according to the MTA analysis.The second step of the GSq approach consists of the compilation of traditional GS model (Equation (1)) and QTL-genotype effect  was considered to be supported against a model with higher DIC; a DIC difference between 5 and 10 was considered as substantial difference between models, and a difference less than 5 was considered as not significant.The Bayesian prediction models were compared in terms of goodness-of-fit, by using Deviance Information Criterion (DIC) , and they*) from validation dataset and the estimated breeding value  and QTL information from GWAS analysis, which was specially implemented in the context of a low-density SNP markers. Importantly, predictive abilities were superior in GSq when the proportion of the variance explained by the significant marker-trait associations (MTAs) was higher than those explained by the polygenic background and SNP markers (that were not found to be significantly associated with a trait). Therefore, we emphasized and hypothesized that both the number of associations and/or the percentage of variation explained by the MTAs are determinants in the effectiveness of the GSq method. This approach can be particularly useful for plant/tree species poorly represented in the high-density SNP arrays, developed for economically important species or when high -density marker panels are not available.


Sex-specific analyses indicated seven additional loci. In total, we identify 32 loci for resting Tpe, 3 for Tpe response to exercise and 3 for Tpe response to recovery modulating ventricular repolarization, as well as cardiac conduction and contraction. Our findings shed light on the genetic basis of resting Tpe and Tpe response to exercise and recovery, unveiling plausible candidate genes and biological mechanisms underlying ventricular excitability.Sudden cardiac death is responsible for half of all deaths from cardiovascular disease. The analysis of the electrophysiological substrate for arrhythmias is crucial for optimal risk stratification. A prolonged T-peak-to-Tend (Tpe) interval on the electrocardiogram is an independent predictor of increased arrhythmic risk, and Tpe changes with heart rate are even stronger predictors. However, our understanding of the electrophysiological mechanisms supporting these risk factors is limited. We conducted genome-wide association studies (GWASs) for resting Tpe and Tpe response to exercise and recovery in ∼30,000 individuals, followed by replication in independent samples , all from UK Biobank. Fifteen and one single-nucleotide variants for resting Tpe and Tpe response to exercise, respectively, were formally replicated. In a full dataset GWAS, 13 further loci for resting Tpe, 1 for Tpe response to exercise and 1 for Tpe response to exercise were genome-wide significant (p ≤ 5 × 10 Sudden cardiac death is a leading cause of mortality and is responsible for approximately half of all deaths from cardiovascular disease.610141), not only in healthy subjects,,The surface electrocardiogram (ECG) is a widely available non-invasive tool, which provides a rapid assessment of underlying cardiac electrophysiology and is therefore a useful method to infer arrhythmic risk. An abnormally prolonged Tpeak-to-Tend (Tpe) interval on the ECG is a risk factor for ventricular arrhythmic mortality and all-cause mortality, independent of age, sex, comorbidities, QRS duration, and corrected QT interval MIM: 6141, not ex vivo ventricular wedge preparation and has not been reproduced in the intact heart.,,Although the general view is that the Tpe interval and the T-wave more commonly reflect spatial dispersion of repolarization in different regions of the heart, the exact nature of this is disputed.,Prior work in twin studies has demonstrated that resting Tpe interval is heritable (52%–63%)Our objective was to identify genetic variants significantly associated with three traits  in a larAnonymized data and materials have been returned to UK Biobank (UKB) and can be accessed per request.UKB is a prospective study of 488,377 volunteers comprising relatively even numbers of men and women aged 40–69 years old at recruitment (2006–2008). The UKB study has approval from the North West Multi-Centre Research Ethics Committee, and all participants provided informed consent.Genotyping was performed by UKB using the Applied Biosystems UK BiLEVE Axiom Array or the UKB AxiomTM Array.Participants were genotyped using a customized array (including GWAS and exome content) and with genome-wide imputation based on HRC and 1000G sequencing data.Detailed information about the phenotypic and genetic quality control (QC) are indicated in Figure S1 and Supplemental Methods. Of the 56,385 individuals from EST-UKB who passed the phenotypic QC, 52,147 complied with genetic QC and were of European ancestry. Similarly, of the 26,467 individuals from IMAGE-UKB who passed the phenotypic QC, 24,999 complied with genetic QC and were of European ancestry. Then, 5,569 individuals who were in both the EST-UKB and IMAGE-UKB cohorts were excluded from the IMAGE-UKB cohort. After exclusions, there were 52,147 individuals from the EST-UKB cohort and 19,430 individuals from the IMAGE-UKB cohort available for genetic analyses ((1),We signal-averaged the heartbeats within a window of 15 s during rest (black), at peak exercise (red), and 50 s after peak exercise (blue) to attenuate noise and artifacts and reveal small variations in the QRS-T-waveform. The onset, peak, and offset timings of the waveforms were located using bespoke software.(2)We derived resting, peak exercise, and recovery Tpe intervals as the temporal differences between the corresponding T-wave end and T-wave peak timing locations.(3)Tpe response to exercise was derived by quantifying the difference between the Tpe intervals at rest (black T-wave) and at peak exercise (red T-wave), normalized by the RR change during this interval, The bicycle ergometer test followed a standardized protocol of 15 s resting period, followed by 6 min of exercise during which the workload was gradually increased, and a 1-min recovery period without pedalling. Pre-processing of the ECG signals from the EST-UKB cohort included low-pass filtering at 50 Hz to remove electric and muscle noise but still allow QRS detection.,We chose lead I for analysis in the IMAGE-UKB cohort to match the EST-UKB signal. We removed baseline wander using a publicly available algorithm see . We pre-For resting Tpe, we pooled the measurements from both EST-UKB and IMAGE-UKB cohorts together, leading to 71,338 individuals with resting Tpe. Inverse-normal transformation of resting Tpe and Tpe response to exercise and recovery was performed, as the distributions were skewed .Figure 2−6, and missingness < 0.0015. Model SNVs are used to learn the parameters from the mixed models for both the heritability estimation and the GWASs (see below).An overview of the study design is provided in Then, we estimated the proportion of resting Tpe, and Tpe response to exercise and recovery explained by additive genetic variation (heritability), as well as their genetic correlation with each other, using a variance components method (BOLT-REML),Next, we performed a GWAS for each trait using a linear mixed model method (BOLT-LMM)−6 from the discovery GWAS for each trait were compiled and mapped to individual loci based on a genomic distance of >500 Kb to each side of another SNV. If multiple SNVs fitted the selection criteria for a single region, only the SNV with the smallest p value was taken forward into replication. As a QC step, we reviewed each selected SNV to check for unrealistically high effect sizes or large standard errors, and none were observed. Regional plots were produced for all selected SNVs and these were carefully reviewed. Twenty-one variants for resting Tpe, 4 variants for Tpe response to exercise, and 7 variants for Tpe response to recovery were taken forward into replication. Replication was confirmed if p ≤ 0.05/21 = 2.4 × 10−3 for resting Tpe, p ≤ 0.05/4 = 1.3 × 10−2 for Tpe response to exercise, and p ≤ 0.05/7 = 7.1 × 10−3 for Tpe response to recovery and the effect was in the direction observed in discovery analyses for each trait in the replication cohort (All SNVs with p < 1 × 10n cohort .−8) from the full dataset GWAS are reported  using BOLT-LMM.reported . To test−6; (2) there was less than a 1.5-fold difference between the lead SNV and secondary association p values on a –log10 scale, i.e., if –log10(plead)/−log10(psec) < 1.5; and (3) there was less than a 1.5-fold difference between the main association and conditional association p values on a –log10 scale, i.e., if –log10(psec)/−log10(pcond) < 1.5.To examine whether there were independent SNVs at these loci, we applied genome-wide complex trait analysis.For each trait, we performed a GWAS for men and women separately in the full cohort including the same covariates in the regression model as specified above, but excluding sex .The percent variance explained of each variant was calculated by estimating the residuals from the regression model against the covariates used in each respective genetic model. We then fitted a second linear model for the trait residuals with all the identified variants plus the top ten principal components. The percent variance explained was the difference between the adjusted R-squared parameters from each model.2 ≥ 0.8 with the traits associated SNVs were considered). LD was calculated using genetic data from UKB in order to calculate pairwise-LD for all associated SNVs. The r2 of pairwise SNVs (minimum r2 = 0.8 and maximum distance between a pair of SNVs is 4 Mb) were computed using PLINK.We performed several analyses to annotate the identified SNVs, at the variant and gene level  with our validated lead SNVs for evidence of mediation of expression quantitative trait loci (eQTL) using the GTEx database, focusing on loci with the strongest evidence of eQTL associations in brain, heart, and adrenal tissue. We also performed colocalization analyses using COLOC2 ≥ 0.8) with our sentinel SNVs and chose the interactors with the SNVs of highest regulatory potential to annotate the loci.We evaluated all SNVs in LD  Gene database and GeneCards: The Human Gene Database were used to obtain official full names and, where relevant, common aliases for each candidate gene product. NCBI’s PubMed was used to interrogate primary literature pertaining to gene function. We also reviewed gene-specific animal models using International Mouse Phenotyping Consortium2 ≥ 0.8) with other traits from published GWAS using PhenoScanner.108980)Finally, to explore shared mechanisms of disease, we assessed association of our identified SNVs  values of resting Tpe in both the EST-UKB and the IMAGE-UKB cohorts was 62 (12) ms . Histogr−8, −6, 21 variants (considering one lead SNV per 1 Mb region) were identified as significant and taken forward into replication in ∼42,000 independent samples from UKB. Of the 21 selected SNVs for resting Tpe, 15 formally replicated (p ≤ 0.05/21 = 2.4 × 10−3) and all had concordant directions of effect  reached genome-wide significance in the full dataset GWAS, all with concordant directions of effect across discovery, validation, and full cohort datasets . ManhattDPT (MIM: 125597), SCN5A-SCN10A (MIM: 600163), and LITAF . Finally, the secondary signal at LITAF, rs570620219, was located 40 kb away from the lead SNV (rs2080512).Conditional analyses showed evidence for four secondary independent signals at loci Taken together, across both the replication stage and full dataset GWAS, we identified 32 SNVs (28 lead SNVs + 4 secondary SNVs) in 28 loci for resting Tpe ; Table 1−6 to take forward into replication in ∼22,000 independent samples. One of the selected SNVs for Tpe response to exercise formally replicated (p ≤ 0.05/4 = 0.0125) and had concordant directions of effect in discovery and replication datasets , GPR1 (MIM: 600239), HEY2 (MIM: 604674), and LIG3 (MIM: 600940). Variants at these loci were not significant (p > 5 × 10−8) in the combined analyses (ETS2 locus (MIM: 164740) for males . This variant was non-significant in the combined sex GWAS (NRXN3 (MIM: 600567) and NOL4L  for resting Tpe were annotated as missense variants. However, we identified regulatory variants that might affect gene expression levels of their target genes in heart and brain tissue by interrogating publicly available eQTL datasets using GTEx  were in high LD (r2 > 0.8) with top eQTL variants in cardiac and brain tissue  in cardiac left ventricle, three genes  in cardiac atrial appendages and three genes  in brain tissue  from the Gene Ontology, increased infarction size (p = 4.04 × 10−6) from the Mouse Phenotype Ontology, the NOS3 PPI subnetwork (p = 2.62 × 10−9) from ENSEMBL, and regulation of actin cytoskeleton (p = 1 × 10−4) from Kyoto encyclopedia of genes and genomes (We observed a significant enrichment of resting Tpe loci in heart tissue . By cons genomes A–S9D.2 > 0.8) were annotated as missense variants or were identified as regulatory variants that might affect gene expression levels of their target genes in heart and brain tissue.None of the lead variants for Tpe response to exercise or to recovery or their close proxies (rETS2 for Tpe response to exercise and KIK3B (MIM: 603754) for Tpe response to recovery whose promoter regions formed significant chromatin interactions with them in the left ventricle (−2) and regulation of skeletal muscle contraction via regulation of action potential  with other traits, including pulse rate, P-wave duration, resting heart rate (MIM: 607276), QT interval, QRS duration, cardiomegaly, Brugada syndrome (MIM: 601144), and atrial fibrillation  for the PR interval, at four loci  and at five loci  for the T-wave morphology restitution were genome-wide significant in our results  for the Tpe response to exercise had previously been associated with other ECG traits including resting Tpe. The main biological processes indicated for resting Tpe involved ventricular repolarization and cardiac conduction and contraction.This is the largest study to date studying the genetic contribution to the Tpe interval, and Tpe response to exercise and recovery. With the unique combination of a robust framework, including independent discovery and replication samples, and dense genetic imputation in ∼72,000 individuals,PRAG1 [MIM: 617344], PYGB [MIM: 138550], CREB5 [MIM: 618262], KCNJ4 [MIM: 600504], MSRA, RUFY1 [MIM: 610327], SERTAD2 [MIM: 617851], DEFB118 [MIM: 607650], GPR1, and HEY2, respectively; Of the total 32 loci discovered in this work, 10  did not overlap with any locus previously reported for another ECG trait  had plausible candidate genes  that is found in the heart. The physiological role of myocardial GP is to provide the energy supply required for myocardial contraction and it is associated with diseases including myocardial infarction (MIM: 608446).KCNJ4, functions closely with KCNJ2 . Both genes encode the human inward rectifier potassium channels Kir2.1 and Kir2.3. These potassium selective ion channels determine the resting membrane potential and terminal repolarization of the cardiac action potential. Importantly, mutations in KCNJ4 are associated with electrolyte imbalance and dilated cardiomyopathy (MIM: 115200).GATA4, a candidate gene at locus MSRA, plays a key role in cardiac development and function.TBX5 (MIM: 601620), it binds to cardiac super-enhancers and promotes cardiomyocyte gene expression, while it downregulates endocardial and endothelial gene expression.,187500),HEY2 encodes a member of the hairy and enhancer of split-related family of transcription factors. Two similar and redundant genes in the mouse are required for embryonic cardiovascular development. Interestingly, the lead variant we have identified at this locus, rs10457469, is in high LD (r2 = 0.97) with rs9388451, which has been reported to be associated with Brugada syndrome through a HEY2-dependent alteration of ion channel expression across the cardiac ventricular wall.HEY2 represses transcription by the cardiac transcriptional activators GATA4 and GATA6 (MIM: 601656).Of the ten resting Tpe-specific loci, a summation of bioinformatics analyses and literature review indicated eight loci  and has one of the largest effect sizes for this trait (1.30 ms). Mutations in KCNJ2 are associated with short QT syndrome 3 (MIM: 609622),KCNH2 and RNF207 (MIM: 616923), both loci previously associated with the QT interval.KCNH2 is another important gene that encodes a crucial potassium repolarizing current, HERG. Finally, RNF207, a RING finger protein, is a known modulator of cardiac repolarization through actions on HERG.Bioinformatics analyses on all loci identified in this study indicate that the main biological mechanism underlying resting Tpe is predominantly driven by cellular processes that control ventricular repolarization. As highlighted above, PYGB, GATA4, and HEY2 (highlighted before), as well as previously reported SCN5A-SCN10A, CAMK2D, and KCND3 are involved. CAMK2D is the candidate gene at the validated locus CAMK2D and is a calcium/calmodulin-dependent protein kinase involved in the excitation-contraction coupling in heart by targeting Ca(2+) influx into the myocyte. KCND3 is the candidate gene at locus KCND3, discovered in the full dataset GWAS, and encodes the Ito carrying KV4.3 channel, and gain-of-function mutations have been associated with Brugada syndrome,An additional biological mechanism underlying resting Tpe is cardiac conduction and contraction. Several candidate genes, such as KCNH2 were genome-wide significant in our results (−10), at KCNJ2, a locus that was also highly significant in our results (p = 4.2 × 10−148). However, their reported suggestive SNV, rs17749681, at GRIN2A (MIM: 138253), was non-significant in our results . Our data suggest there is a significant genetic contribution to resting Tpe, but its response to heart rate changes is mainly influenced by environmental factors. This is a general feature that is emerging from our studies, namely that the heritability of exercise-induced changes in cardiac electrophysiology is lower than those at rest.,Our sex-specific findings strengthen previous studies concluding that there are sex differences in the resting Tpe and its response to heart rate.Despite finding a significant association between the GRS and ventricular arrhythmic events in a training cohort, this significance was not validated in an independent subset of individuals. This might indicate that the common variants modulating resting Tpe do not contribute to the pathophysiological mechanisms influencing ventricular arrhythmic risk. Alternatively, given the low incidence of arrhythmic events in the UK Biobank, which comprises a relatively healthy population, the validation analysis might have been underpowered. Future studies should evaluate the prognostic value of the GRS in well-powered cohorts for validation of our negative results.Our study has some limitations. First, we report results from GWASs including all available samples, which indicate seven loci for resting Tpe, two for Tpe response to exercise, and three loci for Tpe response to recovery with no independent replication, so these loci should be considered as preliminary until they are externally validated. Next, due to the relatively low sample size, we restricted our analysis to common variants (MAF > 1%), so we are unable to comment on the role of rare variants on the Tpe traits. In addition, we only report results for European ancestry as this was by far the largest ancestral group in the UKB cohort. Additional studies will need to investigate whether the findings can be extrapolated to other ancestries. Finally, the range of variation of the Tpe response to exercise and recovery traits is small, and the limited sampling rate of the ECG recordings (500 Hz), corresponding to a temporal resolution of 2 ms, might have hindered the resolution of these measurements.In summary, our findings provide additional loci for Tpe interval traits and reveal the role of ventricular repolarization and cardiac conduction and contraction in modulating them. Our work may guide future studies identifying new therapeutic targets to modulate resting Tpe and its dynamics to prevent and treat ventricular arrhythmias.The authors declare no competing interests.


Photosynthesis has become a major trait of interest for cereal yield improvement as breeders appear to have reached the theoretical genetic limit for harvest index, the mass of grain as a proportion of crop biomass. Yield improvements afforded by the adoption of green revolution dwarfing genes to wheat and rice are becoming exhausted, and improvements in biomass and radiation use efficiency are now sought in these crops. Exploring genetic diversity in photosynthesis is now possible using high-throughput techniques, and low-cost genotyping facilitates discovery of the genetic architecture underlying this variation. Photosynthetic traits have been shown to be highly heritable, and significant variation is present for these traits in available germplasm. This offers hope that breeding for improved photosynthesis and radiation use efficiency in cereal crops is tractable and a useful shorter term adjunct to genetic and genome engineering to boost yield potential. Photosynthetic performance is a key target for yield improvement in cereal crops. Evidence for genetic variation in photosynthesis is reviewed along with new approaches to measure these traits. Indeed, global reserves of cereal grain are again low as a proportion of annual global demand and may be precipitously so (http://www.fao.org/worldfoodsituation/csdb/en/).The challenges of sustainably supplying sufficient food to a burgeoning world population, predicted to exceed 9 billion by 2050, have been well documented  is now geographically fine grained enough to allow us to predict not only global increases in average temperature and the impact on agriculture, but also how this maps onto major cereal-producing regions globally . However, while modelling predicts that these modifications alone and in combination could have quantum effects on photosynthetic performance in crops, as yet only a few of these genetically modified traits have been tested in the field in cereals  and the efforts of the Leibniz Institute for Crop Breeding (IPK) to carry out Illumina sequencing of a large proportion of their barley and wheat germplasm collection (https://bridge.ipk-gatersleben.de).The plummeting costs of genome and transcriptome sequencing means that burgeoning collections of cereal germplasm can be cheaply genotyped, and in many cases high-quality genome re-sequence data are available  and deteindica and japonica rice genotypes with diverse pedigrees and geographic origins: the 3K population, currently being extended to include 10 000 entries (http://snp-seek.irri.org/). Maize germplasm collections and genotyping data are perhaps the best developed and have been utilized for many years  have developed a large sequenced diversity panel of While the genetic resources now exist in a variety of crops to examine allelic variation in all candidate genes/proteins known to be important in controlling photosynthetic flux, Rubisco has been the focus of most efforts to date.2 assimilation within key C3 crops is often limited by the catalytic activity of Rubisco  and algae have revealed substantial diversity in Rubisco catalysis and identified versions better suited to current and predicted future climate scenarios (2 in air (Kcair); (ii) carboxylation speed (kcatc); and (iii) the specificity for CO2 as opposed to O2 (Sc/o)  (2 compared with Aegilops relatives (Screens of 2 (Sc/o) . Until r2 assimilation. However, it is evident in 4 Rubisco provides solutions to improve the current wheat forms. The kcatc and carboxylation efficiency (kcatc/Kcair) of C4 Rubisco outperform those of wheat. Comparison of Rubisco specificity is difficult as Sc/o from that for C4 plants presented. Nevertheless, it is evident that maize Rubisco provides improved CO2 assimilation when compared at 25 °C  and electron transport capacity (J) using the modelling frameworks discussed above  and photosynthetic electron transport (J)  . In the A from gas exchange. In a collection of 20 diverse japonica and indica genotypes, light-saturated A at ambient CO2 concentration varied by >40% from lowest to highest genotypes (A observed in this study (0.17) is indicative of the challenges in using a single point assimilate rate to find the genetic architecture underlying photosynthetic traits.In rice, several studies have been carried out on diverse germplasm to investigate genetic variation in As discussed above, high-throughput surrogates for photosynthetic traits traditionally derived from gas exchange are a priority because: (i) the large scale of experiments necessary to screen germplasm diversity sets and mapping populations proves too costly; (ii) time of day and seasonal variation affect trait expression, compressing time available for measurements; and (iii) measurements may need to be made on several leaves/plant organs at different developmental stages in the crop life cycle to obtain a comprehensive analysis. To obtain accurate mapping of photosynthetic traits in a structured population, such as a recombinant inbred set with high-density genetic maps usually requires in excess of 150 lines grown with replication, in the field, preferably across multiple seasons and often multiple locations , exacerbRecent advances in machine learning coupled with optical sensing systems at the leaf and canopy level offer a potential solution to this issue. Leaf and canopy spectral reflectance measurements can now be made using affordable visible/near infrared spectrometers or more costly full range visible, NIR, SWIR spectrometers, enabling the collection of many hundreds of wavelengths from below 400 nm to 2500 nm. Predictions of leaf traits from spectral reflectance data are made using machine learning to generate statistical models between every wavelength of reflected light from leaves and the trait of interest measured with traditional methods (termed a ‘training set’). These models are then either validated on another set of germplasm or the training set is divided into a training and validation set for testing  or the intrinsic light-harvesting efficiency of PSII (dark-adapted Fv/Fm). NPQ  can also be calculated  which apply a series of ‘flashlets’ to a spot of the canopy up to a few centimetres wide may be useful for field phenotyping . Both Vavilov and commercial checks spread over a similar range of LEF, between 20 µmol e m–2 s–1 and 80 µmol e m–2 s–1. The mean LEF for Vavilov was slightly higher and there were few lines with higher LEF than commercial wheats. In addition, concurrent measurements of SPAD with the same sensor demonstrated a dynamic response of LEF to N content mostly in the Vavilov lines, with commercial varieties presenting higher N. High-throughput screening for leaf photosynthetic-related traits using sensors such as MultispeQ can be useful to identify new diversity in photosynthetic capacity and efficiency in breeding programmes or during surveys of diversity panels at relatively low cost.To investigate the utility of the MultispeQ sensor in a cereal crop, we screened a subset of the pre-breeding ‘Vavilov’ wheat population representing the genetic diversity of the panel  can be monitored by measuring the quenching of chlorophyll fluorescence over a range of light intensities. A mathematical model  can thenapacity’ .Jmax, θ, and Φ) using statistical approaches such as partial least squares regression, random forest, a support vector machine, or neural networks. The combination of hyperspectral imaging and existing methods to detect spikes using image analyses with deep learning  relies upon pre-existing knowledge of these candidates . Using hDespite promising data on genetic diversity and heritability of photosynthetic traits, there are surprisingly few examples of QTL mapping of these parameters in wheat, given its importance globally. There have, however, been several studies targeted to photosynthesis-related traits such as stress tolerance, canopy temperature, stomatal conductance, and transpiration efficiency  and photosynthetic efficiency  to describe the drivers of variation in modelled leaf-level traits. It has been widely reported that in cereal crops Rubisco amount and activity are closely related to N nutrition and leaf N content per unit area  has a strong genetic component and there is considerable variation in wheat. Given the robust models available to predict these parameters from leaf hyperspectral reflectance data (https://iwyp.org/funded-projects/).2 enrichment of a variety of crop species, while often showing increases in yield due to elevated photosynthetic activity, also frequently show sink strength-mediated feedback limitation and limited impact on yield, thought to be due to negative effects of sugar signalling on photosynthetic gene expression, particularly Rubisco and leaf N levels (It has frequently been pointed out that improvements in photosynthetic performance will only be translated into improved grain yield if ‘partitioning’ between source and sink and associated signalling processes are accounted for (reviewed in N levels . VariousWhile sugar feedback could be an impediment to realization of increased yield from photosynthetic improvement, there is also evidence of feedforward effects of photosynthetic performance on sink development. In wheat, the final size of the ear and floret number is determined early in development when the inflorescence is still within the sheath (4 rice, see Current large investments in research for improved crop photosynthetic performance have varying timelines for delivery of new cereal varieties to farmers. Improved tools for phenotyping photosynthesis could identify material for crossing which can then be utilized in breeding programmes almost immediately, but understanding the genetic architecture of photosynthesis, and finding QTLs and causative SNPs in new gene targets requires appropriate diversity sets or biparental populations and then creation of near isogenic lines or gene-edited ‘allele mimics’ to demonstrate causality. This may reasonably be expected to take 3–5 years for the results of field-based activities to be available to crop breeders, assuming genetic material is in place. Deployment of genetically modified traits, once prototyped in model systems, may take even longer after careful validation in multiple environments and in multiple transgenic events. Complex multigene pathway engineering, while becoming rapidly more tractable with synthetic biology and gene editing (for C


GWAS). We explored replication in a mouse model using an advanced intercross line (AIL), which is a multigenerational intercross between two inbred strains. We re-genotyped a previously published cohort of LG/J x SM/J AIL mice  using a denser marker set and genotyped a new cohort of AIL mice  for the first time. We identified 36 novel genome-wide significant loci in the F34 and 25 novel loci in the F39-43 cohort. The subset of traits that were measured in both cohorts  showed high genetic correlations, although the SNP heritabilities were slightly lower in the F39-43 cohort. For this subset of traits, we attempted to replicate loci identified in either F34 or F39-43 in the other cohort. Coat color was robustly replicated; locomotor activity and body weight were only partially replicated, which was inconsistent with our power simulations. We used a random effects model to show that the partial replications could not be explained by Winner’s Curse but could be explained by study-specific heterogeneity. Despite this heterogeneity, we performed a mega-analysis by combining F34 and F39-43 cohorts , which identified four novel loci associated with locomotor activity and body weight. These results illustrate that even with the high degree of genetic and environmental control possible in our experimental system, replication was hindered by study-specific heterogeneity, which has broad implications for ongoing concerns about reproducibility.There has been extensive discussion of the “Replication Crisis” in many fields, including genome-wide association studies ( GWAS) in model organisms can use genetically identical cohorts phenotyped under extremely similar conditions, which would be expected to enhance the success of replication. We sought to investigate replication in model organism GWAS using a mouse advanced intercross line (AIL). The use of GWAS in model organisms such as mice , which are produced by intercrossing F2 mice for additional generations. AILs accumulate additional crossovers with every successive generation, leading to a population with shorter linkage disequilibrium (LD) blocks, which improves mapping precision, albeit at the expense of power . Since then, we have collected genotype and phenotype information from multiple generations, including F34 , which is a reduced-representation sequencing method . No prior publications have described the F39-43 generations (n = 600). The sample size of F34 mice reported in this study (n = 428) is smaller than that in previous publications of F34 (n = 688) because we only genotyped a subset of F34 animals using GBS.All mice used in this study were members of the LG/J x SM/J AIL that was originally created by Dr. James Cheverud . This AIL has been maintained in the Palmer laboratory since generation F34 animals for locomotor activity and locomotor response to methamphetamine , “open field” in the F34 cohort was also measured using Versamax, whereas “open field” in the F39-43 cohort was measured using the EthoVision XT software , and three locomotor activity traits .All phenotypes are listed in Table S1. We have previously described the phenotyping of F34 animals had been genotyped on a custom SNP array on the Illumina Infinium platform , which is a reduced-representation genome sequencing method  (Table S2). All phenotype and GBS genotype data are deposited in GeneNetwork2 (http://gn2.genenetwork.org/).F  did not agree with their recorded sex; we assumed this discrepancy was due to sample mix-ups. To further identify mislabeled samples, we calculated kinship coefficients based on the full AIL pedigree using QTLRel. We then calculated a genetic relatedness matrix (GRM) using IBDLD . Next, we sought to identify animals in which the recorded sex did not agree with the sequencing data. We compared the ratio of reads mapped to the X and Y chromosomes. The 95% CI for this ratio was 196.84 to 214.3 in females and 2.13 to 2.18 in males. Twenty-two Fng IBDLD , which e34, 58,966 autosomal and 824 X chromosome SNPs in F39-43, and 57,635 autosomal and 826 X chromosome SNPs in the combined F34 and F39-43 set (Table S2). GBS genotype quality was estimated by examining concordance between the 66 SNPs that were present in both the array and GBS genotyping results .At the end of SNP and sample filtering, we had 59,561 autosomal and 831 X chromosome SNPs in F2r) was calculated using allele frequency matched SNPs (MAF difference < 0.05) within 100,000 bp distance, as described in Parker et al. (Average LD (34 array genotypes (n = 428) and F34 GBS genotypes (n = 428) were imputed to LG/J and SM/J whole genome sequence data . Imputation to reference panels yielded 4.3 million SNPs for F34 array and F34 GBS imputed sets. Imputed SNPs with r2 > 0.9, MAF > 0.1, HWE p-value > 1.0×10−6 were retained, resulting in 4.1M imputed F34 GBS SNPs and 4.3M imputed F34 array SNPs.Fnce data . For F34LOCO) approach to calculate the GRM, which effectively circumvented the problem of proximal contamination , raw phenotypes were quantile normalized prior to analysis. Coat color traits were coded as follows: albino: 1 = white, 0 = non-white; agouti: 1 = tan, 0 = black, NA = white. Because F34 AIL had already been studied, we used the same covariates as described in Cheng et al.  for body weight, coat color, and locomotor activity, since these traits were measured in the same way in both cohorts. We quantile transformed all continuous phenotypes in each cohort and then combined the transformed phenotypes for the mega-analysis .Separate GWAS were performed using the F34 GWAS were dubious because the flanking SNPs, which would have been expected to be in high LD with the significant SNP (a very strong assumption in an AIL), did not have high -log10(p) values. We only examined SNPs that obtained significant p-values; close examinations revealed that these SNPs had dubious ratios of heterozygotes to homozygotes calls and had corresponding HWE p-values that were close to our 1.0×10−6 threshold (Table S3). We chose the 1.0×10−6 as the filter threshold of the HWE p-values based on a gene-dropping exercise. We used the F33-34 family pedigree and the F34 genetic map to simulate the genotypes in F34  that gave us a conservative set of independent SNPs (Table S4). For the coat color phenotypes, we found that multiple SNPs remained significant even after LD-based clumping, presumably due to the extremely significant associations at these Mendelian loci. In these cases, we used a stepwise model selection procedure in GCTA . We found that the 95th percentile values for these permutations were 4.65, 4.79, and 4.85, respectively, which were very similar to 4.85, the threshold obtained from MultiTrans using the same data. Thus, the thresholds presented here were obtained from MultiTrans but are similar (if anything slightly more conservative) to the thresholds we would have obtained had we used permutation. Because the effective number of tests depends on the number of SNPs and the specific animals used in GWAS, we obtained a unique adjusted significance threshold for each SNP set in each animal cohort (Table S5).We used MultiTrans to set significance thresholds for GWAS . Credible set analysis is a Bayesian method of selecting an interval of SNPs that are likely to contain the causal SNPs; we used LD r2 threshold = 0.8, posterior probability =0.99. The R script could be found on GitHub: https://github.com/hailianghuang/FM-summary/blob/master/getCredible.rWe followed the method described in , and obtained estimates of model parameters including the intercept and the genetic variance component. Using these model parameters, we added the genotype effect to the random numbers generated from the null model to recreate a trait. For each simulated effect size, we scanned every simulated trait 2,500 times and examined the ratio of association tests whose test statistics surpassed the significance thresholds .To estimate the power of replication of a SNP from the discovery set in the replication set, we simulated GWAS with 50 varying effect sizes for the discovery SNP using the LMM model. We first fit the trait in a null model  for the replication study, where M is the number of genome-wide significant variants in the initial study. We computed the “empirical replication rate” as the proportion of variants passing the genome-wide significant threshold in the discovery study that also passed this Bonferroni corrected threshold in the replication study. Since the estimation of the model parameters requires at least two variants, we only applied this method to phenotypes with at least two genome-wide significant variants in the discovery study.We applied each of these models once using FWC and WC+C models explained the observed patterns of replication, we computed the predicted replication rates under each model. For each variant that passed the genome-wide significant threshold in the discovery study, we used the conditional distributions previously learned to compute the probability that the variant passed the Bonferroni corrected threshold in the replication study. For each phenotype, we computed the average of these predicted replication rates and compared this average to the empirical replication rates.To assess how well the 34 and F39-43 populations. We calculated both SNP heritability and genetic correlations between F34 and F39-43 animals using GCTA-GREML analysis and GCTA bivariate GREML analysis ”: GN655; “LGSM AI G34 Palmer (GBS)”: GN656), F39-43 ”: GN657), and mega-analysis cohort ”: GN654) of AIL are uploaded to GeneNetwork2 (http://gn2.genenetwork.org/). Code used to perform the analyses is included in the supplementary materials on figshare: https://doi.org/10.25387/g3.11674221.All relevant data are within the paper and its Supporting Information files. Genotypes and phenotypes of F34  and 305 males and 295 females from generations F39-43. For the F34 AIL 79 traits were available from previous published and unpublished work; for the F39-43 AIL 49 unpublished traits were available (Table S1). F34 mice had been previously genotyped on a custom SNP array (MAF) of those 4,593 array SNPs was 0.388 . The MAF for the GBS SNPs was 0.382 in F34, 0.358 in F39-43, and 0.370 in F34 and F39-43 .We used 214 males and 214 females from generation Fas 0.388 . To obtad F39-43 . There wLMM) as implemented in GEMMA (LOCO) approach to address the problem of proximal contamination, as previously described , we identified 109 significant lead loci in 49 out of 79 F34 phenotypes using the GBS SNPs (Table S7). In contrast, we identified 83 significant lead loci in 45 out of 79 F34 phenotypes using the sparse array SNPs . Among the loci identified in the F34, 36 were uniquely identified using the GBS genotypes, whereas 11 were uniquely identified using the array genotypes. These unique loci could be explained by the disparity of the marker density between the GBS and array genotypes. Some unique loci captured haplotype blocks that were not picked up in the other SNP set. Other unique loci were only slightly above the significance threshold in one SNP set where the corresponding loci in the other SNP set had sub-threshold significance . Overall, GBS SNPs consistently yielded more significant lead loci compared to array SNPs regardless of the clumping parameter values (Table S4), indicating that a dense marker panel was able to detect more association signals compared to a sparse marker panel.We used a linear mixed model ). The physical positions of the SNPs in the credible set were used to determine the boundaries of each locus. As expected, the greater density of the GBS genotypes allowed us to better define each interval. For instance, the lead locus at chr17:27130383 was associated with distance traveled in periphery in the open field test in F34 AILs . A subset of those traits: coat color, body weight, and locomotor activity, were also phenotyped in the F34 AILs. To assess replication, we determined whether the loci that were significant in one cohort (either F34 or F39-43) would also be significant in the other. We termed the cohort in which a locus was initially discovered as its “discovery set” and the cohort we attempted replication in as the “replication set”  in F39-43; similarly, one body weight locus identified in F39-43 was replicated in F34 (P < 0.05). However, none of the locomotor activity loci were replicated in the reciprocal (replication) cohorts.We identified 25 genome-wide significant loci for 21 phenotypes in the Fion set” . Coat co34 cohort and the F39-43 cohorts. Confidence intervals  in the F39-43 cohort was more successful using the discovery QTL region defined by the credible set interval; the p-value at the top SNP within the credible set interval was noticeably more significant  than the p-value at the discovery SNP . Our results suggest that for the most part, the discovery SNP accurately represented the association strength of the loci, presumably because of its strong linkage with the neighboring SNPs. In our case, defining a QTL region by the credible set interval did not increase the count of replicated sites between the two cohorts.We found that using a broader definition of an association region rather than a single SNP did not improve replication between the F39-43  of the coat color, body weight and activity loci were in the same direction between the discovery and replication cohorts. Specifically, we compared whether the sign (direction) of the beta estimates are consistently above or below zero. We found that 11 of 12 comparisons passed this much less stringent test of replication. The one locus (at chr15.67627183) that did not pass the sign test was the locomotor locus “discovered” in F39-43 .In light of the failure to replicate the locomotor activity findings, we conducted a series of 2,500 simulations per trait to estimate the expected power of our replication cohorts. For each phenotype we used the kinship relatedness matrix and variance components estimated from the replication set. For the coat color traits, we found that we had 100% power to replicate the association at either genome-wide significant levels or the more liberal p < 0.05 threshold . For body weight and locomotor activity, power to replicate at a genome-wide significance threshold ranged from 20 to 85%, whereas power to replicate at the p < 0.05 threshold was between 80% and 100% . These power estimates were inconsistent with our empirical observations for the locomotor activity traits, none of which replicated at even the p < 0.05 threshold, where we should have had almost 100% power  or studyWC) only accounts for Winner’s Curse, while the second model accounts for both Winner’s Curse and study-specific heterogeneity due to confounding (WC+C). In this context, we define confounding as any biological or technical effect present in one study but not the other. We applied each of these models once using F34 as the discovery study and once using F39-43 as the discovery study. The models can be used to assess how well Winner’s Curse explains the observed levels of replication. For example, when F34 is used as the replication study for the albino coat color phenotype, the expected value of the replication summary statistics after accounting for winner’s curse is the same as the expected value after accounting for Winner’s Curse and confounding . While the 95% confidence intervals for the WC+C model are larger than the WC model, both models seem to explain the observed data well. However, when F34 is used as the discovery study for the locomotor activity on day 1 or body weight, the WC+C model explains the data better than the WC model.To determine whether these factors could explain the lower than expected rate of replication, we applied a statistical framework that jointly models Winner’s Curse and study-specific heterogeneity in two GWAS studies of the same phenotype  values, indicating that the lack of replication in these phenotypes is more likely to be due to study-specific heterogeneity that is potentially caused by confounding.In order to quantitatively assess how well each of these models explain the observed patterns of replication, we computed the predicted replication rates under each model (Methods) and compared these with the empirical replication rates. In this analysis, we defined the empirical replication rate to be the proportion of variants passing the genome-wide significance threshold in the discovery study that also pass the Bonferroni corrected threshold in the replication study. We used this definition of replication for this analysis instead of replication of lead SNPs to allow for a larger number of variants to be included in the model fitting process. For all phenotypes tested, the enotypes . While t34 and F39-43 generations for body weight, coat color, and locomotor activity phenotypes (Table S11), using GCTA-GREML . In contrast, locomotor activity on day 3 showed a significant but weaker genetic correlation (rG = 0.577), perhaps reflecting variability in the quality of the methamphetamine injection, which were only given on day 3. Overall, these results suggest that genetic influences on these traits were largely similar in the two cohorts; however, the genetic correlations were less than 1, suggesting an additional barrier to replication that was not accounted for in our power simulations.We evaluated whether or not the traits showed genetic correlations across the two cohorts. High genetic correlations would indicate a high degree of additive genetic effect that is shared between the two cohorts, and the low genetic correlations would indicate limited potential for replication. We used all autosomal SNPs to calculate genetic correlations between the F39-43 cohort compared to the F34 cohort, including the Mendelian traits of coat color. The ± 1 × standard error intervals of the F34 and F39-43 SNP heritability estimates for the coat color trait albino overlapped. This observation indicates that SNP heritability for albino in the two cohorts is comparable. In contrast, the ± 1 × standard error intervals of the F34 and F39-43 SNP heritability estimates for the coat color trait agouti did not overlap. We could not explain the differential SNP heritability for the binary trait agouti in the two cohorts. The lower SNP heritability in F39-43 for the rest of the quantitative traits could be a result of increased experimental variance .−9) for body weight were lower than the corresponding p-values for the same loci for F34  and F39-43 , we suspected that a mega-analysis using the combined sample set would allow for the identification of additional loci; indeed, mega-analysis identified four novel genome-wide significant associations . The failure to replicate some of our findings was not predicted by our power simulations. Therefore, we performed an analysis to determine whether Winner’s Curse and study-specific heterogeneity could account for the lower than expected replication rate. Winner’s Curse alone could not explain the failure to replicate. However, modeling both Winner’s Curse and study-specific heterogeneity better explained the observed replication rate. Finally, mega-analysis of the two cohorts allowed us to discover four additional loci. Taken together, our results provide a set of refined regions of association for numerous physiological and behavioral traits in multiple generations of AILs. These loci could serve as benchmarks for future GWAS results in intercross mouse lines. More broadly, this study illustrates the difficulty of replication even when using a highly controlled model system.We used F34 AILs (Csmd1 (CUB and sushi multiple domains 1). Gonzales et al. . We also replicated a locus on chromosome 17 for distance traveled in the periphery in the open field test . One gene at this locus, EphA4, codes for a receptor for membrane-bound ephrins. EPHA4 plays an important role in the activation of the tyrosine kinase JAK2 and the signal transducer and transcriptional activator STAT5B in muscle, promoting the synthesis of insulin-like growth factor 1 (IGF-1) . The lead SNP is immediately upstream of Tlr4, Toll-like receptor 4, which recognizes Gram-negative bacteria by its cell wall component, lipopolysaccharide  between F34 and F39-43. However, the loci for locomotor activity were not replicated. Power analyses predicted a much higher rate of replication, which led us to conduct additional analyses to better understand the lower than expected rate of replication.We considered both the FDrosophila melanogaster. The authors found that sample size was the major determinant for the overestimation of phenotypic variance explained at the significant QTL in both the GWAS-based Drosophila Genetic Reference Panel (DGRP) and the multi-parental Drosophila Synthetic Population Resource (DSPR). When sample size remained constant and the true phenotypic variance explained at the significant QTL was small, the estimation bias was more pronounced. In contrast, estimates for the phenotypic variance explained at all simulated QTL, significant or not, were generally centered at the true values. In an analogous study of power and replication in Collaborative Cross mice, Keele et al.  which h34 cohort and the F39-43 cohort because F33 animals were transported to the University of Chicago from Washington University in St. Louis. In contrast, the breeder animals of the F39-43 cohort have already acclimated to the environment for multiple generations. Another possible source of confounding is that the phenotyping of the F39-43 occurred over five generations (more than a year) during which time numerous environmental factors may have changed . Such factors are known to be an important potential source of confounding;  reported in Interestingly, we found that the genetic correlations between the discovery and replication samples were relatively high for all traits; however, some traits replicated well and others replicated poorly. Our subsequent analysis showed that study-specific heterogeneity was low for the coat color traits, but higher for the body weight and locomotor traits. This makes an important point, namely that a strong genetic correlation can exists in the presence or absence of study-specific heterogeneity. Finally, it was notable that replication (at p < 0.05) was relatively successful for body weight, despite the significant evidence of study-specific heterogeneity and low predicted replication . Power a34 and F39-43 AIL mice. The combined dataset allowed us to identify four novel genome-wide significant associations that were not detected in either the F34 or the F39-43 cohorts presumably because of the increased sample size in the mega-analysis (Finally, we performed a mega-analysis using FIn addition to performing many GWAS and related analyses that led to the identification of dozens of novel loci for locomotor activity, open field test, fear conditioning, light dark test for anxiety, complete blood count, iron content in liver and spleen, and muscle weight, our study also tested our expectations about replication of GWAS findings. We did not obtain the expected rate of replication. We used a method that can distinguish between Winner’s Curse and study-specific heterogeneity to show that the lower than expected rate of replication was due to study-specific heterogeneity. This indicates that study-specific heterogeneity can have a major impact of replication even when in a model system when a genetically identical population is tested under conditions that are designed to be as similar as possible.


Pinus sylvestris trees in a Mediterranean mountainous area distributed in two contrasting elevations. We measured 13 functional traits, in three clusters , and analysed their variation and coordination. We found significant coordination among traits belonging to different clusters that reveals coordinated phenotypes. However, we found fewer correlations within trait clusters than initially expected. Trait correlation structures  differed among individuals at different elevations. We observed more correlations within trait clusters at low elevation compared to those at high elevation. Moreover, the higher number of correlations among different trait clusters and the lower trait variation at the higher elevation suggests that variability decreases under more stressful conditions. Altogether, our results reveal that traits at intraspecific scale are coordinated in a broad network and not only within clusters of traits but also that this trait covariation is significantly affected by environmental conditions.Functional traits have emerged as a key to understand species responses to environmental conditions. The concerted expression of multiple traits gives rise to the phenotype of each individual, which is the one interacting with the environment and evolving. However, patterns of trait covariation and how they vary in response to environmental conditions remain poorly understood, particularly at the intraspecific scale. Here, we have measured traits at different scales and in different organs, and analysed their covariation in a large number of conspecifics distributed in two contrasting environments. We expected significant correlations among traits, not only within clusters of traits as found in global, multispecies studies, but also among clusters, with more relationships within clusters, due to genetic constraints, and among clusters due to more coordinated phenotypes than community level, multispecies studies. We surveyed 100  One of the major challenges in plant ecology over the last decades has been the development of a general plant classification framework based on adaptive plant strategies ,2. The dvs. costly long-lived leaves with slow returns. Similarly, the wood economics spectrum (WS) reflects the trade-offs among important wood functions like transport safety, transport efficiency, and mechanical support. Within WS, wood density works as an integrator of wood properties, related to the mechanical support and resistance to embolism in drought periods [Relationships among functional traits in plant species define coordinated spectra related to common variations in functional strategies of plants. One of the most studied spectrum is the so-called leaf economics spectrum (LES), which predicts how leaf traits should vary across environmental gradients and co-vary among themselves. It describes a universal axis from “slow” to “fast” resource use strategies of plant species –13 confr periods . As a re periods ,16, whic periods ,18.Searching for comprehensive plant strategies, some works addressed trait covariation studies among these spectra or cluster of traits –23. HoweIntraspecific trait variation is gaining increased recognition , 30 and 13C), and lower leaf area and photosynthetic pigments due to low temperatures [Environmental conditions within the geographical and altitudinal distributions of a plant species vary determining important changes in the phenotypic expressions among populations ,38. For eratures ,41. Moreeratures ,43. Evideratures  and barkeratures  comparedPinus sylvestris (Scots pine) in a Mediterranean mountainous area in Spain at two different elevations. We firstly explored the overall trait network at the intraspecific level, analysing trait correlations within and among trait clusters. Secondly, we compared trait coordination in trees at the two very close sites but with contrasting elevations, to quantify the environmental impact on trait networks. We assumed that the environmental differences between both study sites are mainly driven by elevation. We expected to find a high number of correlations among traits within trait clusters, higher than those found in multispecies studies due to the genetic constraints within a species. We also expected a large number of correlations among the three trait clusters, similarly to multispecies studies at the community level due to the existence of important relationships among clusters also at the individual level , i.e. stem, leaf and whole-plant traits, in individual tress of al level .P. sylvestris with no signs of recent management and an uneven age structure. They represent the natural highest and lowest elevational distribution of the species in the area. In both sites, the climate is mountainous Mediterranean with wet, cold winters and warm dry summers. Annual mean temperature is 7.9°C in Ventorrillo and 6.5°C in Pingarrón and annual precipitation is 897 mm in Ventorrillo and 1242 mm in Pingarrón. Mean precipitation during the three driest months is 22 mm in Pingarrón and 24 mm in Ventorrillo  during June and July 2016. Particularly, we chose two sites very close geographically (ca 7km) but with contrasting elevations, namely Pingarrón at 1900m  and Ventorrillo Biological Station at 1440m . Both sites represent natural, dominant and well-conserved stands of rldclim; . Field wJuniperus communis L. subsp alpina (Neilr.) Čelak, Cytisus oromediterraneus (G. López & C.E. Jarvis) Rivas Mart. and Adenocarpus complicatus (L.) Link. Ventorrillo site has a south-west exposure and includes a layer of deciduous Pyrenean oak (Quercus pyrenaica Willd.) under the dominant pine canopy, and the understorey vegetation is mainly composed of Cistus laurifolius L., Cytisus scoparius L. and Genista cinerascens Lange.Pingarrón site is facing north and the understorey vegetation includes oromediterrenean shrublands, such as We conducted an individual-based sampling, with 50 individuals sampled at Pingarrón (1900m) and 50 individuals at Ventorrillo (1440m). We randomly selected individuals among the mature and healthy ones and collected a fully sun-exposed branch from the top of the crown. We also extracted two 5-mm diameter wood cores at 50cm above the ground, using an increment borer.3), and iii) Leaf traits, that balance construction costs vs. growth potential: leaf dry matter content , specific leaf area , leaf nitrogen content , leaf carbon content , leaf carbon isotope , chlorophyll a (μg/g), chlorophyll b (μg/g) and beta-carotene (μg/g). Detailed methods for trait measurements are provided in the Supporting Information  were transformed with a Box-Cox transformation as appropriate, to meet normality assumption using ‘AID’ R package and boxcoxnc function.We first described the magnitude of intraspecific trait variability (ITV) for each trait by calculating the coefficient of variation (CV) and its 95% confidence limits using bootstrapping with replacement (with 500 replicates). We performed a t-test to analyse differences in trait values between sites. The traits . We used the modularity function to measure the structure of the network for both populations. This metric calculates the fraction of edges within the defined clusters minus the expected fractions if the edges were random [Q = 1, the maximum) reflects dense connections within a trait cluster and weak connections among clusters. If the number of connections is close to zero (Q = 0) the trait network has low modularity.We ran a correlation analysis to evaluate pairwise relationships among all traits, pooling data of individuals from both sites and separately. Secondly, we compared the three correlation matrices using a chi-square test. This test provides a χe random ,50. Highscale. We initially used a Detrended Correspondence Analysis (DCA), to estimate the axes length in units of average standard deviation [To assess the influence of elevation on all traits and their relationships, we performed an ordination analysis. Trait data for ordination analysis were standardized using the R function eviation ,52. As teviation , which aeviation ,55. In oeviation . Finallycortest.mat function in the 'psych' package [vegan' package [All statistical analyses were conducted in R version 3.5.1 . Compari package . Multiva package .13C) and WD exhibited relatively low intraspecific variability (ITV) with coefficients of variation (CV) below 15%  showed substantial variability with CV between 29% and 50% (The magnitude of variation at the intraspecific level depended on the study trait Tables  and 2. Below 15% . In cont and 50% .a) and conservation  of resources .We found also correlations among clusters. Plant size traits and stem traits were positively correlated . Among lF1,87 = 4.56; p = 0.001). The first RDA axis represents the traits which differed between the two sites, i.e. chlorophyll a, LDMC and SLA. Despite the small fraction of the total variance explained by altitudinal condition, some leaf trait values were significantly different between both sites  and bark thickness  when all traits were simultaneously considered  have denser connections among clusters and sparse connections within clusters than traits at low elevation (Q = 0.23).The correlation analysis independently run for each site showed a similar number of significant trait correlations, 21 for individuals at high-elevation  and 22 for individuals at low-elevation (28%). We also found a greater number of trade-offs, significant negative correlations, among different clusters at high elevation (13) compared with low elevation (8) . Results13C . For instance, at high elevation, we found that SLA was negatively (and LDMC positively) correlated with bark thickness, crown depth and plant height. Moreover, chlorophyll b was positively related to crown depth and beta-carotene showed a positive correlation with plant height  are highly coordinated among them , reflectStudies at the community level showed that trade-offs proposed by LES between acquisition and resources conservation are not always found ,60–62, rThe plant size traits were tightly correlated, being taller those trees with thicker trunks and larger crowns. This plant size cluster is complemented with stem traits, as bigger trees had denser wood and thicker bark. Wood density is considered a good indicator of mechanical resistance and hydraulic safety –79. NameMoreover, our results showed interactions among leaf traits and other trait clusters within a species, similarly to studies analysing simultaneously trait clusters in different species ,21–23. Fvs. 897 mm), which triggered significant differences in functional traits among pines from both sites. Individuals growing at higher elevation presented greater bark thickness than those at lower elevation Click here for additional data file.S2 AppendixConfidence limits surrounding coefficients of variation (CV) were calculated through bootstrapping with replacement (with n = 500 replicates). Comparison of traits values: a) plant height; b) diameter at breast height (DBH) and c) Age at the two different elevations using a t-test are also shown.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableIt was used the Pearson´s rank correlation analyses. ‘ns’ means ‘not significant’. Brackets indicate the number of individuals at each correlation; number at left side is for Pingarrón correlations and at right side for Ventorrillo correlations.(DOCX)Click here for additional data file.S1 Fig.(DOC)Click here for additional data file.S2 Fig13C leaf carbon isotope discrimination).The orientation of line directions indicates the sign of the correlation among the traits, and the length is related to the strength. Triangles indicate the centroid of both populations Pingarrón in pink (1900m) and Ventorrillo in green (1440m). Click here for additional data file.