We explored the interplay of environmental factors with neurodevelopment and the lipidome, finding that sleep disturbances and unhealthy diet have a convergent lipidome profile  that is also independently associated with poorer adaptive function. In contrast, ASD lipidome differences were accounted for by dietary differences and sleep disturbances. We identified a large chr19p13.2 copy number variant genetic deletion spanning the LDLR gene and two high-confidence ASD genes (ELAVL3 and SMARCA4) in one child with an ASD diagnosis and widespread low-density lipoprotein-related lipidome derangements. Lipidomics captures the complexity of neurodevelopment, as well as the biological effects of conditions that commonly affect quality of life among autistic people.Autism omics research has historically been reductionist and diagnosis centric, with little attention paid to common co-occurring conditions  and the complex interplay between molecular profiles and neurodevelopment, genetics, environmental factors and health. Here we explored the plasma lipidome (783 lipid species) in 765 children (485 diagnosed with autism spectrum disorder (ASD)) within the Australian Autism Biobank. We identified lipids associated with ASD diagnosis ( Detailed analyses pairing deep phenotyping and molecular profiles reveal mediating roles of plasma lipids in genetic and environmental factors associated with autism-related traits in children. Autistic people are greatly interested in these co-occurring conditions as they adversely impact on development, quality of life and long-term health and wellbeing2,3. However, the biological interactions between ASD and co-occurring conditions are understudied. Emerging high-throughput omics technologies that assay molecular traits  may help to improve biological understanding and identify novel biomarkers to improve the detection of ASD and commonly co-occurring conditions.Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social and communication difficulties, restricted and repetitive behaviors and differences in sensory sensitivity. ASD commonly co-occurs with other medical and psychiatric conditions, including sleep disorders, feeding disorders, gastrointestinal complaints, anxiety and seizuresEFR3A4) is associated with nonsyndromic idiopathic ASD, and genetic syndromes of lipid metabolism are frequently associated with neurodevelopmental delay5 . Outside of these rare genetic diagnoses, others have investigated relationships between clinical lipids and ASD diagnosis, with mixed results. Smaller studies have identified potential associations between hypocholesterolemia and ASD diagnosis8, whereas a large study integrating genetic and electronic health record data suggested that there may be a dyslipidemia subtype of ASD9. Indeed, autistic people may be at a higher risk of treatable cardiometabolic disease10.Rare variation in lipid metabolism genes  to disentangle potential mediators of ASD–lipid relationships. In this article, we investigate relationships between the plasma lipidome (783 species) and autism-associated traits, using rich phenotypic and biological data from participants in the Australian Autism Biobank (AAB) and Queensland Twin Adolescent Brain (QTAB) Project. We looked for associations between autism-related traits  and various aspects of the lipidome. We also integrated lipidomics with genetic and environmental data .n = 485 ASD, 160 SIB and 120 UNR, with 500 boys and 265 girls, including 24 UNR participants from the QTAB Project) at the species level (the most granular classification) and class level (which species collapse into) within the lipid ontology21; these can be further annotated by subclass, feature and domain . Depending on the analysis, some of these outlier groups were retained  and unrelated children without a diagnosis (UNR) Fig. . We profretained .Fig. 1OvWe considered three key neurodevelopmental phenotypes: ASD diagnosis, intelligence quotient/developmental quotient composite score IQ/DQ;  and sleeb = 0.018; s.e. = 0.007; P = 5.9 × 10−3). Male sex was associated with ASD diagnosis  and lower IQ/DQ (linear model (IQ/DQ ~ sex); b = 6.6; s.e. = 1.9; P = 4.6 × 10−4). For batch variables, neurodevelopmental phenotypes were associated with sample storage duration but not injection batch or collection time of day, informing our approach to outliers  and lower IQ/DQ scores , both adjusted for age and sex. For medication classes, ASD diagnosis was associated with attention deficit hyperactivity disorder (ADHD)/behavioral medications (odds ratio (OR) = 5.6; s.e. = 0.4; P = 7.9 × 10−5), antipsychotics , anxiolytics/antidepressants , sleep medications  and fish oil/docosahexaenoic acid (DHA) supplements . Lower IQ/DQ was associated with antiepileptic drug use , sleep medications  and fish oil/DHA supplements . Sleep disturbances were associated with ADHD/behavioral medications  and sleep medications .We assessed relationships between neurodevelopmental phenotypes and covariates with potential lipidome effects: demographics, batch, diet and medications. Age was well matched between ASD, SIB and UNR groups, although children with higher IQ/DQ tended to be slightly older  was modestly associated with decreased cholesterol levels  = 0.70–0.97; P = 1.9 × 10−2; Fig. n = 642) or sleep disturbances (n = 607) and either inferred total cholesterol or triglycerides. As expected, body mass index (BMI) was associated with increased dietary cholesterol , inferred lipidome cholesterol  and triglycerides .In the absence of clinical lipid measurements, we inferred total plasma cholesterol and triglycerides from lipidome data . ASD dia22 analysis sis Fig. . As primq < 0.05) variance in age (coefficient of determination (R2) = 86.4%; s.e. = 1.8%; P = ~0; n = 758), Tanner score for pubertal stage , sex  and BMI  -significant (15) Fig. , as wellR2 = 17.5%; s.e. = 6.2%; P = 2.8 × 10−6; n = 642), sleep disturbances  and ASD diagnosis , after excluding storage outliers. There was also a nominally significant association with the adaptive motor domain score of the second edition of the Vineland Adaptive Behaviour Scale (VABS-II) despite a small sample size . The lipidome was not associated with stool consistency .Lipidome associations with the following neurodevelopmental traits were more modest  to test for associations between individual lipids and six traits  with significant lipidome associations in the variance component analyses , IQ/DQ (n = 8) and sleep disturbances (n = 20) , Tanner stage (n = 43), BMI (n = 159) and sex (n = 71)  or ether lipid subclasses and had glycerophospholipid domains. The lipid set enrichment analysis (LSEA) was highly consistent, including for the same LC-PUFAs: linoleic acid, arachidonic acid and DHA  summary statistics from the Busselton Health Study (BHS)24 . Aggregating the genome-wide-significant single-nucleotide polymorphisms (SNPs) for lipids significantly associated with ASD, IQ/DQ and sleep disturbances, the majority of SNPs localized to the FADS gene cluster on chr11:61.4–61.7 Mb  neurodevelopmental trait associations in AAB/QTAB; and (2) strong chromosome 11 FADS region genetic effects in the external BHS lipidomics GWAS24. Hence, for IQ/DQ, we chose the phosphatidylcholine PC(O-18:0/20:4) and for sleep disturbances we chose the phosphatidylethanolamine PE(P-19:0/20:4)(b); both have arachidonic acid groups and both had strong LWAS associations and strong genetic effects related to FADS genes  GWAS lipid trait pair and rs102274 for the sleep duration/PE(P-19:0/20:4)(b) GWAS lipid trait pair.Leveraging this strong genetic signal for neurodevelopment-associated lipids, we investigated potential pleiotropy or causality between lipids and neurodevelopmental traits. We applied summary data-based Mendelian randomization (SMR) by integrating GWAS summary statistics from independent studies, choosing a locus-specific method (over a genome-wide one) as lipids have an oligogenic architectureion Fig. , so we db = −0.037; s.e. = 0.009; PSMR = 1.7 × 10−5) and between PE(P-19:0/20:4)(b) and sleep duration   and sleep duration/PE(P-19:0/20:4)(b) PHEIDI = 0.96 (n = 20 SNPs)), suggesting that the IQ/PC(O-18:0/20:4) association may be due to genetic linkage, whereas the sleep duration/PE(P-19:0/20:4)(b) association is consistent with a SNP-to-lipid-to-trait causal (or pleiotropic) relationship (Supplementary Table SMR identified strong associations between PCO-18:0/20:4) and IQ (−6) Fig. . However:0/20:4 ab = 4.6 × 10−2 ; for sleep disturbances/PE(P-19:0/20:4)(b), b = −2.1 × 10−3 ), probably because genetic prediction of lipids in pediatric cohorts using adult summary statistics is imperfect  study: a larger population-ascertained pediatric dataset. The directions of effect were consistent but not statistically significant (for IQ/PC(O-18:0/20:4), mperfect  and becaFADS1, FADS2 and TMEM258 for individual-level transcriptome-wide association study (TWAS) analyses using evidence from multistep SMR mapping gene expression–lipid trait associations . However, genetically predicted FADS1 , FADS2 (R2 = 2.01% and P = 6.4 × 10−4 for whole blood) and TMEM258 (R2 = 3.11% and P = 2.1 × 10−5 for whole blood) could predict plasma PC(O-18:0/20:4) levels in AAB/QTAB  for 215 trait-associated lipid species from the LWAS analyses  and checked that selected lipid GWAS results within the AAB/QTAB European cohort were comparable to the BHS GWAS results  and demographic, batch, dietary and neurodevelopmental factors to lipid variance. For neurodevelopment-associated lipids, up to 13% of variance could be explained by lipid PGS, demographic variables and the neurodevelopmental trait of interest Fig. . Batch vb = −0.83 ; for IQ/DQ, b = 0.02 ; for sleep disturbances, b = −0.073 ).We investigated the potential contributions of environmental variables to neurodevelopment-associated lipidome profiles. To capture a trait’s lipidome profile in a single variable, we performed principal component analysis (PCA) on the significant LWAS associations (species level) and took PC1. We verified that each trait’s lipidome profile explained the majority (50–70%) of variance in the focal trait’s LWAS associations  for dietary PC3 and b = 0.88  for ASD diagnosis)  , suggesting that the ASD lipidome profile could be partially attributed to reduced meat intake among the ASD group (linear model (ASD lipidome profile ~ dietary PC3 + ASD diagnosis + covariates): is) Fig. . These fge) Fig.  and 20. b = 0.42  for dietary PC1 and b = 1.4 × 10−2  for IQ/DQ). There was no direct association between the dietary and IQ/DQ measures (linear model (IQ/DQ ~ dietary PC1 + covariates): b = 1.66  for dietary PC1), suggesting convergent and independent associations with the IQ/DQ lipidome profile .The IQ/DQ lipidome profile was independently and more strongly associated with a plant-based, healthy diet (dietary PC1) than IQ/DQ itself (linear model (IQ/DQ lipidome profile ~ dietary PC1 + IQ/DQ + covariates): ile Fig. . The IQ/375 Fig.  and 22. b = −0.91  for dietary PC1) and meat (linear model (sleep disturbances lipidome profile ~ dietary PC3 + sleep problems + covariates): b = −0.46  for dietary PC3)  for sleep disturbances, b = −0.90  for dietary PC1 and b = −0.49  for dietary PC3) . The positive association of ADHD medications with sleep disturbances (here and in other studies28) is expected given that these medications are stimulants that interfere with sleep, whereas fish oil supplementation may improve sleep29 and can also lower triglycerides30.The sleep disturbances lipidome profile had the most complex interactions, being associated with decreased intake of a plant-based healthy diet (linear model (sleep disturbances lipidome profile ~ dietary PC1 + sleep problems + covariates): C3) Fig. . The effC3) Fig. . The sletum Fig.  and 24. tum Fig.  and 20. n = 19 ADHD/behavioral and n = 27 fish oil/DHA). In sensitivity analyses conditioning all other covariates on clinical lipid levels  to R2 = 0.96% (P = 2.7 × 10−2); R2 = 3.3% (P = 3.6 × 10−5) to R2 = 3.1% (P = 6.0 × 10−5); We hypothesized that the ASD lipidome may reflect co-occurring conditions better than ASD itself (given the strong association between diet and the ASD lipidome in this cohort) and investigated sleep as a mediating factor. The relationship between ASD and the ASD lipidome profile was attenuated when adjusting for sleep disturbances and demographic and batch covariates  for IQ/DQ and b = −0.32  for sleep disturbances). We found that the sleep disturbances lipidome profile explained significant variance in adaptive function independent of the measured sleep disturbances score (R2 = 3.2% (P = 3.2 × 10−2) to R2 = 1.7% (P = 1.7 × 10−2)). In contrast, the relationship between adaptive function and the IQ/DQ lipidome profile was dependent on IQ/DQ score (R2 = 2.3% (P = 2.9 × 10−3) to R2 = 1.3 × 10−5 (P = 0.94)).Next, we investigated whether omics data relate to adaptive function for children with autism. We used the VABS-II composite score (only collected within the ASD group), which is strongly correlated with increased IQ/DQ and decreased sleep disturbances  measures may not capture the intrinsic heterogeneity of ASD . Furthermore, lipid levels have a relatively oligogenic genetic architecture, which could overlap ASD-associated genetic regions .2, sex, batch, injection order and storage duration) and excluding storage duration outliers, we found that the sphingomyelin SM(34:3) was significantly more variable in the ASD group after multiple testing correction ; (2) visibly fatty plasma samples (n = 12); and (3) individuals with copy number variants   statistical outliers (26) Fig. .Fig. 6Oun = 12 visibly fatty plasma samples and had the three highest concentrations of total deoxyceramide and triglycerides and three of the four highest total diacylglycerol levels .Four of the seven statistical outlier samples had biological explanations. Three were among the els Fig. . Two of LDLR) gene that has key roles in cholesterol regulation. This CNV also spans ASD-associated genes in the Simons Foundation Autism Research Initiative’s SFARI Gene database32, including ELAVL3 and SMARCA4. Rare genetic variation in LDLR causes familial hypercholesterolemia and common LDLR variants are associated with high cholesterol24. Furthermore, others have found a five-exon cluster of the LDLR gene that carries ASD-segregating variation9. This participant’s lipidome was consistent with high levels of LDL particles, with the highest plasma concentrations of cholesteryl ester and dehydrocholesterol ester, and was among the top five highest concentrations for free cholesterol. This was accompanied by high concentrations of a number of sphingolipid classes , GM3 ganglioside and sphingomyelin) that are typically enriched in LDL particles, as well as a number of phospholipid classes   Fig. .n = 12 visibly fatty plasma samples, n = 7 had among the ten highest concentrations of diacylglycerols and n = 5 were among the ten highest for triacylglycerols and alkyldiacylglycerols, suggesting that these lipid classes may be related to fatty plasma appearance. It is possible that dietary differences or a fatty meal preceding sample collection could explain lipidome differences; however, we were unable to test this as only two out of 12 individuals had matching dietary data.Of the FADS gene cluster33 suggested that arachidonic acid metabolism  may have a pleiotropic or mediating relationship with sleep disturbances. As the genetic signal for DHA was weaker, we did not perform SMR and thus do not rule out a causal effect of DHA on sleep. While previous observational37 and small randomized controlled trials41 have investigated omega-3 or DHA supplementation in ASD, our results suggest that sleep disturbances could be the more relevant target29. We have focused our discussion on LC-PUFAs because they are interpretable; however, the species-level associations that we report remain largely uncharacterized and warrant further study.In a large neurodevelopmental lipidomics study, we leveraged extensive phenotypic and omics data to dissect genetic, environmental and behavioral influences on the neurodevelopmental lipidome. The pediatric plasma lipidome was associated with neurodevelopmental traits, including ASD diagnosis, IQ/DQ and sleep disturbances, although there were still stronger associations with age, puberty stage, sex, BMI and dietary traits. ASD diagnosis was modestly associated with lower cholesterol levels, whereas there was no association of IQ/DQ and sleep disturbances with either cholesterol or triglyceride levels. Neurodevelopmental traits were associated with LC-PUFA-containing lipids: specifically, ASD with decreased linoleic acid, IQ/DQ with increased arachidonic acid and sleep disturbances with decreased DHA and arachidonic acid. Furthermore, shared genetic signal within the 42 and ASD diagnosis10. While medications were extensively associated with these neurodevelopmental traits, only ADHD/behavioral medications and fish oil/DHA supplements affected neurodevelopmental lipidome profiles beyond that explained by the trait itself. The sleep disturbances lipidome profile was nominally associated with poorer adaptive function independent of the severity of sleep disturbances. The microbiome analysis identified an association between sleep disturbances and higher microbiome potential for NAD salvage, which is notable given that NAD+ levels have been linked to circadian cycle control46. There was also a decrease in microbiome potential for l-methionine biosynthesis and its derivative S-adenosyl-l-methionine. The latter is involved in the synthesis of neurotransmitters, including dopamine, serotonin and noradrenaline, and is being investigated as a potential therapy for depression48 (whose diagnostic criteria includes sleep disturbances).We investigated the relationship between neurodevelopmental lipidome profiles and environmental factors . Whereas the ASD lipidome profile appeared to be (at least in part) a consequence of diet, lipidome profiles for sleep disturbances and low IQ/DQ converged with unhealthy dietary patterns (independent of the IQ/DQ and sleep phenotypes) and with a potential mediating role for the microbiome. These findings suggest potential mechanisms with links between cardiometabolic disease and both sleep disturbances49, having found convergence in the sleep disturbances lipidome with dietary habits that may predispose to long-term health problems, as well as possible associations with poorer adaptive function. Furthermore, a participant diagnosed with ASD with a large CNV deletion in the chr19p13.2 region (which includes the LDLR gene and multiple high-confidence ASD genes) had a dramatically altered lipid profile reflecting higher levels of LDL particles.Our findings have potential clinical implications. There may be a role for dietary LC-PUFAs or omega-3 or -6 supplementation in sleep disturbances. We also highlight the imperative to screen for and manage sleep disturbances among children with neurodevelopmental differences50, the omics signal for ASD diagnosis is weak compared with other factors, such as age, genetics, diet (which confounded ASD lipidome associations in this study) and co-occurring conditions . Overall, omics studies in children with a pre-existing diagnosis are better suited to interrogating biological associations of co-occurring conditions and quality of life, which are of direct interest to autistic people, rather than the current focus on diagnostic biomarkers.Our results also have implications for ASD biomarker research. We advocate for biobanking, deep phenotyping and careful consideration of covariates to make biological inference about intermediate traits such as the lipidome, where differences reflect genetic, environmental, behavioral and technical effects. As demonstrated here and in previous workThere are some limitations to this work. The sample collection protocol did not include fasting . We attempted to overcome this by performing sensitivity analyses with cosine-transformed sample collection times, to account for cyclical patterns in lipid concentrations, and did not find major differences in the results. Clinical lipids were not directly measured and were instead inferred using lipidomics measures. We used conservative methods (ANCOM version 2.1), multiple testing correction thresholds see  and covaFuture, larger studies with longitudinal designs and recruitment from the broader population will be important to establish a fundamental understanding of the pediatric and neurodevelopmental lipidome. Further work is required to replicate the association between reduced LC-PUFA metabolism  and sleep disturbances, to determine whether this statistical result reflects causality, and thus whether it is clinically relevant. Longitudinal studies are required to confirm relationships between childhood sleep disturbances, pediatric lipidome profiles and long-term cardiometabolic health, both in autistic people and the general population.FADS gene cluster and LDLR) and the environment . Our results point toward metabolic convergence for sleep disturbances and poor diet in autistic children, with implications for long-term wellbeing and quality of life.In conclusion, in this detailed analysis of a deeply phenotyped dataset, we highlight complex relationships between neurodevelopment, physical health, genetics  . At the Queensland recruitment sites, ethics approval was through the Mater Health Services HREC  and The University of Queensland . At the Victoria recruitment site, ethics approval was through La Trobe University . At the Western Australia site, ethics approval was through the Princess Margaret Hospital for Children  and The University of Western Australia . For the QTAB Project, ethics approval was through the Children’s Health Queensland HREC  and The University of Queensland .No method was used to predetermine sample size. As a data-driven project, our aim was instead to maximize the sample size within budget constraints. Randomization was performed within the lipidomics data acquisition process to mitigate batch effects. As a data-driven, exploratory project, there were no interventions to randomize to. The investigators were not blinded to allocation during the clinical and survey assessment relating to phenotypic data for the AAB. However, investigators were blinded in the lipidomics sample preparation and data acquisition process. An in-depth explanation of sample exclusion by analysis is provided in the ‘Study participants’ and ‘Approach to outliers’ sections below.This study included a total of 765 participants : 485 with a diagnosis of ASD, 160 undiagnosed siblings without recorded ASD diagnosis and 120 unrelated undiagnosed children (96 from the AAB and 24 from QTAB)  a distance to the origin in PCA in the 99th percentile of the distribution. These outlier samples were excluded from all analyses except the outlier analysis (see the section ‘Lipidome outliers’), as four  had biological rather than technical explanations  (see the section ‘Approach to outliers’). We also excluded one participant with Smith–Magenis syndrome within the UNR group. Overall, in the main analyses , there were a total of 758 participants . Supplementary Table Seven statistical outlier samples (six ASD and one UNR) were identified that met two criteria: 1) exceeding the 99th percentile of an extremeness score, calculated for each individual and representing the number of  exceedinn = 64 participants in the ASD group for whom the samples had been stored for a longer period of time than other samples . However, they were retained for analyses of other traits  after sensitivity analyses confirmed that they did not significantly affect conclusions. We note that these older samples tended to have less phenotypic data collected  and none had provided dietary data, so they were already excluded from many analyses for these reasons.We also identified a subset of n = 12 participants had plasma samples that were visibly fatty. These were included in all analyses but were also examined as a group of interest for whom there was an a priori expectation of a different lipid profile.A total of 51 with modifications. Analysis of plasma extracts was performed on an Agilent 6495C Triple Quadrupole mass spectrometer with an Agilent 1290 series high-performance liquid chromatography system and a single ZORBAX Eclipse Plus C18 column  with the thermostat set at 45 °C. Samples were randomized to processing batch and injection order.Lipidomics was performed as described previously2O, 30% acetonitrile and 20% isopropanol (vol/vol/vol) containing 10 mM ammonium formate and 5 μM medronic acid) and solvent B  containing 10 mM ammonium formate).Mass spectrometry analysis was performed in both positive and negative ion mode with dynamic scheduled multiple reaction monitoring. The running solvent consisted of solvent A .The following mass spectrometer conditions were used: gas temperature = 150 °C; gas flow rate = 17 l min−1 at 15% B and increased to 50% B over 2.5 min, then to 57% over 0.1 min, 70% over 6.4 min, 93% over 0.1 min and 96% over 1.9 min, and then ramped to 100% over 0.1 min. The solvent was then held at 100% B for 0.9 min . Equilibration was started as follows. The solvent was decreased from 100% B to 15% B over 0.2 min and held for a final run time of 16 min. We included three quality control sample types: pooled quality control, technical quality control and NIST 1950 SRM samples. Pooled quality control samples were included at a rate of 1:20 per sample during the lipid extraction process. These provided an indicator for variance across both the extraction and mass spectrometry analysis. The technical quality control samples were a pre-extracted set of pooled samples identical in composition. These were injected from an independent vial at a rate of 1:20 injections to measure variation across the mass spectrometry run. NIST 1950 SRM is a commonly used standardized reference material for aligning the lipidomic data between different studies and was injected at a rate of 1 per 40 samples. As an indicator of sample variability, we calculated percentage coefficients of variation for the three quality control sample types: technical quality control (6.7%), pooled quality control (7.6%) and the standardized NIST 1950 SRM (7.4%). These quality control types have been described previously51.For the chromatography, we used a stepped linear gradient with a 16 min cycle time per sample and a 1 µl sample injection. The sample analytical gradient started with a flow rate of 0.4 ml minMassHunter Quantitative B08 was used to quantify lipid concentrations from mass spectrometry data. Relative quantification of lipid species was determined by comparison with the relevant internal standard. Lipid class total concentrations were calculated as the sum of individual lipid species concentrations, except in the case of classes triglycerides (TG) and akyldiacylglycerols (TG(O)), for which we measured both neutral loss and single ion monitoring (SIM) peaks and subsequently used the more numerous but less structurally resolved SIM species concentrations for summation purposes when examining lipid totals.Blood samples are difficult to collect in children, and specifically those on the autism spectrum. To improve recruitment, a standardized protocol for collection time of day was not enforced, nor were participants required to fast before sample donation.Overall, the lipidomics assay panel quantified 793 species (out of 825 in the full Baker Institute panel at the time of data acquisition) grouped into 41 lipid classes. The lipid species are the most granular level of lipid ontology and make up the lipid classes. There are also additional categories used in this ontology, including subclasses, features and domains, which are not strictly collapsed within each other. We also inferred clinical lipid levels with total cholesterol, calculated as the sum of the free cholesterol lipid species and the cholesteryl ester lipid class. Triglyceride levels were approximated using the triglyceride (SIM) lipid class measure. Note that the lipidome includes highly correlated data at the species and class hierarchies . We also confirmed that these excluded lipids had negligible association with ASD diagnosis in a model of lipid concentration ~ ASD diagnosis . Hence, after quality control, 783 lipid species remained.We excluded 52 (for older children in the AAB), the age-corrected NIH Toolbox Cognitive Function composite score  and the nonverbal developmental quotient score from the Mullen Scales of Early Learning54 (for younger children in the AAB) as a proxy for cognitive ability and developmental delay); and (3) sleep disturbances, measured using the CSHQ55 total sleep disturbance score . As benchmarking traits and positive controls, we also performed analyses on age, Tanner score  and BMI z score .We focused on three neurodevelopmental traits: (1) ASD diagnosis; (2) IQ/DQ composite score  and batch variables (lipidomics batch and injection order and sample storage duration) and collection time (with cosine transformation to model periodicity) to be noise covariates (explanatory variables) that could affect lipid levels and were therefore adjusted for in most analyses. For the traits age and Tanner score , we did not include age and age2 covariates. For BMI (z score), we did not include age, age2 and sex covariates, as these are accounted for within the population-normed z score.Lipidome measures are molecular traits with both genetic and environmental influences, so lipidomic analysis requires careful consideration of covariates. For the neurodevelopmental traits, we generally considered demographic , which investigated a near-identical plasma lipid panel from the Baker Heart and Diabetes Institute Metabolomics Group. Briefly, their study identified 733 genomic regions with genome-wide-significant evidence (P ≤ 5 × 10−8) for association with lipid levels. Given the sample size of 6,057 individuals, this necessarily reflects that many associations had a large effect size. We used the genome-wide-significant loci to construct PGSs for n = 490 lipid traits. For each lipid species trait, we performed linkage disequilibrium clumping with default settings (variants within a 250 kilobase distance with R2 ≥ 0.10) to identify an independent set of SNPs, using the AAB/QTAB imputed genotypes as a linkage disequilibrium reference. With this set of SNPs, we generated PGSs with the beta statistics from the original GWAS in PLINK57 (version 1.90) using the --score function, which multiplies each SNP effect size by an individual’s allele dosage, then sums across all independent loci. We focused our analysis on European participants, whom we had identified in previous work31, so that ancestries were matched to the BHS GWAS dataset, which increased the predictive ability. We standardized the PGSs within the European dataset (including parents and children who provided SNP genotyping samples but did not have lipidomics data measured here) to have a mean of 0 and a standard deviation of 1.We obtained summary statistics for genome-wide-significant SNPs from a GWAS of 59 were available for a subset of n = 264 children with lipidomics data, of whom 261 remained in the main lipidomics dataset  after excluding outliers. We focused on two forms of dietary data: (1) AES variables quantifying food groups such as proteins, fats , cholesterol and total carbohydrates ; and (2) dietary profiles that were generated by performing compositionally aware PCA on data representing the percentage of energy from various food groups . To generate dietary profiles (referred to as dietary PCs), we applied a centered log-ratio transformation  and then performed PCA. This data processing has previously been described within the AAB/QTAB dataset50, but principal components were regenerated here for the n = 264 individuals for whom there were both lipidomics and dietary data.Dietary data from the Australian Eating Survey (AES)50  were available for a subset of n = 169 children in the AAB and QTAB cohorts  with matching lipidomics and dietary data (and after excluding outliers). We used metagenomics data (described previously50) that included species and multiple functional annotations; for the latter, we focused on the MetaCyc pathways as these had the most easily interpretable functional descriptions. In this analysis, we filtered for common microbiome features by focusing on microbiome features that were present in ten or more individuals in the subset of n = 169 individuals with overlapping lipidomics data. This resulted in n = 511 species and n = 764 MetaCyc pathways.Stool metagenomics dataSix self- or parent-reported medication/supplement categories were considered in this study: ADHD/behavioral, antipsychotics, antidepressants/anxiolytics, antiepileptics, sleep medications or supplements and fish oil/DHA supplements.We considered three batch and technical variables, reflecting lipidomics data generation (processing batch and injection order), storage duration (see the section ‘Approach to outliers’) and collection time of day , we applied the following transformation, dividing by 2,400 for simplicity as the data were provided in 24-h time.To model periodicity in the collection time of day . We also statistically accounted for biological sex by including this as a covariate in all analyses except where specified . We performed analyses to investigate the relationships between sex and the lipidome  were excluded for all analyses except for those within the ‘Lipidome outliers’ section, in which they were specifically interrogated for potential biological explanations. The rationale for this was that including these outliers when investigating mean group differences  could bias the results.The n = 64 storage duration outliers, defined as samples with a storage duration of ≥2,500 days. This was motivated by our observation that the ASD OREML analysis without covariates . Closer inspection of the data revealed that storage time was confounded with ASD diagnostic status . We generated three logistic regression models to assess the conditional associations between diagnosis and dietary versus lipidome cholesterol:First, we examined the effect of dietary cholesterol intake among n = 630 included in the analysis, for whom self-report was available on n = 468, of whom n = 35 self-reported antipsychotic usage and n = 99 reported medications prescribed for ADHD or challenging behavior). We repeated the likelihood ratio test comparing:Second, we performed a sensitivity analysis excluding participants with current or previous self- or parent-reported antipsychotic or ADHD/behavioral medication usage .We estimated the proportion of variance in clinical phenotypes associated with the lipidomics data using the OREML method implemented in the OSCA packageCβ represents fixed-effect covariates, W represents a matrix of standardized lipidome measures for all samples, and the random effects y is below, and OREML solves for:Ao is the omic data-based relationship matrix, where diagonal elements tend toward 1 and off-diagonal elements represent the pairwise correlation of lipid measures between two individuals.Using a mixed linear model fitting all lipids as random effects:2 , sex , batch, injection order and storage duration (covdemog); and, as sensitivity analyses, adding either dietary profiles (covdemogdiet) or collection time of day (covdemogtime). We considered three categories of dietary variables: dietary profiles ; macronutrients ; and dietary diversity (measured using the Shannon index applied to the AES food-level data). For the analysis of dietary profiles, we included as covariates age, age2, sex, batch, injection order and storage duration. For the other dietary variables, we only included the covariates sex, batch, injection order and storage duration and instead performed a sensitivity analysis including energy intake as a covariate (covdemogenergy), reasoning that energy intake is strongly correlated with age and is more relevant to dietary data.We applied a rank-based inverse normal transformation to each lipid measure and considered various sets of covariates: no covariates (nocov); the covariates age, agen = 7 participants identified as statistical outliers in the lipidomics data quality control. For the ASD diagnosis analysis, we additionally excluded the n = 64 storage duration outliers. We tested the effect of excluding the n = 64 storage outliers before and after performing the inverse normal transformation to the lipidomics dataset, finding negligible difference. Furthermore, excluding these outliers had minimal effects on the results for all traits other than ASD diagnosis.For all traits, we excluded the We performed sensitivity analyses accounting for the time of sample collection and dietary data, as these variables are likely to be important but were only available in a subset of participants. We were unable to perform a sensitivity analysis including total cholesterol and total triglyceride levels due to collinearity between these covariates.Our multiple testing strategy is described in the section ‘Multiple testing strategy’. Briefly, we took a phenome-wide approach, using Bonferroni–Hochberg correction across all 18 traits  in the primary analysis.t-statistics of lipid concentrations against outcomes. Covariate-adjusted lipid correlations were calculated by regressing covariates  were the traits of interest and those excluding sex when BMI (z score) or sex were the traits of interest) from the trait of interest against the adjusted lipid concentrations and generating a correlation matrix from the residuals. For each lipid set, the LSEA statistic was calculated as the sum of t-statistics, adjusting for lipid correlation (taking as the denominator the square root of the sum of the adjusted correlation matrix specific to the lipid set). The P value was approximated as the chi-squared of the LSEA statistic squared with one degree of freedom. Under the null hypothesis, the squared LSEA statistic follows the F statistic, which tends to the chi-squared statistic with large sample size.Lipid set annotations were provided by the Baker Heart and Diabetes Institute, corresponding to domain, class, subclass and feature. We used these annotations in correlated random variable LSEA , which tests for enrichment of pathways across the entire lipidome while accounting for the correlation structure between features. We first calculated association 22 (version 0.46). We performed these analyses for ASD diagnosis, IQ/DQ, sleep disturbances, age, Tanner stage and BMI, using the same transformations, subsets of participants and covariate combinations (demographic and batch) as in the OREML analysis.We performed LWASs using the linear/logistic models implemented in OSCA2, sex, batch, injection order and storage duration for all traits; the exceptions were the following: excluding age and age2 as covariates for age, Tanner and BMI and excluding sex as a covariate for BMI and sex). We had also investigated OSCA-MOA (mixed linear model-based omic association)—a mixed model method that accounts for the correlation structure between participants—but found that the median test statistic was lower than expected under a null hypothesis, implying that MOA was susceptible to false negatives when applied to these highly correlated lipidomics data. We also performed sensitivity analyses adjusting for clinical lipids, to determine lipidome changes independent of total quantities of lipoproteins. For these analyses, we included additional covariates representing inferred total cholesterol  and total triglyceride levels ). We performed an additional sensitivity analysis adjusting for collection time of day  for the individuals for whom these data were available (n = 657).As sensitivity analyses to control for false positives, we performed linear or logistic models with and without covariates , analogous to Bonferroni correction. In addition, we performed sensitivity analyses, taking as the multiple testing denominator the number of principal components explaining 95% of the variance (n = 23 for lipid classes and n = 129 for lipid species). Multiple testing correction was applied per trait lipidome analysis .We adjusted for multiple testing in a manner that was sensitive to the strong correlational structure within the lipidomics dataset (see the section ‘Multiple testing strategy’). Instead of applying Bonferroni correction (which would be overly conservative), we applied PCA to the lipidomics datasets  and identified the number of principal components that explained 99% of the variance in the data as the effective number of variablesFor each trait, we then conducted a linear model analysis, fitting simultaneously all significantly associated lipids and applying post-hoc backwards stepwise regression  to identify the most associated set of lipids accounting for the correlation between them.We additionally used the lipid pathway annotations to label the LWAS significant hits.24. We used GCTA (version 1.93.2 beta) MLMA64, using inverse normal-transformed lipidome data for European participants from AAB/QTAB and including age, age2, sex, batch, injection order, storage duration and 20 genotyping principal components representing population stratification.For a handful of lipid traits, we ran GWAS to examine whether genetic control was similar in this pediatric dataset to that of adults from the BHS lipidome GWAS24 in the European subset of the AAB/QTAB cohort, which is pediatric and one-tenth of the size (n = 646).The purpose of this analysis was to replicate the large genetic effects on lipid levels identified in the BHS GWASFocusing on the lipid species that were significantly associated with clinical traits , we dissected the variance in lipid concentrations explained by genetic and clinical data.R2 in a linear regression model. We performed ANOVA of the following models, quantifying R2 for each term:2 and sex + groupLipid trait ~ PGS + age, age2 and sex + dietary variables  + groupLipid trait ~ PGS + age, age2 and sex + batch variables + time of day + groupLipid trait ~ PGS + age, ageWe quantified the variance explained in each lipid to understand the relative contributions of covariates to lipid traits. This was achieved by calculating 65 to external GWAS summary statistics to test for associations between: (1) two lipid species identified in our LWAS analyses (PC(O-18:0/20:4 and PE(P-19:0/20:4)(b))24 and their associated neurodevelopmental traits (IQ25 and sleep duration26); (2) the same two lipidome traits24 and gene expression; and (3) gene expression and the neurodevelopmental traits. For the lipid trait analyses, we selected rs99780 as the instrument to test the IQ/PC(O-18:0/20:4) association and rs102274 for the sleep disturbances/PE(P-19:0/20:4)(b) association. To investigate whether specific genes mediated the lipid trait associations, we performed SMR investigating gene–lipid and gene–trait associations using eQTLGen summary statistics66 for genes in the window of chr11:61050000–62200000, using the same SNP instruments as for the lipid trait SMR. For each analysis, the HEIDI test65 was used to determine whether putative relationships between traits were due to genetic linkage, as opposed to pleiotropy or causality.We applied the SMR (version 1.03) methodFor the LWAS hits, we also investigated whether genetically predicted lipid levels  could predict the neurodevelopmental traits.Within the AAB/QTAB dataset, we did not find significant associations between the neurodevelopmental traits and lipid PGSs, but the direction of effect was consistent for 17 out of 24 lipid species.As a replication analysis to support our main results, we turned to the ABCD dataset to genetically predict levels of the two lipids used in the SMR analysis: PC(O-18:0/20:4) and PE(P-19:0/20:4)(b). As equivalent traits for IQ and sleep disturbances in the AAB/QTAB dataset, we used the NIH Toolbox Total Cognition Score and Sleep Disturbance Scale Total Sleep Score, respectively, among 4,952 European participants.P value clumping and thresholding method, as described in the main analysis. As the P ≤ 5 × 10−8 threshold only yielded one SNP for prediction, we selected the P value threshold that explained the greatest proportion of variance in the lipid trait within the AAB/QTAB dataset. For PC(O-18:0/20:4), we took the threshold P ≤ 5 × 10−6 (AAB/QTAB dataset: R2 = 4.6% and P = 3.7 × 10−8) and for PE(P-19:0/20:4)(b), we selected P ≤ 5 × 10−8 (AAB/QTAB dataset: R2 = 1.5% and P = 1.6 × 10−3).In this analysis, we regenerated PGSs by filtering the full BHS GWAS summary statistics for the PC(O-18:0/20:4) and PE(P-19:0/20:4)(b) lipid species to SNPs in the AAB/QTAB and ABCD datasets, then applying the same In the ABCD dataset, we regressed IQ against the PC(O-18:0/20:4) PGS using a linear model and regressed sleep disturbances against the PE(P-19:0/20:4)(b) PGS using a gamma log-link function, including covariates for age, sex, genotyping principal components and parental socioeconomic status.FADS gene cluster where there was individual-level genotyping data. The rationale for this analysis was that both lipids PC(O-18:0/20:4) and PE(P-19:0/20:4)(b) have a highly oligogenic genetic architecture (at least in adult datasets24); hence, TWASs can help to fine map causal genes underlying these lipids, and may provide supporting evidence for lipid–neurodevelopmental trait associations.We performed an individual-level TWAS analysis, which involved summing TWAS weights to predict gene expression in the FADS cluster gene expression corresponded with lipid levels of PC(O-18:0/20:4) and PE(P-19:0/20:4)(b). We performed an ANOVA analysis (lipid concentration ~ age + age2 + sex + batch + injection order + TWAS estimate + trait) to quantify the variance associated with genetically predicted gene expression, conditioning on covariates. As there was differing availability of genes between TWAS weight datasets, we used TWAS Elastic Net weights from ref. 67 for FADS1 , FADS2  and TMEM258 . For prefrontal cortex prediction, we used weights generated from PsychENCODE68, from which only FADS1  and TMEM258 (6/11 SNPs available) were available. To genetically predict transcription levels, we multiplied TWAS weights by allele dosages for Europeans in the AAB/QTAB and ABCD datasets and then took their sum.First, in the AAB/QTAB dataset, we confirmed that predicted n = 646 Europeans), we turned to the ABCD dataset  to test for associations between genetically predicted FADS cluster gene expression and clinical phenotypes that were proxies for IQ  and sleep disturbances .Given that the AAB/QTAB dataset was relatively small , we performed PCA on the LWAS species-level associations, referring to PC1 as the lipidome profile for that trait. We used linear models to examine relationships between these lipidome profiles, dietary principal components and measures of the focal traits.2, sex, batch, injection order and storage duration. In ANCOM version 2.1, features passing the detection threshold ≥0.7 correspond to significant associations, although a detection threshold of ≥0.6 may also be used. As a sensitivity analysis, we investigated the effect of including dietary principal components as additional covariates.Separately, we investigated the relationships between the three trait lipidome profiles and microbiome species and MetaCyc pathways. We performed analyses in ANCOM version 2.1, taking LWAS PC1 as the dependent variable and including the covariates age, age2 + sex + batch  + medications associated with that trait lipidome profile + associated dietary principal components + associated microbiome species or MetaCyc pathways determined using the nominally significant associations from ANCOM version 2.1 (detection threshold ≥ 0.6).We used ANOVA to determine the proportion of variance in each neurodevelopmental trait’s lipidome profile that could be attributed to each explanatory variable (in the following order): age + ageWe compared two ANOVA models to explore the association between ASD and the ASD lipidome profile, with and without conditioning on sleep disturbances:We performed a similar analysis for sleep disturbances, this time conditioning on ASD diagnosis:To investigate the relationship between VABS-II composite score, IQ/DQ and sleep disturbances and their associated lipidome profiles (note that the VABS-II was only assessed within the ASD group), we performed the following ANOVA models and assessed whether the trait-associated lipidome profile explained additional variance in the VABS-II composite score beyond the neurodevelopmental trait alone:n = 64 participants who were storage duration outliers, as: (1) storage duration was considered to be an undesirable batch effect; and (2) all of these outliers were in the ASD group, meaning that they may bias the group comparison. Our multiple testing strategy for this analysis is described in the section ‘Multiple testing strategy’.We tested for differences in variance between groups (ASD versus non-ASD) using Levene’s test. We accounted for covariates  by regressing these effects out from each lipid concentration measure and taking the residuals. For this analysis, we included the outliers that had been identified using the quality control pipeline, which we justified due to finding that many of these outliers had a biological explanation. However, we excluded the Here we were investigating whether any specific lipids were associated with a given trait.X.The null hypothesis was that no lipids were associated with trait n = 783 tests for each trait. For the lipid class level, we performed n = 41 for each trait  and that has historical precedence in gene-based analyses, which similarly contend with highly correlated data . To this end, we:Applied PCA to the lipidomics datasetn = 302 for the lipid species and n = 32 for the lipid classes)Estimated the effective number of independent lipids by taking the number of principal components accounting for >99% of variance in the dataset  as:Calculated the corrected For the LWAS analysis at the lipid species level  and in other omics fields . For the lipid species data, significance was declared at P < 1.66 × 10−4 and for the lipid classes it was declared at P < 1.56 × 10−3.We applied multiple testing correction within each trait, as opposed to across all traits, consistent with current conventions in the lipidomics field  with three neurodevelopmental traits  Fig. . Using tWe also performed extensive sensitivity analyses assessing different combinations of covariates , sex, VABS-II adaptive motor domain, stool consistency (Bristol Stool Chart), protein, fats, carbohydrate, sugars, cholesterol, dietary PC1, PC2 and PC3 and dietary diversity. In this case, we applied a Benjamini–Hochberg correction to control the FDR across the 18 tests with the covdemog set of covariates , sex , batch, injection order and storage duration). We did not perform Bonferroni correction as these traits are correlated and this approach would be overly conservative. As data were not complete across all phenotypes, we were not able to perform the effective number of variables approach described above.For the OREML analysis Fig. , we perfAgain, we performed extensive sensitivity analyses to complement the main analyses and did not include these tests in multiple testing for the same reasons described above.Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41591-023-02271-1.Supplementary InformationSupplementary Figs. 1–20.Reporting SummarySupplementary TablesSupplementary Tables 1–25.


Triticum turgidum subsp. durum) offers the opportunity to better understand the effects of breeding on the genetic structures that regulate the expression of traits of agronomic interest. Furthermore, the identification of DNA markers useful for marker-assisted selection could also improve the reliability of technical protocols used for variety protection and registration.The recent boosting of genomic data in durum wheat , ancient (OC) and modern cultivars (MC), were evaluated in two locations, for 34 agronomic traits, including UPOV descriptors, to assess the impact of changes that occurred during modern breeding.Q.Yb-6A) in a region containing the nadh-ubiquinone oxidoreductase subunit, a gene involved in starch metabolism. The Q.Kcp-2A harbored the PPO locus, with the associated marker (Ku_c13700_1196) in LD decay with Ppo-A1 and Ppo-A2. Interestingly, the Q.FGSGls-2B.1, identified by RAC875_c34512_685 for flag leaf glaucosity, mapped less than 1 Mb from the Epistatic inhibitors of glaucousness (Iw1), thus representing a good candidate for supporting the morphological DUS traits also with molecular markers. LD haplotype block approach revealed a higher diversity, richness and length of haploblocks in MC than OC and LR , suggesting a possible effect exerted by breeding programs on genomic regions associated with the agronomic traits.The association mapping analysis, performed with 4,241 SNP markers and six multi-locus-GWAS models, revealed 28 reliable Quantitative Trait Nucleotides (QTNs) related to plant morphology and kernel-related traits. Some important genes controlling flowering time and plant height were in linkage disequilibrium (LD) decay with QTNs identified in this study. A strong association for yellow berry was found on chromosome 6A (Our findings pave new ways to support the phenotypic characterization necessary for variety registration by using a panel of cost-effectiveness SNP markers associated also to the UPOV descriptors. Moreover, the panel of associated SNPs might represent a reservoir of favourable alleles to use in durum wheat breeding and genetics. Triticum turgidum subsp. durum) is a major staple crop in the Mediterranean basin, and Italy and Spain are among the main producers  testing of new varieties as a complement to, or replacement of, morphological observations listed in International Union for the Protection of New Varieties of Plants (UPOV) guidelines .In our previous study  a large A core collection of 123 durum wheat genotypes was derived from a larger panel of accessions previously developed by www.r-project.org), with location as random effects in the model [Y = lmer (X~(1|LINE) + (1|LOC) + (1|LINE : LOC)] (The list of phenotypic traits and their acronyms used afterward were reported in  : LOC)]  to gener : LOC)] . Normal  : LOC)] . For allhttp://www.traitgenetics.com). The Svevo v.1.0 reference genome was used to assign the physical position to each SNP marker  GWAS models  implemented in the R package mrMLM v4.0  were useTo evaluate changes in the LD haplotype block during the Italian durum wheat breeding, the collection was divided into three groups , according to the partition by http://www.gramene.org/) using Ensembl Plants (https://plants.ensembl.org/index.html) and biomart (Candidate genes flanking the significant marker-trait associations’ (MTAs) regions were searched based on the LD haplotype blocks and LD decay distance calculated by  biomart .Significant QTNs were annotated using the Svevo durum wheat high-confidence gene models . To detep.value > 0.05), with the exception of Kernel thickness (KT), Blackstain Blackpoints (BP) and Plant height (PH), which showed p.value < 0.05  was detected by all methods and it explained a phenotypic variance of up to 32%. Other eight QTNs  were identified by five models in all environments, with the only exception of pKWmEB which failed in detecting the associations. These QTNs showed LOD values up to 9 for TCC and KerLen and explained a phenotypic variance ranging between 13% for PH and 29% for the FGSGls.A total of 436, 464 and 376 QTNs associated with thirty-four traits (LOD score ≥ 3) were identified at Foggia (FG), Metaponto (MT) and by using BLUPs, respectively Figure 12 ≥ 10% at least in one environment), whereas the remaining two were minor , followed by TCC and KerPhe (three QTNs), and PH, KerLen, FGSGls and SpkCol (two QTNs each). All the remaining traits had only a QTN. Among QTNs associated with HD, three of them, on chr. 2B, 5B and 7B, were major  on chr. 2A was associated both with TCC and KerPhe.We have also identified some QTN clusters for different traits , 3.72 Mb (chr. 2B) and 3.93 Mb (chr. 1B) for LR, OC and MC, respectively Table S5The analysis of genes within LD haplotype blocks showed important differences between the three groups. We retrieved 2,285 functionally characterized genes, out of which 425, 251 and 1,609 were specific to LR, OC and MC, respectively. To further understand their biological role, they were categorized in MapMan Figure 3saur-like auxin-responsive” and “btb/poz and math domain-containing protein” were organized in clusters. Other genes related to ethylene signaling and response pathway were in linkage with Cytochrome P450 and WD-repeat proteins on chr. 2A (blocks 106 and 120) in OC.To provide additional insight into the genes within LD blocks, we performed GO enrichment analysis separately for LR, OC and MC Table S6telo2-interacting protein, Ribulose-phosphate 3-epimerase, Peroxidase, dna-3.methyladenine glycosylase) were found in blocks with genes involved in plant growth and development such as 26S proteasome non-ATPase regulatory and nac domains.In addition, the GO term “response to stimulus” (GO:0050896) was also enriched in LR. Many “NBS-LRR disease resistance proteins” were in linkage with CLAVATA3/ESR (CLE)-related protein and Photosystem I reaction center subunit III, as well as genes related to abiotic stresses and adaption  known as key genes in flowering time. In addition, other genes related to the spikelets per spike phenotype  and photoperiod and phototropism such as radialis, Phytochrome kinase (PKS), nonphototropic hypocotyl 3  were found.In MC, thirty-five genes were related to the biological function “enzyme regulator activity” (GO:0030234) and were localized on chr. 1B, 2A, 3A, 3B, 4B, 5B, 6A, 6B and 7A. The main ancestor GO Term were associated with enzyme inhibitor activity and defense response. The genes included clusters of disease resistance protein  in tandem with Phloem protein 2 genes (block 35 on chr.1B), and Q.Ka-3B, Q.Lp-5B and Q.Ls-7B) , while one was found between LR and OC (Q.Kcp-4B). Among these, the largest haplotype block was detected in LR (2870.68 kb) on chr. 7B, whereas the lowest (0.03 kb) in LR on chr. 5A. A variation in size, as well as in frequency of the haplotypes was observed for the three blocks including the QTNs Q.Ka-3B, Q.Lp-5B and Q.Ls-7B. The first had a lower size in LR (647.549 kb) than in OC and MC (1904.04 kb), and also the alleles and their frequencies changed over the three populations  Table S7Improving grain production while maintaining environmental sustainability under climate change remains the main breeding goal in the coming years, especially in vulnerable environments, such as the Mediterranean Basin . UnderstIn the current study, we exploited the ML-GWAS approach to study the genetic basis of 34 morpho-phenological and agronomic traits and identify QTNs to be exploited for marker-assisted selection and/or in UPOV protocol for durum wheat varietal protection. In addition, we defined haplotype blocks, using the LD-based approach , to idenMost of the traits investigated in our panel showed normal distributions, typical of traits controlled by multiple QTLs and highly vulnerable to environmental factors. The six ML-GWAS methods confirmed the genetic complexity, revealing different traits-associated QTNs across all wheat chromosomes. The BLUP prediction method, a parameter to minimize the environmental bias allowing the estimation of the true individual genetic value , increasQ.Ph-2B (Kukri_c36879_83) was in LD decay with candidate genes affecting PH such as diminuito/dwarf1 gene, Flowering-promoting and gras transcription factors involved in steroid synthesis   .Q.Yb-6A) in a region containing a nadh-ubiquinone oxidoreductase gene, involved in starch metabolism in rice  at 43.43 Mb on chr. 5A, associated with the black point disease resistance, was previously reported as an important player conferring resistance to Septoria and Powdery Mildew in winter wheat  and coincided also with QTL for thousand kernel weight (TKW) identified by Q.Kcp-2A harbors the PPO locus, with the marker Ku_c13700_1196 in LD decay with Ppo-A1 and Ppo-A2 genes, confirming the markers associated with this trait as one of the most suitable genetic models to support DUS phenotypic protocols.The kernel coloration with phenol in durum wheat is the result of the conversion of phenol and other phenolic derivates into melanin pigments in the presence of tyrosinase . Since tQ.FGSGls-2B.1 identified by the RAC875_c34512_685 marker mapped less than 1 Mb from the Epistatic inhibitors of glaucousness (Iw1) locus previously described by Unlike the previous one, which was already known in the literature, the associations found for flag leaf gloucosity (both lamina and sheath) represented a new result in durum wheat. In fact, the In our previous study  we compaA likely cause of the observed differences in length and number of HBs among the three genetic groups could be related to the breeding effect exerted on genes involved in response to abiotic and biotic stresses and gravitropism and phototropism; these latter are considered an adaptive response of crucial importance in plants .saur-like auxin-responsive genes was found in the HBs of LR and OC, although on different chromosomes. These are a family of auxin-responsive genes involved in plant growth and in response to internal and external signals , strong associations were found through GWAS and LD haplotype block analysis, proving their power in detecting genetic variants associated with the traits of interest and for evaluating the impact of breeding programs. In particular, the increase in the number and length of haplotype blocks in MC suggested the high selective pressure exerted by breeders for the traits of agronomic interest.We believe that the results reported here will not only expand the knowledge regarding the genetic architecture of many of these traits but also allow the employment of diagnostic/perfect genetic markers in current UPOV descriptive protocols, strongly influenced by environment and visual assessment, thus improving the plant variety protection and registration system.10.6084/m9.figshare.11835663).Publicly available datasets were analyzed in this study. This data can be found here: Figshare repository (doi: FT and PV conceived the work, coordinated and supervised the activities. FF, SR, MS, LG and GT phenotyped the genetic materials. FT and SE performed statistical, QTNs and LD block analyses and data visualization. PV, FT, and SE contributed to interpretation and wrote the manuscript. All authors critically revised the manuscript. All authors read and approved the final manuscript.