The method of regional heritability mapping (RHM) has become an important tool in the identification of quantitative trait loci (QTLs) controlling traits of interest in plants. Here, RHM was first applied in a breeding population of popcorn, to identify the QTLs and candidate genes involved in grain yield, plant height, kernel popping expansion, and first ear height, as well as determining the heritability of each significant genomic region. The study population consisted of 98 S1 families derived from the 9th recurrent selection cycle (C-9) of the open-pollinated variety UENF-14, which were genetically evaluated in two environments (ENV1 and ENV2). Seventeen and five genomic regions were mapped by the RHM method in ENV1 and ENV2, respectively. Subsequent genome-wide analysis based on the reference genome B73 revealed associations with forty-six candidate genes within these genomic regions, some of them are considered to be biologically important due to the proteins that they encode. The results obtained by the RHM method have the potential to contribute to knowledge on the genetic architecture of the growth and yield traits of popcorn, which might be used for marker-assisted selection in breeding programs.  Zea mays L.) is among the most important cereal crops in the world in addition to wheat and rice [Maize  based on the significant associations between genotypic markers and phenotypic data . The ideThe SNP has been widely used in association studies for the detection of a large number of QTLs and candidate genes involved in the yield and growth ,14,15. DResearch in human genetics has shown that multiple independent loci with different frequencies and allelic effects are commonly located in the same gene region or short-segment regions ,20. ThesTo overcome this drawback and capture most of the genetic variance that cannot be captured by association studies, recent studies have proposed a method called regional heritability mapping (RHM) ,17, whicEucalyptus [Phaseolus vulgaris L.) [Manihot esculenta Crantz) [Eucalyptus and observed that RHM allowed the identification of 13 more QTLs than association analysis. In addition, the authors observed that RHM outperformed the genome-wide association study analysis for all traits evaluated, capturing, in general, two or three times the amount of genomic heritability. Similarly, RHM allowed a greater proportion of genomic heritability to be explained compared to association analysis, in accessions of Phaseolus vulgaris L. [To date, studies based on RHM analysis are not well consolidated for plant species, since most research using this type of methodology has been developed to identify QTLs related to complex traits in humans and animals ,22,23. Tcalyptus , in commaris L.) , and cas Crantz) . Resende Crantz)  comparedgaris L. .To the best of our knowledge, this is the first study to apply RHM to understand the genetic basis of complex traits in popcorn. The objectives of this study were to identify genomic regions and candidate genes associated with growth (plant height and ear height), popping expansion, and grain yield of popcorn, as well as to examine the genomic heritability attributed to these traits, using regional heritability mapping with a mixed model approach.p-value < 0.001) in ENV1 and ENV2, with values of r = 0.21 and 0.26 for EH-GY , r = 0.80 and 0.76 in EH-PH  and r = 0.24 and 0.31 in PH-GY .The average values for ear height (EH), plant height (PH), and grain yield (GY) differed between the two environments, with EH and PH higher values in ENV2  to ENV1, while GY in ENV1 was 20% higher than ENV2 . On the 21,442 polymorphic loci were identified among the maize plants from the breeding population throughout the Capture Seq method . IndividThe pattern of LD was estimated for each chromosome considering all SNPs belonging to the same chromosome. The slowest LD decay was observed in chromosome 8, in which half of the decay occurred at ~151 Kb (kilobase pairs), while chromosome 4 decreased the fastest, in which half of the decay was observed at ~76 Kb. The average half of the LD across the chromosomes was ~110 Kb ± 6.82 . This rep-value ≤ 0.001, or −log10  ≥ 3) found in ENV 1 and ENV 2, respectively.Seventeen QTLs were found by RHM in ENV1, each encompassing between 2 and 8 SNPs on chromosomes 1, 2, 4, 5, 6, and 8, while in ENV2, five QTLs were mapped, which ranged between 2 and 9 SNPs on chromosomes 2, 4 and 7 . In ENV1As the main findings by RHM in both environments, possible candidate genes were identified based on the physical position of reference genome B73  and regiHalf of the LD decay varied among chromosomes (76–151 Kb), which suggests differences in their rate of recombination . The linHeritability estimated based on RHM was low for most traits and in both environments , with EN1 and F2 and using the maximum likelihood approach by interval mapping for low heritability QTLs and high SNP density, obtained a value of approximately 95% heritability for GY when using a sample number of 200 [In ENV1, for GY—one of the key traits of interest in popcorn—a series of heritability values were obtained, which ranged from 11% to 93%. This variation may be due to population effects, environmental factors, or experimental precision , or, morr of 200 .Phaseolus vulgaris L, demonstrating that the RHM method was effective in detecting the hereditary portion of the trait in the population.Previous studies have reported that the trait heritability of GY varies from low to moderate ,67, indiOn the other hand, in ENV1, the inheritable proportion of total variability of PH and EH traits was the lowest . Some hyAmong the QTLs observed in ENV1, some genes were considered to be biologically important due to their encoded proteins and proven participation in the studied traits.The GRMZM2G069618 gene reported to be involved in the expression of GY encodes the protein containing the tetratricopeptide repeat domain (TPR). The TPR is involved with numerous outstanding functions in plant organisms, for example, hybrid sterility in rice , hormonaThe GRMZM5G846057 gene, linked to the GY trait, is related to the AP2 domain (AP2) protein. APETALA2/ethylene proteins (AP2/EREBPs) are the main regulators of responses to plant development, growth, and stress ,78. AP2/The GRMZM2G414114 gene, which is related to the expression of the protein DNAj (??) domain//TCP family transcription factor//Transposase-associated domain, is involved with grain yield. This protein, called TCT, refers to transcription factors observed in plants and is involved in the growth and development of several species , includiThe GRMZM2G051958 gene, responsible for coding the enzyme phosphoenolpyruvate carboxykinase ATP (PEPCK), is involved in the expression of the trait PE. Studies report that this protein plays a significant role in the photosynthesis of C4 plants of the Poaceae family, in which corn is inserted , as wellGRMZM2G069618, GRMZM2G414114, and GRMZM2G051958 encode proteins that would directly affect GY by regulating the weight and size of grains, increase in the photosynthetic rate, and the physical and chemical properties of popcorn. Moreover, these genes are involved in the growth and development of plants, and the development process of popcorn kernels, key aspects in the GY. Particularly, GRMZM2G051958 has shown a significant increase in the grain yield in wheat through an increase in the photosynthetic rate. On the other hand, GRMZM5G846057 encodes a protein AP2, which regulates the GY in conditions of abiotic stress. In this sense, Xu et al.  overexprDigitaria ciliaris and Oryza sativa [A relationship between PH and the GRMZM2G133175 gene, which encodes a cysteine-rich TM module stress tolerance (CYSTM), was observed. The cysteine-rich transmembrane module CYSTM, commonly found in eukaryotes, is composed of a small family of molecular proteins . The CYSa sativa , and resa sativa . Xu et aa sativa  suggest in Arabidopsis thaliana [In ENV2, the GRMZM2G060630 gene, which encodes the mitochondrial phosphate transporter (MPT) member 3 protein, was related to EH. Mitochondrial phosphate transporters have been identified as responsible for plant responses to salt stress thaliana  and indithaliana . This lethaliana  suggesteThe RHM approach to the analysis of SNP data has the potential to explain part of the missing heritability by capturing QTL variance from small regions of the genome, which escape the standard association, single SNP-based studies. For example, Mafra et al. , in thisThe study was carried out in August 2016, at the Research Station of the Antônio Sarlo State College of Agriculture , in the county of Campos dos Goytacazes (Rio de Janeiro—RJ) (hereafter ENV1), and on an experimental field of the State University of Northern Rio de Janeiro—UENF (21°38′45.2 S 42°03′16.3” W), on the Ilha Barra do Pomba, county of Itaocara (Rio de Janeiro—RJ) (henceforth ENV2). The ENV1 has a dry tropical climate with an average annual temperature around 24 °C and annual precipitation of 1112 mm , while EThe study population consisted of 98 S1 families derived from the 9th recurrent selection cycle (C-9) of the open-pollinated variety UENF-14 ,97,98, wThe cycles of recurrent selection to obtain the studied population were obtained through the strategies described in The tests were designed in incomplete blocks with three orthogonal repetitions. Seeds of each family were sown in a 5 m long row, at a spacing of 0.90 m between rows and 0.20 m between plants. When necessary, cultural treatments were carried out as recommended for the crop.−1. PE was measured by weighing 30 g kernels per plot on a precision scale; then, these were inserted into kraft paper bags and microwaved for 2 min 20 s. Afterward, the popped kernels were filled in 2000 mL beakers, and the resulting volume was divided by the initial kernel weight (30 g of grains). PH was measured from the soil level to the flag leaf insertion in six healthy plants per plot (with a ruler). Similarly, EH was measured in six healthy plants per plot, from the soil level to the insertion of the first ear (with a ruler). Data from the phenotypic evaluations are shown in Z1,2 are incidence matrices of random effects.At both locations, the main traits of agronomic interest for the crop were measured: grain yield , kernel popping expansion , plant height , and the first ear height . GY was measured after the threshing process of all ears in the plot, where the weight  in Kg of the kernels per plot was transformed to kg haY and PE ; b is a LSMeans) ; e is thGenomic DNA was extracted from the young leaves of 200 plants, from the breeding population, using the standard CTAB method of Doyle and Doyle  with modr2 statistics, using PLINK software [From the panel of 10,507 SNPs and 196 individuals, the Genetic Relationship Matrix (GRM) was calculated, with the rrBLUP package , based osoftware . To analsoftware  was fittsoftware .1 is the vector with fixed effects—intercept, repetition and covariables such as the number of plants per plot, counted immediately after thinning and kernel moisture for the traits GY and PE; b is the vector of block effects; r is the vector of random regional genomic additive effects; u is the vector of polygenic effects; e is the residual effect vector of the model. X is the incidence matrix of systematic fixed effects, and Z1,2,3 are incidence matrices of random effects.Regional heritability mapping provides heritability estimates for genomic segments containing common and rare allelic effects . The RHMG is the genomic matrix of kinship, calculated with the rrBLUP package [Greg is a matrix similar to G, but uses a subset of matrix W ). These subsets were determined by 100 kb long genomic “windows” or “regions”, with overlapping stretches of 50 kb , corresponding to the estimated LD—see Results section); b, r, u, and e, respectively. These components were estimated by REML, using the average information (AI) algorithm. RHM was adjusted by the Regress package [p-value ≤ 0.001, or −log10  ≥3.The following was assumed: package , based o package ; Greg is package , in lang package , accordi package . Signifir, g, and e, respectively.For each segment of the genomic window, the regional heritability was determined by the following equation:https://phytozome.jgi.doe.gov/pz/portal.html, (accessed on 10 November 2020)). The gene search regions were defined according to the start and end position of each region. The genes detected within these regions were defined as candidate genes.The QTLs identified by RHM analysis were subjected to gene identification analysis, using the public maize genome data set, based on the reference genome B73 version 3 (gene annotation in the B73 Zea mays AGPv3 assembly) . FunctioRHM allowed us to combine common and rare variants within the same analysis, which provided valuable information on the genetic architecture of yield and growth traits. It was confirmed that RHM has the potential to explain some portion of missing heritability by capturing variance caused by QTL with low MAF and multiple independent QTL within a region, as determined for grain yield. Moreover, the genomic regions identified by RHM allowed further examination towards candidate genes discovery. In this regard, a total of 22 genomic regions were identified in both tropical environments, which may be related to the adaptability of the genotypes to each environment. Subsequent gene identification study revealed associations with several genes with a known function in plants. Forty-six candidate genes were associated with these genomic regions, of which seven are considered to be biologically important, encoding proteins that participate in plant development processes, and suggesting different roles in the performance of functions related to popcorn growth and yield. The QTLs and candidate genes detected in this study broaden the knowledge on the molecular basis of these important agronomic traits and could potentially help to increase the efficiency of popcorn breeding programs, creating new opportunities for the development and selection of elite breeding genotypes.


Oxytocin is a neuromodulator and hormone that is typically associated with social cognition and behavior. In light of its purported effects on social cognition and behavior, research has investigated its potential as a treatment for psychiatric illnesses characterized by social dysfunction, such as schizophrenia and bipolar disorder. While the results of these trials have been mixed, more recent evidence suggests that the oxytocin system is also linked with cardiometabolic conditions for which individuals with severe mental disorders are at a higher risk for developing. To investigate whether the oxytocin system has a pleiotropic effect on the etiology of severe mental illness and cardiometabolic conditions, we explored oxytocin’s role in the shared genetic liability of schizophrenia, bipolar disorder, type-2 diabetes, and several phenotypes linked with cardiovascular disease and type 2 diabetes risk using a polygenic pathway-specific approach. Analysis of a large sample with about 480,000 individuals (UK Biobank) revealed statistically significant associations across the range of phenotypes analyzed. By comparing these effects to those of polygenic scores calculated from 100 random gene sets, we also demonstrated the specificity of many of these significant results. Altogether, our results suggest that the shared effect of oxytocin-system dysfunction could help partially explain the co-occurrence of social and cardiometabolic dysfunction in severe mental illnesses. Schizophrenia (SCZ) and bipolar disorders (BD) are associated with reduced life expectancies , 2, partIn addition to lifestyle habits such as smoking, poor diet, and a lack of exercise , some anEmerging evidence suggests that oxytocin-system dysfunction might play a pleiotropic role in the etiology of psychotic disorders and MetS risk factors –18. OxytOxytocin-pathway gene variants have been associated with features of psychotic disorders, such as social cognition and emotional processing , 33. TheOXTR) or CD38, which regulates the secretion of oxytocin [A common method for examining the relationship between oxytocin gene variants and phenotypes of interest is to use candidate gene approaches, typically investigating the oxytocin-receptor gene  from the Max Planck Institute for Molecular Genetics website (cpdb.molgen.mpg.de)  and extroxt) using PRSice-2 (version 2.3.3) [oxt was based on a meta-analysis of the European-ancestry subset of the 2020 Psychiatric Genomics Consortium schizophrenia GWAS [oxt was based on a meta-analysis of the European-ancestry subset of the 2019 PGC bipolar disorder GWAS [oxt scores were calculated from thresholds ranging from 5 × 10−8 to 1, in increments of 0.001. Using a permutation approach included in PGSice-2 [p < 0.05 for schizophrenia (including 427 SNPs), p < 0.02 for bipolar disorder (including 198 SNPs), and p < 0.4 for T2D (including 1107 SNPs) and used PGSs computed at these thresholds for the main analysis, after a transformation to z-scores. We also mapped the SNPs included in each PGSoxt (using the -print-snp flag in PRSice) using the snp2gene component of FUMA [Supplementary notesoxt  , one eacn 2.3.3) . The schnia GWAS , the bipder GWAS , and theder GWAS . For eacPGSice-2 , we dete of FUMA  and subs of FUMA , 58 usin.3 , one oxt), as in the main analysis (PC1oxt and PC2oxt). The first PC reweights the variants included in the PGS to achieve maximum variation over all PGS thresholds used  on the whole range of PGSs computed for each GWAS, following previously used methods –61 and tThe following metabolically relevant variables were considered Table : anthropoxt’s, controlling for sex and age [oxt on a dependent phenotype changes, depending on the value of one or more other variables. For dichotomous variables, we tested their independence using Pearson’s χ2 test and then fitted logistic regression models, also controlling for sex and age [https://osf.io/gpdxm/.All statistical analyses were done using R version 4.0.0 2020-04-24) . To anal020-04-24p’s < 0.05, FDR-corrected).Pairwise correlation analyses revealed strong associations between BMI variables, trunk fat, and whole-body fat, and between grip strength and waist-to-hip ratio Fig. . HierarcA principal component analysis was performed within each cluster, and the first principal component of each was then extracted. The first principal component of the BMI/body fat cluster explained 83% of the variance of that cluster, the first principal component of the dietary cluster explained 71% of the variance of that cluster, and the first principal component of the grip strength/waist-to-hip ratio cluster explained 70% of the variance within that cluster  only between BD PC2oxt and the body-fat cluster principal component , while BD PC2oxt was significantly negatively associated with BMI , with these results being in the lower end of effect sizes compared with the random gene-set PGS models . Of the dietary variables, only total sugar intake was significantly negatively associated with BD PC2oxt (pfdr’s < 0.001) but not BD PGSoxt or BD PC1oxt (both pfdr > 0.05), with an effect size at the lower 5% of the distribution of random gene-set PGS models. The model showed significant positive interaction effects between BD PC2oxt and the other two variables in the dietary cluster (Supplementary Table oxt and BD PGSoxt (pfdr < 0.001), with effect sizes in the top 5% of the random PGS model distribution, and with significant negative interaction effects between BD PGSoxt, PC1oxt, and mean sugar intake  but not BD PC2oxt (pfdr > 0.5), with effect sizes at the lower 5% of the random gene-set PGS model effect size distribution. These models did not show evidence of significant interaction effects between BD PGSoxt and BD PC1oxt and the other social variables in their respective models , but the effect sizes were on average compared to those of the random gene-set PGS model distribution. None of the polygenic score models with the presence of social contacts showed any significant association .Logistic-regression models of loneliness on BD PGSoxt, SCZ PC1oxt, and SCZ PC2oxt were statistically significant and demonstrated specificity to the oxytocin pathway  only between the body fat cluster principal component and SCZ PC2oxt, and between the waist-to-hip/grip-strength cluster principal component and SCZ PC2oxt , but not with SCZ PGSoxt, SCZ PC1oxt . Linear-regression models of waist-to-hip ratio showed significant positive associations with SCZ PGSoxt and SCZ PC1oxt (both pfdr < 0.001), but not with PGSoxt or PC2oxt (pfdr > 0.05) with effect sizes in the top 5% of the random gene-set PGS model distribution , but only the latter had an effect size in the top or bottom 5% of the random gene-set PGS model distribution .Models between the first principal component of each continuous-phenotype cluster and SCZ PGSxt Figs. . Analyziion Fig. –23. Signoxt and SCZ PC1oxt , though these significant effects were around the average within the random gene-set PGS model distribution. None of the other social variables showed any significant association with PGSoxt, SCZ PC1oxt, or PC2oxt .Logistic-regression models of loneliness showed consistently significant positive associations with SCZ PGSoxt, T2D PC1oxt, and T2D PC2oxt did not show significant or pathway-specific effects , with these effects in the top 10% of the random gene-set PGS model distribution  and trunk-fat percentage (both pfdr’s < 0.001) showed significant positive associations, but these effects are on average within the random gene-set PGS model distributions , with the effects of the T2D PGSoxt and T2D PC1oxt models in the top 5% of the random gene-set PGS model distribution, with a significant negative-interaction component between T2D PGSoxt and T2D PC1oxt and grip strength in each respective model (Supplementary Table oxt and T2D PC1oxt (both pfdr’s < 0.001), but not T2D PC2oxt (pfdr > 0.05), with effect sizes in the bottom 5% of the random gene-set PGS model distributions. Both these models also showed significant negative interaction effects between waist-to-hip-ratio and T2D PGSoxt and T2D PC1oxt, respectively (Supplementary Tables oxt showed significant negative associations (pfdr’s = 0.01), with this effect size in the bottom 5% of the random gene-set PGS model distribution, and the model showed significant interaction effects between T2D PC1oxt and the two other variables in the dietary cluster , with an effect size at the very top of the random gene-set PGS model distribution  may be better for predicting overall associations, pathway-specific PGS can be more suitable for investigating specific components of the underlying pathology [The PGSathology , potentiathology . This isathology –76.oxt for T2D and the second PC–PGSoxt for SCZ, and a negative association with the second PC–PGSoxt for BD, and the body-fat cluster principal component showed a positive association with the second PC–PGSoxt for SCZ. The food-intake cluster points to an involvement of the oxytocin pathway in the regulation of caloric intake, appetite, and preference for sweet food, consistent with previous reports [In the initial pairwise correlation and clustering analysis, we identified three main clusters of metabolic predictors: BMI, trunk fat, and total body fat (body-fat cluster); energy intake, sugar intake and food weight (eating-habit cluster), and grip strength and waist-to-hip ratio (strength/WH-ratio cluster). The body-fat cluster variables showed a positive association with the oxytocin-specific PGS and the first PC–PGS reports . While t reports , 77.distressing for all these individuals, and oxytocin-pathway variants were found to be associated with reported dissatisfaction with the quantity or quality of social contact.Four variables were included to investigate the role of oxytocin-pathway variants on social behavior: loneliness, the ability to confide, social contacts, and social activity. Loneliness and the ability to confide represent the subjective dissatisfaction with one’s relationships, with the ability to confide significantly associated with the oxytocin-pathway PGSs. While the level of social contacts was strongly linked with loneliness, it was not associated with most of the oxytocin-specific PRSs, and while these variables were found not to be independent, the logistic regression models found no significant interactions between them. These associations with the oxytocin pathway are in line with previous findings on emotional withdrawal in individuals with schizophrenia  and on tIt has recently been proposed that the primary purpose of the oxytocin-signaling system is the support of allostasis, which is the process of maintaining stability in changing environments . This isThere has been a growing interest in oxytocin’s role in behavior . HoweverAltogether, the shared effect of oxytocin-system dysfunction helps provide a better understanding of why social and metabolic dysfunction often co-occurs in severe mental illnesses. Future investigations examining the effects of long-term effects of intranasal oxytocin administration on behavior and cognition in severe mental illnesses, such as schizophrenia, should also explore oxytocin’s effects on metabolic measures in tandem.Supplementary materials


HBLUP and GHBLUP+GBLUP, respectively). We found that the accuracies using haploblock approach varied with different LD thresholds. The LD thresholds (r2 ≥ 0.5) were optimal for most scenarios. Our results suggested that LD-based haploblock approach can improve accuracy of genomic prediction for carcass traits using both HD chip and imputed WGS data under the optimal LD thresholds in Chinese Simmental beef cattle.A haplotype is defined as a combination of alleles at adjacent loci belonging to the same chromosome that can be transmitted as a unit. In this study, we used both the Illumina BovineHD chip (HD chip) and imputed whole-genome sequence (WGS) data to explore haploblocks and assess haplotype effects, and the haploblocks were defined based on the different LD thresholds. The accuracies of genomic prediction (GP) for dressing percentage (DP), meat percentage (MP), and rib eye roll weight (RERW) based on haplotype were investigated and compared for both data sets in Chinese Simmental beef cattle. The accuracies of GP using the entire imputed WGS data were lower than those using the HD chip data in all cases. For DP and MP, the accuracy of GP using haploblock approaches outperformed the individual single nucleotide polymorphism (SNP) approach (GBLUP_In_Block) at specific LD levels. Hotelling’s test confirmed that GP using LD-based haplotypes from WGS data can significantly increase the accuracies of GP for RERW, compared with the individual SNP approach (∼1.4 and 1.9% for G Genomic prediction (GP) has been widely used in the past decades . Many apMany previous studies have shown that genomic selection using haplotypes is more reliable than that using individual SNPs for both simulated and real data, even when the marker density is low . CuyabanHaploblocks can be constructed through multiple strategies including the fixed block length based on centimorgans (cM) , base paThe WGS data can provide more potential causative polymorphisms, thus imputation from low density marker panels to WGS for datasets with a large number of individuals may be an effective approach to increase the accuracy of GP . A recenHBLUP), SNP-based prediction model (GBLUP), and the combination of haplotype and SNP prediction model (GHBLUP+GBLUP); and (3) investigate the LD-based haplotypes with different thresholds on the prediction accuracies.The objectives of current study were to (1) evaluate the predictive performance of carcass traits using HD chip and WGS data in Chinese Simmental beef cattle; (2) compare the differences of predictive accuracies between haplotype-based prediction model  .Data available comprised a total of 1,233 Simmental cattle born between 2008 and 2015 from Ulgai, Xilingol League, and Inner Mongolia, China. After weaning, cattle were moved to Jinweifuren Co., Ltd.  for fattening under the same feeding and management conditions. A more detailed description of the management processes was reported in previous studies , 2017. Ap < 10–6), and individual call rate (<0.90). Missing genotypes were imputed using BEAGLE (v4.1)  . IndividE (v4.1) . ConsequForty-four unrelated individuals  were selected as the reference population for imputation. The whole genome sequencing of these individuals was performed using Illumina Hiseq2500 instruments . All processes were performed according to the standard manufacturer’s protocols.2), which is an estimate of the squared correlation between the most probable and the true reference dose. The average imputation accuracy was 0.83 when the MAF was larger than 0.05.The SNPs from the HD chip were imputed to the sequencing level using BEAGLE (v4.1) . The impPhenotypes were adjusted for the fixed effects, including sex, year, and the covariates of body weight upon entering the fattening farm, and the number of fattening days. Subsequently, the adjusted phenotypes were used for further analyses. Variance components were estimated using the following univariate animal model in ASREML (v4.1).y is the vector of the adjusted phenotypes, n1 is an n × 1 vector with entries equal to 1; μ is the overall mean; G is the additive genomic relationship matrix constructed using all SNPs and Z is incidence matrix linking a to y; and I is the identity matrix and hwhere r2). For two bi-allelic loci (A1/A2and B1/B2), r2 was calculated as,The LD-based haploblocks were generated separately for each chromosome. A group of SNPs was defined as a haploblock if the LD between every two SNPs in the group was greater than or equal to the threshold value (D = pA1B1pA2B2 − pA1B2pA2B1.where r2)  were set as the thresholds in this study.Seven different LD levels  model including the haplotype/SNP effect was used for DP, MP, and RERW as described in Eq. (1). Three approaches based on (a) the SNPs, (b) the haploblock only, and (c) the haploblock and the non-blocked SNPs were considered for predictions. Seven different M denotes the -encoded genotype matrix, ip is the MAF of marker i, m is the number of markers, and P is a matrix with columns equal to i2p.We performed genomic prediction using GBLUP for all SNP markers, and the genomic relationship matrix was calculated as Genomic prediction using GBLUP for the SNP markers inside of the block in HD chip and WGS data were defined as GBLUP_770K_In_Block and GBLUP_WGS_In_Block, respectively.HBLUP) was performed for all markers. The haplotype-based genomic relationship matrix in GHBLUP was constructed as the product of the haplotype allele matrix (HM) and expressed as HM is the pseudo-markers matrix with entries 0, 1, and 2 representing the number of copies of each haplotype allele in a haploblock, and QH is the total number of haplotype alleles of whole genome. In the GHBLUP+GBLUP model:The haplotype-based genomic best linear unbiased prediction . The accuracy of genomic prediction was assessed using fivefold cross-validation (CV). The CV procedure was applied by assigning animals randomly into five separate subsets. This procedure was randomly repeated 10 times.The regression coefficient of the adjusted phenotype on GEBVs for individuals in the validation set was obtained to measure the degree of inflation/deflation of prediction, which was defined as follows:The average Pearson correlation coefficient between the adjusted phenotypic values and genomic estimated breeding values (GEBVs) in the validation set divided by square root of heritability was used as a measurement of prediction accuracy. The prediction accuracy was calculated as :y∗ is adjusted phenotypic values, gebv is the genomic estimated breeding values (GEBVs), and h2 is the heritability.where HBLUP, and GHBLUP+GBLUP) and marker densities (HD chip and WGS), we used t statistic , the jhr is the cor, and the khr is the cor. If P(T ≥ t) ≤ α(α =  0.05), then the hypothesis (H0:jkr = jhr) is rejected. Hence, we can conclude whether correlations were significantly different.where r2 > 0.2, we observed 68,775  and 634,662  blocks from the HD chip and WGS data, while the number of SNPs out of blocks were 298,454 and 3,240,315 and haplotype allele counts were 840,676 and 3,370,157. Details about the total number of haplotype alleles (variables), haploblocks, and non-blocked SNPs with different r2 are presented in r2. The average number of SNPs per haploblock ranged from 3.3 to 5.3 for the HD chip data and from 4.5 to 5.6 for the WGS data. According to our results, we found that the method based on haploblock reduced the number of variables  for the WGS data. However, as for the HD chip data, the haploblock approach increased the number of variables compared with the SNP approach. This result mainly depends on the data type used for haploblock construction (HD or WGS).Based on the HD chip data, the estimated heritabilities of DP, MP, and RERW using univariate animal model were 0.27, 0.17, and 0.23, respectively, and the statistical description is shown in r2 was calculated for each 1-kb window size. LD decay suggested that the HD chip data had a faster LD decay than WGS data (r2 decreased from 0.8 to 0.2 as the marker distances of the HD chip (from 0 to 35 kb) and WGS data (from 0 to 25 kb) increased. However, no obvious difference was found when r2 < 0.2. Therefore, we chose the LD thresholds (r2 ≥ 0.2 to r2 ≥ 0.8) to construct the haploblocks in our study.We also evaluated the LD decay between 0 and 100 kb for BTA1 in the HD chip and WGS data, respectively. The average WGS data , thus prHBLUP, and (c) GHBLUP+GBLUP, were considered for the comparisons. As shown in HBLUP+GBLUP had better performance for DP and MP than GHBLUP. We also found GHBLUP+GBLUP_770K yielded ∼1.8% higher accuracy than GHBLUP_770K for DP on average. However, as for the RERW, GHBLUP_770K had a slight higher accuracy than GHBLUP+GBLUP_770K, and the GHBLUP+GBLUP_WGS had better performance than GBLUP_WGS_In_Block.Three different approaches, including (a) GBLUP, (b) GHBLUP_770K and GBLUP_770K_In_Block for all scenarios  were selected. As for the GHBLUP+GBLUP, no significant differences were found between HD chip and WGS. It should be noted that the accuracies of the three traits decreased obviously for GBLUP_770K_In_Block compared with the GBLUP_770K  on the prediction accuracy, we constructed haploblocks (from r2 ≥ 0.2 to r2 ≥ 0.8) using seven different levels. In our study, we found that the haploblock approach (including GHBLUP and GHBLUP+GBLUP) was better than the individual SNP approach (GBLUP_In_Block) at specific LD thresholds (r2) , the GHBLUP+GBLUP_770K showed significant improvement compared with GBLUP_770K_In_Block for DP (To investigate the influence of LD levels (lds (r2) . The acck for DP .HBLUP, and GHBLUP+GBLUP were 4.42, 5.41, and 41.5 min using fivefold cross-validation respectively (repeated 10 times). The time for constructing haplotype-based genomic relationship matrix (HG) were 8.22 and 203.97 h on average for the HD chip and WGS data, and 0.898 and 9.12 h for the SNP-based genomic relationship matrix (G).In our study, the average computation time of GBLUP, GIn this study, we compared the accuracies of genomic prediction using both the HD chip and WGS data. A previous study suggested that prediction of breeding value was expected to be more accurate using the WGS data compared with the high-density chip because the causal mutations are assumed to be included in the WGS data . GenomicHBLUP+GBLUP had the highest accuracy and GHBLUP was better than GBLUP at different LD levels for DP and MP  can increase the prediction accuracies for milk production traits (up to 3.1%) compared with the individual SNP approach. Also, the accuracies of GHBLUP+GBLUP and GHBLUP can be influenced by the genetic architectures of different traits  with potential QTLs were not included in the model; therefore, adding the non-blocked SNPs may increase the prediction accuracy without loss of information. Also, we found that the regression coefficients using haplotype approach including GHBLUP+GBLUP and GHBLUP is close to 1 for all three traits, compared with SNP approach ; however, due to a large number of rare haplotype alleles with small effects or no effect were included in sequencing data  were estimated in the GHBLUP+GBLUP model, thus the long time was required for this approach. In addition, our results suggested that the haplotype approach for WGS data requires more time to construct the genomic relationship matrixes than the SNP-based approach. It should be noted that the haplotype-based genomic relationship matrix need to be recoded using numerical dosage coding strategies for each haploblock  . Written informed consent was obtained from the owners for the participation of their animals in this study.LYX designed the experiments. HL analyzed the data and wrote the article. LiX, ZW, LeX, PZ, and HG collected the data. PG, YC, XG, LZ, HJG, WC, LYX, BZ, and JL discussed and improved the article. All authors read and approved the final article.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Significant phenotypic variation was observed across genotypes, treatments, and their interactions across seasons for all the 22 traits measured. Significant positive correlations were observed among grain yield and yield-attributing traits and root traits. Across seasons, a total of 233 marker-trait associations (MTAs) associated with fifteen traits of interest at different levels of nitrogen  were detected using 9,474 genome-wide single nucleotide polymorphism (SNP) markers. Of these, 45 MTAs for 10 traits in the N0 treatment, 100 MTAs for 11 traits in the N60 treatment, and 88 MTAs for 11 traits in the N120 treatment were detected. We identified putative candidate genes underlying the significant MTAs which were associated directly or indirectly with various biological processes, cellular component organization, and molecular functions involving improved plant growth and grain yield. In addition, the top 10 lines based on N response and grain yield across seasons and treatments were identified. The identification and introgression of superior alleles/donors improving the NUE while maintaining grain yield may open new avenues in designing next generation nitrogen-efficient high-yielding wheat varieties.Nitrogen is one of the most important macronutrients for crop growth and metabolism. To identify marker-trait associations for complex nitrogen use efficiency (NUE)-related agronomic traits, field experiments were conducted on nested synthetic wheat introgression libraries at three nitrogen input levels across two seasons. The introgression libraries were genotyped using the 35K Axiom The global demand for nitrogen currently stands at about 117 million metric tons with a projected annual increase of approximately 1.5% expected in the near future FAO, . FarmersGS2 gene in wheat and rice  for both yield and its response to N deficiency in wheat under field conditions have been documented. Detection of genotypes and underlying QTLs for maintaining yields at low N levels are of value in wheat breeding programs designed to increase N-deficiency tolerance. Some QTLs influencing N uptake have been genetically mapped in wheat under different doses of fertilizer application using biparental populations  and multi-parent advanced generation inter-cross (MAGIC) populations have shown a potential for mining novel genetic variation in rice . Nested introgression line libraries developed from the synthetic wheats, PDW233/Ae. tauschii acc. pau 14135 and PBW114/Ae. tauschii acc. pau 14170 (N-SHW) constituting a set of 352 lines were used in the present study. The N-SHW library was made up of subsets from four populations  and other unique parents . The breeding scheme that was used to develop the N-SHW introgression library is summarized in A total of 31 cultivated and 12 synthetic wheats were evaluated at 6 nitrogen (N) levels  in 3 replications in 2015 and 2016 during the rabi seasons at Punjab Agricultural University, Ludhiana, India. The synthetic wheats, PDW233/−1, P: 69 Kg ha−1; K: 116 Kg ha−1, Zn: 4.77 parts per million (ppm), Cu: 1.73 ppm, and Fe: 7.30 ppm. The N-SHW libraries were evaluated in rabi seasons of 2018 and 2019. Details of the number of lines tested and experimental design is provided in st of November and 18th of November in 2018 and 2019, respectively. In both the years, the experiments were conducted at three N levels  referred to as N0, N1, and N2, respectively. The recommended dose of phosphorus, potassium, and manganese was applied at the time of sowing. Half of N was applied at the time of sowing while the other half was applied in two equal splits, the first at crown root initiation stage and the remaining at the maximum tillering stage in both the N1 and N2 experiments. N0 was treated as a control. Recommended fungicides and insecticides were applied to control stripe rusts, brown rusts, and aphids at jointing, booting, and 10 days after anthesis to prevent diseases and pests. Weeds were controlled manually.The N-SHW library, six parents, and two synthetic hexaploid wheats were assessed under field conditions at the experimental farms of School of Agricultural Biotechnology, PAU Ludhiana  over 2 years in 3N levels (6-year x N combinations). The soil analysis of experimental plots showed soil pH: 7.3; EC: 0.157 ds/m; organic content: 0.29%; N: 99 Kg haUnder field conditions, a total of 22 traits were assessed in all experiments across both seasons except the maximum root length and root angle which were measured in 2018 only. The details of the nitrogen use efficiency (NUE)-related traits, root and plant morphological traits, grain yield, and yield attributing traits are presented in t-test was used to determine the significant difference among the breeding lines, treatments, and to estimate the interactions. The correlation analysis among traits was performed in R. v.1.1.423.Analysis of variance, and experiment-wise mean for each season was calculated using a mixed model analysis in PBTools V 1.4.0. for augmented design and in STAR Version: 2.0.1 for the split plot design. In the split plot design, the N levels were considered as the main plot and the breeding lines as subplot. Fisher's http://bbi.irri.org/) was used to explain the relationship between G and the seasons.To evaluate the phenotypic stability and GY adaptability of the breeding lines across seasons and treatments, the genotype and genotype × environment (GGE) biplot analysis was performed, considering the effects of genotype (G) and genotype by environment (GE) as random. The best linear unbiased prediction (BLUP) values of the G and GE effects were calculated. The multiplicative model in PB tool version 1.3 . The quality pre-processing of 35,143 markers obtained from the 35K chip was done using PLINK software  reps after burn-in in order to assess the population structure in the 352 breeding lines using a total of 9,474 SNPs. The consistency and accuracy of the results were validated across 10 runs for each K. The K value with maximum likelihood over the 10 runs was used to estimate the most appropriate number of clusters  and kinship matrix (K) estimated from the genotyping data. The Bonferroni correction method was used to correct for false positives in the analysis, using the stringent p-value benchmark. The Bonferroni multiple test correction was performed  and the calculated threshold value was found to be 5.28 × 10−6. The allelic effect of all the significant markers associated with the measured traits was determined by comparing the mean phenotypic values and the significant allelic variants for the trait/s using a Kruskal–Wallis test in R.Significant marker-trait associations were identified using a compressed mixed linear model (CMLM)/P3D (population parameters previously defined) in GAPIT executed in R. Identity by state (IBS) values and a relatedness matrix were used to estimate the random effect and genetic similarity of the accessions, respectively. The statistical power of the association studies was further improved by considering the population structure .Single nucleotide polymorphisms that exhibited a false discovery rate (FDR) corrected E ≤ 1 0−5) was performed using the CloudBlast tool against Triticum (nr_subset)  and NCBI non-redundant database (http://www.ncbi.nlm.nih.gov), followed by the InterPro using CloudIPS, followed by GO mapping, and annotation configuration. The GO terms were then used to generate the semantic similarity-based scatterplots/interactive graphs/tag clouds by using REVIGO (http://revigo.irb.hr/).The functional annotation and gene ontology (GO) of the identified putative candidate genes were performed using OMIX box software. Blast  genotypes or the top grain yielders across seasons and treatments. On the other way around, the genotypes that were low yielding or not able to maintain the GY with the minimal application of N fertilizer when compared to the recommended or standard N fertilizer application, were considered as the N responsive (NR) genotypes or the poor grain yielders across seasons and treatments.The 352 N-SHW lines, six parents, and two synthetic hexaploid wheat donors were screened for 22 traits in six growing conditions (2 years x 3 nitrogen levels). The ANOVA revealed significant genetic variation for the root, plant morphological, and agronomic traits among genotypes, treatments, seasons, and their interactions  . The det−1 and it ranged from 564 to 4,092 kg ha−1 , NPT , fresh root weight (FRW) , fresh shoot weight (FSW) , dry shoot weight (DSW) , and with root surface area (RSA) = . Across treatments, the GY showed a negative correlation with days to flowering (DTF), and shoot biomass (SB) showed a positive correlation with GY.We calculated the Pearson's correlation coefficients between all the traits measured in N0 , N60 Fi, and N12K explaining the population structure was K = 3 at minor allele frequency (MAF) ≥ 5%  distributed across all 21 wheat chromosomes. The most appropriate AF) ≥ 5% . The kinAF) ≥ 5% . The firAF) ≥ 5%  until thlue of 3 .® Wheat Breeder's Array. Using the -log(P) ≥0.001 at 5% significance level, a total of 233 marker-trait associations (MTAs) were detected across seasons associated with fifteen traits of interest at different N levels  for the MTAs associated with nitrogen use efficiency (NUE)-related traits, root traits, and yield/yield related traits measured in the present study at different levels of N are presented in The Manhattan plots depicting the significant -log (http://plants.ensembl.org/index.html). Detailed information on the identified candidate genes is presented in In order to identify candidate genes underlying the consistent MTAs, we surveyed putative candidates in a 1 Mb upstream and 1 Mb downstream region the identified significant SNPs using EnsemblPlants , root morphological , plant morphological  and GY/yield-attributing traits (days to flowering (DTF), SPS, NPT, SB, and GY]. Most of the putative candidate genes in NUE-uptake related traits across treatments were associated with protein phosphorylation/proteolysis, recognition of pollen, molybdoprotein cofactor biosynthetic process, and transmembrane transport . Some we−1) compared to the respective recipient parent averaged across two seasons under three different N treatments is presented in To identify stable breeding lines across treatments and seasons, a GGE biplot method was used. The first two principal components (PCs) explained 77.7%  of the total GGE variation in the data . The ranFurther analysis was undertaken to assess the significant differences between the mean values of the allelic classes of MTAs for root growth and GY using the Kruskal–Wallis test. The presence of favorable alleles with significant differences was checked in promising breeding lines. This allowed for the selection of 20 promising breeding lines possessing favorable allele combinations for improving the plant root growth  and graiAe. tauschii wild accessions to cultivated wheat  populations successfully exploited the genetics of complex traits and facilitated the discovery of candidate genes in rice  keeping into account, the genetic effects produced in each genetic background. The associated SNPs were used to track the potential candidate genes associated with a particular trait of interest. The presence of high phenotypic variability in the nested synthetic introgression libraries coupled with the high marker density across the whole genome provided a strong base for the association mapping.Interestingly, the genes responsive to nutrient uptake under water stress -related traits in wheat. Significant phenotypic variations for the NUE-related traits, yield, and yield-related traits among genotypes, treatments, seasons, and their interactions  were observed. Stable marker-trait associations (MTAs) identified for different traits measured in the present study comigrating with various genes associated with nitrogen (N) uptake/utilization and improving grain yield (GY) may help to harness their benefits in genomics-assisted breeding programs. The identification of N-efficient breeding lines may serve as novel donors in genomics-assisted introgression programs. The identification and introgression of superior haplotype improving NUE while maintaining GY using haplotype-based breeding may open new avenues in designing next-generation N-efficient high-yielding wheat varieties.The original data and related information presented in the study are included in the article/NS and PC designed this study. AK provided the genotypic data of two populations and contributed to the development of nested introgression libraries. NS and MS conducted the field experiments. NS analyzed the data and wrote the manuscript. NS, SK, and PC provided resources. AK, MS, SK, VP-S, AS, AB, TB, and PC revised the manuscript. VP-S provided support with SPAD meter. All authors contributed to the article and approved the submitted version.The work was compiled under the projects funded by the Department of Biotechnology, Govt. of India (Grant No. BT/IN/UK-VNC/42/RG/2015-16 and BT/PR30871/BIC/101/1159/2018).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Zymoseptoria tritici and powdery mildew (PM) caused by Blumeria graminis f.sp tritici (Bgt) are among the forefront foliar diseases of wheat that lead to a significant loss of grain yield and quality. Resistance breeding aimed at developing varieties with inherent resistance to STB and PM diseases has been the most sustainable and environment-friendly approach. In this study, 175 winter wheat landraces and historical cultivars originated from the Nordic region were evaluated for adult-plant resistance (APR) to STB and PM in Denmark, Estonia, Lithuania, and Sweden. Genome-wide association study (GWAS) and genomic prediction (GP) were performed based on the adult-plant response to STB and PM in field conditions using 7,401 single-nucleotide polymorphism (SNP) markers generated by 20K SNP chip. Genotype-by-environment interaction was significant for both disease scores. GWAS detected stable and environment-specific quantitative trait locis (QTLs) on chromosomes 1A, 1B, 1D, 2B, 3B, 4A, 5A, 6A, and 6B for STB and 2A, 2D, 3A, 4B, 5A, 6B, 7A, and 7B for PM adult-plant disease resistance. GP accuracy was improved when assisted with QTL from GWAS as a fixed effect. The GWAS-assisted GP accuracy ranged within 0.53–0.75 and 0.36–0.83 for STB and PM, respectively, across the tested environments. This study highlights that landraces and historical cultivars are a valuable source of APR to STB and PM. Such germplasm could be used to identify and introgress novel resistance genes to modern breeding lines.Septoria tritici blotch (STB) caused by the fungal pathogen  Zymoseptoria tritici and Blumeria graminis f.sp tritici (Bgt), respectively, are the major devastating foliar diseases that cause significant yield loss in wheat-growing regions. These fungal diseases can significantly reduce the yield and quality of the wheat crop under conducive environmental conditions (Septoria tritici blotch (STB) and powdery mildew (PM) caused by fungal pathogens nditions . Both STnditions . In thisnditions  coupled nditions . Neverthnditions . Hence, nditions .Stb genes) are detected hitherto and mapped on 14 wheat chromosomes against the STB pathogen , adult stage , or at all growth stages  of the wheat plant  are identified and mapped across wheat chromosomes  are identified, mapped and provide quantitative PM resistance   . Howevercereale) . For ins quality . Besideshe field . On the he field .With the continuous decline of genotyping cost coupled with the increasing availability of various single-nucleotide polymorphism (SNP) fingerprinting platforms, genomic-assisted breeding has become a suitable method in various plant-breeding schemes. Linkage mapping and genome-wide association study (GWAS) are successfully applied to detect QTLs/genes in various crops. The GWAS method overcomes the two common limitations  raised by the bi-parental linkage mapping approach . GWAS wa1. This preserved germplasm has encompassed a rich genetic variation for various traits of agronomic importance  preserved winter wheat accessions comprising landraces and historical cultivars collected from the Scandinavian countriesportance , includiportance , adult-pportance , 2007, aportance . The curThis study used 175 winter wheat genotypes comprising both cultivars and landraces. The details of genotypes can be found in 2 plot size comprising six rows, and the distance between adjacent plots and rows within plots was 50 and 20 cm, respectively. Disease assessment for both STB and PM was done once at the flag leaf stage [Zadok’s growth stage (GS) 73–75] on June 26, 2019. The Estonia field trial was done at Jõgeva for two growing seasons (2018–2019 and 2019–2020). The plot size was 1 m2 with six rows, and the distance between adjacent plots and rows within plots was 50 and 20 cm, respectively. Both disease assessments were done twice each year (June 6 and 20 in 2019 and July 2 and 14 in 2020) on the flag leaves. The Lithuania field trial was carried out at Dotnuva for two growing seasons (2018–2019 and 2019–2020) with a plot size of 1 m2 encompassing six rows, and the distance between plots and rows within plots was 80 and 20 cm, respectively. The disease evaluation for PM was scored for 2 years on June 3, 2019 and June 23, 2020. However, the STB assessment was done only in 2020 for two rounds (June 23 and July 4). The field trial in Sweden was carried out in Svalöv in 2019 with a plot size of 1 m2 having six rows. Possible covariates such as plant height and phenology  were also recorded at all locations except Denmark.Field evaluation of genotypes was carried out in four different countries, including Denmark, Estonia, Lithuania, and Sweden for two growing seasons (2018–2019 and 2019–2020). Genotypes were screened under natural infections for STB and PM and scored on a 1–9 scale, which was used previously by The phenotypic data analysis of individual experiments  and combined environment was evaluated using the Multi-environment Trial Analysis with R (META-R) software package . The anawhile with covariates, phenotypic data adjustment was made with the model:Yijk is the disease traits, μ is the overall mean effect, and Repi and Blockj(Repi) denote the effect of the ith replicate and the jth incomplete block within the ith replicate, respectively. Genk represents the effect of the kth genotype; εijk is the effect of the error associated with the ith replication, jth incomplete block, and kth genotype, which is assumed to be the independent and identically distributed random variables (iid) normal with mean zero and variance σ2ε. Cov is the effect of the covariate (plant height and/or days to heading). All, except the overall mean and covariate, are treated as random effects and the iid normal with mean zero and effect-specific variances. Genotypes were considered as fixed effects during the BLUEs estimation. Days to heading and plant height were incorporated into the analysis whenever they were significantly correlated with the disease scores. BLUEs and variance components for combined environment were estimated using the model:where i and Envi × Genl are the ith environment and the G × E interaction effects, respectively. Environment refers to the location–year combination.where EnvThe broad-sense heritability of the combined environment analysis was calculated with the formula:while for individual environments, repeatability among replications was calculated with the formula:2g and σ2e are the genotype and error variance components, respectively, σ2ge is the G × E interaction variance component, nRep is the number of replicates, and nEnv is the number of environments. Frequency distribution and box plots of the phenotypic data were analyzed using Minitab 18 , while Pearson’s correlation between phenotypic traits was computed using cor function in R environment (where σironment .The genotype plus genotype-by-environment interaction (GGE) biplot analysis was conducted based on the BLUEs values of 175 Nordic winter wheat genotypes collected from four countries using the “GGEBiplots” package in the R environment .Genotypic data that was previously published  was usedP < 0.001 [–log10(P) > 3.0] was used as a threshold to report significant MTAs as described earlier  between SNP markers was calculated in Trait Analysis by Association, Evolution, and Linkage (TASSEL) with 100 sliding window size. To estimate the extent of LD between pairs of loci, r2 values were plotted against physical distance (cM). The LD decay curve line was then fitted on the scatterplot using the smoothing spline regression line at the genome level following the procedure by Genome-wide linkage disequilibrium (LD) analysis was performed using the entire genotypic dataset. Pairwise squared allele-frequency correlations (Genomic prediction was explored for each environment and environments combined using the ridge regression BLUP (RR-BLUP) mixed model package “rrBLUP”  in R envX and Z are the designed matrices for fixed and random effects, respectively. β and μ represent the vectors of fixed and random effects, respectively, while y is a vector of phenotypic values (BLUEs), and ε is the residual error. GP was further tested using a model called the weighted ridge regression BLUP (wRR-BLUP)  . To do tCross-validation was implemented to evaluate the prediction accuracy of the model. For this purpose, the dataset was divided randomly as a training and testing set and conducted for 500 iterations. The percentage of training and testing set was 80 and 20%, respectively. Predictive ability was estimated as the correlation coefficient between the observed BLUEs of the genotypes and predicted values of the test set based on the effect estimates of genotypes in the training set. Prediction accuracy was then calculated from prediction ability divided by the square root of traits’ broad sense heritability .The phenotypic variation in STB and PM disease scores based on the individual environment and environments combined BLUEs is summarized in 10(P) > 3.0 for each environment and combined analysis are reported in The GWAS analysis was conducted using the BLUEs from the two diseases using six different models including GLM, MLM, SUPER, MLMM, FarmCPU, and BLINK, in which the first three are single-locus while the last three multi-locus models. Population structure was modeled using PCA, and the optimal numbers of PCs were estimated based on the BIC. Ten PCs were initially included to select the best numbers of PCs with the highest BIC values, and the result indicated that three PCs were the optimal number to be included in the GWAS analysis models. The six models extensively detected stable and environment-specific QTNs for both STB and PM resistance across chromosomes. The detected QTNs with −logr2 value of the genome was 0.12, and the LD decay started at r2 of 0.48 and reached half-decay at 0.23. The LD decay curve intersected with the half-decay and the standard critical (r2 = 0.3) at 2.0 and 1.6 cM, respectively . For instance, the SNP marker wsnp_Ex_c33012_41567026 on chromosome 4A was significantly associated with STB resistance in Estonia trial 2019, Estonia 2 years combined, Estonia and Lithuania combined, as well as in all environments combined , Estonia 2 years combined, and Estonia and Lithuania combined mean values. In addition to the marker RAC875_c68120_285, five markers  positioned within 1-cM interval had shown a significant association with STB scored from Estonia in 2020 , Lithuania 2 years combined, Lithuania 2 years and Estonia 1 year combined, Sweden and Denmark combined, as well as with the all five environments combined mean values. The marker wsnp_Ex_c790_1554988 had a significant association with noticeable PM disease score effect across its alleles , Sweden explaining 19% from the total phenotypic variance (R2), Denmark and Sweden combined (R2 = 19.7%), and overall combined PM mean values. The SNP marker wsnp_Ex_c14340_22315611 on chromosome 3A was the other stable QTN showing significant association with PM scores from Lithuania, Sweden, Denmark, and Lithuania and Estonia combined mean values. A total of 36 SNP markers on chromosome 3A  positioned within 3-cM interval (88–91 cM) exhibited a significant MTAs with PM scores from Lithuania trial conducted in 2019 , while the five environments combined BLUEs mean values scored the highest GP accuracy (0.43) . For PM This study evaluated 175 genetically diverse winter wheat genotypes from the NordGen genebank to detect QTLs and estimate genomic breeding values of genotypes for adult-plant disease resistance for STB and PM. These two foliar diseases are regarded as two major biotic threats for European winter wheat production. The panel integrates landraces and cultivars originated from Sweden, Denmark, Finland, and Norway, representing the previous century winter wheat breeding practices in the region . GenotypThe panel was previously tested for seedling resistance to STB with artificial inoculation in Biotron greenhouse chamber, and genotypes had a significant genetic variation for STB resistance response . In thisstb genes, several QTLs have been involved for resistance to STB in wheat with moderate-to-small effects  for adult-plant STB resistance in European Winter Wheat, which is found close to the currently detected marker. Similarly, BS00040648_51, 594 MB) for adult-plant STB resistance using an eight-founder MAGIC winter wheat population. The other stable marker detected in this study was BS00021714_51 (78 cM), according to wsnp_Ex_rep_c109742_92411838 (81 cM) discovered previously by Kukri_c63163_141 that explained 8.0% from the total phenotypic variance. Chromosome arm 5AL is a region in which stb17 gene was discovered previously in adult plants of synthetic hexaploid wheat  in which the race specific resistance gene stb15 was detected previously  but not from each of the simulated 500 training set (TS). For unbiased estimates in wRR-BLUP, separate GWAS must be conducted for each of the 500 TSs to detect significant SNPs fitted in the wRR-BLUP model as fixed effects. Unfortunately, this is highly computationally demanding  because The panel was tested in field conditions for PM resistance in five environments. The results indicated a highly significant variation in resistance response toward the pathogen . A signiPm genes and quantitatively by small-to-moderate effect genes  on chromosome arm 7AL, where the Pm1 gene was detected previously (Pm genes as well as other singleton and meta-QTLs (BobWhite_c15773_166 (752.09 MB) on the chromosome arm 2AL was the other source for stable QTN discovered in all environments for PM APR. The chromosome arm 2AL is the other gene-rich region for PM resistance including the Pm4a, b, and d alleles detected on the chromosome homoeologous group two, which was initially introgressed from other progenitors species of wheat (wsnp_Ex_c14340_22315611 on chromosome arm 3AL (89 cM) was another stable QTN discovered in this study. Previous studies reported QTLs on this chromosome region nearby to this marker for PM APR  earlier in both single as well as environment-combined analysis. The GP prediction accuracy of PM ranged between 18 and 62% across environments for the RR-BLUP model. The wRR-BLUP improved the GP prediction accuracy, and the score was in a range of 36–83% across the tested environments. In a previous study, This study analyzed 175 winter wheat genotypes from NordGen comprising historical landraces and cultivars for GWAS and GP analysis. The phenotypic data indicated a significant variation between genotypes for adult-plant disease resistance response to STB and PM. The GWAS analysis identified various valuable QTNs for both STB and PM APR that could be incorporated in future marker-assisted selection and genomic selection programs. GWAS significantly improved the GP accuracy in predicting the genomic breeding values of genotypes for both STB and PM APR. The result of this study supports the potential of GP in predicting the GEBVs of the vastly available genetically rich landraces by developing appropriate models. The genomic-assisted germplasm selection with superior alleles for disease resistance in wheat could be then integrated into active breeding programs.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/AC conceived and designed the study. TH performed field the trials in Sweden. JS and LJ performed the field trials in Denmark. GB and ŽL performed the field trials in Lithuania. RK, BI, and MK performed the field trials in Estonia. AA and DSG performed the data analysis and wrote the first draft of the manuscript. All authors contributed to the data interpretation and approved the final version of this manuscript.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.


Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented.P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations.We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.The online version contains supplementary material available at 10.1186/s12864-021-07745-5. White blood cell and platelet traits are important indicators of health status and can be used to diagnose and assess the risk of cardiovascular diseases –4, immunDARC null variant (rs2814778) associated with lower WBC and NEU in African populations [In addition, the majority of large-scale white blood cell and platelet count genomic studies were conducted in populations of European ancestry (EA) despite differences in the genetic architecture of white blood cell and platelet traits across ancestral groups . For exaulations , 27.The Population Architecture using Genomics and Epidemiology (PAGE) Study funded by the National Human Genome Research Institute and the National Institute on Minority Health and Health Disparities was initiated to systematically characterize the genetic architecture underlying complex diseases and related quantitative traits among underrepresented minority populations in the U.S. through large-scale genetic epidemiological research . We prevP < 2E-9, based on minor allele frequency (MAF)-specific P-value thresholds) [P < 5E-8) in the single-trait analyses combining all ancestries were 37 and 46 for WBC, 3 and 4 for BAS and EOS, 4 and 7 for LYM, 14 and 21 for MON and MPV, 29 and 35 for NEU, and 19 and 26 for PLT, respectively. The ancestry-combined analysis identified more genome-wide significant and suggestively significant loci compared to the ancestry-specific analysis for all phenotypes, except for WBC and NEU. The largest numbers of significant findings for WBC and NEU were from AA-specific analysis, most of which showed significant associations only in the AA   or suggeINSIG1 and IGF1, P < 5E-8, Table IGF1 is common (MAF > 20%) and the association signals for LYM were driven by all three ancestral groups , NEU (MED13L), and MPV (HADHB and PPP1R16B). Among these four novel associations at established loci, three showed ancestry specificity with the lead variants being monomorphic in at least one ancestral population   after exDARC, NRBP1, HLA, and MED24) and nine out of eighteen  regions were associated with WBC and PLT, respectively, and harbored more than one gene, which could indicate either multiple functional genes at these regions or co-regulation by variant predictors and correlation of expression levels of neighboring genes  and WBC and PLT levels, identifying 207 significant genes that mapped to previously reported loci [false discovery rate (FDR)< 0.05, Supplemental Table P<2E-9). A total of 1953 variants at 148 loci showed significant evidence of association with at least one of the eight traits after Bonferroni correction  and 5526 variants at 805 loci had P< 0.05 in PAGE  or targeted chips (Metabochip or Exomechip) –22, 31 iP< 3.13E-3, 0.05/16, Supplemental Table DARC null variant . Since the paper that reported these loci was also a part of the BCX Consortium, we further calculated effect sizes in EA populations to account for the winner’s curse [We then evaluated loci reported by the largest Euro-centric GWAS  at the t’s curse . The resDARC for WBC and NEU; MED24 for WBC; and BAK1, HBS1L, AK3, and SH2B3 for PLT, P<2E-9): each locus harbored two independent variants in PAGE  with the lead variant overlapped with enhancer and repressor activities  and transcribed regions , respectively  dataset  and IGF1 (associated LYM), have not been linked to any white blood cell or platelet traits before, although we acknowledge lack of replication. These two genes  are both closely related to glucose and lipids homeostasis, which have a complex interplay with inflammation and related traits. The lead variant and its LD proxies at the INSIG1 locus are located in an intergenic region close to another gene HTR5A (5-hydroxytryptamine receptor 5A), which has been reported for sulfasalazine-induced agranulocytosis in EA populations [TG locus which was reported for PLT and EOS and was associated with WBC in PAGE, the lead variant and multiple LD proxies are intronic variants of the TG (thyroglobulin) gene and also exhibited association with gene expression level of LRRC6 (leucine rich repeat containing 6) in whole blood . Polymorphisms in TG gene are associated with susceptibility to autoimmune thyroid diseases (AITD) while defects in LRRC6 gene are a cause of primary ciliary dyskinesia-19, which features chronic infections and persistent inflammation of the respiratory system [PPP1R16B-rs567151067) is available only in one of the participating studies of European ancestry. This may be because this variant is of low frequency in Europeans (MAF=0.001 in PAGE European populations and MAF=0.0004 in BCX Consortium).The two novel loci, ulations . Two LD ulations  while EZulations . Future y system . FunctioMED13L and HADHB) and the lead variant at INSIG1 exhibited relatively lower MAF in EA. Among the six loci showing evidence of independent signals in the stepwise conditional analysis, two of them harbored variants whose associations were mainly driven by AA and/or HL populations. One is the DARC locus, where the two variants associated with WBC or NEU were independent of the well-established functional variant rs2814778 [SH2B3 locus, where the most significant variant, rs3742003, was associated with expression levels of multiple genes in various tissues and showed a DANN rank score > 0.9 (deleterious) and an Eigen PC phred score ≥17 . These annotation findings indicated potential functions of these independent variants at the two established regions and merit further investigation. More significant loci were identified in the ancestry-combined analysis except for WBC and NEU, where most significant results were in AA, suggesting improved power when combining all samples and the potential to uncover loci driven by all ancestral groups.Our findings highlighted the potential of uncovering additional genetic loci in ancestrally diverse populations, especially those showing ancestry-specificity in underrepresented populations and were possibly missed by previous Euro-centric analyses. Among the six novel findings we identified in the ancestry-combined meta-analysis, half of them were mainly driven by association signals in AA and HL populations. Among these three loci, two of them harbored lead variants that were monomorphic in EA populations , and extra caution should be taken when using European-derived effect estimates in other ancestral groups.The ancestrally diverse samples in PAGE also enabled evaluation of previously reported loci through comparison of effect sizes and explained phenotypic variances across diverse populations. The statistically significant attenuation of effect sizes in AA and HL populations was pervasive even after adjusting for the winner’s curse, with loci explaining a higher proportion of phenotypic variance in EA populations. The only exception was NEU, with the Duffy/Compared to the PAGE global paper , the curIn conclusion, the ancestrally diverse populations in the PAGE Study facilitated the discovery of both ancestry-specific and ancestry-agnostic findings at putative novel loci and previously established regions for association with white blood cell and platelet traits. Successful replication of multiple previously reported loci in PAGE indicated considerable shared genetic architecture underlying these traits. Our results emphasize the importance of improving diversity and inclusion in genetic association studies by incorporating participants with diverse ancestral backgrounds.We identified six potential novel findings for five of the eight examined white blood cell and platelet traits in the ancestrally diverse populations from PAGE. Multiple established loci were confirmed in our analysis and independent signals were identified in six reported regions. Systematic evaluation of white blood cell and platelet traits associated loci from a Euro-centric GWAS showed global attenuation of effect sizes in AA and HL compared to EA populations. Our results indicated the importance of diversity and inclusion in genetic association studies, which will lead to an improved understanding of these complex traits.In the discovery stage, our analysis included up to 64,784 participants of self-identified AA n=16,201), HL , or EA  race/ethnicity from four cohort studies and one biobank .9/L).We studied eight hematological traits as defined in the standard clinical complete blood count (CBC) analysis, measuring properties of white blood cells  and platelets (PLT and MPV). Counts of white blood cells and the five subtype cells as well as platelets were measured using automated hematology cell counters and following standardized laboratory protocols from blood draws at the earliest available visit. Each count was reported in trillions of cells per liter , center (when applicable), and the first 10 principal components (PC). For samples previously genotyped on either other Illumina or Affymetrix arrays, residual values for each trait were calculated from linear regression models after adjustment for age, age2, sex (when applicable), center (when applicable), and the first 10 PCs calculated from an LD-pruned set of genotypes in each individual study. In the univariate GWAS analysis, we tested the association of each genetic variant with the rank-based inverse-normally transformed residual values in MEGA samples and in each individual study, respectively. All MEGA samples were pooled together for testing while association testing was performed by study and ancestral group in non-MEGA samples. These association analyses were performed using SUGEN, which is based on generalized estimating equations (GEE) allowing correlated errors for first or second-degree relatives and independent error distributions by self-reported race/ethnic group [In the discovery stage, we performed both univariate GWAS analysis for each of the eight traits and aSPU simulation-based method which jointly tested all eight traits . For WBCic group . Associaic group . Both anP value for each variant [https://github.com/kaskarn/aspu_julia).To identify additional novel loci and evaluate evidence for shared genetic effects across all eight traits, we combined the trans-ethnic meta-analysis results from each univariate trait analysis using aSPU to generate a joint  variant . The aSP variant  ) to evaP<2E-9 and P<5E-8, respectively [http://www.mhi-humangenetics.org/en/resources). There are two studies, BioMe and WHI, that were included in our discovery analysis and the BCX results as well. The fixed effect meta-analysis provided by BCX is a combination of the results of overlapping samples in WHI and BioMe and other remaining samples in BCX. As we know the association results for the overlapping samples in WHI and BioMe we can “invert” the fixed effect meta-analysis reported on the BCX website to obtain the results of all non-overlapping samples in BCX.Genome-wide and suggestive significant cutoffs were set as ectively , 30. UsiP<2E-9), we identified the lead variant with the lowest P value and defined a 2 Mb region centered on the lead variant. At the DARC locus, the examined region was extended to ~ 6.5 Mb due to the extensive LD. We included the genotype dosage for the lead variant in each region as an additional covariate in the regression model. We did not stop the conditional analysis until all variants in each region showed P>2E-9.To identify distinct association signals at previously reported loci, we performed stepwise conditional analysis in each study, followed by meta-analysis. At each known locus that reached genome-wide significance in the ancestry-combined meta-analysis  with the most significant variant within ±1 Mb region. The LD proxies of the six novel findings were generated using either ancestry-specific or ancestry-combined data . Primary mononuclear cells, monocytes, neutrophils, natural killer cells, T and B cells from peripheral blood and primary hemotapoietic stem cells, the seven most relevant cells to the eight studied white blood cell and platelet traits, were selected to examine chromatin immunoprecipitation-sequencing (ChiP-seq) signals associated with enhancers (H3K27ac and H3K4m1), repressors (H3K27me3), and transcribed regions (H3K36me3) [Functional annotation of the novel findings listed in Table 3K36me3) .https://molpath.shinyapps.io/ShinyHeme/) [In addition, we examined our novel findings and their LD proxies, as well as the independent signals at previously reported loci, in two comprehensive data sets combining chromatin accessibility data derived from ATAC-seq in hematopoesis-related cell types and GWAS results for various blood cell traits in the UKBB   and the nyHeme/)  . These nyHeme/) .Additional file 1.Additional file 2.


Behavioral and physiological ecologists have long been interested in explaining the causes and consequences of trait variation, with a focus on individual differences in mean values. However, the majority of phenotypic variation typically occurs within individuals, rather than among individuals (as indicated by average repeatability being less than 0.5). Recent studies have further shown that individuals can also differ in the magnitude of variation that is unexplained by individual variation or environmental factors . The significance of residual variation, or why individuals differ, is largely unexplained, but is important from evolutionary, methodological, and statistical perspectives. Here, we broadly reviewed literature on individual variation in behavior and physiology, and located 39 datasets with sufficient repeated measures to evaluate individual differences in residual variance. We then analyzed these datasets using methods that permit direct comparisons of parameters across studies. This revealed substantial and widespread individual differences in residual variance. The magnitude of individual variation appeared larger in behavioral traits than in physiological traits, and heterogeneity was greater in more controlled situations. We discuss potential ecological and evolutionary implications of individual differences in residual variance and suggest productive future research directions. Animals are typically quite unpredictable in behavior with the majority of variance unexplained. We returned to raw data of previously published work, and found almost ubiquitously that individuals vary in their predictability. We discuss potential ecological and evolutionary implications of individual differences in residual variance and suggest productive future research directions. In contrast to morphological and life‐history variation, behavioral and physiological traits offer additional challenges due to their lability over short temporal scales. While individuals commonly differ in their mean level trait, as evidenced by significant repeatability  can differ between individuals, so that some individuals are predictable about their mean, relative to others which at the same time, situation and context are unpredictable  the magnitude of individual differences in rIIV, (b) whether rIIV commonly correlates with the individual's trait mean; and where possible (c) whether rIIV correlates among individuals across different traits. In light of these analyses, we then discuss the biological factors, which may lead to variation in rIIV, and covariance of individual rIIV with individual “personality” (means).2P.Our initial aim was to review the literature on individual variation in rIIV. A secondary aim to meta‐analyze these results grew organically out of this endeavor. We collated suitable datasets from the literature looking broadly for animal ecology papers quantifying individual variation in behavior or physiology . We aimed for well‐sampled datasets, with a minimum of 20 individuals, sampled at least five times each, which was viewed as a minimum to yield some power to test for variation in rIIV  were the best represented species, with seven datasets (16 estimates) from four independent laboratories, followed by hermit crabs . Many estimates represented either activity (20 estimates) or boldness (20), with additionally three studies and seven estimates of metabolic traits, two studies and estimates of hormone traits, among a number of other traits.The compiled datasets were highly heterogeneous in the traits and taxa they represented including eight different mammal species, six fish species, three crustacean species, three bird species, two reptile species, one gastropod species, one amphibian species, and one cnidarian species. Guppies analyzed with “brms” Bürkner,  where reEquation  for metaEquation  and requWe also extracted the correlation of rIIV  with the mean level trait (at mean‐centered random slopes where they were present). In a few instances, we located studies that measured more than one trait, which permitted us to examine whether animals are generally (un)predictable across multiple traits. In those cases, we ran multivariate DHGLMs as discussed in Ref.  created a worse negative skew. As the positive skew was minor and unlikely to cause a meaningful bias, we left the estimates untransformed. Among studies, however, there was a clear log‐normal distribution. To account for this, we modeled CVP on the log‐scale through the use of the nonlinear commands in “brms”  understand the grand weighted‐mean CVP across all studies, (b) assess whether CVP generally differs between behavioral (n = 53 estimates) or physiological traits (n = 11 estimates), (c) evaluate whether traits differ in their CVP by fitting a fixed effect with five levels , and (d) evaluate whether field studies display higher CVP owing to the lack of control of environmental conditions . All models were run with a random intercept effect of estimate (to acknowledge variation in CVP within studies), and variance was separated by trait type  for models b‐d. Study identity was also fit as a random intercept as either multiple traits or subpopulations were sometimes present within a study. Similar analyses were conducted on the other extracted parameters, which we discuss below.Due to the heterogeneity in the taxa and traits considered (while not having large samples), we ran only simple meta‐analyses. Single estimates of CVBürkner, , which a33.1P of 0.26 . We did not pick up clear signs of publication bias in the studies considered, with no effect observed of whether the original publication considered variation in rIIV . The lack of a difference was largely driven by a single hemolymph density study in shore crabs  to highly pronounced and visually obvious 0.76 Figure . This waDifferent factors of the endogenous state could create variability over different periods of time  and activity rates (n = 20 estimates), where we had a reasonable amount of data to assess generality in these correlations. Most of the boldness assays related to latency to emerge following a fright stimulus. Time perception is typically thought to scale logarithmically . For activity rates, there was little indication for a general correlation of more active individuals being more consistent . For both traits, there was little consistency among estimates from differing studies—indicating little generality among taxa, populations, or metrics of these behaviors. This was indicated by the substantial variation among estimate . Both trait types showed no correlation between the mean and rIIV (r = 0) to be within one standard deviation of the mean coefficient, indicating there was variance not just in the magnitude of the correlation, but also the direction.There was a slight tendency for bolder individuals to exhibit higher rIIV, though this effect was nonsignificant overall  and y (the fitness related consequence) have a linear relationship, the mean of y can be directly calculated from the mean of x—without regard for the variance. When the relationship is nonlinear, the mean of y then becomes a function of both the mean and variance of x.When selection gradients are nonlinear, we need to understand both the mean and variance of the trait score. This line of reasoning has led researchers to speculate that extreme values of behavior could often be more consequential than the mean . For bold individuals (those that exhibit a short latency or flight initiation distance), high variability would be more likely to lead to instances of highly costly behavior, and death. For low‐risk individuals, even extreme deviations from their mean behavior are unlikely to lead to a costly outcome. While in the example of boldness to predator survival, a meta‐analysis did not reveal a correlation between boldness and survival at the among‐individual level predictable across multiple traits. Such correlations are predicted to arise if residual variance represents instabilities in underlying latent state variables, such as physiological or informational state . This means that individuals that were predictable in one trait tended to also be predictable in other traits. However, data were limited with just 19 estimates from 8 studies, and many of these estimates were very imprecise (as reflected by the large 95% CRI) and therefore largely uninformative to the model. Estimates also came from highly heterogeneous studies, and are therefore hard to generalize and should be treated as indicative only.Here, we extracted the among‐individual correlations of intercepts of the pairs of traits and the correlation in rIIV of the same trait. As we were interested in the degree to which the means were correlated (not the direction of the correlation), we used the r2 of the mean intercepts, indicating that when traits are strongly linked, this extends to rIIV. While this effect also overlapped with 0 —reflecting a lack of power—the effect is likely real. Had we have instead evaluated the residual correlation , the effect would be partially underpinned by mathematical dependencies. A more suitable null hypothesis when traits are strongly linked would therefore be a perfect correlation of rIIV across traits . Quantifying the residual correlation was not possible as some of our  number of relevant datasets were not measured concurrently in time. Further, existing methods to estimate the residual covariance in a DHGLM assume a constant correlation coefficient, while the variances are changing. The covariance (and slope) therefore scale with the variances to maintain a constant correlation coefficient—an assumption that is biologically unrealistic and likely to yield spurious results.There was also the predicted positive correlation between the among‐individual correlation in rIIV and the 3.4The existence of individual variation in rIIV has many potential consequences to the fitness of an animal. The evolution of homeostasis is a clear example of directional selection reducing rIIV Cannon, , whereby2 consumption). Therefore, behavior will affect measurements of physiological and performance traits. Many physiological measures require an animal to reach a peak capability, such example datasets may include maximum metabolic rate , activity will also increase the body temperature predictability, with studies which may tie rIIV to individual fitness.Individual variation in residual variance was almost ubiquitous across a wide range of taxa and traits considered in this paper. However, the extent of variation in rIIV was highly variable across the different datasets, as was the relationship of individual differences in rIIV to trait means. Contrary to predictions, heterogeneous residual variance did not appear to occur due to a lack of control of environmental factors—in contrast, laboratory studies showing a higher CVNone declared.David J. Mitchell: Conceptualization ; Data curation (lead); Formal analysis (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Christa Beckmann: Conceptualization ; Supervision (supporting); Writing‐review & editing . Peter A. Biro: Conceptualization ; Formal analysis (supporting); Funding acquisition (lead); Investigation ; Supervision (lead); Writing‐review & editing .Appendix S1Click here for additional data file.