Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies.Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders.https://github.com/precimed/mixer.The software is available at: Bioinformatics online. The rapid technological advances of the last years have provided an enormous amount of genetic data, promoting the development of statistical methods aimed at unraveling the genetic architecture of complex traits Holland . ImportaRecently, we developed a model which allows the breakdown of SNP-heritability into the number of variants influencing a given phenotype (non-null variants) and the distribution of their effect sizes using summary statistics from GWAS and detailed population-specific linkage disequilibrium (LD) structure  analysis  only estHere, we extensively tested AI-MiXeR on synthetic GWAS data generated under various setups to establish scenarios where it reconstructs the underlying parameters correctly. We then applied AI-MiXeR to GWAS summary statistics for 11 complex phenotypes representing a range of diverse human traits and diseases. Our analysis suggests that both densities and effect sizes of non-null variants vary considerably across different genomic annotation categories and reveals diverse patterns of genetic architecture in different phenotypes.π) and the variance of their effect sizes . The pure (i.e. not induced by LD) effect of the kth variant (z-score) of the jth variant is then given by: jth and kth variants and z-scores). To reduce computational burden, we randomly select a subset of 106 variants of all GWAS variants to use for maximization of the likelihood function. For specific details of the model and its implementation, please refer to the following sections.We consider an additive model of genetic effects ignoring gene–environment interactions, epistasis and dominance effects. Variant effect sizes are modeled with point-normal mixture priors, where both proportion of non-null variants and distribution of their effect sizes can vary between different predefined functional genomic categories. Each functional category in the model is characterized by the proportion of non-null variants  whenever jth variant. Any variant j with Consider a quantitative phenotype standardized to mean 0 and variance 1. Let ith individual). Using (1) and this notation we can write:j observed in the GWAS (jth column of j estimated in GWAS (j: Consider now a GWAS on a quantitative phenotype. Let j and k from a multiple linear regression in an infinite population (as discussed above).where Assuming the considered phenotype is complex, i.e. it is influenced by many variants each explaining only a tiny fraction of phenotypic variance, then the variance of the simple linear regression error is approximately equal to the sample variance of the phenotype, z-score of variant j observed in GWAS:Combining In By construction . Trans-chromosome correlations as well as correlations between variant genotypes at z-scores observed in different subsets of 106 randomly selected variants.We applied the model to GWAS summary statistics on 11 phenotypes . Like inThe simulations with synthetic data demonstrate that the true parameters are estimated accurately when the proportions of heritability carried by both functional categories are comparable and each category individually carries >2% of the total heritability; if one of the functional categories carries a negligible fraction (<2%) of the total heritability the model often fails to reconstruct its parameters accurately . Selecteπexonic and πnon-exonic) can be converted into the number of non-null variants by multiplying them by the total number of variants within the corresponding annotation category. The numbers ensuing for the analyzed phenotypes are presented in The model was used to estimate exonic and non-exonic polygenicities, discoverabilities and heritabilities of 11 complex phenotypes presented in The majority of 11 analyzed phenotypes fall into the portion of parameter space where, according to our simulations, the model is expected to produce robust parameter estimates , Fig. 1.The model suggests, that despite having similar heritability, phenotypes may differ substantially in polygenicity and discoverability of non-null variants. For example both AI-MiXeR and partitioned LDSC provide comparable estimates of total and partitioned heritability for LDL and T2D (AI-MiXeR LDL: 2= 0.06) . However2= 0.06) . HoweverWe present the AI-MiXeR model, which can be used to decouple and partition a phenotype’s heritability into functional category-specific polygenicity (proportion of non-null variants in a given category) and discoverability (variance of non-null effect sizes) components and thus better characterize the phenotype’s genetic architecture. This may inform the design of future genetic studies, as efforts to improve discoverability in different genomic categories are likely to have different impact across complex phenotypes depending on their unique genetic architecture.It is widely assumed that protein-coding exons contain a higher proportion of causal variants (higher polygenicity) and have on average stronger effects (higher discoverability) on complex phenotypes compared to non-exonic regions  reported 66 exome-wide significant LDL susceptibility variants within 14 loci  and varied only the heritability (z-scores distribution only through their product [follows from formula (5)], our simulation scenario with N = 105 and N = 7 × 105 and AI-MiXeR relies on design and implementation quality of the specific GWAS. In general, model predictions for a given phenotype may differ depending on a GWAS’s sample size, as well as on the coverage of the tested variants. The sample sizes of the GWAS tested here vary by more than one order of magnitude, from approximately 5 × 10N = 489) was used to estimate LD The model underlying AI-MiXeR is sensitive to the LD structure estimates. Ideally, the LD structure should be estimated on the same sample used for association testing. However, this is mostly impractical if not impossible. Here, in the analysis of GWAS summary statistics for 11 phenotypes, we estimated the LD structure using the 1000 Genomes Phase 3 genotype panel. Inconsistencies between the LD structure of the samples used for association testing and that of the 1000 Genomes Phase 3 panel could skew the model’s results. Additionally, roughening the LD structure . However, with the current implementation, the computational cost of the optimization increases rapidly as the number of annotation categories grows. For this reason, we restricted the main analysis of this study to exonic and non-exonic categories, which we reckoned could be informative in the context of whole-exome studies. An exploratory analysis of several other functional annotation categories  for SCZ and T2D can be found in The AI-MiXeR method presented here considers predefined annotation categories allowing both different proportions of non-null variants and different effect size distributions in various functional annotation categories, which is not possible with other methods available to date . The abibtaa568_supplementary_dataClick here for additional data file.


P = 6.79 × 10−23, OR = 1.23; Ncases = 4714, Ncontrols = 1,011,520). The variant also confers risk of intervertebral disc disorders  suggesting a common pathogenesis in part or a true pleiotropy.Bell’s palsy is the most common cause of unilateral facial paralysis and is defined as an idiopathic and acute inability to control movements of the facial muscles on the affected side. While the pathogenesis remains unknown, previous studies have implicated post-viral inflammation and resulting compression of the facial nerve. Reported heritability estimates of 4–14% suggest a genetic component in the etiology and an autosomal dominant inheritance has been proposed. Here, we report findings from a meta-analysis of genome-wide association studies uncovering the first unequivocal association with Bell’s palsy (rs9357446-A;  Other symptoms vary from person to person and may include mild facial pain, numbness or stiffness and headache. The annual incidence is about 20–30 cases per 100,000, with about 1 in 60 lifetime risk. The disorder usually occurs in individuals aged 15–40 and affects men and women equally2. Risk factors include hypertension3, diabetes4, immunosuppression5, Lyme disease6, and in rare cases inflammatory demyelinating neuropathy such as Guillain-Barré syndrome7.Bell’s palsy, also known as idiopathic facial paralysis, is an acute or subacute weakness of the facial muscles innervated by the seventh cranial nerve . Symptoms are usually unilateral although both sides can be affected. Although symptoms usually subside within one month, up to 30% of cases show delayed or incomplete recovery with mild to severe residual facial weakness, contracture, and/or hemifacial spasm6. The literature has highlighted several viral illnesses such as herpes simplex type 1, which has been detected in 31–79% of cases9, human immunodeficiency viruses10, varicella-zoster, and Epstein-Barr11. After a primary infection, these latent viruses reside in the geniculate ganglion or in lymphocytes. At times with e.g. stress, the viruses may be reactivated and cause local damage to the myelin8. The two most common treatments are corticosteroids and antivirals. However, studies have found that antivirals alone are not helpful and antiviral treatment in combination with corticosteroids have conflicting results12. Although the heritability has been estimated to be only 4–14%13, numerous familial cases have been reported.While the exact etiology of Bell’s palsy is unknown, it has been suggested that the disorder is caused by inflammation or compression of the seventh cranial nerveHere, we report the first meta-analysis of genome-wide association studies (GWASs) of Bell’s palsy, combining data from Iceland (deCODE genetics), the UK (UK Biobank), Denmark , and Finland (FinnGen), which uncovers an association with a common intergenic variant.Ncases = 290, Ncontrols = 342,122), the UK , Denmark , and Finland . Bell’s palsy cases were assigned ICD-10 (G51.0) diagnoses by a physician in all study cohorts , that confers risk of Bell’s palsy   Table . The fulP-het ≤ 0.05) describing a modest difference in the four datasets. Therefore, we applied a random-effects model which assumes that there may be different underlying true effects estimated in each dataset. The variant maintains a weighted Bonferroni significance (P = 6.83 × 10–23) based on predicted functional impact of the association signal.There is a small evidence of heterogeneity  and of those, eleven are highly correlated  with any known coding or structural variants.Based on the Icelandic data, twenty-two non-coding variants in the region correlate with rs9357446 r > 0.2 ancis-eQTL in blood and adipose tissue in Iceland and did not find an association between rs9357446 and expression of nearby genes (Supplementary Note). The variant did not affect expression in any nearby genes in 18 other databases listed in Supplementary Table N = 1236, P = 0.024). However, this CpG is generally methylated and a slight increase in methylation is not likely to have any biological effect.We examined 15. The Bell’s palsy meta-analysis associates with seven lifestyle traits  and poor health .We performed genetic correlation analyses using the Bell’s palsy meta-analysis and 600 GWASs from the UK Biobank16. This finding replicates in the Icelandic dataset . We did not find association with other lung related phenotypes including asthma, chronic obstructive pulmonary disease, or tuberculosis combining the Icelandic, UK and Danish data in meta-analyses and we did not find associations with other reported risk factors such as hypertension and type 1 diabetes . rs9357446-A is among the top variants at the locus for IDD P = 6.79 × 10–23, OR = 1.23), in a meta-analysis of four study cohorts.The precise pathophysiology of Bell’s palsy is unknown and biologically targeted treatment is lacking. The aim of the study was to search for variations in the human genome affecting risk of Bell’s palsy and thus use genetics to uncover biological underpinnings of this somewhat mysterious disease. Here, we report the first unequivocal association between Bell’s palsy and a sequence variant, rs9357446-A , several sequence variants in the closest gene, cell division cycle 5 like (CDC5L), have been found to associate with related phenotypes, including osteoarthritis17, bone density18, ossification of the spine19, and lung function measured by FVC and forced expiratory volume16. Another potential candidate at the locus, SLC35B2, is a key component of a protein sulfation pathway where it plays a part in modifying C–C Motif Chemokine Receptor 5 (CCR5), that is one of HIV’s host proteins for entry into the cell20. Several other studies point to the importance of CCR5 in determining disease severity of other viral infections in animal models22. In addition, SLC35B2 is involved in proteoglycan synthesis. Cartilage is particularly rich in proteoglycans, and changes in the structure and composition of glycosaminoglycans, forming the sugar chain attached to the protein core of proteoglycans, have been found in osteoarthritis. Mutations in slc35b2 in zebrafish have been shown to cause deformation of the craniofacial skeleton23.While rs9357446-A confers risk of Bell’s palsy, it also confers low risk of IDD as well as reduced lung function measured by decreased FVC24. In Bell’s palsy, a viral infection is considered one of the possible causes8. The main treatment is corticosteroids, which supports the theory of edema induced entrapment neuropathy. The facial nerve traverses through the temporal bone  and branches to innervate the facial muscles. The narrowest part of the canal, the labyrinthine segment, is where most cases of compression occur, which may result in ischemic neuropathy of the facial nerve25.Bell’s palsy is still idiopathic since we do not know what causes it; there are no biomarkers to support the diagnosis and the diagnosis requires exclusion of any other cause of facial paralysisWe were unable to determine a gene at 6p21.1 involved in Bell’s palsy pathology. However, the association of rs9357446-A with IDD brings up the possibility that the variant may confer risk of Bell’s palsy through an effect on cartilage and bone development. Alternatively, the association between Bell’s palsy, IDD, and reduced lung function may be a result of a true pleiotropy. Although more studies are needed, the identification of this variant contributes to the limited understanding of Bell’s palsy.All Icelandic data were collected through studies approved by the National Bioethics Committee  following review by the Icelandic Data Protection Authority. Participants donated blood or buccal samples after signing a broad informed consent allowing the use of their samples and data in all projects at deCODE genetics approved by the NBC. All personal identifiers of the participants' data were encrypted by a third-party system, approved and monitored by the Icelandic Data Protection Authority.The UK Biobank data were obtained under application number 24898. All phenotype and genotype data were collected following an informed consent obtained from all participants. The North West Research Ethics Committee reviewed and approved UK Biobank’s scientific protocol and operational procedures (REC Reference Number: 06/MRE08/65).The Danish Data Protection Agency (2007-58-0015) and the National Committee on Health Research Ethics (NVK-1700407) approved the genetic study on Danish Blood Donor Study (DBDS). The data requested for this study was approved by the DBDS steering committee. Samples from the Copenhagen Hospital Biobank (CHB) were included as part of the study on pain related diseases under the genetics of pain and degenerative musculoskeletal disease protocol (NVK-1803012).Samples and phenotype data were collected from the Finnish biobanks and the national health registers, respectively, for the FinnGen database. The Coordinating Ethics Committee of the Helsinki and Uusimaa Hospital District evaluated and approved the FinnGen research project. The project complies with existing legislation . The official data controller of the study is University of Helsinki.To identify Bell’s palsy cases in Iceland, we searched for patients over the age of 18 with International Classification of Diseases (ICD-10) diagnosis code G51.0 and ICD-9 code 351.0 at the National University Hospital of Iceland and all Icelandic primary and secondary care centers. Records spanned from 1983 to 2019.The UK Biobank study is a large prospective cohort study of ~ 500,000 individuals in the age range of 40–69 from across the UK. Extensive phenotype and genotype data have been collected for participants, including ICD diagnosis codes. Bell’s palsy diagnoses in the UK Biobank were obtained by searching for cases with ICD-10 code G51.0 from UK Biobank General Practice clinical event records  and hospital diagnoses . Only individuals of Caucasian ancestry were included.26. Genetic ancestry quality control was performed in two stages. Firstly, ADMIXTURE v1.2327 was run in supervised mode with 1000G populations CEU, CHB, and YRI28 as training samples and Danish individuals as test samples. Input data for ADMIXTURE had long-range LD regions removed29 and was then LD-pruned with PLINK v.190b3a30 –indep-pairwise 200 25 0.3. Danish samples with less than 0.9 assigned CEU ancestry were excluded. In the second stage, remaining Danish samples were projected onto a principal component analysis (PCA), calculated with in-house tools on an in-house European reference panel. The UMAP package in R31 was used to reduce the coordinates of test samples on 20 principal components to two dimensions. Additional European samples not in the original reference, were also projected onto the PCA and UMAP to help identify the ancestries represented in clusters. A polygon informed by visual inspection was drawn to include all samples with very similar ancestries to the main Danish cluster.The CHB is a research bank, which contains left over samples from diagnostic procedures on in- and outpatients in hospitals in the Danish Capital Region. The DBDS GWAS study is a nationwide prospective cohort study of ~ 100,000 blood donors. We applied for ICD-10 code G51.0 from CHB and DBDShttp://r3.finngen.fi).The phenotype data from the FinnGen study was produced from several national health registries. All Bell’s palsy cases were diagnosed by a physician and categorized using ICD-10 code G51.0, ICD-9 code 351.0, and ICD-8 code 350. The summary statistics for available phenotypes, including Bell’s palsy, were imported on June 16th 2020 from a source available to consortium partners  and 100,000 controls (DBDS) from Denmark, and 1017 cases and 121,360 controls from Finland; in total, 4714 cases and 1,011,520 controls.33. In short, 37.6 million high-quality sequence variants were identified by sequencing 49,962 Icelanders using GAIIx, HiSeq, HiSeqX, and NovaSeq Illumina technology to a mean depth of at least 17.8 × . SNPs and indels were identified and their genotypes called using joint calling with Graphtyper34. Additionally, over 165,000 Icelanders  have been genotyped using various Illumina SNP chips and phased using long-range phasing35, which allows for improving genotype calls using the information about haplotype sharing. The genotypes of the high-quality sequence variants were imputed into the chip-typed Icelanders36. To increase the sample size and power to detect associations, the sequence variants were also imputed into relatives of the chip-typed using genealogic information. All the tested variants had imputation information over 0.8.The preparation of samples and the whole-genome sequencing of 49,962 Icelanders has been described in detail elsewhere37, and the Affymetrix UK Biobank Axiom array38 in the remaining participants. Imputation was performed by the Wellcome Trust Centre for Human Genetics using a combination of the Haplotype Reference Consortium39, UK10K haplotype resources40, and 1000Genomes phase 3 panels28. A total of 96 million variants have been imputed.The UK Biobank samples were genotyped with a custom-made Affymetrix chip, UK BiLEVE Axiom in the first 50,000 individuals41. Samples and variants with less than 98% yield were excluded. We used the same methods as used for the Icelandic data35 to create a haplotype reference panel by phasing the whole-genome sequence genotypes (N = 8635) using the phased chip data , and to impute the genotypes from the haplotype reference panel into the phased chip data. Whole genome sequencing, chip-typing, quality control, and the subsequent imputation from which the data for this analysis were generated was performed at deCODE genetics.Over 332,000 samples from the CHB and DBDS together with ~ 238,000 genotyped samples from Northwestern Europe were long-range phased using Eagle2A custom-made FinnGen ThermoFisher Axiom array  was used to genotype ~ 135,600 FinnGen samples at Thermo Fisher genotyping service facility in San Diego. Imputation was performed using the Finnish population specific WGS backbone. More than 16 million variants have been imputed.32. We used LD score regression to account for distribution inflation due to cryptic relatedness and population stratification in the Icelandic, UK, and Danish data42. In the Icelandic association analysis, we adjusted for sex, county of origin, current age or age at death (first and second order term included), blood sample availability for the individual, and an indicator function for the overlap of the lifetime of the individual with the time span of phenotype collection. In the UK association analysis, we adjusted for sex, age, and the first 40 principal components to adjust for population stratification. In the Danish association analysis, we adjusted for sex, whether the individual had been chip-typed and/or sequenced, and the first 20 principal components. The FinnGen association analysis was adjusted for sex, age, the genotyping batch, and the first 10 principal components.We performed logistic regression using the Icelandic, UK, and Danish data and combined the results with imported association results from Finland to test for association between sequence variants and Bell’s palsy using software developed at deCODE genetics43 to combine the results from the four study groups in which the groups were assumed to have a common OR but allowed to have different population frequencies for alleles and genotypes. Variants with imputation information below 0.8 were excluded from the analysis. Sequence variants were mapped to NCBI Build38 and matched on position and alleles to harmonize the four datasets. We estimated the genome-wide significance threshold and corrected for multiple testing with a Bonferroni procedure weighted for variant classes and predicted functional impact44. A likelihood ratio test was performed for the genome-wide significant variant to test the heterogeneity of the effect estimate in the four datasets. The null hypothesis is that the effects are the same in all datasets and the alternative hypothesis is that the effects differ between datasets.For the meta-analysis, we used a fixed-effects inverse variance methodConditional analysis was performed to identify the most likely causal variant at the locus and to identify any secondary signals. We used the true genotypes of participants in the study from Iceland, the UK, and Denmark. LD between variants was estimated using a set of 8700 WGS Icelanders and was restricted to variants within one Mb from the index variant.P ≤ 0.05/600 = 8.33 × 10–5) from the UK Biobank15 with effective sample size over 5000 were performed using LDSC method45. The LDSC method suggests minimal effective sample size of 5000 for a GWAS trait to get unbiased estimates of genetic correlation and heritability. Since the published GWAS studies used in the analysis have Caucasian ancestry, we used pre-computed LD scores from a 1000 genome panel with r2 from HapMap3 excluding HLA region. The HLA region was excluded for its complex genetic pattern and associations with wide number of traits. Moreover, the default parameters of LDSC method were used to compute genetic correlation and heritability estimates.Genetic correlation analyses between the Bell’s palsy meta-analysis and 600 published GWAS traits .48. MinION and PromethION devices with FLO-106/R9.4 flow cells were used for base calling. Methylation on CpGs was called using Nanopolish (version 0.6.1)49.We performed whole-methylome-sequencing on 4133 samples using ONT Ligation Sequencing Kit (SQK-LSK108) or Rapid Sequencing Kit (SQK-RAD003)Supplementary data.Supplementary information.


The subdomains nonverbal communication (NVC), social interaction (SI), and peer interaction (PI) shared genetic risk factors, while the subdomains of repetitive sensory-motor behavior (RB) and restricted interests (RI) were genetically independent of each other. The polygenic risk score (PRS) for ASD as categorical diagnosis explained 2.3–3.3% of the variance of SI, joint attention (JA), and PI, 4.5% for RI, 1.2% of RB, but only 0.7% of NVC. We report eight genome-wide significant hits—partially replicating previous findings—and 292 known and novel candidate genes. The underlying biological mechanisms were related to neuronal transmission and development. At the SNP and gene level, all subdomains showed overlap, with the exception of RB. However, no overlap was observed at the functional level. In summary, the ADI-R algorithm-derived subdomains related to social communication show a shared genetic etiology in contrast to restricted and repetitive behaviors. The ASD-specific PRS overlapped only partially, suggesting an additional role of specific common variation in shaping the phenotypic expression of ASD subdomains.Autism spectrum disorders (ASD) are highly heritable and are characterized by deficits in social communication and restricted and repetitive behaviors. Twin studies on phenotypic subdomains suggest a differing underlying genetic etiology. Studying genetic variation explaining phenotypic variance will help to identify specific underlying pathomechanisms. We investigated the effect of common variation on ASD subdomains in two cohorts including >2500 individuals. Based on the Autism Diagnostic Interview-Revised (ADI-R), we identified and confirmed six subdomains with a SNP-based genetic heritability  The genetic architecture of ASD is highly complex comprising common, rare inherited and de novo genetic variants. Common variants show small effects, but collectively have a substantial impact on ASD susceptibility explaining ~50% of ASD liability2. Phenotypic subdomains with high heritability3 and rather low cross-trait genetic correlation estimates are reported previously4. Albeit high heritability estimates in ASD, the genetic and biological contribution of individual ASD domains remains largely unknown. This can be attributed to its heterogeneous genetic and phenotypic complex architecture. An approach to address this difficulty and ravel the ASD complexity is to focus on ASD phenotypic domains and subdomains, which have been proposed to reduce genetic heterogeneity and thus increase statistical power5.Autism spectrum disorder (ASD) is a phenotypically heterogeneous neurodevelopmental disorder. The diagnostic criteria according to DSM-5 6. Categorizing these domains further into independent phenotypic subdomains has shown evidence for an underlying strong genetic susceptibility as published by Liu et al.3. Based on the diagnostic algorithm items of the Autism Diagnostic Interview-Revised (ADI-R), they identified 6 subdomains in the Autism Genome Project (AGP) cohort, namely joint attention (JA), social interaction and communication (SI), nonverbal communication (NVC), and peer interaction (PI) related to domain A, and repetitive sensory-motor behavior (RB), compulsion/restricted interests, or insistence on sameness (RI) related to domain B. Linkage-based common-variant heritability of these ASD subdomains ranged between 29% (PI) and 65% (RI), which is comparable to the additive SNP-based heritability of 40–60%2 or the twin-based additive genetic heritability of 62–81%4 of the categorical ASD diagnosis.The phenotypic independence of the two DSM-5 dimensions has been shown previouslyrg) between domain A and domain B to be ranging between ~10 and 50%, and varying between males and females4. Another study investigating 189 twins with at least one affected individual reported that the overall co-twin co-trait correlations were small between five phenotypic subdomains derived from the Development and Wellbeing Assessment instrument7 )8.ASD domains and subdomains are likely to show distinct underlying genetic risk. Twin studies reported the genetic correlation  and RB (19q13.3). In addition, numerous quantitative genome-wide association studies (qGWAS) have focused on different ASD-related dimensional traits derived from the ADI-R diagnostic algorithm 10, the SRS total score11, or single items of the ADI-R12, the ADOS and the SRS12. None of the reported genome-wide significant findings were observed in another ASD subdomain or trait, or independently replicated in any study. This may be attributed to small genetic effects or limited sample size13; still, it may also indicate a differing underlying genetic etiology and implicated neurobiological mechanisms of different ASD subdomains.The high genetic heritability and the low genetic correlation between domains and subdomains suggest that the previously reported statistically independent ADI-R subdomains14, and genetic overlap with different traits observed in the population15. Given the polygenic etiology of ASD as categorical diagnosis and the assumed differential polygenic etiology of variance in phenotypic subdomains, it is of prime interest to study the overlap of general polygenic risk on ASD with specific genetic risk for the subdomains.In addition, an overlap with common genetic risk for ASD as categorical diagnosis has not been assessed in previous studies. To capture the additional value of studying phenotypic subdomains, their genetic correlation with ASD as a categorical diagnosis also needs to be explored. Regarding the small genetic effect of single SNPs, a powerful approach to capture the role of common genetic variation is to study the combined effect of SNPs by a polygenic risk score (PRS). This approach has been taken in ASD research to identify cross-disorder genetic risk, to study the role of common variation in different ASD subtypes, such as low and high IQ16. ASD-associated genes are implicated in synaptic scaffolding, neuronal transmission, chromatin remodeling, protein synthesis or degradation, or actin cytoskeleton dynamics14. Previous research has also shown that ASD-associated genes are involved in numerous biological processes, such as the mammalian target of rapamycin (mTOR)17, Wnt18, and calcium (Ca2 + ) signaling pathways19. Although these pathways are well known for their role in ASD, there is still a great need to understand how dysregulation of these pathways is involved in modulating the subdomains of ASD. From a biological perspective, we hypothesize that the phenotypic domains of social interaction and stereotyped behavior show differential underlying pathomechanisms. This assumption is based on the observation that genetic animal models for ASD show inconsistent phenotypes. For example, Nlgn3 (Neuroligin) adult knockout (KO) mouse model showed normal direct social interaction, but was engaged in repetitive behavior20, whereas Nrxn2α (Neurexin 2α) KO mice showed social deficits, but did not exhibit stereotyped repetitive behavior21. Moreover, magnetic resonance imaging (MRI) studies in humans have shown that inferior frontal gyrus, amygdala, prefrontal, and temporal cortices are related to defects in social language processing and social attention23, whereas the orbitofrontal cortex and basal ganglia have been associated with repetitive and stereotyped behavior of ASD24. Given the concept of ASD as an early developmental disorder, another biologically plausible argument for a differential genetic regulation of ASD-related subdomains stems from the finding of distinct transcriptomic signatures during development of these brain regions.Regarding the implicated neurobiology, similarly to the assumed differential genetic risk, specific underlying neurobiological mechanisms are expected for the different ADI-R algorithm-based subdomains. Studies on neurobiological mechanisms in ASD as categorical diagnosis converge with regard to abnormal neuronal function and early-age brain growth abnormalityWe hypothesize that distinct common genetic variants will modulate ADI-R-derived ASD subdomains, which are related to specific underlying biological processes, and gene-regulatory signatures. Thus, we performed a qGWAS on ADI-R-derived ASD subdomains dissecting their genetic etiology, and investigated their relation to the polygenic risk for ASD.n = 625 trios, n = 53 duos, and n = 27 singletons) and the AGP cohort (n = 2730 trios and n = 5 duos). Diagnosis was based on thorough clinical assessment using Social Communication Questionnaire (SCQ), ADI-R, and/or ADOS. Exclusion criteria and QC were based on the AGP cohort25. For final analysis, only the index patients  with ADI-R and genotype information available were included .We included a German (DE) cohort  applying predictive mean matching (pmm) in R package mice28 .From Liu et al.psych29 as published10. Components were selected based on the Kaiser criterion. Confirmatory factor analysis (CFA) was performed in the DE cohort implementing R package lavaan30. For identified components, the sum of items with loading above 0.4 was calculated .Subdomains were identified based on ADI-R data of the AGP cohort using principal component analysis with “varimax” rotation in R package R² = 0.06) in the DE cohort, 1.5% in the AGP cohort, and 1.2% in the combined cohort with a genome-wide significance threshold of alpha = 5e−8. Power analysis was performed using Quanto (http://biostats.usc.edu/Quanto.html). See the power analysis for performing genetic heritability in the supplementary material.The implemented algorithms require large sample sizes. To increase power, SNP-based analyses were performed in the combined cohort (AGP and DE). For quantitative GWAS, the sample size had a power of 1-beta > 80% to explain 6% of the variance (http://pgc.unc.edu). Polygenic risk score analysis was performed using PRSice tool31  in the merged cohort. P values for shared etiology were corrected using false discovery rate (FDR).To identify the shared etiology between an ASD diagnosis and the phenotypic subdomains, we performed a polygenic risk score analysis implementing the Psychiatric Genomics Consortium (PGC) summary statistics of ASDs (see h2SNP) was calculated using the GCTA software32 based on the genetic relationship matrix (GRM) between pairs of individuals. For genetic correlation (rg) analysis, bivariate genomic GREML analysis was performed in GCTA  using the merged cohort.SNP-based heritability .SNP-based association analysis was performed in combined as well as individual cohorts. However, for further downstream analyses, we only used findings replicated in the GWAS of individual cohorts. Linear mixed-effect regression models with the subdomains as dependent variables were applied with fixed effects for gender, age, first four dimensions of the multidimensional scaling results (population stratification) from plinkv1.9v1.0635. qGWAS results of the individual cohorts were used. To reduce false-positive findings, we included only genes with Ppermuted ≤ 0.05 replicated in both datasets for further analysis.This analysis was performed separately on the individual cohorts based on their respective GWAS output. The simultaneous joint effect of multiple SNPs was determined using Multimarker Analysis of GenoMic Annotation (MAGMA) software package Ppermuted <0.05) and overlapping genes from the MAGMA analysis resulting from both the cohorts were subjected to these analysis. Gene ontology (GO) and pathway analysis was performed using GO-Elite36. Brain network analysis was based on published gene lists of the 29 transcriptome modules (kindly provided by Dr. Kang) co-regulated during the development of the human brain37. Replicated genes from MAGMA analysis for each subdomain were tested for enrichment using Fisher-exact test.The significant  were available for 1895 AGP and 614 DE cases with no difference in gender distribution across cohorts (P = 0.373). The DE cohort was older at diagnosis and showed a higher IQ compared with AGP sample . JA and RB were lower in the DE compared with the AGP cohort, while NVC was higher  proportion of genetic variance of all subdomains. The best PRS model explained 3.3% of variance (R²) in SI and 2.3% in JA and in PI. In contrast, R2 was lower for NVC (0.7%) and RB (1.2%), whereas for RI, the best model explained 4.5% of variance. P-value thresholds used for SNP selection of the subdomain GWAS in the best models ranged from 0.031 to 0.411  was identified for all subdomains with the highest h2SNP observed for SI , and the lowest for RB  . Moderate correlations were observed between SI and PI , SI and JA , and SI and RI , while the least correlation was observed between SI and RB . However, JA and PI were highly correlated . Moderate correlations were observed between JA and NVC  and RI . The lowest rg with respect to JA was observed with RB . For PI, middle-range correlation was observed with SI , and NVC , whereas lower rg values were seen for RB  and RI . Lowest rg of NVC was observed with RB , whereas moderate rg with RI . RB showed very low genetic correlation with RI . Overall, RB showed no significant rg, i.e., P < 0.05 with any other subdomain , rs377634870, P = 4.8 × 10−08 at 1p22.3 (no gene within 10 kb), rs34459814, P = 2.5 × 10−08 at 7q11.23 (CLIP2), rs34083004, P = 3.7 × 10−08 at 7q11.23 (CLIP2), one for PI, i.e., rs10115292, P = 1.8 × 10−08 at 9p21.1 (no gene within 10 kb), and three for RB, i.e., rs13274146, P = 2.1 × 10−08 at 8p21.3 (no gene within 10 kb), rs7837513, P = 4.2 × 10−09 at 8p21.3 (no gene within 10 kb), and rs7824610, P = 2.0 × 10−09 at 8q21.11 (no gene within 10 kb). No significant hit was identified for RI. For locus plots, see Supplementary Fig. GWAS (combined cohort) identified eight genome-wide significant SNPs Fig. , 5, whicPpermuted < 0.05) genes associated with any of the subdomains . For JA, 35 genes were associated and nriched for GO terms, e.g., “carbohydrate and energy metabolism” and “chromatin modification”. For PI, 59 genes were identified, which are implicated in “hormone processing” and “plasma membrane” processes. For NVC, 47 genes were enriched for GO terms related to protein catabolism, and at brain level, the brain-expressed module 27  was enriched. The brain-enriched module P values were tested for multiple correction within the individual subdomain but not across subdomains. RB-associated 49 genes were enriched for “skeletal muscle tissue development”, “DNA binding”, and “transmembrane receptor activity”. For RI, 59 genes were identified and implicated in “postsynaptic” and “intracellular mediated” signaling along with regulation of MAPKKK (mitogen-activated protein kinase kinase kinase) cascade.MAGMA identified 292 replicated  SNPs were shared between SI, JA, and PI; 27 SNPs between SI, PI, and NVC. No nominal overlaps were identified between RI and any other phenotype. At gene level Ppermuted < 0.05, we observed three overlapping genes between SI and JA , two genes between SI and PI (MNS1 and IL20), one gene between NVC and PI (TM4SF4), SI and RB (RGS10), JA and PI (LHB), and JA and NVC (COBLL1)  and identified two factors, i.e., RSM and IS similar to our identified subdomains of RB and RI, respectively10. Thus, the identified subdomains in our study have been well replicated in independent ASD datasets before, and are plausible targets for quantitative genetic analyses.We identified and confirmed the six-factor structure of the ADI-R algorithm items first reported by Liu et al.h2SNP has been studied in large ASD samples to quantify additive heritability explained by genome-wide SNPs41. Our study is the first estimating SNP-based heritability of specific phenotypic subdomains in ASD. We assumed that the heterogeneous phenotype of ASD may prevent a clear picture of the role of SNP in each subdomain. Overall, we observed low SNP-based heritability for the individual subdomains; however, in our study, h2SNP for all subdomains was higher than previously reported estimates of the categorical phenotype ASD (~17%)42. Although, without replication, we cannot generalize our findings for the specific subdomains, we still describe higher heritability estimates for domain A-related subdomains. However, we observe a difference between the subdomains of domain B, which showed the lowest estimate for RB but higher estimates for RI. From the results of our study, we suggest a differential role of common and rare variants in domain A and also within the subdomains of domain B. This also may explain the lower SNP-based heritability of the categorical ASD phenotype, because it is defined by symptoms in domains A and B.Phenotypic subdomains with high SNP-based heritability but lack of genome-wide significant hits, such as RI, might underlie many variants with low effect sizes. Subdomains with only little variance explained by SNP heritability where genome-wide hits are identified, might in contrast underlie few variants with a moderate-to-high effect size. Thus, we conclude that the genetic architecture underlying the phenotypic variance in ASD individuals is likely to be different across the domains.43. A complete genetic correlation of 1 was found for JA and PI, suggesting strongly overlapping common genetic variation underlying SI and NVC, or JA and PI. Moreover, we observed that subdomains of domain A are also highly phenotypically correlated than the subdomains of domain B . Similarly, a population-based twin study did not find genetic covariation between SI and RB scores45; however, a twin study of ASD individuals reported a strong genetic overlap of the extreme values of impaired social communication and restricted behaviors derived from SCQ46. The contrasting findings may be explained by a differential role of common and rare variation in social communication-related subdomains and RB, especially in ASD individuals, with rare variation playing a stronger role in RB47.The highest genetic correlation was identified between SI and NVC (0.97), mirroring the correlation at the phenotypic levelPATJ (aka INADL) at SNP as well as at gene level. PATJ is coding for a scaffolding protein CIPP, and regulates surface expression of the acid-sensing ion channel 3 in sensory neurons48. The Uniprot Protein Database (https://www.uniprot.org/) predicts, based on sequence similarities, an interaction of PATJ with glutamatergic NMDA receptors and ASD candidate genes NLGN2 and HTR2A. Rare loss-of-function variants in PATJ have also previously been found in ASD49, thus strengthening our findings. The second genome-wide significant hit for SI mapped to CLIP2 gene is located at 7q11.23. Duplication carriers of this region show a high rate of ASD51. Furthermore, SI-associated genes were enriched in a co-expressed brain gene set (module 6). This module is mainly active in cortical structures during early childhood. In the hippocampus, module 6 is activated before birth, silenced prior to puberty, and then reactivated. This supports previous findings of early cortical maturation impairments in ASD52, and of the important role of the hippocampus in social behavior53.With respect to specific genetic variation underlying the different subdomains, several genome-wide significant hits and novel candidate genes were identified in the present study. For SI, we observed an association with DAGLA gene implicated in seizures and neurodevelopmental disorders, including autism54, and the COBLL1 gene involved in epilepsy55 and language impairment56.No genome-wide hit was identified for JA. At the gene level, JA was associated with 57. Among the significant genes (Ppermuted < 0.05) enriched for PI, we identified a sodium voltage-gated ion channel gene SCN5A that was found to be a hub protein in an ASD-associated protein-interaction module58. Other ASD-associated significant PI genes include CECR2, a 7.2-kb exonic loss, which was found in an ASD female54.The only genome-wide significant SNP in PI is rs10115292 mapped to an intergenic region at chr. 9p21.1, known for ASD-associated CNVs59. SLC26A5 at 11p15.4 was among the top hits from the gene-based analysis; mutations in this gene are potential candidates for causing neurosensory deafness60. This region is linked with delayed development of speech61. The NVC-associated regulatory gene set (module 27) is expressed in the hippocampus, striatum, and mediodorsal nucleus of the thalamus until puberty . It is possible that a variant may carry a large genetic risk to increase expression of one phenotypic subdomain but a smaller risk on another. Thus, to identify the overlapping SN-based genetic risk with high confidence, it requires a larger sample size to attain an adequate statistical power. However, we followed a conservative approach to minimize proneness of false positives by performing the gene analysis in two independent ASD datasets, and to classify genes as replicated only if they have an empirical 73. Thus, we again chose the more conservative approach. However, we suggest replicating the analysis in a genetically more homogeneous sample.Another limitation of our study is the mixed ethnicity in the two cohorts and higher ASD severity scores in the AGP sample. However, we accounted for the mixed ethnicity in our GWAS analysis, and to overcome false-positive associations, we followed a conservative approach by performing gene-based analysis in two independent ASD datasets and only interpreted overlapping hits. In addition, several genes mapped from GWAS hits of the combined cohort were found at gene level. For the PRS analysis, we used the combined cohort, which contained PGC ids as well, but since our research question was focused on dimensional phenotypes rather than categorical, so we did not exclude those samples from our cohort. In the heritability and genetic correlation analysis, we did not account for covariates. This might have led to an overestimation of estimates. Still, a recent study has shown that the inclusion of covariates can result in inflated and biased genetic correlations and heritability estimatesIn summary, our results suggest that the genetic architecture of subdomains is distinct between A- and B-related subdomains and differs within the two B-related subdomains RB and RI. We replicated several previously implicated genes in ASD, but also describe new candidate genes for specific subdomains. Involved biological pathways and gene expression patterns strengthen the previous observations that ASD phenotypic variability is influenced by pathways regulating neuronal development of different brain areas, including the hippocampus, amygdala, and cortical areas.The results of our study need to be replicated in larger samples with different ethnic backgrounds. In addition, a combined analysis of common and rare variants may clarify the specific role of common variants in shaping the ASD phenotype in relation to the reported subdomains.Supplementary Material fileSupplementary Table 2Supplementary Table 6Supplementary Table 7Supplementary Table 8


Phaseolus vulgaris L.). In this research, a panel of 174 accessions representing Croatian common bean landraces was phenotyped for seed content of eight nutrients , and genotyped using 6,311 high-quality DArTseq-derived SNP markers. A genome-wide association study (GWAS) was then performed to identify new genetic sources for improving seed mineral content. Twenty-two quantitative trait nucleotides (QTN) associated with seed nitrogen content were discovered on chromosomes Pv01, Pv02, Pv03, Pv05, Pv07, Pv08, and Pv10. Five QTNs were associated with seed phosphorus content, four on chromosome Pv07, and one on Pv08. A single significant QTN was found for seed calcium content on chromosome Pv09 and for seed magnesium content on Pv08. Finally, two QTNs associated with seed zinc content were identified on Pv06 while no QTNs were found to be associated with seed potassium, iron, or manganese content. Our results demonstrate the utility of GWAS for understanding the genetic architecture of seed nutritional traits in common bean and have utility for future enrichment of seed with macro– and micronutrients through genomics-assisted breeding.Micronutrient malnutrition is one of the main public health problems in many parts of the world. This problem raises the attention of all valuable sources of micronutrients for the human diet, such as common bean ( Phaseolus vulgaris L.) is a species of great interest for human diet worldwide, gaining attention as functional food offering benefits for human health  has become a popular approach for studying traits of agricultural importance and has gained popularity particularly for screening a great number of accessions to gain insight into understanding the genetic basis of complex traits . In commThe basic goal of GWAS is to detect markers that are either associated with a trait of interest directly or are in linkage disequilibrium  with a quantitative trait locus (QTL) that controls it. Cited GWAS studies on content of micronutrients detected numerous QTLs associated with Fe, Ca, Zn and Mn content, located an all chromosomes. Most of them represent minor genes, usually explaining around 10% or less of total phenotypic variation, with only a few exceptions. Earlier studies employing classical QTL analysis summarily detected a large number of QTLs; some of them were associated with major genes, but they were usually population or environment specific , 2011. Pr2). While uncorrected r2 indicates strong LD even for the SNPs located on the opposite ends of the chromosome  associated with variation in seed content of eight nutrients . The secondary aim of the study was to compare different methodology options and identify their possible pitfalls and shortcomings.The study was performed using 174 accessions representing the most commonly used Croatian landraces of common bean  held at 1. The quality of DArTseq-derived SNP markers was determined by the parameters ‘reproducibility’ , ‘call-rate’ (percentage of samples for which a given marker was scored) and ‘MAF’   . Marker vulgaris  using BLvulgaris . Final Svulgaris . The impvulgaris : (1) cenvulgaris , (2) norvulgaris , (3) domvulgaris , and (4)vulgaris . Additior2. Besides straightforward r2, corrected measures designed to remove the bias caused by population structure (rS2), kinship (rV2) and both (rVS2) were also estimated . Row p-values from TASSEL were converted into Storey’s q-values using R package “qvalue”  assigning the accessions of Mesoamerican origin  to cluster A, while the accessions of Andean origin (phaseolin type II or III) formed the cluster B. At K = 3 , cluster B defined for K = 2 split up into two clusters separating the great majority of accessions of phaseolin type II (“H” or “C”) from those having phaseolin type III (“T”). At K = 3, 48 accessions (27.59%) belonged to cluster A, 29 (16.67%) to cluster B1, and 80 (45.96%) to cluster B2. For 17 accessions (9.77%), membership probabilities were lower than 75% in any of the clusters and were thus considered as “mixed origin.” Phaseolin type and cluster membership of each accession are given in Q-values of each accession obtained at K = 3 were used for the control of genetic background in GWAS.In concordance with the results described by Out of 17,514 polymorphic markers 8,092 (46%) had high scoring reproducibility (>0.95), high call-rate (>0.90) and minor allele frequency (MAF) higher than 5%. From 8,092 SNP sequences, 6,599 (82%) high-quality SNPs were aligned to 11 chromosomes of common bean. The average number of SNPs per chromosome was 599.91, ranging from 403 on chromosome 4 to 834 on chromosome 2. The mean number of SNPs per Mbp was 12.85 or, on average, one SNP every 77,828 base pairs. Two hundred and eighty-eight SNPs for which more than 5% of accessions were heterozygous were removed for further analysis, and missing data were imputed for the remaining 6,311 SNPs .r2). Bias caused by relatedness and/or population structure was removed by adjusting r2: (a) using kinship estimates (rv2), (b) using Q-values obtained by STRUCTURE (rs2), or (c) using both (rvs2). Fitted model for non-adjusted estimate r2  at approximately 1 Mbp. Both measures, rv2 and rvs2, shown on rvs2 based on centered IBS was also shown in rvs2 values based on four other kinship matrices. Estimation curves for normalized IBS and distance-based kinships were completely overlapped, and the difference between them in comparison to centralized IBS was visible only due to magnification achieved by reducing both axes to approximately 1/10 of their total range. Two dominance-based kinship curves were completely overlapped as well, but characterized with lower decay rate, reaching 0.1 threshold at the distance of approximately 3 Mbp. The results suggested that adjustment for relatedness preferably using a centralized IBS kinship matrix was a necessary requirement for GWAS.Linkage disequilibrium, non-independence of alleles at different loci was assessed as the squared correlation between loci  were trimmed, thus removing from one to ten the most extreme data points per trait. Descriptive statistics for all minerals based on imputed data set are given in 10(p) peaks were observed at two pairs of QTNs located on chromosome 3 (Pv03) and chromosome 10 (Pv10), both explaining 7% of total phenotypic variation. Five QTNs were associated with P: four on chromosome 7 (Pv07) and one on chromosome 8 (Pv08)  , explain8 (Pv08)  and Mg o8 (Pv08) , explain8 (Pv08) , explainAs expected, a multi-locus model fitting in MLMM resulted in much less marker-trait association discoveries. Out of 22 QTNs associated with N by TASSEL, only two were confirmed by MLMM: one out of four on Pv01 and the first of the two QTNs on Pv10. Similarly, only one QTN out of four found by TASSEL on Pv07 was associated with P. An additional discovery by MLMM is QTN associated with N on the Pv05, not previously detected by TASSEL.p-values below the threshold for N, and only one for P.Regarding the relationships between sizes of different variance components estimates obtained by MLMM, comparison of the residual sum of squares (RSS) plots for N and P  could beR2 value for each marker, only a cumulative value for the full set of markers could be extracted from MLMM output. The associated markers were distributed over the whole genome, except for the chromosomes Pv04 and Pv11; N was the trait with the largest number of discovered associations, but individual effects of markers were lower than for other traits. When the TASSEL discovered a sequence of QTNs within up to 0.3 Mbp distance range, MLMM would likely retain just one of them. Finally, most of the markers were positioned closer to the chromosome ends, and just a few closer to the centromeric region.The summary of all marker-trait associations detected either by TASSEL or MLMM is given in Strong effect of population structure in N, clearly visible on Concerning the origin of analyzed common bean accessions, we performed a model-based cluster analysis based on microsatellite markers which revealed the presence of three clusters in nearly complete congruence with the results of phaseolin type analysis. The results are consistent with previous studies showing that the European germplasm originates also from Mesoamerican and Andean gene pools, where the Andean is more prevalent . SimilarAs one of the requirements for successful GWAS, the presence of a reasonable amount of phenotypic diversity amongst genotypes in the study panel was already established in an earlier study . For mosrv2 and rvs2, and finally, as estimated by rvs2, LD decayed to 0.1 at a distance of approximately 1 Mbp. In addition to the conclusions of rv2 and rvs2 measures support The major issue in GWAS is to separate the true signal of marker-gene association from a plethora of false signals created by population structure and relatedness of individuals. The impact of kinship and population structure on LD estimates in common beans has been extensively discussed in a recent study by There are two possible reasons for the discrepancy in the number of marker-trait associations identified by TASSEL and MLMM: (1) stepwise reduction of available genetic variability in MLMM; (2) the use of different methods for estimation of genetic and error variance.p-values for fixed markers are below the threshold.Stepwise reduction of available genetic variability in MLMM: Single locus model used in TASSEL explores the entire genetic variability for fitting each SNP, while the multilocus model (MLMM) reduces available genetic variability for the next step by fixing selected SNP at each fitting step. At the final forward step, the genetic variability is completely exhausted; MLMM stops and runs the backward part of the stepwise fitting, as illustrated by RSS plots in N is equal to 0.548, thus substantially larger than 0.377, the value of equivalent ratio calculated using the TASSEL null model variance estimates.The use of different methods for estimation of genetic and error variance in TASSEL and MLMM: Stepwise reduction of available genetic variability in MLMM: it is not possible to make a straightforward comparison of variance estimates from TASSEL and MLMM, because MLMM output provides only relative values of genetic and error variance, used to create RSS plots on p values obtained by the two options (“re-estimate” vs. P3D), the results were in disagreement in terms of the number of discoveries. Namely, using the selected threshold of q = 0.2, the analysis using P3D option detected no significant SNPs, because the smallest estimated q-value was as high as 0.25. It is likely that this outcome is related to panel size as well as the number of markers.Furthermore, as the results of the TASSEL analysis were obtained by using the option to re-estimate variance estimates for each marker fit, the analysis was redone using the timesaving P3D (“population parameters previously determined”) option. P3D approach uses null model estimates for all marker fits . Despiteq-value of 0.2 as the cutoff point yielded the reported set of QTNs. It comes with the cost of 20% false positives, i.e., when the analysis of N content in TASSEL detected 22 marker-trait associations, 5 of them were actually false. Although other common bean studies used lower FDR cutoff values of 0.01–0.10  Two QTNs associated with seed iron content found on chromosome 6 by Classical QTL analyses also yielded numerous marker-trait associations, which can also be used for comparison with GWAS findings, knowing the approximate positions of the QTLs. This is at least possible for meta-QTLs, thanks to their physical positions provided by The important aggravating factor for the detection and use of marker-trait associations is the fact that a lot of them are either environment or population specific. In studies that collected phenotypic data from more than one environment, most of the QTNs were environment-specific; for example, two iron QTNs detected in two different seasons by –1 for Ca and 0.33–0.40 g kg–1 for Mg, but for the dry weight of seed coat (in contrast to whole seed in present study). Among the various possible strategies for use of QTNs in the biofortification breeding programs, The SNP dataset generated for this study is included in the ZŠ and KC-S: conceptualization. JG and ZL: methodology. KC-S, BL, MV, MP, and ZL: investigation. JG and KC-S: writing-original draft preparation. ZŠ and ZL: writing – review and editing. All authors 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.