Evaluation of the extent of genetic variation within and between the populations of crop genetic resources are of paramount importance in any breeding program. An experiment aimed at assessing the extent of variation among barley lines and the degree of association between hordein polypeptide and agronomic traits was hence executed.Field experiment was conducted in six environments between 2017–2019 involving 19 barley lines. Hordein bands were separated using vertical Sodium Dodecyl Sulphate Poly- acrylamide Gel Electrophoresis (SDS-PAGE).−1) across environments, 3.6 ton ha−1 at Holleta, and 1.93 ton ha−1 at Chefedonsa. At Arsi Negelle a different line Acc# 17146–9 was the highest yielding (3.15ton ha−1). SDS-PAGE-based analysis of barley lines separated 12 hordein bands between C (four bands) and B (eight bands) subunits. Interestingly bands 52, 46a, and 46b were uniquely conserved in the four naked barley lines . A considerably high proportion of genetic diversity within the populations than among the populations could be a repercussion of high gene flow which substantiates the longstanding and dominant informal seed exchange system among the farmers. The significant positive association between grain yield and band 50 evocates the expression of this allele may code for higher grain yield. The negative association between days to maturity and band 52 perhaps stipulates earliness in barely lines upon the manifestation of the band. Band 52 and 60 appeared to be associated with more than one agronomic trait  and could be the result of pleiotropic characteristics of the genes residing in these banding regions.The analysis of variance revealed significant variation among lines and wider range units were observed for the agronomic traits. The line  was superior, producing the highest grain yield (2.97 ton haThe barley lines exhibited substantial variation for hordein protein and agronomic traits. However, imparted the need for the implementation of decentralized breeding as a consequence of genotype-by-environment interaction. Significant hordein polypeptide and agronomic traits association advocated the utilization of hordein as a protein marker and perhaps consider them in the parental line selection.The online version contains supplementary material available at 10.1186/s12870-023-04117-x. To put the most effective and efficient crop improvement program in place, access to crop genetic resources is quite fundamental. The characterization of these crop genetic resources for different agro-morphologic traits and the evaluation of the extent of genetic variation within and between the populations are most pivotal in any breeding program . More spHor-2, and Hor-1 respectively [Hor-1 and Hor-2 are located on the short arm of chromosome 5, 1H [Hor 3 locus located on the long arm of chromosome 5 [Proteins in barley grain can be separated into albumin, globulin, prolamin (hordein), and gluten fractions . The horectively . Hor-1 ame 5, 1H , 19. Themosome 5 . Hordeinmosome 5 , 20, 21.mosome 5 . This memosome 5 . Relatiomosome 5 , 25 basemosome 5 ; betweenmosome 5 , 27, 28;mosome 5 . Little p < 0.001) among the barley lines for all characters under scrutiny pooled over the six environments  with 12.14 cm range units, DH , DM  and the other traits revealed relatively lower range unit.The REML variance component analysis was employed in an attempt made to dissect the phenotypic diversity among the barley lines. The same analysis showed that there was a very highly significant variation p < 0.00 among thThe distributions of mean performance of the genotypes (BLUP) for GY in each of the environments are described in boxplots Fig. . Amidst −1) across environments, 3.6 ton ha−1 at Holleta and 1.93ton ha−1 at Chefedonsa (Table S−1) and when combined over the environments the same line with a mean grain yield of 2.84 ton ha−1 had no significant difference compared to the best line Acc# 16811–6. The mean performance of pheno-agronomic traits other than GY combined across the environments is also presented in Table SThe genotype (Acc# 16811–6) was found to be a stunning line with respect to grain yield producing the highest yield  were common with frequencies > 0.100 and all the bands except bands 50 and 47 at the Hor-2 locus occurred at higher frequencies (Table In the current experiment we report the frequency of appearance of particular band across the 19 barley lines (the number of times a given band appeared) and the frequency of a banding pattern which shows how often the same group of bands occurred across the lines. Out of the four bands at the es Table . The banThe estimation of the coefficient of Nei’s genetic similarity between the populations of the barley lines revealed the lowest similarity index was observed between the barley population from Guraghe and Awi (0.468) and the highest was between Guraghe and Hadiya (0.977) with an average of 0.757 across all the population Table S, Fig. 3.Na,), effective number of alleles (Ne), Shannon diversity index (H), percentage of polymorphic loci (p%), mean genetic diversity (he) span between 0.250–1.333 , 1.000–1.620 , 0.000–0.53 (H), 0.00–91.67 (p%), 0.042–0.360 (he)  was also observed among the barley lines. The AMOVA results highlighted, a greater proportion of genetic variance was observed within the populations, which accounted for 76%, whereas among populations the recorded variance was 24%  of the variation in the dependent variable, thousand kernel weight in this case. The degree of association among the characters that exhibited significant association with the hordein bands spanned between 61.1% (GFP) and 73.1% (TKW).In an attempt made to unpack the degree of association among the agronomic traits and biochemical makers, multiple regression was run. For this analysis, nine agronomic traits (each as a dependent variable) and 12 hordein bands (independent variables) were included. Before running the regression analysis, however, Pearson correlation was carried out to diagnose whether there was a collinear relation among the independent variables Table S. By doinht Table . It was p < 0.05.In the dissection of the contribution of each of the hordein bands (independent variables) with respect to each of the agronomic characters, we used the same analysis (regression). The analysis uncovered that among the six predictor variables (hordein bands) four were significantly associated with agronomic traits Table . DistincStepwise regression analysis was applied to further identify those hordein bands that should remain in the model to best explain the association. The analysis was done in such a way that it combined forward selection and backward elimination. The level of significance used in forward selection (penter) was 0.05 and for backward elimination (prem) was 0.1. With this procedure, GY was best explained by three bands 50, 52 and 60. For GFP among the two bands that showed a significant association, only band 58 remained in the model, band 60 was eliminated from the model after stepwise regression. Band 52 remained in the model to best explain DM and TKW  in the barley genome in which case 12 distinct bands were separated at these loci out of which four bands were separated at Hor 1. According to [Hor 1 alleles which in turn contributed to a high level of polymorphism in hordein. The detection of nine banding patterns out of which seven were unique indicated that there was an important set of genetic polymorphisms among the barley lines included in the study. The existence of seven unique migration patterns in the electrophoresis further signified the effectiveness of SDS-PAGE electrophoresis as a screening test in discriminating cultivars. This is possibly due to hordein migration pattern is varietal characteristics and being less influenced by the environment [Hor 1 and the fast-migrating Hor 2 giving rise to 53 distinct banding patterns from 53 Lebanese landraces [Hor 1 and two at Hor 2) even though naked barley lines had different geographic origins and these bands were not detected in any of the hulled barley lines. This may be an indicator of the utilization of these bands as a genetic marker for naked caryopsis characteristics in barley and of the effectiveness of SDS-PAGE in differentiating the naked versus hulled barley varieties. However previous findings suggested the gene conferring naked caryopsis in barley, the nud gene is located on the long arm of chromosome 7H [Hor 1 and Hor 2 locus where these unique bands were separated are located on the short arm of chromosome 5 (1H). This may call for further investigation with a larger panel of naked and hulled barley genotypes to confirm this region of the barley genome whether it has any relation with naked caryopsis. In study made to differentiate between spring and winter cultivars [Genetic variation in a panel of germplasm has an essential role in the identification of varieties. Variation in the electrophoretic banding pattern of proteins is linked with the genotype’s genetic constitution and thus it can be exploited to comprehend the genetic makeup . In the rding to  the Ethiironment , 40, to ironment . Therefoironment  detectedironment , the majironment . A totalandraces . This vaosome 7H , 44 as oultivars  reported2). This justifies that some of the observed variations in the agronomic traits could one way or the other be related to the expression of the hordein bands. Such association between hordein polymorphism and variation of agro-morphological traits may unarguably lay a fertile ground for breeders to broaden the germplasm source and support parental choices to be used in their breeding programs [The estimation of genetic parameters showed the variation within the four populations was quite different such that the percentage of polymorphic loci ranged from 0.000 to 91.67, and the Shannon diversity index ranged between 0.000 to 0.531 for Guraghe and Arsi populations respectively. This suggested that the lines included in the sampled population from Guraghe showed no variation where all the lines share similar hordein bands. Conversely, all the lines from Arsi responded quite differently with regard to the hordein test conserving no similar hordein bands among each other with few exceptions. In their study on 90 different accessions that comprised four populations collected from Jordan  reportedprograms . The sigprograms –27 and iHor 1 and Hor 2, hence when selection occurs towards the direction of the powdery mildew resistance allele the chance of indirect selection for some hordein alleles will be very high [The detected association between the agronomic traits and hordein polypeptide bands was perhaps the repercussion of linkage among genes conferring a particular agronomic characteristic and a specific hordein banding . It coulery high , 46. Amiery high , 27. Theery high . Howeverery high .The observed variation for agronomic traits in the current study is pivotal to marking parental lines out for hybridization and introgression of desirable traits into the available genetic base in the national breeding programs. Differential response of the barley lines over the test locations rule in the imperativeness of decentralizing the breeding programs and variety release scheme of the country to best exploit the potential of the varieties. The banding pattern based on SDS-PAGE showed hulled and naked barley lines could be differentiated using hordein data. However, there is a need to undertake further study to confirm this finding with a diverse set of barley lines where naked and hulled barley types are included. The presence of unique bands conserved only in the naked caryopsis augments the application of hordein bands as a genetic marker. Markedly high gene flow among the population and a higher proportion of genetic variance within the population than among the populations was additional evidence that supports future barley improvement programs should give due emphasis to variation within the population. The regression analysis reaffirmed that the hordein bands are somehow associated with some of the agronomic traits. This dictated selection to the direction of these bands may either positively or negatively affect the agronomic performance and hence positive associations are considered an effective indicator for parental choice in crossing programs.http://www.ebi.gov.et) and an improved variety HB-1307 received from Holleta Agricultural Research Center (http//www.eiar.gov.et) were included. These barley lines represent five different administrative zones among the major barley-growing regions of the country with altitudes ranging between 2500–3200 m.a.s.l. , Arsi Negelle (for two years) and Chefedonsa (for one year) hence the experiment was conducted in six environments between 2017 and 2019. Holleta and Chefedonsa are located in the central highland and Arsi Negelle is located in the African Rift Valley belt. The plots were fertilized with DAP and Urea fertilizers as per the recommended rate of applications for the three sites and the seeds were planted by hand drilling. Planting was carried out in early July at Holleta, in mid-July at Arsi Negelle and in late July at Chefedonsa. Relevant crop management practices were carried out following the standard agronomic recommendations.The experiment was laid according to Randomized Complete Block Design with three replications in six rows of 2.5 m length, 0.2 m row spacing where each plot of 3 mData collection was based on the descriptor list developed by Bioversity International . Three p0c followed by centrifugation for 10 min at 14krpm. The supernatant was then transferred to a clean tube and an equal amount of gel loading buffer (50 mM Tris–HCl (pH6.8), 2% SDS, 0.1% bromophenol blue, 10% glycerol) was added to the supernatant. The separation was done using 10% acrylamide gel (acrylamide (10%), Tris–HCl (pH 8.8), 10% SDS, TEMED and 10% APS) and 5% stacking gel , SDS, APS and TEMED. The extracted hordein from each sample was loaded in the gel and a protein ladder  was included to estimate the molecular weight of hordeins separated by electrophoresis. The gel was run at 100 V-constant power until the indicator dye reaches the bottom of the separating gel. The gel was stained overnight using a staining solution containing 0.25 gm Coomassie blue diluted in 100 ml ethanol, 100 ml distilled water and 25 ml acetic acid. Destaining was done for 12 h using a mixture of ethanol and distilled water (1:1) and the gel was photographed by a digital imaging system. The hordein banding pattern was then determined by scoring the presence and absence of all examined bands. The naming of the bands was done in relation to their position with the respect to the protein ladder.Hordein separation was conducted using vertical (SDS-PAGE) from the grain harvest of 2019. Seed storage proteins are known to be highly stable and genetically fixed hence seeds only from Holleta site third year were used for hordein analysis. A barley flour sample was obtained by grinding individual seeds. Extraction was done by mixing 0.5 gm of barley flour and 200 µl extraction buffer  and keeping it for 1 h at 60ggplot-2 function [ijk is the trait of interest, µ is the general mean, Envi is the effect of the ith environment, Repj(Envi) is the effect of the jth replicate within the ith environment, Blockk(EnviRepj) is the effect of the kth incomplete block within the ith environment and jth replicate, Genl is the effect of the lth genotype, Envi x Genl is genotype by environment interaction and Ꜫijkl is the error associated with the ith environment, jth replication, kth incomplete block and the lth genotype.The analysis of variance (ANOVA) was based on the restricted maximum likelihood (REML) algorithm and REML was used to produce the best linear unbiased prediction (BLUP) mean. The BLUP means combined over the six environments were calculated using the META- R statistical software version 6.04  and thesfunction . Pooled function . The REMlm function. In the regression analysis, the agronomic traits were considered as an outcome variable and the hordein bands as a predictor variable. Stepwise regression analysis was also computed to further identify those bands that should remain in the multiple regression analysis model using the olsrr function to best explain the association.Bands of hordein protein scored as present (1) or absent (0) were used for statistical analysis. The software GenAlEx version 6.5  was usedAdditional file 1: Table S1. Estimates of the range, range unit and mean along with standard errors computed using pooled data of the six environments for the nine quantitative traits used.Additional file 2: Table S2-1. Best linear unbiased predictor mean for grain yield (ton ha-1)of 19 six rowed barley lines pooled over years within the location and overall environments. Table S2-2. The mean performance of 19 barley lines for eight pheno-agronomic traits combined across six environments.Additional file 3:Table S3. Pairwise Population Matrix of Nei’s Genetic Distance  and Nei’s Genetic Identity  based on 2 hordein loci between six-rowed barley lines.Additional file 4: Table S4. Correlation among the independent variables (hordein bands) based on grain yield.Additional file 5: Table S5. List of the barley lines included in the study, their geography of origin and caryopsis type.


Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9–10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.The online version contains supplementary material available at 10.1007/s10519-022-10123-w. This approach has been fruitful because individual differences in almost all traits are likely caused by both genetic and environmental factors. While twin, adoption and extended pedigree data may be used to disaggregate and estimate the relative contributions of genetic and environmental factors to trait variance, the classical twin study (CTS) has proved to be the dominant design. The rationale underlying the CTS is that the difference in phenotypic similarity between monozygotic  and dizygotic  twin pairs can be used to infer and distinguish the influences of environmental and genetic factors. An advantage of using both MZ and DZ twin pairs is that the effects of rearing by the same parents in the same household  may be separately quantified from the effects of environmental events that are unique to each twin . These components, along with additive genetic variance (A), referring to the cumulative effects of individual genetic loci, generate the ‘ACE’ model acronym. Prior estimates of additive genetic, common and specific environmental effects based on published studies were comprehensively reported  who are interested in the relative degrees to which individual variation in children’s mental, physical, or brain structural traits is likely caused by genetic or environmental factors to peruse the results for selected phenotypes on demand. Given the broad scope of the ABCD Study assessments, a thorough review of relevant literature on sources of variability  for all domains of measurement would be prohibitively long for publication in a single research article. Instead, interested readers can refer to concise reviews of prior genetically-informative studies of each primary domain, including structural neuroimaging, in our online supplementary material.We begin here by summarizing basic twin methodology and describing the ABCD twin dataset. We then explain our analysis methods in detail, followed by a results section composed of commentary on key results from the twin modeling, along with a link to an omnibus online table of results. We conclude with commentary on the assumptions of the classical twin study as they relate to ABCD data, as well as a brief discussion of similarities and differences between the ABCD results and the extant literature for four key domains—brain structural, cognitive, psychiatric, and morphometric features —and offer suggestions for future avenues of exploration. Specifically, the discussion will review previous findings on the components of variance of four phenotypic domains: structural neuroimaging, neurocognition, child psychopathology, physical and other traits captured in ABCD, where childhood findings effectively formed our expectations as we applied ACE modeling to the ABCD twin dataset. We then review how the results from analyzing the ABCD data using a consistent variance components approach compare with previous findings, highlight some novel findings and thus provide a foundation for multivariate, developmental, causal and moderation applications evaluating the role of gender, race/ancestry and socioeconomic status (SES). For additional information about the ABCD Study, a key literature resource for the rationale, instrument selection, data processing and interpretation of ABCD Study results is the 2018 special issue  of Developmental Cognitive Neuroscience  from the four ABCD ‘twin sites’, namely the University of Colorado Boulder (CUB), the University of Minnesota (UMN), Virginia Commonwealth University (VCU) and Washington University at St. Louis (WUSTL), for whom zygosity was assigned using genomic data from the smokescreen array  includes results from all tabulated  continuously distributed variables in the open access 3.0 dataset, described in the ABCD Data Release documentation, including those discussed here, and an additional 38,672 task Magnetic Resonance Imaging (MRI) related variables. The online resource includes an app that allows the user to search for specific variables and select specific output columns for display. A second app generates brain images with selected parameters. Additional figures graphing ACE results are also included.In keeping with NIH’s policy of zero embargo time for investigators collecting the data, only publicly available data from the NIMH National Data Archive (NDA) are analyzed, specifically the baseline wave of data collection from the ABCD 3.0 data release. All continuous variables, and those with more than twelve categories were included in the analyses, totaling 53,172 variables. We focus the discussion of the results on 14,500 variables, covering structural neuroimaging, neurocognition, childhood psychopathology, physical and other traits. Note that below we only describe the ABCD capture of the most commonly-used phenotypes. Conversely, tables in our Supplemental Online Resource  was administered and includes seven tasks that measure episodic memory (NIHTB-CB Picture Sequence Memory), executive function , attention (NIHTB-CB Flanker Task), working memory (NIHTB-CB List Sorting Working Memory), processing speed (NIHTB-CB Pattern Comparison Processing Speed), and language abilities . We report here on the uncorrected Toolbox scores. Composite indices of fluid and crystallized reasoning are also derived , a widely used test of auditory learning and memory, was implemented for this purpose. An automated version of the Matrix Reasoning subtest from the Wechsler Intelligence Test for Children-V (WISC-V)Wechsler  was admiIn the baseline assessment of 9- and 10-year-old children, parents rate their children’s behavior using the Child Behavior Checklist . The impacts of racist ideologies and policies continue to besmerch behavior genetics—which has the noble scientific goal of understanding the sources of individual differences—and are partially responsible for the dramatic underrepresentation of non-white individuals in behavioral and psychiatric genetic research cohorts , NIH (https://www.genome.gov/about-genomics/fact-sheets/Eugenics-and-Scientific-Racism) and Pan UK Biobank (https://pan.ukbb.broadinstitute.org/) for highlighting the impacts of institutional racism on science and contribute this work and the related online resource to the ABCD Study’s own proclamation of their values, including anti-racism, inclusivity, equity, and diversity (https://abcdstudy.org/families/better-together).Twin studies, which were first suggested by Galton, have undesirable historical associations with individuals who held socio-political beliefs that are now widely regarded as inhumane, immoral, and inherently racist. Acknowledging this history is a critical part of combating scientific racism. Twin studies can provide valuable insight regarding how environmental and genetic factors jointly contribute to the composition of phenotypic variance, and behavioral geneticists must emphasize accurate interpretations of their research and promote comprehensive and nuanced understandings of the results so that neither the public nor other scientists derive gross misunderstandings about the nature of heritability or human health. One example of emphasizing greater nuance and understanding is clarifying the relationship between social constructs like self-identified race and biological constructs like genetic ancestry/variation. These constructs are not interchangeable. The US race categories are based on US Census options, categorical, and correlated with social advantages/disadvantages and experiences of racism. Genetic ancestry/variation is continuous and based on differences in allele frequencies. Neither self-identified race nor continental ancestry assignments capture the full range of genetic variation. Given these shortcomings, we should embrace a multidimensional, continuous view of genetic variation and ancestry rather than using continental ancestry categories as suggested by a recent policy forum in Science  the phenotypic distribution of the variables; ii) the independence of the sources of variance; and iii) the equal environmental sharing assumption. First, the maximum likelihood (ML) model-fitting method used assumes that the data from twins conform to the bivariate normal distribution, albeit with different correlations for MZ and DZ pairs. This assumption was not explicitly tested, and was certainly violated at times, particularly when scale scores are derived as factor or sum scores of items where most items are rarely endorsed—such as happens with scales designed to detect clinical diagnosis or severity. Fortunately, ML is robust to failures of this type, but some underestimation of correlations is likely, compared to treating the variables as ordinal  nor interaction (GxE) between the variance components. The no covariance assumption would fail if, e.g., allele frequencies systematically differ between those living in low and high SES environments. Since SES covaries with race, and race differences in allele frequencies are well known  is a key assumption of modeling data from a classical twin study. Many critics of the twin method ascribe findings of greater MZ than DZ correlation to violations of the assumption, citing evidence of greater environmental sharing by MZ twins than by DZ twins as sufficient evidence that it causes MZ correlations to be greater than DZ ones. However, two arguments counter this claim. First, the environments shared must be trait-relevant; if they are, then within either MZ or DZ pairs greater sharing should associate with greater phenotypic similarity. Historically, this phenomenon has rarely been observed, but improved measures of trait-relevant environmental factors are possible. Second, EEA violations upwardly bias estimates of A only when the environmental factors subject to greater sharing by MZs are not The ACE model assumes random mating in the parental generation; and in the presence of phenotypic assortative mating, may lead to underestimation of A and overestimation of C. While random mating may be a reasonable assumption for structural neuroimaging measures, it is certainly not for neurocognitive measures, a limitation that should be considered when viewing positive estimates of shared environmental variance. Conversely, unless interactions with specific environmental modifiers are explicitly modeled, variance due to A and AxC are confounded in the classical twin study design and a component of the estimated A for such neurocognitive variables may plausibly represent genotype x shared environment interaction effects Eaves , 1977.Variance component patterns for sMRI endophenotypes were, in general, remarkably consistent with the extant literature  find particularly strong genetic influences in similar regions, but with a leftward-predominance, i.e., preferentially influencing language centers. Leftward-predominant heritability in language centers is conceptually appealing, as it may be indicative of recent evolutionary influences in humans. The reasons for the observed discrepancies between the studies are unclear. Differences in cerebral parcellation have a relatively large influence on thickness heritability estimates and may partially explain the observed differences. There is also strong evidence that the heritability of thickness actively changes during childhood  include a searchable data dashboard that displays the twin model results for all tested baseline continuous variables. Users can adjust the output to display the twin correlations by zygosity and sex, and the standardized and unstandardized parameter estimates of the ACE model either for the full sample or by sex, or both. Due to the size of the data and a desire to minimize loading time, the results have been organized by research domain, which users can select at the top left of the screen in the section “Select a Variable Category”. The dashboard also hosts the figures from this manuscript and background content salient to the ABCD Study and twin methods. A link to three-dimensional visualizations of parameter estimates for brain regions can be viewed using either the Desikan-Killiany or the Destrieux atlas at https://schmittje.shinyapps.io/ABCD_brainviz (linked in the ABCD dashboard in the section entitled “3D Brain Images”). To launch the application, users select the parameter and participant subset  of interest, a brain atlas for plotting, and click “Render Plot”. A concise overview of the abbreviations is included as a reference under “Additional Information”. Users should note that the Destrieux atlas takes considerably longer to render than the Desikan-Killiany atlas, owing to the greater level of detail in the former.The supplemental online resources . Opportunities to evaluate the moderating effects of sex, race/ethnicity/ancestry, SES and other potential covariates are abundant. This capability improves with both longitudinal assessments and genetic marker data, both of which are integral parts of the ABCD Study®. The complexity and diversity of measures in ABCD will support not only brain-wide association scans but also genome-wide association studies once the genotype data is available. Moreover, as the breadth and depth of the longitudinal data increases, so too will opportunities to apply and develop new analytic approaches to characterize developmental trajectories, predict outcomes, and investigate the interplay of genetic, neurological, social, behavioral, and cognitive factors.Supplementary file1 (PDF 18 kb)Supplementary file2 (DOCX 244 kb)Below is the link to the electronic supplementary material.


The Hybrid Rat Diversity Panel (HRDP) is a stable and well-characterized set of more than 90 inbred rat strains that can be leveraged for systems genetics approaches to understanding the genetic and genomic variation associated with complex disease. The HRDP exhibits substantial between-strain diversity while retaining substantial within-strain isogenicity, allowing for the precise mapping of genetic variation associated with complex phenotypes and providing statistical power to identify associated variants. In order to robustly identify associated genetic variants, it is important to account for the population structure induced by inbreeding. To this end, we investigate the performance of four plausible approaches towards modeling quantitative traits in the HRDP and quantify their operating characteristics. In particular, we investigate three approaches based on genome-wide mixed model analysis, and one approach based on ordinary least squares linear regression. Towards facilitating study planning and design, we conduct extensive simulations to investigate the power of genetic association analyses in the HRDP, and characterize the impressive attained power. In simulation of eQTL data in the HRDP, we find that a mixed model approach that leverages leave-one-chromosome-out kinship estimation attains the highest power while controlling type I error. The Hybrid Rat Diversity Panel (HRDP) is large panel of 99 inbred rat strains consisting of three subpopulations: the HXB/BXH recombinant inbred (RI) panel (30 strains), the FXLE/LEXF RI panel (34 strains), and a group of divergent classic inbred strains (35 strains) . The HRDDespite the large number of SNPs statistically associated with complex traits, the mechanism by which genetic variation affects complex traits is often not well understood. One mechanism for the genetic control of complex traits is the modulation of gene expression by SNPs . A substAnalyzing QTLs in a panel of inbred organisms with complex relationships among individuals (such as the HRDP) requires modeling considerations for the effect of population structure. Initial analyses of the HMDP were substantially confounded by failure to properly account for the effect of population structure, resulting in poor type I error control  . RigorouThis paper examines four methodological approaches towards modeling eQTLs in the HRDP. In particular, we analyze a 43-strain subset of the HRDP for which we have both RNA expression levels and genotype information. This 43-strain subset consists of 30 strains from the HXB/BXH RI panel as well as 13 classic inbred strains; the 43 strains comprising this subset are listed in 2 exposure and quickly decapitated. Brains were cut in half sagittally, placed immediately in RNAlater, and stored in a −80°C freezer.The HRDP is large panel of 99 inbred rat strains consisting of three subpopulations: the HXB/BXH recombinant inbred (RI) panel, the FXLE/LEXF RI panel and a group of divergent classic inbred strains . We obtahttp://www.snp-star.eu/; A genetic marker set  from the STAR consortium . The brain was split sagitally and half of the brain was used for RNA sequencing. The RNAeasy Plus Universal Midi Kit (Qiagen) was used to separate long (>200 nucleotides) and short (<200 nucleotides) fractions. The long RNA fraction was purified using the RNeasy Mini Kit (Qiagen). Sequencing libraries for the long RNA fraction were constructed using the Illumina TruSeq Stranded Total RNA Sample Preparation Kit with Ribo-Zero ribosomal RNA reduction chemistry (Illumina) in accordance with the manufacturer’s instructions with the exception that for later batches multiple Ribo-Zero washes were used to reduce the amount of rRNA sequenced. Samples were sequenced in eight batches on an Illumina HiSeq2500 or HiSeq4000  in High Output mode to generate 2 × 100 or 2 × 150 paired end reads.https://genome.ucsc.edu/) were also eliminated. This alignment was done using Bowtie 2 (v.2.3.4.3)  . The rem2.3.4.3)  using HI2.3.4.3)  (v.2.1.02.3.4.3)  algorith2.3.4.3)   eliminating the few isoforms that were not detected above background, as defined above, after re-quantitation, 2) eliminating individual samples with low sequencing efficiency , 3) eliminating individual samples when a few transcripts dominate the read count , and 4) eliminating extreme outliers. Extreme outliers were systematically identified by examining all pairwise correlations among a sample and all other samples using read counts that had been transformed using a regularized log(rlog) . If moreThe final count matrix was adjusted for batch effects and other technical effects using the first factor from an RUV (Removing Unwanted Variance)  analysisThis paper compares four methods for the analysis of QTLs. These models are as follows: 1) a linear regression model, 2) a linear mixed model, 3) a linear mixed model with leave one chromosome out kinship matrix calculation, and 4) a linear mixed model stratified by subpopulation followed by meta-analysis.Linear regression, where each SNP is pairwise associated with the expression levels of each isoform, is often used for eQTL analyses  Veyrier when samLinear mixed models are a popular tool for conducting eQTL analyses in the presence of confounding by shared ancestry  Kang et. The linAn adjustment to the linear mixed model as described above is the linear mixed model with leave-one-chromosome-out (LOCO) kinship matrix calculation, henceforth GEMMA LOCO. This approach has some theoretical benefits as compared to the standard GEMMA approach . An assup-values) through a meta-analysis approach. For this strategy, we estimate separate GEMMA models for the recombinant inbred population and the classic inbred population and combine the results using Stouffer’s method  and by the number of significant eQTL identified. We cannot directly characterize the type I error rate in the real data application as in this case the true causal SNPs are unknowable. Thus, we instead use the genomic inflation factor to assess the type I error rate. The genomic inflation factor is defined as the ratio of the median observed R2) from a one-way ANOVA with strain as the predictor and transformed isoform expression as the outcome was used to estimate broad-sense heritability for each isoform. To derive a null distribution of the broad-sense heritability, strain labels were permuted and a distribution of resulting heritabilities were compared to observed distribution. In addition to a broad-sense heritability that does not include information about population structure, SNP-based heritability was estimated to be the proportion of variance explained (PVE) estimated via GEMMA  from 175 RNA-Seq libraries. Sixteen libraries were eliminated due to poor quality. After limiting to only transcripts that were greater than 200 nucleotides in length and transcripts that had an estimated read count of at least 1 in at least 2/3 of the samples, 170,595 transcripts remained and were requantitated using RSEM. After this requantitation, 99,376 genes  met the criteria of expression above background (estimated read count of at least 1 in at least 2/3 of the samples) and 135 samples met our quality control standards both prior to and after normalization, transformation, and batch correction.Observed kinship values ranged from 0.33 to 0.99 . Of the R2) to the distribution of the permuted R2 to investigate the genetic effects on the transcriptome. The center of the distribution of the broad-sense heritabilities from the brain transcriptome is significantly higher than the heritabilities based on permuted values . Transcripts with evidence for a bimodal or trimodal distribution of expression values tended to have higher SNP-based heritabilities  have a unimodal distribution consistent with many SNPs or non-genetic factors individually contributing a small portion of the variation in expression. A much smaller proportion  have a bimodal distribution indicative of a single genetic variant with a large effect. A still smaller proportion  have a trimodal distribution. Modality of the RNA expression levels of a transcript was associated with the SNP-based heritability  for each of the 99,376 brain RNA expression phenotypes . Thus, ep-value less than The number of significant cis-SNPs identified by each of the four approaches was compared. The linear regression model has been invalidated by its inability to control type I error, but we include its results here for completeness. A SNP was determined to be significant if it had a cis-eQTL . The draThe above results, coupled with the reasonable type I error control of the standard GEMMA and GEMMA LOCO methods, is evidence that the subpopulation meta-analysis approach may be underpowered. However, given that we do not know the true causal structure in application to real data, this apparent difference in power must be confirmed in simulation. p-value is less than The simulation study facilitates the empirical determination of the type I error rate and the power of the methodological approaches in eQTL analysis of the HRDP. Because the linear model was ruled out by the real data analysis, our simulation study only compared the performance of standard GEMMA, GEMMA LOCO and the subpopulation meta-analysis. We assessed the ability of these methods to detect cis-eQTLs, as our approaches do not have power to detect trans-eQTLs in this simulation. Recall that the two simulation settings with cis effects assume that some large proportion of the variance is attributable to cis effects; thus, power differs for cis and trans effects in this simulation. We considered results at the transcript level, as we are not investigating fine mapping approaches that account for the effect of linkage disequilibrium. That is, if at least 1 significant cis-SNP is identified in a gene that has at least 1 truly nonzero cis effect, we count that as a true positive. That is the case even if the associated SNP is not identical to the truly nonzero SNP. Defining other events  follows from this logic. For example, a false positive occurs when we identify one or more associated cis-SNPs with our approach, but there is no truly associated cis-SNP. As previously, a SNP is identified as significant if its association test Power and type I error  for the three analysis methods are assessed in the 43-strain setting. These results indicate that standard GEMMA and GEMMA LOCO are appropriate methods for the analysis of the HRDP data, while the subpopulation meta-analysis approach is substantially underpowered and thus suboptimal. First, we detail type I error and power results from the 43-strain simulation, then discuss the power of GEMMA LOCO applied to the subpopulation simulation since it was the method with the highest power in general. In eQTL studies, statistical power varies between transcript because of cis-heritability therefore, power is estimated separately for different ranges of cis-heritability.The no cis effect simulation, in which cis and trans SNPs are simulated from the same distribution, is used to calculate the empirical type I error rate. Note that type I error is only assessed for the no cis effect simulation in the case where no cis-SNP has a simulated nonzero effect size. Due to the potentially high degree of correlation among cis-SNPs and the fact that we do not employ a fine mapping approach, it is not feasible to assess type I error except for the “global null” case where all cis-SNPs are non-causal. In the 43-strain setting, each of the three methods has a type I error rate less than 0.015 . This isThe one and multiple cis effect simulations in the 43-strain setting are used to assess the power of the analysis methods. The 43-strain HRDP has greater than 80% power to detect cis effects via the standard GEMMA and GEMMA LOCO approaches when the cis heritability exceeds 0.4, and reasonable power to detect cis effects when the cis heritability is between 0.1 and 0.4 . In the The 43-strain setting results indicate that standard GEMMA, GEMMA LOCO, and subpopulation meta-analysis are all appropriate for the analysis of the HRDP data, although the subpopulation meta-analysis is underpowered in comparison. Next, we applied GEMMA LOCO to four populations: the FXLE/LEXF recombinant inbred population, the HXB/BXH recombinant inbred population, the classic inbred population, and 92 strains of the HRDP. These results may inform study design decisions and how to select subpopulations from the HRDP for an eQTL study.GEMMA LOCO has adequate type I error control when applied to all four populations, as shown in This paper discusses several feasible methods for eQTL analyses in the Hybrid Rat Diversity Panel. In particular, we rigorously investigate the performance of four approaches to QTL analysis: linear regression, linear mixed model, linear mixed model with leave one chromosome out kinship matrix calculation, and linear mixed model by subpopulation followed by meta-analysis. We apply these methods to brain RNA expression levels in 99,376 transcripts in the HRDP and characterize the performance of these methods via genomic inflation and the number of associated cis-SNPs. We further explore the type I error and power of these methods via simulation. Finally, we characterize the power of QTL analyses in subpopulations of the HRDP data, and empirically relate the power to the phenotypic heritability. We conduct the power analyses by subpopulation and for the entire set of 92 strains to facilitate study design decisions regarding the HRDP data.From this analysis, we conclude that the standard GEMMA and GEMMA LOCO approaches both control type I error sufficiently and maximize power. Therefore, both are feasible choices for eQTL analyses of the HRDP data. GEMMA LOCO identifies the most significant cis-SNPs in the real data application and has the best power in simulation while maintaining the nominal level of type I error control in simulation. The genomic inflation incurred by the GEMMA LOCO analysis of the real data is some evidence that it may have weaker type I error control than the other approaches. For applications where controlling type I error is of great emphasis, we believe that it is also appropriate to use the standard GEMMA approach, as this approach is only marginally less powerful than GEMMA LOCO.The simulations were conducted under the assumption that data can be easily pooled across the three subpanels, i.e., the HXB/BXH RI subpanel, the FXLE/LEXF RI subpanel, and the divergent classic inbred strain subpanel. In certain situations, this may not be possible, for a variety of reasons . We would caution that investigators should carefully assess whether data can be pooled prior to and during analysis. In occasions where data cannot be pooled, meta-analysis may be the desired approach, given its robust type I error control and modest decrease in power.Powerful and parsimonious eQTL mapping is relevant to systems genetics approaches towards studying complex traits. We will enumerate a few such applications here. The transcriptome-wide association study framework  uses genThese results establish a baseline of modeling recommendations and study design considerations for eQTL analyses of the HRDP. Given the central role transcript variation plays in the regulation of complex disease and the substantial effect of genetic variation on transcript variation, characterizing the genetic control of transcript variation is of great interest. Transcriptome analysis in human subjects with complex disease is often confounded by substantial environmental variation, making model organism analysis an attractive alternative for further parsing genetic control of the transcriptome. We hope this study will facilitate rigorous statistical analyses of well-powered studies involving the HRDP, thus furthering systems genetics applications towards the understanding of complex disease.


DGAT1) is regarded as a candidate gene for variation in milk and meat traits in cattle. The objective of this study was to use a PCR single-strand conformation polymorphism approach to explore sequence variation in two regions of ovine DGAT1 and to assess its effect on meat traits in New Zealand Southdown sheep. Three variant nucleotide sequences were identified in each region, with two single nucleotide polymorphisms (SNPs) and one nucleotide deletion being detected in intron 1 and two SNPs being found in exon 17. The effect of the exon 17 variation was not investigated due to one variant being predominant and the other two variants occurring at low frequencies. In intron 1, one variant (B1) was found to be associated with increase loin meat yield, suggesting that this may have value as a gene marker for improving meat traits.Diacylglycerol O-acyltransferase 1 (DGAT1) is a microsomal enzyme that plays a key role in the synthesis of triglycerides. Its gene ( Fat is an unpopular constituent of meat for many consumers, as it is considered unhealthy and high intakes of saturated fat are reported to be associated with increased heart disease risks . HoweverDGAT1) is one of the most extensively investigated lipid metabolism genes in livestock. It encodes a microsomal enzyme that catalyzes the terminal and only committed step in triacylglycerol synthesis; thus, it has a key role in the metabolism of cellular glycerolipids [DGAT1 variation on livestock production, with the research focusing on an exon 8 polymorphism . Currently, the most notable effect of this polymorphism is on milk production, and for a variety of cattle breeds from different countries, including the Netherlands [The diacylglycerol O-acyltransferase 1 (DGAT1) gene . These are c.-73C/A in the 5′-unstranslated region, c.191+411C/T and c.191+1265C/T in intron 1, c.279+1415C/T in intron 2, c.897+72C/T in intron 10, and c.1461C/T in exon 17 [DGAT1 and some meat/carcass traits , but these were carried out in Iranian and Chinese indigenous sheep breeds and focused on the exon 17 SNP. There has been only one study investigating the effect of another SNP  and undertaken using a European origin sheep breed: the Texel [Sheep  exon 17 ,21. Prev exon 17 ,23,24 rehe Texel . FurtherDGAT1 that contained the intron 1 SNP (c.191+411C/T) and the exon 17 SNP (c.1461C/T) in a variety of sheep breeds. We then assessed associations between nucleotide sequence variation in those two regions and carcass traits in Southdown lambs.In this study, we set out to investigate variation in two fragments of ovine DGAT1 and comprised 120 sheep selected from a variety of common breeds and composites in New Zealand (NZ), including NZ Romney, Coopworth, Perendale, Corriedale, Merino, Texel, Suffolk, Southdown, Poll Dorset, and Borderdale. These sheep were sourced from 20 different farms in NZ. The second group consisted of 87 Southdown lambs that were produced by 15 Southdown sires using artificial insemination in ovulation-synchronized Southdown ewes, which were not the ewes in the first group studied. These lambs were born and raised in the same environment and with the same nutrition on a single farm in South Canterbury, NZ, over 2017–2018. They were slaughtered at approximately 120 days of age in late January 2018 at an average weight of 44.8 ± 4.86 (mean ± standard deviation) kg.A total of 207 sheep in two groups were investigated. The first group was used to ascertain the nature of variation in ovine longissimus dorsi (eye) muscle was measured at the 12th rib, along with the subcutaneous fat depth in that region.Carcass data for the Southdown lambs from the second group were collected from a meat-processing plant. Hot carcass weights (HCW) were measured on the processing chain. They are the weight of the carcass minus the pelt, head, and gut. VIASCAN analysis  was useA blood sample from each sheep was collected by piercing the animal’s ear then placed onto Munktell TFN paper  and air dried. Genomic DNA was purified from a dried blood spot using a two-step washing procedure .DGAT1, based on a published ovine DGAT1 sequence (GenBank EU178818). One fragment was in intron 1 and covered a previously reported SNP c.191+411C/T [Two pairs of PCR primers  were des1+411C/T , while t1+411C/T .2+, 0.5 U of Taq DNA polymerase , and 1 × reaction buffer supplied with the enzyme. For the two regions amplified, the reactions contained 0.25 µM of one or the other of the primer pairs. The amplifications were undertaken in Bio-Rad S1000 thermal cyclers , and the thermal profile included an initial denaturation step at 94 °C for 4 min, followed by 35 cycles of 30 s at 94 °C, 30 s at 64 °C, and 30 s at 72 °C, with a final extension step of 5 min at 72 °C.The PCR amplifications were undertaken using 15 µL reactions that contained the DNA on a 1.2 mm punch of the TFN paper, 150 µM of each dNTP , 3.0 mM MgFollowing amplification, a 0.7 µL aliquot of the PCR products was mixed with 7 µL of loading dye . The samples were denatured at 95 °C for 5 min followed by rapid cooling on wet ice. They were then loaded into 16 cm × 18 cm acrylamide:bisacrylamide (37.5:1) (Bio-Rad) gels and electrophoresed with 0.5× TBE buffer using Protean II xi cells (Bio-Rad) and the conditions described in PCR amplicons representing different SSCP patterns were selected for DNA sequencing in both directions and using the matched PCR primers at the Lincoln University DNA Sequencing Facility, NZ. Amplicons with simple SSCP banding patterns (and hence that appeared to be homozygous) were purified and then directly sequenced. For those rarer SSCP patterns that only appeared to be found in heterozygote forms, a sequencing approach described in Gong et al.  was usedSequence alignments and translations were undertaken using DNAMAN version 5.2.10 .DGAT1 and variation in carcass traits. For those variants that had associations with p < 0.20 and which could thus potentially impact the trait, a second set of models was run with these variants factored in as explanatory elements. This enabled determination of the independent effect of each variant. In all the models, gender and birth rank were fitted as fixed factors, and sire was fitted as a random factor. The model(s) were: Yijklm = µ + Gi + V1j + V2k + Brl + Sm + eijkl; where Yijklm is the trait marginal mean, µ is the group raw mean for the trait, Gi is the effect of gender, V1j is the effect of the jth variant (presence and absence), V2k is effect of the second variant presence/absence (if factored in), Brl is the effect of first birth rank, Sm is the effect of the mth sire and, eijklm is the random residual effect. Unless otherwise indicated, all p values were considered statistically significant when p < 0.05, and trends were noted at p < 0.10.The statistical analyses were undertaken using Minitab. General linear mixed models were firstly used to test associations between the presence or absence of individual variants of A1, B1, and C1  and one insertion/deletion (c.191+499delC) were identified in the other two sequences. The variants occurred at frequencies of 70.8%, 23.3%, and 5.9% for A1, B1, and C1, respectively, in the group of sheep used for variation screening. The sequences of the variants were deposited into GenBank with accession numbers OP235451–OP235453. In the 87 Southdown lambs studied for the association analyses, the variant frequencies were 59.2%, 31.6%, and 9.2% for A1, B1, and C1, respectively, and the genotype frequencies were 28.7% (n = 25), 44.8% (n = 39), 16.1% (n = 14), 8.1% (n = 7), and 2.3% (n = 2) for A1A1, A1B1, A1C1, B1B1, and B1C1, respectively.PCR-SSCP analysis of intron 1 amplicons revealed three different banding patterns A, and DN, and C1 . The seqA2, B2, and C2 , whereas B2 and C2 were much less common, with frequencies of 9.2% and 1.7%, respectively. Because of the low frequencies, the association analyses were only carried out for variation in intron 1, but not the exon 17 region.For exon 17 amplicons, three PCR-SSCP patterns B represe, and C2  were alsB1 variant was found to be associated with an increase in loin yield, shoulder yield, and leg yield in the single-variant model  animals showing a decrease in the values while CC (corresponding to A1 and C1 in this study) animals showed an increase. These associations were either not detected with the Southdown sheep or possibly, in the case of ribeye area, slightly at odds with the associations described here. While the B1 variant was associated with loin meat yield, no effect on either eye muscle width or depth was observed, suggesting that the effect, if any, was a result of variation in muscle length or shape (the muscle is ovoid).The effect of the intron 1 SNP c.191+411C/T was investigated previously in 255 Texel lambs by Armstrong et al. , but theDGAT1 on the measured carcass traits.While this is speculative, there were other differences in the sheep investigated. For example, the Texel lambs described by Armstrong et al.  appear tThe carcass traits were also only measured in male lambs for the Texel sheep , whereasB2 variant reported here) for the exon 17 SNP c.1461C/T. They described associations between this SNP and various carcass and meat traits. However, in our study, we found that allele C (corresponding to variants A2 and C2) was most common, whereas the T allele equivalent occurred at an overall frequency of only 9.2% in the NZ sheep from a variety of breeds and composites of differing breeds. The sheep genome assembly sequence (NC_056062.1) also has allele C at this SNP. It is unknown whether this difference in allele frequencies reflects genetic difference in breeds or is the consequence of selection for production traits, and this requires further investigation.In Iranian and Chinese indigenous sheep breeds, Ala Noshahr and Rafat , MohammaDGAT1 and report three sequence variants for each region. An intron 1 variant (B1) was found to be associated with increased loin meat yield in NZ Southdown sheep. The effect of variation in exon 17 was not investigated, but the frequencies of variants were notably different to those reported in other sheep breeds.In this study, we analyzed sequence variation in intron 1 and exon 17 of ovine