Schizophrenia is a heterogeneous disorder, exhibiting variability in presentation and outcomes that complicate treatment and recovery. To explore this heterogeneity, we leverage the comprehensive Danish health registries to conduct a prospective, longitudinal study from birth of 5432 individuals who would ultimately be diagnosed with schizophrenia, building individual trajectories that represent sequences of comorbid diagnoses, and describing patterns in the individual-level variability. We show that psychiatric comorbidity is prevalent among individuals with schizophrenia (82%) and multi-morbidity occur more frequently in specific, time-ordered pairs. Three latent factors capture 79% of variation in longitudinal comorbidity and broadly relate to the number of co-occurring diagnoses, the presence of child versus adult comorbidities and substance abuse. Clustering of the factor scores revealed five stable clusters of individuals, associated with specific risk factors and outcomes. The presentation and course of schizophrenia may be associated with heterogeneity in etiological factors including family history of mental disorders. Schizophrenia is a complex disorder where individuals experience different symptoms and outcomes that can be captured by patterns in other diagnoses. Here the authors use computational approaches to summarize these patterns and suggest they are associated with genetic and environmental exposure. Schizophrenia, in particular, can be seen as the canonical example of a syndrome with varying clinical presentations, inconsistent treatment response, and longitudinal prognostic instability5. From one perspective, the heterogeneity of schizophrenia implies that the diagnosis is too broad because it is sensitive to various unique clinical presentations. From another perspective, however, it is too narrow, as the diagnosis is often not unitary, but regularly appears in constellation with multiple comorbid symptoms, diagnoses, or pathologies10. This necessitates questions into the nature of this phenomenon—does heterogeneity in schizophrenia merely reflect a constellation of random phenomena with distinct etiologies?Psychiatric disorders have been classified for close to a century using a categorical and syndromic approach based on subjectively observed and reported psychiatric symptoms, rather than objective biomarkers, which limits their specificity and utility for guiding interventions3, but previous subtypes were used infrequently11, had modest longitudinal stability12, inadequately described symptom heterogeneity, and have been abandoned in DSM-511 and ICD-1113. This reflects initiatives to describe the clinical heterogeneity in schizophrenia on symptom dimensions14 and factor analysis of symptom dimensions15. However, these approaches have been limited by their cross-sectional design , short follow-up , or retrospective data collection . Despite this, interest in describing heterogeneity in schizophrenia persists7 and longitudinal cohorts, with broad phenotyping, and representative ascertainment are well poised to make important contributions to this topic.The idea of subtypes within schizophrenia dates back more than a century18 genetic differences are also present20, which indicates that different pathological mechanisms are implicated, at least to some extent, depending on the psychiatric diagnoses21. Since psychiatric comorbidity is very common in schizophrenia8 different comorbid diagnoses might reflect differences in underlying biology and therefore there is a need for broader investigations into the nature of clinical heterogeneity in schizophrenia, especially with respect to longitudinal outcomes and comorbid diagnostic patterns.Although there is substantial pleiotropy, or sharing of genetic risk factors, across different psychiatric disorders24 has provided nearly complete coverage of the health service usage of the complete population of Denmark for more than 50 years, including psychiatric hospital contacts23. The Psychiatric Central Research Register (PCRR)23 follows the population from birth in a longitudinal and prospective manner, providing a unique, time-stamped, and reliable28 diagnoses for an individual at each hospital contact. These data, then, more closely reflect real-world clinical practice than retrospective case-control diagnoses because they objectively catalog preceding and succeeding psychiatric contacts. Previous studies have used these powerful data to define rates of comorbid diagnoses in psychiatry8 and describe specific patterns of transitions among diagnoses to demonstrate difficulties classifying individuals with mental disorders at first admission29. Danish register data have also been used to describe both prodromal states30 and premorbid traits in individuals later diagnosed with schizophrenia31, and to record longitudinal stability in patterns of comorbidity32. A systematic, data-driven study of life-course patterns of comorbid diagnoses and their relation to etiological factors has not been pursued but could contribute greatly to how we understand heterogeneity within schizophrenia.The Danish health registers offer a unique perspective on life-course heterogeneity in the clinical presentations of individuals with schizophrenia. Established in 1968, the Danish registration systemN = 5432). We quantified inter-individual differences in comorbidity trajectories using Sequence Analysis33 and examined the structure in the differences using multidimensional scaling (MDS) and cluster analysis of the resulting first principal MDS dimensions. To investigate whether the heterogeneity within clinically defined schizophrenia (ICD-10 F20.0-F20.9) is linked to etiological heterogeneity, the individual patient projections onto the leading principal dimensions of trajectory dissimilarity from the MDS analysis were tested for association with known risk factors and clinical outcomes. This could help uncover biological heterogeneity and motivate more personalized clinical care to improve outcomes in schizophrenia.We hypothesized that predictable trends exist in the patterns of longitudinal, comorbid psychiatric diagnoses among individuals with schizophrenia. To test this hypothesis, we obtained complete psychiatric hospitalization records for all persons born in Denmark between 1981 and 2002 that had received a diagnosis of schizophrenia by the end of 2012 (N = 5432) were identified together with 10,864 random age- and sex-matched population controls and followed until December 31st, 2016 (Table N = 3790) of all participants received at least one other psychiatric diagnosis before their first schizophrenia diagnosis  by the end of 2012  for 22 of 56 pairs of comorbid diagnoses (not including schizophrenia) for individuals with schizophrenia were significant (33 (see “Methods” section) to define trajectory dissimilarity between all pairs of trajectories. Multidimensional scaling (MDS)35 identified three principal components that explained 79% of the variance in the dissimilarity matrix defined by SA  and therefore proceeded with the 1-year increment size.We constructed comorbid psychiatric trajectories  contained individuals with comorbid childhood disorders (cumulative incidence (CI)F7-F9: 100%), cluster 2 (N = 1580) contained individuals with multiple adult comorbidities (CIF1-F6: 99%), cluster 3 (N = 734) contained individuals with only mood disorder comorbidities (CIF3: 100%), cluster 4 (N = 729) contained individuals with comorbid substance abuse (CIF1: 100%), and cluster 5 (N = 1792) contained individuals with little comorbidity (CIF1-F9: 46%)  Fig. . With th6%) Fig. , these c6%) Fig. . The tra38. After Bonferroni correction for 25 tests, the three leading principal MDS dimensions associated globally with parental age , parental history of psychiatric disorders , birth measures , hospital treatment for infections , severity and long-term outcome , and educational attainment polygenic risk score  to relate the three principal MDS dimensions with 23 selected genetic, clinical, and environmental measures that have shown prior associations to a schizophrenia diagnosis. We additionally examined a number of hospitalizations and time spent hospitalized for psychiatric diagnoses, as commonly-used proxies of severityβNumber of hospitalizations = 2.64, P = 2.0 × 10−29; βTotal time hospitalized = 0.49, P = 1.5 × 10−10), parental history of psychiatric disorders , maternal smoking during pregnancy , hospital treatment for bacterial infections , and hospital treatment for viral infections . The second principal MDS dimension (associating with comorbid childhood disorders) was associated negatively with one severity measure  and positively with a maternal diagnosis of schizophrenia . The third dimension (associating with comorbid substance use disorders) showed positive associations with severity measures , and parental psychiatric diagnoses , and negative associations with birth length , birth weight , with parental age , and with PGS for educational attainment , associated positively with the two severity measures (P < 0.002) with membership in the five clusters for 8 of these 13 variables. Post-hoc single cluster-single risk factor associations were significant (P < 0.00096) in 12 out of 52 tests and showed patterns broadly concordant with the post-hoc dimensional analysis (Supplementary Table P < 0.002). We believe this is due to a lack of sensitivity induced by the clustering and note that all 13 associations were at least nominally significant (P < 0.05). Thus, the intuitive factor loadings in the dimensions were also largely reflected in the five clusters. In order to enable external replication and use of these clusters, we trained a decision tree on the k = 5 MDS-based clustering that produces a largely overlapping classification  ascertained from other iPSYCH-cohorts39 and diagnosed with schizophrenia after 2012. This cohort does not have the same population representativeness of the initial cohort due to its ascertainment which should lead to underestimates of the robustness of our initial findings. Regardless, we found sign concordance for 12 of the 13 univariate dimensional associations (Supplementary Table P = 0.0085), maternal age (P = 0.0003), maternal smoking during pregnancy (P = 0.0007), number of hospitalizations (P = 0.0002) and total time hospitalized . We compared the complete trajectories across the entire follow-up period and identified three latent principle dimensions that explained a plurality of the variance. These three dimensions clustered individuals with schizophrenia into five stable subgroups with intuitive, interpretable, and clinically relevant factor loadings and revealed distinct patterns of associations with schizophrenia risk factors. Thus, our exploratory and data-driven analyses revealed substantial, stable disorder-course heterogeneity among individuals with schizophrenia, which could potentially be rooted in etiological differences.40. In contrast to this, clusters 1, 2, 4, and 5 were fairly stable and most of the associations with risk factors and outcomes seen in dimensional representation were also associated with clusters 1–3 when comparing to cluster 5. This indicates that features loading on these clusters  form more distinct trajectories and risk factor and outcome profiles. This could be consistent with childhood disorders and substance abuse having more symptoms clearly distinguishable from typical schizophrenia symptoms, marking more stable heterogeneity, while a prior depression diagnosis may be more epiphenomenal .There are many reasons an individual may receive a particular diagnosis prior to schizophrenia but not all would result in diagnoses that are necessarily meaningful for considering subgroups. We found that 734 individuals had a trajectory in cluster 3 characterized by affective disorder diagnoses, mostly occurring before the onset of schizophrenia. However, this cluster had low stability and only a few of the putative risk factors showed significant associations with Cluster 3, which could indicate that comorbid affective disorder diagnosis alone does not capture any specific pathology. Notably, prodromal symptoms of schizophrenia can overlap with depression symptoms41. For example, dimension 1, reflecting a disorder-course characterized by multiple co-occurring psychiatric diagnoses, was particularly associated with parental history of mental disorders, lower parental age at birth, maternal smoking during pregnancy, birth complications, a history with a higher load of hospital-treated infections, and a more severe disorder course as captured by both number of hospitalizations and total time hospitalized for their psychiatric illness. There has been much work previously on infections being a risk factor for schizophrenia42, which have previously been shown to increase the risk of schizophrenia in a dose-response relationship with the number of severe infections43, that did not appear to be confounded by the genetic risk for schizophrenia44. Our work suggests that beyond a diagnosis, infections may predispose to a disorder course. Along these same lines, we also find associations for risk factors that have more mixed support in the current literature, such as parental age46. Our work may add clarity to this, suggesting the effects of risk factors are not uniform throughout the population of individuals with schizophrenia and may only associate with certain segments of the population. Other associations did not seem supported by previous literature e.g., the finding that family history of mental disorder is associated with a disease course with multiple psychiatric comorbidities.Our work may add additional context to well-described case-control discriminating risk factors47. The second dimension, which was, in particular, capturing clinical trajectories denoted by increased prevalence of comorbid childhood psychiatric diagnoses could reflect the predominance of an earlier developmental pathway that presents in childhood, possibly overlapping with what have been described as premorbid schizophrenia symptoms47. This is in line with a recent study by Dickinson et al.48 who used a cross-sectional design and found a sub-group of individuals with schizophrenia with signs of a more neurodevelopmental course. This dimension was associated with a maternal schizophrenia diagnosis. Notably, the pioneering work by Fish et al.49 described the developmental pandysmaturation syndrome among newborns of mothers with schizophrenia. This dimension was associated with a less severe disease course after the schizophrenia diagnosis as measured by the number of admissions. Interestingly, Dickinson et al.48 also found that individuals with schizophrenia and a pre-adolescent cognitive impairment had less severe symptoms after onset, than those with more typical pre-adolescent cognitive performance that declined (adolescent disruption of cognitive development). Both dimensions 1 and 2 could co-adhere with subtypes of schizophrenia with more neurodevelopmental components, where particularly dimension 1 has a higher level of early life risk factors for schizophrenia, which could indicate that neurodevelopmental pathology plays a particularly important role for these dimensions.This work is also relevant to the position of schizophrenia as a neurodevelopmental disorder50. Comorbid substance use could also lead to a more severe, pro-longed course, by limiting the ability to obtain and maintain treatment51. Thus, this dimension could represent the presentation of individuals who express schizophrenia within the context of a challenging environment.Dimension 3 was primarily related to comorbid substance use disorders. This was associated with a more severe disorder course as measured by the number of hospitalizations and also more parental psychiatric diagnosis, increased birth complications, younger parental age at birth, and lower PGS for educational attainment. When taken together, these risk factors could suppose a less resilient or robust environment for the individual, in that birth complications, parental age, and education level may be correlated with socio-economic status and access to support54. While socio-economic factors and the home environment may directly influence disorder course , any genetic factors associated with specific, life-trajectories of schizophrenia could alternatively affect the home environment. This second scenario could occur if a more debilitating form of the disorder leads to more home disruption or if parents, who by way of Mendelian inheritance would carry these same genes, expressed any behavioral changes that affect the home environment. This kind of gene-environment correlation across generations can be tested by studying non-transmitted alleles as has been done for educational attainment56. We should note that in this study, relationships between environmental risk factors and family history of psychiatric disorders are not completely confounded, implying that environmental factors are likely to make independent contributions to disorder course. The notion of independent effects of socio-economic factors and parental history of psychiatric disorders has found previous support in contributing to risk for schizophrenia54, and provide an interesting motivation for future gene by environment or causal inference studies of heterogeneity in outcomes and disorder course for individuals with psychiatric disorders. Given the current weight of evidence, it is more likely that the home environment modifies disease course, rather than the other way around, although more work is needed, especially given the emerging perspectives of cross-generation generational genetic effects confounding intuitive causal relationships.All three dimensions showed some associations with variables that are known to be correlated with socio-economic factors, such as income level and parental education, which may affect the home environment . However, the direction of causality is difficult to infer57. However, the nature of these later-recorded childhood disorders requires more targeted follow-up.Our initial motivation for pursuing trajectory analysis in schizophrenia was to identify heterogeneity in factors that precede the first diagnosis of schizophrenia. However, we observed many diagnoses of childhood psychiatric disorders (intuitive predecessors) recorded after the first diagnosis of schizophrenia, such that they appear in adolescence or adulthood. Although counterintuitive, earlier onset disorders may go undetected until deeper psychiatric examinations are prompted by treatment for other indications or that a large portion of care for these has been administered at primary care facilities not captured by the registers. The abundance of these highlights the importance of studying for example the safety of administering stimulants to individuals with a history of psychosis58 have pursued analyses that do not focus on the primary disorder polygenic score, a common design . While intuitive, there is not a theoretical requirement to believe case-control associated variants are the only relevant predictors of heterogeneity in outcomes. In fact, while some studies found SCZ-PGS to be related to chronicity59 and negative or disorganized symptoms20, several well-powered studies have been unable to find primary disorder-related PGS to be associated with clinical heterogeneity including treatment response in schizophrenia60 and age of onset in bipolar disorder61. We find it particularly interesting that PGS for educational attainment best discriminate individuals with different trajectories, as reports show educational attainment has large genetic overlap  with schizophrenia, but without consistent correlation in the direction of the per locus effects 62. In Frei et al.62, the authors demonstrated that co-occurring associations without evidence of genetic correlation can be consistent with etiological heterogeneity, a hypothesis that finds support in this work. Additionally, a recent study by Dwyer et al.16 characterizing symptom trajectories after the onset of psychosis also found an association with educational attainment PGS which further supports our finding that genetic variants linked to educational attainment are associated with longitudinal heterogeneity.While polygenic scores for educational attainment and schizophrenia both discriminate schizophrenia cases from controls in this sample, only the educational attainment score was associated with the structure of differences in comorbidity trajectories among individuals with schizophrenia. Relatively few studiesThe structured, stable variability in clinical course among individuals with schizophrenia, and its associations to possible etiological factors, as indicated by our MDS analyses have at least three possible implications for clinical care. First, the principal MDS dimensions of the comorbidity trajectories were differently associated with aspects of the downstream need of care, e.g., number of hospitalization and total time hospitalized, suggesting sub-group predictors could have implications for planning and prioritizing healthcare among those most in need. Second, many of the etiological factors associated with clinical course captured by the comorbidity trajectories were early life factors , suggesting it could be possible to predict the expected clinical course at early stages . Finally, psychiatric classifications are continually updating and could be further refined on the basis of stable differences in outcomes and disorder course having implications for nosology and diagnostic practice more broadly.The strengths of this study are the population-wide design that encompasses all individuals with schizophrenia in the birth cohort at the time of ascertainment, the use of complete longitudinal records of all hospital-assigned diagnoses, and admissions over 16-36 years of follow up, and an exploration of potential etiology heterogeneity.63 and different comorbidity profiles8, and thus early-onset trajectories might less dominate the overall structure. As such this study can be viewed as a minimal summary of the structure within the variability among the course of schizophrenia and a first step towards more thorough characterizations of the nature of underlying heterogeneity.The present trajectory-based method is limited by the relatively young age of the population birth cohort (1981–2002) where some individuals may still go on to develop schizophrenia or other psychiatric disorders and including these additional late-diagnosed individuals may add or change the representations of the structure in trajectory variability. For example, female individuals with schizophrenia may have later onset23, and it has been found that substance use disorders in admissions for schizophrenia were underdiagnosed64. This limitation may be mitigated by the notion that more severe cases, such as those diagnosed with schizophrenia, tend to be treated within the hospital system23 and typically have multiple hospital contacts such that the register coverage of comorbidities might be better in these clinical populations. Numerous prior studies have shown the overall diagnoses in the registers, including for schizophrenia, to be reliable28. This may reflect that registered diagnoses are consensus assignments made at the end of hospitalization by the college of specialized psychiatrists and based on the full clinical course and set of examinations, treatments, and outcomes. Although we see evidence for the change in diagnostic system from ICD8 to ICD10 occurring in 1994 in the rates of specific comorbidities among individuals with schizophrenia, our specific results appear robust to this effect. However, in the interpretation of the parental history, it should be kept in mind that while the majority of diagnoses in the probands were assigned under ICD-10, many of the parental diagnoses were assigned under ICD-8, which does not completely overlap with current diagnostic criteria65. Future extensions of this work should still be mindful of potential complications.The use of psychiatric diagnoses to describe symptom presentation is subject to a number of limitations including imperfect inter-rater reliability and the reliance on imperfect classification systems. Further, the study could be viewed as somewhat limited by the use of health register diagnoses as opposed to standardized research-based diagnoses. Registers provide complete coverage of public hospital in- and outpatient diagnoses, but treatments by private psychiatrists or general practitioners are not recorded15 or Dwyer et al.16). We acknowledge that diagnoses may not capture the full nuance of a change in clinical presentation over time and thus think our results speak to how etiological differences may contribute to longer-term, life-course presentations. We feel this is still important for conceptualizing how to manage long-term care and is somewhat complementary to investigations of symptom scales on short timelines. Further, the potential limits of cruder diagnostic changes may be balanced by the strengths of our unique data resource—long follow up, population-wide, birth cohort—that are not currently mirrored in cohorts with deeper symptom-scale phenotyping. Developing such cohorts would be highly informative and complement our work here. While we focused on detecting and describing heterogeneity within schizophrenia, more studies are needed to uncover how this relates to other diagnostic categories, for example, broadening to a wider spectrum of disorders involving psychosis, and our results should be replicated externally, preferably using cohorts from another country.Another potential limitation is that we cluster individuals according to changes of diagnostic states in 1-year windows, rather than using finer-grained symptom scales in shorter time intervals .Finally, we acknowledge that we have surveyed only a very limited set of potential disorder-course altering risk factors: aggregate measures of common SNPs instantiated as polygenic risk scores (PGS), rare single nucleotide variants, and clinical variables with prior support, but we feel that more far-reaching searches, including other measures of the social environment , copy number and structure variants, broader collections of clinical factors, and PGS from multiple traits and diseases are needed to more fully characterize the nature of disorder-course heterogeneity in schizophrenia. In addition, future work could emphasize longer-term outcomes, such as participation in the labor market.23. We included all individuals with an inpatient or outpatient hospital contact discharge code corresponding to schizophrenia in the International Statistical Classification of Diseases and Related Health Problems 10th revision (ICD-10) codes F20.0-F20.9.A cohort consisting of all singleton births by a known mother in Denmark between May 1, 1981 and December 31, 2002 and diagnosed with schizophrenia before December 31, 2012, was identified through the Danish Psychiatric Central Research Register (PCRR)22 and the PCRR23. The PCRR contains data on diagnoses given during hospital admission and from 1995 onwards it also contains diagnoses given at outpatient clinics. For every individual with a schizophrenia diagnosis and 10,864 age and sex-matched population controls  for almost every Dane born after 198167. The quality of amplified DNA (aDNA) from DBS samples for genotype analyses has been shown to be equivalent to high-quality DNA samples68. The sample consisted of two sub-cohorts: The GEMS cohort (Genomic Medicine for Schizophrenia69), born 1981–1996; and a subset of the iPSYCH cohort39, born 1981–2002 and not included in the GEMS cohort. aDNA genotyping using DBS samples has been performed on both cohorts but using different chips (Illumina 610k and Illumina PsychArray). In the present analyses, all individuals with a schizophrenia diagnosis were included in the phenotypic characterization, but only the iPSYCH cohort was used in genetic association analyses to avoid batch effects. Quality control procedures for genotype data including procedures to account for genetic ancestry have been described elsewhere19. Briefly, the Infinium PsychChip v1.0 array was used for amplified DNA obtained from dried blood spots. Of the ~550,000 genotyped SNPs, 246,369 were deemed good quality, these were phased using SHAPEIT370, and imputed with Impute271 using the 1 000 genomes project phase372 as reference. Imputed genotypes were filtered on imputation quality (INFO > 0.2), association with imputation batch (P > 5 × 10−8), association with genotyping wave (P > 5 × 10−8), Hardy–Weinberg equilibrium , differing imputation quality between cases and controls (P > 1 × 10−6), and minor allele frequency (MAF > 0.01). After these steps, 8,019,760 variants remained. EIGENSOFT v6.0.173, was used to select individuals of homogeneous genetic. KING v1.974 was used to estimate kinship and from each pair of patients with closer than third-degree kinship one was removed. No samples had high levels of missing genotypes (>1%), abnormal heterozygosity, or genotype/recorded sex discordance. For a subset of individuals, whole-exome sequencing (WES) was performed. Quality control and count of disruptive or damaging mutations were calculated from exomes according to the definitions in Ganna et al75. In brief, the WES was performed using the Illumina nextera Rapid Capture Exome kit, with a mean coverage of 77×. Mapping and variant calling followed the Broad Institute Pipeline75. Samples were excluded based on contamination, sex-mismatch, non-European ethnicity, and genetic relatedness75. Variants were excluded based on read depth (<20), HWE (P < 0.000001) or call rate (<0.80). Protein truncating variants were, classified using VEP version 85. Family history of psychiatric disorders was obtained from the PCRR using the parental CPR obtained from the Danish Civil Registration System24. Family history of schizophrenia was defined as ICD-8  codes 295.x9 excluding 295.79; ICD-10  codes F20.0-F20.9. Similarly, a history of maternal infection during pregnancy was obtained from DNPR22 and birth-related variables were obtained from the Danish Medical Birth Register76 , data on all psychiatric diagnoses assigned before December 31, 2016 were obtained from The Danish National Patient Register (DNPR)39. The use of this data is considered to be in accordance with the WMA Declaration of Taipei77.The Danish Scientific Ethics Committee, the Danish Health Data Authority, the Danish data protection agency, and the Danish Neonatal Screening Biobank Steering Committee approved this study. This is in keeping with the strict ethical framework and the Danish legislation protecting the use of these samples. At the time of blood sampling, parents were informed in writing that the blood spots are stored in the Danish Neonatal Screening Biobank and can be used for research, pending approval from relevant authorities and about how to prevent or withdraw the sample from inclusion in research studies39 as part of either a different sub-cohort  or as part of the random population sample. This independent cohort was reserved for replication of results.An additional cohort consisting of individuals diagnosed with schizophrenia between January 1, 2013 and December 31, 2016 was obtained using individuals that were included in the iPSYCH studyN = 65) or death (N = 154) according to the civil registration system or at the end of follow-up . Within the primary cohort diagnosed with schizophrenia before December 31, 2012, we computed the hazard ratio of getting a diagnosis from each of the eight comorbidity categories given each of the other seven categories, using a Cox Proportional Hazard model, defining censoring as described above, with the first diagnosis as a time-dependent covariate and adjusting for age and sex. Apart from the two diagnoses being modeled, other diagnoses were not included in the model.Cumulative incidence was computed separately for each of the eight comorbid disorder categories. We considered an individual censored on the date of whichever came first of emigration  categorical outcomes016 Fig. , and a s78 was used to calculate dissimilarities between pairs of sequences. This method aligns sequences using state substitutions, insertions, and deletions (indels). As sequences had varying observation lengths (16–35 years) depending on the year of birth, for all sequences, we computed the probabilities of later states up to length 36 using a first-order Markov chain based on population-wide estimates of transition probabilities  varying from 1 to 13. Additionally, we calculated stress plots for each value of k. Stability of the MDS results was assessed using a permutation test. Scores for each retained dimension were obtained for every individual. To bring the dissimilarities to a more easily interpretable form , we used the scores from the MDS to perform a Ward’s hierarchical agglomerative clustering37 and selected the number of clusters where the proportion of variance explained leveled off. A permutation test of the stability of the clustering was undertaken.Based on the calculated dissimilarities, a classic MDS was performed. We calculated the cumulative Multivariate analysis of covariance (MANCOVA) was used to test for association between selected MDS dimension scores of dimensions 1–3 (dependent variables) and 18 putative risk variables from the registries, including pregnancy and birth-related variables, hospital contacts due to an infection both in the individual and maternal infections during the pregnancy period, and family history of psychiatric disorders  and count of rare deleterious or damaging mutations. PGS were calculated by using genetic loci previously found associated with a trait. For each locus, an individual can have 0, 1, or 2 risk alleles. The PGS is the sum of risk alleles carried by an individual weighted by the effect size of that allele in the original study. Seven PGSs from publicly available summary statistics of published GWASs were computed (Supplementary Table F-tests conducted (P < 0.05/25 = 0.002), we performed post-hoc univariate regressions to determine which particular dimension was associated with the variable.If a given MANCOVA showed a significant association with the independent variable after a Bonferroni correction for the total number of MANCOVA k = 5) clustering. These were conducted sequentially with the cluster membership as a dependent variable and each of the predictor variables as an independent variable with adjustment for sex and age and for genetic variables additional adjustment for 10 PCs and genotyping wave. Odds ratios were reported treating cluster 5 as reference. Since these were selected based on association in the MANCOVA, we conservatively applied the same significance threshold (P < 0.05/25 = 0.002).A multinomial logistic regression was conducted to test for associations between the variables with a significant association in the MANCOVA and the cluster membership in the selected  were associated with the average yearly number of hospital admissions under a schizophrenia diagnosis after the first diagnosis of schizophrenia and the average number of days spent in hospital per year. For these analyses, we used MANCOVAs as described above adjusting for age and sex.All significant associations from the MANCOVAs were tested in an independent replication cohort consisting of individuals born within the same years but diagnosed with schizophrenia between January 1, 2013 and December 31, 2016. Using the same SA dissimilarity metric, these individuals were projected onto the MDS dimensions of the primary cohort.78 . Dissimilarities were then computed without imputation and using three different substitution cost schemes, three different indel costs, and two measures of distance between state distributionsFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Swimmer puppy syndrome is a disease found in neonatal puppies mainly characterized by the inability to stand, but its direct cause is unknown. Since swimmer puppies were observed infrequently but continuously among the Labrador retriever colony at the Hokkaido Guide Dogs for the Blind Association in Japan, based on their birth record and pedigree, factors related to the onset of swimmer puppy syndrome in Labrador retrievers were examined.th-day body-weight trait was equally high at 0.78, both of which strongly suggest genetic involvement.The total number of offspring over seven years was 436, of which 16 were swimmer puppies. Most of the affected puppies except one recovered steadily. As for the swimmer puppies, the litter size was significantly lower, and the body weights on the 10th and 28th day after delivery were significantly higher than the non-symptomatic puppies. These results suggested that the onset may be related to weight gain in the neonatal stages due to a small litter size. According to the genetic analysis, 26 ancestors common to the affected individuals were confirmed, but the causative individual could not be identified with the inbreeding coefficient. The heritability of the swimmer-puppy onset trait was 0.80, and the heritability for the the 10In this study, the onset of swimmer puppy syndrome in the Labrador retrievers was associated with litter size and early weight gain, and result of study suggests that genetic influence might be involved. Swimmer puppy syndrome is a disease found in dogs and cats during the neonatal period in which all four limbs are splayed laterally, and the main symptom is a ‘swimmer’-like movement , called Orthopedic, nutritional, neurological, genetic, and environmental factors have been discussed as causes of this syndrome , 2, 4–8,. examined 2443 puppies for the relationship between the onset of swimmer puppy and the various factors, including breed, breed size, gender, litter size, and flooring types. Among them, they suggested that overweight due to a decrease in the litter number may be one of the risk factors [Nganvongpanit et al factors . HoweverOn the other hand, at the Hokkaido Guide Dogs for the Blind Association in Japan, swimmer puppies have been seen every year continuously. Although symptoms of the affected individual varied, accurate records of the onset data were kept, and the pedigree could be followed.Therefore, in this study, the risk factors of swimmer puppies found in Labrador retrievers maintained under the unified management protocol were examined. In addition, based on the pedigree information, the involvement of genetic factors was investigated based on the inbreeding coefficient and the BLUP animal model.The total number of pups was 436. Of these, 16 were diagnosed as swimmer puppies, and 420 were non-symptomatic individuals (Table p > 0.1).In the swimmer puppy group, 6 were male and 10 were female . In the distribution graph, the swimmer puppies tended to be in the region with low litter size and heavy body weight (upper left of the distribution map) compared to the non-symptomatic puppies . Calculating the inbreeding coefficient, four ancestors showed high values in more than 10 of the swimmer puppies  was used for genetic analysis. It was excluded from this study for the dogs which could not be tracked with accurate postnatal records.Based on the inspection data by the veterinarian in charge of the Guide Dog Association, a swimmer puppy was defined as followed. Dogs that were seen to have all the following three symptoms at the same time between the 14th and 28th days after birth: 1) the forelimbs, hind limbs, or all four limbs are laterally deployed, 2) flat chest, 3) unable to stand. All individuals were inspected by the same veterinarian.Data were collected for incidence rate, prognosis, breed, sex, weight, and the litter size. The incidence rate of swimmer puppy was expressed as a percentage by dividing the number of affected individuals by all births. The prognosis survey was traced back with all postnatal data. The litter size, and body weight records at birth, 10 days, and 28 days after birth were used and compared between litters where swimmer puppy appeared (swimmer puppy group) and the litters without swimmer puppy (non-symptomatic group). The correlation between the litter size and the body weight at each period was statistically analyzed.http://www.obihiro.ac.jp/~suzukim/masuda/software_inbcoef.html), which applied the method of Meuwissen and Luo [The inbreeding coefficient was calculated based on the assumption that the onset of swimmer puppy is a qualitative trait and a hereditary disease due to a single recessive gene. An ancestor that frequently appears among the blood relatives of the affected individual was identified, and when that individual was designated as the common ancestor (A), the inbreeding coefficient derived from A was calculated for all individuals with records using the following formula.mentclasspt{minimahttp://nce.ads.uga.edu/~ignacy), which could handle threshold traits, was used to estimate variance components and best linear unbiased estimators (BLUE) and BLUP [2 was shown as additive genetic variance (A) / additive genetic variance + residual variance (A + R). To calculate the posterior means and standard deviations of (co)variances, heritabilities and genetic correlation, 400 000 samples after a burn-in of 100 000 iterations were used. Convergence was determined by using visual inspection of the plots of Gibbs samples.Assuming that the onset of swimmer puppy is a quantitative trait, the heritability was calculated. Heritability is a measure of how much an individual's phenotypic value is determined by additive genetic effects. A total of 436 records with 10,946 individuals in pedigree records were used to estimate genetic parameters of Labradors. The onset trait of swimmer puppy in retriever species was estimated using the best linear unbiased prediction (BLUP) animal model below , 14.\docand BLUP . Then Hehttps://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmed.html) was used for statistical analysis, and the chi-square test and Mann–Whitney U test were performed as statistical processing.EZR (EazyR:


Generally, Korean Hanwoo males produced under a 24-month production cycle (PROD24) are evaluated as a part of the progeny test program. However, there is little information on other males outside the PROD24, such as those raised under a 30-month production cycle (PROD30) for higher profits. Therefore, we investigated PROD30 males for important carcass traits  using a reasonably large dataset to understand their genetic merit. To do so, we estimated the genetic parameters of traits using animal model. Our analysis revealed moderate to high heritability values for the studied traits. The marbling score was found to be highly heritable at 0.56. The genetic correlation between traits was mostly moderate to low, and the backfat thickness was poorly correlated with the marbling score. These results are consistent with many previous reports on PROD24. Our study suggests that PROD30 and PROD24 males might have somewhat similar genetic potential, as well as similar genetic backgrounds. Thus, it could be concluded that there is further scope for PROD30 males to improve Hanwoo males’ overall prediction accuracy, especially under a genomic selection program, together with PROD24 males.2 of CWT, EMA, BFT, and MS were 0.35 ± 0.04, 0.43 ± 0.05, 0.48 ± 0.05, and 0.56 ± 0.05, respectively. The strongest genetic correlation (rg) was obtained between CWT and EMA (0.49 ± 0.07), whereas it was negligible between CWT and BFT. EMA and MS were also moderately correlated, whereas there was a relatively low negative correlation between EMA and BFT (−0.26 ± 0.08). Our study revealed a consistent indirect genetic improvement in animals from 2005 onwards. Although Hanwoo improvement has mainly focused on males under a 24-month production cycle, we observed PROD30 males to have somewhat similar genetic potential. Our results provide useful insights into the genetic merits of PROD30 males for the first time, which may facilitate future studies on them and their integration into the Hanwoo National Evaluation for genomic selection. Understanding animals’ genetic potential for carcass traits is the key to genetic improvements in any beef cattle. In this study, we investigated the genetic merits of carcass traits using Hanwoo males raised in a 30-month production system (PROD30). We achieved this using a dataset comprising 6092 Hanwoo males born between 2005 and 2017 and measures of four carcass traits . Genetic parameters were estimated using a multiple-trait animal model through the AIREMLF90 program. According to the multiple-trait model, the h Korean Hanwoo cattle are the most prized source of beef in Korea due to their high marbling and palatability ,2. DecadThe Korean National Hanwoo improvement policy has mainly focused on the PROD24 system since the 1980s. However, Hanwoo cattle face particular challenges from a population genetics standpoint due to their limited gene pool. At the same time, the Hanwoo breeding policy does not encourage the inclusion of foreign genetic resources due to the strict pure breeding strategy. Thus, it is crucial that all potential sources of genetic variation within existing populations, such as PROD30 males, are exploited properly. Although the PGT program showed significant successes in improving carcass performance over the years ,6, the fThe genetic potential for economic traits is usually assessed by population genetic parameters such as heritability and genetic correlation estimates. More accurate knowledge of these parameters leads to better genetic evaluation and breeding programs with maximum selection response . Heritab2: 0.46–0.51 [2: 0.48–0.63) in various reports [Previously, the genetic parameters for the PROD24 population have been reported in numerous studies ,15,16,17.46–0.51 ,14,18. S reports ,14,18,19 reports . The pur2), backfat thickness , and marbling score —were recorded. An MS value of 1 indicated the poorest degree of marbling, whereas a score of 9 indicated the greatest degree of marbling in beef. The feeding of PROD30 males was variable across farms and ad libitum in nature, especially during the last few months before slaughter when they were mostly provided with high-energy diets to increase MS. It is important to note that this feeding practice was somewhat different from commonly used feeding practices for PROD24 males [The study population comprised 6092 Hanwoo males in the PROD30 population that were born between 2005 and 2017 in various designated Hanwoo breeding farms across Korea. The data on carcass phenotypes were provided by the Hanwoo Improvement Center, Seosan, Korea. All the males were classified into three groups based on their purposes of use . The fir24 males , where tA pedigree related to the studied males was obtained from KAIA. The pedigree was composed of 25,032 animals, where the longest ancestral path was traced up to 14 generations, with about 38.13% of those being inbred. Moreover, a significant proportion (96%) of the inbred animals had lower inbreeding rates (0–5%). The highest inbreeding coefficient was 0.26. However, the average inbreeding coefficient was observed as being as low as 0.01 in the pedigree. The software package CFC 1.0  was usedWe selected appropriate fixed and random effects for the genetic analyses of traits . A list Similarly, slaughterhouse and slaughter date were tested as either fixed or random effects and with and without interaction terms. The slaughter age factor was fitted as a fixed covariate with all of the models. Finally, the Akaike information criterion (AIC)  and resiy is the vector related to the observations of carcass traits, b is the vector related to the fixed effects of TYSL and SAGE, a is the vector related to the random additive genetic effects of animals, e is the vector related to the random residuals. , and Var (The (co)variance components of carcass traits were estimated through a multiple-trait animal model using the AIREMLF90 software package . The fin2) was obtained by the equation h2 = g) between two traits was estimated asp) between traits using the phenotypic variance and the covariance of two traits. Note that phenotypic correlation is provided for completeness only. We obtained the approximated standard error (SE) of genetic parameter estimates from (co)variance components using the appropriate AIREMLF90 option (‘se_covar_function’), which uses a Monte Carlo method for the computation of SE, as suggested by Houle and Meyer [g) was also calculated as the ratio of a trait’s genetic standard deviation . However, the phenotypic variability was generally lower in CWT and EMA traits.2 and correlation estimates between traits using the multiple-trait model analysis. We classified all the h2 values into three categories: low (0.00 to 0.20), medium (0.21 to 0.40), and high (0.41 to 1.0), according to Bailey [2 values of 0.56 ± 0.05 and 0.48 ± 0.05, respectively. However, for CWT and EMA, the h2 values were mostly moderate, at 0.35 ± 0.04 and 0.43 ± 0.05, respectively. The CVg of 27% in MS was the highest value among all four traits. The lowest genetic variation was found in CWT in our study.o Bailey . Both MSg estimate indicated a moderate genetic correlation of 0.49 ± 0.07 between CWT and EMA, which was the highest correlation among all pairs of traits. This result suggests that the selection of CWT could lead to improved EMA and vice versa. The strength of association between EMA and MS was moderate and positive (0.48 ± 0.07). Between CWT and MS, the rg was weakly positive (0.23 ± 0.08). However, the rg value was found to be weakly negative between EMA and BFT (−0.26 ± 0.08), while it was very weakly negative between BFT and MS (−0.09 ± 0.08). We also observed an association between CWT and BFT that was statistically not different from zero.Conversely, the rp < 0.001). Similarly, EMA rose from 1.2 cm2 to 4.5 cm2 . However, the EBV of BFT reduced from −0.50 to −0.90 mm . Although an overall decline was obvious in BFT-EBVs, the changes were somewhat inconsistent over time. We observed desirable changes in the EBV of MS over the study period, with values going from 0.5 to 0.75 .2 of 0.35 using males from the PROD24 population. Another recent Hanwoo study reported a h2 of 0.36 [2 values of 0.29 ± 0.04 and 0.30 ± 0.04 in Hanwoo cattle [2 values we found were somewhat similar to Simmental [2 of EMA, we also found agreement with Park et al. [2 value (0.38 ± 0.05) in Hanwoo steers. However, other Hanwoo reports were generally consistent with our report [2 estimate within the range of other breeds such as Nelore cattle (0.29 ± 0.07 to 0.33 ± 0.03), Japanese Brown cattle (0.29), Australian Angus and Hereford bulls (0.26–0.38), Japanese Black steers (0.43 ± 0.06), and Simmental bulls (0.26 ± 0.05 to 0.46 ± 0.05) [2 values appeared to be somewhat intermediate compared to other breeds, for which moderate to high values of h2 from 0.35 ± 0.05 to 0.58 were reported [2 estimate in this study. The finding of Park et al. [2 values ranging from 0.48 to 0.54 were reported [2 values by Shin et al. [2 values of 0.58 and 0.62 ± 0.07, respectively.This study found all carcass traits to be moderate to highly heritable in PROD30 males. Our estimated heritability for CWT was consistent with the value of Park et al. , who rep of 0.36 , which i of 0.36 ,16,18,28o cattle . In compimmental ,32,33. Fk et al.  reportedr report . We also ± 0.05) ,38,39,40 ± 0.05) ,16,18,19 ± 0.05) ,34,41. O ± 0.05) . Additioreported ,38. MS, reported ,18,41. Oreported ,38,43. An et al.  were slig values ranging from −0.09 to −0.45 for Hanwoo cattle [g = 0.01–0.04) with findings in other breeds such as Brahman and Angus [g = from −0.19 to 0.66 ± 0.01) for other breeds [This study revealed a wide range of genetic correlation estimates between traits. We found both CWT and EMA to be moderately and positively correlated traits in PROD30 males. This could be due to the pleiotropy wherein a gene or a set of genes influences two traits and results in the genetic correlation between these traits . This coo cattle ,41,50. So cattle  and otheo cattle  providedo cattle . Howevero cattle , where tnd Angus ,52,53. Tr breeds ,51,54,552 and rg, of PROD30 males for all carcass traits were broadly similar to those reported by previous studies. However, most of the previous reports were based on PROD24 males. Such similarities could be because both these populations share common genetics through common sires, and thus there is a possible common genetic base for traits. Although parameters were somewhat similar across studies, they do not necessarily indicate that the distribution of traits would be similar as well. In fact, our study showed lower CVs (more homogeneity in traits) compared to Park et al. [Our estimates suggest that the genetic parameters, i.e., hk et al. , especiaHowever, the slight difference in parameter estimates among reports could be for multiple reasons. First, the genetic variance of populations under consideration could vary between studies. It is known that heritability can change at different periods, even in the same breed or population, based on the extent of external interventions such as artificial selection. Second, the difference in sample size and analysis methods could partition the additive genetic variance differently for similar traits among populations, thus generating different genetic variances ,13. The In this study, we also demonstrated genetic trends in carcass trait EBVs since 2005. Generally, CWT, EMA, and MS are subjected to positive selection in PROD24 males, whereas BFT is negatively selected because lean meat is targeted ,14. For We already mentioned that a ssGBLUP-based GS is ongoing with the PROD24 population . In thisThis was the first study involving males raised under the Korean National Genetic Evaluation program from a 30-month production period. To conclude, we found close agreement in the estimates between the current population (PROD30) and many earlier reports on animals using the PROD24 production system. Heritability estimates of carcass traits were moderate to high. There was evidence of similar genetic correlations among traits that have been frequently reported for PROD24 males. In this study, we demonstrated the genetic potential of the PROD30 population: our results suggest that PROD30 males could be a potential source of newer genetic variations, which could benefit genomic selection using genomic marker information. We proposed a model for the evaluation based on the present dataset. As such, we believe that this study also holds significance in understanding the genetic architecture of the population. The present study could help with making decisions on whether to integrate the PROD30 males into the national level evaluation. We also believe that their integration into the ongoing national Hanwoo evaluation could result in the robust estimation of animal genetic effects, especially under the umbrella of genomic selection.


The protein α-Klotho acts as transmembrane co-receptor for fibroblast growth factor 23 (FGF23) and is a key regulator of phosphate homeostasis. However, α-Klotho also exists in a circulating form, with pleiotropic, but incompletely understood functions and regulation. Therefore, we undertook a genome-wide association study (GWAS) meta-analysis followed by Mendelian randomization (MR) of circulating α-Klotho levels.Plasma α-Klotho levels were measured by enzyme-linked immunosorbent assay (ELISA) in the Ludwigshafen Risk and Cardiovascular Health and Avon Longitudinal Study of Parents and Children (mothers) cohorts, followed by a GWAS meta-analysis in 4376 individuals across the two cohorts.P < 5 × 10−8), namely ABO, KL, FGFR1, and two post-translational modification genes, B4GALNT3 and CHST9. Together, these loci explained >9% of the variation in circulating α-Klotho levels. MR analyses revealed no causal relationships between α-Klotho and renal function, FGF23-dependent factors such as vitamin D and phosphate levels, or bone mineral density. The screening for genetic correlations with other phenotypes followed by targeted MR suggested causal effects of liability of Crohn’s disease risk [Inverse variance weighted (IVW) beta = 0.059 ] and low-density lipoprotein cholesterol levels [−0.198 ] on α-Klotho.Six signals at five loci were associated with circulating α-Klotho levels at genome-wide significance (B4GALNT3 and CHST9, contribute to genetic influences on α-Klotho levels, presumably by affecting protein turnover and stability. Subsequent evidence from MR analyses on α-Klotho levels suggest regulation by mechanisms besides phosphate-homeostasis and raise the possibility of cross-talk with FGF19- and FGF21-dependent pathways, respectively. Significance statement: α-Klotho as a transmembrane protein is well investigated along the endocrine FGF23-α-Klotho pathway. However, the role of the circulating form of α-Klotho, which is generated by cleavage of transmembrane α-Klotho, remains incompletely understood. Genetic analyses might help to elucidate novel regulatory and functional mechanisms. The identification of genetic factors related to circulating α-Klotho further enables MR to examine causal relationships with other factors. The findings from the first GWAS meta-analysis of circulating α-Klotho levels identified six genome-wide significant signals across five genes. Given the function of two of the genes identified, B4GALNT3 and CHST9, it is tempting to speculate that post-translational modification significantly contributes to genetic influences on α-Klotho levels, presumably by affecting protein turnover and stability.Our GWAS findings suggest that two enzymes involved in post-translational modification,  The endKL (α-klotho) gene polymorphisms have previously been reported in association with urolithiasis, cardiovascular disease, cancers and longevity ], as well as other characteristics with evidence of genetic correlation.P = 2.3 × 10−27], mapped to the second intron of the gene CHST9 on chromosome 18. The second top-associated variant rs8176672  mapped to the first intron of the ABO gene on chromosome 9. A strong association was also observed for another conditionally independent variant at this locus, rs532436, not in linkage disequilibrium (LD) with rs8176672 (r2 = 0.02 in the 1000 Genome Europeans). A variant at a further locus, rs1056008 , is located in the longest exon of B4GALNT3 on chromosome 12. We also identified SNPs rs7333961 , upstream of the gene coding for α-Klotho on chromosome 13, and rs881301 , upstream of FGFR1 on chromosome 8. Genetic associations were broadly similar in both cohorts, with rs8176672 and rs12607664 reaching genome-wide significance in each  and Avon Longitudinal Study of Parents and Children (ALSPAC) mothers . Using these α-Klotho-associated SNPs as instruments of MR, IVW analyses showed no causal effect of α-Klotho on CKD risk (based on an eGFR <60 ml/min/1.73 m2) or eGFR derived from either creatinine or cystatin-C  , which tstatin-C . Bi-direstatin-C . Similarstatin-C .Given findings from LD score regression suggesting that α-Klotho might have functional relationships with other endocrine FGFs, we extended our MR analyses to traits related to FGF19 and FGF21. We found no causal effect of α-Klotho on inflammatory bowel disease. However, in bi-directional MR, IVW analyses revealed evidence of a positive relationship for genetic liability to inflammatory bowel disease and, particularly, Crohn’s disease on α-Klotho levels , with siIVW and sensitivity analyses revealed no causal relationship between α-Klotho and body mass index (BMI), lipids and traits related to insulin sensitivity  and S9. cis-eQTLs for ABO in multiple tissues  databases GTEx v8 and eQTLGen, rs8176672 and rs532436 were strong  tissues . Likewisher loci . The B4GB4galnt3-deficient mice. However, no clear differences were observed when comparing wild-type, heterozygous and homozygous animals , localizing to the Golgi apparatus. This enzyme transfers N-acetylgalactosamine  onto N-acetylglucosamine-beta-benzyl to form GalNAcβ1,4-GlcNAc structures on protein epitopes, also known as N,N′-diacetyllactosediamine (LacdiNAc)  assay. One potential explanation for this apparent discrepant finding is that, rather than altering actual levels of α-Klotho, genetic alterations in LacdiNAc content affect the epitope binding with the human ELISA assay used in our study. Furthermore, different functional effects of B4GALNT3-dependent α-Klotho modification in mice and humans cannot be ruled out.As to the mechanisms underlying this genetic association, acdiNAc) . We prevl tissue , an impol tissue . The prel tissue . On the CHST9 expresses the enzyme carbohydrate sulphotransferase 9, which catalyzes the transfer of a sulphate to terminal LacdiNAc sequences. The two enzymes, B4GALNT3 and CHST9, thus both contribute to generating the terminal SO4-4-GalNAcβ1,4GlcNAcβ structure  and alpha 1–3-galactosyltransferase (EC:2.4.1.37). These enzymes add UDP-N-acetyl-galactosamine and UDP-galactose, respectively, to glycoprotein fucosyl-galactosyl residuals  are mediated by β-Klotho, as opposed to α-Klotho . In addiMany of the genetic factors associated with Crohn’s disease risk represent inflammatory mediators, such as IL-23 , which cThe main limitation of the present study is the relatively small GWAS sample size. That said, the genetic signals which we identified seemed plausible and provided a relatively strong genetic instrument for subsequent MR analyses. In addition, other characteristics which we examined in relation to α-Klotho were supported by well-powered GWASs that were derived from large data sets. In terms of other limitations, the commercial ELISA employed to measure α-Klotho in the GWAS samples has inferior performance compared with the IP-IB assay, though the latter is unsuitable for use in large cohorts due to its labour-intensive nature .B4GALNT3 and CHST9, it is tempting to speculate that post-translational modification contributes to genetic influences on α-Klotho levels, presumably by affecting protein turnover and stability. In subsequent MR analyses, we found no causal relationship between α-Klotho and CKD or FGF23-dependent pathways. However, there was evidence of a causal effect of Crohn’s disease risk and, to a lesser extent, LDL levels on α-Klotho levels, pointing to novel interactions which require further study.In conclusion, we present findings from the first GWAS of circulating α-Klotho levels, in which we identified six genome-wide significant signals across five genes. Given the function of two of the genes identified, The LURIC study is a prospective cohort study of individuals with and without cardiovascular disease and was designed to investigate environmental and genetic risk factors for the development of cardiovascular diseases. Between July 1997 and January 2000, 3316 participants of German ancestry were enrolled in the cardiology unit of a tertiary care medical centre in Southwestern Germany. The inclusion criteria were defined as clinical stability except for acute coronary syndromes (ACS), German ancestry and availability of a coronary angiogram . Exclusion criteria were pre-specified as any acute illness other than ACS, any chronic disease where non-cardiac disease predominated and a history of malignancy within the past 5 years. The detailed study protocol has been published . Writtenhttp://www.bristol.ac.uk/alspac/researchers/our-data/). Genotyping of ALSPAC samples was conducted on the Illumina Human660W-Quad platform and genotype calling using Illumina GenomeStudio. Quality control was carried out in PLINK v1.90 . Of these initial pregnancies, there was a total of 14 676 fetuses, resulting in 14 062 live births and 13 988 children who were alive at 1 year of age. Detailed information on the health and development of children and their parents were collected from regular clinic visits and completion of questionnaires ,41. EthiNK v1.90 . GenotypNK v1.90  The resuα-Klotho was measured in plasma samples from both cohorts using the human circulating α-Klotho assay kit  . The lowB4galnt3−/− mice were generated by breeding B4galnt3tm1c(EUCOMM)Wtsi male mice , having LoxP sites introduced upstream of exon 8 and downstream of exon 9 of the B4galnt3 gene, with female mice expressing cre recombinase under the control of the phosphoglycerate kinase-1 promoter (PGKcre)  and Dexdomitor  before they were bled and euthanized with cervical dislocation. The animal experiments were approved by the Ethics Committee at University of Gothenburg, and the care of the animals was according to their guidelines. Circulating α-Klotho in mouse serum of B4galnt3 wild-type, heterozygous and knockout mice was measured by an established IP-IB assay at the UT Southwestern  ((PGKcre) . The offTX, USA) . Differen = 2234 for LURIC and n = 2142 for ALSPAC  were used as covariates in LURIC, where sex, age and the first 10 PCs were used as covariates in ALSPAC. The final sample sizes were r ALSPAC . Resultsr ALSPAC , the poor2 > 0.9) were ignored, and those situated >10 Mb away were assumed to be in complete linkage equilibrium. A reference sample of 8890 unrelated individuals of ALSPAC mothers was used to model patterns of LD between variants. The reference genotyping data set consisted of the same 6.44 million variants assessed in our GWAS meta-analysis. Conditionally independent variants that reached GWAS significance were annotated to the physically closest gene with the hg19 gene range list available in dbSNP (https://www.ncbi.nlm.nih.gov/SNP/).To detect multiple independent association signals at each of the genome-wide significant α-Klotho loci, we carried out an approximate conditional and joint genome-wide association analysis using the software package GCTA-COJO . SNPs inTwo analyses were conducted using LD score regression ,47. FirsWe examined the evidence for a causal relationship between plasma α-Klotho levels and several renal phenotypes using two-sample MR . PrimaryLD score regression suggested possible relationships with IBD and metabolic outcomes, with which the other endocrine FGFs, FGF19 and FGF21, have previously been implicated , 14 indeIn addition, given lipid traits are correlated with each other, multivariable MR  was condLDL-C, HDL-C and TG versus α-Klotho.LDL-C, TG and APOB versus α-Klotho.HDL-C and APOA1 versus α-Klotho.Cis-acting effects of the genome-wide significant GWAS signals were examined by lookups in the GTEx database v8  to estimate such posterior probabilities (PP) . A lack of evidence (i.e. a PP <80%) in the co-localization analysis goes against the hypothesis that the cis-gene mediates the effect of the genetic signal in question on α-Klotho levels. The gene expression data were extracted from the eQTLGen database. We treated co-localized findings (PP ≥80%) as ‘colocalised’ and other results that did not pass co-localization as ‘not colocalised’.abase v8  and the abase v8 . Regulatabase v8 . To furties (PP) . The defP-value <5e-8) were intersected with regions using bedtools intersect (DNase-I hypersensitivity site data was obtained from the ENCODE database  or two rntersect . In addintersect  were obtI.G., J.Z., T.F.M.A., N.M.S., D.R., L.F., S.M.S., C.O., W.M., J.V. and J.H.T. contributed to the concept, interpretation, critical writing and/or analysis of the data. All authors contributed to revision of the intellectual content and final approval of the version to be published.Supplementary_Material_final-v2_ddab263Click here for additional data file.


We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments  Triticum aestivum L.) is an essential cereal to secure global food security  integrates genome-wide dense markers and, as presented by Meuwissen et al. , is a diThe GS has been widely used in wheat breeding to predict various traits, such as yield, disease resistance, grain weight, heading, iron and zinc contents, end-use quality, and physiological traits  were evaluated for two consecutive wheat seasons (2017 and 2018) to adapt to the diverse environments of North and Central India. To evaluate the performance of multi-environment (ME) GS models on our specific set of selection environments, we tested different GS CV schemes mimicking the breeding schemes where untested lines and environmental performance are highly valuable to achieve the desired selection gains. This study is highly relevant particularly in the South Asian context where trial sizes are relatively small and broadly adapted wheat lines are sought after.T. aestivum L.) were selected from the International Yield Trial of CIMMYT International Nurseries (elite germplasm). These lines constitute a diverse association panel. The seeds of 141 genotypes were obtained from the Germplasm Resource Unit, CIMMYT (Mexico). Detailed information with a pedigree for each genotype is given in A set of 141 South Asian spring wheat lines  at Jabalpur (JBL) (23°14′00.6N and 80°04′40.7E) and Ludhiana (LDH) (30°59′28.74N and 75°44′10.87E), locations during the crop season for 2 years (2017 and 2018), genotypes were phenotyped and evaluated across all trials for four traits  . The expGenomic DNA was extracted from the fresh leaves of seedling wheat using the modified cetyltrimethylammonium bromide (CTAB) method   for eachYik is the trait of interest, μ is the mean effect, Repi is the effect of the ith replicate, Genk is the effect of the kth genotype, ϵikis the error associated with the ith replication and the kth genotype, which is assumed to be normally and independently distributed, with mean 0 and homoscedastic variance. For across environments, Yijk is the trait response and the ith environment, Repj(Envi) is the effect of jth Rep in the ith environment, and Envi×Genk is the environment × genotype interaction. The resulting analysis produced the adjusted trait phenotypic values in the form of BLUPs within and across environments. The BLUPs model considers genotypes as random effects, minimizing the effect of screening time and other environmental effects.where In addition, the components of the phenotypic variance of a given trait at an individual environment and across environments were also extracted to calculate the broad-sense heritability using the formula as follows:where he BLUPs  and the he BLUPs  using Fahe BLUPs  and facthe BLUPs .The baseline SE genomic prediction analysis was implemented in the BWGS program  package T~N, g is the genomic relationship matrix. Ei represents the effect of the ith environment. gEij is the random term that takes into account the interaction between the genomic effect of jth line and the ith environment with gE= (g1………gj)T~N . The highest averaged GRYLD over environments/years was observed at JBL18 (9.4 ton/ha), followed by JBL17 (8.7 ton/ha), LDH17 8.2 ton/ha), and LDH18 (7.9 ton/ha). Similarly, the TGW trait also showed variation across environments. The highest averaged TGW over environments/years was observed at JBL17 (69 g), followed by JBL18 (59.5 g), LDH17 (58.4 g), and LDH18 (53.5 g). We observed significant G × E interaction for the GRYLD and DAYSMT in JBL18 and LDH17 , 2. For  ton/ha, The phenological traits DTHD and DAYSMT displayed the strongest positive correlation (0.88), followed by a weak positive correlation TGW-GRYLD (0.15), while GRYLD and DTHD (−0.73) demonstrated negative correlations. The lowest correlation was observed between GRYLD and DAYSMT (−0.76) traits. The principal component analysis (PCA) of multivariate analysis enables the easier understanding of effects and networks among different traits and elucidates genotypic difference among a set of given traits, i.e., the first two PCs explained 92% of the total variation. The PC1 explained 70.3% of the total variance and PC2 explained 21.7% of the total variance .Table 4; A GS scenario representing SE breeding programs was tested. In this model, the PAs of the GS models for each of the four traits were separately generated for all four tested environments. In other words, the environments were treated as independent. Overall, the PA of the SE model was significantly lower among the three tested GS scenarios Table 4; . PA was Table 4). Interestingly, identical trait rankings were also observed for two ME models, where the DTHD ranked the highest (0.85) and GRYLD ranked the lowest (0.69–0.73) among all four traits. While the ME models had identical trait rankings, the environments ranked slightly differently for the two models for all traits. For instance, both years (2017 and 2018) at the LDH location had higher overall PA compared to JBL for all traits.The inclusion of environmental information in ME models significantly improved the PA over SE models across all traits and environments . A very Crop breeders regularly evaluate the performance of genotypes and collect multiple traits data in various environments. The genotype-based selection on phenotypic and GBS marker information using genomic prediction models is gradually acquiring acceptance in breeding with the initiation of economical next-generation sequencing (NGS) technologies  for 2 years (2017 and 2018) . This study evaluated four traits  for use in an ME trait GP model. GRYLD and related traits were positively correlated to each other in two sets  . This poOur results showed that the heritability of the traits ranged from moderate  to high . Among the four traits, the phenological traits  and TGW particularly showed high stable broad-sense heritability ranging from 0.71 to 0.96. It suggests the high quality of the phenotypic measurements and significant predictive potential of the traits. GRYLD, a highly quantitative and environmentally sensitive trait , and senescence into the genomic prediction framework to improve the selection efficiency of spring wheat in the South Asian 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/VT and DS drafted the manuscript. VT, DS, GD, and YC analyzed the data. UK, JP, and RS designed the field trials, conducted genotyping, and provided breeding lines. VT and YG collected field data. UK, BT, JP, RS, and AJ supervised the overall study. All authors contributed to the article and approved the submitted version.This study was supported by the United States Agency for International Development (USAID), Feed the Future Innovation Lab for Applied Wheat Genomics (Cooperative Agreement No. AID-OAA-A-13-00051), and CGIAR Research Program on Wheat (CRP) Partner Grant to BISA (Grant Code: A5017.09.64).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.


Glycine max (L.) Merr.] yield losses each year in rain-fed production systems of many regions. Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed management. The objectives of this study were to confirm previously reported soybean loci and to identify novel loci associated with canopy wilting (CW) using a panel of 200 diverse maturity group (MG) IV accessions. These 200 accessions along with six checks were planted at six site-years using an augmented incomplete block design with three replications under irrigated and rain-fed treatments. Association mapping, using 34,680 single nucleotide polymorphisms (SNPs), identified 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 SNPs coincident with previous studies that likely tagged 68 loci and 101 novel SNPs that likely tagged 84 loci. We also determined the ability of genomic estimated breeding values (GEBVs) from previous research studies to predict CW in different genotypes and environments. A positive relationship  was found between observed CW and GEBVs. In the vicinity of 188 significant SNPs, 183 candidate genes were identified for both coincident SNPs and novel SNPs. Among these 183 candidate genes, 57 SNPs were present within genes coding for proteins with biological functions involved in plant stress responses. These genes may be directly or indirectly associated with transpiration or water conservation. The confirmed genomic regions may be an important resource for pyramiding favorable alleles and, as candidates for genomic selection, enhancing soybean drought tolerance.Drought causes significant soybean [ Glycine max (L.) Merr.] is exposed, drought causes the most severe yield losses and greatest year to year variation for rain-fed production systems throughout soybean-growing regions , and there were 11 years in which the proportion of the soybean production area impacted by drought exceeded 20%. Total estimated economic losses due to drought during this same time period (adjusted to the consumer price index) were $217 billion in the United States (https://www.ncdc.noaa.gov/billions/events/US/1980-2020). It is likely that climate change will exacerbate the unpredictability of rainfall and will lead to an increased frequency of drought and flooding in the future  in soybean is a promising trait for crop improvement. Carter et al. , 2006 scSlow wilting is associated with the conservation of soil moisture when soil moisture is plentiful, which can then be used when soil moisture in fast-wilting genotypes has been depleted  were selected for phenotypic evaluation of CW. Of the 200 accessions in this new panel, 100 represented the most genetically diverse genotypes (based on molecular marker data) from the original 373-accession panel used by Kaler et al.  germplasm collection  the Pine Tree Research Station (PT), AR  on a Calloway silt loam ; (2) the Rohwer Research Station (RH), AR  on a Sharkey silty clay ; (3) the Bradford Research Center in Columbia (CO), MO  on a Mexico silt loam ; and (4) the Maricopa Agricultural Center, University of Arizona at Maricopa (MC), AZ  on a Casa Grande series  soil. However, in this study, we did not include the 2019 cropping season data from RH and CO, because timely rainfall throughout the season eliminated drought.Soil test analyses were conducted, and P and K were applied as recommended at all site-years. In the PT and RH locations, 9 row plots were sown with a drill having 19 cm between rows and with a plot length of 4.57 m. The plots consisted of four rows at CO with rows 3.96 m in length and with 0.15 m between rows. At MC, there were three-row plots with rows 4.87 m in length and with 0.19 m row spacing. Herbicides and insecticides were applied as recommended to control weeds and insects at all site-years. For the IR treatment, drip irrigation was used at CO, flood irrigation was used at PT, and furrow irrigation was used at RH and MC. At PT and RH, irrigation was applied to IR and DR treatments before the V6 stage when the estimated soil moisture deficit exceeded 50 mm  with correlation coefficients ranging between 0.43 and 0.77 (data not shown). Individual rating values also agreed closely (0.72 ≤ r ≤ 0.91) with the average rating for a given site-year-treatment combination. Previous reports of CW have also found similar ranking among genotypes and high correlations between individual rating dates  + Bm(l) + GRil(j) + GBim(l) + TRkl(j) + TBkm(l) + GTRikl(j) + , and the three-way interaction GSTijk. The random effects included the following: Rl(j) = effect of the lth replicate nested in site-year j, Bm(l) = effect of the mth incomplete block nested in rep l, GRil(j) = effect of the interaction of the ith genotype with the lth replicate, GBim(l) = effect of the interaction of the ith genotype with the mth incomplete block, TRkl(j) = effect of the interaction of the kth treatment with the lth replicate, TBkm(l) = effect of the interaction of the kth treatment with the mth incomplete block, GTRikl(j) = effect of the interaction of the ith genotype, kth treatment, and lth replicate, and the residual error consists of the interaction of the ith genotype, the kth treatment, and the mth incomplete block.For ANOVA, the PROC MIXED procedure (α = 0.05) of SAS 9.4 was used with a model: YPROC VARCOMP of SAS 9.4 with the restricted maximum likelihood (REML) method was used to estimate the variance components for the calculation of broad-sense heritability on an entry-mean basis. The best linear unbiased prediction (BLUP) values for each independent environment, as well as across environments, were estimated using META-R, and these values were then used for association analysis. Association analysis was conducted in three ways: (1) for each of the 12 environments, (2) averaged over site-years for IR (Ave_IR) and DR treatments (Ave_DR), and (3) averaged across all environments (AAE). CW BLUP values for the 12 environments, Ave_IR, Ave_DR, and AAE are provided in www.soybase.org)  of alleles in the TASSEL 5.0 software , which is equivalent to P ≤ 0.0003, was used to identify SNPs. This threshold P-value was chosen based on a formula that uses marker-based heritability  , consisting of MGs from MG000 to MGX, were used as a testing population. The 373 accessions from Kaler et al. (www.soybase.org)   lower than those of the two slow-wilting checks, namely, PI416937 and PI471938. At the other extreme, there were three genotypes for the IR treatment and four genotypes for the DR treatment with wilting scores significantly (P ≤ 0.05) higher than those of the two fast-wilting checks, namely, A5959 and 08705_16  ranged from 39 to 76% for the IR treatment and from 62 to 84% for the DR treatment. Averaged across all the site-years, H2 was 71% for the IR treatment, 79% for the DR treatment, and 86% when averaged over all the environments , and all two-way and three-way interactions (ronments .P) ≥ 3.5; P ≤ 0.0003] for individual environments, plus loci averaged over site-years by the IR and DR treatments, and plus loci averaged over all the environments  on chromosomes Gm10, Gm11, Gm12, and Gm18, respectively, were common between IR and DR treatments, averaged IR and DR treatments, and averaged across all environments . Two genThe SNPs that were not coincident with previous studies were considered novel loci . Of 101 P ≤ 0.05) between GEBVs and observed CW that ranged from r = 0.26 (PT18IR) to r = 0.49 (PT19IR) (r = 0.2 (RH18DR) to r = 0.5 (MC18DR). GEBVs averaged across the IR (r = 0.45) and DR treatments (r = 0.43) and averaged across all environments (r= 0.45) also showed significant positive correlations.Averaged CW scores of the 373 accessions from Kaler et al.  combined(PT19IR) . For theP ≤ 0.05) between observed CW and GEBVs for individual environments that ranged from r = 0.2 (Pine Tree 16) to r = 0.39 , and when averaged across environments (r = 0.37)  .P ≤ 0.05) between observed CW and GEBVs for individual environments that ranged from r = 0.35  to r = 0.46 , and when averaged across environments (r = 0.5)  .Averaged CW scores of the 100 new accessions from this study combined with the 373 accessions reported by Kaler et al.  and the G. max genome assembly version Glyma.Wm82.a1.v1.1 in SoyBase (www.soybase.org)  CW scores under both IR and DR treatments. At the other extreme, PI507407 and PI507408 had wilting scores significantly (P <0.05) greater than those of fast-wilting checks under both the IR and DR treatments  positive correlations (r = 0.8) for CW between the IR and DR treatments, and moderate to high heritability (39% ≤ H2 ≤ 84%), indicating that CW was relatively stable across the environments. Similar results of correlations and heritability were reported in previous mapping studies for CW  correlations between the IR and DR treatments within a site-year and between the Ave_IR and Ave_DR ratings  can be found in the article/SC and AK wrote initial drafts of the manuscript. SC, AK, and JG collected genomic information and analyzed data. LP coordinated and supervised the project. All authors were involved in planning, executing, collecting data from field experiments, and read and approved the final 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.


Triticum aestivum) breeding panel specifically built to capture the diversity across the 75,000 CIMMYT wheat cultivar collection. Twelve phenotypes were correlated to variation in TPS and TPP genes including plant height and biomass (source), spikelets per spike, spike growth and grain filling traits (sink) which showed indications of both positive and negative gene selection. Individual genes explained proportions of heritability for biomass and grain‐related traits. Three TPS1 homologues were particularly significant for trait variation. Epistatic interactions were found within and between the TPS and TPP gene families for both plant height and grain‐related traits. Gene‐based prediction improved predictive ability for grain weight when gene effects were combined with the whole‐genome markers. Our study has generated a wealth of information on natural variation of TPS and TPP genes related to yield potential which confirms the role for T6P in resource allocation and in affecting traits such as grain number and size confirming other studies which now opens up the possibility of harnessing natural genetic variation more widely to better understand the contribution of native genes to yield traits for incorporation into breeding programmes.Trehalose 6‐phosphate (T6P) signalling regulates carbon use and allocation and is a target to improve crop yields. However, the specific contributions of trehalose phosphate synthase (TPS) and trehalose phosphate phosphatase (TPP) genes to source‐ and sink‐related traits remain largely unknown. We used enrichment capture sequencing on TPS and TPP genes to estimate and partition the genetic variation of yield‐related traits in a spring wheat . Here, for the first time, we analysed comprehensive exome SNP information for TPS and TPP genes and dissected the genetic architecture of yield‐related traits in a spring wheat panel specially designed to represent the genetic diversity of 75,000 CIMMYT lines . The data showed significant relationships of TPS and TPP genes with agronomic traits with evidence of historical selection and identified opportunities for future selection of TPS and TPP genes and strategic crossing for yield improvement. All these examples show the centrality of the pathway in determining yield processes and significantly both TPS and TPP genes are listed as having been modified during domestication in maize  as a signal of sucrose availability , consists of bread spring wheat lines constructed from elite high‐yielding material, pre‐breeding lines, landraces and synthetically derived lines selected for high yield and biomass. Marker‐trait associations have recently been published for this population , harvest index (HI), yield , thousand grain weight , grains per m2 (GM2), percentage of grain filling (PGF), grain filling rate , spikes per m2 (SM2), grains per spike (GSP), grain weight per spike , spikelets per spike , infertile spikelets per spike (InfSpS), spike length , awn length , rapid spike growth phase , days to initiation of booting (DTInB), days to anthesis (DTA), days to maturity (DTM), thermal time to initiation of booting (TTInB), thermal time to anthesis (TTAnth), thermal time to maturity (TTPM) and thermal time to anthesis +7 days (TTA7H). For further detail on trait evaluation see Molero et al., , peduncle length , biomass at physiological maturity  of the trehalose biosynthetic pathway using the Cytoscape ClueGo plug‐in  was generated based on the modified Euclidean distance using the ape R package  was calculated following Weir and Cockerham  analysis using the SNPRelate R package  calculated for each pairwise combination in PLINK v.1.9  between and within trehalose genes using the square allele frequency correlation coefficient  in GAPIT v3.0 R package  functions in the FREGAT R package  using the p.adjust R function. Finally, quantile–quantile (Q‐Q) plots were used to verify the fitness of the model and plotted using the CMplot R package (https://github.com/YinLiLin/R‐CMplot).Following recommendations that region‐based tests have different assumptions about the genetic effects and weighting functions  using an optimized Poisson‐based model (dNdScv) in the dndscv R package  from Ensembl Plants per chromosome. Global ω estimates across all genes were estimated per chromosome. A global q‐value ≤ 0.1 (without considering InDels) was used to identify statistically significant genes. A confidence interval (α = 0.95) was calculated per gene. Selection was measured as positive (ω > 1), negative (ω < 1) and neutral  substitutions per synonymous site  by fitting a single‐point association test (Q+K model) using the FREGAT R package. Briefly, beta is the absolute additive effect of the minor alleles on the phenotype in standard deviations  approach  model controlling for population structure  and exome profiles (FST = 0.06) revealed mild genetic structuring between groups and a substantial within‐group variation in elite lines, suggesting that these genotypes have gene‐specific mutations  associated with infertile spikelets per spike (SpS)  Table . Interes3.3TPS1 on chromosome 1A) and synonymous variants (TPP on chromosome 3D) positively interacting with each other to enhance the percentage of grain filling. Furthermore, our results also showed that six genes affected multiple distinct phenotypic traits (i.e. pleiotropic effects), particularly the TPS1 on chromosome 1D  of global information (i.e. variation of the mutation rate across genes in each sub‐genome) for the chromosomes 1D and 2B   Figure  and a TP) Figure .3.5By quantifying the contribution of each variant to the genetic variance of the trait, we observed contrasting patterns of the beta densities, clearly showing differences in the peak and distribution across families Figure . AdditioTPS1 homologues explained a high fraction of heritability (with some significant regions) for thousand grain weight, grains per m2, plant height, final biomass and harvest index  Figure . When we4The T6P signalling pathway is a central regulatory system of resource allocation and source‐sink interactions and is emerging as an important target in crops such as maize, rice, wheat and sorghum  reflecting the impact of local recombination, meaning that such genes could have been selected quickly during the breeding process.Contrasting patterns of gene LD have been reported across a range of studies in maize . Additionally, a TPS1 gene known to regulate flowering time in Arabidopsis  . T6P regulates flowering time in Arabidopsis through FT (Wahl et al., FT may have a broader role in productive development beyond flowering time including effects on spike development, spikelet number and fertility (Liu et al., We propose that the trehalose gene family will support designing strategic crossing and pre‐breeding in various ways. First, we identified several promising genes that in conjunction with gene‐editing techniques could be used to study their more exact role in source/sink pathways, for example In conclusion, our study shows the importance of the trehalose pathway as a contributor to crop improvement both historically and for the future. The wealth of information will direct strategies of crossing and selection and in‐depth mode of action studies to better define the specific contribution of TPS and TPP genes to yield traits.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.D.H.L performed statistical and quantitative genetic analysis. A.W, C.A.G and A.A.I contributed to the exome data analysis and interpretation of the biological results. G.M and M.R conducted the field experiment, assisted with the phenotypic data, and generated the 35 K SNP Chip data. R.J and A.H designed enrichment capture probe set and performed bioinformatics analysis. K.H.P assisted with the bioinformatics analysis. D.H.L and M.J.P. wrote the article, which all authors edited and approved.Supplementary MaterialClick here for additional data file.Table S1Click here for additional data file.