Vigna subterranea L. Verdc.) is considered an emerging crop for the future and known as a crop for the new millennium. The core intention of this research work was to estimate the variation of landraces of Bambara groundnut considering their 14 qualitative and 27 numerical traits, to discover the best genotype fitted in Malaysia. The findings of the ANOVA observed a highly significant variation (p ≤ 0.01) for all the traits evaluated. There was a substantial variation (7.27 to 41.21%) coefficient value, and 14 out of the 27 numerical traits noted coefficient of variation (CV) ≥ 20%. Yield (kg/ha) disclosed positively strong to perfect high significant correlation  with traits like fresh pod weight, dry pod weight, and dry seed weight. The topmost PCV and GCV values were estimated for biomass dry (41.09%) and fresh (40.53%) weight with high heritability (Hb) and genetic advance (GA) Hb = 95.19%, GA = 80.57% and Hb = 98.52%, GA = 82.86%, respectively. The topmost heritability was recorded for fresh pod weight (99.89%) followed by yield (99.75%) with genetic advance 67.95% and 62.03%, respectively. The traits with Hb ≥ 60% and GA ≥ 20% suggested the least influenced by the environment as well as governed by the additive genes and direct selection for improvement of such traits can be beneficial. To estimate the genetic variability among accessions, the valuation of variance components, coefficients of variation, heritability, and genetic advance were calculated. To authenticate the genetic inequality, an unweighted pair group produced with arithmetic mean (UPGMA) and principal component analysis was executed based on their measurable traits that could be a steadfast method for judging the degree of diversity. Based on the UPGMA cluster analysis, constructed five distinct clusters and 44 accessions from clusters II and IV consider an elite type of genotypes that produce more than one ton yield per hectare land with desirable traits. This study exposed an extensive disparity among the landraces and the evidence on genetic relatives will be imperative in using the existing germplasm for Bambara groundnut varietal improvement. Moreover, this finding will be beneficial for breeders to choose the desirable numerical traits of V. subterranea in their future breeding program.Bambara groundnut ( Vigna subterranea L. Verdc.; Syn: Voandzeia subterranea L. Thouars) is an underutilized grain legume that belongs to the family of Fabaceae and subfamily of Faboidea grown mostly in Africa [Vigna subterranea (L.) Verdc and its chromosome number is 2n = 2x = 22 [Arachis hypogaea L) and cowpeas  in Africa [The Bambara groundnut , University Agricultural Research Park, University Putra Malaysia (UPM), Malaysia. Based on the Global Positioning System (GPS), the research location was 2°58′54.0′′N latitude and 101°42′53.8′′E longitude. The seeds of accessions were sown in open field conditions during the 2018-2019 cropping season. During the planting season, the mean climatic situation is stated in One hundred fifty accessions of Bambara groundnut were selected for this current research work, all representing the African accessions collected from the local market of Nigeria. The list of Bambara groundnut accession used in this research was displayed in 2O5/ha, and 100% K = 45 kg K2O/ha). The total portion of phosphorus (100% P) and potassium (100% K) was applied during land preparation; hence, 70% N was applied at 5 weeks after sowing [The experiment was conducted in a randomized complete block design (RCBD) with three replications. The experimental plot comprised of single rows measuring 2.10 m × 0.80 m. The distance between the plant to the plant was 30 cm and row to row distance was 1 m and the distance between replication was 2.0 m according to . During r sowing .Both qualitative and quantitative data were taken following the Bambara groundnut descriptors . Twenty-p ≤ 0.05) and to compare among the means of significant traits. The result of this research was expressed as mean, the square of mean, genetic parameter, and lastly, the Pearson correlation was measured to find out the intercorrelation ships among the traits. The correlations between the quantitative variables were determined using the Pearson [The SAS  version 9.3 was followed to test the significant differences using the analysis of variance (ANOVA) procedure at the level of LSD ( Pearson  correlat Pearson  noted th(1)Analysis of variance (ANOVA) estimation: this analysis was done to discover the uniqueness among the accessions and estimate the effects of environment and genes on several traits(2)σg2 is the genotypic variance, σp2 is the phenotypic variance, MSG is the genotypic mean square, MSE is the error mean square, and r is the replication numberThe genotypic and phenotypic variation was calculated as per following the formula given by :(1) σg2(3)(a)(b)(c)RD = (PCV − GCV/PCV) × 100where PCV is the phenotypic coefficient of variation, GCV is the genotypic coefficient of variation, σp2 is the phenotypic variance, σg2 is the genotypic variance, and RD is the relative difference between PCV and GCV.The coefficient variation of phenotypic (PCV) and genotypic (GCV) were estimated as per formula given by :(a)PCV=The estimated values of PCV and GCV were categorized by , 49 like(4)hb2): it refers to the proportion of genotypic variance (σg2) with the phenotypic variance (σp2) multiplied by a hundred. For estimation of (hb2), the formula given by [σg2 = Genotypic variance and  σp2= Phenotypic variance. In accordance with [Broad sense heritability K) following the method of [K is the constant that indicates the intensity of selection. According to Adewale et al. [K is at 5%. hb2 is the broad sense heritability, and Genetic advance (GA) (as a percentage of mean) was calculated with a 5% selection intensity (ethod of . Geneticethod of :(3)GA%=e et al. , the rat(6)Genetic gain (%): estimated as genetic advance (GA) × 100; it is also categorized  as betweFurther, to determine the expected gain from selection, estimation of heritability simultaneously with the values of the genetic advance can be an effective tool of crop enhancement program. Valuation of this component is an extensively fundamental step that must be taken into consideration before commencing any breeding program. In our current research, the enhancement of Bambara groundnut yield preference of the selection method was taken based on the magnitude of variation that exists in the gene pool of this crop, measurement of variance component, heritability, and genetic advance.To determine the genetic or hereditary divergence, cluster analysis was used for 27 numerical traits that were considered in this study. Based on the Euclidian distance method, data was analyzed for investigation of genetic diversity. In addition to this, based on the unweighted pair group method using arithmetic average (UPGMA) and following the algorithm and sequential, agglomerative, hierarchic, and nonoverlapping (SAHN) method, the genetic interrelationship among the Bambara groundnut was estimated using the SAS version 9.3 software. Using similar software, the principal component analysis (PCA) was done with subsequent correlation coefficients applied to construct a dendrogram to observe the groupings and relatedness among the accessions of the Bambara groundnut. The NTSYS version 2.1  and Exeter Software   were useH) is another index that is generally used to categorize the species diversity in a certain community. Shannon's diversity index is an account for both richness and evenness present in the species also used for a wide diversity of fields. It is also known as phylogenetic indices or phylogenetic metrics, which is a numerical estimation that indicates how many types (such as species) of variation are present in a community and simultaneously can consider the phylogenetic relations among the individuals. However, the Shannon evenness index is a synonym for the Shannon equitability index and was calculated [H is Shannon's diversity index, Hmax is the maximum diversity possible, ln is the natural logarithm of a number, pi is the proportion of the population made up of species ‘i', S is the number of species or species richness in a sample, N is the number of total samples, and E = equitability = evenness = H/Hmax.Diversity indexes are statistics regarded to summarize the diversity of a population in which each member belongs to a unique group. Shannon's diversity index  for all the traits studied among the accessions. For replication, all the variables showed a highly significant difference (p ≤ 0.01) except the variables no. of branch per plant, fresh biomass weight per plant, immature pod per plant, and shelling percentages observed no significant differences. The minimum and maximum values across overall plants were shown in p ≤ 0.01). The average no. of branches per plant with the standard error was observed 34.56 ± 0.33, for total no. of pod per plant 60.68 ± 0.48, for hundred seed weight 270.19 ± 1.56 and yield 1049.19 ± 14.93 kg/ha. The highest value of standard deviation (SD) was found for the trait yield kg/ha (SD = 316.64) with standard error (SEm: ±14.93) while the lowest was for internode length  , standard deviation (St. Dev), and coefficient of variation (CV%) revealed by analysis of variance among the investigated 27 quantitative traits were displayed in : ±0.03) . We foun: ±0.03) .Twenty-three accessions were notified as elite stranded among the whole morphotypes based on a higher yield performance and other yield contributed quantitative traits , and ther < 0.25) significant correlation with yield . Days to maturity had positive and intermediate (0.25 ≤ r < 0.75) highly significant association with yield . Plant height had positive and weak (0.0 ≤ r < 0.25) highly significant relation with yield  but intermediate correlation with seed length . Significantly positive and intermediate association (0.25 ≤ r < 0.75) was found between biomass fresh weight , biomass dry weight , seed length , and seed width  with yield kg/ha. Correlation values for total no. of pod had positive and moderate (0.25 ≤ r < 0.75) highly significant association with yield  while strong (0.75 ≤ r < 1.00) and significant  relation was found with number of mature pod, dry seed weight , and hundred seed weight . Fresh pod weight detected positive and strong (0.75 ≤ r < 1.00) highly significant correlation with yield  along with dry pod weight  whereas dry pod weight showed positive perfect correlation (r = 1.00) with yield. The positive and intermediate (0.25 ≤ r < 0.75) highly significant association with yield was noted for hundred seed weight  per plant  varied from internode length (0.25) to seed yield (100025). Unvaryingly, for the trait internode length, the least phenotypic variance ( σp2), the value was 0.30 whereas for seed yield kg/ha was reported the topmost (100273.24) value. Seemingly, in the case of all traits, the phenotypic variance ( σp2) is greater than genotypic variance (σg2). Topmost PCV and GCV values were computed for biomass dry weight per plant and biomass fresh weight with a value of 41.09% and 40.53%, respectively. The lowest estimated values for GCV had 6.74% for shelling percent, whereas the lowest PCV values were found 7.26% for the trait days to maturity. The estimated both GCV and PCV values were found more than 20% for the traits such as number of stem per plant (PCV 23.51% and GCV 22.08%), biomass fresh (PCV 40.83% and GCV 40.53%) and dry (PCV 41.09% and GCV 40.09%) weight, fresh (PCV 33.02% and GCV 33%) and dry (PCV 30.18% and GCV 30.14%) pod weight, dry seed weight (PCV 26.31% and GCV 25.68%), and yield (PCV 30.19% and GCV 30.15%) kg/ha which indicate high variability among these traits and for the improvement of accession further selection could be done since the variation of these traits is due to the effect of additive genes. Rest of the traits showed GCV and PCV ≤ 20% although the traits days to maturity (PCV 7.26% and GCV 6.82%) and Shelling percent (PCV 8.47% and GCV 6.74%) showed below 10% coefficient of variation which indicates the limited scope of selection based on respected traits due to the effect of environment on their phenotypic expression.The investigation of covariance (genotypic and phenotypic), the genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV), relative differences (RD), broad-sense heritability, and genetic (GA) advance (as a percentage of mean) were displayed in (1) Relative Difference (RD). The relative difference (RD) is referred to as an estimation of the ratio of GCV in association with the respective PCV and the estimated RD values varied from 0.05% (fresh pods weight) to 38.67% for no. of branch per plant (er plant . The tra(2) Heritability in a Broad Sense (hb2). Heritability refers to the ratio of the total variation of phenotypic traits in each population between the individuals due to genetic variation. Typically, the estimated values of heritability in the broad sense were high (hb2 > 30) for almost of the all traits evaluated (hb2 ≤ 60%) heritability values were marked for the traits like branch number per plant (37.61%), no. of immature pods per plant (52.73%), and pod width (44.98%) whereas the rest of the traits expressed high (hb2 ≥ 60%) heritability values, which indicate that the degree of heritability is less affected by the environment.valuated . The ran(3) Genetic Advance (GA). Genetic advance (as percentage mean) was ranged between the lowest (11.06%) for shelling percent and biomass fresh weight (82.86%) ((82.86%) . Geneticnd average maximum yield (23.5%) while cluster III was constructed by only 10% of the total accessions with lowermost average yield (12.48%). Additionally, we recorded 46.51%, 16.90%, and 2.67% higher (+) mean yield compared to average grand mean yield (1049.19 kg/ha) for cluster II, cluster IV, and cluster I, respectively, while cluster III (36.11%) and cluster V (32.53%) gave lower (-) yield. So, cluster II (23) and cluster IV (21) had accessions adjacent to each other on the aspect of yield traits  and 27 morphological traits (b) exposed by PCA was shown in Principal component analysis (PCA) has been vastly used in crop research for sorting the traits and grouping of accessions. In the current research first, eight principal components (PC) had accounted for 78.99% of the cumulative variation . The 1stH) and evenness (EH) for the twenty-seven traits is shown in H = 5.01) diversity was estimated for maturity date and shelling percent followed by the traits like fifty percent flowering date, nodes number per stem, internode length, pod width, seed length, and seed width which (H = 5.00) indicated that maximum diversity was present among these traits while the lowermost diversity (H = 4.93) was noted for the trait biomass fresh and dry weight per plant. Similarly, maximum (EH = 1.00) values of evenness were marked for almost all the traits whereas minimum (EH = 0.98) was noted for biomass fresh and dry weight per plant.The Shannon–Weaver diversity index was used to assess the phenotypic diversity for each trait. The estimation of the Shannon–Weaver diversity index (Effective selection is done when significant hereditary variation present with high magnitude among the accessions has been reported by Hahn  and AdebVigna subterranea (L.) Verdc [Vigna unguiculata L) [Vigna subterranea (L.) Verdc [p ≤ 0.01) among the accessions and varies from 103 to 163 days, which was more or less similar range reported by Masindeni [p ≤ 0.01) was found between yield and yield contributing traits such as biomass fresh and dry weight, pod and seed number per plant, fresh and dry pod weight, dry seed weight, pod length and width, mature and immature pods per plant, and hundred seed weight showed high genetic variation among these traits. A similar variation in yield contributing traits was also stated by Goli et al. [Arachis hypogaea L.) which was introduced from South America, the calculated yield was 1058.8 kg/ha from a cultivated area of 6,207,414 hectare in West Africa; for this reason, Bambara groundnut was treated as a neglected and underutilized crop due to its low production in West Africa [The findings of this work displayed that there was a vast genetic variation that exists among the Bambara groundnut accession estimated for twenty-seven numerical traits listed. The similar descriptive statistics analysis  confirmed the genetic diversity of the .) Verdc  and the ulata L) . Our resulata L) . Relevanulata L)  and indiulata L)  in Botswulata L)  and Tourulata L) . In Cameulata L)  and Sobdulata L) . Among tulata L)  out of 1ulata L)  ranged fulata L)  and playulata L) . The floulata L)  and very.) Verdc . Identic.) Verdc  ranging .) Verdc  reported.) Verdc  observed.) Verdc , also he.) Verdc , and in asindeni  and Goliasindeni . The matasindeni . Long phasindeni . In our asindeni  in Southi et al.  and suggi et al. ; [18, 63i et al. ). Our cai et al. ; his obsi et al. . On the i et al. . But fort Africa .The correlations assessment indicated a relationship of some morphological traits with the characters of yield. The association or correlation is an influential tool for the researchers to prefer the traits to be integrated into the genotype selection program . The corIn past research findings on heritability, it was reported that the selection constructed for certain trait improvement does not only depend on available genetic variation but also the degrees of heritability for such variations , 85. BesThe estimated result of relative difference (RD) was very high for traits like branch number per plant, no. of nodes per stem, pod width, and no. of immature pod per plant. Bello et al.  and UmarGA = 62.02%), dry seed weight , and yield kg/ha  had high heritability alongside with high genetic advance and suggested greater additive effect of genes which provide effective selection for traits improvement directly. This result has an uninterrupted background by the previous research of [Vigna subterranea (L.) Verdc. can be obtained through selection by the measurement of heritability and genetic advance.Estimation of heritability and genetic advance play a vital role in assuming the divergence of phenotypic values which were broadly considered as breeding values. Johnson et al.  and Asseearch of , 90, 96.earch of . Onwubikearch of  and Jonaearch of  that theearch of . FinallyCapsicum annuum L. genotypes. The cluster analysis based on the UPGMA model using numerical traits constructed four distinct groups of Bambara groundnut genotypes in south Africa reported by Unigwe et al. [In the present, investigation of clustering was supported in the past research, observed by Unigwe et al. , Gbaguide et al. , and Atoe et al.  also conTypically, the principal component analysis (PCA) is the rejustification tool of cluster analysis. Genetically, identical accessions were clustered into the same group stated by Falconer  also genH′ index varied from 1.60 to 2.07 for twenty quantitative traits of Bambara lines, Olukolu et al. [H′ index of nineteen qualitative traits of Bambara groundnut varied from 0.1 to 0.15 and twenty-eight numerical traits of 124 accession of Bambara groundnut which showed H′ index values between 0.09 and 0.16 across the four African regions. Bonny et al. [Vigna subterranea L verdc.) in Cốte d'Ivoire of similar findings with me. Nonetheless, the values of H′ index for traits appeared statically more or less similar, suggesting a similar genetic diversity. The report from Alvarez et al. [In our study, the observed diversity index value was more than 4.93 for most of the traits evaluated. This finding supported by Aliyu et al.  reportedu et al.  reportedy et al.  evaluatez et al. , Robert z et al. , and Thoz et al.  showed tVigna subterranea L. Verdc) can be obtained through selection by the determination of different genetic parameters analysis. Additionally, the degree of divergence recorded for almost all the agromorphic variables was studied. However, it can be beneficial to the advancement of agromorphic traits of Bambara groundnut by the plant breeders. The current research also resolved strongly to the perfect association between the morphological traits and the field yield. The yield-related traits like no. of the stem, no. of the petiole, no. of mature pods, biomass fresh and dry weight, fresh and dry pod weight, pod weight, dry seed weight, and yield kg/ha recorded high GCV and PCV values were ≥20% with high genetic advance also a low relative difference. It is evident from this present study that the enhancement of yield and other yield-related traits can be attained through effective selection based on estimates of heritability and genetic advance. The result from the principal component and cluster analyses depict that 23 accessions from cluster II and 21 accessions from cluster IV considered high-yielding accessions and can be suggested as large-sized seed associated with high yield potentials. These 44 elite accessions among 150 of Bambara groundnut lines were suggested to grow for further evaluation via conventional breeding alongside with molecular study for confirming and identifying the best 20 accessions.It is noticeable from this current research that the enhancement of yield and other yield-related traits of Bambara groundnut (


Extensive heterogeneity in autism spectrum disorder (ASD) has hindered the characterization of consistent biomarkers, which has led to widespread negative results. Isolating homogenized subtypes could provide insight into underlying biological mechanisms and an overall better understanding of ASD. A total of 1093 participants from the population-based “Healthy Brain Network” cohort  were selected based on score availability in behaviors relevant to ASD, aged 6–18 and IQ >= 70. All participants underwent an unsupervised clustering analysis on behavioral dimensions to reveal subgroups with ASD traits, identified by the presence of social deficits. Analysis revealed three socially impaired ASD traits subgroups: (1) high in emotionally dysfunctional traits, (2) high in ADHD-like traits, and (3) high in anxiety and depressive symptoms. 527 subjects had good quality structural MRI T1 data. Site effects on cortical features were adjusted using the ComBat method. Neuroimaging analyses compared cortical thickness, gyrification, and surface area, and were controlled for age, gender, and IQ, and corrected for multiple comparisons. Structural neuroimaging analyses contrasting one combined heterogeneous ASD traits group against controls did not yield any significant differences. Unique cortical signatures, however, were observed within each of the three individual ASD traits subgroups versus controls. These observations provide evidence of ASD traits subtypes, and confirm the necessity of applying dimensional approaches to extract meaningful differences, thus reducing heterogeneity and paving the way to better understanding ASD traits. The heterogeneity of ASD, reflected in its etiology, development, and biological phenotypes, presents an enormous challenge in the delineation and understanding of the disorder. It is therefore fundamental to define distinct subgroups of ASD, and dimensional approaches have been proposed as one way to perform this.Autism spectrum disorder (ASD) is a complex array of neurodevelopmental conditions typically characterized by social interaction and communication impairments, and restricted and repetitive behaviors5. Core autistic traits are often concomitant with behavioral traits varying in type and degree, thus further complicating the characterization of ASD. Common symptoms reported in ASD patients include anxiety6, depressive symptoms7, aggression8, attention deficits9, hyperactivity10, and sleep difficulties11. ASD patients are also more likely to present medical issues including seizures12, immune system abnormalities13, and gastrointestinal disorders14. Behavioral symptoms often indicate the presence of comorbid psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD), major depressive disorder, anxiety disorders, and conduct disorders. This complexity enforces the legitimacy of implementing a dimensional approach to examine continuous autistic traits and fluctuating behavioral symptoms as inter-correlated constructs varying in expression. A dimensional approach can promote stratification according to behavioral and biological features as suggested by the Research Domain Criteria15.It has been suggested that autistic traits, in particular social and communication deficits, are distributed along a continuum extending into the general population17. Several case-control studies have reported various changes in thickness including increases18 and decreases20. Surface area in ASD has been less investigated, with most results reporting no differences22. Studies have also reported significant global increases in surface area at younger ages24, as well as decreases in later stages of life24. Gyrification observations within ASD on the other hand have greatly reported increases26, with few showing decreases28. Though neuroanatomical investigations in ASD have remained largely unreplicated, few observations have persisted. This includes early brain overgrowth in frontal and temporal lobes31, causing children to achieve a nearly developed brain volume earlier than controls. Also, longitudinal studies in cortical thickness have shown a general trajectory of accelerated thinning with age in ASD patients versus controls in frontal, temporal, and parietal areas32. We presume that the general variability and lack of reproducibility is due to the frequently encountered practice of combining heterogeneous ASD patients into one group within case-control studies.A prominently studied biological feature in ASD is cortical morphometry. In particular, ASD is accompanied by a range of aberrant cortical patterns present in both volumetric and surface-based morphology studies33. Extracted subgroups were subsequently combined into one autistic traits group and compared in cortical surface features  to the remainder of the population , thus mimicking a case-control study. We show that the case-control paradigm does not extract meaningful cortical features and that behavioral stratification is required. Therefore, in order to achieve clinically relevant morphometric signatures, we ran morphological analyses comparing each of our isolated socially impaired subgroups to controls. This should provide us with a better understanding of underlying heterogeneity present in the physiology of autistic traits, and ultimately ASD.The objective of the present study is to thus disentangle and better understand the behavioral heterogeneity in ASD by using subjects with autistic traits to extract refined cortical morphometry features. To observe how behavioral dimensions distribute in a general population of children and adolescents that vary in degree of social impairment spreading across the spectrum, we designed the following experiment. We chose a broad age range (5–18), including males and females, within a large-scale multidimensional population-based cohort in order to capture a larger effect variance (compared to a purely ASD cohort) by focusing on behavioral constructs, and not an ASD diagnosis. We then conducted an unsupervised clustering analysis on the z-scores of several behaviors, especially chosen due to their manifestation in ASD, in order to isolate data-driven subgroups high in our dimension of interest: social impairment, which is a surrogate of autistic traits34. It comprises a population of individuals at-risk for developing psychiatric disorders and typically developing participants. Subjects were recruited through flyer dissemination and subsequently assessed on clinical questionnaires at three sites in New York City, USA: Staten Island, Mobile Van, Midtown.The Healthy Brain Network (HBN) cohort initiative within the Child Mind Institute began in 2015 with the goal of collecting brain imaging, cognitive/behavioral, and genetic data for 10,000 children and adolescents (5–21 years old) to investigate the heterogeneity behind neuropsychiatric and neurocognitive development39. Full-scale IQ was measured using the Wechsler Adult Intelligence Scale  or the Wechsler Intelligence Scale for Children (WISC-III). Written informed consent was obtained from legal guardians and from participants themselves. This cohort study initiative was approved by the Chesapeake Institutional Review Board.In the status of the HBN cohort, consensus diagnostics are not available for most of the subjects enrolled; however, this does not preclude the possibility to carry out our dimensional study since subjects were not selected based on an ASD diagnosis, but rather on the presence of behavioral constructs relevant in the field of ASD. There were 1800 subjects participating at the time of this study, of which 1093 remained based on available overlap in behavioral scores assessing social deficits, hyperactivity, anxiety, irritability, depression, aggression, and attention problems, and having a full-scale Intelligence Quotient (FSIQ) >= 70. We selected these seven behaviors due to their presence in comorbid psychiatric disorders commonly reported in ASD patients, and therefore their frequent emergence along the autistic behavioral spectrum, implicating them in the understanding of ASD behavioral neuropathology44. Though not a diagnostic tool, the SRS exhibits high inter-rater and cross-cultural reliability, and correlates greatly with the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview—Revised (ADI-R) diagnostic assessments for ASD from the DSM-5, making it a robust measure to use in the dimensional study of ASD behaviors45. Similarly, for the remaining behaviors we did not use diagnostic assessments but rather scales measuring behavioral trait severity. Hyperactivity levels were determined using the hyperactivity subscale within the Strengths and Difficulties Questionnaire (SDQ)46; anxiety was measured using the total score from the Screen for Child Anxiety Related Disorders Parent-Report (SCARED-P)47; irritability was defined using the total score of the Affective Reactivity Index Parent-Report (ARI-P)48; and lastly, levels of depression, aggression, and attention problems were determined using subscales of the same names within the Child Behavioral Checklist (CBCL)49.One of the most prominent dimensions in ASD patients is social impairment. Here, we used data from the widely used 65-item parent social responsiveness scale (SRS) as a quantitative measure of clinical autistic traits, making it the central variable of interest in our study and in fact the score from which we separate out our autistic traits individuals. The SRS has been proven as a valid measure of autistic traits and thus has been used as a measure of autistic traits (for the purpose of understanding ASD) in several behavioral, genetic, and neuroimaging studies50. A substantial challenge in such analyses lies in determining the number of clusters, which is a user-defined parameter. To address this problem, the chosen number of clusters k was determined using a Bayesian Information Criterion (BIC) distribution , SciPy (version 1.1.0), and Matplotlib (version 1.5.1).Mean behavioral scores, FSIQ and age were compared between subgroups using non-parametric 2-sided Mann–Whitney 34).MRI acquisition took place at three different sites: mobile 1.5T Siemens Avanto in Staten Island, 3T Siemens Tim Trio at Rutgers University Brain Imaging Center, and 3T Siemens Prisma at the CitiGroup Cornell Brain Imaging Center . For more information on precise methods of image analysis and the construction of anatomical information for each individual done by this software, refer to53. Briefly, the FreeSurfer analysis stream includes intensity normalization, skull stripping, and segmentation of gray  and white matter surfaces52. Subsequent tessellation, as well as various topology corrections and inflation, leads to 3D meshes of cortical surfaces in different resolutions. Our work is based on a tessellation with ~160,000 vertices per hemisphere and used the FreeSurfer fsaverage template. We focused on three morphological measures of which the processing stream created vertex-wise maps for analysis: cortical thickness (CT), surface area (SA), and gyrification (lGI). The local gyrification index is measured as the ratio between buried and visible cortex54. All images were manually inspected in-house, in addition to using the Euler number as a metric of quality by retaining images at a threshold of -217, as specified in Rosen et al. 55.T1-weighted images were processed using the FreeSurfer software version 6.0.0 , and including the residual error:p = 0.01. We report clusters with cluster-wise p-value (cwp) of cwp <0.05. These p-values were corrected for multiple comparisons using the mri_glmfit-sim precomputed MonteCarlo simulation.Vertex-wise statistical analyses were conducted using the command-line group analysis stream in FreeSurfer. Cortical surfaces for each participant were first registered to a study-specific template, then smoothed using a full-width-at-half maximum (FWHM) kernel of 10 mm for CT and SA, and 5 for lGI. A general linear model was fit at each vertex k value of 9 was retained as our supervised partitioning for this study , thus indicating a high level of social impairment, providing us with greater confidence that subjects within these subgroups have “autistic-like” traits , n = 107 ; the second maintained normal levels in all behavioral scores except for attention problems and hyperactivity, n = 82 (described as attention problems—Attn); and the third showed high levels of anxiety and depression, as well as attention deficits, n = 61 (described as anxiety depression—AnxDep)  with the aim of creating a representative general population without autistic traits subjects. Studies often barely obtain additional behavioral information on their controls other than a “non-diagnosis” or “low SRS”. By combining these remaining six subgroups into one control group, we smooth out several behavioral heterogeneities and yield a control group composed of a wide behavioral spectrum, while still maintaining low mean levels of SRS  . We also compared each autistic traits subgroup to controls and observed significant differences in all behavioral scores, except for reactivity in the Attn subgroup. Lastly, we decided to combine all three high autistic traits subgroups into one “autistic-like” group  to compare against controls, which yielded highly significant differences in every behavioral score (Table Comparisons to controls”).Upon comparing the three socially impaired subgroups to one another, several significant differences in behavioral scores were found (Table Comparisons between subgroups”). Upon comparing each subgroup to controls, we again found no differences in gender. We did however find differences in age between all subgroups and controls, except for Emot. Although age differences were present, age ranges were again similar. FSIQ differed between all subgroups and controls, except for AnxDep, which is to be expected since autistic traits are generally accompanied by differences in FSIQ. Lastly, the comparison between the hSRS group and controls yielded significant differences in age and FSIQ, but not gender (Table Comparisons to controls”). Due to these differences, we deemed it important to control for FSIQ, age and gender in the subsequent neuroimaging analysis.With respect to demographic information, there were no significant differences in gender and FSIQ between all subgroups. However, there were reported differences in age, though age ranges were similar  . To delineate the interest and significance of subtyping in an autistic traits population, we first combined all three subgroups into one large group  and compared cortical thickness, local gyrification and surface area against controls. Indeed, this comparison did not yield significant differences in any of the measured surface features. We then compared the same surface features between each of our three subgroups against controls. After correction for multiple comparisons, the Emot subgroup exhibited decreases in gyrification in the right hemisphere in two separate clusters, one spanning the precuneus  , and another in the temporal lobe  (denoted as PC and pITG)  . Additionally, the Attn subgroup also exhibited two separate clusters in the left hemisphere showing increases in surface area in the precentral cortex  , and superiorfrontal regions  (denoted as PreC and SF)  , and decreases in thickness in the left posterior middletemporal gyrus lining the superior temporal sulcus (pMTG/STS)   Fig. . The AttSF) Fig. . Lastly,55) Fig.  (Table 3Emot), (2) high in attention deficits and hyperactivity (Attn), and (3) high in anxiety and depression, as well as attention deficits (AnxDep). Furthermore, upon neuroanatomical investigation, we found that comparing each subgroup to controls uncovers unique cortical signatures. Namely, after correcting for multiple comparisons, the Emot subgroup showed decreased gyrification in precuneus and posterior inferior temporal regions (PC and pITG); the Attn subgroup displayed increases in gyrification in the lateraloccipital (LO) and increases in surface area in the precentral and superiorfrontal regions (SF); and lastly, the AnxDep subgroup exhibited an increase in gyrification in the postcentral cortex (PostC), as well as a decrease in thickness in the posterior middletemporal and superior temporal sulcus area (pMTG/STS). Most interestingly, we found that by comparing the structural brain features of one heterogeneous autistic traits group  to controls, we were unable to uncover any cortical signatures. Simply comparing behaviorally diverse ASD-like cases to controls proved far too rudimentary to yield consistent features.ASD studies have unceasingly demonstrated heterogeneity, warranting a shift in focus towards initially characterizing these differences before subsequent analysis, and steering away from case-control studies. To this end, a dimensional approach proves most relevant. To the best of our knowledge, this is the first study using an unsupervised clustering analysis on a population-based cohort to investigate how autistic traits cluster with other behavioral dimensions into subgroups, with subsequent isolation of subgroup cortical signatures. Recent evidence advocates that autistic traits fall along a continuum within the general population, which was why this study was not limited to diagnosed individuals, but rather focused on autistic traits as absolute constructs in order to avoid potential selection or environmental biases often accompanying diagnosed patients. We obtained three autistic traits subgroups in our unsupervised clustering analysis with the following behavioral profiles: (1) high aggression, reactivity, and ADHD-like traits  as well as in visuospatial processing, empathy and memory, while the temporal lobe correlates to memory, audition, theory of mind and visual processes68. Considering that this group bears high in aggression, studies have also reported a general decrease in gyrification in aggressive patients70, as well as decreased functional connectivity between the precuneus and other brain regions in patients exhibiting higher aggression traits, possibly due to its role in the DMN and empathy72. Additionally, this subgroup exhibited high ADHD-like symptoms which have also shown links to precuneus regions of the brain74. In our second subgroup, Attn, we observed increases in surface area in the precentral (primary motor) cortex, which is involved in voluntary motor control76, and the superiorfrontal gyrus, which is part of the motor control network and also harbors functions in attention, working memory, executive functioning and in the default mode network78. A study has even suggested that early motor impairments are predictors of future social communication delays, further indicating the importance of understanding this region in relation to ASD risk79. Specifically, within ASD, atypical motor functioning has been measured in patients from infancy until well into adulthood81. Seeing as how we observed extensive structural alterations throughout the motor control network in the Attn subgroup, this warrants further investigation into the relationship between ADHD-like traits and motor control in ASD and autistic traits populations. The Attn subgroup also presented decreases in gyrification in the lateraloccipital region, which is heavily implicated in visual perception, and specifically in face recognition, which greatly influences social communication83. The last subgroup, AnxDep, exhibited increases in gyrification in the postcentral region (primary somatosensory cortex), which functions as the main sensory receptive area of the brain85. In ASD, atypical sensory reception, more specifically over-responsivity to tactile sensory inputs, is a very prevalent symptom88. This suggests that autistic individuals could easily be overwhelmed, perhaps forging a link to the development of anxious and depressive behaviors, as observed in this subgroup. The AnxDep subgroup additionally showed a decrease in thickness in the pMTG/STS region, which has been greatly implicated in language and social aspects, and thus an extremely important cortical region of interest in ASD behavioral studies91. Therefore, taking into account dimensional constructs of behavior in ASD can better prepare subgroups for the identification of biological mechanisms. Further investigation is warranted into the relationship between affected regions and corresponding subgroup behaviors in the context of ASD since these regions have been consistently reported within the ASD literature.Compared to our high vs. low/absent autistic traits contrast that yielded no results, by isolating behaviorally refined autistic traits subgroups we observed cortical signatures despite having lower statistical power than the combined sample. Decreased gyrification detected in the right PC and pITG region in the 39. Reported comorbid disorders include ADHD, depression disorders, anxiety disorders, obsessive compulsive disorder (OCD), and conduct disorders39. This high degree of comorbidity (based on diagnostic information) corresponds to our dimensional results, which describe these associations in an even more descriptive and spectral manner using behavioral constructs. Having access to a multidimensional cohort containing assessments of behaviors reported in ASD allowed us to explore how core autistic traits inherently distribute with other symptoms in a dimensionally continuous population. By running a data-driven clustering analysis on a population-based cohort, we isolated three main autistic traits subgroups. The AnxDep subgroup is composed of subjects high in anxiety, depression and attention deficits. This is in line with findings reported in the literature where anxiety and depression appear to be some of the most common psychiatric comorbidities in ASD patients39. The Attn subgroup could represent an isolated population consisting purely of ADHD and autistic traits in an otherwise behaviorally muted subclass. This may perhaps become the optimal subgroup for studying the overlap between ADHD and ASD. Lastly, the Emot subgroup has ADHD-like traits in combination with emotional regulation abnormalities as evidenced by high degrees of aggression and reactivity. The Emot subgroup suggests a third combination of behavioral traits showing that the aggressive behaviors often observed in autistic traits participants can in effect co-occur with ADHD-like traits. The diverse behavioral profile of each subgroup highlights the importance of combining independent behaviors into one multivariate analysis to observe how they distribute. For example, as mentioned previously, both the Attn and Emot subgroups show high levels of attention problems and hyperactivity, and remain relatively close in anxiety and depression levels. The Emot subgroup, however, exhibits exceptionally high levels of aggression and reactivity, a factor that separates one ADHD-like autistic traits subgroup into two (i.e. Attn and Emot), thus increasing behavioral homogeneity and the likelihood of extracting biological features from cortical images.The behaviors observed in our subgroups enforce the fact that ASD is highly concurrent with several psychiatric conditions in up to 80–95% of patients92. However, this gender disequilibrium is not entirely surprising as this difference can be explained by the fact that studies sampling from the general population often show a lower ratio (3:1)93, and that overall variability may play a role. Moreover, several studies have reported ratios ranging from 2:1 to 7:197, indicating a heterogeneity that warrants further exploration as well as a diversity in gender ratio that depends on how cohorts are built. Most importantly, within the current investigation gender differences were controlled for in the neuroimaging analysis.Notably, the unsupervised clustering analysis yielded high autistic traits subgroups with gender ratios  differing from those usually reported ASD populations 99. Also, the average age of subjects in the current study (~11.4 years old) could indicate that our cortical results are consequences of differential child development, a hypothesis however that can only be confirmed using a longitudinal, prospective design. Clinical diversity in autistic traits may be further explained by other modalities, thus next steps would involve considering genetic, volumetric, diffusion, and functional differences between the acquired subgroups.This study has several potential limitations. Firstly, though the SRS included a repetitive behavior subscale, it would have been interesting to include an independent repetitive behavior component within the clustering analysis. Concerning the unsupervised clustering, inherent limitations include the somewhat arbitrary determination of the number of clusters, and difficulties to reproduce the same partitioning in another dataset. Also, it is challenging to account for covariates in unsupervised clustering analyses. Alternative approaches could also have been applied on this dataset that would prove interesting in future studies including clustering based on SRS subscales , or clustering on a broader range of scales  with subsequent isolation of subgroups high in SRS. Additionally, the present study used a general population-based cohort, and not one tailored for ASD studies, thus warranting the careful isolation of behavioral variables relevant to our objective. Within the morphometric results, it is possible we did not observe further thickness differences due to the wide age range of our cohort. Thickness changes more with age and environment and may thus present larger heterogeneities than does gyrification , leading us to observe greater gyrification alterations within our resultsIn conclusion, we showed that subtypes of autistic traits yield refined signatures and therefore stress the importance of stratification using a dimensional approach. Several studies, including the current one, have demonstrated the difficulty in yielding significant biological features in case-control comparisons, leading to large-scale inconsistencies within ASD literature. Since several of the behavioral associations and affected cortical regions discussed in this study have similarly been implicated in ASD studies, our findings maintain the growing assumption that outcomes in autistic traits are related to variations observed in ASD patients. By uncovering better-defined subtypes of ASD, studies can finally begin to truly understand the underlying genetic, biological and behavioral mechanisms of this syndrome.Supplementary FiguresSupplementary Table


Maternal pre-pregnancy body mass index (BMI) is positively associated with offspring birth weight (BW) and BMI in childhood and adulthood. Each of these associations could be due to causal intrauterine effects, or confounding , or some combination of these. Here we estimate the extent to which the association between maternal BMI and offspring body size is explained by offspring genotype, as a first step towards establishing the importance of genetic confounding.n ≤11 498). Bivariate Genomic-relatedness-based Restricted Maximum Likelihood implemented in the GCTA software (GCTA-GREML) was used to estimate the extent to which phenotypic covariance was explained by offspring genotype as captured by common imputed single nucleotide polymorphisms (SNPs). We merged individual participant data from all cohorts, enabling calculation of pooled estimates.We examined the associations of maternal pre-pregnancy BMI with offspring BW and BMI at 1, 5, 10 and 15 years, in three European birth cohorts  between maternal BMI and offspring phenotype was 0.15  for offspring BW, increasing to 0.29  for offspring 15 year BMI. Covariance explained by offspring genotype was negligible for BW [–0.04 ], but increased to 0.12  at 15 years, which is equivalent to 43%  of the phenotypic covariance. Sensitivity analyses using weight, BMI and ponderal index as the offspring phenotype at all ages showed similar results.Offspring genotype explains a substantial fraction of the covariance between maternal BMI and offspring adolescent BMI. This is consistent with a potentially important role for genetic confounding as a driver of the maternal BMI–offspring BMI association. Maternal body mass index (BMI) is associated with offspring weight at birth and BMI in childhood and adulthoodEach of these associations could be due to causal intrauterine effects, or confounding , or to some combination of theseOur study suggests that a substantial part of the maternal BMI–offspring BMI association is explained by offspring genotype, but that in contrast the maternal BMI–offspring birth weight association is not explained by offspring genotypeThis is a first step towards establishing the importance of genetic confounding of the maternal BMI–offspring BMI association,,,It has been hypothesized that development in the uterus of an obese mother may programme a fetus for increased risk of obesity in subsequent postnatal life.,,,Mendelian randomization (MR),,,,IJE online). This could be because the confounders that were adjusted for were measured poorly, or because other unmeasured confounders explain the association; maternal genotype inherited by the offspring could be an important unmeasured confounder. General population data suggest that the narrow-sense heritability [the proportion of phenotypic variance due to additive genetic effects (denoted by h2)] of BMI is at least 30%,,,In published studies adjustment for numerous potential confounders makes a negligible difference to the strength of the association between maternal (pre-)pregnancy adiposity and offspring adiposity in childhood or adulthoodHere we aimed to estimate the extent to which the covariance between maternal BMI and offspring body size from birth to adolescence is explained by offspring genotype, as a first step towards establishing the importance of genetic confounding.,IJE online. Ethical approval for NFBC1966 and NFBC1986 was obtained from the University of Oulu Ethics Committee and the Ethical Committee of the Northern Ostrobothnia Hospital District, and for ALSPAC was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees.We analysed data from three prospective population-based birth cohorts: the Northern Finland Birth Cohort (NFBC) 1966,IJE online).We excluded stillbirths, multiple births and individuals with missing genotype data, and removed one member of any sibling pairs present at random. We then excluded participants with missing maternal BMI or offspring BMI/birth weight (BW) data. For our main analyses we used Genomic-relatedness-based Restricted Maximum Likelihood implemented in the GCTA software (GCTA-GREML), which requires that cryptic (unknown) relatedness be removed to avoid confounding due to familial environment and non-additive genetic effects.IJE online. During QC, individuals with non-European ancestry were excluded. For all three cohorts, array genotypes were harmonized and imputed to the Haplotype Reference Consortium (HRC) imputation reference panelGenotyping was carried out using genome-wide microarray chips followed by standard quality control (QC) procedures; details of genotyping and QC for each cohort are given in full in IJE online). We also analysed BMI data at 31 and 46 years in NFBC1966. We calculated maternal pre-pregnancy BMI using pre-pregnancy weight reported by the mothers during early pregnancy and either self-reported or measured height (IJE online). Offspring sex, BW, length and gestational age were obtained from the birth record or measured by research staff (IJE online). In childhood and adulthood offspring weight and height were obtained from clinical examination, growth records or questionnaires (IJE online). For all weight, height and BMI variables we set outlying values that we judged to be physiologically implausible to missing. We standardized maternal and offspring phenotypic variables to give mean zero and variance one in the pooled dataset, using the usual formula (IJE online). With standardized variables, phenotypic covariance is equivalent to phenotypic correlation, enabling direct comparison of phenotypic covariance for offspring phenotypes that are measured in different units. Although BMI variables were positively skewed, sensitivity analyses indicated that results were similar when using a variety of normalizing transformations (IJE online), therefore we used untransformed variables for our primary analyses. IJE online, gives details of other pregnancy variables that we used in sensitivity analyses.For our primary analyses we examined the associations of maternal pre-pregnancy BMI with offspring weight at birth, and BMI at 1, 5, 10 and 15 years, in all studies . The simplest GCTA-GREML model is a univariate model,rG) between two phenotypes, which quantifies the extent to which the additive genetic effects on phenotype one are shared with those on phenotype two (IJE online). However, bivariate GCTA-GREML also enables estimation of the proportion of phenotypic covariance that is explained by the set of SNPs. This has previously been applied to two phenotypes measured in the same individual.,GCTA-GREML has been extended to a bivariate model that partitions the phenotypic covariance between two traits,P is the covariance between maternal BMI and offspring phenotype (BW or BMI) estimated using the usual formula (IJE online), CovG is the contribution to this covariance from additive genetic effects captured by the offspring’s imputed SNPs genome-wide, estimated using bivariate GCTA-GREMLE is the residual (unexplained) covariance, which is a combination of additive genetic effects not captured by SNPs, non-additive genetic effects and environmental effects . A detailed description of our statistical approach is given in IJE online.Assuming independence between additive genetic effects and other contributing factors, we can partition the phenotypic covariance as follows:G to CovP is our quantity of interest and has been termed the bivariate heritabilityG and CovE have the same sign, CovG:CovP is equivalent to the proportion of phenotypic covariance that is explained by additive genetic effects. If CovG and CovE are opposite in sign then CovG:CovP may be negative or >1; in this case CovG:CovP cannot be interpreted as a proportion, but still gives an indication of the extent to which phenotypic covariance is explained by genotype.The ratio of Covr2) >0.3 and lack of evidence for Hardy-Weinberg disequilibrium (P>1e-6); hard called (best-guess) genotypes (as output by the minimac3 software packageGCTA-GREML requires computation of a genetic relatedness matrix (GRM) containing a SNP-based estimate of relatedness for each pair of individuals in the sample. We used imputed autosomal SNPs with minor allele frequency (MAF) >0.01, imputation quality score (IJE online) to examine the impact ofWe conducted sensitivity analyses  SNPsusing different MAF and imputation varying the other covariates, as well as the number of PCs, that were fitted as fixed effectsvarying the relatedness exclusion threshold3] at all ages.using alternative phenotypes including weight, BMI and PI  but increased over childhood, reaching 0.12  at 10 years and 0.12  at 15 years, which is equivalent to 44%  and 43%  of the phenotypic covariance at 10 and 15 years respectively. This pattern continued into adulthood, with high CovG:CovP estimated in NFBC1966 at 31 years [1.25 ] and 46 years [0.78 ], albeit with wide confidence intervals (IJE online).IJE online), and estimates changed little as we varied covariates, phenotypes  or normalizing transformations (IJE online). Results from analyses in which we varied the relatedness exclusion threshold or the set of SNPs used to calculate the GRM suggested that our primary analyses are unlikely to be substantively biased, and estimates for CovG:CovP and SNP heritability were not attenuated as we varied the number of PCs fitted as fixed effects between zero and one thousand (Supplementary Notes/Tables/IJE online). Finally, we fitted the univariate GCTA-GREML model with disjoint halves of the genome and found little evidence of inflation of SNP heritability estimates due to cryptic relatedness or population stratification (IJE online).Standard meta-analysis using a random effects model gave similar estimates to the pooled IPD estimates, although with wider confidence intervals  using imputed SNPs with MAF >0.01 gave a similar SNP heritability estimate for BMI to the gold standard GCTA-GREML-LDMS-I model.Simulation studies suggest that the GCTA-GREML model is robust to violation of several of its assumptions.IJE online). We conducted extensive sensitivity analyses to explore the likelihood of bias due to confounding by familial environment,IJE online). Given reassuring results from analyses in which we (i) varied the relatedness exclusion threshold, (ii) fitted a large number of principal components as fixed effects, and (iii) used disjoint halves of the genome to test for inflation due to population structure, we feel that neither coarse nor fine population structure are likely to pose a serious threat to the validity of our findings.Our study has several important strengths. We analysed rich prospective data from three birth cohorts, collected from early pregnancy to adolescence (and until middle age in one study). Our use of bivariate GCTA-GREML enabled inference on the combined effects of hundreds or thousands of genetic variants that individually would not be observable. Furthermore, we meta-analysed data from three cohorts, giving sufficient sample size to obtain statistically robust evidence for genetic covariance. However, replication in other birth cohorts would be desirable, particularly as the mothers in our cohorts were lean compared with many present-day populations in high-income countries.IJE online), suggesting that this phenotypic association is unlikely to be meaningfully affected by selection bias. Although we are unable to rule out an effect of selection bias on our genetic covariance estimates, it seems unlikely that such an effect would be of sufficient magnitude to wholly account for our results. Finally, weight at birth and BMI from childhood to adulthood are imperfect proxy measures for adiposity. However, there is evidence that the correlation with directly measured adiposity is strong for child and adult BMI,Several limitations apply to this work. First, assortative mating has been observed for BMI,In conclusion, our data are consistent with, although do not confirm, the hypothesis that genetic confounding explains a substantial part of the association between maternal pre-pregnancy BMI and offspring adolescent BMI. It will be important to confirm whether this is the case, because if there is substantial genetic confounding then intervention to reduce maternal pre-pregnancy BMI with the aim of reducing offspring obesity risk will have a smaller effect than if such confounding did not exist.http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). T.A.B. is supported by the Medical Research Council (UK) [grant number: MR/K501281/1]. D.M.E. and D.A.L. work in / are affiliated with a unit that is supported by the UK Medical Research Council [grant number: MC_UU_00011/6] and D.A.L. is a NIHR Senior Investigator [grant number: NF-SI-0611–10196]. I.P. is funded by the World Cancer Research Fund (WCRF UK) and World Cancer Research Fund International [grant number: 2017/1641] and the Wellcome Trust [grant number: WT205915].NFBC1966 and 1986 have received financial support from the Academy of Finland ; University Hospital Oulu, Biocenter, University of Oulu, Finland ; NIHM [grant number: MH063706]; Juselius Foundation; NHLBI [grant number: 5R01HL087679-02] through the STAMPEED program [grant number: 1RL1MH083268-01]; the European Commission , the Medical Research Council, UK ; the EU H2020 DynaHEALTH action [grant number: 633595]; the EU H2020-HCO-2004 iHEALTH Action [grant number: 643774]; the EU H2020-PHC-2014 ALEC Action [grant number: 633212]; the EU H2020-SC1-2016-2017 LifeCycle Action [grant number: 733206]; the EU H2020-MSCA-ITN-2016 CAPICE Action [grant number: 721567]. The DNA extractions, sample quality controls, biobank upkeep and aliquoting were performed in the National Public Health Institute, Biomedicum Helsinki, Finland and supported financially by the Academy of Finland and Biocentrum Helsinki. The UK Medical Research Council and Wellcome [grant number: 102215/2/13/2] and the University of Bristol provide core support for ALSPAC. Genotyping of the ALSPAC maternal samples was funded by the Wellcome Trust [grant number: WT088806] and the offspring samples were genotyped by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. This study was also supported by the US National Institute of Health [grant number: R01 DK10324] and the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013) / ERC grant agreement [grant number: 669545]. A comprehensive list of grants funding is available on the ALSPAC website (This publication is the work of the authors and T.A.B., M.-R.J. and D.A.L. will serve as guarantors for the contents of this paper.dyz095_Supplementary_DataClick here for additional data file.


Puroindoline-b gene, which is known for influencing endosperm texture. Two SNPs on chromosome 4A were tentatively associated with WRC, accounting for 4.6% and 4.4% of phenotypic variation. The favorable alleles at the SNP sites were present in only 15% (friability) and 34% (WRC) of lines, indicating a need to develop new germplasm for these whole-grain end-use quality traits. Validation of these findings in independent populations will be useful for breeding winter wheat cultivars with improved functionality for whole grain food applications.Bran friability (particle size distribution after milling) and water retention capacity (WRC) impact wheat bran functionality in whole grain milling and baking applications. The goal of this study was to identify genomic regions and underlying genes that may be responsible for these traits. The Hard Winter Wheat Association Mapping Panel, which comprised 299 lines from breeding programs in the Great Plains region of the US, was used in a genome-wide association study. Bran friability ranged from 34.5% to 65.9%  and WRC ranged from 159% to 458% . Two single-nucleotide polymorphisms (SNPs) on chromosome 5D were significantly associated with bran friability, accounting for 11–12% of the phenotypic variation. One of these SNPs was located within the  Numerous studies have associated the consumption of high-fiber foods with decreased risk of type 2 diabetes, obesity, and heart disease ,2. ConsuUnlike refined white flour, whole wheat flour contains the all the anatomical portions of the grain are together in the same proportions as they occur in the intact kernel . The braAs wheat is milled, the bran may fracture into many small particles or remain as larger pieces. The tendency for bran to fracture into smaller pieces is known as friability. Initial research on wheat milling was focused on a reduction in bran friability (ability to retain large particles) to facilitate efficient separation of white flour from the bran by sifting . HoweverIn recent years, genome-wide association studies (GWAS) have been used extensively to determine the genetic basis of agronomic, physiological, and disease resistance traits in bread wheats ,31,32,33Our previous study showed that among eight traits measured on wheat bran, including phenolic compounds, antioxidant activity, protein, ash, lipoxygenase activity, and free thiol groups, bran particle size and WRC showed the most significant impact on whole wheat bread volume and texture among the other chemical and physical parameters tested . Therefo2 plots with fungicide application, and the harvested seeds from these genotypes were used in this study.The study comprised 299 genotypes from the HWWAMP. These genotypes included varieties released since the 1940s and represent existing germplasm of the hard winter wheat growing regions of the US, including Colorado, Kansas, Michigan, Montana, Nebraska, North Dakota, Oklahoma, South Dakota, and Texas ,43. The Wheat kernels from each of the HWWAMP genotypes were cleaned and tempered to 15.3% moisture for 24 h. Milling was performed on a laboratory mill  as described previously . The milFriability of bran was measured by sieving the bran fraction through two testing sieves stacked on top of each other . Sieves were selected according to published guidelines, where coarse bran is separated from “shorts”, or fine bran, by sifting on a No. 20 sieve, and any remaining flour is separated from shorts by sifting through a No. 60 sieve . Friabilg for 15 min. The supernatant was then carefully decanted and test tubes were drained upside down over paper towels for 10 min. Following decanting, the tubes were weighed and the weight of the bran plus absorbed water was calculated. WRC was calculated as the weight of bran plus absorbed water divided by the dry weight of bran, expressed as a percentage.The WRC of bran was analyzed according to the approved method for flour , with soData distributions of friability and WRC were visualized using histograms. The normality of the data was analyzed using the Shapiro–Wilk test using the “shapiro.test” package in R .The HWWAMP panel was previously genotyped using the 90 K SNP wheat iSelect assay and the marker data is available at the T3 wheat database . The majThe two traits recorded in this study, bran friability and WRC, had one phenotypic value per line. This was similar to one phenotypic measurement generally available on other quality traits in a cultivar development program . A singlThe GWAS was conducted in TASSEL. Several linear models were examined to achieve the best fit possible, including: general linear model (GLM)  using on−40 and sequence identity greater than 50%. The annotations for the underlying or flanking genes were compared against the trait of interest to determine the candidate genes that were likely important for bran functionality traits.The annotations of the candidate genes underlying or flanking (±5 kb) the SNPs associated with a trait were obtained from the International Wheat Genome Sequencing Consortium (IWGSC) . If annoThe Pina-D1, Pinb-D1b, and Pinb-Wild molecular marker information on 44 lines in the HWWAMP was obtained from the regional breeding programs, Northern Regional Performance Nursery (NRPN) and Southern Regional Performance Nursery (SRPN) from 2005–2009 dataset . The “coWith the emphasis on consumption of whole grain foods by governmental agencies , it is rp = 0.83). The wide range in this trait suggested a significant variation in milling performance among wheat genotypes in the HWWAMP.Bran friability was normally distributed from 34.5% to 65.9% with a median of 51.1% (p < 0.001). The broad range in WRC data suggested a significant genetic effect on this trait. However, unlike friability, the distribution departed slightly from normal.The WRC ranged from 159–458% with a median of 331% b. The WRp = 0.0002).Correlations among bran yield, friability, and water retention were calculated in order to determine their application as a covariates in the GWAS models. Only bran yield and WRC were significantly correlated  and TraesCS5D02G001200 . TraesCS5D02G001200 encodes for sucrose membrane transfer proteins.As a part of cell membranes, these proteins could play an additional role in the friability of bran during milling. The other gene, TraesCS5D02G004300, was annotated as Pinb-D1b at Ensembl Plants [Puroindoline-b in the IWGSC functional annotations [Pinb-D1b marker  and Pinb-Wild  was relatively stronger compared to Pina-D1 . This result is probably because the distance between Pinb-D1 forward primer start position and the friability-associated SNP marker (BS00000020_51) is 222 bp. Overall, these observations indicate that the SNP marker associated with bran friability  is in linkage disequilibrium and potentially tagged to the Pinb-D1 marker, and is a functional variant for Pinb locus. This also suggests that Pinb-D1 locus may be involved in regulating bran friability.The two markers that were significantly associated with bran friability, BS00000020_51  and Excalibur_c49805_63 , were located in genes otations . A BLASTPinb-D1 genes encode for puroindoline proteins, which make up a complex multicomponent complex called fraibilin [raibilin . Friabilraibilin . The resTraesCS4A02G251100  and TraesCS4A02G251300 , respectively. The TraesCS4A02G251300 gene did not have a characterized function in wheat. However, it did have high similarity to the Zm00001d033902_T001 gene present in maize , which was reported to encode for cysteine-rich secretory proteins. Interestingly, in wheat, nongluten cysteine-rich proteins are harmful for breadmaking because they interfere with gluten macropolymerization [The two SNP markers that were tentatively associated with WRC, IWA4867  and IWA4698 , were located close to In conclusion, our study identified novel SNPs associated with bran friability and tentative SNPs associated with WRC. One of the SNP markers associated with bran friability was tagged to the well-characterized Pinb locus in wheat, which suggests that in addition to endosperm texture, Pinb may also have a role in bran texture (friability). The favorable alleles at the trait-associated SNP sites were present in only a fraction of lines (mostly recent releases and very few heritage or older cultivars) in the HWWAMP, which comprises breeding lines from many of the breeding programs in the US. This result indicates that the traits beneficial for whole wheat flour are neither selected for or against, just randomly, and are relatively new in the breeding history. Additionally, the lines carrying favorable alleles for both traits were randomly distributed in a PCA biplot, suggesting the absence of groupings among lines carrying favorable alleles. A conscious effort is thus needed to breed for cultivars that would be more desirable for whole-grain milling. Upon validation in independent populations and multiple environments, the novel markers identified in this study may help breeders select lines that have high bran friability and low WRC, which are preferred in whole grain bread-making. Additionally, the lines carrying favorable alleles identified in this study can be used as parental lines while making new crosses in breeding programs for developing cultivars suitable for whole wheat grain flour. Further, if the genomic regions for bran friability and WRC are validated in multiple backgrounds and environments, they can then become potential targets for function characterization studies such as knockout or overexpression experiments and thus help narrow down genes important for these traits in bread wheat. These efforts overall could lead to release of wheat lines with high whole grain flour functionality and identification and characterization of genes important for whole grain bread-making.


The age at onset (AAO) is an important determinant in Parkinson’s disease (PD). Neuroimaging genetics is suitable for studying AAO in PD as it jointly analyzes imaging and genetics. We aimed to identify features associated with AAO in PD by applying the objective-specific neuroimaging genetics approach and constructing an AAO prediction model. Our objective-specific neuroimaging genetics extended the sparse canonical correlation analysis by an additional data type related to the target task to investigate possible associations of the imaging–genetic, genetic–target, and imaging–target pairs simultaneously. The identified imaging, genetic, and combined features were used to construct analytical models to predict the AAO in a nested five-fold cross-validation. We compared our approach with those from two feature selection approaches where only associations of imaging–target and genetic–target were explored. Using only imaging features, AAO prediction was accurate in all methods. Using only genetic features, the results from other methods were worse or unstable compared to our model. Using both imaging and genetic features, our proposed model predicted the AAO well (r = 0.5486). Our findings could have significant impacts on the characterization of prodromal PD and contribute to diagnosing PD early because genetic features could be measured accurately from birth. Like other neurodegenerative diseases, age at onset (AAO) in PD patients is important because it is related to clinical characteristics and progression7. Young-onset PD patients show a good response to levodopa, more dyskinesia, dystonia, and slower progression4, while late-onset PD patients demonstrate more axial symptoms, cognitive impairment, and genitourinary symptoms7. As such, there are differences in PD characteristics according to AAO. Many previous studies revealed that neuroimaging and genetic factors play an important role for AAO in PD patients12. Finding the significant features from both neuroimaging and genetic factors related to AAO in PD could enable understanding of the pathological mechanisms underlying the associations between AAO and PD.Parkinson’s disease (PD) is the second most common neurodegenerative disorder and presents with various motor and non-motor symptomsNeuroimaging genetics is an emergent transdisciplinary approach, where the associations between genetic variations and neuroimaging measures are explored. Neuroimaging genetics is more sensitive than a conventional genome-wide association study (GWAS) as it integrates richer neuroimaging information compared to binary diagnosis. Neuroimaging provides a rich quantitative characterization of disease and promises to aid in identifying genetic variations that are correlated with the clinical variables of interest. Neuroimaging genetics is different from conventional GWAS where associations between genetic factors and phenotypes are directly explored. By identifying the associations between genetic factors and imaging measurements, neuroimaging genetics intends to model and understand how genetic factors influence the structure or function of the human brain. Canonical correlation analysis (CCA) is a popular method of imaging genetics to combine high-dimensional data types. The CCA finds the best linear transformations for imaging and genetic features so that the correlation between imaging and genetic can be maximized. In practice, the sparse CCA (SCCA) method is adopted to find a sparse  set of features through regularization, such as with least absolute shrinkage and selection operator (LASSO). The existing SCCA was focused only on finding the best linear transformations for imaging and genetic features, respectively. Therefore, the resulting set of features has a high imaging–genetic association, but it does not necessarily have a strong association with the disease being studied. This makes interpreting the selected features challenging as we are not sure of the feature–target disease association. To improve the interpretation, we extended the sparse CCA to include a third data type related to the target task , which leads to simultaneously exploring the associations of the imaging–genetic, genetic–target, and imaging–target pairs. Our rationale was to explore all three possible pair-wise associations to leverage the available data fully. We named the approach objective specific neuroimaging genetics emphasizing the third added data type related to the main objective.13. Then, we applied our proposed method with the FA values, PD-related SNP alleles, and AAO as three input data types. The results were then compared with existing methods.In this study, we applied the objective-specific neuroimaging genetics approach to identify genetic and imaging features associated with AAO in PD patients, not in healthy controls. We also aimed to construct an analytical model to predict the AAO of PD using the identified genetic and imaging features simultaneously. Neuroimaging and single nucleotide polymorphisms (SNPs) data from the Parkinson’s Progression Markers Initiative (PPMI) were obtained. Neuroimaging features that can reflect the differences in the brain structure and function with respect to AAO of PD were computed from the fractional anisotropy (FA) of diffusion tensor imaging (DTI) that is a sensitive method to assess PD pathophysiology and severity14. However, our identified SNPs might serve as early biomarkers for AAO in PD as they can be measured accurately from birth.We hope to better understand how genetic variations of specific risk genes lead to alterations in the brain structure and function with neuroimaging genetics. Such an understanding could have tremendous potential for accurately diagnosing and improving therapy for PD. The imaging and genetic feature used to predict AAO of PD could help with characterizing the prodromal period of PD. This study focused on AAO in PD patients only still, our study could be extended to predict diagnosis by replacing the AAO term with diagnosis status and ultimately contribute to the early diagnosis of PD. In particular, our study could make a significant contribution to preventive medicine in PD, combining the identified SNPs with the known environmental factors of PD. The neurodegenerative process has already substantially progressed when the diagnosis of PD is established based on the widely accepted clinical criteria15. We used DTI, T1-weighted MRI, and DNA genotyping data. Inclusion criteria were aged 30 years or older, diagnosis of PD , disease duration of 1–24 months, Hoehn and Yahr (H&Y) stage of 1–2, and presence of the T1-MRI, DTI, and genotyping data. AAO was based on patients’ recollection of the first parkinsonian motor symptom3. Additionally, we divided the patients into four subgroups based on AAO , according to Pagano et al3. The demographic and clinical data of enrolled subjects were illustrated in Table This study was a retrospective analysis, and institutional review board (IRB) approval was obtained from Sungkyunkwan University. Our study was performed in full accordance with the local IRB guidelines. Informed consent was obtained from all subjects. Our study data of 146 PD patients were obtained from the PPMI database3). In the case of DTI, the acquisition protocol included a 3D magnetization prepared rapid gradient-echo sequence and a 2D single-shot echo-planar DTI sequence . Further details of the MRI acquisition and processing are available from the PPMI website (https://www.ppmi-info.org/). Most DTI scans were obtained with cardiac gating except for a few cases (less than 5%) in which a clear cardiac signal was not available.MRI data were obtained using a standard protocol for 3 T scanners. T1-weighted MRI were acquired using the following imaging parameters , genotype call rate (< 95%), Hardy–Weinberg equilibrium p value (< 10–6), and sex-matching were performed in PLINK v1.9 software18. Then, we converted this VCF file into the binary PLINK format to enforce a minimum genotyping quality (GQ) score threshold of 20 and recode the data into a text file containing the number of copies of the minor allele of each variant for each subject using the PLINK18.All genetic data  were genotyped by NeuroX genotyping arrays. We compiled the quality-controlled data into a single VCF file to extract the variants specified  and splatted multi-allelic sites into two using BCFtoolsWe mainly used the two types of high-dimensional information  for our study. We extended the SCCA to incorporate the third data type related to the target task (AAO of PD). This led to exploring imaging-genetic associations as well as target-imaging and target-genetic associations simultaneously Fig. . Detailshttps://fsl.fmrib.ox.ac.uk/fsl/fdt)19. The DTI data were processed using the diffusion tensor analysis toolkit (FDT) of FSL. Head motion and image distortions induced by eddy currents were corrected by applying a 3D full-affine alignment of each image to the mean no-diffusion-weighting (b0) image. DTI data were averaged and concatenated after the correction of distortion. We generated voxel-wise maps of FA, which quantifies how elongated the diffusion tensor ellipsoid is. Additionally, it is known to be sensitive to a wide range of pathologies20. FA maps were registered onto the common MNI space using the same procedure to process T1 MRI. We adopted the automated anatomical labeling (AAL) atlas defined on the MNI space to specify the region of interests (ROIs). We finally calculated the FA values of 90 ROIs using the sample mean of voxel-wise FA values from each ROI.Before DTI processing, T1-weighted images need to be processed first. The T1-weighted images were skull-stripped and nonlinearly registered onto the standard Montreal Neurological Institute (MNI) spatial frame using the FMRIB Analysis Group Software Library  and Y denotes q features of genetics  for each SNP), and Z denotes features related to target objective . Our proposed objective-specific SCCA (os-SCCA) is a special case of conventional three-way SCCA (TSCCA), where the third data type is from the target task. This leads to the optimization of three pairwise SCCAs simultaneously22. By replacing one data type of TSCCA with objective-relevant information, we can find imaging genetic associations that are relevant to a particular task . The formulation is defined as follows:Below we use boldface lowercase letters to denote a vector and boldface uppercase letters to denote a matrix. Given datasets Z is an n × 1 vector w become a scalar (1 × 1 vector). We can rewrite the objective functions for os-SCCA as follows21: 21.Z21. Optimal value of regularization parameters  in the inner loop. We calculated the average metric score over the five folds in (https://github.com/Ji-Hye-Won/os-tscca).Due to a limited number of samples, we adopted a nested five-fold cross-validation. For the outer loop, we separated the data into training and test sets using a fivefold split. For the inner loop, the training data were subject to another fivefold cross-validation where the inner training set was used to train the model and the remaining set was used as the validation set to tune the hyperparameters of the model. The value of w was set using the formula given in a previous study adjusted for the dimensions of 23.We applied FA values, the number of minor alleles  of PD-related SNP, and AAO of PD as three inputs for training samples to os-SCCA. The elements with non-zero coefficients of the loading vectors were the selected imaging and genetics features. Then, we constructed random forest regression models based on the selected imaging and genetic features to predict the AAO of PD. We trained a random forest of 500 regression trees using the selected features. Three regression models with only the selected imaging features, genetic features, and combined imaging and genetic features were built. This resulted in five sets of performance metrics of the models, which were averaged. We assessed the performance of the prediction using Pearson’s correlation between the actual and predicted AAO of PD in the test fold. The root mean square error (RMSE) was also usedhttps://www.webgestalt.org/2019/). The ORA compares sets of genes annotated to pathways to a list of the identified genes that are significantly deferentially expressed. A hypergenometric test was adopted to detect an over-representation of the high-ranking genes among all the genes in a given category. The whole genome was selected as the reference gene list.The selected SNPs were annotated with the genes, and they were processed with an over-representation enrichment analysis (ORA) using an online database WebGestalt (The selected features and prediction models from the os-SCCA were compared with existing sparse multi-view feature selection methods. We compared our approach with LASSO and the minimum redundancy maximum relevance (mRMR), where only associations of genetic-target and imaging-target were explored. Our method extracts significant SNP and imaging features at the same time. Contrastingly, LASSO and mRMR select significant features of a target variable  directly from larger sets of candidate predictors corresponding to features from imaging and genetic separately. In the case of mRMR, the number of selected features is a user parameter. This number was set as the same number of features obtained from our os-SCCA. We also constructed random forest regression models using imaging, genetic, and combined features from LASSO and mRMR based the training set data. We validated the models by applying them to test sets. In summary, the same procedures were applied using different selected features from the LASSO and mRMR approaches.15. The FA values of 90 regions were computed from the DTI preprocessing. The 72 allelic statuses were selected from Parkinson’s disease-associated variants for PPMI subjects. Our analysis is different from GWAS as we performed our experiments with only SNPs related to PD. Thus, the identified SNPs were not subject to statistical rigors of GWAS and should be interpreted as important variables of the prediction model. We applied the os-SCCA approach using these two types of data and target information  in a five-fold cross-validation. The identified features were similar in the five sets of results. We only reported results that were common to all five sets.We used FA values and genetic data of 146 PD patients from the PPMI databaseWe identified FA of 14 ROIs related to both SNPs and AAO of PD in all five training sets. The 14 selected regions were as follows: left and right precentral gyrus (PreCG); left and right median cingulate and paracingulate gyri (DCG); left and right posterior cingulate gyrus (PCG); left and right superior occipital gyrus (SOG); left and right lenticular nucleus, putamen (PUT); left and right lenticular nucleus, pallidum (PAL); and left and right thalamus (THA).We identified 24 variants related to both the FA and AAO of PD. The 24 selected variants were as follows: rs823118, rs4653767, rs6430538, rs353116, rs12497850, rs11724635, rs6812193, rs3910105, rs199347, rs591323, rs13294100, rs11060180, rs8005172, rs14235, rs737866, rs174674, rs740603, rs165656, rs6269, rs4633, rs2239393, rs4818, rs4680, and rs165599. Table Table −16 afterward. The imaging features of the five folds constantly above the threshold of 0.2 in a descending order based on the FS score were as follows: left PCG, left lenticular nucleus, PAL, right PAL, right PCG. The FS scores of the genetic features decreased gradually without sudden drops. The 26 genetic variants of the five folds are shown in Table The same number of features were used between our approaches and mRMR to enable a fair comparison. Each feature was ranked using the feature selection (FS) score provided from the mRMR, and we analyzed only a few features  with the highest FS scores. The FS scores were typically high, above 0.2, for the first few imaging features and then dropped significantly to 10The number of features selected for each training fold was not constant, ranging from 7 to 20. In some cases, no genetic features could be selected. For imaging features, we identified FA of 3 ROIs related to the AAO of PD for all five folds. The three selected regions were as follows: right inferior frontal gyrus opercular part (IFGoperc), left caudate nucleus (CAU), and left PAL. For genetic features, the selected SNP was only rs4653767 , which was found more than three times in five folds.p = 0.0105; averaged) between the predicted and real AAO of PD over five left out test folds. The mean RMSE between the predicted and real AAO of PD was 8.1197. Using mRMR resulted in a correlation of r = 0.5260 with p = 0.0026, while using LASSO led to a correlation of r = 0.5586 with p = 0.0022. The prediction plots are displayed in the first column of Fig. We constructed random forest regression models using the selected imaging, genetics feature, and the combined features from os-SCCA to explain the AAO of PD in a five-fold cross-validation. Additional prediction models were built using the features obtained from mRMR and LASSO approaches and compared with those of os-SCCA. Using imaging features only, all three approaches worked well. Our proposed model showed a meaningful correlation  between the predicted and real AAO of PD over five left out test folds. The mean RMSE between the predicted and real AAO of PD was 9.1678. Using mRMR resulted in a correlation of r = 0.1568 with p = 0.4457, while using LASSO led to a correlation of r = 0.0829 with p = 0.6684. In the case of LASSO, there were even negative values of r. The correlation values derived from our approach were higher than those derived from mRMR and LASSO. The second column of Fig. Using genetic features only, the models were less successful than using imaging features Fig. b. The LAp = 0,051; averaged) between the predicted and real AAO of PD over five left out test folds. The mean RMSE between the predicted and real AAO of PD was 7.9764. Using mRMR resulted in a correlation of r = 0.3029 with p = 2,186, while using LASSO led to a correlation of r = 0.5453 with p = 0.0025. The prediction plots using both imaging and genetic features are shown in the third column of Fig. Using both imaging and genetic features, our model showed prediction improvement  compared to using only imaging features. However, using mRMR and LASSO led to degraded performance (decrease in correlation) compared to using only imaging features even if more features were adopted. Our proposed model showed an improved correlation  directly related to AAO as the outcome. This translates to optimizing only the imaging–target associations. However, os-SCCA optimizes additional genetic–target and imaging–genetic associations simultaneously, which leads to holistic modeling of the multimodal data. The strengths of os-SCCA were well demonstrated in the predictive model with genetic features. Therefore, it becomes challenging to select a few related to the outcome. Using LASSO, there were folds with no genetic features selected. Using mRMR, although we ensured that the same number of features were used as in os-SCCA, the FS of the selected genetic features were very low, which might obscure the selection process. Only with jointly optimizing the genetic–target and imaging–genetic associations using our approach, could meaningful genetic features be selected. We were able to predict AAO with an RMSE of less than 10 years using our approach. This implies that approximate estimation at a 10-year interval is possible. Our results show that it was possible to predict the AAO of PD using only the genetic features from os-SCCA. These findings could be used as potential biomarkers related to AAO of PD, and help elucidate the associated pathomechanism of PD. Still, the identified SNPs need to be confirmed in additional validation studies with more samples. Most patients in our study have AAO between 50 and 60. Thus, we constructed a simple additional model using only the sample mean of all data . The RMSE was 9.4689, which fared worse than those using both imaging and genetic features (7.9764), only imaging features (8.1197), and only genetic features (9.1678).The effectiveness of os-SCCA was seen by evaluating the performance of the predictive model using both genetic and neuroimaging data. By considering all thee pair-wise associations in selecting the features, the performance improved over models that used only imaging and genetic features in every fold. In models using features from mRMR, even if the model could use more features than those of os-SCCA, the performance decreased. In the case of LASSO, the performance was lower than os-SCCA. Overall, we confirmed that imaging and genetic features should be selected, considering all possible associations when using genetic and neuroimaging data at the same time in an imaging genetic framework.31. ACMSD is associated with aging and risk of PD, tentatively suggesting that this enzyme might influence pathogenesis32. Additionally, COMT, an enzyme involved in the degradation of dopamine, is a critical determinant of the availability of this neurotransmitter in the prefrontal cortex. COMT modulates the activity of the enzyme, affects cognition, and is magnified in the aging brain33. There are also studies showing that COMT is related to the late-onset of PD, and COMT is a modifier of the AAO in PD35. We investigated rationales for the identified brain regions. The PCG and PreCG are the regions that have age-related differences in the structure of the brain37. Other brain structures such as THA found in our study were also reported in early PD patients39. The ratio of dopamine transporter binding of PUT is reported to be related to the difference between old-onset PD and young-onset PD groups40. In particular, PCG is one of the regions that showed substantially different gene profile changes with age41. As symptoms of PD vary according to AAO, an in-depth study of the brain regions of our study combined with the PD symptoms might contribute to the enhanced characterization of PD subtypes and possibly lead to a redefinition of the subtypes42. Considering that neurodegenerative changes start a few decades before AAO, diverse changes in various brain regions are quite feasible. However, most previous studies have focused on the brain structure related to clinical features in PD patients, and very few of them assessed the association between AAO and brain structure in PD patients. Clinical characteristics of young-onset PD are already well-known from many previous studies4, but it was difficult to determine whether the relevant brain structures in our study were from AAO itself or were also linked with clinical features in young-onset PD patients. It is challenging to control for all the clinical diversity present in PD patients, and our results were consistent with previous studies which revealed the known involved structure in early PD patients. Considering there is no previous study focused on the AAO of PD, our results should be interpreted cautiously. Our primary objective was related to AAO of PD, and we did not distinguish between healthy controls (HC) and PD. This was partly because there was an ambiguity in HC from the PPMI database. The PPMI data are not follow-up data with a long duration, thus HC cases could develop PD in the future or stay healthy. This can be examined further in future studies using well-designed follow-up data. A longitudinal study is better suited for AAO of PD as it might offer more accurate AAO information of PD along with neuroimaging data near the time when neurodegeneration starts.We identified 25 genes annotated from 24 SNPs and 14 brain regions in all training sets. One of the resulting genes, NUCKS1, is known to have a functional association in the brain of people with PD. A significant association of expression and transcription levels of NUCKS1 with PD has been observed1. Considering that AAO is related to various parkinsonian symptoms and progression, it is difficult to eliminate all the confounding factors. Therefore, it is difficult to control for all possible clinical characteristics and to focus only on those relevant to AAO. In a similar vein, we identified brain imaging–SNP pairs related to AAO in PD using only SNPs related to PD to make feature selection and interpretation tractable. To use the os-SCCA for other purposes, a similar reduction in feature dimension of the input modalities might be necessary especially for the genetic factors. Lastly, no replication using another dataset is a limitation of our study. Our approach requires multimodal data requirements and we could not find any open database satisfying our data requirements to the best of our knowledge.Our study has some limitations. Our results were obtained using a cross-sectional design. Therefore, it is difficult to distinguish between PD progression and risk. Although imaging genetic analysis needs a smaller sample size compared to GWAS, we recruited a relatively small number of patients. Even with these limitations in study design and sample size, we were able to suggest significant prediction models for AAO of PD using genetic and neuroimaging features from os-SCCA. We hope this study is the first step to identifying the pathomechanism and related genetic factors in PD patients. Second, PD is a wide-spectrum disorder with diverse motor and non-motor symptomsIn this study, we investigated relevant genetic factors and brain regions associated with the AAO of PD and suggested a prediction model using os-SCCA. Our study jointly models genetic factors and neuroimaging and thus could be a more suitable model compared to studies using genetic factors and neuroimaging only. Our proposed os-SCCA is a special case of TSCCA. Thus, the scalability of TSCCA applies. Our approach could be extended to included additional imaging modalities hence becoming four-way SCCA. TSCCA belongs to the family of linear models and thus could be easily scaled in terms of computational speed and size of data sets. We hope our study can be the first step toward accurate early diagnosis and improved therapy for PD.