The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse;  N = 1,000–100,000) to be statistically powerful  have developed standardized multi-site phenotype extraction and genetic analyses pipelines. In this manuscript, we consider the impact of analysis method for the estimation of heritability. We compared four approaches: two commonly used genetic analysis packages (SOLAR-Eclipse and OpenMx), and two recently developed accelerated heritability estimation methods . These packages use the same variance component model and definition of heritability, but use different numerical methods and data preprocessing steps to calculate the proportion of variance attributed to additive genetic factors. We performed this study to (A) analyze if heritability estimates derived by the four packages’ analyses are comparable to one another and; (B) develop a homogenization approach that minimizes the variability in heritability estimates across the four packages.http://enigma.ini.usc.edu/ongoing/dti-working-group/). This included the use of the ENIGMA protocol for following the QA/QC steps for each site, registration to the ENIGMA-DTI target, extraction of white matter skeleton, followed by extraction of tract-average FA values.We performed these analyses using two datasets: a simulated—with known additive genetic contribution and an experimental—consisting of fractional anisotropy (FA) measurements collected in twins and siblings by three independent studies. FA is the most commonly analyzed scalar parameter extracted from diffusion tensor imaging  gains in computational efficiency. This allows performing voxel-wise heritability analyses on a single workstation. While APACE is only intended for twin or twin-plus-sibling designs, FPHI can use any kinship structure, like SOLAR-Eclipse.APACE model and FPHI use statistical approximations to estimate heritability values. APACE uses a regression approach based on the squared differences of twin pairs, a variant of a U-statistic  in simulated and experimental data using twin family study designs. For experimental data we used DTI acquisitions from three different studies. The human connectome project  twins, 250 dizygotic (DZ) twins, and 500 founders (not included in the phenotype file) was created using SOLAR-Eclipse simulate function. SOLAR-Eclipse simulation functionality was also used to produce a data set of 10,000 traits with heritability estimates varied uniformly between 0 and 100%. All simulated traits had normal distribution and did not include effects of covariates.A simulated Subjects: the cohort contained 481  healthy participants of the HCP for whom the scans and data were released in June 2014 (humanconnectome.org) after passing the HCP quality control and assurance standards . The total imaging time for collection of diffusion data was approximately 1 h.Imaging: diffusion data was collected at Washington University in St. Louis using a customized Siemens Magnetom Connectome 3-Tesla scanner with a 100 mT/m maximum gradient strength and a 32-channel head coil. Details on the scanner, image acquisition and reconstruction are provided in Ugurbil et al. 4. DiffusSubjects: the cohort consisted of 246 adults , recruited from the NTR and consisted of 72 MZ pairs, 48 DZ pairs, and six siblings. Exclusion criteria consisted of having any metal material in the head, having a pacemaker, a history of any major medical conditions or psychiatric illness .Imaging: DTI data were collected on a 3-Tesla Philips Intera MR scanner . It included 42 MZ and 57 DZ twin pairs that were recruited from families participating in the BrainSCALE cohort . More information may be found in Brouwer et al.  is the proportion of the total phenotypic variance (σP2) that can be explained by the genetic effects of genes (σg2),Heritability analyses were performed in the simulated and FA traits. Heritability . Narrow sense heritability is defined as the fraction of phenotypic variance σP2 attributable to additive genetic factors. In twin designs a third variance parameter is can be identified and may be added to the model, σc2, for the common environment shared by twins and siblings growing up in the same family. This three-parameter model is known as the ACE model, while the two-parameter model (Equation 2) is referred to as the AE model.where Φ is the kinship matrix representing the pair-wise kinship coefficients among related individuals, σg2 is constrained to zero with that of a model in which σg2 is estimated. Twice the difference between the loge likelihoods of these models yields a test statistic, which is asymptotically distributed as a 1/2:1/2 mixture of a X2 variable with 1 degree-of-freedom and a point mass at zero.The variance parameters are estimated by comparing the observed phenotypic covariance matrix with the covariance matrix predicted by kinship  computational acceleration relative to the iterative MLE estimation in SOLAR-Eclipse, while the graphics processing unit (GPU) version further improves this performance (~106) vs. iterative MLE approach.SOLAR-Eclipse’s iterative MLE approach is accelerated by the use of a data transformation based on the eigenvectors of the kinship matrix Φ  with the expected heritability values and with each other . In the simulated dataset, the two ML-based methods show zero bias  and the average spread in heritability estimates of 1.2%. The APACE and FPHI methods showed excellent overall agreement with expected values . APACE showed significantly higher average spread than the FPHI method: 3.7 vs. 2.2% (p = 10−10).ch other . We quan2, age × sex, and age2 × sex as covariates. The two ML-based method showed excellent agreement in all three datasets . The least agreement (~5% average spread) between two ML-based approaches was observed in HCP . Intermediate results were observed in the NTR dataset . Hence, we averaged the heritability values produced by the two ML methods to create a “ground truth” reference for the two approximation methods.The heritability analyses were performed in FA data for 49 tracts in HCP, NTR and BrainScale cohorts using age, sex, agedatasets . The besβ = 0.97–1.04) and intercepts (α = 0.01–0.26) vs. APACE , in all three cohorts. For FPHI, the improvements were more subtle and were mainly observed as decrease in bias and spread. The bias for APACE increased for NTR cohort (from −0.01 to 0.08). Both approximation methods showed a 50% improvement in the percentage spread vs. the average ML-based estimate, yet, the % spread for FPHI remained about half that for APACE .Trait normalization brought improvements in the agreement between the estimates by two approximation approaches and the average ML-based estimation . APACE mW > 0.94, p < 0.05; r < 0.20, all p > 0.4). Furthermore, some traits that visibly contributed to dispersion of heritability values, for example the inferior fronto-occipital tract-left (IFO-L) and superior corona-radiata-right . The histograms for SLF-L and SCR-L showed only modest kurtosis (kurtosis = −0.2 and 0.15 for SLF-L and SCR-L), but visibly varied from the normal distribution , uncinate fasciculus-right (UNC-R), external capsule-right (EC-R) and superior corona radiate-left (CR-L), failed the null hypothesis for normal distribution  voxel-wise imaging genetics analyses using fast approximation approaches. We performed the evaluation in a simulated dataset and imaging data from three independent datasets. In the simulated data, we observed an excellent agreement between all heritability estimate approaches. The two MLE approaches accurately replicated the expected heritability values, with the unity slope and near zero intercept and measurement bias. The two approximation techniques likewise showed excellent agreement in the simulated data, with only slight spread . In neuroimaging data, the two MLE approaches produced consistent estimates of heritability for all cohorts. We used the average MLE as the reference measures for approximation techniques because the true additive genetic contribution is unknown . However, the traits with high dispersion in heritability estimates did show deviations from normality in the underlying dataset. The heritability estimates produced by the FPHI approach were generally closest to that produced by MLE estimates. The agreement among all methods was significantly improved following data normalization approach that ensured normality for quantitative traits. This data normalization approach is now available as a part of SOLAR-Eclipse distribution.We conducted a careful evaluation of four quantitative genetic approaches used by imaging genetic studies to measure heritability—the proportion of variance attributable to the additive genetic factors. Two of the methods (SOLAR-Eclipse and OpenMX) used an iterative MLE approach. Two methods (APCE and FPNI) were developed specifically to accelerate  and other multi-study initiatives aim to overcome the challenge of limited power by performing meta-analytical analyses. In these initiatives, phenotypic and genetic analyses are performed by individual sites and meta-analytical aggregation is used to derive the overall estimates of genetic effects. The main challenge in this approach is overcoming the diversity and complexity of analytical and statistical approaches that may lead to variance in phenotype extractions and estimation of effect sizes  that use approximation to obtain fast inference of genetic variance.In our evaluations, we observed excellent agreement between estimates produced by the two MLE-based approaches that were the corner stone of imaging genetic research in the past. The main disadvantage of MLE approaches is the long calculation times associated with the iterative maximization of the likelihood. In imaging genetic studies, up to a million voxel-based imaging traits may be analyzed  was not significant. We explored four cases of visible outliers. Some traits  failed assumptions for normality, but other outliers passed normality according to Shapiro-Wilk’s test. We concluded that approximation approaches may be more sensitive to the noise and deviation from data normality and may produce biased heritability estimates even in traits whose distributions pass the standard tests for normality.r > 0.95) was observed for averaged ML-estimates before and after inverse normal transformation. Enforcing normality upon data reduced the dispersion in h2 values and improved the average spread for the approximation approaches. This was especially noticeable for FPNI approach where the correlations with ML-estimates became high (r > 0.97) for all cohorts.We found that the use of inverse normal transformation improved the agreement between ML and approximation-based approaches and resolved the outlier heritability estimates observed in uncorrected data. The inverse normal transformation did not alter the pattern of ML-based estimates: high correlation (The ML estimations were used as the reference to compare the performance of approximation-based approaches in the simulated and imaging data. The two ML approaches produced convergent heritability estimates in both simulated and imaging datasets. However, this does not constitute the “ground truth” especially in imaging datasets where ML approaches may be biased despite convergence.We have conducted a careful comparison of four heritability estimation methods for imaging data. Based on “ground-truth” simulations, four packages can produce low-bias, low-variance heritability estimates, with ML-based methods understandably performing slightly better than the approximation methods. In real data, the approximation methods exhibit more variability relative to the ML-based methods, but this variability was reduced with the use of a rank-based inverse normal transformation, suggesting that this may be an important tool to maximize inter-method reliability.This study performed secondary data analyses in anonymized human subjects.PK, BP, HG, BD, MR, XC, NJ, PT and TN designed experiment, performed analyses, and wrote the manuscript. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


F-tests for multivariate mixed-effect models is not well-recognized. Thus, we compared the F-tests for univariate and multivariate mixed-effect models with simulations. The superiority of the multivariate F-test over the univariate test varied depending on three parameters: phenotypic correlation between variates (r), relative size of quantitative trait locus effects between variates (ad), and missing proportion of phenotypic records (mprop). Simulation results showed that, when mprop was low, the multivariate F-test outperformed the univariate test as r and ad differ, and as mprop increased, the multivariate F-test outperformed as ad increased. These observations were consistent with results of the analytical evaluation of the F-value. When mprop was at the maximum, i.e., when no individual had phenotypic values for multiple variates, as in the case of meta-analysis, the multivariate F-test gained more detection power as ad increased. Although using multivariate information in mixed-effect model contexts did not always ensure more detection power than with univariate tests, the multivariate F-test will be a method applied when multivariate data are available because it does not show inflation of signals and could lead to new findings.Genome-wide association mapping (GWA) has been widely applied to a variety of species to identify genomic regions responsible for quantitative traits. The use of multivariate information could enhance the detection power of GWA. Although mixed-effect models are frequently used for GWA, the utility of  Genome-wide association mapping (GWA) has been widely applied to humans, animals, and plants to identify genomic regions responsible for quantitative traits, which has been made feasible by decreases in the cost and time required to obtain genome-wide single-nucleotide polymorphisms (SNPs) and sequences. Whereas various statistical methods have been proposed for GWA, particularly in recent animal and plant studies, mixed-effect models are often used to correct population stratification  mapping have been proposed Piepho, , and theF-test for multivariate mixed-effect models of general form; both empirical and analytical evaluations were performed to elucidate the properties of the F-tests for multivariate mixed-effect models. The F-test  is usually used for association analysis based on univariate mixed-effect models , X is the covariate matrix, including intercepts, genotypes of the SNP to be tested, and principal components for adjustment of population structure, B is the fixed effects of the covariates, Z is the design matrix, u is the polygenic effects, and e is the residuals. u and e follow multivariate normal distributions,where A is the genomic relationship matrix defined by genome-wide SNPs  p (−log10P) values. Nevertheless, we propose that the methods and results presented here are useful in practice because p-values obtained for negative SNPs  were not underestimated in simulations presented later, and thus the reduction of degrees of freedom would not affect GWA given the number of samples usually used in GWA. This issue is revisited in the Results and Discussion.where A multivariate mixed model can be written asYm is the vector of response variables of length n × d, where d is the number of variates . Xm is the matrix of covariates, including the intercepts, SNP genotypes, and so on. For example, when d is two,Here, Bm is the vector of fixed effects of length p × d where p is the number of fixed effects for each variate. Zm is the design matrix, which for the example of d = 2, takes the following form,um and em are the polygenic effects and residuals, respectively, andd × d genetic covariance matrix, andwhere ⊗ indicates the Kronecker product and d × d residual covariance matrix.where F-test introduced here for this multivariate mixed-effect model, SNP effects on all of the variates are tested simultaneously. The F-statistic in the multivariate case isIn the Here, F-statistic follows an F distribution with the numerator degrees of freedom being f and the denominator degrees of freedom being n × d − p. When Ym includes missing records, the dimensions of Ym, Xm, and Vm and the denominator of H is constructed as follows. For example, when d is two (variates 1 and 2) and 1 and μ2 are the intercepts and B1 and B2 are the effects of a SNP, H becomesWe assume that when the fixed effect of interest is 0, the F-test.We refer to this test as the multivariate F-test for bivariate mixed-effect models, and GEMMA provides the Wald, likelihood ratio, and score tests . Among the QTLs, one QTL was randomly selected as a “target QTL,” and the remaining ones were used as “background QTLs.” For the background QTLs, the additive genetic effects were simulated using a multivariate normal distribution,Bm,bQTL is a vector of background QTL effects with length d (number of variates), and Σ is a d by d correlation matrix represented with a single correlation parameter r, i.e.,where For the target QTL, the QTL effect on the first variate was generated from the standard normal distribution,jth variate (d ≥ j > 1) was determined asand then the effect on the aj is a real value between −1 and 1. We investigated the detection power of the multivariate F-test by assessing the power on the target QTLs while varying aj and r values. Total genetic effects, um, were generated by summing all of the products between the QTL effects and QTL genotypes. The residuals of individual i were generated using the same correlation matrix Σ,where S is a diagonal matrix whose elements were the standard deviations of the total genetic effects (um). Thus, the heritability was 0.5 throughout the simulation. The phenotypic correlation was largely Σ. Because, as illustrated in the previous section, the F-value is influenced by the phenotypic covariance (V) rather than the individual variance components  was determined as the intermediates between 1 and ad with constant intervals. For example, when d = 4 and a4 = 0.4, a2 = 0.8 and a3 = 0.6, respectively.When F-tests using the background QTLs. The rationale for this is as follows. When we draw random variables from a multivariate normal distribution and represent the variables using ad, a wide range of ad values are obtained. For example, when we draw random values fromWe did not assess the detection power of ad < 0.9, and even 10% of pairs show ad < 0. However, as we show later, ad has an impact on the detection power of multivariate F-tests. Thus, using the background QTLs, the power of multivariate F-tests could not be assessed appropriately.Approximately 80% of variable pairs are represented with d = 2, 4, and 8, the maximum missing proportions of phenotypic values were (2 × 200–200)/(2 × 200) = 0.5, (4 × 200–200)/(4 × 200) = 0.75, and (8 × 200–200)/(8 × 200) = 0.875, respectively . When d = 2, mprop was set to 0, 0.125, 0.25, 0.375, and 0.5; when d = 4, mprop was 0, 0.188, 0.375, 0.563, and 0.75; and when d = 8, mprop was 0, 0.219, 0.438, 0.656, and 0.875. Note that when mprop is the maximum value, every individual has a phenotypic value only for a single variate. When mprop > 0, r was set to 0, 0.1, …, 0.8, 0.9, 0.95, and ad was 0, 0.1, 0.2, …, 0.9, 0.95, and 1 when d = 2. When d = 4 and 8, r was 0, 0.4, 0.8, and 0.95, and ad and aj (d > j >1) were set as when mprop = 0.Missing phenotypic values were randomly generated, such that every individual had a phenotypic value for at least one variate. Thus, when In the analysis of each scenario, we used SNPs that were not selected as QTLs to construct the genomic relationship matrix for the polygenic effects. Then, the effect of the target QTL was tested. Principal components were not added. Other than the QTL genotypes, only intercepts for each variate were added to the model as the fixed effects.F-tests were implemented using R , we compared the–log10p-values of the multivariate F-tests with those of the univariate tests for the first variate, which was expected to be highest among the variates.We assessed the power of the multivariate d = 2 and the data had no missing records, the multivariate F-test outperformed the univariate test as the differences between ad and r became large . The numerator can be expressed as a function of ad and r by simplifying model assumptions as follows. Suppose that d = 2, A = I. Then, the numerator of the F-value isWhen me large . This tex is the vector of the genotypes of the QTL. The heat map of f , shown in d = 2, shown in where d = 4 and 8 differ from that when d = 2, particularly when both r and ad are near zero (aj (d > j >1) as the intermediate between 1 and ad with constant intervals . For example, when d = 4 and a4 = 0, we set a2 and a3 to 0.666 and 0.333, respectively. This means that the QTL effects on the first to fourth variates become BtQTL,1, 0.666BtQTL,1, 0.333BtQTL,1, and 0, respectively. If we focus on variates 1 and 2 and examine the heat map for d = 2 , the multivariate F-test is superior to the univariate test within the range from r = 0 to 0.3, which is consistent with the results when d = 4  became similar (flatter) among the simulation scenarios. Note that, from d = 8 and mprop = 0.656 because of a failure of variance component estimation in 98.1% of analyzes. We return to this issue later. When mprop was the maximum, i.e., when every individual had a phenotypic value only for a single variate, the superiority of the multivariate F-test depended on ad, i.e., as ad increased, the test outperformed the univariate one. However, it was also observed that the gain in–log10p was minimal. The fact that r became less influential when mprop was the maximum can be illustrated analytically. Suppose that d = 2 (variates 1 and 2) and the first half of individuals (group X) has values only for variate 1 and the second half (group Y) has values only for variate 2. LetFor each ncreased . This teThen, the phenotype (co)variance matrix appearing in equation (2) can be written asIX and IY are the identity matrices for groups X and Y, respectively. Because groups X and Y do not have values for variates 2 and 1, respectively, the relevant rows and columns are removed from Vm resulting in:where r disappears from the residual covariance (the second term). Although r still appears in the genetic covariance (the first term), the influence of r on the F-value is expected to decrease.The parameter F-test not inflating the p-values of negative SNPs, QQ plots were drawn for the SNPs that were on the same chromosome as the target QTLs and were not associated with any QTLs  occasionally failed to converge within the default iteration number  or to return a positive-definite matrix. As mentioned above, when d = 8 and mprop = 0.656, the estimation failed in most cases in this scenario. In other scenarios, the frequencies of failure were 0.004, 0.054, and 0.081 when d = 2, 4, and 8, respectively. This issue would be solved using Gibbs sampling or by estimating the covariance for each pair of variates independently, as is often done in the genetic analysis of multiple traits d3. Considering this computational difficulty, meta-analyzes will be an attractive alternative to use multivariate information. For example, TETAS can combine p-values obtained by univariate mixed-effect models considering correlations between variates  and (2) do not follow the ulations , and thund Roger . The metlnosaier . AlthougF-tests can be conducted for imputed data. Phenotype imputation is essential for several estimation algorithms for multivariate mixed-effect models because of transformation using eigen vectors, which is required for high computational efficiency  as observed here because it will influence the imputation accuracy regardless of the imputation methods.When missing records are included, both the multivariate and univariate The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Nevertheless, these genomes are at various levels of completeness and few have been anchored to chromosome scale and/or are haplotype-phased. We highlight the impact of a high-quality genome assembly for plant breeding and genetic research by showing how it affects our understanding of the genetic architecture of important traits and aids marker selection and candidate gene detection. We compared the results of genome-wide association studies and genomic selection that were already published using a blueberry draft genome as reference with the results using the recent released chromosome-scale and haplotype-phased blueberry genome. We believe that the benefits shown herein reinforce the importance of genome assembly projects for other non-model species.The decreasing costs of next-generation sequencing and the improvements in  Vaccinium corymbosum) is an outcrossing tetraploid species (2n = 4X = 48), and 48 pseudomolecules from the northern highbush cultivar 'Draper' were recently assembled and phased [Assembling plant genomes using short-read–based sequencing is a challenging task, especially because most plant genomes are large, highly repetitive, and have undergone ancient and recent rounds of polyploidization. Thanks to the new sequencing methods, researchers have been able to achieve chromosome-scale haplotype-phased genome assemblies more inexpensively and quickly than in previous decades. The cultivated blueberry  analyses in a southern highbush blueberry (SHB) breeding population in order to detect single-nucleotide polymorphisms (SNPs) associated with fruit-related traits . At that−10 and identity of 80% [Brassica species [A total of 31,063 probes of 120-mer were originally designed based on the 'W8520' genome for targeted Capture-Seq genotyping by RAPiD Genomics . Probes were designed for enrichment of genic and single mapped genomic regions. The probe sequences were then aligned against the high-quality 'Draper' genome using blastn with e-value threshold of 10GWAS can provide the first insights into the genetic architecture of a trait by identifying the number of significant loci, genomic position, mode of gene action, and effect on the phenotypic variation. In this step, the high-resolution positioning of SNPs in the chromosome-scale assembly played an important role in unraveling the genetic architecture of the traits. Using this new pipeline, which also included more accurate genotype calling using the updog R package , we wereCUFF.54762.1 and CUFF.14779.1) were incomplete, and no significant similarity was found in the non-redundant blast protein database. However, for all the nearest genes predicted in the 'Draper' genome, we could find orthologs and/or functional annotation (Triticum aestivum L.) allowed the full resolution of a quantitative trait locus (QTL) in a region that was disrupted in the previous wheat assemblies due to lack of ordering and incomplete gene predictions [GWAS also provides candidate genes for subsequent validation. A high-quality genome assembly results in a more complete and accurate prediction of the gene repertoire for candidate gene mining. To exemplify, we looked at the nearest gene of the associated SNPs mentioned in Fig. 2A for scar size and pH traits in the 'W8520' genome. Both predicted sequences (notation . SimilarGenomic selection (GS) has become a new tool in breeding programs, assisting the selection of promising materials and maximizing the genetic gains. For its implementation, a high marker density is required in order to capture most of the linkage information between QTLs and SNPs. However, many studies have been showing that improvements in prediction accuracies reach a plateau afterwards despite the increased marker density . MoreoveAltogether, we can conclude that investing time and resources to obtain a high-quality reference genome is worthwhile given the benefits it confers to downstream genetic analyses and in the decision-making process for breeding programs. In the case of the blueberry, the benefits were as follows: (i) selection of a superior set of uniquely mapped probes for GWAS and GS, which will help reduce future targeted genotyping costs because fewer probes are needed; (ii) a higher precision about the location, number, and gene action of QTLs, thereby improving our understanding of the genetic architecture of the traits through GWAS analyses; (iii) higher chances to find the molecular mechanisms underpinning the trait variation in future studies by being able to explore a more complete gene repertoire; and (iv) achievement of similar genomic predictive ability with fewer genotyping probes. All this will translate into less time and funds needed to implement marker-assisted and genomic selection in the breeding program, and hopefully the achievement of higher genetic gains in shorter breeding cycles.10.5061/dryad.kd4jq6h). The 'Draper' genome from Colle et al. [https://genomevolution.org/coge/GenomeInfo.pl?gid=36464) and GigaScience database (doi: 10.5524/100537) . The 'W8520' genome from Bian et al. [http://www.igbquickload.org/blueberry/V_corymbosum_scaffold_May_2013/).The data used herein were mostly retrieved from published articles. Specifically, the phenotypic and genotypic data from the SHB population can be obtained from Ferrão et al.  at DryadSupTable_gene_mining_old_newgenome.xlsx.GBLUP: Genomic Best Linear Unbiased Prediction; GS: genomic selection; GWAS: genome-wide association study; kb: kilobase pairs; maf: minor allele frequency; QTL: quantitative trait locus; SNP: single-nucleotide polymorphism; SHB: southern highbush blueberry.The authors declare that they have no competing interests.This work was funded by the University of Florida royalty fund generated by the licensing of blueberry cultivars.J.B. performed the probe selection, SNP calling, and annotation of the genes. L.F.V.F. performed the GWAS analyses. R.R.A. performed the GS analyses. P.M. supervised and provided overall guidance. J.B. wrote the manuscript with revision from all authors. All authors read and approved the final manuscript.giz068_GIGA-D-19-00128_Original_SubmissionClick here for additional data file.giz068_GIGA-D-19-00128_Revision_1Click here for additional data file.giz068_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz068_Reviewer_1_Report_Original_SubmissionRobert Henry -- 4/23/2019 ReviewedClick here for additional data file.giz068_Supplemental_FileClick here for additional data file.


Drosophila melanogaster can serve as a translational model for studies on substance abuse, since flies have a dopamine transporter that can bind cocaine and methamphetamine, and exposure to these compounds elicits effects similar to those observed in people, suggesting conserved evolutionary mechanisms underlying drug responses. Here, we used the D. melanogaster Genetic Reference Panel to investigate the genetic basis for variation in psychostimulant drug consumption, to determine whether similar or distinct genetic networks underlie variation in consumption of cocaine and methamphetamine, and to assess the extent of sexual dimorphism and effect of genetic context on variation in voluntary drug consumption. Quantification of natural genetic variation in voluntary consumption, preference, and change in consumption and preference over time for cocaine and methamphetamine uncovered significant genetic variation for all traits, including sex-, exposure- and drug-specific genetic variation. Genome wide association analyses identified both shared and drug-specific candidate genes, which could be integrated in genetic interaction networks. We assessed the effects of ubiquitous RNA interference (RNAi) on consumption behaviors for 34 candidate genes: all affected at least one behavior. Finally, we utilized RNAi knockdown in the nervous system to implicate dopaminergic neurons and the mushroom bodies as part of the neural circuitry underlying experience-dependent development of drug preference.Illicit use of psychostimulants, such as cocaine and methamphetamine, constitutes a significant public health problem. Whereas neural mechanisms that mediate the effects of these drugs are well-characterized, genetic factors that account for individual variation in susceptibility to substance abuse and addiction remain largely unknown.  Drosophila melanogaster, presents an excellent model for identifying evolutionarily conserved genes that affect drug consumption, since genetic background and exposure can be controlled precisely. We took advantage of natural variation in a panel of inbred wild derived fly lines with complete genome sequences to assess the extent of genetic variation among these lines for voluntary consumption of cocaine and methamphetamine and to explore whether some genetic backgrounds might show experience-dependent development of drug preference. The drug consumption traits were highly variable among the lines with strong sex-, drug- and exposure time-specific components. We identified candidate genes and gene networks associated with variation in consumption of cocaine and methamphetamine and development of drug preference. Using tissue-specific suppression of gene expression, we were able to functionally implicate candidate genes that affected at least one consumption trait in at least one drug and sex. In humans, the mesolimbic dopaminergic projection plays a role in drug addiction. We asked whether in Drosophila the mushroom bodies could play an analogous role, as they are integrative brain centers associated with experience-dependent learning. Indeed, our results suggest that variation in consumption and development of preference for both cocaine and methamphetamine is mediated, at least in part, through a neural network that comprises dopaminergic projections to the mushroom bodies.Illicit use of cocaine and methamphetamine is a major public health problem. Whereas the neurological effects of these drugs are well characterized, it remains challenging to determine genetic risk factors for substance abuse in human populations. The fruit fly,  Illicit use of cocaine and methamphetamine constitutes a significant public health problem that incurs great socioeconomic costs in the United States and worldwide –3. CocaiAmphetamine and methamphetamine are used clinically to treat attention deficit hyperactivity disorder and narcolepsy. Long term use of these compounds, however, can lead to addiction, and ultimately death . The addSusceptibility to the effects of cocaine and methamphetamine is likely to vary among individuals and be determined both by environmental and genetic factors. However, there is limited information regarding the genetic basis of susceptibility to the effects of these drugs in human populations . Twin anDrosophila melanogaster is an excellent model for identifying genes that affect drug consumption behaviors since both the genetic background and environment, including exposure to drugs, can be controlled precisely. These results have translational potential since 75% of disease-causing genes in humans have a fly ortholog , loco, Tao) and ethanol-related behaviors  [D. melanogaster. In addition, the genes implicated by the GWA analyses are enriched for multiple gene ontology (GO) categories and pathways [While not formally significant, we identified genes previously associated with cocaine-related behaviors   in D. mepathways ,54 at a These results suggest a highly polygenic architecture for variation in consumption and drug preference, and that the genetic underpinnings for variation in consumption or preference are both shared and distinct for cocaine and methamphetamine, consistent with the quantitative genetic analyses.P = 9.99 × 10−4) network of 81 candidate genes  to functionally test whether reduced expression of candidate genes implicated by the GWA analyses affect consumption phenotypes. We selected 34 candidate genes for RNAi mediated suppression of gene expression from the set of GWA analyses . A totalUAS-RNAi and control genotype, separately for males and females , solution  and exposure . A significant L effect denotes a difference in overall consumption between the RNAi and control genotypes; a significant S effect indicates a difference in preference between sucrose alone and sucrose with drug; and a significant E effect indicates a difference in consumption between exposures 1 and 3. Significant L×S and L×E interaction terms denote, respectively, a difference in preference between the RNAi and control genotypes, and a difference in consumption between exposures 1 and 3 between the two genotypes. A significant L×S×E interaction indicates a change in preference with repeated exposure between the RNAi and control genotypes. We are most interested in the main effect of genotype and interactions with genotype; i.e., consumption, preference, change of consumption and change of preference.We performed three-way fixed effect ANOVAs for each  females  Tables. GAL4 driver crossed to all 34 UAS-RNAi genotypes and their respective controls. All candidate genes had a significant (P < 0.05) effect on at least one of the consumption traits in at least one drug or sex combination. A total of 22 (25) genes affected consumption of cocaine (methamphetamine), 21 (23) affected a change of consumption with exposure to cocaine (methamphetamine), 16 (10) affected cocaine (methamphetamine) preference, and 11 (11) affected a change in cocaine (methamphetamine) preference with exposure in males and/or females  from among those that showed phenotypic effects when targeted by RNAi under the ubiquitous driver and which have known effects on the nervous system. We assessed functional effects of these genes on consumption traits when their corresponding RNAi constructs were expressed under the control of the neuronal-specific elav driver or glial-specific repo driver. All of these genes had a significant (P < 0.05) effect on at least one of the consumption traits in at least one drug or sex combination under the elav driver, and all but Snoo had significant effects on at least one of the consumption traits in at least one drug or sex combination under the repo driver. With neuronal-specific suppression of gene expression, 9 (10) genes affected consumption of cocaine (methamphetamine), 6 (7) affected a change in consumption with exposure to cocaine (methamphetamine), 2 (7) affected cocaine (methamphetamine) preference, and 3 (6) affected a change in cocaine (methamphetamine) preference with exposure in males and/or females  that showed robust phenotypic effects when targeted with a corresponding elav-driven RNAi. Knockdown of all four genes with a mushroom body specific driver resulted in significant effects on consumption of cocaine and/or methamphetamine for at least one drug and sex combination  defines 11 criteria for substance use disorder in humans, all related to continuing to use of the substance despite adverse social and physiological effects and the development of tolerance with repeated exposure. The DSM-V also recognizes that there is individual variability of unknown etiology for the propensity both to experiment with psychostimulants and to develop symptoms of substance abuse following initial exposure. Previous studies of effects of cocaine –39,73–76P < 5 x 10−5. We hypothesized that these candidate genes would be enriched for true positive associations despite the low power of the GWA analyses and that choosing genes for functional evaluation from this list would be more productive than choosing genes at random. Observations supporting this hypothesis are that mutations in several candidate genes have previously been shown to affect cocaine or ethanol-related phenotypes in Drosophila [P = 9.9 x 10−3) to occur by chance. The candidate genes in the significant genetic interaction network are enriched for several canonical signaling pathways as well as all aspects of development, including nervous system development. These observations suggest that subtle genetic variation in nervous system development is associated with variation in propensity for consumption of psychostimulant drugs. Nearly 90% of the genes in the network have human orthologs and are candidates for future translational studies.To begin to understand the nature of the genetic basis for variation in drug consumption and preference, we performed GWA analyses for all consumption traits, separately for cocaine and methamphetamine, using 1,891,456 DNA sequence variants present in the 46 DGRP lines with minor allele frequencies greater than 0.05 . We idenosophila , that thGAL4 driver affected consumption traits, using the same experimental design as for the DGRP lines. All of these genes affected at least one consumption trait/sex/drug. However, there is considerable variation in the effects of different drivers on consumption, preference and change in preference for cocaine and methamphetamine, which likely reflects variation in the effects of RNA interference on different neural elements of a complex integrated neural circuitry. Indeed, several candidate genes, functionally implicated by RNAi, are associated with neural development and represent several early developmental signaling pathways. Snoo has been identified as a negative regulator of the decapentaplegic signaling pathway [ed, is an immunoglobulin domain containing membrane protein of adherens junctions that interacts with multiple developmental signaling pathways, including Egfr, Notch and Hippo signaling [msi, is a neural RNA binding protein that interacts with Notch signaling to determine cell fate [MB-GAL4 or TH-GAL4 drivers show different effects on consumption, change in consumption, preference and change in preference for the two drugs . Two capillaries  containing 4% sucrose (Sigma Aldrich) + 1% yeast (Fisher Scientific) or 4% sucrose + 1% yeast + drug, with a mineral oil (Sigma Aldrich) overlay (to minimize evaporation), were inserted in the top of each vial. Cocaine and methamphetamine were obtained from the National Institute on Drug Abuse under Drug Enforcement Administration license RA0443159. Flies were allowed to feed for 16–18 hours with the vials placed in an enclosed plastic chamber wrapped in a plastic bag under a 12 hour light/dark cycle with constant humidity of 50%. For each experiment, an identical set of vials without flies was included in each chamber to determine evaporation loss. The capillaries were then removed and the volume of food consumed (1 mm = 0.067 μl) in each calculated as described previously [We used a four-capillary Capillary Feeder (CAFE) assay –46 to meeviously . The capWe defined four behaviors: total amount of each solution consumed, drug preference, and change in consumption and change of preference between exposures 3 and 1. Preference was quantified in two ways: as the difference between the amount of drug and sucrose consumed (Preference A), and as this difference scaled by the total amount consumed (Preference B).Y = μ + L + E + S + X + (L × E) + (L × S) + (L × X) + (E × S) + (E × X) + (S × X) + (L × E × S) + (L × E × X) + (L × S × X) + (E × S × X) + (L × E × S × X) + ε, where Y is consumption; μ is the overall mean; L is the random effect of line; E, S, and X are the fixed effects of exposure (day 1–3), solution , and sex ; and ε is the residual variation between replicate vials. The main effect of L and all interaction terms with L are genetic factors affecting drug consumption. We also ran the same ANOVA models to compare the effects of cocaine and methamphetamine on consumption, separately for males and females. The full model for variation in change in consumption over time is Y = μ + L + S + X + (L × S) + (L × X) + (S × X) + (L × S × X) + ε. We assessed variation in the development of preference using the model Y = μ + L + E + X + (L × E) + (L × X) + (E × X) + (L × E × X) + ε. We also assessed whether there is natural variation in the change of preference over time using the model Y = μ + L + X + (L × X) + ε. We also ran reduced models for each trait. All ANOVAs were performed using the PROC GLM function in SAS. We used the R function pf to assign exact P-values.We performed four-way factorial mixed model analyses of variance (ANOVA) to partition variation in consumption in the DGRP: P-values for the segregating genetic variation for each trait. We computed broad sense heritabilities as the sum of all genetic variance components divided by the total phenotypic variance for each model, and broad sense heritabilities of line means as the sum of all genetic variance components divided by the sum of all genetic variance components plus the environmental variance/10, where 10 is the number of replicate vials per line, sex, exposure and treatment. We computed pairwise genetic correlations as σL1 and σL2 are the among line standard deviations from the one-way ANOVA for each condition. We computed Pearson product-moment correlations of line means to estimate phenotypic correlations between different traits.We used the SAS PROC MIXED function to estimate variance components for each of the random effect terms in the full and reduced models. The R package lmer and lmerTest were utilized in combination with the pchisq function to assign http://dgrp2.gnets.ncsu.edu/) [We performed GWA analyses on line means for all consumption traits using the DGRP pipeline (su.edu/) . This piWe annotated candidate genes identified by the GWA analyses using Flybase release 5.57  and mapphttp://pantherdb.org/) [We carried out gene ontology (GO) enrichment analysis with PANTHER 11.1 (db.org/) ,54.GAL4-UAS system for RNAi-targeted knockdown of expression of candidate genes associated with variation in consumption of cocaine or methamphetamine with a weak ubiquitous driver (Ubi156-GAL4) and drivers specific for neurons (elav-GAL4), glia (repo-GAL4), mushroom bodies (201Y-GAL4) and dopaminergic neurons (TH-GAL4). We crossed 3 homozygous GAL4 driver males to 5–7 homozygous females harboring a unique UAS-RNAi transgene or the progenitor control to generate F1 GAL4-UAS-RNAi and GAL4 control progeny. We assessed the consumption traits exactly as described above for the DGRP lines. Differences between RNAi lines and their corresponding control lines for consumption were assessed with a fixed-effect ANOVA, separately for males and females. The full model was: Y = μ + L + E + S + (L × E) + (E × S) + (L × S) + (L × E × S) + ɛ, where Y denotes the mean consumption, E denotes the different exposures, L is the line (Control or RNAi), S denotes the different solutions (sucrose or cocaine/methamphetamine), and ɛ the error variance. Differences between RNAi lines and controls for change in consumption and preference were also assessed with fixed-effect ANOVAs. The full model for change in consumption was: Y = μ + L + S + (L × S) + ɛ, while the full model for preference was Y = μ + L + E + (L × E) + ɛ. All ANOVAs were run using R.We used the binary S1 Table(A) Cocaine experiment. (B) Methamphetamine experiment. F: female; M: male.(XLSX)Click here for additional data file.S2 TableE: Exposure; X: Sex; S: Solution; L: DGRP Line; df: degrees of freedom; MS: Type III mean squares; F: F-ratio test; P: P-value; σ2: variance component estimate; SE: standard error; H2: Broad sense heritability. Significant P-values are shown in red font. (A) Cocaine experiment. (B) Methamphetamine experiment.Exposure, Sex, Solution, and their interaction are fixed effects, the rest are random. Mixed model three-way factorial ANOVAs are given for males and females, as well as reduced models by Exposure, Sex, and Solution. (XLSX)Click here for additional data file.S3 Table(A) Cocaine experiment. (B) Methamphetamine experiment. Means are given in mm; 1 mm = 0.067 μl.(XLSX)Click here for additional data file.S4 TableP-values are indicated in red font. (B) Pair-wise phenotypic correlations. Entries in the cells are the correlation coefficients and the cell color denotes the P-value. Red: P < 0.0001; orange: P < 0.001; yellow: P < 0.01; green: P < 0.05; white: P > 0.05.(A) Cross-sex, cross-exposure and cross-solution genetic correlations. Significant (XLSX)Click here for additional data file.S5 TableP < 5 ×10−5) and associated genes for each trait. (B) Variants and genes for the cocaine traits, the methamphetamine traits, and variants and genes overlapping between the two experiments. (C) Pathway and gene ontology enrichment analysis for the cocaine GWA analyses. (D) Pathway and gene ontology enrichment analysis for the methamphetamine GWA analyses.(A) Top variants ((XLSX)Click here for additional data file.S6 TableThe references indicate which of the human orthologs have been associated with addictive phenotypes.(XLSX)Click here for additional data file.S7 Table(A) Genes in network. (B) Human orthologs. (C) Pathway and gene ontology enrichment analysis.(XLSX)Click here for additional data file.S8 TableUbi156-GAL4. (B) elav-GAL4. (C) repo-GAL4. (D) 201Y-GAL4. (E) TH-GAL4. Data are given in mm; 1 mm = 0.067 μl.(A) (XLSX)Click here for additional data file.S9 TableUbi156-GAL4. (B) elav-GAL4. (C) repo-GAL4. (D) 201Y-GAL4. (E) TH-GAL4. Data are given in mm; 1 mm = 0.067 μl.(A) (XLSX)Click here for additional data file.S10 TableP: P-value. Significant P-values are shown in red font. (A) Ubi156-GAL4. (B) elav-GAL4. (C) repo-GAL4. (D) 201Y-GAL4. (E) TH-GAL4.Fixed effect three-way factorial ANOVAs are given for males and females as well as reduced models by Exposure and Solution. E: Exposure; S: Solution; L: RNAi or control genotype; df: degrees of freedom; MS: Type III mean squares; F: F-ratio test; (XLSX)Click here for additional data file.S11 TableP: P-value. Significant P-values are shown in red font. (A) Ubi156-GAL4. (B) elav-GAL4. (C) repo-GAL4. (D) 201Y-GAL4. (E) TH-GAL4.Fixed effect three-way factorial ANOVAs are given for males and females as well as reduced models by Exposure and Solution. E: Exposure; S: Solution; L: RNAi or control genotype; df: degrees of freedom; MS: Type III mean squares; F: F-ratio test; (XLSX)Click here for additional data file.S12 TableGAL4 driver lines.(A) DGRP lines. (B) RNAi lines and control genotypes. (C) (XLSX)Click here for additional data file.S1 FigP < 0.0001; orange: P < 0.001; yellow: P < 0.01; green: P < 0.05; white: P > 0.05.Red: (PDF)Click here for additional data file.S2 FigL×S terms  or significant L×S×E terms from the full ANOVA models. Exact P-values are given in (A) Cocaine preference, females. (B) Cocaine preference, males. (C) Change in cocaine preference between third and first exposures, females. (D) Change in cocaine preference between third and first exposures, males. Asterisks represent significant (PDF)Click here for additional data file.S3 FigL×S terms  or significant L×S×E terms from the full ANOVA models. Exact P-values are given in (A) Methamphetamine preference, females. (B) Methamphetamine preference, males. (C) Change in methamphetamine preference between third and first exposures, females. (D) Change in methamphetamine preference between third and first exposures, males. Asterisks represent significant (PDF)Click here for additional data file.S4 FigP < 0.0001; orange: P < 0.001; yellow: P < 0.01; green: P < 0.05; white: P > 0.05.Red: (PDF)Click here for additional data file.S5 FigP < 0.0001; orange: P < 0.001; yellow: P < 0.01; green: P < 0.05; white: P > 0.05.Red: (PDF)Click here for additional data file.


GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 , which uses singular value decomposition, to characterize the underlying latent structure of genetic associations of 2,138 phenotypes. Despite these successes, there are challenges to translating findings to clinical advances, much due to the widespread pleiotropy and extreme polygenicity of complex traits, which are the presence of genetic effects of a variant across multiple phenotypes and multiple variants across a single phenotype5. In retrospect, this is not surprising given that most common diseases are multifactorial. However, it remains unclear exactly which factors, acting alone or in combination, contribute to disease risk and how those factors are shared across diseases. With the emergence of sequencing technologies, we are increasingly able to pinpoint alleles, possibly rare and with large effects, which may aid in therapeutic target prioritization13. Furthermore, large population-based biobanks, such as the UK Biobank, have aggregated data across tens of thousands of phenotypes14. Thus, an opportunity exists to characterize the phenome-wide landscape of genetic associations across the spectrum of genomic variation, from coding to non-coding, and rare to common.Human genetic studies have been profoundly successful at identifying regions of the genome contributing to disease risk15, can be used to combine information from several (likely) correlated vectors to form basis vectors, which are guaranteed to be orthogonal and to explain maximum variance in the data, while preserving the linear structure that helps interpretation. In the field of human genetics, SVD is routinely employed to infer genetic population structure by calculating principal components using the genotype data of individuals16.Singular value decomposition (SVD), a mathematical approach developed by differential geometers19, to characterize the underlying (latent) structure of genetic associations using summary statistics computed for 2,138 phenotypes measured in the UK Biobank population cohort14. We apply our approach, referred to as DeGAs — decomposition of genetic associations — to assess associations among latent components, phenotypes, variants, and genes. We highlight its application to body mass index (BMI), myocardial infarction (MI), and gallstones, motivated by high polygenicity in anthropometric traits, global burden, and economic costs, respectively. We assess the relevance of the inferred key components through GREAT genomic region ontology enrichment analysis21 and functional experiments. The results from DeGAs applied to protein-truncating variants (PTV) dataset indicate strong associations of targeted PTVs to obesity-related traits, while phenome-wide association analyses (PheWAS) uncover the differential region-specific regulation of our top candidates in fat deposition. For these reasons, we prioritize adipocytes as our experimental model system for the follow-up functional studies of our candidate genes. Given that the roles of adipocytes in regulating metabolic fitness have been established at both local and systemic levels of pathology associated with obesity, it is likely that the differentiation and function of adipocytes may shape the effects of our candidate genes at the cellular and molecular level.To address the pervasive polygenicity and pleiotropy of complex traits, we propose an application of truncated SVD (TSVD), a reduced rank approximation of SVD23, we performed separate analyses on three variant sets: (1) all directly-genotyped variants, (2) coding variants, and (3) PTVs  of a variant, respectively. Given its computational efficiency compared to the vanilla SVD, we applied TSVD to each matrix and obtained a decomposition into three matrices W = USVT . This reduced representation of K = 100 components altogether explained 41.9% , 62.8% (coding) and 75.5% (PTVs) of the variance in the original summary statistic matrices  of 2,138 phenotypes from the UK Biobank Fig. . We perf24 ; and a genetic variant proximal to AC105393.1 (rs62106258: 0.46%); 2) fat mass and percentage measurements (61.5%) and the same three FTO and AC105393.1 variants ; 3) bioelectrical impedance measurements (38.7%), a standard method to estimate body fat percentage27, and genetic variants proximal to ACAN (rs3817428: 0.64%), ADAMTS3 (rs11729800: 0.31%), and ADAMTS17 (rs72770234: 0.29%); 4) eye meridian measurements (80.9%), and two intronic variants in WNT7B  and a genetic variant proximal to ATXN2 (rs653178: 0.96%); and 5) bioelectrical impedance and spirometry measures  and genetic variants proximal to FTO (rs17817449: 0.17%), ADAMTS3 (rs11729800: 0.11%), and PSMC5 (rs13030: 0.11%) . Using the individual and phenotype PCA plots, we found a fewer number of components that explains most of the variance and several phenotypes, such as traffic intensity of the nearest major road and creatinine (enzymatic) in urine, are dominantly driving the top phenotypic PCs   altogether explained over 69% of the genetic associations , alcohol intake (PC100), and sleep duration and food intake  that collectively corresponded to 36% of the genetic associations  and water intake (PC64), as well as bioelectrical impedance of whole body (PC67) corresponding to 51% of genetic associations altogether  phenotype ontology and set p = 5 × 10−6 as the Bonferroni-corrected statistical significance threshold (Method)33. For each key component, we applied GREAT and found an enrichment for the mouse phenotypes consistent with the phenotypic description of our diseases of interest21. The top component for BMI, identified as the body fat measures component (PC2), showed enrichment of several anthropometric terms including abnormally short feet (brachypodia) , increased birth weight , and increased body length   and aortitis  , which is known to be involved in gallstone development34 To test the specificity of the enriched ontology terms while considering the correlation structure within ontology terms, we took the top five enriched terms for each DeGAs component, obtained the list of genes annotated with these top terms, and measured their pairwise gene set similarity across 100 DeGAs components using Jaccard index (Methods). Jaccard index is a set similarity measure ranges between zero and one, where one means the complete match and zero means complete disjoint of the two sets. We found the median of the pairwise similarity to be 0.029 , and ZFAT (3.4% gene contribution score for PC1) , GPR151 (12.3%), and ABTB1 (8.5%), whereas PC3 was driven by PTVs on TMEM91 (8.6%), EML2-AS1 (6.7%), and KIAA0586 (6.0%)  and PDE3B (rs150090666) being key contributors to the top two components of genetic associations for PTVs and BMI , whole-body fat mass , trunk fat mass , hip circumference , right leg fat-free mass  and body weight  . We did not find evidence of association with homozygous carriers , presumably due to lack of power  than the average UK Biobank participant  driving the genetic associations between PTVs and BMI  cells, two well-established preadipocyte models used for studying adipocyte differentiation (i.e. adipogenesis) and function37.We sought to illustrate the potential application of DeGAs in prioritizing candidate genes using functional follow-up experiments. Several of our most interesting findings were observed from strong associations between PTVs and obesity-related traits. Variants in BMI Fig. . In addifat Fig. , while PPDE3B increased dramatically during both mouse and human adipogenesis, while GPR151 maintained a low expression level throughout the differentiation   or siGpr151. Not surprisingly, Gpr151-deficient lipid-poor adipocytes showed dramatically lower lipolysis, along with the impaired capability of responding to isoproterenol (ISO), a β-adrenergic stimulus of lipolysis  Fig. , as wellp4) Fig. . Furthersis Fig. . These dGPR151 driven by either EF1α or aP2 promotor , albeit in a very low level  providing an example of how to interpret the results from DeGAs effectively and how to select candidate genes for the relevant experimental models; and (2) initiating an application of DeGAs in biological research and inspiring more state-of-art translational studies of candidates predicted from DeGAs.We developed DeGAs, an application of TSVD, to decompose genome-and phenome-wide summary statistic matrix from association studies of thousands of phenotypes for systematic characterization of latent components of genetic associations and advanced the understanding on polygenic and pleiotropic architecture of complex traits. Applying DeGAs, we identified key latent components characterized with disease outcomes, risk factors, comorbidity structures, and environmental factors, with corresponding sets of genes and variants, providing insights on their context-specific functions. We demonstrated the robustness of the results by applying DeGAs with different parameters. With additional biological characterization of latent components using GREAT, we find component-specific enrichment of relevant phenotypes in mouse phenotype ontology. This replication across species highlights the ability of DeGAs to capture functionally relevant sets of both coding and non-coding variants in each component.Our comparison of DeGAs to an alternative approach — decomposition of individual phenotype data followed by GWAS — highlights the ability of DeGAs to capture most of the variation in the genetic associations and to enable identification of biomedically relevant genetic signals as latent components connecting sets of genetic variants and phenotypes. As an illustration of in-depth analysis of genetic variants with different functional consequences, we reported applications of DeGAs for different functional categories.40. By leveraging the ability of TSVD to efficiently summarize most of the variance in the input association statistic matrix, DeGAs provides a systematic way to interpret polygenic and pleiotropic genetic architecture of common complex traits.In DeGAs, we provided multiple ways to investigate the biological relevance of latent components, including quantitative scores and ontology enrichment analysis. These metrics are useful to annotate and interpret latent components, which are otherwise just mathematical objects in a high-dimensional space. For example, we found a significant contribution of anthropometric traits among the top 5 components, which reflects the pervasive polygenicity of these traits  and explored their functionality in fat cells, in order to illustrate the application of DeGAs computational analysis in biological research.To select candidate genes and their most relevant experimental models for the functional studies in an unbiased manner, we applied DeGAs to PTVs dataset. First, we identified PC1 as the top phenotype contribution component to BMI. Second, based on the fact that the main drivers of PC1 phenotypes were fat mass-related measurements, we chose adipocytes as our experimental model to approach candidate gene studies. Last but not least, we unbiasedly selected the top two genes contributing PTVs to PC1   Fig. . GPCRs aPDE3B, on the other hand, did not affect differentiation of preadipocytes significantly in our study. There is evidence that PDE3B plays a more notable role in differentiated mature adipocytes, the primary component of adipose tissue. As an essential enzyme that hydrolyzes both cAMP and cGMP, PDE3B is known to be predominantly expressed in tissues that are important in regulating energy homeostasis, including adipose tissue47. White adipose tissue in Pde3b knockout mice behaves more as “beige” fat with improved mitochondrial activity and energy-burning properties, leading to a reduction of visceral fat mass as compared to the wild-type littermates48. Moreover, Pde3b knockout in mice confers cardioprotective effects49, and human PDE3B “knockout” subjects display lower circulating triglycerides and higher HDL-cholesterol in blood50. There is a growing body of evidence that cardiometabolic health is linked to improved body fat distribution 51. Our PheWAS analysis suggests that PDE3B PTVs have the strongest association to hip circumference  . Based on the information provided in Protocol 44532 the Stanford IRB has determined that the research does not involve human subjects as defined in 45 CFR 46.102(f) or 21 CFR 50.3(g). All participants of UK Biobank provided written informed consent (more information is available at https://www.ukbiobank.ac.uk/2018/02/gdpr/). All DNA samples and data in this study were pseudonymized.This research has been conducted using the UK Biobank Resource under Application Number 24983, “Generating effective therapeutic hypotheses from genomic and hospital linkage data”  and the UK Biobank Axiom Array ), which were designed for the UK Biobank study. The initial quality control was performed by the UK Biobank analysis team and designed to accommodate the large-scale dataset of ethnically diverse participants, genotyped in many batches, using two similar arrays14.The UK Biobank is a population-based cohort study collected from multiple sites across the United Kingdom. Information on genotyping and quality control has previously been described14 and the hg19 human genome reference for all analyses in the study. To minimize the variabilities due to population structure in our dataset, we restricted our analyses to include 337,199 White British individuals based on the following five criteria reported by the UK Biobank in the file “ukb_sqc_v2.txt”:self-reported white British ancestry (“in_white_British_ancestry_subset” column)used to compute principal components not marked as outliers for heterozygosity and missing rates (“het_missing_outliers” column)do not show putative sex chromosome aneuploidy (“putative_sex_chromo- some_aneuploidy” column)have at most 10 putative third-degree relatives (“excess_relatives” column).We used genotype data from the UK Biobank dataset release version 2https://github.com/konradjk/loftee) and variant quality control by comparing allele frequencies in the UK Biobank and gnomAD version 2.0.1 (gnomad.exomes.r2.0.1.sites.vcf.gz)54.We annotated variants using the VEP LOFTEE plugin  region (chr6:25477797-36448354) based on the Genome Reference Consortium GRCh37 definitionThese variant filters are summarized in Supplementary Fig. 14 in 11 distinct groups  were used for disease outcomes, cancer phenotypes, and family history definitions, respectively12. We also used additional data fields and data category from the UK Biobank14 to define the phenotypes in the following categories as well as 19 and 42 additional miscellaneous binary and quantitative phenotypes: medication, imaging, physical measurements, assays, and binary and quantitative questionnaire Supplementary Table We organized 2,138 phenotypes from the UK BiobankSome phenotype information from the UK Biobank contains three instances, each of which corresponds to (1) the initial assessment visit (2006–2010), (2) first repeat assessment visit (2012–2013), and (3) imaging visit (2014–). For binary phenotype, we defined "case" if the participants are classified as case in at least one of their visits and "control" otherwise. For quantitative phenotype, we took a median of non-NA values. In total, we defined 1,196 binary phenotypes and 943 quantitative phenotypes.14, where the array type indicates if a participant was genotyped with UK Biobank Axiom Array or UK BiLEVE Axiom Array. For binary phenotypes, we performed Firth-fallback logistic regression using PLINK v2.00a (17 July 2017)56. For quantitative phenotypes, we applied generalized linear model association analysis with PLINK v2.00a (20 Sep. 2017)56. We applied quantile normalization for phenotype , where we fit a linear model with covariates and transform the phenotypes to normal distribution N. while preserving the original rank. We used the following covariates in our analysis: age, sex, types of genotyping array, and the first four genotype principal components computed from the UK Biobank14.Association analyses for single variants were applied to the 2,138 phenotypes separately. We used the following covariates in our GWAS analyses: age, sex, array type, and the first four genetic principal components provided by UK Biobank in the sample QC file57.To test the effects of population stratification correction on the association analysis, we performed additional GWAS with age, sex, types of array, and the first ten genotype principal components as covariates for the five quantitative traits and five binary traits. For each pair of GWAS summary statistics with four and ten genotype principal components, we computed the genetic correlations and confirmed that the two GWAS run yielded the almost identical results i,k is an orthonormal matrix of size N × K whose columns are phenotype (left) singular vectors, S is a diagonal matrix of size K × K whose elements are singular values, and V = j,k is an orthonormal matrix of size M × K whose columns are variant (right) singular vectors. While singular values in S represent the magnitude of the components, singular vectors in U and V summarize the strength of association between phenotype and component and variant and component, respectively. With this decomposition, the k-th latent component  are represented as a produ of k-th column U, k-th diagonal element in S, and k-th row of VT. For TSVD on the summary statistics, we used implicitly restarted Lanczos bidiagonalization algorithm (IRLBA)58 (https://github.com/bw.ewis/lba) implemented on SciDB59 to compute the first K components in this decomposition.For each summary statistic matrix, we applied truncated singular value decomposition (TSVD). The matrix, which we denote as k-th component = sk2/VarTot(W) where, sk is the k-th diagonal element in the diagonal matrix S and VarTot(W) is the total variance of the original matrix before DeGAs is applied.A scree plot , we performed additional analyses with K = 90 and K = 110, and investigated the first five latent components as well as the top three components for the three phenotypes of our interest.To demonstrate the robustness of the DeGAs components with respect to the number of latent components , one can investigate the contribution of the phenotypes or variants for specific principal components by simply plotting factor scores24 Fig. . Specifi61. Specifically, we display phenotypes projected on phenotype principal components (Fp = US) as a scatter plot. We also show variants projected on variant principal components (Fv = VS) as a separate scatter plot and added phenotype singular vectors (U) as arrows on the plot using sub-axes  for the top five TSVD components and the top three key components for our phenotypes of interest are summarized in Supplementary Table We used stacked bar plots for visualization of the contribution score profile for each of the components. We represent phenotypes, genes, or variants with large contribution scores as colored segments and aggregated contributions from the remaining ones as “others” in the plot Figs. . To helpi and variant squared cosine score for a given variant j as Conversely, we can also quantify the relative importance of the latent components for a given phenotype or variant with squared cosine scores. We denote phenotype squared cosine score for a given phenotype S quantify the importance of latent components for the global latent structure, the phenotype or variant squared cosine score quantifies the relative importance of each component in the context of a given phenotype or a variant. The squared cosine scores for the phenotypes highlighted in the study is summarized in Supplementary Figs. By definition, the sum of squared cosine scores across a latent component for a given phenotype or variant equals to one, i.e. p, phenotype squared cosine score identifies k as the top key component, but phenotype contribution score for k identifies p′ (p′ ≠ p) as the top driving phenotype for the component k. This is because the two scores, contribution score and squared cosine score, are both defined by normalizing singular vector and principal component vector matrices, respectively, but with respect to different slices: one for row and the other for column.Note that squared cosine scores and contribution scores are two complementary scoring metrics to quantify the relationship among phenotypes, components, variants, and genes. It does not necessarily have the inverse mapping property. For example, it is possible to see a situation, where for a given phenotype 62, we applied TSVD on the imputed and normalized phenotype matrix and characterized the first five latent components and visualized the scree plot as well as the phenotype and individual PCs in scatter plots.To characterize the latent components in the raw phenotype data, we first applied median imputation for missinge values on the phenotype data followed by Z-score transformation. Using Python scikit-learn package63.Using the results of the phenotype decomposition described above, we defined principal components of the individual’s phenotype (phenotype PCs) and applied genome-wide association analysis using the same procedure we used for the original quantitative traits. We used R package qqplot to generate Manhattan plot64. We summarized the estimated genetic correlation (rg) as heatmap and characterized the median value of absolute value of rg among the top k phenotype PCs as a function of k.To compare the results of association analysis of phenotype PCs, we computed genetic correlation using LD score regression21. We used the mouse genome informatics (MGI) phenotype ontology, which contains manually curated knowledge about hierarchical structure of phenotypes and genotype-phenotype mapping of mouse33. We downloaded their ontologies on 2017-09-28 and mapped MGI gene identifiers to Ensembl human gene ID through unambiguous one-to-one homology mapping between human and mouse Ensembl IDs. We removed ontology terms that were labeled as “obsolete”, “bad”, or “unknown” from our analysis. As a result, we obtained 709,451 mapping annotation spanning between 9,592 human genes and 9,554 mouse phenotypes.We applied the genomic region enrichment analysis tool (GREAT version 4.0.3) to each DeGAs components21. Since we included the non-coding variants in the analysis, we focused on GREAT binomial genomic region enrichment analysis based on the size of regulatory domain of genes and quantified the significance of enrichment in terms of binomial fold enrichment and binomial p-value. Given that we have 9,554 terms in the ontology, we set a Bonferroni-corrected p-value threshold of 5 × 10−6.For each DeGAs component, we selected the top 5,000 variants according to their variant contribution score and performed enrichment analysis with the default parameters65, where each of the key components are displayed in the innermost track with their phenotype squared cosine score to be proportional to their angle, and the resulted significant ontology terms are represented as the bars. To focus on the significant signals with large effect size, we imposed additional filter of binomial fold ≥2.0 and binomial p-value threshold of 5 × 10−7, The binomial fold change is represented as the radius and the binomial p-value is represented as color gradient in a log scale in the plot  indicates set of genes annotated with term t. We computed all the pair-wise similarity across the top k DeGAs components and summarized their median as a function of k.To test the specificity of the GREAT enrichment of each of the 100 DeGAs components, we computed Jaccard index similarity scores. For each DeGAs latent component, we looked at the GREAT enrichment and took the top five enriched terms sorted by GREAT binomial fold. To measure the similarity between these enriched terms, we identified the set of genes annotated for those terms and computed Jaccard index defined below:https://biobankengine.stanford.edu/intensity/rs114285050https://biobankengine.stanford.edu/intensity/rs150090666To investigate the quality of variant calling for the two PTVs highlighted in the study, we manually inspected intensity plots. These plots are available on Global Biobank Engine.p < 0.001) associations with phenotypes with at least 1000 case count or 1000 individuals with measurements with non-missing values for binary and quantitative phenotypes, respectively . We report the statistically significant (http://www.cog-genomics.org/plink/2.0/)56. After removing individuals with missing values for BMI and genotype, we performed linear regression for BMI  with age, sex, and the first four genomic PCs as covariates:To quantify the effects of the two PTVs on obesity, we performed univariate regression analysis. We extracted individual-level genotype information for the two PTVs with the PLINK2 pgen Python API  with the DeGAs contribution scores and summarized as scatter plots −1 of penicillin G and 100 μg mL−1 of streptomycin) at 37 °C in a humidified atmosphere containing 5% CO2. To obtain fully differentiated adipocytes, 3T3-L1 preadipocytes were grown into 2-day post-confluence, and then differentiation was induced by using a standard differentiation cocktail containing 0.5 mM of IBMX, 1 μM of dexamethasone, 1 μg mL−1 of insulin, and 10% FBS. After 48 h, medium was changed into DMEM supplemented with 10% FBS and 1 μg mL−1 of insulin and replenished every 48 h for an additional 6 days.3T3-L1 preadipocytes were obtained from ATCC  and cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) containing 10% fetal bovine serum (FBS) and antibiotics (100 U mL−1 streptomycin and 100 U mL−1 penicillin (0F medium) supplemented with 10% FBS in a 5% CO2 incubator. To initiate differentiation, confluent cells were stimulated by 0F media supplemented with 0.01 mg mL−1 human transferrin, 0.2 nM T3, 100 nM cortisol, 20 nM insulin, 250 μM IBMX, 25 nM dexamethasone and 2 μM rosiglitazone. After day 4, the differentiating cells were kept in 0F media supplemented with 0.01 mg mL−1 human transferrin, 100 nM cortisol, 20 nM insulin and 0.2 nM T3 for additional 8–10 days until cells were fully differentiated.SGBS cells, a gift from Dr. Martin Wabitsch , were cultured in DMEM/F12 containing 33 μM biotin, 17 μM pantothenate, 0.1 mg mgGpr151 , Pde3b , or scrambled negative control (Origene #SR30004) using Lipofectamine™ RNAiMAX Transfection Reagent (Invitrogen) following the manufacturer’s protocol. The 3 siRNAs against the same target were diluted in a 1:1:1 ratio to reach the total concentration of 50 nM, when transfecting as a pool. The transfected cells were incubated for 48 h and then subjected to differentiation.At 80% confluence, 3T3-L1 preadipocytes were transfected with 50 nM siRNA against GPR151 (Hs00972208_s1), Gpr151 (Mm00808987_s1), PDE3B (Hs00265322_m1), Pde3b (Mm00691635_m1), Pparg (Mm00440940_m1), Cebpa (Mm00514283_s1), Fabp4 (Mm00445878_m1), PPIA (Hs04194521_s1), Ppia (Mm02342430_g1).Total RNA was extracted using TRIzol reagent (Invitrogen), following the manufacturer’s instruction. RNA was converted to cDNA using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Quantitative PCR reactions were prepared with TaqMan™ Fast Advanced Master Mix (Thermo Fisher Scientific) and performed on ViiA 7 Real-Time PCR System (Thermo Fisher Scientific). All data were normalized to the content of Cyclophilin A (PPIA), as the endogenous control. TaqMan primer information for RT-qPCR is listed below: Cells were washed twice with PBS and fixed with 10% formalin for 1 h at room temperature. Cells were then washed with 60% isopropanol and stained for 15 min with a filtered Oil Red O solution (mix six parts of 0.35% Oil Red O in isopropanol with four parts of water). After washing with PBS 4 times, cells were maintained in PBS and visualized by inverted microscope. After taking pictures, Oil Red O stain was extracted with 100% isopropanol and the absorbance was measured at 492 nm by a multi-well spectrophotometer (Bio-Rad).Glycerol release into the culture medium was used as an index of lipolysis. Fully differentiated 3T3-L1 adipocytes were serum starved overnight and then treated with either vehicle (DMSO) or the lipolytic stimuli isoproterenol  for 3 h. The culture medium was collected and the glycerol content in the culture medium was measured using an adipocyte lipolysis assay kit (ZenBio #LIP-1-NCL1). Glycerol release into the culture medium was normalized to the protein content of the cells from the same plate.GPR151 construct was obtained from Addgene (#66327). This construct includes a cleavable HA signal to promote membrane localization, a FLAG epitope sequence for cell surface staining followed by codon-optimized human GPR151 sequence66. We PCR-amplified the above sequence with stop codon and assembled it into a lentiviral plasmid (Addgene #85969) with either EF1α promoter (Addgene #11154) or aP2 promoter (Addgene #11424). EF1α-GPR151 or aP2-GPR151 lentiviral plasmid were transfected into human embryonic kidney 293T cells, together with the viral packaging vectors pCMV-dR8.91 and pMD2-G. 72 h after transfection, virus-containing medium was collected, filtered through a 0.45-μm pore-size syringe filter, and frozen at −80 °C. 3T3-L1 preadipocytes at 50% confluence were infected with the lentivirus stocks containing 8 µg mL−1 polybrene. Two days after transduction, lentivirus-infected 3T3-L1 preadipocytes were subject to differentiation.The TM Cell Sorter. Cells without FLAG antibody staining were used to determine background fluorescence levels. Cells were sorted based on APC fluorescence and collected directly into TRIzol reagent for RNA extraction.Day 6 differentiating 3T3-L1 adipocytes were collected and washed with ice cold FACS buffer (PBS containing 2% BSA). Cells were first resuspended into FACS staining buffer (BioLegend # 420201) at ~1 M cells per 100 μl and incubated with anti-mouse CD16/CD32 Fc Block (BioLegend #101319) at room temperature for 10–15 min. Cells were then incubated with APC-conjugated FLAG antibody (BioLegend #637307) for 20–30 min at room temperature in the dark. Following washing and centrifugation, cells were resuspended in FACS buffer and sorted using a BD InfluxLysate aliquots containing 50 μg of proteins were denatured, separated on a 4–10% SDS-polyacrylamide gel, and transferred to nitrocellulose membranes using a Trans-Blot® SD Semi-Dry Transfer Cell (Bio-Rad). Membranes were blocked in 5% non-fat milk and incubated overnight at 4 °C with primary antibodies: anti-GPR151  or anti-beta-actin . Subsequently, the membranes were incubated for 1 h at room temperature with IRDye® 800CW goat-anti-mouse antibody . Target proteins were visualized using Odyssey® Fc Imaging System (LI-COR). Uncropped blots were provided in the Source Data.t-test was used for single variables, and one-way ANOVA with Bonferroni post hoc correction was used for multiple comparisons using GraphPad Prism 7 software.Data are expressed as mean ± SEM. Student’s Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Source Data


Most studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.We assessed heritability based on data of Swedish siblings  with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50.Given the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research. Unlike most twin studies, the Swedish sibling sample uses clinical diagnoses derived from medical in- and out-patient treatment registers, instead of surveys. Second, we use summary data of eight large samples of subjects with measured SNPs (h2-SNP). The uniqueness is estimating heritability from very large samples, based on genetic similarities inferred from distantly related people. As in the h2-national design, case status in the h2-SNP design is based on diagnostic criteria.Using two different methods, this study capitalizes on the largest and most powerful data-sets to date, to estimate the heritability of eight psychiatric conditions: (1) alcohol dependence (AD), (2) anorexia nervosa (AN), (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder (ASD), (5) bipolar disorder (BIP), (6) major depressive disorder (MDD), (7) obsessive-compulsive disorder (OCD), and (8) SCZ. First, we use a large Swedish national cohort  and SNP-based data for eight psychiatric conditions. Our hypothesis is that h2-SNP is lower, but correlates positively with the family-based estimates. In addition, our design can illustrate the differences in etiology between the psychiatric conditions, guiding future directions for etiological research in psychiatry.The aim of this study is to provide a test of the homogeneity in heritability estimates between family-based data , 9 (1987–1996), or 10 (1997–present). The ICD codes for the eight disorders are presented in Table S1 in the Supplementary. We used the Multi-Generation Register to link individuals to their full and maternal half-siblings registered as living in Sweden since 1961 and born in Sweden since 1932. Only two siblings per family were included, starting with the oldest siblings in each family followed by the next oldest sibling, but only if born within 5 years of the first sibling to maximize the probability that they had experienced a similar rearing (i.e. shared) environment. If the two eldest were born more than 5 years apart, we proceeded to the second oldest sibling pair within the family, and so on. The final sample size of full- and maternal half-siblings varied by diagnosis. To ensure that the younger sibling in each pair had lived long enough to receive a potential diagnosis, pairs in which the younger sibling was younger than 5 (for ADHD and ASD), 10 , or 15 years old (BIP and SCZ) were excluded.2-national), and shared and non-shared environmental variance. The analyses were carried out in Mplus  that full siblings share an average of 50% of additive genetic effects and 25% of non-additive genetic effects, (b) that maternal half-siblings share an average of 25% of additive genetic effects and 0% of non-additive genetic effects, and (c) that shared environmental effects are 100% shared between both full and maternal half-siblings, and that (d) non-shared environmental effects are unique to each individual. By comparing the observed tetrachoric correlations between the binary diagnoses for full and maternal half-siblings, we estimated the contribution of genetic variance , adjusted for the prevalence of the disorder, is inferred from the slope of the regression.The hd = h12 − h22, Z = d/s.e.(d) gives a test of the null hypothesis that the difference d is zero, by comparing the value of Z to the standard normal distribution, except for AN, BIP, and OCD where the h2-SNP estimates did not significantly differ from h2-national (corrected p > 0.30). However, these differences should be interpreted with caution as due to the somewhat smaller samples sizes of these particular disorders, the standard errors (s.e.) of h2-national were relatively wide for AN, BIP, and OCD. Of note, the s.e. is in general sensitive to sample size, and in particular for h2-national because the full and half-sibling groups only differ by 0.25 in genetic relatedness. Additionally, the nature of summary data of large consortium designs implies that included samples have been genotyped on different platforms and chips, potentially increasing the s.e. of h2-SNP.Estimates of shared environmental effects were non-significant in the sibling analyses. The heritability estimates  showed sr 0.19). However, this correlation was mainly driven by OCD that showed the highest h2-SNP and lowest h2-national; when removing OCD this correlation increased to 0.50. The high h2-SNP is probably due to the fact that the OCD sample is heavily ascertained from highly multiplex families and early age of onset cases, and consists thus of the most severe and genetically loaded cases.Heritability estimates from the two designs correlated positively  suggesting that a higher family-based heritability is associated with a larger (aggregated) effect of SNPs. This supports the hypothesis that, apart from rare variants, common genetic variants play an important role in psychiatric disorders, and thus confirms the polygenic nature of these complex traits  were remarkably similar to previous twin studies of psychiatric traits  were, as expected, lower than the family-based designs  genotypes, whereas the other design is based on familial relationships and hence includes estimates of genetic factors shared by relatives that are rare in populations. The largest differences between the family-based and h2-SNP estimates were observed for the neurodevelopmental traits ADHD and ASD, and for SCZ. Indeed, rare variant risk effects have been reported for ADHD, ASD, and SCZ  . As mentioned previously, the SNP-based estimate of OCD was based on a clinical sample of most severe and therefore probably most genetically loaded cases. Yet, standard errors for both the family-based and SNP-based estimate of OCD were relatively large, so these findings should be interpreted with caution. In a similar vein, one should not stretch the interpretation of the SNP based AD estimate as it derives from one of the smaller genetic samples.In general, the nature of the large consortium designs on which SNP-based heritability is based likely increases the standard error, all of which will impact on het al., et al., et al., et al., The family-based estimates showed substantial differences in the relative contributions of genes and environment across the eight psychiatric conditions: Heritability estimates for AD, AN, MDD, and OCD were relatively low, ranging from 30 to 41%. However, the prevalence of AN in the family data was low and hence, statistical power was limited, as illustrated by the large standard error in these data. Still, the estimate of 41% for AN in the family data confirms heritability estimates based on twin studies . We aimed to minimize potential time effects by limiting age differences between siblings to a maximum of 5 years. Moreover, additional analyses comparing siblings born within 1–2 v. 4–5 years apart showed very similar results (data not shown). Second, in the national sibling design, we assume that the shared environment of full and half siblings is the same. This assumption seems correct: A recent study showed that the vast majority of both Swedish full and maternal half-siblings tend to live together throughout childhood , suggesting that the Swedish data are fairly comparable with other data from European descent.Our study has potential limitations. First, the concept of shared environment is not straightforward in the sibling design. Siblings are born at different points in time, do not share the prenatal environment, and are born into different family structures (e.g. the first child born to young parents et al., et al., Lastly, both designs assume random mating but a large-scale study in psychiatric populations (Nordsletten In sum, this study presents a converging picture of the etiology of eight psychiatric disorders. SNP-based estimates were as expected lower but correlated with the family-based estimates. Additionally, the findings highlight large differences in genetic and environmental influences between psychiatric disorders. In contrast to ASD, ADHD, BIP, and SCZ, where genetic influences are most important, non-genetic influences play a large role in AD, AN, MDD, and OCD.