N = 132,113; 21% cases). We identified two genome-wide significant loci . POU showed positive genetic correlations with the two largest available GWAS of OUD and opioid dependence . We also identified numerous additional genetic correlations with POU, including alcohol dependence (rg = 0.74), smoking initiation (rg = 0.63), pain relief medication intake (rg = 0.49), major depressive disorder (rg = 0.44), chronic pain (rg = 0.42), insomnia (rg = 0.39), and loneliness (rg = 0.28). Although POU was positively genetically correlated with risk-taking (rg = 0.38), conditioning POU on risk-taking did not substantially alter the magnitude or direction of these genetic correlations, suggesting that POU does not simply reflect a genetic tendency towards risky behavior. Lastly, we performed phenome- and lab-wide association analyses, which uncovered additional phenotypes that were associated with POU, including respiratory failure, insomnia, ischemic heart disease, and metabolic and blood-related biomarkers. We conclude that opioid misuse can be measured in population-based cohorts and provides a cost-effective complementary strategy for understanding the genetic basis of OUD.The growing prevalence of opioid use disorder (OUD) constitutes an urgent health crisis. Ample evidence indicates that risk for OUD is heritable. As a surrogate (or proxy) for OUD, we explored the genetic basis of using prescription opioids ‘not as prescribed’. We hypothesized that misuse of opiates might be a heritable risk factor for OUD. To test this hypothesis, we performed a genome-wide association study (GWAS) of problematic opioid use (POU) in 23andMe research participants of European ancestry ( Opioid use disorders (OUD) represent a global epidemic . Every dOPRM1, which encodes the µ-opioid receptor [Although OUD is known to be moderately heritable , 7, genoAn alternative, complementary approach is to study the genetic liability for OUD across different stages, particularly the transition from use to misuse. By taking this approach, we can explore specific aspects of OUD liability, such as initial use, subjective drug response, transition to hazardous use, dependence, withdrawal, and relapse. This strategy can be applied in population-based cohorts, allowing for a dramatic increase in sample sizes for a fraction of the cost.n = 132,113) of European ancestry. We asked a single question “Have you ever in your life used prescription painkillers (taken not as prescribed), e.g., Vicodin, Oxycontin?”, and conducted a GWAS defining cases as those who answered ‘yes’. We used a variety of bioinformatic analyses to explore the relationship between POU and OUD as well as other substance use disorder-related behaviors. We also explored POU’s genetic relationship with various psychiatric, behavioral, and medical conditions.In the present study, we pursued a novel strategy in which we measured problematic prescription opioid use (POU) in 23andMe research participants (www.eandireview.com). During 4 months in 2015 and 14 months in 2018–2020, participants responded to a decision-making survey that, depending on branching logic, included up to 139 questions pertaining to aspects of impulsivity and substance use and abuse. A single item in this survey asked, “Have you ever in your life used prescription painkillers (taken not as prescribed), e.g., Vicodin, Oxycontin?”. A total of 132,113 individuals responded, with 27,805  answering ‘yes’ (cases) and the remaining 104,308  answering ‘no’ (controls). Controls were not screened for prior opioid use, so they represent a combination of individuals who have taken opioids but only as prescribed, and others who have never taken an opioid. Only individuals who were categorized as being of European ancestry based on empirical genotype data [We utilized a cohort of 132,113 male and female research participants of European ancestry. All participants were drawn from the customer base of 23andMe, Inc., a direct-to-consumer genetics company. Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services  and to study their functional consequences. We also used MAGMA v1.08 [p < 2.56E−06). Gene sets were obtained from Msigdb v7.0 . We also used Hi-C coupled MAGMA . We applied a Bonferroni correction based on the total number of gene-tissue pairs tested (p < 9.55E−07).We used a variety of bioinformatic methods to further characterize the loci identified by the GWAS. First, we used the default version (v1.3.6a) of the FUMA web-based platform  to identMA v1.08 , 20 to pH-MAGMA; ) to assihttps://www.gtexportal.org/) as the reference transcriptome dataset. For S-PrediXcan and S-MultiXcan analyses, we chose to use sparse (elastic net) prediction models, which are available at http://predictdb.hakyimlab.org/. We applied a conservative Bonferroni correction based on the total number of gene-tissue pairs tested .Lastly, we used S-MultiXcan v0.7.0 (an extension of S-PrediXcan v0.6.2 ) to idenhttps://www.genome.jp/kegg/drug/) and the World Health Organization Collaborating Center for Drug Statistics Methodology (https://www.whocc.no/atc_ddd_index/). We used the R package circlize v0.4.1 [We examined the 17 genes that were significantly associated with POU in the MAGMA gene-based analysis (10% FDR) for known interactions with prescription medications using the Drug Gene Interaction Database v3.0 (dgidb.genome.wustl.edu) . We usede v0.4.1  to visua2). We used pre-computed LD scores (“eur_w_ld_chr/”), which are publicly available . LD scores were computed for every SNP using individuals from European ancestry from the 1000 Genomes Project. We restricted the analysis to well-imputed SNPs, filtered to HapMap3 SNPs, with MAF above 1%. We removed InDels, structural variants, strand-ambiguous SNPs and SNPs with extremely large effect sizes (χ2 > 80). Heritability was calculated on the liability scale by accounting for differences in population prevalence (4%) and sample prevalence (21%). The population prevalence was retrieved from the 2018 National Survey on Drug Use and Health [We used the LD Score regression (LDSC ) python d Health .rg) between POU and 935 other traits or diseases  [p < 5.35E−05); however, this correction is overly conservative because many of the 935 traits are highly correlated with one another.We used LDhub or local LDSC  to calcu3 local) . Local tp value threshold of 2.00E−05, 5.00E−08,1.00E−05, 1.00E−05, 5.00E−08, and 1.00E−05, respectively, for risk-taking behavior, smoking, cannabis use disorder, alcohol dependence, chronic pain, and OUD. We then computed genetic correlations using the POU summary statistics adjusted for the covariates of interest.We used mtCOJO  to indivcluster package [Previous studies have shown that consumption and misuse/dependence phenotypes have a distinct genetic architecture. To explore whether POU clustered more with consumption or misuse/dependence phenotypes, we used a data-driven unsupervised machine learning method known as agglomerative hierarchical clustering analysis (HCA) . HCA for package .The product of HCA is a dendrogram, formed with multiple brackets called “branches”. Phenotypes on the same branch are more similar to each other based on their pairwise genetic associations with each other and with all other phenotypes on that branch. Branches can form subbranches of more specific clustering.To explore whether pleiotropic effects for POU are associated with an array of other medical ailments and biomarkers, we performed phenome-wide and lab-wide association scans , respectively. These analyses were conducted using data from the Vanderbilt University Medical Center (VUMC). The project was approved by the VUMC Institutional Review Board . VUMC is an integrated health system with individual-level health data from electronic health records (EHR) for about 3.2 million patients. The VUMC biobank contains clinical data from EHR as well as biomarkers obtained from laboratory assessments. A portion of the individuals from VUMC also have accompanying array genotyping data. This cohort, with over 66,903 patients, is called BioVU , 35.For each of the unrelated genotyped individuals of European ancestry from BioVU, we computed polygenic risk scores (PRS) for POU using the PRS-CS “auto” version . Genotyphttps://phewascatalog.org/phecodes) and a minimum of 100 cases for inclusion of a phecode. The disease phenotypes included 145 circulatory system, 120 genitourinary, 119 endocrine/metabolic, 125 digestive, 117 neoplasms, 91 musculoskeletal, 85 sense organs, 76 injuries & poisonings, 65 dermatological, 76 respiratory, 69 neurological, 64 mental disorders, 42 infectious diseases, 42 hematopoietic, 34 congenital anomalies, 37 symptoms, and 31 pregnancy complications.To identify associations between the PRS for POU and clinical phenotypes, we performed a PheWAS in BioVU. We fitted a logistic regression model to each of 1338 case/control disease phenotypes (“phecodes”) to estimate the odds of each diagnosis given the POU PRS, while adjusting for sex, median age of the longitudinal EHR, and the first ten PCs. Analyses were conducted using the PheWAS v0.12R package . We requTo identify associations between POU PRS and biomarkers, we performed a LabWAS  in BioVUN = 2304 cases) and OUD (N = 1799 cases). We applied FDR (5%) correction for both PheWAS and LabWAS analyses.Lastly, to explore whether the POU PRS effects could be mediated by the diagnosis of substance use disorders (SUD) or OUD in EHR, we repeated the PheWAS and LabWAS analyses after adjustment for SUD (GC = 1.097 with an LDSC intercept of 1.004 (SE = 0.008), suggesting that the majority of the inflation was due to polygenicity. The SNP heritability of POU on the liability scale was SNP-h2 = 0.04 ± 0.01.We examined 11,311,983 SNPs in all 132,113 study participants. The inflation factor of the GWAS was λKDM4A and PTPRF (p = 3.80E−08), and rs640561 near LRRIQ3 .We identified two genome-wide significant loci: rs3791033 near the genes N > 360,000) revealed that rs3791033 has been previously implicated in smoking phenotypes , other psychiatric conditions, such as ADHD (p = 2.76E−10), and educational outcomes, such as educational attainment (p = 1.01E−10). KDM4A, which is the nearest gene, has been previously implicated with similar traits across several independent GWAS studies  with neurological, metabolic and other psychiatric traits, such as ADHD , a phenome-wide scan in UKB revealed nominal associations  or even nominally significant (p < 0.05) associations with any of the SNPs that have been previously associated with OUD and opioid dependence  and H-MAGMA  had a total of 464 interactions with drugs belonging to ATC drug classes . We identified significant genetic associations between POU and 253 of these 935 traits , with opioid dependence versus unexposed controls (European) as measured by the Psychiatric Genomics Consortium , and with opioid medication use  in participants from UKB.Notably, POU showed positive genetic correlations with OUD as measured by the Million Veteran Program , to high levels of consumption , to misuse  and dependence , and other smoking behaviors, such as maternal smoking around birth . Interestingly, hierarchical HCA with AGNES found that POU clustered closely with substance use disorders, as opposed to consumption phenotypes , and prevalent pain conditions such as back and knee pain, and headaches . As expected, we found positive genetic associations with chronic pain  and higher pain relief medication intake .We also identified a number of associations with UKB pain phenotypes, including positive genetic correlations with several pain ICD diagnoses , risk-taking behavior , major depressive disorder , insomnia , loneliness , and irritability .We also identified positive genetic associations between POU and other behavioral and psychiatric traits, such as mood swings  and intelligence .Lastly, POU was negatively correlated with educational attainment , alcohol-related disorders  and substance addiction and disorders , which included both opiates and other substances.We conducted a PheWAS with EHR data to test the association between polygenic risk for POU and liability across thousands of medical conditions from hospital-based cohorts. Our PheWAS identified significant associations between POU and 53 medical traits across 13 categories  and chest pain . POU was also significantly associated with various psychiatric disorders, including mood disorders , anxiety disorders , depression  and posttraumatic stress disorder .Another similarity between the PheWAS and the genetic correlations was the positive association between POU and pain phenotypes measured in EHR, such as acute pain , insomnia  and ischemic heart disease .Finally, we discovered additional associations between POU and other EHR traits, including respiratory failure  and blood glucose , and lower HDL cholesterol , calcium , folate , and calcitriol . We also found associations with blood-related biomarkers, such as high erythrocyte distribution , increased leukocytes , reduced carbon dioxide in serum/plasma  and creatinine .In addition to the PheWAS, we also examined laboratory results, which we refer to as LabWAS. The LabWAS identified 15 biomarkers significantly associated with POU  in 132,113 23andMe research participants of European ancestry. This represented a novel approach to studying OUD in a population-based cohort. Our results show that this single question captured a genetic signal that is correlated with signals from well-characterized cohorts that have been clinically diagnosed with OUD. Notably, the genetic correlations with OUD persisted even after correcting for risk-taking behavior and other putatively similar dimensional phenotypes. We also identified novel associations with laboratory-based biomarkers, demonstrating the overarching impact of POU on health. While previous GWAS of OUD have used clinically ascertained cohorts, our results suggest that POU provides a cost-effective alternative to diagnosed OUD that is viable in non-clinically ascertained populations, making it possible to rapidly obtain large sample sizes that can aid in OUD gene discovery.Obtaining a large enough sample size to effectively identify risk loci has been a common obstacle for OUD GWAS . Our stuThe most important finding in this study is the identification of the polygenic architecture of POU that is highly comparable to the findings from GWAS of clinically ascertained OUD cohorts. We observed a positive association of POU with OUD and opioid dependence measured by two of the largest available GWAS, MVP, and PGC, and with a GWAS of opioid medication use in UKB participants. As might have been expected, we also observed strong positive genetic correlations with alcohol dependence and tobacco smoking, as well as with various psychiatric traits associated with OUD, including mood swings, risk-taking, anxiety, depression, and insomnia. These sets of genetic correlations mirror those from previous GWAS of clinically ascertained OUD samples –8. HowevWe initially speculated that some of the genetic signal associated with POU could be confounded by genetic factors unrelated to opioid misuse, such as risk-taking behaviors, comorbid substance use  or pain. Previous reports have indicated that genetic studies using an unscreened sample for relevant comorbid conditions , includiKDM4A, which is a protein-coding gene that belongs to the Jumonji domain 2 (JMJD2) family of histone demethylases [KDM4A has known interactions with medications that are used to treat other psychiatric conditions, such as depression, or target dopaminergic or serotonergic systems, as well as drugs used in opioid anesthetics, suggests that KDM4 demethylases may be implicated in relevant biology for opioid behaviors and could represent promising druggable targets [KDM4A, the lead variant in this region, rs3791033, is an eQTL for several other nearby genes PTPRF, HYI, ST3GAL3, MED8, CCDC24, ATP6V0B, WDR65, ARTN, TIE1. Our second strongest signal was rs640561, which is near the gene LRRIQ3, which is a protein-coding gene of leucine rich repeats and IQ Motif Containing 3. rs640561, or SNPs in strong LD, have been previously associated with other substance use traits, including smoking [This study has also revealed novel biological insights for opioid misuse. While we were unable to replicate the previously reported GWAS signals associated with OUD, we discovered two novel loci associated with POU. Rs640561 replicated in the largest available GWAS of OUD . Both lthylases . KDM4 dethylases , includithylases –49 and, thylases  and alcothylases , 52. The targets . However smoking –56 and a smoking , educati smoking , but theWe examined the genetic associations between POU PRS and thousands of clinical diagnoses and biomarkers measured in a hospital-based setting via PheWAS and LabWAS analyses. These analyses revealed significant associations for a wide range of health conditions, implicating POU across all major body systems, some of which had been previously associated with various clinical OUD cohorts and others that are novel.For example, we consistently identified strong associations between POU and poor metabolic function , which can result in serious downstream health consequences. These results are consistent with previous studies showing poor metabolic function in individuals who misuse opioids , and in This study is not without limitations. The screening and composition of the control group is crucial for studies of substance use and abuse , 31, 43.In summary, we have shown that the genetic signature for opioid misuse that we were able to capture via self-report in an unselected population is similar to genetic risk for OUD. Our work sets the stage for future analyses incorporating a multivariate framework , and larger sample sizes. Our approach provides new, cost-efficient tools for genetic research related to OUD and provides insights into an intermediate behavior that may set the stage for later transition to OUD.Supplementary TablesSupplementary Tables


Parus major), we assessed the association between single nucleotide polymorphisms (SNPs) in the serotonin transporter gene (SERT) and two repeatable behavioural traits, i.e. female-female aggression and female hissing behaviour. For female-female aggression, a trait showing age-related plasticity, we found no evidence for associations with SERT SNPs, even when assessing potential age-dependent effects of SERT genotype on aggression. We also found no strong support for associations between SERT SNPs and hissing behaviour, yet we identified two synonymous polymorphisms (exon 13 SNP66 and exon 12 SNP144) of particular interest, each explaining about 1.3% of the total variation in hissing behaviour. Overall, our results contribute to the general understanding of the biological underpinning of complex behavioural traits and will facilitate further  research on behaviour-related genes. Moreover, we emphasize that future molecular genetic studies should consider age-dependent genotype–phenotype associations for behavioural trait (co)variation, as this will vastly improve our understanding of the proximate causes and ultimate consequences of personality variation in natural populations.Quantifying variation in behaviour-related genes provides insight into the evolutionary potential of repeatable among-individual variation in behaviour . Yet, individuals typically also plastically adjust their behaviour in response to environmental conditions and/or age, thereby complicating the detection of genotype–phenotype associations. Here, using a population of free-living great tits ( Traditionally, this among-individual behavioural variation was often attributed to random (i.e. non-adaptive) noise around an adaptive mean3. Today, it is apparent that personality can be heritable6, influence fitness8 and therefore potentially evolve adaptively in response to selection10. Although quantitative genetic studies have revealed substantial genetic variation underlying variation in personality traits (e.g.5), our knowledge about the specific genetic architecture of personality variation still remains in its infancy13. Yet, understanding how genes contribute to the shaping of behavioural phenotypes is essential to assess their evolutionary potential and how personality variation is maintained in natural populations.Individuals within single populations are often found to vary in their average behaviour across repeated observations, known as animal personality15. Consequently, effects of genes on behavioural phenotypes (and hence heritability) might not be constant across environments and/or the life span of individuals, as characterized by genotype-by-environment (i.e. G × E) and genotype-by-age (i.e. G × A) interactions, respectively16. In other words, effects of certain genes or gene polymorphisms on behavioural phenotypes might be reduced, shut down or altered under specific environmental conditions and/or at certain ages, indicating that genes can act in an environment- and/or age-dependent manner (e.g.18). Although still limited in number, studies in natural populations are increasingly revealing heterogeneity in the associations between genetic polymorphisms and behavioural traits across populations, suggesting the existence of G × E in behaviour . In contrast, studies investigating G × A in behaviour, which can be considered a specific form of G × E, are very scarce , and we are unaware of any study assessing whether the association between specific genes (or polymorphisms) and behavioural traits interact with age in natural populations. Modelling the interaction between genotype and age is important for traits that show age-related phenotypic plasticity since the latter can complicate the detection of associations between genes and behavioural phenotype.Besides being repeatable, behavioural trait expression is typically also plastic, with individuals adjusting their behaviour in response to external  and internal (e.g. age) factors13). One promising candidate gene particularly with regard to anxiety- and aggression-related behaviour is the serotonin transporter gene (SERT) (e.g.27). The serotonin transporter has an important role in the regulation of extracellular and synaptic serotonin concentrations, and hence the magnitude and duration of serotonergic neurotransmission. In human and non-human primates, allelic variation in SERT has been associated with anxiety, harm avoidance, dominance, aggression and sexual behaviour29. Additionally, a handful of recent studies suggest that single nucleotide polymorphisms (SNPs) in SERT can be associated with behavioural variation in free-living bird species. Notably, SNPs in SERT have been associated with variation in flight initiation distance in dunnocks (Prunella modularis23) and novel object and anti-predator responses in great tits .A variety of so-called candidate genes have been hypothesized to be involved in the expression of particular behavioural phenotypes (recently reviewed in34). In this population, both behavioural traits have been shown to be short-term (i.e. within-year) and long-term (cross-year) repeatable, but to not covary on the among-individual level . The latter raises the, as of yet untested, possibility that aggression and hissing behaviour are influenced by different underlying genetic mechanisms. Moreover, we recently revealed that female-female aggression, but not hissing behaviour, decreased within individuals with age and that individuals differed in their level of age-related plasticity in aggression . Since G × A can underlie I × A15, the influence of genes on aggression might vary with age, but whether this is the case remains to be evaluated. Hence, using a behavioural dataset collected over four years (see37) and SERT as a candidate gene, we assessed the association between SERT SNPs and both aggression and hissing behaviour in female great tits. For aggression, we additionally assessed whether the potential associations with SNPs interacted with age, which would be characterized by age-dependent aggression-SNP associations (i.e. G × A).Here, using a population of free-living great tits, we aimed to identify genomic variation underlying two female personality traits, that is, female-female aggression and female anti-predatory nest defence , which has been monitored since 1997 and at present consists of approximately 150 nest boxes for great tits. As part of long-term monitoring, all birds in the population are provided with a metal leg ring as nestlings or upon first capture (in winter or when feeding nestlings), and all adults receive a unique combination of three plastic colour rings. A blood sample (20 µl) is collected from the brachial vein for all birds, either as nestling or as adult upon first capture. Reproductive activities of all breeding pairs are monitored throughout the nesting cycle to determine lay date, clutch size and onset of incubation.37. First, female-female aggression was assessed using simulated same-sex territorial intrusion tests (henceforth aggression test), following35. In short, a stuffed female great tit  was placed on top of the focal female’s nest box at day 2 and 5 of the egg-laying period (with day 1 the day the first egg was laid). The focal female’s behaviour was observed for 5 min, starting when she entered within a 15 m radius around the nest box, or when she was already present at the start of the test. The observer , standing at a distance of approximately 15 m, scored the following aggression parameters using a customized handheld tally counter device: the number of alarm calls produced, the time spent on the decoy (in s) and the number of attacks towards the decoy. Also, approach distance (in m) was scored, representing the minimum distance of the focal female to the decoy during the observation period.For four years (2016–2019), behavioural experiments were performed on females with first clutches34. This so-called hissing behaviour was assessed using simulated predator intrusion tests (henceforth hissing test), following38. Specifically, the observer (one of 11) entered the head of a stuffed great spotted woodpecker  into the entrance hole of a focal female’s nest box, at day 2 and 5 of the incubation period, thereby blocking the only entrance to the nest box and preventing the incubating female from escaping. The woodpecker was held in this position for one minute, during which the number of hissing calls produced were counted, which can easily be heard from outside the nest box. The number of hissing calls produced was used as a measure of hissing behaviour.Second, when confronted with a predator inside the nest cavity, some incubating and brooding females produce loud broadband hissing calls, often accompanied with intense flapping of the wings and lunging at the predator37). Age of females (with age = 0 representing age of birth) was determined using hatching records (resident birds) or plumage characteristics upon first capture (first-year or older). Absolute age was therefore known for all local recruits  and immigrant birds first captured as first-year-olds . For immigrant birds with an adult plumage upon first capture , we assumed they recruited into the population as 2-year-olds37. We were able to genotype 306 of the 312 females in our behavioural dataset (detailed below).Over the course of 2016–2019, a total of 686 aggression tests and 866 hissing tests were successfully performed on 290 and 311 unique females, respectively, with 289 of these females tested for both behaviours during the same breeding attempt , and 2.0 µl of DNA at 10 µM.DNA was extracted from 306 blood samples using a commercial kit (Macherery-Nagel NucleoSpin® blood kit), following manufacturer’s instructions. Next, using polymerase chain reactions (PCRs), we amplified the 13 exonic regions of Genotypes were determined via direct sequencing of PCR amplicons, performed at the genomics core facility of the University of Antwerp using a sanger sequencing platform. Sequences where aligned to the great tit reference genome using Geneious Prime 2020.0.4 and single nucleotide polymorphisms (SNPs) were identified by manual inspection. At first, a subsample of 46–48 females  were genotyped across all loci, and sequenced in both directions (i.e. forward and reverse primers). This revealed SNPs with genotype frequencies > 5% in 8 exonic regions, i.e. exons 1, 2, 3, 5, 6, 9, 12 and 13. All other exons either contained rare SNPs  or were monomorphic (exon 4), and were not further considered. Next, all other females in the dataset were genotyped for the eight polymorphic exons, which were sequenced in the direction that gave the best coverage across a particular locus (i.e. either forward or reverse). Sample sizes differ somewhat between loci due to sequencing failures of some SNPs for some individuals.Hardy–Weinberg package in R39). Linkage disequilibrium (LD) statistics (D’ and r) among the remaining SNPs within SERT were estimated using SNPStats40 (Supplementary Table p.adjust function in R41).Within the eight exonic regions, we excluded SNPs in which minor allele frequencies (MAF) were < 10%  with eigenvalue > 1 (EV = 1.40), explaining 49% of variance. High scores on PC1 correspond to closer approach distance, more time on the decoy and more attacks. In contrast, low scores on PC1 correspond to the production of more alarm calls from a larger distance  scored during territorial intrusion testsSERT SNP and behavioural traits was performed using linear mixed models fitted with the lmekin function (coxme package in R42). For each behavioural trait, we fitted two standard allele effect models, i.e. the additive and the overdominant effect model, thereby covering the complete range of allele effects . Genotype was coded as a continuous covariate  in the additive effect models, and as a two-level factor  in the overdominant effect models. Based on previous findings, we also included lay date (relative to the first-egg date in the given year) and clutch size as fixed effects in the models for hissing behaviour37. Clutch size was centred and standardized within individuals, thereby partitioning effects of clutch size on behavioural traits into its among-individual  and within-individual  components44. For aggression, we included age and its two-way interaction with genotype as fixed effects. We pooled individuals of 3 years old or older into one age-class to avoid small numbers of individuals in any genotype-age class . Nonetheless, for certain SNPs this still resulted in a small number of individuals in certain genotype-age classes and the interaction between age and genotype was only included in the models where sufficient individuals (≥ 5) were available in each genotype-age class  were not included36. All models included random intercepts for female identity (ID) and the unique combination of ID and year (ID_Year), the latter denoting a period (i.e. breeding season) during which repeated observations were obtained for individuals (see45). To control for the effect of relatedness among females, we constructed a relatedness matrix  using pedigree information based on social matings (2004–2019) and entered this matrix into all models. Relatedness was modelled this way because, due to high immigration and dispersal, we failed to collect behavioural data for a sufficient number of related females to fit animal models . We also did not include random intercepts for observer or decoy identity since previous results using the same behavioural dataset revealed that they explain little to no variation in focal behavioural traits (see37). For significant SNPs we calculated the proportion of variance in behaviour explained using the marginal coefficient of determination (R2m48).Second, association testing between each All analyses were performed in R 3.6.1 . Response variables were standardized to unit variance prior to analyses and, based on visual inspection of model residuals, all models were fitted assuming Gaussian error distribution. Interaction effects were removed from final models when non-significant. Results are presented as means with standard error (SE), unless stated otherwise, and we report significance levels of effects before  and after adjustment for FDR (P.adjust).This study was approved by the ethical committee of the University of Antwerp (ID 2017-23 and 2017-61), performed in accordance with Belgian and Flemish laws regarding animal welfare, adhered to the ASAB/ABS guidelines for the use of animals in behavioural research and teaching, and complies with ARRIVE guidelines. The Royal Belgian Institute of Natural Sciences (KBIN) provided ringing licences for all authors and technicians.SERT SNPs at 27 loci across 8 exonic regions in our population , where SNP66 genotype explained 1.3% of the total variance in hissing behaviour (R2m = 0.013). Specifically, homozygous females for SNP66 produced on average 3.26 hissing calls less compared to heterozygous females . Specifically, the number of hissing calls produced decreased on average by 2.92 with the successive replacement of C-alleles in SNP144 . In general, we also found no strong support for associations between SERT polymorphisms and hissing behaviour. Yet, genomic sequence variation at one synonymous locus in SERT (SNP66 in exon 13) showed a strong, marginally non-significant, tendency to be associated with variation in hissing behaviour. Another synonymous polymorphism (SNP144 in exon 12) also appeared to be associated with hissing behaviour, although not significantly so after correcting for multiple testing. As expected for single loci influencing complex behavioural traits, each of these SNPs explained a small amount (1.3%) of the total variance in hissing behaviour. Overall, our results therefore suggest that the SERT gene might be involved in heritable variation in hissing behaviour, but not in female-female aggression, although caution is warranted given the possibility for false positive results (i.e. type I errors).Identifying genes that underlie personality can improve our understanding of how among-individual variation in behaviour is maintained in natural populations. Here, in a population of free-living great tits, we found no evidence for associations between 37, we found no evidence for (age-dependent) associations with genomic sequence variation in the exonic regions of SERT. It should be noted that for some SNPs there were very few females in certain genotype-age classes and whether genotype-by-age interactions occur for these SNPs remains unknown. Moreover, we cannot rule out that factors other than age, such as external environmental conditions, may result in induced or inherited changes in a gene’s expression49. For example, in free-living great tits, dopamine receptor D4 (DRD4) genotype was associated with exploratory behaviour in only one out of four populations, potentially explained by cross-population environmental differences modifying genetic effects19. More recently, environmentally induced epigenetic modifications (i.e. DNA methylation) have been suggested to play a role in the association between SERT genotype and novelty responses across urban and rural great tit populations21. Hence, it will be interesting to investigate whether genotype-by-environment interactions occur for the association between female same-sex aggression and SERT polymorphisms, both within and across populations.Although we have previously demonstrated that female-female aggression is both repeatable and plasticity adjusted according to ageSERT polymorphisms. Yet, variation in hissing behaviour was marginally non-significantly associated with genomic sequence variation in exon 13 (SNP66), where homozygous females produced on average less hissing calls compared to heterozygous females. Additionally, we revealed some tentative indications for an additive genetic effect of SNP144 in exon 12 on hissing behaviour. As noted before, these associations might represent false positive results (i.e. type I errors), especially in the case of exon 12 SNP144 as the latter association was far from reaching significance after correcting for multiple testing. Nonetheless, our results are informative for studies aiming to reconcile the effects of SERT on behaviour, including those aiming to identify biological pathways involved in the expression of hissing behaviour. That is, the here identified SNPs in exon 12 and 13 were not detected in another great tit population where the association between hissing response and SERT SNPs was assessed31. Instead, the latter study found that SERT SNP187 in exon 1 explained about 16% of the variation in whether or not (i.e. binary response) females produced hissing calls upon predator confrontation. Although we found a SNP at the same location in exon 1 (SNP187), this was not associated with the same nucleotide change . Nonetheless, genuine cross-population differences in gene-behaviour associations are common50 and can be the result of, amongst other factors, divergent selective pressures, different mutations, and gene-by-environment interactions51. Notably, since the proportion of females not producing hissing calls  can greatly differ among great tit populations , it will be of interest to investigate whether population differences in SERT-hissing behaviour associations represent genuine population-specific effects, preferably across a large number of populations.Overall, we also found no strong support for associations between hissing behaviour and 12. Although small from a statistical viewpoint, from a genetic point of view these effects can be non-negligible and substantial, particularly for a genetic association with complex behavioural traits53. Hence, the here identified SNPs with small effects can guide our further understanding of the biological underpinning of hissing behaviour26. At present it indeed remains unclear how these potential effects come about, especially since the associated polymorphisms are synonymous and do not determine amino acid sequences of the encoded protein. However, there is growing evidence that synonymous polymorphisms can have functional effects via their influence on transcription, splicing, mRNA stability or translation, any of which could alter the phenotype55. Alternatively, synonymous SNPs can be linked with variation in non-coding regions that influence a gene’s expression, or with functionally significant polymorphisms (i.e. non-synonymous SNPs) in other genes56. Hence, a next step would be to investigate linkage disequilibrium patterns between exonic SNPs  and variation in intronic regions of SERT, as well as in the wider gene region .As expected for quantitative behavioural traits, single loci had only small effects on hissing behaviour in our population, each explaining about 1–2% of variationSERT that might be involved in the expression of hissing behaviour, but not female same-sex aggression. Different findings for different traits might be related to the behavioural context in which they are expressed. That is, growing evidence in rodents, humans and non-human primates suggests that different forms of aggression could be influenced by different components of the serotonergic system, as well as interactions with other neurochemical systems59. In the case of female-female aggression, females can decide whether or not to (aggressively) engage during same-sex conspecific confrontation in their territory, therefore likely reflecting a female’s tendency for offensive aggression. In contrast, hissing behaviour is typically expressed in situations where predator confrontation is unavoidable due to predators often blocking the only entrance/exit of the nest cavity, therefore reflecting a female’s tendency for fear-induced defensive behaviour. Hence, our findings raise the possibility that the serotonergic system might be differentially involved in the expression of same-sex offensive aggression and fear-induced defensive behaviour in an avian species. Yet, this remains to be determined since the observed differential involvement of SERT might also be the result of differences in the heritability of our focal behavioural traits, which we were unfortunately unable to estimate due to the relatively low number of related females in our data. Nonetheless, repeatability estimates are useful in this regard as they enable the quantification of the upper limit to heritability5. Since cross-year repeatability is high for hissing behaviour (R = 0.64) but low for aggression , it could hence have been more difficult to detect underlying loci for aggression than for hissing behaviour. Also, although variation in hissing behaviour has been linked to reproductive investment and success in our population60, it remains to be investigated whether the here detected synonymous SERT polymorphism are subject to selection54.In general, our study identified SNPs in SERT genotype might play a potential role in the expression of hissing behaviour, but replicated studies are necessary to verify our results. Yet, the two here identified SNPs with small effect provide valuable starting points in continuing to reveal underlying biological pathways involved in the expression of hissing behaviour. In contrast, we found no support for associations between SERT genotype and female same-sex aggression, even when assessing potential age-dependent effects. Whether age-dependent associations occur between aggression and other genes, including the role of epigenetic mechanisms on age-dependent behavioural trait expression, remains to be evaluated. In general, since genotype-by-age interactions are expected to be common for behaviour15, there is a clear scope for future molecular genetic studies to consider genotype-by-age interactions for behavioural trait (co)variation. With our study, we hope to stimulate research in this direction as this will vastly improve the proximate and ultimate understanding of the development of personality variation in natural populations.Our findings suggest that Supplementary Tables.


Genomic selection improves accuracy and decreases the generation interval, increasing the selection response. This study was conducted to assess the benefits of using single-step genomic best linear unbiased prediction (ssGBLUP) for genomic evaluations of milk yield and heat tolerance in Thai-Holstein cows and to test the value of old phenotypic data to maintain the accuracy of predictions.The dataset included 104,150 milk yield records collected from 1999 to 2018 from 15,380 cows. The pedigree contained 33,799 animals born between 1944 and 2016, of which 882 were genotyped. Analyses were performed with and without genomic information using ssGBLUP and BLUP, respectively. Statistics for bias, dispersion, the ratio of accuracies, and the accuracy of estimated breeding values were calculated using the linear regression (LR) method. A partial dataset excluded the phenotypes of the last generation, and 66 bulls were identified as validation individuals.Bias was considerable for BLUP (0.44) but negligible (−0.04) for ssGBLUP; dispersion was similar for both techniques . The ratio of accuracies was 0.33 for BLUP and 0.97 for ssGBLUP, indicating more stable predictions for ssGBLUP. The accuracy of predictions was 0.18 for BLUP and 0.36 for ssGBLUP. Excluding the first 10 years of phenotypic data  decreased the accuracy to 0.09 for BLUP and 0.32 for ssGBLUP. Genomic information doubled the accuracy and increased the persistence of genomic estimated breeding values when old phenotypes were removed.The LR method is useful for estimating accuracies and bias in complex models. When the population size is small, old data are useful, and even a small amount of genomic information can substantially improve the accuracy. The effect of heat stress on first parity milk yield is small. The development of a sustainable animal production system in tropical countries in the face of climate change is challenging. Heat stress is an important concern in dairy production systems in places that are always hot and humid, such as Thailand . The effet al. +an+ahtn [f(THI)i]+pn+phtn [f(THI)i]+eijklmn,yijklmn is the test-day milk yield of cow n; hmyi is the random effect of the ith combination of herd-month-year of the test ; fsj is the fixed effect of the jth combination of farm and calving season (j=1-218); bgdimk is the fixed effect of the kth combination of the breed group and the days in milk group (k=1-30); afcl is the fixed effect of the lth class of age at first calving (l=1-7); bgm is a fixed effect of the mth breed group (m=1-3); a is a general random additive genetic effect (without considering heat tolerance); aht is the random additive genetic effect for heat tolerance; p is a general random permanent environmental effect (without considering heat stress); pht is the random permanent environmental effect of heat tolerance; e is the random residual effect; and f(THI) is a function of the THI defined as follows:in which yf(THI)=The repeatability model with random regressions on the THI function captures the effects of heat stress on milk yield because it models milk yield as a function of degrees of heat stress (reaction norm approach) affecting the animals. The intercept additive genetic effect, which is independent of the THI function and represents the intercept, indicates the animal’s capacity to produce milk under thermoneutral conditions. The additive genetic effect regressed on the THI function quantifies the animal’s sensitivity to heat stress; it represents the slope of the reduction in milk yield per THI unit increase above the threshold of thermoneutral conditions . To defiThe (co)variance structure among all random effects in the model was:2 is the variance; σ is the covariance; and a, aht, p, and pht are defined as above.in which A is the numerator relationship matrix; I is the identity matrix; σAnalyses were performed with and without genomic information using ssGBLUP and Best linear unbiased prediction (BLUP), respectively. In ssGBLUP, the inverse of the pedigree relationship matrix (A) in the mixed model equations is replaced by the inverse of the realized relationship matrix (H) , which i-122 is the inverse of the pedigree relationship matrix for genotyped animals only and is G-1 the inverse of the genomic relationship matrix (G), constructed as in VanRaden [22, the elements for genotyped animals in A. The method to analyze scaling to make G compatible with A followed by Chen et al. [In which AVanRaden  using cun et al. .Estimated breeding values were calculated following :htBV=a+f(THI)*aht of one animal is −0.1 kg, and it is 0.0 kg for a second animal, the second animal has a superiority of 0.1 kg at f(THI)=1 and of 0.5 at f(THI)=5, assuming that is equal for both animals.in which is the regular breeding value, is the heat-tolerance breeding value, and f(THI) is defined as above. The effects of heat tolerance determine the change in breeding values of animals per THI unit increase. For example, if aStatistics from the LR method  were useStatistics for bias, dispersion, the ratio of accuracies, and accuracies were calculated as follows.Bias was computed as: and  are the mean estimated breeding values obtained with the partial and whole datasets, respectively; μwp has an expected value of 0 if the evaluation is unbiased.in which  on  is expected to be 1 if there is no over/under dispersion. It was computed as:The slope or dispersion of the regression of  is the covariance between breeding values obtained with the whole and partial datasets, and  is the variance of estimated breeding values obtained with the partial dataset.In which  and  as follows:An estimator of the ratio of accuracies was calculated as the correlation between The ratio of accuracies is an estimator of the consistency between subsequent evaluations, and a greater value indicates more stable predictions.The accuracy of genomic predictions was calculated as follows:in which is the average inbreeding coefficient in the individuals in the validation group, and is the additive genetic variance.2, and for the slope genetic variance of heat tolerance, it was 0.004±0.002 kg2. Therefore, considerably low genetic variation was found for heat tolerance. This is possibly because the Thai-Holstein population is composed of crossbred animals, and the local breeds that are crossed with Holstein are well-adapted to hot environments. In addition, this population has been under selection for heat tolerance since 1992. Aguilar et al. [The threshold for heat stress in this population was estimated to be a THI of 76, which means that milk yield began to decrease at this THI level. To define the threshold, analysis with the full dataset was undertaken by varying the THI values from 72 to 80. The THI level in the analysis with the lowest −2logL value was selected as the threshold . The estimate for intercept additive genetic variance at a THI of 76 was 4.89±0.03 kgr et al.  found thet al. [Statistics for bias, dispersion, the ratio of accuracies, and accuracy are presented in et al.  suggesteet al. [The ratio of accuracies is a metric of the stability of genetic evaluations. It can also be interpreted as the inverse of the relative gain in accuracy due to the addition of new phenotypes . As expeet al.  in a reset al. [In general, the LR statistics deteriorated for a THI of 80, especially the bias from BLUP predictions. This is possible because of the low genetic variance found for heat tolerance. Combining genomic information with the data helped to keep bias, dispersion, the ratio of accuracies, and accuracy at more similar levels under a THI of 76 and 80. Fragomeni et al.  showed tet al. [et al. [The accuracy of predictions is a crucial component in animal breeding programs because it has a direct relationship with the selection response. The accuracy of ssGBLUP predictions doubled compared with those from BLUP, which has the potential to increase the selection response by two-fold. The gain in accuracy due to the use of genomic information is explained by more accurate estimates of the Mendelian sampling terms . Macedo et al.  reported [et al.  showed aThere is a question as to why the accuracy doubled despite having only a few genotyped animals. First, the accuracies are relatively small because the population size is small. The genomic improvement with genomic relationships is due to the estimation of clusters of chromosome segments . With feAnother possible factor to explain the two-fold increase in the accuracy of predictions is related to the identification of “hidden relationships” in the pedigree. The percentage of animals with both parents missing was 23.1%, and with one parent missing, it was 7.2%. Therefore, animals unrelated through pedigree relationships may become related in the genomic relationship matrix, contributing to a more precise estimation of the effects of independent chromosome segments. Unknown parent groups are widely used in the modeling of missing pedigrees. In this method, breeding values of animals with missing pedigrees are adjusted according to the fraction of genes originating from each unknown parent group . The accThe statistics for bias, dispersion, the ratio of accuracies, and accuracy  in generGenomic selection could be used to improve milk yield production in the Thai-Holstein population with the potential to double genetic gain. It is important to make use of all the available information to maximize the accuracy of genomic predictions. The accuracy of breeding values for thermo-neutral and extreme environments were similar mainly because marginal genetic variation was found for heat tolerance and a small reference population dataset was used. It is important to highlight the necessity of future research, including data from more parity to explore the potential of genetic progress for heat tolerance.PS: Analyzed the data. PS, JH, DL, IM, and WB: Drafted the manuscript. PS, JH, IM, and DL: Reviewed and edited the manuscript. IM, PS, JH, and DL: Contributed to conceptualization. SB: Analyzed the samples. WB and VC: Supervised the research. All authors read and approved the final manuscript.


Pinus taeda L. (loblolly pine) clonal varieties genotyped with 16920 SNP markers were used to evaluate genomic selection for fusiform rust disease caused by the fungus Cronartium quercuum f. sp. fusiforme. The 723 clonal varieties were from five full-sib families. They were a subset of a larger population , field-tested across 26 locations in the southeast US. Ridge regression, Bayes B, and Bayes Cπ models were implemented to study marker-trait associations and estimate predictive ability for selection. A cross-validation scenario based on a random sampling of 80% of the clonal varieties for the model building had higher (0.71–0.76) prediction accuracies of genomic estimated breeding values compared with family and within-family cross-validation scenarios. Random sampling within families for model training to predict genomic estimated breeding values of the remaining progenies within each family produced accuracies between 0.38 and 0.66. Using four families out of five for model training was not successful. The results showed the importance of genetic relatedness between the training and validation sets. Bayesian whole-genome regression models detected three QTL with large effects on the disease outcome, explaining 54% of the genetic variation in the trait. The significance of QTL was validated with GWAS while accounting for the population structure and polygenic effect. The odds of disease incidence for heterozygous AB genotypes were 10.7 and 12.1 times greater than the homozygous AA genotypes for SNP11965 and SNP6347 loci, respectively. Genomic selection for fusiform rust disease incidence could be effective in P. taeda breeding. Markers with large effects could be fit as fixed covariates to increase the prediction accuracies, provided that their effects are validated further.In this study, 723  Threshold traits (dichotomous or polychotomous characters) are discrete variables when assessed in genetic studies . DichotoCronartium quercuum f. sp. fusiforme is usually assessed as a discrete dichotomous response. The pathogen has co-evolved with the host pine species in the region over millions of years  named Fr genes. A resistant single dominant locus, Fr1, was mapped by association of the phenotype with random amplified polymorphic (RAPD) DNA genetic markers in four specific families of P. taeda  markers were used in GWAS to map QTL and explore genomic selection for fusiform rust disease. In two independent clonally replicated P. taeda in the southern US, the disease incidence is assessed as a binary outcome and is analyzed as a continuous trait on liability scale  per family ranged between 1 and 238 with an average of 49. Clonal varieties were produced via somatic embryogenesis  in the southeastern US. Each site consisted of six incomplete blocks with row-column configuration. Not all the trials were established in the same year because of logistics of producing plantlets. Trials were established in seven test series starting in 2006 and were completed in 2008. The number of tests established per series ranged between 2 and 6. Test sites were strongly connected within a test series (69–396 genotypes in common), whereas the overlap between tests across series was weak. The average number of trees  per clonal variety was ∼17 trees across all the test series. The layout of the reps and number of crosses and clones differed among sites.TM AxiomTM array from ThermoFisher Scientific. SNPs loci were initially screened with a 192 sample set of diverse genotypes from across the natural range of P. taeda. Candidate SNP sequences used in the screening were developed as a part of the PINEMAP project . SNPs were selected for inclusion on the final array based on a call rate success and a minor allele frequency of 5%. Markers violating a nonzero minor allele frequency less than 0.02 and a lower than expected proportion of heterozygotes were discarded. Missing genotypes were imputed with LinkImpute software  were assessed for the incidence of fusiform rust disease  at age 6 years. For this study, 723 clonal varieties from five full-sib families were genotyped. Most of these families were related sharing a parent or a grandparent . The genotyped varieties were distributed across all 26 trials. The number of clonal varieties per family ranged between 46 and 224. For genotyping, DNA was extracted from young needle tissues of trees. A proprietary 17 K custom SNP array was developed using the Applied BiosystemsThe incidence of fusiform rust disease of 1831 genotypes was analyzed using the following generalized linear mixed model.Various variance-covariance structures were fit to the additive genetic effects  structure was fitted for nonadditive genetic effects in the model. This structure provides a single constant nonadditive genetic variance across all sites but no covariance between pairs of sites. Nonadditive genetic by environment interaction was not significant. Residual log-likelihood ratio test was used to assess the significance of additive and nonadditive genetic variances.Using the observed variance components from the above model, clone-mean narrow-sense, and broad-sense heritabilities were estimated using the derivations according to 3 = 3.29 . The modThe solutions from r), column (c) and blocking (b) effects were fit as random factors, with the same assumptions as in In the model, the ) is regressed on The additive genetic relationships were calculated using the pedigree. Realized genomic relationship matrix was calculated using the regression method according to The intercept and the slope estimated from Equation (6) were used to calculate the realized genomic relationship matrix as The relationship matrices were calculated using the AGHmatrix package in R statistical software , 0.178 (AB), and 0.009 (BB). Minor allele frequency ranged between 0 and 0.50 with a mean of 0.10. The marker matrix was used to identify clusters of genotypes into families in order to better understand the structure in the population. Spectral decomposition was performed on the marker matrix using principal component analysis in R statistical software . HeatmapWe fit the following whole-genome regression model to estimate genomic estimated breeding values (GEBV) and SNP marker effectswhere Ridge regression implementation of Equation (9)  assumes  squares .(10)β^RP. taeda , a method called Bayes Cπ (π is unknown. The effect of each SNP comes from a mixture of multivariate student’s t-distributions (0 = 0.99–0.10) to assess the completely polygenic vs oligogenic inheritance for the trait. All genomic prediction models were fit using the R package BGLR  for fusiform rust disease compared to Bayes Cπ model. The following cross-validation (CV) scenarios were implemented to test the predictive ability of all SNP markers using ridge regression and Bayes B models. A summary of the cross-validation scenarios (sampling) is given in Supplementary Table S1.Random-CV: A fivefold cross-validation scenario was performed by randomly selecting 80%  of the population as the training set and the remaining 20%  as the validation set. The random sampling was repeated 30 times. In this cross-validation scenario, the family structure and the relatedness were ignored.Family-CV: Four full-sib families out of five were used as the training set, and the remaining one full-sib family was used as the validation set. The size of the training and validation sets differed for each family-CV scenario. Depending on the full-sib families included, the training set ranged between 503 and 677 clonal varieties while the testing set ranged between 46 and 224 clonal varieties.Within-family-CV: In this scenario, 80% of each full-sib family was randomly selected to create the training set. The training population size was about the same  as random-CV scenario. The validation sets (the remaining 20% of each full-sib family) had a range of 9–45 clonal varieties, depending on the family size. Within each family, the random sampling was repeated 30 times.Predictive ability between the training and validation sets was estimated as i-th effect, Bayes B and Bayes Cπ models revealed three SNP loci with large effects. The following generalized linear mixed model was fit to estimate allelic substitution effect for significant loci detected in the Bayesian whole genome regression models.where, Fusiform rust disease incidence had an overall mean of 9.7% in the studied population. The five genotyped full-sib families had a mean of 4 to 14% disease incidence. The mean incidence varied for clonal varieties within the families, ranging between 0 and 100% .ar = 0.95) . Genotype-by-environment interaction was negligible, as shown by the high additive genetic correlations between pairs of sites ( = 0.95) .Principal component analysis of SNP marker data put 723 clonal varieties into five distinct clusters, each representing a full-sib family . The clu0 ≥ 0.90) detected three SNP loci  with large effects on fusiform rust disease incidence . The model with three SNP loci as fixed covariates while accounting for polygenic effects had a lower average standard error of pair-wise prediction (1.02) and prediction error variance (1.04) compared to the model with polygenic effects only .We validated the effects of three QTL as fixed covariates in GWAS. All three SNPs were significant at the α = 0.05 level  was 16 times higher than the odds of outcome for the homozygous haplotype AAAAAA . This homozygous haplotype comprised 28%  of the population. More than half (114) of these individuals were in family GxE and the rest were in families CxE (66) and CxD (23). Family GxE had the lowest incidence of fusiform rust disease in the population .Random-CV resulted in the highest average predictive ability estimates for Bayes B (0.71) and Bayesian ridge regression (0.76). This scenario also produced higher rank correlations and smaller mean squared error variance (better) compared to other CV scenarios . These results agree with previously published studies, reporting that among-family prediction accuracies were low due to weak genetic relatedness between training and validation sets  was another caveat of the study. Low incidence of fusiform rust disease is not surprising and has been widely reported from cloned progeny tests . Hence, we used 80% of individuals from other families to make the training population size comparable to the d trials .P. taeda. These markers should be further validated in an independent study to be considered for marker-assisted selection. Within-family genomic selection for fusiform rust incidence could be effective in P. taeda breeding. A training population that is genetically linked to the prediction population is recommended for predicting individuals within families. Bayesian models could capture loci with large effects in fusiform rust disease in southern pines and should be preferred over the whole genome regression models with uniform distribution.We identified three SNP loci with a large effect on fusiform rust disease outcome in a set of clonally replicated full-sib families of


Urinary nitrogen leakage is an environmental concern in dairy cattle. Selection for reduced urinary nitrogen leakage may be done using indicator traits such as milk urea nitrogen (MUN). The result of a previous study indicated that the genetic correlation between MUN in Australia (AUS) and MUN in New Zealand (NZL) was only low to moderate (between 0.14 and 0.58). In this context, an alternative is to select sequence variants based on genome-wide association studies (GWAS) with a view to improve genomic prediction accuracies. A GWAS can also be used to detect quantitative trait loci (QTL) associated with MUN. Therefore, our objectives were to perform within-country GWAS and a meta-GWAS for MUN using records from up to 33,873 dairy cows and imputed whole-genome sequence data, to compare QTL detected in the GWAS for MUN in AUS and NZL, and to use sequence variants selected from the meta-GWAS to improve the prediction accuracy for MUN based on a joint AUS-NZL reference set.PAEP on chromosome 11 and DGAT1 on chromosome 14. We selected 50,000 sequence variants that had the same direction of effect for MUN in AUS and MUN in NZL and that were most significant in the meta-analysis for the GWAS. The selected sequence variants yielded a genetic correlation between MUN in AUS and MUN in NZL of 0.95 and substantially increased prediction accuracy in both countries.Using the meta-GWAS, we detected 14 QTL for MUN, located on chromosomes 1, 6, 11, 14, 19, 22, 26 and the X chromosome. The three most significant QTL encompassed the casein genes on chromosome 6, Our results demonstrate how the sharing of data between two countries can increase the power of a GWAS and increase the accuracy of genomic prediction using a multi-country reference population and sequence variants selected based on a meta-GWAS.The online version contains supplementary material available at 10.1186/s12711-022-00707-9. Urinary nitrogen leakage is an environmental concern in dairy cattle . While mFor many novel traits, international collaboration can help to increase the size of the reference population for genomic prediction and thereby to increase prediction accuracy . In a reSeveral studies have shown how sequence variants selected from genome-wide association studies (GWAS) can be used to increase the accuracy of genomic prediction compared to the use of standard single nucleotide polymorphism (SNP) chips –11. SequGWAS for MUN may help to identify quantitative trait loci (QTL) in addition to aiding the selection of variants for genomic prediction. Several GWAS have reported QTL associated with MUN –17. HoweOur objectives were (1) to perform a meta-GWAS for MUN using records from up to 33,874 dairy cows and imputed whole-genome sequence data, (2) to compare QTL detected in the GWAS for MUN in AUS and in NZL, and (3) to use sequence variants to improve the prediction accuracy for MUN based on a joint AUS-NZL reference set.Table MUN in AUS was derived from milk samples using commercial equations based on wet chemistry from Bentley instruments and MUN in NZL was derived using a FOSS MilkOscan FT + analyser .Because of the heterogeneity of the crossbreds and the small number of Australian Reds and Ayrshires in the dataset, prediction accuracies were estimated for Holstein and Jersey only. All AUS and NZL Holstein and Jersey cows were randomly allocated to one of five validation folds. Validation was carried out within population: for each of the five folds, there were four validation sets of 2532 AUS Holstein, 371 AUS Jersey, 452 NZL Holstein and 305 NZL Jersey cows. For each of these validation folds, a mixed breed reference population was designed and included all the cows that were neither pure Holstein nor pure Jersey, plus the Holsteins and Jerseys that were not in the validation population.Bos taurus cattle in Run8 of the 1000 Bulls Genome Project [http://www.1000bullgenomes.com/) and the sporadic missing genotypes were imputed using the Beagle v4.1 package [2 higher than 0.9. The pseudoautosomal region (PAR) of the X chromosome was not imputed. We used only sequence variants with a Minimac imputation R2 ≥ 0.4 and a minor allele frequency ≥ 0.005, which resulted in 16,824,460 variants that were used for the GWAS and genomic prediction.All animals used in our analyses were genotyped. We used SNP genotypes from the BovineHD Genotyping BeadChip (HD) as well as imputed whole-genome sequence data for our analyses. The NZL cows and most of the AUS cows were genotyped with low-density (~ 8500) SNP panels that had an overlap of approximately 7000 SNPs with the Illumina Bovine 50K panel. A small number of the AUS cows were genotyped on the Illumina Bovine 50K panel. All raw genotypes underwent quality control checks based on the GenCall (GC) score and were mapped to the ARS-UCD1.2 reference genome . Any ani Project , 21. Seq Project , and the Project  was used package . Prior tTo account for breed effects, we used the first principal component (PC1) of a principal component analysis (PCA) performed with all AUS and NZL HD genotypes . The PCAWithin-country GWAS were carried out using GCTA . Phenoty–6 and considered that all the variants with a p-value below this threshold were significant. The false discovery rate (FDR) corresponding to a p-value threshold of 10–6 was estimated as To define QTL, we used a threshold of 10All variants with the same direction of effect in both the AUS and NZL GWAS were selected.2 threshold of 0.9, to avoid selecting redundant variants associated with the same QTL.Linkage disequilibrium (LD) pruning was performed in this selected variant set combining the genotypes of AUS and NZL animals in the reference population using the PLINK sofware  with an From the remaining variants, the 50,000 variants with the smallest p-value based on the meta-analyses were selected, to reduce the number of variants to that comparable with commonly used commercial SNP chips and to reduce computational demand. We refer to this set of variants as SEQ.Sequence variants were selected using the following steps:We selected six different sets of SEQ. The first set was done using the full dataset, and was only used to estimate genetic parameters. For genomic prediction, a new set of SEQ was selected for each fold, using only the reference population of that fold. Accuracies were then estimated for the validation population, which was not used for the selection of SEQ.Genetic parameters were estimated using the AIREMLF90 software . The folmethod 1 , \documeng (APY)  as impleng (APY)  to stay WC_HD = within-country reference population, using HD variants,WC_SEQ = within-country reference population, using SEQ variants,MC_HD = multi-country reference population, using HD variants,MC_SEQ = multi-country reference population, using SEQ variants.We estimated the accuracy of genomic prediction for the following scenarios:The same bivariate model described above was used to train the five reference populations and predict genomic estimated breeding values (GEBV) for the animals in the validation populations. Genetic parameters were re-estimated for each of the scenarios. Then, prediction accuracy was calculated as the correlation between the GEBV of the validation populations and their phenotypes corrected for fixed effects. Prediction accuracies were first obtained for each of the five validation populations, and subsequently the average of the five correlations was calculated. The bias was estimated as the regression coefficient of phenotypes corrected for fixed effects on GEBV.Figure The average within- and between-population genomic relationships of the GRM constructed with either the HD or SEQ variants were similar , and 105,135,506 bp (p = 4.8 × 10–8) in the NZL GWAS. However, the peak detected with the AUS GWAS on chromosome 14  was not present in the NZL GWAS. The meta-GWAS  gene, located at 103,271,858 bp on chromosome 11. This variant had p-values of 1.7 × 10–20, 5.4 × 10–16 and 8.8 × 10–7 in the meta-analysis, AUS GWAS and NZL GWAS, respectively. There were many significant variants on chromosome 11, of which 720 were located between 100,851,568 and 104,180,090 bp, encompassing multiple genes including progestagen-associated endometrial protein (PAEP). The QTL region detected on chromosome 6 contained 225 significant variants and spanned several genes, including casein alpha S1 (CSN1S1), casein alpha S2 (CSN1S2), casein beta (CSN2) and casein kappa (CSN3). The most significant variant was an intergenic variant located at 85,635,311 bp with a p-value of 1.9 × 10–11, 5.8 × 10–7 and 7.3 × 10–6 in the meta-analysis, AUS GWAS and NZL GWAS, respectively. The third most significant QTL interval was located between 520,394 and 633,066 bp on chromosome 14. The most significant variant, at 525,863 bp, was a downstream variant in ENSBTAG00000053637 with p-values of 6.6 × 10–11, 9.3 × 10–19 and 9.3 × 10–1 in the meta-analysis, AUS GWAS and NZL GWAS, respectively. The QTL region on chromosome 14 contained 102 significant variants and included the diacylglycerol O-acyltransferase homolog 1 (DGAT1) gene. Additional file Figure –17 to 0.019.Figure Selecting variants within the reference populations designed for the cross-validation process resulted in five different sets of 50,000 variants each. Additional file Table In the bivariate analyses to estimate the genetic correlation between MUN in Australia and MUN in New Zealand, a core set of 9157 animals was required to explain 95% of the variance of Figure Using the SEQ variants instead of the HD variants resulted only in minor changes in accuracy using the within-country reference populations. However, the SEQ variants did substantially increase the accuracy in all populations using the multi-country reference population. Averaged across populations, accuracies obtained with the SEQ variants were equal to 0.20 using a within-country reference population and 0.39 using a multi-country reference population.The SEQ variants increased the bias substantially when a within-country reference population was used Fig. . For exaWe detected several QTL for MUN and showed how sequence variants that were selected based on GWAS for MUN can be used to substantially increase the prediction accuracy for MUN in AUS and NZL when using a multi-country reference population.DGAT1 gene, a major QTL associated with fat yield in dairy cattle [Interestingly, while several peaks were present in both the AUS and NZL GWAS, the major peak in the AUS GWAS that was on chromosome 14 was not present in the NZL GWAS. The relatively low genetic correlations between MUN in AUS and in NZL  indicatey cattle . MUN in y cattle , which m–6 in the meta-analysis. The largest three QTL intervals, on chromosomes 6, 11 and 14, all included well-known QTL associated with milk traits in dairy cattle [CSN1S1, CSN1S2, CSN2 and CSN3, which are associated with milk production traits [GLT6D1 gene as the most significant variant. GLT6D1 is associated with periodontitis in humans [PAEP, a gene that is assumed to underlie a well-known QTL associated with milk production traits in dairy cattle [alpha 1-3-N-acetylgalactosaminyltransferase and alpha 1-3-galactosyltransferase (ABO), which determines blood type in humans [ABO and a QTL for protein yield and protein percentage in dairy cattle. The most significant variant on chromosome 14 was a downstream variant in ENSBTAG00000053637, and that QTL interval included DGAT1, a major QTL for milk production traits in dairy cattle [stearoyl-CoA desaturase (SCD) gene that has previously been associated with MUN [We detected 14 QTL located on eight chromosomes with p-values ≤ 10y cattle –35. The y cattle . While tn traits , 34. Then traits . This inn humans . The QTLn humans  and has n humans . van denn humans  reportedy cattle . The moswith MUN  and milkwith MUN .Several previous studies have demonstrated that sequence variants selected from a GWAS can be used to improve the accuracy of genomic prediction –11, as wBoth the heritability and genetic correlation between AUS and NZL were substantially higher with the SEQ than the HD variants. A possible explanation for this observation could be that the SEQ variants may be closer to the causal mutations for MUN than the HD variants, resulting in a larger proportion of the genetic variance being captured by the SEQ variants than by the HD variants. Alternatively, several studies have shown that using genomic markers to estimate heritability can lead to upward biased estimates compared to heritabilities estimated using pedigree data , 43. TheWhile the genetic correlation between populations increased using SEQ variants, the genomic relationships in the SEQ GRM were largely similar using either the HD or SEQ variants. Larger differences were observed for Ayrshires and Australian Reds, but their small datasize makes it difficult to interpret these differences, and exclusion of Australian Reds and Ayrshires in the genomic prediction analyses did not have a substantial impact on the prediction accuracy using either HD or SEQ variants . Interestingly, the genomic relationships between AUS and NZL populations indicated that while the genetic links between AUS and NZL Holsteins and crossbreds are small, the AUS and NZL Jerseys showed stronger genomic relationships.Although the results using the SEQ variants for multi-country prediction are promising, we tested our approach only for the specific purpose of combining AUS and NZL data for MUN. Hence, results may be different for other traits and when combining different populations, such as multiple breeds, and further testing is required to investigate whether the strategy used to select SEQ variants can help increase prediction accuracy for a wider range of traits and populations.The discovery populations used to select the SEQ variants were the same as the reference populations used for genomic prediction, and reference and validation populations were highly related due to the design of the cross-validation scheme and the available data for this study. For example, it was possible for half sisters to be present in both the reference and validation populations in the same fold, which likely resulted in higher accuracies compared to cross-validation designs where reference and validation populations are less related. The promising results that we obtained in terms of increased genetic correlation and genomic prediction using a meta-GWAS and a joint reference population of AUS and NZL cows need to be assessed when validation cows are selected based on date of birth and when validation cows that are less related to the reference population are used. Furthermore, it will be useful to confirm our results by selecting variants from a dataset that does not include animals in the reference population used for genomic prediction as recommended in . We did Our results demonstrate how, in spite of the low genetic correlation between MUN measured in two countries using a standard SNP chip, sharing data from different sources internationally can be beneficial to increase the power of a GWAS and to increase the accuracy of genomic prediction using a multi-country reference population and sequence variants selected based on GWAS. Our approach of selecting sequence variants to increase prediction accuracy may be useful for multi-breed and multi-country prediction, but first this needs to be tested on a wider range of traits and populations.Additional file 1: Table S1. Genomic relationships using HD and SEQ variants. Average genomic relationships within  and between populations  of genomic relationship matrices constructed using high-density (HD) or selected sequence (SEQ) variants; AUS = Australia, NZL = New Zealand.Additional file 2: Table S2. Genes located within QTL regions. chr = chromosome, start and end = start and end of QTL region in base pair (bp) on the ARS-UCD1.2 annotation, genes = list of genes located within QTL region.Additional file 3: Figure S1. Overlap between sets of sequence variants selected in cross-validation folds. Ref1, ref2, ref3, ref4 and ref5 are the five reference populations that were used to select five sets of 50,000 sequence variants with the same direction of effect for MUN in AUS and NZL that were most significant in the meta-analysis for the GWAS five sets of variants, and the numbers represent the overlap between the different sets of variants.Additional file 4: Table S3. Composition of the animals in the core set. Number of animals of each population included in the core set.Additional file 5: Table S4. Genomic prediction accuracies for each of the five folds. validation = validation population, reference = reference validation, fold1-5 = prediction accuracy in fold 1–5, AUS_HOL = Australian Holstein, AUS_JER = Australian Jersey, NZL_HOL = New Zealand Holstein, NZL_JER = New Zealand Jersey, WC = within country reference population, MC = multi country reference population, HD = variants on the Illumina Bovine HD BeadChip, SEQ = selected sequence variants.


Brassica juncea (L). Czern & Coss to recognize promising genotypes for effective breeding. Two hundred and eighty-nine diverse accessions of Indian mustard belonging to four continents were analyzed for yield and yield-related traits (20 traits) over two seasons (2017–2018 and 2018–2019) using an alpha lattice design. The genetic variance was found to be significant (P ≤ 0.01) for the individual and under pooled analysis for all of the evaluated traits, demonstrating the presence of significant genetic variability in the diversity panel, which bids greater opportunities for utilizing these traits in future breeding programs. High heritability combined with high genetic advance as percent of mean and genotypic coefficient of variation was observed for flowering traits, plant height traits, seed size, and seed yield/plant; hence, a better genetic gain is expected upon the selection of these traits over subsequent generations. Both correlation and stepwise regression analysis indicated that the main shoot length, biological yield, total seed yield, plant height up to the first primary branch, seed size, total siliqua count, days to flowering initiation, plant height at maturity, siliquae on the main shoot, main shoot length, and siliqua length were the most significant contributory traits for seed yield/plant. Also, promising genotypes were identified among the diversity panel, which can be utilized as a donor to improve Indian mustard further. These results indicated a greater scope for improving seed yield per plant directly through a selection of genotypes having the parsimonious combination of these nine traits.The response to selection in any crop improvement program depends on the degree of variance and heritability. The objective of the current study was to explain variance and heritability components in Indian mustard  Brassica juncea (L.) Czern & Coss (AABB) is the second most important edible oilseed crop in India after the soybean. B. juncea is a natural allotetraploid of two diploid species viz., Brassica rapa (AA) and Brassica nigra (BB). Rapeseed is a key species from the Brassica genus and a high-value crop for oil and biofuel industries. In India, during 2018–2019, rapeseed–mustard was cultivated over an area of 5.96 million hectares with production and productivity of 8.32 million tons and 1,397 Kg/ha, respectively  to use restricted maximum likelihood/BLUP-based method to assess variance, genetic parameters, and genotypic performance of mustard genotypes in multiyear trials, (ii) to fit stepwise regression model for identifying highly significant traits to form a path diagram that explains the relationship of cause and effect among seed yield-related traits, and (iii) to group the germplasm lines sharing a common attribute based on Mahalanobis distance. This study unravels the nature of genetic variability in Indian mustard and would be helpful in the selection of superior genotypes for yield and related traits, which further augment the ongoing and future mustard breeding programs.B. juncea germplasm obtained from Punjab Agricultural University, Ludhiana, under Indian Council of Agricultural Research (ICAR)—National Agricultural Science Fund-sponsored project, including varieties, cultivars, introgression lines, derived lines, and exotic and indigenous collections from the diverse origin such as from India, Australia, Europe, Germany, and Canada were evaluated for phenological and morphological traits under timely sown irrigated conditions. The details of the germplasm accessions used are presented in Two hundred and eighty-nine diverse accessions of 2. Five representative plants from each treatment were selected from the middle two rows for evaluation of agronomic performances. Recommended agronomic practices were followed in both seasons. Data of five characters viz. days to flowering initiation (DFI), days to 50% flowering (DFF), days to 100% flowering (DHF), days to flowering completion (DCF), and days to maturity (DMT) were recorded on a plot basis. Morphological data of 15 characters recorded on five plants each include plant height at flowering (PH_Fl), plant height up to the first primary branch (PH_FPB), plant height at maturity (PH_M), number of primary branches (PB), number of secondary branches (SB), main shoot length (MSL), siliquae on the main shoot (SMS), total siliquae count (TSC), siliqua length (SL), seeds per siliqua (SPS), seed size (SS), seed yield/plant (SY/Plant), total seed yield/plot (TSY/Plot), biological yield/plot (BY/Plot), and harvest index (HI).Trials were laid out in a randomized alpha lattice design with two replications; each plot consisted of four rows of 2-m length in a plot size of 2.4 m2g, σ2gy, and σ2e) for each of the 20 traits were calculated using the restricted maximum likelihood  in lme4 R-package . The beskelihood  estimatii is the fixed effect of year i; gj is the random effect of genotype, j and is ∼NID; rik is the random effect of replication, k in year i and is ∼NID; bikl is the random effect of block l nested with replication k in year i and is ∼ NID; (gy)ij is the random effect of the relations between genotype j and year i and is ∼NID; and εijkl is random residual effect and ∼ NID. Diagnostic residual plots were used to check the normality and homogeneity of the response variable. If the residuals from the fitted model did not meet the assumptions, data were subjected to transformation. This final model was utilized to generate the BLUP for each genotype.where μ is the grand mean; yr) between BLUP values for each pair of genotypic traits was estimated using the “corrr” package  values of 20 characters pooled over the years, and the residual variance–covariance matrix generated using vcov function of lme4 package was subjected to grouping using the D2 statistic according to The heritability parameter across the year was estimated by analysis of variance using the ratios of Meteorological observations recorded during the season (October–March) are represented in p-values in figures. The analysis indicated a significant mean difference between seasons for each trait. The mean values of all traits except siliquae on the main shoot and harvest index were slightly higher in the season 2017–2018 than 2018–2019  ranging from 1.80% for days to maturity to 30.0% for harvest index under normal sown conditions. High CV for some of the yield traits such as TSC, SY/P, TSY/Plot, BY/Plot, and HI was due to longer duration and photoperiod sensitivity of some Canadian accessions—CN-105305, CN-34005, and CN-34008. Most of the traits were approximately normally distributed apart from flowering traits and PH_FPB . Also, t018–2019 . The meaP ≤ 0.01) for all the studied traits for both years, representing the presence of considerable genetic variability in the germplasm for all the traits. Significant G × E interactions were observed for all the traits during pooled analysis, suggesting different genotypes response to environmental conditions differentially.Significant effects of the sources of variation are presented in 2g), G × E variance (σ2gy), and error variance (σ2e). Furthermore, genotypic and G × E variances were compared with total phenotypic variance to identify the magnitude of genotypic contribution for Brassica improvement. In the present study, most of the traits were highly heritable (>0.60) as per the scale of Robinson (1966) in combined environments except SB and HI, which showed low heritability (>0.30). Similarly, PB, TSC, SPS, TSY/Plot, and BY/Plot showed moderate heritability  with total seed yield per plot (r = 0.43), seed size (r = 0.37), main shoot length (r = 0.44), siliquae on main shoot (r = 0.37), siliqua length (r = 0.31), seeds per siliqua (r = 0.15), total siliquae count (r = 0.33), number of secondary branches (r = 0.22), biological yield per plot (r = 0.36), and harvest index (r = 0.17). Seed yield per plant exhibited a significant negative correlation with flowering characters such as days to 50% flowering (r = −0.38), days to 100% flowering (r = −0.39), days to flowering completion (r = −0.40), days to flowering initiation (r = −0.35), days to maturity (r = −0.28), and plant height characters such as plant height at flowering initiation (r = −0.34), plant height up to the first primary branch (r = −0.42), and plant height at maturity (r = −0.20), whereas number of primary branches showed a nonsignificant relationship.The utility of independent traits in the selection can be expected by their significant association with seed yield (dependent trait). In the present study, genotypic correlations between 20 character pairs were studied in all possible combinations . The priR2 value of 0.46  followed by siliquae on the main shoot (ρX4 = 0.225) and seed size (ρX5 = 0.215). However, this trait had an important negative direct effect through plant height up to the first primary branch (ρX2 = −0.185) and plant height at maturity (ρX9 = −0.150). Siliqua length and biological yield/plot showed a positive indirect effect on yield . However, days to flowering initiation (ρ = −0.350) and plant height up to the first primary branch (ρ = −0.235) contributed with negative indirect effects on seed yield. The present study also entrusted to identify promising genotypes for significantly associated traits identified by regression and path analysis , IV, and V (one genotype each). Again, cluster I was found to have around 26 subclusters (a to z) at Mahalanobis D2 value of 20.0 (B. juncea. Subcluster Ic consists of a breeding line from east European countries (2), and subcluster I (d–i) is dominated by high-yielding cultivars and advanced breeding lines of Indian origin. Most of the indigenous collection (IC) based on B. juncea var. rugosa and Canadian gene pool got together in subcluster I (j–z)  at Mahalanobis  of 60.0 . D2 dist of 20.0 . Subclus I (j–z) .The relative contribution of each character indicated that seed size (13.14%), days to 50% flowering (10.85%), and siliqua length (7.88%) contributed maximum toward total divergence . All theAlthough genotypes in cluster I were from the different geographical locations, genotypes belonging to the same location or same breeding program that tend to fell together in the same subclusters such as DRMRIJ-17 series, i.e., 17-41,17-42, 17-43, and 17-52 derived from ICAR-Directorate of Rapeseed and Mustard Research, Bharatpur, fell on subcluster Id of cluster I. Similarly, the indigenous collections from Arunachal Pradesh, such as IC-597870, IC-597871, and IC-597881 (subcluster Io) and IC-597904 and IC-597949 (subcluster In), and most of the Canadian gene pool from subclusters I (t–y) followed the same pattern as the DRMRIJ series. In contradictions to the observation discussed earlier, a few accessions collected from the same region, such as Canadian germplasms, did not fell in a single cluster or subcluster, indicating that geographical proximity does not always result in genetic similarity.D-values ranged from 8.30 to 15.68 ; therefore, intercrossing the genotypes from these two clusters may create wider variability and is estimated to throw high yielding transgressive segregants in the mustard breeding program. The minimum inter-cluster D-value (8.30) detected between clusters I and IV showed the higher genetic similarities between these clusters. Intra-cluster distance (D) revealed that cluster II showed maximum intra-cluster distance (6.29) followed by cluster III (4.68). Owing to solitary genotype, clusters IV and V did not show intra-cluster distance. The magnitudes of the intra-cluster distances were not always proportional to the number of genotypes in the cluster, as intra-cluster distance in cluster I was found to be moderate (4.12) regardless of maximum genotypes (281).Based on the similarity matrix, the distance between clusters, inter-cluster to 15.68 . The higThe exploitation of genetic diversity present in a species can lead to the improvement of traits of the economic importance of mandated crops with the intervention of plant breeders to benefit the farmers and consumers . Yield iBrassica juncea is a major oilseed crop of Rabi (winter) season and is highly sensitive to weather parameters such as temperature and solar radiation, which affect growth, phenological events, and crop yield  resulted in subsequent yield loss.op yield . Intermiop yield . The senB. juncea cultivar. The greater variability observed in the present study could be due to the use of genotypes from diverse geographical origins. The lowest coefficient of variation for the number of days to maturity (1.80%) showed its best genetic potential and genetic influence, whereas the highest coefficient of variation for harvest index (30.0%) showed more influence of environmental fluctuations  for seed yield.The analysis of variance revealed highly significant differences for all the characters representing the presence of variability, which can be utilized through genetic improvements. Significant variance due to G × E interactions for all the 20 characters confirmed that the genotypes respond differently in diverse environments. Therefore, it is possible to exploit different environments by developing environmentally specific varieties from the diversity panel. The importance of G × E interactions had also been observed by The observed variation in a population may be either due to genetic or environmental or both. Only those due to genetic components remain heritable. Heritability alone does not infer the estimate of the actual amount of genetic gain in the selection program, as it is also inclusive of nonadditive genetic factors . All theper se in the Brassica breeding program  in pooled analysis with a high GA of 60.8% indicated that the results would be repeatable and rewarding over generations of selection cycles, which is a boost for the breeding program. This concurred with previous studies . These r program .per se may be ineffective for such traits as harvest index in the present study. None of the traits exhibited low heritability with low genetic advance.Estimates of heritability for yield component traits varied from low (22%) to high (85%). There is a need to identify the traits that should be targeted for improving the seed yield in mustard. Flowering traits, plant height traits, and seed size showed high heritability and high GA, as few of them are governed by a few major quantitative trait loci reported earlier by Higher PCV values than their corresponding GCV for most of the traits showed the considerable role of environment in the expression of these traits; therefore, the variation in the genotypes is due to both genotype and the environment . The widCorrelation analysis indicated that seed yield per plant was significantly correlated with biological yield/plot, total seed yield/plot, seed size, plant height up to the first branch, total siliqua count, days to flowering initiation, plant height at maturity, siliqua length, and siliqua on the main shoot, which implies that selection in improving these traits would lead to improved seed yield . Genotypviz. total siliquae count (r = 0.57) and biological yield/plot (r = 0.33). Therefore, the selection of genotypes with higher siliqua count and biological yield/plot would indirectly improve the number of secondary branches per plant in successive generations. This is in accordance with the findings of For instance, the number of secondary branches with very low heritability was significantly positively correlated to high heritability traits R2) of the dependent variable  coupled with high GA% of the mean (>20%). These traits also showed a highly significant (p ≤ 0.001) correlation with seed yield/plant. Specific direct and indirect effects of traits and relative importance of each trait in determining the key goal, i.e., seed yield, was accompanied through path coefficient analysis . Path anD2 distance over the Euclidian distance is that it can take account of the correlation between a highly correlated variable and can scale the contribution of individual variables to the distance value according to the variability of each variable  was much higher than earlier reports related to D2 clustering studies by The advantage of genetic diversity analysis based on Mahalanobis variable . The MahBroad variability in the current material holds great promise to use these genotypes from different clusters (such as II and IV) for future breeding programs. The highest-yielding genotype (IC-597867) identified in the present study was present in cluster IV, whereas genotypes with a high harvest index were contained in cluster II. So, the improved cultivars from cluster I can be utilized to exploit the genotypes present in clusters II and IV for further yield increments and genetic diversification through hybridization.Brassica improvement program. Flowering and plant height-related traits were found to be more heritable, although these were negatively correlated with yield. High PCV, GCV, and genetic advance values with low to moderate heritability were observed for total seed yield, which indicated that the yield performance still needs to be improved to produce superior varieties. Moreover, the current study could aid breeders in enhancing the seed yield by considering the traits that have a good correlation with seed yield. Cluster analysis revealed that genotypes under study were more diverse, which could be utilized for future hybridization programs, and it can release transgressive segregants for economic trait improvement. The greater variability among the studied genotypes and the association between seed yield and secondary traits in the current study suggested the exploitation of superior genotypes in the near future.The study assessed a diversity panel representing 289 genotypes across four continents for the existence of genetic variability for seed yield and yield-related traits over two seasons. The results revealed an enormous genetic variability for the traits under study, which can be exploited to acquire further breeding gains. The use of BLUP values for genotypes provided higher selection accuracy by reducing residual error, which permitted the identification of potential genotypes for the The original contributions presented in the study are included in the article/DY conceptualized, supervised the research, and contributed to reviewing the manuscript. RS and SLS performed the field trials and prepared the original draft manuscript. SS performed first-year field trial. SM conducted the data analysis, curated the data, and contributed in reviewing the manuscript. RC helped in field trials and data entry. YP and NS coordinated the study and revised the manuscript. SV administered the project and revised the manuscript. All authors read the manuscript and agreed with its content.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.