Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity. A study on the effects of gene-environment interactions on body mass index (BMI) in the UK Biobank cohort finds BMI genetic loci that interact with lifestyle traits such as physical activity, smoking, and alcohol consumption. However, the effect sizes of individual variants discovered by GWAS are relatively small—in the range of 1.01–1.20—such that the polygenic risk score (PRS), summing up all effects of GWAS single nucleotide polymorphisms (SNPs), explains less than 10% of the phenotypic variance in most traits7. Therefore, heritability (the estimated genetic effects on traits) based on SNPs was far lower than that estimated by traditional analyses based on twin and family studies7, even though they may be inflated due to confounding with shared environmental effects with in families or twins8. Recent studies, including those using the Genome-wide complex trait analysis, a method calculating the SNP heritability from all possible common variants, suggested that common genetic variants can explain up to 70% of the variation in height and 40% of the variation in body mass index (BMI)12. Despite these progresses, substantial missing heritability remains in most complex traits16.Genome-wide association studies (GWASs) have uncovered several genetic variants that affect complex traits21. Genetic variants within the FTO gene that exhibit the largest effect size on obesity were found to interact with environmental factors, such as physical activity, diet, alcohol consumption, and sleep duration27. The effect size of FTO variants on BMI was reduced by increased physical activity and enhanced by decreased physical activity25. These findings indicate that genetic susceptibility to disease can be modulated by altering environmental factors29, and also suggest that the G × E interaction may contribute to missing heritability30.Complex traits are also affected by environmental factors, and the environmental effect can modify genetic risk factors, known as gene-environment (G × E) interaction31. In addition, the analysis of G × E interaction generally requires a higher sample size than that used in genetic association analysis20. Therefore, the identification of genetic variants having true G × E interaction effects in complex human traits has been challenging33. Nevertheless, recent progress in the analysis of G × E interaction has shed light on the importance of their effect on trait phenotype. Poveda et al. investigated the G × E interaction in cardiometabolic traits in the VIKING study, which involved a cohort of 16,430 Swedish adults from 1682 extended pedigrees34. They found statistically significant effects of gene-age and gene-alcohol intake interactions on weight, as well as effects of gene-age interactions on systolic blood pressure, using quantitative genetic analysis in extended pedigrees. Further, Justice et al. performed a genome-wide analysis of gene-smoking interaction on obesity-related traits in 241,258 samples  to understand the effects of smoking on genetic susceptibility to obesity35. They identified 31 genetic loci for gene-smoking interaction, and gene set analysis using these genetic loci revealed various pathways, including response to oxidative stress and addictive behavior, where dysregulation may lead to increased susceptibility to obesity. In addition, two studies investigated the presence of G × E interaction in BMI and estimated genetic risk using PRS calculated from GWAS statistics29. They used the UK Biobank GWAS data, which include 500,000 participants with lifestyle measurements, and found significant G × E interactions of PRS with physical activity, alcohol consumption, and socioeconomic status in BMI. These studies demonstrate the statistical significance of the G × E interaction in complex traits. However, it is still unclear how much the G × E interaction can explain the effect of phenotypic variance on the extent of heritability.Generally, it is difficult to accurately measure environmental exposures, because many cases depend on self-reported questionnaires for past exposures36. They found an evidence for the genetic interaction effect with smoking behavior, which was estimated to contribute 4.0% to the BMI variation. Sulc et al. investigated the contribution of G × E interaction to obesity-related traits from the UK Biobank using a maximum likelihood method37. Previously, Wang et al. showed the presence of G × E interaction by estimating the difference in phenotype variance in different genotype groups of subjects31. Sulc et al. applied the assumption of Wang et al. that the estimation of G × E interaction in a genome-wide level is presumably affected by all environmental risk factors. Using this approach, Sulc et al. found that G × E interaction effects of genome-wide SNPs explained 1.9% of the variance in BMI in addition to the 15% contributed by genetic risk factors. However, Sulc et al. did not use the real measurements of environmental factors; therefore, their findings for the effect size of G × E interaction on BMI require validation.Recently, two studies estimated the extent by which the G × E interaction explained the phenotypic variance of obesity. Robinson et al. estimated the difference in the heritability of G × E interaction in eight self-reported lifestyle variables, including diet, exercise, and smoking39.In this study, we aimed to (1) provide evidence for the heritability of G × E interaction for BMI using real environment measurements, (2) estimate the effect size of the G × E interaction heritability for BMI, and (3) identify novel genetic loci that interact with environmental factors to affect BMI. We used the UK Biobank data for this analysis, which included 331,282 participants with 4,143,506 SNPs and 14 obesogenic lifestyles. To calculate G × E interaction heritability, we used both linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer)https://github.com/Nealelab/UK_Biobank_GWAS). The lifestyle characteristics of the 331,282 participants are summarized in Table We selected unrelated 331,282 UK Biobank participants of “White-British” European ancestry for this study, similar to those used in the Neale lab , metabolic equivalent task (MET) scores (category ID: 54), mental health (category ID: 100060), smoking (category ID: 100058), alcohol (category ID: 100051), sleep (category ID: 100057), household (category ID: 100066), and baseline characteristics (category ID: 100094) have been repeatedly reported to be related to obesityP < 3.57 × 10−3) with BMI adjusted for age and sex in both raw and processed variables. We found positive correlations between BMI and pack years of smoking, smoking status, time spent watching TV, fed-up feelings, neuroticism score, townsend deprivation index at recruitment (TDI), nap during day, alcohol intake frequency, and time spent using computer. In contrast, negative correlations were seen between BMI and MET score, sleep duration, moderate physical activity, alcohol intake status, and average total household income before tax , alcohol intake frequency (1.17), and sleep duration (1.17) were higher than 1.1, we applied the genomic control correction using lambda values to these lifestyle factors. As a result, the intercept values become decreased after the correction: alcohol intake status, 0.99; alcohol intake frequency; 0.96; sleep duration 0.98  in the lifestyle factors analyzed, as shown in Supplementary Table P < 5.00 × 10−6 are provided in Supplementary Table We tested the effect of G × E interactions between the 4,143,506 SNPs  and individual lifestyle factors on BMI as a continuous trait, using fixed effect models of linear regression (PLINK v.1.90). To investigate genomic inflation, we estimated the genomic control lambda and the intercept value of LD score regression on each GWIS result  with the G × E interaction in BMI and that of rs12438181 (interacting with smoking status) with pulmonary function in smokers were reported, and two lead SNPs showed eQTL genes as sentinel SNPs (r2 = 1).Further, we investigated functional annotation of these two G × E interaction lead SNPs. First, we searched for functional importance of these SNPs using RegulomeDB (Supplementary Table https://github.com/bulik/ldsc) , time spent watching television (P = 1.13 × 10−3), pack years of smoking (P = 1.74 × 10−4), alcohol intake frequency (P = 1.26 × 10−6), and fed-up feeling (P = 2.24 × 10−3) showed statistical significance, based on the Bonferroni multiple correction (P < 3.57 × 10−3)  Table 38. Amongcy P = 1.6 × 10−6,http://dougspeed.com/ldak/), which is based on a different algorithm from LDSC42. The heritabilities of G × E interaction calculated using the LDAK-SumHer method are presented in Table P = 1.28 × 10−4), pack years of smoking (P = 3.04 × 10−5), and alcohol intake frequency (P = 5.15 × 10−4) showed statistical significances, based on the Bonferroni multiple correction (P < 3.57 × 10−3) (Table P = 1.29 × 10−2) and fed-up feelings (P = 9.74 × 10−3) did not reach a significance level based on the multiple correction in the LDAK-SumHer analysis. The heritability of G × E interaction for alcohol intake frequency was hG×E2 = 0.0032, that for MET score was hG×E2 = 0.0045, and that for pack years of smoking was hG×E2 = 0.0052 in LDAK-SumHer analysis. The P-values for the heritability of G × E interaction calculated by LDSC and LDAK-SumHer methods are compared in Fig. To confirm these estimates of G × E heritability calculated by LDSC, we used another approach, the LDAK-SumHer v.5.1 method (P < 4.16 × 10−3) with all obesity-related traits (Supplementary Table β) in all obesity-related traits, whereas alcohol intake frequency and pack years of smoking showed positive effect size (β) in all obesity-related traits. The distributions of raw and processed variables for obesity-related traits in the population are shown in Supplementary Fig. To investigate whether MET score, pack years of smoking, and alcohol intake frequency also showed statistically significant heritability of G × E interaction in other obesity-related traits, we tested the G × E interaction in waist circumference (WC), hip circumference (HC), waist-to-hip ratio adjusted with BMI (WHRadjBMI), and body fat percentage (BFP). The lifestyle characteristics of participants related to these obesity-related traits are summarized in Supplementary Tables Further, we examined correlations among these obesity-related traits in the study population  for G × E interaction in the MET score and pack years of smoking, while two statistically significant signals (rs5729295 and rs11642015 at the FTO locus) were identified in alcohol intake frequency (Supplementary Table P < 4.17 × 10−9) SNPs  were associated with diverse traits, including BMI, and showed eQTL genes as sentinel SNPs (r2 = 1).Using the same method as that for BMI analysis, we tested G × E interactions between the 4,143,506 SNPs and MET score, pack years of smoking, and alcohol intake frequency in WC, HC, WHRadjBMI, and BFP adjusted age, sex, genotyping array, and PC1–PC10. To investigate genomic inflation, we estimated the intercept of LD score regression from the each GWIS result in the Supplementary Table P < 5.00 × 10−6) with the results in other obesity-related traits  was also statistically significant for WC  and BFP .Additionally, we compared the results of suggestive G × E interactions in BMI (P < 4.17 × 10−3), the heritabilities of G × E interaction for pack years of smoking in WC , alcohol intake frequency in WC , pack years of smoking in HC , and alcohol intake frequency in WHRadjBMI  were statistically significant according to the LDSC analysis. In case of LDAK-SumHer analysis, the heritabilities of G × E interaction for MET score in WC , pack years of smoking in WC , alcohol intake frequency in WC , pack years of smoking in HC , and MET score in BFP  were statistically significant.Based on these results, we calculated the heritability of G × E interaction for MET score, pack years of smoking, and alcohol intake frequency using LDSC and LDAK-SumHer for each trait Fig. –5. UsingIn this study, to estimate the proportion of missing heritability that could be explained by G × E interaction, we determined the heritability of G × E interaction for BMI and other obesity-related traits in a large sample of 331,282 participants from the UK Biobank. Three lifestyle factors—MET score, pack years of smoking, and alcohol intake frequency—showed statistically significant interaction with genetic factors for BMI in both LDSC and LDAK-SumHer analyses. The G × E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer were as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, we identified the statistical significance of the G × E interaction heritability of these three lifestyle factors in WC, HC, WHRadjBMI, and BFP. Additionally, we identified two genome-wide significant loci interacting with lifestyle factors in these obesity-related traits.29 and Tyrrell et al.28 calculated the PRS for BMI in the European sample referring to SNPs discovered by Locke et al.43. They investigated the interactions between the PRS and lifestyle factors using the linear regression model. Rask-Andersen et al. found physical activity, alcohol intake frequency, and socioeconomic status to interact with PRS in BMI. Tyrrell et al. found physical activity and socioeconomic status to interact with PRS in BMI. Similarly, we also found a statistical significance in the G × E interaction heritability of physical activity (MET score) and alcohol intake frequency in BMI. While smoking (pack years of smoking) was found to be significant only in this study, Robinson et al. previously demonstrated the significance of G × E interaction smoking interaction heritability34.Recently, Rask-Andersen et al.36. They found evidence for the contribution of G × E interaction for smoking behavior to BMI, which explains 4.0% of the phenotypic variance. Our analysis estimated statistically significant heritability of G × E interaction in the pack years of smoking in BMI with somewhat less value . Sulc et al. also provided evidence for G × E interaction effect on BMI based on the calculation of phenotypic variance across the different PRS groups and found PRS × E to contribute 1.9% to BMI37. If we assume that there is no correlation between G × E interaction of three lifestyle factors, the G × E interaction effect on BMI may be calculation by the summation of the heritabilities of three lifestyle factors. The summed values account to 1.3% for LDAK-SumHer and 2.38% for LDSC in this study. Sulc et al. reported TDI and alcohol intake frequency as lifestyle factors for the PRS × E contribution of 1.9% to BMI. We also found marginally significant heritability for G × E interaction in TDI . And Shin et al. estimated heritability of G × E interaction in BMI using the GxEsum program, which was built on LDSC approach44. The GxEsum is a method for estimating the phenotypic variance explained by genome-wide G x E terms for large-scale biobank dataset. They provided the heritability of G × E interaction for age , neuroticism score , physical activity (hG×E2 = 0.3% P = 0.026) and alcohol intake frequency  in BMI. Moreover, we also found marginally significant heritability for G × E interaction using the LDSC method as follows: neuroticism score , MET score (hG×E2 = 0.65% P = 3.38 × 10−3) and alcohol intake frequency .Robinson et al. proposed the heritability of G × E interaction by estimating the difference in heritabilities between subgroups classified by environmental exposure using mixed-effect modelsP < 3.57 × 10−9 on BMI, P < 4.17 × 10−9 on obesity-related traits)  gene cluster on chromosome 15 has been well-reported to be associated with smoking addiction48. For rs12438181, which showed significant effects on BMI by interacting with smoking behaviors in this study, eQTL analysis of GTEx (ver. 8) data revealed that this SNP is associated with both CHRNA3 and CHRNA5 genes in the brain  gene is a well-known, strong genetic factor for obesity, of which the mechanism is mainly attributable to the role in energy metabolism50. Previous studies demonstrated that the FTO gene is not only associated with BMI, but also interacts with lifestyle factors to influence BMI31. In this study, rs11642015, and rs57292959 at the FTO locus interacted with alcohol intake frequency to affect BMI, WC, and BFP, respectively ] interacts with the frequency of alcohol consumption in BMI, which supports our findings on the interaction of these lead SNPs with alcohol intake frequency24.Our analysis for the G × E interaction found two genome-wide significant loci for multiple testing  and 0.67% (LDAK-SumHer), and hG×E2 of HC was 1.02% (LDSC) and 0.43% (LDAK-SumHer), indicating that if our results are inflated, the inflation may be small.There are several limitations to our study. First, some of the GWIS results may be statistically inflated. This can be inferred from the Q–Q plot and the intercept values calculated through the LDSC . The sample filters are as follows: PCA calculation filter for selection of unrelated samples; sex chromosome filter for aneuploidy removal; filter of principal components (PCs) for European sample selection to determine British ancestry; and filters for selection of self-reported “White-British”, “Irish”, and “White”. In addition, we selected samples based on the filter for self-reported “White-British”, remaining the final sample as 331,282.We used the UK Biobank database, which is a population‐based database that recruited more than 487,409 individuals aged 40–69 years during 2006–1054. UK Biobank has been given ethical approval to collect participant data by the North West Multicentre Research Ethics Committee, the National Information Governance Board for Health & Social Care, and the Community Health Index Advisory Group.All participants provided signed consent to participate in the UK Biobank55. Genotyping imputation was performed using UK10K Project and 1000 Genome Project Phase 3 reference panel. We performed quality control analysis using PLINK v.1.9056, based on the following exclusion criteria: SNPs with missing genotype call rates >  0.05, MAF < 0.05, and Hardy–Weinberg equilibrium P-value < 1.00 × 10−6. We excluded SNPs with MAF smaller than 0.05 to avoid potential false-positive results due to the coincidence of a low-frequency variant31. Consequently, 4,143,506 SNPs were retained for further analysis.Baseline genetic imputation data of 93,095,623 SNPs were available in 487,409 participants. UK Biobank participants used the UK Biobank Axiom Array and the UK BiLEVE Axiom Array from Affymetrix Participants’ weights were assessed using various methods during the initial UK Biobank assessment center visit. Additionally, standing height was measured on a SECA 240 Height Measure. For BMI, we used data-field 21001, which is constructed from height and weight measured. For this study, BMI was transformed using log transformation.40. The 14 lifestyle factors were metabolic equivalent task score , moderate physical activity (field ID: 884), time spent using computer (field ID: 1080), time spent watching TV (field ID: 1070), neuroticism score (field ID: 20127), fed-up feelings (field ID: 1960), smoking status (field ID: 20116), pack years of smoking (field ID: 20161), alcohol intake frequency (field ID: 1558), alcohol intake status (field ID: 20117), sleep duration (field ID: 1160), nap during day (field ID: 1190), average total household income before tax (field ID: 738), and TDI (field ID: 189)  in GWIS analysis.38 and LDAK-SumHer39 to estimate the G × E interaction heritability from the 14 GWIS summary statistics. The statistical evaluation method is different between two methods. LDSC methods estimates genetic heritability using a regression model, whereas LDAK-SumHer methods calculated genetic heritability using a likelihood model. When using LDSC to estimate G × E interaction heritability, it is necessary to LD score of SNP. Bulik-Sullivan et al. estimated the 1,217,312 SNPs of LD score based on the European 1000 Genomes database and Hap-Map3 SNPs (https://github.com/bulik/ldsc)38. We also used LD score to estimate G × E interaction heritability. When using LDAK-SumHer, it is required for well-imputed common SNPs panel. So, we remained SNP satisfied criteria as follows41: (1) 1000 Genomes imputation database, (2) Non-ambiguous SNPs, (3) SNPs not in MHC region (http://dougspeed.com/)39. Based on these SNPs, we used GCTA model of LDAK-SumHer to estimate the G × E interaction heritability39.We used LDSCwww.r-project.org). We used qqman package for Manhattan plots and corrplot package for calculating correlation coefficients and plotting the correlogram. The ggplot2 package was used for plotting bar plots and lme4 package was used for association analysis.We created Manhattan plots, histograms, Q–Q plots, bar plots, and correlograms and performed association and correlation analysis in R version 4.0.3 (59 (https://www.ebi.ac.uk/gwas/). We investigated the eQTL information for lead SNPs shown statistical significance using GTEx version 8 database41 . We searched for functional importance of lead SNPs using RegulomeDB60 (https://regulomedb.org/regulome-search/).To investigate the biological function and possible effects of significant variants on various traits, we investigated this information on the GWAS catalog, RegulomeDB, and GTEx version 8. For GWAS catalog, we determined any association of genetic variants existing within the 10 Mb flanking region of the lead SNPs with obesity or obesity-related traits, a direct association of the lead SNPs with any traits using the GWAS catalogFurther information on research design is available in the Supplementary InformationReporting Summary


Malus domestica Borkh.) are controlled by multiple small-effect quantitative trait loci (QTLs). Genomewide selection (genomic selection) might be an effective breeding approach for highly quantitative traits in woody perennial crops with long generation times like apple. The goal of this study was to determine if genomewide prediction is an effective breeding approach for fruit quality traits in an apple scion breeding program. Representative apple scion breeding germplasm , high-quality single nucleotide polymorphism (SNP) data (nSNPs = 977), and breeding program fruit quality trait data at harvest were analyzed. Breeding parents `Honeycrisp' and `Minneiska' were highly represented. Moderate to high predictive abilities were observed for most fruit quality traits at harvest. For example, when 25% random subsets of the germplasm set were used as training sets, mean predictive abilities ranged from 0.35 to 0.54 across traits. Trait, training and test sets, family size for within family prediction, and number of SNPs per chromosome affected model predictive ability. Inclusion of large-effect QTLs as fixed effects resulted in higher predictive abilities for some traits (e.g. percent red overcolor). Postdiction (i.e. retrospective) analyses demonstrated the impact of culling threshold on selection decisions. The results of this study demonstrate that genomewide selection is a useful breeding approach for certain fruit quality traits in apple.Many fruit quality traits in apple ( Malus domestica Borkh.) breeding is a resource-intensive, time-consuming process; therefore, apple breeders are always looking for ways to make the breeding pipeline more efficient. Significant emphasis has been placed on identification and characterization of quantitative trait loci (QTLs) underlying breeding relevant traits. Although many QTLs associated with fruit appearance and quality traits in apple have been reported , few hah et al.  and Evanh et al. ). In addh et al. , 3–5. Fir) between the observed and predicted values of test set individuals [Hordeum vulgare L.), maize (Zea mays L.), soybean (Glycine max (L.) Merr.), and wheat (Triticum aestivum L.) . In appa et al. . Factorsa et al. ). TherefThe University of Minnesota (UMN) apple breeding program scheme has three main phases of evaluation: 1) unreplicated seedling testing; 2) clonal testing; and 3) grower trials/pre-commercialization testing . In eachFew studies have examined the utility of genomewide prediction in an apple breeding program. In this study, our goal was to determine the utility of genomewide prediction within the context of the UMN apple breeding program for several fruit quality traits: percent red overcolor, fruit weight, two instrumental firmness measures , an instrumental crispness estimate (Cn), SSC, and TA. Prediction and postdiction (retrospective explanation), a term that has been used in previous studies e.g., , were usp < 0.001) and individual (p < 0.0001) had significant effects on all traits. Normal QQ plots  indicated that random effects were normally distributed for most traits  and Ma (Chr16) as fixed effects did not result in significantly higher predictive abilities for TA , and 3 for TA . When an3 for TA . When a 3 for TA . For per3 for TA . For exa3 for TA . Both `H3 for TA . Inclusi3 for TA . Overalls for TA . Howevery for TA .The number of offspring and advanced selections that would have been culled depended on the selection method  and independent culling thresholds applied  and S6. The fewest number of individuals would have been culled using thresholds estimated with ‘Honeycrisp’s trait values  had been used compared to thresholds estimated with ‘Honeycrisp’s trait values  and S6. High numbers of individuals would have been culled if culling thresholds , calculated based on percentile values, had been used  and S6. Results from this study demonstrate that genomewide prediction could be utilized to predict fruit quality traits in apple. Moderate to high predictive abilities were observed for most of the examined fruit quality traits with predictive abilities varying with trait, training-test sets, and SNP number. In postdiction analyses, selection decisions  for a mean of 64% of unselected offspring and 66% of advanced selections would have been the same regardless of selection method . Depending on the culling thresholds, 16% to 70% of advanced selections examined would have been culled if phenotypic selection had been used whereas 3% to 45% of advanced selections examined would have been culled if genomewide selection had been used. Results of this study demonstrate the utility as well as challenges of genomewide selection for fruit quality traits in apple breeding programs that utilize `Honeycrisp'-derived germplasm.Moderate to high predictive abilities for most fruit quality traits at harvest were observed across training-test sets indicating that genomewide selection is an effective breeding approach for certain traits. Predictive abilities estimated were similar to those reported in previous studies , 19, 22.Model predictive abilities varied significantly with trait, training set size and composition, and test set. This was not surprising as significant variation for model predictive abilities has been observed within and among previous studies in apple , 19, 22.. As Cn in this study only had moderate predictive abilities, Cn might not be a good target for genomewide prediction and different phenotypic measures of crispness  should be explored. Similar to Cn, a relatively low proportion of the SSC variation was associated with individual (Fragaria × ananassa Duchesne) [e.g., In this study, the proportion of trait variation associated with individual affected predictive abilities. Since many individuals in this study were unreplicated, variation among individuals contained components of both genetic and environmental effects. Previous studies in apple and other crops  have demonstrated that high heritability traits often have higher predictive abilities compared to low heritability traits e.g., , 25, 26.dividual . Howevere) e.g., , 28. TheInclusion of SNPs at previously detected QTLs as fixed effects resulted in higher predictive abilities for certain traits. In this study, including a SNP at the Chr9 red color locus e.g., , 29, 30 Moderate predictive abilities were achieved for most traits using a reduced set of SNPs. In general, more SNPs per chromosome was associated with higher predictive abilities across traits. This was not surprising as previous studies in other crops e.g.,  have demGenomewide selection within families is a promising breeding approach  and S4. Apple breeders simultaneously target multiple traits with no single trait completely driving selection decisions. For example, the overall consumer experience is created by multiple appearance, texture, and flavor traits and people can vary in their preferences . TherefoCulling based on observed values is likely more stringent compared to genomewide selection when considering individual traits. In general, higher numbers of individuals would have been culled using phenotypic selection compared to genomewide selection for individual traits. This was not surprising as most individuals with more extreme phenotypes  were predicted to have values towards the middle of the distribution. For example, the two offspring with the lowest 9.17 lbs) and highest (24.65 lbs) M1 BLUPs had predicted values of 16.38 and 18.91, respectively, which were similar to the population mean (15.78 lbs). Therefore, as observed by Muranty et al. [ lbs and During the unreplicated seedling phase of the breeding program, a selection decision for a given individual is made during a single evaluation. Environmental or genotype × environment effects might significantly impact fruit quality traits and therefore the selection decision. In this study, across-year trait BLUPs, which might have accounted for some environmental or genotype × environment effects, were used as phenotypic values. The stringency and accuracy of phenotypic selection based on individual traits or overall eating experience are influenced by the phenotypic data  that are relied upon.In an apple breeding program, individuals are evaluated based on their overall eating experience and stringent culling thresholds for specific fruit quality traits are not often used. Therefore, determining culling thresholds for targeted traits is challenging and could potentially limit the application of genomewide selection. Not surprisingly, the choice of culling thresholds significantly affected the number of individuals that would have been culled or selected for further evaluation regardless of the selection method. Use of trait levels of `Honeycrisp' or advanced selections to set culling thresholds resulted in the culling of few advanced selections. Culling using genomewide selection generally would not have been effective with either of these thresholds as the number of individuals culled would have been very low (≤ 4%). In contrast, some culling thresholds were likely too stringent as released cultivars like `Minneiska', `MN33', and `MN55' would have been culled. For example, 19 advanced selections including `Minneiska', `MN33', and `MN55' would have been culled with genomewide selection using culling thresholds based on the 90% percentile for fruit weight, 10% percentiles for SSC and TA, and the 25% percentiles for M1 and M2  and S8. r = 0.43 – 0.72) by Evans et al. [One of the first challenges is which fruit quality traits to target. Apple fruit quality is affected by a multitude of traits and factors (reviewed by Musacchi and Serra ). Additis et al.  and Teh s et al. , had rels et al.  concludeGermplasm targeted in this study included 852 unselected offspring from 13 full-sib families representing 14 breeding parents . Full-siAs described by Kostick and Luby , all indFruit were evaluated at harvest using the standardized phenotyping protocol described by Evans et al. . Starch Individual fruit were rated for the percentage of the fruit’s surface with red color. Percent red overcolor was rated on a 1 (none) to 5 (≥ 75% red) scale previously described by Evans et al. . Fruit sTexture analysis was performed using a computerized penetrometer, the Mohr® Digi-Test MDT-1 or, in later years, the MDT-2 , which have previously been described by Mohr and Mohr , Evans eEvaluation of fruit juice samples for SSC and TA was previously described in detail by Miller et al.  and is shttps://www.r-project.org/). Linear mixed models fit with restricted maximum likelihood (REML) via “lme4” R package [https://inkscape.org/; accessed 30 January 2023).Prior to statistical analysis, data for percent red overcolor, fruit weight, M1, M2, Cn, SSC, and TA for fruits within a given year were averaged for each individual. Most statistical analyses in this study were carried out in R version 4.0.4 . Adjust package  and werey = Xβ + Zu + ε where β corresponded to a vector of fixed effects, u corresponded to a vector of random effects, and ε corresponded to the residual error. In this study, model inputs were y which corresponded to the phenotypic trait values (i.e. trait BLUPs) and Z which corresponded to the SNP marker matrix of the training set where marker genotypes were coded as 1 and −1 for the homozygous genotypes  and 0 for heterozygous genotype (i.e. AB). Multiple training – test set scenarios were examined. The Pearson correlation coefficient (r) between the predicted and observed (trait BLUPs) trait values for individuals in the test set was used to calculate model predictive ability.Genomewide prediction analyses were conducted within the “rrBLUP” R package . As descGenomewide prediction models were tested using all unselected offspring via two training-test set schemes: 1) random samples of 10, 25, 50, or 75% of population as training sets and 2) all but one untested family as the training set. In the random subset method, a proportion of the population was randomly selected to serve as the training set while the remaining proportion of the population was used as the test set. For example, at the 10% sampling level, a random sample of 10% of the population was used as the training set while the remaining 90% were used as the test set. For each sampling level , 500 iterations were completed. In the untested family method, one family served as the test set while the remaining families were used to train the model. For example, if the `Honeycrisp' × `Minnewashta' family was considered the test set, offspring from all remaining families would be used as the training set. This procedure was repeated so that each family served as the test set.Utility of genomewide prediction models for prediction of fruit quality traits within families was tested by a delete-one procedure. For example, when testing a genomewide prediction model within the `Honeycrisp' × `Minnewashta' family (n = 150), the performance of each offspring was predicted from the performance of the remaining 149 offspring.To examine the effect of family size on predictive ability of within-family genomewide prediction, random samples of 20, 40, 60, 80, 100, and, if possible, 120 offspring from large full-sib families  were selected to represent the family. For each sampling level, 100 iterations were completed. Genomewide prediction models were tested within these subsets using single untested offspring from the subset as the test set and the remaining offspring as the training set. This procedure was repeated until all offspring had been used as the test set.To examine the effect of number of SNPs per chromosome on model predictive ability, random samples of five, 10, 15, 20, 25, and 30 SNPs per chromosome were included as random effects in the genomewide prediction model. SNPs were sampled from the 977 SNPs retained after curation with SNPQC software. One family served as the test set while the remaining families were used to train the model. For each sampling level, test set, and trait combination, 100 iterations were completed.Ma3) and Chr16 (Ma) [e.g., Ma [X which corresponded to the design matrix for the fixed effect SNP(s) and previously described y and Z. One family served as the test set while the remaining families were used to train the model. This was repeated until all families had been used at the test set. Paired t-tests were used to determine if there were significant differences in model predictive abilities between models with SNPs at large-effect QTLs included as fixed effects versus models that included all SNPs as random effects.For percent red overcolor, TA, M1, and M2, individual SNPs near or within previously reported large-effect QTL regions were selected. Specifically, SNPs near the Chr9 color locus e.g., , 19, 29,e.g., Ma , loci wee.g., Ma  was usede.g., Ma  was applTo examine the utility of genomewide prediction within the context of an apple breeding program, postdiction (retrospective) analyses were carried out to determine if advanced selections identified via traditional selection procedures would have been kept or culled via genomewide selection for a set of fruit quality traits.In addition to the 852 unselected offspring from the 13 full-sib families , 92 advahttps://inkscape.org/; accessed 30 January 2023).Fruit weight, M1, M2, SSC, and TA were selected because they had breeding relevance, sufficient phenotypic variation, moderate to high observed predictive abilities, or lacked available relevant locus-specific DNA tests. Five different culling threshold scenarios relevant to the breeding program were examined in this study and are described in Web_Material_uhad088Click here for additional data file.


Plant growth‐promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth‐related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome‐wide association analyses to examine maize growth‐related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386–1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome‐wide association analysis as potential markers for growth‐related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome‐wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral‐based phenotyping for studying maize growth‐related traits in response to PGPB inoculation. B+with plant growth‐promoting bacterial inoculationB−without plant growth‐promoting bacterial inoculationGWAgenome‐wide associationPGPBplant growth‐promoting bacteriaPHplant heightSDstalk diameterSDMshoot dry massSNPssingle nucleotide polymorphisms1Sustainably increasing food production is required considering growing demands, especially in developing nations  , homogenized, and seeded. Irrigation, weed control, and application of fertilizer (excluding nitrogen) were carried out according to crop requirements. After germination, each plot was thinned to only one plant per plot. The plants were evaluated when most had six expanded leaves, that is, approximately 33 days after sowing. Manually measured phenotypes included plant height (PH), stalk diameter (SD), and shoot dry mass (SDM). A total of 1512 plots (plants) were evaluated under B+ and B− conditions (756 B+ and 756 B−). Detailed information on the experimental design can be found in Yassue  and without (B−) PGPB inoculation. Each experimental unit consisted of one pot containing one plant. Maize seeds were co‐inoculated with the PGPB strains n Yassue , 2022.2.2The genomic data used in this study have been previously published . The middle portion of the last completely expanded leaf was used as the region of interest for hyperspectral imaging. A dark room with an additional light supply was used to minimize light variation. Radiometric calibration was performed according to the manufacturer's instructions. For each plant, a hyperspectral cube image was produced, which contained 150 bands with wavelengths in the 386–1021 nm range. Image processing through the Spectral Python Boggs,  module w2.4ykli is the vector of phenotypes  for the kth replication, lth block nested within replication, and ith genotype; μ is the overall mean; r and b are the fixed effects for replication and block nested within replication, respectively; wij is the incidence matrices of marker covariates for each SNP coded as 0, 1, or 2; and αj is the jth marker effect. The prior of αj was as follows: σα2 is the common marker genetic variance, π is a mixture proportion set to 0.99, and ϵ is the vector of residuals. A Gaussian prior N was assigned to the vector of residuals, and a flat prior was assigned to μ, r, and b. The scaled inverse χ2 distribution was assigned to σα2 and σϵ2 with the degrees of freedom equal to 4 and choosing the scale parameter such that the prior mean of the variance equals half of the phenotypic variance. The variance components obtained from univariate BayesC were used to estimate genomic heritability hg2=σg2σg2+σe2nr, where hg2 is the genomic heritability, σg2 and σe2 are the additive genomic and residual variances, respectively, and nr is the number of replication (2).BayesC =δj= indicating whether the jth marker effect for trait t is zero or nonzero and βj follows a multivariate normal distribution with null mean and covariance matrix Σα=σα12σα12σα21σα22, where α1, α1, and α12 (α21) are marker genetic variance for trait 1, marker genetic variance for trait 2, and marker genetic covariance between traits 1 and 2, respectively, and the residuals were assumed independently and identically distributed multivariate normal vectors with null mean and covariance matrix Σϵ  and W−1, respectively. We assumed all possible combinations for δj, namely, , , , and  having nonzero probability.A recent study showed that hyperspectral image data can be used to perform phenomic prediction for PH, SD, SDM with reasonable accuracy  of genomic heritability estimates for hyperspectral wavelengths and indices were 0.45 (0.06) and 0.40 (0.08) for B+ and 0.44 (0.04) and 0.43 (0.08) for B−, respectively .Eight SNPs influenced both the manually measured and hyperspectral phenotypes, which were visualized in a phenome‐wide association plot Figure . In gene4The utility of hyperspectral imaging technology for phenotyping has recently gained increasing attention in genetic studies because such data can capture the resonance of certain physicochemical compounds in plants and associated with genomic regions  of B− plants may indicate an association between PH and leaf pigments, such as carotenoids, chlorophyll a and b, and nitrogen concentrations . The association between near‐infrared spectra and plant biomass in maize was reported in maize in a previous study . These genes have been previously reported as part of The Shiny web application with an interactive interface is a powerful tool for the visualization and interpretation of GWA analysis of multiple phenotypes. Two‐way Manhattan plots can be used to investigate all associations across traits and managements, including nonsignificant results that are not elaborated on here. In addition, phenome‐wide association plots can be used to identify and visualize markers with mutual influence across hundreds or thousands of phenotypes. The Shiny application can be easily extended to other high‐throughput phenotyping data, such as longitudinal, fluorescence, and thermal data.5The hyperspectral signatures captured some genetic variability in the maize diversity panel and were associated with growth‐related traits under PGPB inoculation. GWA analysis of hyperspectral data identified genomic regions that influenced both manually measured phenotypes and hyperspectral bands. In addition, a Shiny web application for multiple‐phenotype GWA was developed.Rafael Massahiro Yassue: Conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing—original draft; and writing—review and editing. Giovanni Galli: Investigation; methodology; and writing—review and editing. Chun‐Peng James Chen: Methodology and writing—review and editing. Roberto Fritsche‐Neto: Conceptualization; funding acquisition; supervision; and writing—review and editing. Gota Morota: Conceptualization; methodology; funding acquisition; supervision; writing—original draft; and writing—review and editing.The authors declare that there is no conflict of interest.PLD3_492_SupplementalMaizeHyperspectralGWAS.pdfClick here for additional data file.


Sunflower is one of the most important oilseed plants in the world and its oil has nutritional and high economic value. Selection of high‐yielding hybrids is important in sunflower breeding. In this regard, 11 new hybrids along with four cultivars were evaluated in a randomized complete block design with four replications during the 2018–2020 growing seasons. The phenological and agronomic traits including days to flowering, days to ripening, plant height, stem diameter, head diameter, seed number per head, thousand‐seed weight, oil content, and seed yield were measured. In this study, the methods of genotype × trait (GT) and genotype × yield × trait biplot (GYT) were used to identify interrelationships between different traits and to select the best sunflower hybrids based on multiple traits. According to the results, GYT biplot method was more efficient compared to the GT biplot method. Considering both superiority index (SI) and GYT biplot, the genotypes G8, G11, G5, and G3 were superior in terms of agronomical attributes such as flowering and maturity times, stem and head diameter, plant height, thousand‐seed weight, and seed number per head in close relationship with grain yield. Oil content of the hybrids G8, G11, G5, and G3 was 47.9%, 46.4%, 45.8%, and 46.3%, respectively. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics  in sunflower. Overall, the GYT graphical biplot method provides a practical and efficient new approach for the identification of suitable hybrids according to the set of intended characteristics in sunflower improvement under multi‐years or multi‐locations. In this study, the methods of genotype × trait (GT) and genotype × yield × trait biplot (GYT) were used to identify interrelationships between different traits and to select the best sunflower hybrids based on multiple traits. Generally, the graphical method of the GYT biplot provides a practical and efficient new way to identify superior genotypes based on multiple traits in sunflower breeding programs. The GT biplot analysis has been used for this purpose in some crops such as soybean , Karaj, Alborz Province  for two growing seasons (2018–2019 and 2019–2020). All the hybrids that were used in this study are produced based on cytoplasmic male sterility (PET system + Fertility restorer genes) in Karaj . Specific mating design has not been used to produce these hybrids. But to evaluate the combing ability of the relevant parent‐inbred lines in the past years, Line × Tester method has been used. All the evaluations were not necessarily done in one experiment and the lines were gradually identified at different times and used in the final crossings to produce the hybrids in this study. The pedigree of the hybrids is presented in Table 2.12.1.1In order to test the normality of the data, the Shapiro–Wilk test was used by SPSS (Ver. 19) software SPSS, . Combine2.1.2The biplots for GT were generated by the first two principal components obtained from singular value decomposition (SVD) using GGEbiplot software  was significant for days to flowering, head diameter, stem diameter, and thousand‐seed weight Table . The eff3.2According to the results of GT‐biplot analysis, the first two principal components accounted for 35.7% and 21.9% variability of standardized data, respectively , in the first year Figure . The polIn the second year, the GT biplot for the dataset explained 58%  of the total variation of the standardized data Figure . AccordiResults of GT biplot analysis for an average of 2 years indicated that the first two principal components explained 36.6% and 24.9%, variation of the standardized data, respectively  Figure . Accordi3.3The vector view is one of the applications of the GT biplot to examine interrelationships between environments. In the vector view of the GT biplot, the lines that connect each trait to the origin are called the vector. The angle between the vectors is an indicator of the relationship between the traits. The angles lower and more than 90° imply positive and negative correlations, respectively. Also, if the angle is near 90°, it showed no relationship. According to Figure The vector view in the second year Figure  indicate3.4In the first, second, and average of 2 years, breeding hybrids G8, G3, G12, and G11 had the highest values for Y*SNPH, Y*PH, Y/DTR, Y/DTF, Y*HD, and Y*SD in polygon view of the GYT biplot  indicated that, regarding all of the yield–trait combinations, hybrids G8, G11, G5, and G3 were superior to four commercial hybrids and others hybrids, while the hybrids G4, G1, G9, G2, and G15 were unfavorable  can be used as the selection criterion and so on because of high correlation of days to flowering, days to ripening, and plant height, the simplest one  could be used as suitable selection criterion.5The unique feature of this study is that it showed that GYT biplot method can be used as a comprehensive and effective method for the evaluation and selection of sunflower single cross hybrids based on a set of different agronomical traits under multi‐years or multi‐locations. The results showed that it is possible to select hybrids based on multiple traits for improving genetic material in sunflower breeding programs. The breeding hybrids G8, G11, G5, and G3 were superior regarding the relationships of all the studied traits with sunflower grain yield, which implies an increase in selection efficiency. Oil content of the hybrids G8, G11, G5, and G3 was 47.9%, 46.4%, 45.8%, and 46.3%, respectively. The results also indicate that simultaneous improvement of some of agronomic characteristics such as early maturity, plant height, stem, and head diameter is possible in sunflower breeding programs.Amir Gholizadeh: Formal analysis (lead); investigation ; methodology ; resources (lead); software (lead); validation ; writing – original draft (lead); writing – review and editing . Mehdi Ghaffari: Conceptualization (lead); data curation (lead); funding acquisition (lead); investigation ; methodology ; project administration (lead); supervision (lead); visualization (lead); writing – review and editing .The authors declare that they have no conflict of interest.This study does not involve any human or animal testing.


Peer victimisation is a prevalent occurrence in childhood and adolescence and can often have long‐lasting consequences. Previous research using polygenic scores (PGSs) have revealed various genetic vulnerabilities as predictive of victimisation in childhood. However, findings were based on self‐report and may therefore be influenced by varying self‐perceptions. Previous investigations also focused on average victimisation across childhood, and thus do not capture variability in polygenic predictability over time. The present study, therefore, aimed to investigate associations between PGSs and victimisation using separate and combined reports from teachers and peers in childhood, as well as self‐reports in later adolescence to explore trajectories of victimisation.n = 536). Ten PGSs related to mental health, cognitive abilities and physical traits were examined as possible predictors of victimisation using linear regressions and growth curve models.Data were derived from the Quebec Newborn Twin Study. Participants were assessed for victimisation using self‐reports from 7 to 17 years and using teacher ratings and peer nominations between 7 and 10 years  when predicting a multi‐informant measure of victimisation. No PGS predicted changes in victimisation over time.Findings revealed that PGSs associated with victimisation are consistent across informants, but to varying extent according to estimated effect sizes. Self‐reported victimisation was predicted by PGSs related to mental health, while PGSs related to cognitive and physical traits had larger effect estimates when predicting teacher‐ and peer‐reported victimisation. The PGS for educational attainment was consistently negatively associated with victimisation across informants, producing the largest effect estimates (While the PGS for educational attainment is a robust predictor of victimisation, many PGSs are differentially associated with victimisation depending on the informant. Such findings highlight the need to pay close attention to the phenotypic assessment of victimisation, and show that using multiple informants can both strengthen and provide unique insight into how associations may occur. A host of genetic predispositions have been shown to predict the likelihood that someone becomes a victim of bullying. We show for the first time, that these vulnerabilities and their association with peer victimisation vary depending on the informant. Such findings show that using multiple informants can help strengthen predictions of developmental outcomes based on genetic information, as well as provide unique insight into paths by which these may occur. Previous research using polygenic scores (PGSs) has shown that peer victimisation is predicted by various traits and vulnerabilities. The extent to which these genetic dispositions reflect self‐perception biases, however, is unknown.This study shows for the first time that genetic predictors of peer victimisation are influenced by the informant, with self‐reports more associated with genetic risk for mental health problems, and teacher‐ and peer‐reports more closely linked to cognitive and physical traits. PGSs associated with educational attainment, however, is a robust predictor of victimisation across informants.Our findings highlight the need to pay close attention to the phenotypic assessment of victimisation and demonstrate that using multiple informants can strengthen predictions of developmental outcomes based on PGSs.Peer victimisation occurs when an individual is repeatedly exposed to physical, verbal or relational aggression from the same peer or groups of peers , which implies that associations between genetic factors and exposures are free from reverse causation. The twin design, which disentangles the relative contributions of genetic and environmental factors, can thus be used to elucidate underlying mechanisms and tackle with more confidence the direction of associations . PGSs are derived from largely populated genome‐wide association studies (GWASs) whereby millions of genetic variants are scanned to identify those associated with a phenotype. Such common variants are summed and incorporated into an individual‐specific propensity score, each weighted by the magnitude of their association with the targeted phenotype . Families with twins born between 1995 and 1998 in the Greater Montreal area were contacted, and 662 (67%) agreed to participate when the twins were 5 months ; 7: α = .67; 10: α = .72; 13: α = .67; 15: α = .84; and 17 years: α = .83). Correlations between self‐reports across time ranged from low to moderate, with higher correlations observed between those assessed in closer proximity . As per the self‐reports, a mean score was computed using at least two teacher rated scores at ages 7, 10 and 12 years. Correlations between teacher‐reports across time were slightly higher than those found using self‐reports  made fun of by other children, (2) hit or pushed by other children, and (3) called names by other children’. Responses were all recorded on a three‐point scale  and averaged. Such items showed adequate internal consistency , and averaged at each age. According to standard procedures for peer nomination data . To account for dependency between twins, the analysis was conducted using a robust maximum likelihood estimator in Mplus, version 7.0 . These scores were used in the main analyses so that participants had two overall victimisation scores, one at age 7 and another at age 10 years. The three separate informant scores yielded significant loadings at both 7 and 10 years; 0.60 and 0.72 for peer‐reported victimisation; 0.58 and 0.63 for teacher‐rated victimisation; and 0.37 and 0.43 for self‐reported victimisation, respectively.A two‐factor confirmatory analysis (CFA) was used to combine scores from the different informants  to provide an indication of overall victimisation. This was carried out using data for all three informants, at ages 7 and 10 years . All genotyped children were of European descent, with the majority also of white ethnicity . In PRSice, the best‐fit scores are determined using the highest R2 estimate from the regression analysis. These analyses controlled for 10 principal components (PCs), and all PGSs were standardised. Correlations between the best‐fit PGSs can be found in the Supporting Information , weighted by their effect estimates reported in the original GWAS. These were used to construct PGSs using imputed genotypes. SNPs with a minor allele frequency (MAF) <0.01, and an imputation quality score <0.8 were removed. Clumping was carried out to remove SNPs in linkage disequilibrium (LD) at To first replicate previous associations between childhood victimisation and PGSs , we then included significant PGSs to assess their independent contributions (multi‐PGS models). Multicollinearity was not an issue within these models as correlations between PGSs were no larger than 0.35 Table . Every mLongitudinal growth curves were then fit using the ‘lme4’ package in R  and parental education. Both play an important role in shaping a child's academic trajectory, with research indicating genetic and environmental influences of SES on educational attainment (Wang et al., Beyond educational attainment, our study also replicated other associations previously reported (Schoeler et al., Our study emphasised the value of using multiple informants to gain deeper insight into predictors of peer victimisation. It also highlighted the benefit of combing such reports into an aggregated composite. Findings based on our multi‐informant measure produced the largest estimates across our victimisation outcomes, with associations also surviving FDR correction. Such associations are likely to reflect more robust relations compared to single informant measures because potential measurement error is reduced by considering all three perspectives. One downfall, however, is that by extracting scores based on common perspectives, we minimise the ability to explore distinct viewpoints. Thus, future studies should carefully consider how informant reports are used. This decision should depend on the research goals and expectations of convergence (Ladd et al., Finally, in addition to showing how informant reports can strengthen and complement our understanding of victimisation, we also revealed subtle differences in predictors of victimisation with age. While some vulnerabilities, such as the PGS for depression, were associated with mean victimisation scores in both childhood and later adolescence, some were unique to each time point. When investigating whether differences were statistically different using mixed effect models exploring trajectories, however, findings revealed that while a genetic risk to depression may be a more important predictor of victimisation trajectories in childhood, effects are not significantly larger than those in adolescence. This implies that although modest, the role of a genetic propensity for depression in victimisation is stable over time.It is important that the findings on age differences are interpreted with some caution given the limited sample size (see Appendix Other limitations of the current study are that our sample of twins may restrict the generalisability to singletons. It is possible that having a cotwin offers some protection from victimisation, either physically by intervening, or by providing unique social support. Family effects were adjusted for using linear mixed effects models, and peer nominations were standardized within classrooms to allow comparisons with other children. Nevertheless, the use of twin data may be one explanation why our results differ to Schoeler et al. .Finally, our findings must be considered in relation to the different measures used to assess victimisation. The factor analysis scores were only based on three informant reports, and our peer nomination measure did not include questions about the frequency of victimisation. Some students may therefore have incorrectly nominated peers for infrequent or minor acts of teasing. When compared to the standardised teacher‐ratings, the degree of variation in the peer‐reports was ssimilar, however, information about frequency is crucial to understanding dose‐response relationships and detecting those most at risk. In addition, unlike self‐reports, which covered both direct and relational forms of victimisation, teacher and peer assessments focused on direct forms. Differences between the informant reports and vulnerabilities may thus reflect distinct predictors of overt and relational types of victimisation rather than unique perspectives. Future research should attempt to compare different forms of victimisation using multiple informants to explore potential underlying differences.Overall, our findings implicate some pre‐existing genetic vulnerabilities as risk factors for victimisation in childhood and adolescence. In particular, genetic proxies associated with educational attainment. Further research should explore this finding further to understand how, and in which contexts, genetic dispositions may increase or decrease vulnerability to peer victimisation. This will be crucial to developing more targeted prevention strategies.The authors have declared that they have no competing or potential conflicts of interest.Parental and child informed consent was obtained from the ethics review board at Université Laval, Quebec.Jessica Armitage: Conceptualization; Formal analysis; Investigation; Methodology; Project administration; Visualization; Writing – original draft; Writing – review & editing. Geneviève Morneau‐Vaillancourt: Conceptualization; Formal analysis; Writing – review & editing. Jean‐Baptiste Pingault: Conceptualization; Writing – review & editing. Till Andlauer: Data curation; Formal analysis. Stéphane Paquin: Conceptualization; Writing – review & editing. Stéphanie Langevin: Methodology. Mara Brendgen: Writing – review & editing. Jean Séguin: Conceptualization. Guy Rouleau: Conceptualization. Michel Boivin: Conceptualization; Investigation Methodology; Writing – review & editing.Supporting Information S1Click here for additional data file.Supporting Information S2Click here for additional data file.


Conjunctional false discovery rate analyses identified 85, 41, and 198 shared loci between irritability and SCZ, BD I, and MDD, respectively. Multiple genetic loci were associated with irritability and three main psychiatric disorders. Given that irritability is a cross-disorder trait, these findings may help to elucidate the genomics of psychiatric disorders.Irritability is a heritable core mental trait associated with several psychiatric illnesses. However, the genomic basis of irritability is unclear. Therefore, this study aimed to 1) identify the genetic variants associated with irritability and investigate the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability; 2) explore the relationships between irritability and various traits, including psychiatric disorders; and 3) identify additional and shared genetic variants for irritability and psychiatric disorders. We conducted a genome-wide association study (GWAS) using 379,506 European samples  from the UK Biobank. We utilized various post-GWAS analyses, including linkage disequilibrium score regression, the bivariate causal mixture model (MiXeR), and conditional and conjunctional false discovery rate approaches. This GWAS identified 15 independent loci associated with irritability; the total SNP heritability estimate was 4.19%. Genetic correlations with psychiatric disorders were most pronounced for major depressive disorder (MDD) and bipolar II disorder (BD II). MiXeR analysis revealed polygenic overlap with schizophrenia (SCZ), bipolar I disorder (BD I), and MDD. Conditional false discovery rate analyses identified additional loci associated with SCZ (number [ Identifying genetic variants associated with irritability, a heritable condition, has revealed some variants that are also associated with major psychiatric disorders. Researchers in Seoul, South Korea, led by Hong-Hee Won at Sungkyunkwan University and Woojae Myung at Seoul National University, performed a genome-wide association study with 379,506 individuals of European ancestry from the UK Biobank database. The study compared millions of genetic variants between 105,975 people with self-reported irritability and 273,531 controls. Multiple genetic analyses illustrated the genetic overlap between irritability and psychiatric disorders such as major depressive disorder, bipolar disorder, and schizophrenia. The study also investigated specific biological pathways associated with these variants. The findings help shed light on the genetic basis of psychiatric disorders, which may pave the way for new treatment. Irritability can also be defined as a temperament type with a predisposition to feeling negative emotions  and a lack of control over excessive emotional responses2.Irritability is an emotional response characterized by disproportionate reactivity to negative stimuli5. Genetic studies have suggested that irritability is genetically associated with various psychiatric illnesses. For instance, studies have indicated that a genetic predisposition toward schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and MDD can manifest as irritability8. In addition, youth at familial risk of bipolar disorder (BD) are more likely to demonstrate chronic irritability than controls; chronic irritability in turn is associated with increased rates of BD, depressive disorders, disruptive behavior disorders, and ADHD9. Moreover, irritability is a diagnostic criterion for the following mental illnesses: generalized anxiety disorder, depressive disorders , bipolar I disorder (BD I) and bipolar II disorder (BD II), acute stress disorder and posttraumatic stress disorder, oppositional defiant disorder, and personality disorders 10. Essentially, irritability is a common component of various psychiatric disorders and bridges the internalizing–externalizing divide.Various psychopathological outcomes have been associated with irritability. Correlational and longitudinal studies have shown that irritability during adolescence predicts major depressive disorder (MDD) and generalized anxiety disorder in adulthood and is associated with current depressive symptoms, neuroticism, impulsivity, borderline personality pathology, and suicidality12. While prior studies have examined the neurophysiological associations of irritability with brain structures and their respective functions14, there is a paucity of literature on the genome-wide basis of irritability. The genetic relationship between irritability and psychiatric disorders is an emerging issue that merits further exploration.Irritability is heritable. Twin studies have shown that its heritability estimates range from 31% to 37%In the present study, we had three main objectives: 1) to clarify the genetic architecture of irritability by identifying associated genetic variants via a genome-wide association study (GWAS) and investigating the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability via post-GWAS analyses; 2) to explore the relationships between irritability and various traits, as well as psychiatric disorders, using linkage disequilibrium score regression (LDSC) and the bivariate causal mixture model (MiXeR); and 3) to apply conditional false discovery rate (condFDR) and conjunctional false discovery rate (conjFDR) approaches to identify additional and shared genetic variants for irritability and psychiatric disorders via overlapping SNP-based associations.https://www.ukbiobank.ac.uk/about-biobank-uk. For the present study, individuals were excluded if they were non-European, if their genetically determined sex did not match their self-reported sex, if they presented with sex chromosome aneuploidy, if they did not provide sex information, or if they withdrew from the UK Biobank. Finally, 379,506 participants of European ancestry who answered the baseline irritability question (“Are you an irritable person?”) were included in this study.The UK Biobank is a nationwide cohort of approximately 500,000 individuals aged between 40 and 69 years at the time of recruitment. Individuals were recruited during a four-year baseline period (from 2006 to 2010) at multiple centers located in the UK. The collected data include computer-assisted interviews, touchscreen-based self-report questionnaires, physical health measures, and biological samples (including genotype data). More details regarding the UK Biobank can be found at n = 105,975; controls, n = 273,531; Supplementary Table The UK Biobank included the above irritability question as part of the baseline assessment. Participants who responded “yes” to this question (“Are you an irritable person?”) were defined as irritable cases, and those who responded “no” were defined as controls. A total of 379,506 participants of European ancestry who answered the above irritability question were included in the analysis  < 0.5%, or imputation quality scores (INFO) < 0.4, as in a previous study18. These QC criteria resulted in the inclusion of 9,575,249 SNPs.The UK Biobank released the genetic data for 487,409 individuals in March 2018 (version 3). These samples were genotyped using either Affymetrix UK BiLEVE Axiom or Affymetrix UK Biobank Axiom arrays , which include > 800,000 variants. The UK Biobank researchers applied extensive quality control (QC) procedures to the genotype data19. This is a logistic mixed-model approach that analyzes binary traits with unbalanced case‒control ratios. This method also accounts for sample relatedness and effectively controls the inflation of type I error rates using saddle point approximation. Age, sex, genotyping array type, and the first 10 principal components of genetic ancestry supplied by the UK Biobank (field 22009) were included as covariates in the multivariate analyses as reported in a previous study20. We used 844,770 pruned genotyped markers to estimate the genetic relationship matrix with the following parameters: a window size of 500 base pairs (bp), a step size of 50 bp, and a pairwise r2 values < 0.2.We performed genome-wide association analyses using the scalable and accurate implementation of the generalized mixed model (SAIGE)https://zzz.bwh.harvard.edu/plink) to define regional lead SNPs with r2 values > 0.221. Variants that exceeded the genome-wide significance threshold of P < 5 × 10−8 were identified as associated with irritability. LD was calculated using UK Biobank samples that had passed the same QC criteria that were used for the current GWAS. Regional plots of these variants were generated using LocusZoom software (http://locuszoom.sph.umich.edu)22.The summary statistics were processed via linkage disequilibrium (LD) clumping using PLINK software (P < 5 × 10−8) in the meta-analysis and repeated this test 10 times.As a sensitivity analysis, we conducted a two-stage GWAS to examine whether our association results were robust. We randomly divided the 379,506 European samples into 10 subsets. One subset was used as a replication set, and the remaining nine subsets were used as a discovery set, creating 10 pairs of discovery and replication sets. For the 15 lead SNPs, we conducted an association test in a discovery set and a replication set and performed a meta-analysis of the results from the discovery set and replication set. We tested if the lead SNPs were significant  database  and PsychENCODE25 database (http://resource.psychencode.org) were performed to identify the genes that were significantly associated with lead SNPs26. Statistically significant eQTL associations were identified at a false discovery rate (FDR) < 0.05. The enrichment of gene sets was examined using the MAGMA package implemented in FUMA to analyze the biological pathways determined by the Gene Ontology Consortium27.The functional mapping and annotation (FUMA) platform was used to examine functional annotations and evidence of expression quantitative trait loci (eQTL) and to conduct pathway analyseshttps://github.com/bulik/ldsc). Variants in the major histocompatibility complex region were excluded, and common autosomal variants with a MAF > 1% in the European population were included. We performed partitioned heritability analyses using LDSC to evaluate the enrichment of 53 genomic annotations within the full baseline model28.We used LDSC to estimate SNP-based heritability for irritability using precomputed European LD scores from the 1000 Genomes Project v3 (https://github.com/bulik/ldsc)29. We used several gene sets for cell type-specific analyses, including multitissue gene expression (including both GTEx30 and Franke lab32 data) and multitissue chromatin (including both Roadmap Epigenomics33 and ENCODE34 data), as well as gene sets previously described by Finucane et al. (2018)29 and Cahoy et al. (2008)35.We used LDSC applied to specifically expressed genes (LDSC-SEG) to identify enrichments in tissue-specific gene expression and chromatin modification using gene sets obtained from the LDSC website (rg) between irritability and other phenotypes were examined using LDSC36 with the default options: imputation quality scores > 0.9 and MAF values > 1%. We used European GWAS summary statistics for 90 phenotypes (these can be downloaded publicly). In addition, European GWAS summary statistics for nine psychiatric disorders were used to analyze shared genetic backgrounds with respect to irritability. Among the well-studied GWASs, we selected those that focused on psychiatric disorders from which we could obtain European samples with the least overlap with the UK Biobank37. These psychiatric disorders were classified into three groups: mood and psychotic disorders , early-onset neurodevelopmental disorders , and disorders with compulsive behaviors 37. Tourette’s syndrome was classified as both an early-onset neurodevelopmental disorder and a disorder with obsessive behaviors in a prior study that used genomic structural equation modeling. We classified this disorder as an early-onset neurodevelopmental disorder in the present study based on the factor loading value in this previous study.Cross-trait genetic correlations  of the brain region of interest (ROI) as well as diffusion tensor imaging (DTI) was used to analyze the genetic correlations between irritability and neuroimaging traits  was used to normalize interpolations and color scales. The UK Biobank Brain Imaging Documentation provides a detailed description of brain volume and DTI measures . We applied FDR correction for the ROI and for each DTI scalar .The volumes of the ROIs in the brain were mapped using the ROI atlas42 (http://github.com/precimed/mixer) to quantify polygenic overlap and evaluated genetic correlations. Univariate analyses using MiXeR yield the number of trait-influencing loci for each trait  and the average magnitude of additive genetic associations among these variants 43. Bivariate analysis models were used to determine additive genetic effects as a mixture of four bivariate Gaussian components, representing variants not affecting either trait, variants affecting only one of the traits, and variants affecting both traits42. To evaluate polygenic overlap, MiXeR was implemented to calculate the Dice coefficient . Model fit evaluated via the Akaike information criterion (AIC) was based on the maximum likelihood of GWAS z scores and was illustrated with conditional quantile‒quantile (Q-Q) plots.We used GWAS summary statistics for nine major psychiatric disorders (Supplementary Table https://github.com/precimed/pleiofdr) was applied to identify additional loci associated with psychiatric disorders and irritability44. This technique was used because combining GWASs for the two associated phenotypes increases statistical power, allowing for the identification of new common variants associated with both phenotypes that were not identified in the original GWASs44. Additionally, the conjFDR approach was employed to identify loci shared between irritability and psychiatric disorders44. This method assigns the maximum condFDR values of each trait as the conjFDR value between two traits and allows the identification of genomic loci related to both traits44. Psychiatric disorders with reasonable polygenic overlap in MiXeR were included in this analysis. We plotted all SNPs within an LD block in relation to their chromosomal location in a conditional/conjunctional Manhattan plot and identified the strongest signal in each LD block by ranking all SNPs based on the conditional/conjunctional FDR value. Independent genomic loci were defined using the FUMA protocol. To calculate how many loci were additionally identified in this study compared to the original GWAS results, we determined if the variants were located within ± 1 Mbp of the variants reported in previous studies. Finally, eQTL mapping was applied to link-defined loci with related genes, and tissue specificity analyses were performed with mapped genes using the GTEx database within the FUMA platform. Overall, MiXeR and cond/conjFDR provided a more comprehensive overview of the genetic relationships between traits, as these methods are not affected by the effect direction.The condFDR approach . These analyses included genome-wide association tests, SNP-based heritability and partitioned heritability analyses, multiple tissue analyses, genetic correlation analysis, and MiXeR analysis.P < 5 × 10−8) were identified (Table P < 5 × 10−8) in meta-analyses of 10 randomly assigned discovery and replication sets , H3K4me3 (indicative of active promoters), and H3K9ac (indicative of active promoters or enhancers).The total SNP heritability of irritability was estimated to be 4.19% (standard error = 0.2%). In the partitioned heritability analysis, four of the 53 annotations passed the FDR threshold of 0.05  showed strong enrichment at an FDR < 5% (Supplementary Table rg) , functional digestive disorders (rg = 0.37), MDD (rg = 0.56), and positive answers to “ever depressed for a whole week” (rg = 0.47) and “ever unenthusiastic/disinterested for a whole week” (rg = 0.53). Statistically significant negative correlations were observed with satisfaction-related traits (rg range = −0.56 to −0.36), never smoking (rg = −0.30), and leisure/social activities . These correlations suggest common genetic associations between irritability and multiple health and lifestyle factors.We performed LDSC on the summary statistics for health-associated traits to compute genetic correlations (rg) Fig. . A totalrg = −0.17) at an FDR < 5%.LDSC analysis was likewise conducted to test the genetic correlations of irritability with brain region volumes and connectivity via structural MRI and DTI, respectively  and BD II (rg = 0.38) in the category of mood and psychotic disorders.Genetic correlations between irritability and nine major psychiatric disorders are presented in Supplementary Tables rg estimate of the LDSC analyses, MDD showed considerable polygenic overlap (Dice coefficient = 0.56), sharing approximately 5300 variants with irritability 50, while 95 loci remained unique.For condFDR analyses of SCZ, BD I, and MDD (using irritability as an associated phenotype), we employed a conditional Manhattan plot to visualize the localization of genetic markers Fig. . We iden48. Fig. −16. MoreMRPS33) was detected in all three disorders. Furthermore, 117 genes mapped with additional loci for SCZ were enriched in five tissue types, 45 genes mapped with BD I loci were enriched in seven tissue types, and 90 genes mapped with MDD loci were enriched in eight tissue types, with enrichment in the brain demonstrated in all three disorders  to genes via eQTL analysis and identified 117, 45, and 90 genes, respectively  and conjFDR analyses between irritability and psychiatric disorders, we also generated a conditional/conjunctional Manhattan plot to visualize the localization of the genetic markers , H3K4me3 , and H3K9ac . Approximately 2.6% of the total SNP heritability was attributed to the conserved regions, representing the largest proportion of enrichment in any category. The importance and significance of the conserved regions have been reported in prior studies on intelligence, physical illnesses and traits, SCZ, and BD56. H3K4me3 and H3K9ac annotations were similarly enriched in SCZ, BD, and MDD, while the DHS was associated with physical and emotional functioning58. The LDSC-SEG analysis suggested that the CNS was strongly enriched, indicating its significant involvement in the biological basis of irritability  and BD II (rg = 0.38) in the category of mood and psychotic disorders  and BD I (rg = 0.15), indicates that most of these shared variants have different directions of effects on each trait. These polygenic relationships between irritability and psychiatric disorders provide important evidence for subsequent studies to examine and clarify the genetic basis of irritability and the overlap of biological mechanisms with those of psychiatric disorders. As irritability is a symptom of many psychiatric disorders, including MDD (in children and adolescents), BD I/II, generalized anxiety disorder, and oppositional defiant disorder10, we expect that investigating the genomics of irritability more comprehensively will help to elucidate the genomics of other associated psychiatric disorders and vice versa.Based on the polygenic associations between irritability and SCZ, BD I, and MDD, we identified additional and shared SNPs associated with the above four phenotypes via the condFDR and conjFDR approaches Fig. . Compare64. For example, the GWASs of risk-taking behavior and mood instability, which were based on a single question, identified and replicated loci associated with the respective traits and revealed a shared genetic basis with other psychiatric traits66. In the future, studies incorporating more comprehensive questionnaires, such as the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto Questionnaire2, may be used to support our findings. Second, although irritability is predicted to be heritable, environmental factors also influence this trait. Thus, our results should not be utilized to predict an individual’s temperament but rather as evidence of the genetic basis of irritability for a comprehensive evaluation of the associated neurobiological and genetic manifestations of this trait. Third, although our analyses identified strong enrichment in multiple brain tissues, we were unable to determine specific brain regions. This may be partly due to the use of limited functional datasets for brain tissues and cell types25. Additionally, we observed significant enrichment in neurons but not in oligodendrocytes and astrocytes  to define irritability. The results should be interpreted carefully because exploring irritability with a simple question could only reveal certain aspects of the complex characteristics of human behavior. Numerous previous studies investigating personality traits using large-scale biobank data share this limitation (of relying on simple questionnaires) but have sufficient statistical power for genetic analyses due to the hundreds of thousands of participants; such studies have nonetheless yielded novel genetic discoveries and insights into human behaviorSupplementary Information