Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur at two levels: (i) the genotypic level, which we call interclonal, and (ii) the plot level irrespective of the type of genotype, which we call interplot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection (GS) model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that <10% increase in accuracy was achieved with GS-interclonal competition model, but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations, we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance.Plants competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava ( The main goals of field evaluation are prediction of genetic values for targeted genotypes and selection of elite genotypes for advancement and commercialization. In field trials, above and below ground interplot competition can be expected for resources such as water, light, nutrients, and space. It is important to account for competition when present, as ignoring it can result in increased error in genetic value estimates that can subsequently limit the advancement in the breeding program . Bias duCompetition is more important in scenarios where genotypes are placed in small plots without bordering. In such plots, performance is more strongly influenced by the competitive ability of neighboring plots. For example, when there is a significant height difference, the yields of shorter genotypes are often depressed by shading from the taller ones , which aFrom an agronomic perspective, there are two popular analytical approaches in estimating competition: the phenotypic interference model , where cIn field experiments, error in prediction of genotypic values can also occur due to the presence of spatial heterogeneity. There are ways to incorporate both spatial and competition effects in the same mixed effect model. Spatial correlation can be modeled as a random effect and competition can be modeled by an effect given to the neighboring plots . To accoManihot esculenta Crantz), a root crop, in order to enhance the selection process in the breeding program. Cassava is a subsistence crop in sub-Saharan Africa, and is the main source of calories for half a billion people , we analyzed the competitive ability in cassava , root dry matter content (DM), fresh weight of shoots (SHTWT), and harvest index (HI). The DM is the percentage dry root for 100 g of fresh root. The FYLD is the fresh root weight measured in kilograms. The SHTWT is the total fresh weight of harvested foliage and stems measured in kilograms. The HI is the proportion FYLD to the total harvested weight (FYLD + SHTWT)  population in a preliminary yield trial (PYT), population from cycle 1 , and cycle 2 . In summary, 83 parents from GG population gave rise to 2187 progenies for C1. Later, 84 C1 and 13 GG clones  were selected as parents giving rise to 2466 progenies for C2. The clones were assigned to a field in a randomized design using replicated check clones (1–10 check clones). The fields were partitioned into Ranges and Columns, with unreplicated test clones belonging to the same family assigned to adjacent rectangular plots in a Range in C1 and C2 trials. The relatedness of genotypes was calculated based on the additive relationship matrix using all the SNP markers with >1% minor allele frequency. A detailed description of the population used for this study, experimental design, field and plot orientation, plot dimension, and method for calculating the relatedness of genotypes studied can be found in a companion paper ; Z1 is the design matrix for genotypic effect, n is the number of observations, and g is the number of unique genotypes in the data; g is the vector of genotypic effect; I is the incidence matrix for residual error; and K is the genomic relationship matrix .In a GS model framework, the phenotype of a plot is affected by the genotypic effect for the trait of the clone in the plot. This effect can be estimated using the genomic relationship matrix as follows:Z2 is the incidence matrix for the genotypic effect for competitive ability whose construction will be discussed below; and c is the vector of competitive ability genotypic effects.However, in breeding evaluation trials, the phenotype of a plot is affected both by the genotypic effect for the trait of the clone in the plot and by the genotypic effect for competitive ability of clones in neighboring plots, as well as by the impact of that competition on the trait . Thus, aII is the incidence matrix for competition error whose construction is similar to that of Z2; p is the vector of competition errors; r is the vector of residual errors; and K is the genomic relationship matrix.As for the measurement of the trait in the plot, there can be error in the estimate of the competitive ability coming from a neighboring plot. For example, a clone planted in an adjacent plot will exert less influence if it was established from weak propagules and therefore does not grow vigorously. Thus, as for the genetic effect, there can be error in the measurement of the trait and of the competitive ability, as well as a covariance between those errors. While modeling this scenario, the dimension of the incidence matrix is the as same as that of the residual error, but, unlike in residual error, the diagonal is zero because the incidence of this competition error is to the neighboring plots. A covariance between residual and competition errors can also be expected. In summary, the residual error , ,…} ,…} . The FD K) was also provided for the dataset in use.Because the competition model is complicated, we discuss the approach we used to simulate competitive effects before we discuss analysis of real data. This discussion will serve to highlight intricacies of the statistical model that needs to be fit. We conducted a simulation study to test the ability of the GS-competition Model 3 to (a) reduce error, (b) correctly partition phenotypic variance, and (c) improve accuracy of the estimation of genetic effects of measured traits. A dataset containing 829 genotypes, including 11 check genotypes and their field coordinate information, was used to start the simulations. A genomic relationship matrix , correlation between competition error and residual error (Ecor), and fraction of competition error (fraE) were also provided as simulation parameters. A genetic variance-covariance matrix (G) was produced using Gcor, and the two genotypic variance values:G (chol.G) and K (chol.K) matrices was performed. Second, two separate random effect values (geno) following normal distribution of mean 0 and SD of 1 were generated for each genotype. Third, genotypic effects (geno.G) were calculated using chol.G matrix as following:K matrix, as follows:gr) can be formulated asTrait genotypic effects were simulated with zero mean and unit variance. Values for competition genotypic variance , and to vary from 1 to 0.1 when the ratio values were low (0.3). Two scenarios of correlation between direct and competitive ability genotypic effect were considered: (i) no correlation and (ii) a correlation of 0.4. In scenarios where competitive genotypic effect is very high, the variance due to it contributes mostly to the total genotypic variance. To illustrate this, consider the scenario of unit variance for both direct and competitive genotypic ability. When it comes to phenotypic expression, the competitive influence on the neighboring plots is calculated as indicated by the Two scenarios of correlation between competition and residual error effects were accounted for: (i) no relation and (ii) a high positive correlation of 0.8. A combination of values ranging from 0.3 to 0.9 corresponding to the fraction of competition to total error variance was used simulate the competition and residual error effects. The combination was used to determine the variance of competition and residual error effects.Three scenarios for clone replication were considered in the simulation. First, the scenario with minimum replication contained only the checks replicated at least twice with all the test clones represented once. Second, 50% of the test clones were replicated twice in addition to the presence of replicated check clones. A third scenario was considered where all clones were replicated at least twice.Z2 matrix modified using NN, FD, and SD competition functions was tested along with the Base model on simulated data. The simulation and analysis was repeated 20 times. The best model was selected as the one with the lowest root mean squared error (RMSE), i.e., the mean squared deviation between the estimated and the simulated genotypic effect. Accuracy (the correlation between true and estimated genotypic effects) was also used as a criterion for selecting these models.The GS-competition Model 3 with Model 3 and its two variants (Model 1 and 2) were used to evaluate potential competition effects in real data. A 10-fold cross validation (CV) repeated five times was used. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) between observed (Y) and predicted (Ŷ) response values for the test dataset. Models using various incidence matrices to account for competition were compared to the Base model having no competition component, and a model was selected based on its predictability and significance. Details on the CV method are given in the companion paper . A Chi-square test with a significance threshold of 2.706 at e of 0.1  was usedftp://ftp.cassavabase.org/manuscripts/Elias_et_al_2017_datasets/.Total phenotypic variance was calculated as the sum of variance values from different components of the model after analysis. The datasets and SNP files used for performing this study can be found in the following link: ftp://ftp.cassavabase.org/manuscripts/Elias_et_al_2017_competition.zip.Functions were written to automate the real and simulated data analysis. For the real data analysis, the minimum requirements for the function are a .csv file having field coordinates (Ranges and Columns), trait(s), and clone names, and a genotypic relationship matrix based on marker or pedigree data. Providing a plot dimension  can help to identify the best model. The function can take care of the initial processing of the dataset including removal of missing values for a particular trait, matching the genotypes with those in the relationship matrix, and removal of outlier points whose residuals are >2.5 times the error SD (after testing using the Base model). The output of the algorithm will be saved in the working directory of R. The output will contain the predictions for direct and competition genotypic effect, pRMSE, and predicted correlation (pCOR) values, number of failed models as trapped by the “try” function in R during the evaluation of a model, and summaries of the Base and the selected model including the parameter values (if the selected model is different from the Base). For the simulations, a dataset with genotypes and field coordinates must be given along with different parameter values, such as correlation of direct to competition effect, correlation of competition to residual error, genotypic ratio, competition variance, fraction of competition to total error, and plot dimension. The output contains a .csv file of RMSE and correlation between true and estimated genotypic direct and competition effects, error effects, fraction of competition error, genotypic ratio, and number of models failed. The functions are available in the following web link: File S1 contains ANOVA tables from analyzing the simulated data. The legend of Table S1 in File S1 compiles the parameter values used for simulation studies. Table S5 in File S1 provides information on the best model selected after CV in all the trial-trait scenarios. Table S6 has information on scenarios where Model 5 fit best. Figure S1 in File S1 shows the original observation, direct genotypic BLUP, and competitive effect from Model 2 or 3 for all the trial-traits mentioned in Tables S1–S4 in We showed from our real data studies that estimates of the trait genotypic effect are affected even when the competition variance was small . Therefore, we conducted simulation studies to see the impact of instances where competitive variance would be larger  and vice versa . The FD File S1). Increase in genotypic ratio was advantageous to Model 3, and clear distinction was observed while analyzing data where true competition followed a SD pattern . Usage of distance based non-NN function also helped to identify the distance at which the competition stopped or diminished markedly (File S1).We observed a reduction in pRMSE as high as 4.5%, and an increase in pCOR as high as 25% when significant competition was explained by Model 1 or 2 . The resFile S1) also showed that accounting for interplot competition modified the trait genotypic variance and reduced the error variance. An exceptional scenario was found in Ikenne_2014_C1 for DM, which can be an artifact of the competition function. In this case, the residual variance was found to be negligible, and all the variance in the phenotype was explained by direct or competition genotypic effect, even though the competition explained only 2.6% of total variance. The variance explained by interplot competition component was <10% in all of the scenarios.Results from Model 2 (Heritability of the traits ranged from 0.27 to 0.84 . Slight File S1). Weak correlation between genotypic trait and competition variance indicated that not all the highly productive genotypes were aggressive, and not all the high aggressive genotypes were highly productive.The trait genotypic effect was moderate to strongly positively correlated to observed phenotypic value, while the competition effect was weakly correlated to it. The competition effect was also weakly correlated to the trait and residual effects. The exception to this behavior was HI and DM from 2014_C1 trials conducted in Ibadan and Ikenne, exhibiting a moderately negative correlation between competition and residual effects (Table S5 in Strong positive correlation (0.7) between above- and below-ground competition effect in Ibadan_2013_C1 indicated that the competitiveness was similar for the whole plant in these genotypes. In Ikenne_2013_C1 moderate positive correlation (0.45) between above- and below-ground impact of competition for the genotypes was observed. The HI showed most instances of a significant competition effect. Significant competition in HI indicated that cassava could change its biomass allocation strategy when influenced by a neighboring competitive genotype. The competition effect, which explained only a very small percentage of variance in phenotype, was, however, weakly correlated with the trait genotypic effect in all the traits. However, all the trials with significant competitive effects exhibited it for below ground biomass where the harvest product of interest was located.File S1), no pattern of border effect with similar colored plots around the edge of the field was evident. This observation was consistent for observed phenotypic values, and for both of the genotypic effects. The genotypic effects were distributed randomly and among the family of genotypes.From the visual assessment (File S1) and Ibadan_2014_PYT SHTWT , the variance due to competition was explained by the competition error. In Mokwa plots with low competitive ability placed together across the Ranges and Columns could be contributing to the low FYLD values. Some evidence of spatial correlation can also be found for this particular trait (Table S6).In Mokwa_2014_C1 FYLD . All the trial/traits except in Ibadan_2014_C1 HI, interclonal competition and spatial correlation along with trait genotypic variance explained significant variance in the phenotype. In Ibadan_2014_C1 HI, accounting for competition variance along with trait genotypic variance was found to be better in explaining the variation present in the phenotype.We analyzed these data for the same traits to evaluate the presence of spatial correlation  compared to that on the shorter edge . This difference was accounted for in the distance-dependent competition functions. However, we have no information on the intraplot competition in multiple row plots of the same clone.All plants in a single row plot might be able to use the water and nutrients provided in the interplot space fairly equally. Moreover, cassava being a clonally propagated plant, the portion of stem used as planting material could also determine the amount of interplot competition. A plot planted with vigorous stem cuttings might capture more resources than the same clone planted with less vigorous cuttings. To account for this source of error, we included a competition error term—a fourth innovation in the GS models. Understanding the competition error could improve the choice of planting material to mitigate errors in performance due to propagule vigor.The genotypes selected based on trait and competitive genetic effects can be used for the next cycle of breeding or for commercialization. When spatial correlation is present, it should be adjusted using the proposed model before selecting the elite genotypes. The trait genotypic effect should be the first component to consider while selecting, as performance was moderate to strongly positively correlated to it. Competition variance for genotypes were <10% of their trait genotypic variance in most the scenarios in real data studies. During such instances, genotypes with high trait genotypic effect (after adjusting for the competition effect) can be selected as elite. Therefore, a wise selection for yield increase would be that with high direct and moderate competitive ability. If highly competing plants are selected, they may decrease whole plot performance . SelectiThrough real data studies, we showed that a small but significant increase in predictive ability was attained when interclonal competition was taken into account in a GS model. This predictive ability was markedly increased when accounting for the competition error if present. Results from simulation studies indicated that noticeable increases of accuracy in predicting the breeding value could be attained. These increases occurred especially when competitive influence reached beyond the adjacent neighbors and was accounted for in the GS model. Knowing the competitive ability of a genotype can help in selecting genotypes for breeding advancement. This knowledge can also be useful in modifying the spatial arrangement when genotypes with marked differences in competitive ability need to evaluate together in clonal evaluation trials. This knowledge can also facilitate adjusting the timing and quantity of irrigation and nutrient application, and application of management practices such as pruning and trimming.www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300354/-/DC1.Supplemental material is available online at Click here for additional data file.


Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach.Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the  One of the major challenges in modern biology is to understand the link between molecular genetic variation and quantitative trait variation. For the vast majority of quantitative traits and diseases, phenotypic variation is caused by the joint effects of multiple segregating genetic variants, their interactions, environmental effects, and genotype-environment interactions and correlations . KnowledTechnological advancements in molecular biology, in particular the development of array-based and high-throughput sequencing platforms, have enabled large scale genome-wide scans for statistical associations between single nucleotide polymorphisms (SNPs) and quantitative traits and diseases . These gOne of the major challenges with GWAS is the inability to detect all causal SNPs, or all SNPs correlated with the causal variants. Stringent genome-wide significance thresholds are needed in order to efficiently control the false positive rate. Because most SNP effect sizes are small to moderate, the majority of the causal variants will remain undetected . The maihttp://psoerensen.github.io/qgg/). We have successfully applied this modeling approach to cattle . A particularly useful resource is the Drosophila Genetic Reference Panel, DGRP (We have previously described the SNP set test approach – the covariance association test (CVAT) – as a powerful method for associating a set of SNPs with human diseases and complex traits  to investigate the applicability of CVAT to rank genes within a set of associated genes; and (2) to provide functional validation for the findings of the genomic feature models. First, we used the genomic feature prediction models to identify large sets of genes, here defined by gene ontology (GO) categories , that weD. melanogaster. Collecting data on a new phenotype instead of using published data has the advantage of allowing us to perform the functional validation in the same manner as we did in phenotyping the DGRP, as well as potentially providing new biological knowledge of a complex trait phenotype. Locomotion is an important fitness component that is central for an individual’s survival and reproduction because it allows animals to localize mates and energy resources, defend territories, and escape from predators and environmental stress elements. Locomotor activity is a complex trait, and the genetic component is governed by the joint segregation of multiple quantitative trait loci, and likely their interactions. As a measurable trait, locomotor activity encompasses a broad range of different types of activity measures, some of which are species specific. Despite species- and trait specific differences, quantitative genetic analyses have revealed abundant genetic variation for different measures of locomotor activity across species   were obtained from the Vienna Drosophila Stock Center (http://stockcenter.vdrc.at)  was obtained from the Bloomington Drosophila Stock Center (http://flystocks.bio.indiana.edu). The UAS-RNAi and GAL4 lines were maintained at the Drosophila laboratory at Department of Bioscience, Aarhus University  on oatmeal-yeast-sugar-agar medium, 25°, 70% humidity, and a 12-h light-dark cycle).The DGRP lines . Since phenotyping was performed in two different laboratories, there were small experimental differences between the two experimental designs (see details below).We used an assay to quantify locomotor activity that relies on video tracking software  was mounted above the behavioral arena to obtain five minutes of video recordings from each of the 24 behavioral chambers. Locomotor activity was then quantified as the total distance traveled for individual flies, extracted from the video files using the tracking software EthoVision XT (v. 10.0) .The behavioral arenas were constructed in transparent polycarbonate with 4 × 6 behavioral chambers (each of 16 mm in diameter and 6 mm in height) behavioral arena. The locomotor assays were performed in a behavioral room  between 8:00-11:00 2 and transferred to the behavioral arenas 16-18 hr prior to the assay. During this time, the flies had access to food, which was removed at the start of the assay.We obtained locomotor activity measurements for approximately 24 individual males for each of 204 DGRP lines. For logistical reasons the DGRP lines were divided into eight blocks, such that each block contained approximately 28 DGRP lines, and every block was assayed over six consecutive days. Two to five day old flies were anesthetized with COam.The behavioral arenas used for the gene expression knockdown experiments were likewise constructed in transparent polycarbonate, but contained 6 × 6 behavioral chambers (each of 16 mm in diameter and 6 mm in height) per behavioral arena. The behavioral arenas were illuminated from below by a light box  to ensure high contrast between the flies and the background. An iPad Air  mounted above the behavioral arena was used to obtain ten minutes of video recordings. All behavioral tests were performed in a behavioral room  between 8:00-11:00 UAS-RNAi line, and the corresponding control line, approximately 20 virgin females were crossed to five tubulin-GAL4 males. Approximately 30 F1 male offspring containing the UAS-GAL4 construct were assayed. The flies being tested were gently moved to the individual behavioral chambers (without anesthetization using an aspirator), and the video recordings were obtained immediately after loading the flies to the behavioral arenas. The observed phenotype of the UAS-GAL4 offspring was compared to the offspring of the control line crossed to the GAL4 line using a standard linear model accounting for experimental effects .For each lme4 package for R .The DGRP genotypes were obtained from D. melanogaster reference genome (org.Dm.eg.db (The feature sets considered were genes and gene ontology (GO) categories . First, Dm.eg.db . A totali-th SNP, and A central component for predicting trait values using genomic best linear unbiased prediction (GBLUP) is a matrix that captures the genetic similarity between all pairs of individuals. The additive genomic relationship matrix can be computed as G=WW'/m , where mThe common use of GBLUP models is to model a single random genomic effect, thus, assuming that all SNP effect sizes are drawn from a common Gaussian distribution. If including multiple random genomic effects, this assumption can be relaxed by allowing SNP effect sizes to have different distributions. Incorporating multiple random effects requires the computation of additional genomic relationship matrices based on a subset of SNPs, for example, those within a genomic feature . Our R package qgg is accessible at qgg/http://psoerensen.github.io/, including examples on how to perform the genomic feature analyses.Estimating the variance components in Model 2 and Model 3 was performed using the average information restricted maximum likelihood (AI-REML) procedure  as impleTo partition the genomic variance of a predictive GO category to genomic variance at the gene level we adapted the covariance association test (CVAT)  in locomotor activity, with an approximate fourfold difference between the least active and the most active DGRP lines (activity .D. melanogaster aggressive behavior (We used the GBLUP model (Model 2) to predict the genomic values for locomotor activity by estimating genomic parameters on 90% of the data, and using those parameters to predict the genomic values in the remaining 10% of the data (Equation 1). The validation sets were chosen randomly and this procedure was repeated a total of 50 times on different training and validation sets. The GBLUP model uses all available SNPs assuming the effects are drawn from a common Gaussian distribution. The performance of the model was quantified as the correlation (2=r2=PA2 . The her+σ^e2n¯) , where σThe one component GBLUP model assumes all SNP effects are from a common Gaussian distribution; however, this assumption is not likely to be true . A relaxi.e., those that contain many SNPs, tend to explain the least genomic variance  and less active (Rim2, CG17930, CG14160, Shawn, p-value = 0.005, Given our list of 15 candidate genes potentially affecting locomotor activity, we set out to functionally validate these genes by investigating the phenotypic consequence of gene expression knockdown in adult flies using the bipartite ckground . The genCG14160, CG15553, and CG17930 have not previously been phenotypically annotated in D. melanogaster, thus, here we provide first evidence that the genes are involved in explaining variation in a behavioral phenotype. The gene Shawn is a mitochondrial carrier in the Drosophila nervous system (Rim2 encodes a deoxynucleotide transporter located within the mitochondria. Both Rim2 and Shawn have conserved human homologous gene sequences, SLC25A36 and SLC25A40, respectively, which have been found to contain susceptibility loci for bipolar disorder (The genes D. melanogaster (e.g., Parkinson’s and Huntington’s diseases, are associated with locomotor deficits, whereas some neuropsychiatric disorders, e.g., attention-deficit/hyperactivity disorder and depression, are associated with changes in activity levels (Fruit flies and mammals have a common evolutionary origin of basic biological processes, including development of the nervous system (y levels . TherefoRim2 and Shawn have conserved human homologous gene sequences, SLC25A36 and SLC25A40, respectively, which have been found to contain susceptibility loci for bipolar disorder (D. melanogaster as a model organism to study complex human psychological and behavioral disorders.For example, Parkinson’s disease has been shown to be linked to degeneration of certain dopaminergic neurons , and dopIn conclusion, we provide functional support both for the candidate genes detected by CVAT, and for the ranking of effect sizes suggested by CVAT. These results are important because they provide evidence for the two challenges relating to GFM analyses; namely the need to have an efficient method to rank genes within a larger set of associated genes, and to perform biological validation of the genomic findings from GFM. Thus, these results demonstrate that the findings from the GFM analyses not are statistical artifacts, but indeed have biological relevance.


Yet, the majority of research focuses on variables collected from a single time point. This aim of this study was to test for main effects on a quantitative trait across time points using a constrained maximum-likelihood measured genotype approach. This method simultaneously accounts for all repeat measurements of a phenotype in families. We applied this method to systolic blood pressure (SBP) measurements from three time points using the Genetic Analysis Workshop 19 (GAW19) whole-genome sequence family simulated data set and 200 simulated replicates. Data consisted of 849 individuals from 20 extended Mexican American pedigrees. Comparisons were made among 3 statistical approaches: (a) constrained, where the effect of a variant or gene region on the mean trait value was constrained to be equal across all measurements; (b) unconstrained, where the variant or gene region effect was estimated separately for each time point; and (c) the average SBP measurement from three time points. These approaches were run for nine genetic variants with known effect sizes (>0.001) for SBP variability and a known gene-centric kernel When compared to results using two time points, the constrained method utilizing all 3 time points increased power to detect association. Averaging SBP was equally effective when the variant has a large effect on the phenotype, but less powerful for variants with lower effect sizes. However, averaging SBP was far more effective than either the constrained or unconstrained approaches when using a gene-centric kernel-based test.We determined that this constrained multivariate approach improves genetic signal over the bivariate method. However, this method is still only effective in those variants that explain a moderate to large proportion of the phenotypic variance but is not as effective for gene-centric tests. Contemporary analytical approaches to identify individual genetic variants associated with complex disease phenotypes often rely on analyzing phenotypic data from a single time point. However, longitudinal epidemiological studies often collect information on the same quantitative phenotypes from multiple time points . Two recMAP4 region on chromosome 3, and a randomly ascertained equivalent region on chromosome 1, to determine if this was more efficient at identifying genetic association for complex disease.Herein, we extend this method to all available time points in the 200 replicates from the Genetic Analysis Workshop 19 (GAW19) simulated family data. For comparison, we first conducted a univariate approach of the average of systolic blood pressure (SBP) measurements from 3 time points using measured genotype analysis of 9 single nucleotide variants (SNVs). We had previously identified these informative SNVs from our constrained bivariate analysis . We thenThe GAW19 family data set contains 849 Mexican American individuals from 20 extended pedigrees from the Type 2 Diabetes Consortium. Each of the 200 simulated data sets includes the following information for each individual for 3 time periods along with gender: age, SBP, diastolic blood pressure, hypertension status, blood pressure medication status, and smoking status .p <0.05. This measured genotype model was fitted to the data and compared with the null model of no difference in trait mean by genotype using a likelihood ratio test. Twice the difference in log-likelihoods of these models was distributed as a Chi-square random variable with 1 degree of freedom.Maximum likelihood methods were used to determine association for the average SBP measurement across all three time points independently in a measured genotype model available in SOLAR  . CovariaWe also applied maximum likelihood methods in our multivariate longitudinal association analyses. This method investigates the relationship of all highly correlated phenotypes simultaneously. This approach tests the null hypothesis that variance components relating to major gene effects on multiple repeat measurements of a trait are equal to zero. Our proposed method extends standard multivariate variance component methods  to invesFor these analyses we used the same covariates from the average univariate analysis along with the same 9 SNVs. We then compared these results to a multivariate model where the effect of the SNV on the mean trait value of each of the three measurements of the phenotype was estimated independently (distributed as a Chi-square distribution with 3 degrees of freedom). Significant results were then added over the 200 GAW19 replicates to determine which method provided the best power to detect association.MAP4  as a positive test across all 200 simulated replicates. This method applies gene-specific relationship matrices to determine the proportion of the trait’s variance explained by an individual gene as a result of the departure of its localized empirical kinship estimate from the pedigree-derived theoretical kinship estimates. A new variance component parameter (h2geff) is introduced into the standard variance component model in SOLAR. Significance of each h2geff parameter is then obtained from a standard likelihood ratio against the null model and distributed as a ½:½ mixture of a 1-degree-of-freedom Chi-square test and a point mass of 0 [We applied a gene-centric analysis under a variance component model in SOLAR  to two gp values less than 0.001, 5.0E−5, and 5.0E−9. All analyses identified the MAP4 variant 3_48040283 as genome-wide significant . The MAP4 SNP, 3_47957996 was significant in 200 of the constrained longitudinal tests and averages tests and in 199 of the unconstrained tests. Additional variants, 1_66075952 from LEPR and MAP4 variants, 3_47956424 and 3_48040284, demonstrated low numbers of genome-wide significant associations for the constrained longitudinal test and only the chromosome 1 variant demonstrated genome-wide significance for the unconstrained method. When compared to our previous GAW18 results using two time points [p values of less than 0.01 were detected 3.5 % of the time for the constrained approach and 0.2 % for the average univariate method. This demonstrates no systematic inflation of p values under the null (data not shown).Table MAP4. The average time-point gene-centric test provided higher power than all of the other tests for MAP4. This is expected based on the underlying simulation model that has stable genetic effects over time/age and the average is dampening down stochastic effects in these data. It can be assumed that if gene by age interactions were included in the simulated model the average method would do worse. The gene-centric method for the MAP4 region provided less support than some of the individual SNVs from the same gene. This also may be expected as the gene-centric model provides separate estimates of the proportion of the variance because each time point has the same kernel, which introduces stochastic noise into the data. Consequently, we would expect the constrained multivariate test perform worse than the average. When the constraint is lifted, there is greater statistical noise and the unconstrained test performs the worst. This may be because we incorporated all of the available variants within the gene and did no prioritization based on potential functional or regulatory aspects of the included SNVs.As expected, none of the models we tested showed any significant results for the chromosome 1 region. Table In this paper, we present a constrained multivariate approach to increase power to detect association with a variant by constraining the effect of the SNV on the phenotype using a variance-component model for all available measurements of a given phenotype. The model is an extension of our previous work that demonstrated that constraining the beta-coefficient for SNVs across two time points increased genetic signal for variants that had a moderate to large effect on the phenotype over a univariate approach or one where the beta was allowed to fluctuate. The criteria for this constrained method are multiple repeat measurements of the same phenotype or different measurements of the same phenotype  . Our cur


In studies of child psychopathology, phenotypes of interest are often obtained by parental ratings. When behavioral ratings are obtained in the context of a twin study, this allows for the decomposition of the phenotypic variance, into a genetic and a non-genetic part. If a phenotype is assessed by a single rater, heritability is based on the child’s behavior as expressed in the presence of that particular rater, whereas heritability based on assessments by multiple raters allows for the estimation of the heritability of the phenotype based on rater agreement, as well as the heritability of the rater specific view of the behavior. The aim of this twin study was to quantify the rater common and rater specific contributions to the variation in children’s behavioral problems. We estimated the heritability of maternal and paternal ratings of the Child Behavior Checklist (CBCL) 6–18 empirical emotional and behavioral problem scales in a large sample of 12,310 7-year old Dutch twin pairs. Between 30 and 59% of variation in the part of the phenotype parents agree upon was explained by genetic effects. Common environmental effects that make children in the same family similar explained less variance, ranging between 0 and 32%. For unique views of their children’s behavioral problems, heritability ranged between 0 and 20% for maternal and between 0 and 22% for paternal views. Between 7 and 24% of the variance was accounted for by common environmental factors specific to mother and father’s views. The proportion of rater shared and rater specific heritability can be translated into genetic correlations between parental views and inform the design and interpretation of results of molecular genetic studies. Genetic correlations were nearly or above 0.7 for all CBCL based psychopathology scales. Such large genetic correlations suggest two practical guidelines for genome-wide association studies (GWAS): when studies have collected data from either fathers or mothers, the shared genetic aetiology in parental ratings indicates that is possible to analyze paternal and maternal assessments in a single GWAS or meta-analysis. Secondly, if a study has collected information from both parents, a gain in statistical power may be realized in GWAS by the simultaneous analysis of the data.The online version of this article (doi:10.1007/s10519-016-9823-1) contains supplementary material, which is available to authorized users. To assess children’s behavioral and emotional problems, researchers often rely on parental ratings. However, parents are not always in agreement on the behavior of their child. Maternal and paternal ratings on the Child Behavior Checklist (CBCL) 6–18, for example, correlate around 0.75, which is lower than the average test–retest reliability of the instrument, which is 0.89 for the empirical subscales (Achenbach and Rescorla The Child Behavior Checklist 6–18 (CBCL 6–18) assesses child behavioral and emotional problems on a number of scales that indicate problems in the internalizing (INT) domain  and externalizing (EXT) domain  as well as social, thought, attention problems, dysregulation, which sums anxious/depressed, aggressive behavior and attention problems . SNP-heritability can be obtained from genomic-relatedness-matrix restricted maximum likelihood (GREML) analysis  to the raters agreement and disagreement of the all empirical scales of CBCL 6–18 in a large sample  of twins around age 7 years and in this way inform molecular studies. These twins participate in an ongoing longitudinal data collection for the Netherlands Twin Register , which is a population-based longitudinal study of the health and life style of twins and their families. Participants are voluntarily registered with the NTR and the data collection protocol was approved by the Medical Research Ethics Committee of the VU University Medical Center. For 12,629 twin pairs, born between 1986 and 2006, maternal and paternal ratings were available. Data from 312 pairs were excluded since one or both twins had an illness or handicap that interfered with daily functioning. For the same-sex twin pairs zygosity was determined by blood group (n pairs = 194), DNA polymorphisms (n pairs = 1558) or by parental zygosity questionnaire (n pairs = 6661). Twins for whom zygosity was unknown (n pairs = 7) were also excluded from the analysis. The final sample comprised 12,310 twin pairs: 2079 monozygotic male (MZM), 2086 dizygotic male (DZM), 2324 monozygotic female (MZF), 1924 dizygotic female (DZF) and 3897 opposite-sex pairs (DOS). CBCL data were collected when the twin pair was about 7 years old . Maternal questionnaires were available for 12,086 pairs, paternal questionnaires for 8555 pairs. Either the CBCL 4-18 . It contained parental twin1-twin2 correlations, the parental cross-correlations between twins (e.g. father rating of twin1 and mother rating of twin2) and the parental agreement correlations Table  and BartMeans and standard deviations for boys and girls for mother and father ratings are given in Table Correlations, estimated in 4 × 4 matrix for each of the five zygosity by sex groups are summarized in Table A), Shared Environmental (VC) and Unique Environmental (VE) variation to the total phenotypic variation. For attention problems MZ correlations were lower than one and larger than twice the DZ correlations, indicating Additive Genetic (VA), Dominant Genetic (VD) and Unique Environmental (VE) variation. Thus, for all traits a VACE variance decomposition model was fitted, except for attention problems, for which an VADE model was used.Table For all scales, the common component of the phenotype assessed by both parents was substantial, with parental ratings correlations varying between 0.62 and 0.74 Fig. ; Table 4In this study we employed a psychometric model to determine to what extent parental assessments of a child’s behavioral problems around age 7 reflect common and parent specific aspects of the child behavior or if parents disagree due to rater bias. We observed interparental phenotypic correlations between 0.62 and 0.74, reflecting substantial but incomplete agreement between parents. Incomplete agreement may result in different heritability estimates between a single phenotype as assessed by different informants. Different informants provide information about child’s behavior and it is important to identify, prior to large GWAS efforts, whether the additive genetic effects on a trait strictly are found in the phenotypic variation which correlates between raters. Our analyses showed these were fairly highly correlated and that genetic correlations ranged from moderate to high Fig. ; Table 4In the NTR exploration of parental rater bias effect were conducted earlier for anxious/depressed  in a study of Haberstick et al.  and 0.40  respectively. For externalizing behavior scale estimates of SNP-heritability were 0.44  for teacher’s ratings and 0.12  for maternal ratings. The differences in SNP-heritability estimates are consistent with the findings obtained from twin studies, which account for rater specific effects. As was suggested in a study of attention problems by , MANOVA, and combined multivariate analyses (CMV) were explored by Medland and Neale , Minica In current study we considered parental ratings and did not make an attempt to analyze rater effects based on teachers ratings or on self-assessments of children. Inclusion of other raters will convey additional information about possible combined or separate analysis of problem behaviors assessed by multiple raters.Based on the results reported in this paper, we conclude that aggregating multiple raters’ in genetic studies of childhood psychopathology potentially will improve power. At age 7, our study showed that heritability of phenotypes reflecting a shared perspective on the child’s problem behavior is substantially higher than that of unique view. These results suggest a model in which genome wide analysis of different raters are combined into a single trait, accounting for genetic correlation, and differences in heritability, could prove optimal. For traits with a (somewhat) lower genetic correlation or if including further raters, for which substantial rater specific genetic effects are present , a multitude of multivariate genetic analysis tools exist.Supplementary material 1 (DOCX 103 kb)Below is the link to the electronic supplementary material.


Supplemental Digital Content is available in the text. Structural integrity of the white matter is a marker of cerebral small vessel disease, which is the major cause of vascular dementia and a quarter of all strokes. Genetic studies provide a way to obtain novel insights in the disease mechanism underlying cerebral small vessel disease. The aim was to identify common variants associated with microstructural integrity of the white matter and to elucidate the relationships of white matter structural integrity with stroke, major depressive disorder, and Alzheimer disease.This genome-wide association analysis included 8448 individuals from UK Biobank—a population-based cohort study that recruited individuals from across the United Kingdom between 2006 and 2010, aged 40 to 69 years. Microstructural integrity was measured as fractional anisotropy- (FA) and mean diffusivity (MD)-derived parameters on diffusion tensor images. White matter hyperintensity volumes (WMHV) were assessed on T2-weighted fluid-attenuated inversion recovery images.VCAN [versican]: rs13164785; P=3.7×10−18 for MD and rs67827860; P=1.3×10−14 for FA). LD score regression showed a significant genome-wide correlation between FA, MD, and WMHV . In polygenic risk score analysis, FA, MD, and WMHV were significantly associated with lacunar stroke, MD with major depressive disorder, and WMHV with Alzheimer disease.We identified 1 novel locus at genome-wide significance (VCAN gene may play a role in the mechanisms underlying microstructural integrity of the white matter in the brain measured as FA and MD. Mechanisms underlying white matter alterations are shared with cerebrovascular disease, and inherited differences in white matter microstructure impact on Alzheimer disease and major depressive disorder.Genetic variants within the  Despite these numbers, the pathogenesis of cSVD is largely unknown, and this knowledge gap is a major factor behind the lack of specific therapies to delay cSVD progression.4 However, studies suggest that microstructural tissue alterations underlying the cerebral white matter  detected using diffusion tensor imaging (DTI) are more predictive of cognitive decline.5The most commonly studied marker of cSVD is white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI). The prevalence and severity of WMH increases with advancing age and is associated with cardiovascular risk factors, stroke, dementia, and depression.7 However, there are only few published GWAS with a limited sample size that investigate the microstructural integrity of the white matter.10 The aims of this analysis were 2-fold: (1) to identify genetic variants associated with microstructural integrity of the white matter (FA and MD) in 8448 population-based individuals in UK Biobank and (2) to elucidate the relationships of FA, MD, and white matter hyperintensity volumes (WMHV) with clinical end points .Small vessels in the brain are difficult to investigate in vivo. Genetic studies provide a way to obtain novel insights in the disease mechanism underlying cSVD. Previous genome-wide association studies (GWAS) in population-based individuals and patients with stroke identified 12 genome-wide significant loci associated with WMH.http://www.ukbiobank.ac.uk/).The genetic and phenotypic UK Biobank data are available on application to the UK Biobank (http://www.ukbiobank.ac.uk). The study collects extensive data from questionnaires, interviews, health records, physical measures, biological samples, and imaging.UK Biobank is a prospective study that recruited 502 620 community-dwelling participants from across the United Kingdom between 2006 and 2010, aged 40 to 69 years  or no genetic data were excluded (Table I in the online-only Data Supplement). Participants with consistently extreme outlying tract-averaged water diffusion biomarker values were removed after visual inspection of the data by the authors.A subset of the participants also underwent brain MRI. In the present study, we used the second release of MRI data, which included 9066 subjects who underwent brain MRI, on average 6.6 years  after initial recruitment at mean age 55.5 years  and had usable T2-weighted fluid-attenuated inversion recovery or DTI images. Patients with a baseline diagnosis of stroke, multiple sclerosis, Parkinson disease, dementia, any other neurodegenerative problem . All participants provided informed consent to participate. The present analyses were conducted under UK Biobank application number 19463.http://www.ukbiobank.ac.uk).11Procedures for brain imaging acquisition and initial quality check have been described previously and are available on the UK Biobank website .In brief, all brain MRI data were acquired on a single standard Siemens Skyra 3T scanner  using the standard Siemens 32-channel radiofrequency receiver head coil. Sagittal T1-weighted scans were acquired using a 3-dimensional magnetization-prepared rapid acquisition gradient-echo sequence . Sagittal T2-weighted fluid-attenuated inversion recovery scans were obtained using a 3-dimensional SPACE sequence . DTI scans were acquired with a spin-echo echo-planar imaging sequence and multishell acquisition  maps were projected onto the subject skeleton, using the FA-derived alignment parameters. A set of 48 standard space tracts have been defined previously,14 these are then used as masks to generate tract-specific masks from the skeletonized images. These are then used to produce a mean FA or MD from each tract for each subject. This is similar to the processing applied in the ENIGMA project (http://enigma.ini.usc.edu/protocols/dti-protocols).15After gradient distortion correction and further correction for head movement and eddy currents, diffusion tensors and scalar diffusion parameters  were calculated using the b=1000 shell (50 directions) and DTIFIT from the FSL software.Principal component analysis was applied on the 48 tracts to extract a latent measure. The first principal component of FA (FA.PC1) and MD (MD.PC1) was used in subsequent analyses as dependent variable.16 In brief, 2 closely related arrays from Affymetrix, the UK BiLEVE Axiom array , and the UK Biobank Axiom array were used to genotype ≈805 426 markers with good genome-wide coverage. Phasing was performed using SHAPEIT3 and imputation to a merged Haplotype Reference Consortium reference panel  and UK10K and 1000 Genomes Phase 3 panel was performed using the IMPUTE4 package.18 Imputed genotypes were available for 487 442 individuals in this study.16 From the resulting data set, we excluded (1) individuals who did not segregate with European samples based on principal component analysis, (2) individuals with high-level heterozygosity and missingness (>5%), and (3) individuals whose reported sex was inconsistent with sex inferred from the genetic data. In addition, only SNPs imputed from the HRC panel were included in this analysis.We used the June 2017 release of the imputed genetic data from UK Biobank . Details of the design of the arrays, sample processing, and stringent quality control have been described elsewhere.P<1.7e-8, accounting for the 3 phenotypes studied. At this threshold, we had 80% power to identify variants explaining >0.5% of the trait variance.In this analysis, we first subset the genetic data on the individuals who also had MRI imaging data. We performed a GWAS of FA, MD, and log (WMHV), using SNPTEST v2.5.4-beta3, including age at MRI, sex, genotyping batch, and the first 10 ancestry informative principal components as covariates. We set the study-wide significance threshold at 19 We then used these Bayes factors to calculate the posterior probability that each variant is causal and the 95% credible set for each association  as described in the study by Maller et al.20For all SNPs in linkage disequilibrium (LD) with the lead SNP (r2>0.1), we calculated Bayes factors from the effect sizes and SEs using Wakefield approximation.To identify additional independent signals at genome-wide significant loci we performed a forward stepwise regression using SNPTEST.21 the Genotype-Tissue Expression Project,22 and the Brain eQTL Almanac.23To evaluate whether the genome-wide significant variants potentially influence gene expression, we examined genome-wide cis-expression quantitative trait loci (eQTL) data in multiple tissues from 3 major eQTL databases: the Blood eQTL Browser,24 major depressive disorder,25 ICH,26 and MRI-confirmed lacunar stroke27), of which details are provided in Table II in the online-only Data Supplement and Methods in the online-only Data Supplement.We performed a lookup of the genome-wide significant SNPs in available GWAS summary statistics of relevant clinical end points .We estimated the heritability of FA, MD, and WMHV and tested for genetic correlation between the white matter measures using LD score regression.P<0.0001, P<0.05, P<0.5) from the GWAS data of FA, MD, and WMHV and tested them for association with Alzheimer disease, major depressive disorder, ICH-, and MRI-confirmed lacunar stroke using an inverse-variance weighted method using summary statistics data. We derived an independent (r2 <0.1 or 500 Kb apart) set of SNPs at each threshold using an LD-clumping procedure used using plink v1.90b3.45. Risk score analysis was performed in R using the gtx package.We derived polygenic risk scores at 3 different levels of significance (online-only Data Supplement).In total, 8448 individuals were included in the present analysis  and showed high phenotypic and genetic correlation  years. Among the individuals who passed genetic quality control checks, FA and MD measures could be calculated in 8239 individuals, and WMHV were available in 8429. The 3 measures were heritable (P<1.7×10−8) for FA, MD, and WMHV with their corresponding alleles and effect sizes. The inflation of test statistics (λ) was equal to the inflation expected for the sample size. Manhattan plots and Q-Q plots are displayed in Figure online-only Data Supplement, respectively. Regional plots for the genome-wide significant hits for FA, MD, and WMHV are provided in Figures III, IV, and V in the online-only Data Supplement.Table P=1.3×10−14; top SNP for MD: rs13164785, P=3.7×10−18; LD r2=1 for these 2 SNPs).We identified 1 region harboring genome-wide significant SNPs for FA or MD. This locus, which was shared between FA and MD, was on chr5q14 .online-only Data Supplement). Results of the joint model containing both SNPs are given in Table IV in the online-only Data Supplement.To identify additional independent signals at the genome-wide significant loci, we performed a forward stepwise regression using SNPTEST on both regions, which yielded a second independent genome-wide signal for both FA and MD in the chr5q14 region .Functional annotation of the genome-wide significant SNPs in the chr5q14 region using the Genotype-Tissue Expression and Brain eQTL Almanac resources identified no significant eQTLs at genome-wide significance. However, there was significant cis association with expression of P=0.01) was found in the MRI-confirmed lacunar stroke data set, but the effect was in the opposite direction. No other significant associations for any of the traits were found.A nominally significant SNP for the chr5q14 locus  and 17q25 . The top SNPs for MD and FA at the chr5q14 locus were nominally associated with WMHV .Both SNPs showed the expected direction of effect in the association with both FA and MD, but only rs146896516 was nominally associated with MD , FA , as well as WMHV . All other correlations were not significant  for association with clinical end points. There was a polygenic association of all 3 MRI traits with MRI-confirmed lacunar stroke, with a similar association across all traits .30SNPs in chr5q14 have been linked to intracranial aneurysm of which one of the lead SNPs in the previous study is among our genome-wide significant SNPs  of the participants were of non-European descent. Thus, the results may not be applicable to other populations. Another limitation is that we were not able to replicate our findings in independent samples because unfortunately no large-scale replication resources are currently available.We identified a novel locus that is genome-wide associated with microstructural integrity of the white matter in the brain measured as FA and MD. The results contribute to the growing evidence that mechanisms underlying white matter alterations are shared with cerebrovascular disease and highlight that inherited differences in white matter microstructure, possibly age related, impact on multiple diseases.We thank the International Genomics of Alzheimer Project (IGAP), the Psychiatric Genomics Consortium, the genetics of magnetic resonance imaging–confirmed lacunar stroke collaboration, and the Intracerebral Hemorrhage Genetics collaboration for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and provided data but did not participate in the analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families.This project has received funding from the European Union Horizon 2020 research and innovation program under grant agreement number 667375. This work was, in part, supported by a British Heart Foundation Programme Grant (RG/16/4/32218). Dr Rutten-Jacobs was supported by a British Heart Foundation Immediate Research Fellowship (FS/15/61/31626). Dr Tozer is supported by the Cambridge Universities National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre. Dr Dichgans received funding from the European Union Horizon 2020 research and innovation program under grant agreement number 666881 (SVDs@target) and from the German Research Foundation through the Collaborative Research Centres 1123 (B3) and the Munich Cluster for Systems Neurology (EXC 1010 SyNergy). H.S. Markus is supported by an NIHR Senior Investigator award, and his work is supported by the Cambridge Universities NIHR Comprehensive Biomedical Research Centre. International Genomics of Alzheimer Project: the i–Select chips was funded by the French National Foundation on Alzheimer disease and related disorders. European Alzheimer Disease Initiative was supported by the LABEX (laboratory of excellence program investment for the future) Development of Innovative Strategies for a Transdisciplinary Approach to Alzheimer's Disease (DISTALZ) grant, Inserm, Institut Pasteur de Lille, Université de Lille 2, and the Lille University Hospital. Genetic and Environmental Risk in Alzheimer's Disease (GERAD) was supported by the Medical Research Council (grant No. 503480), Alzheimer Research UK (grant No. 503176), the Wellcome Trust (grant No. 082604/2/07/Z), the and German Federal Ministry of Education and Research: Competence Network Dementia grant numbers 01GI0102, 01GI0711, and 01GI0420. Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE) was partly supported by the National Institutes of Health (NIH)/National Institute on Aging grant R01 AG033193 and the National Institute on Aging AG081220 and Age, Gene/Environment Susceptibility Study (AGES) contract N01–AG–12100, the National Heart, Lung, and Blood Institute grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. Alzheimer's Disease Genetics Consortium was supported by the NIH/National Institute on Aging grants U01 AG032984, U24 AG021886, and U01 AG016976 and the Alzheimer Association grant ADGC–10 to 196728. Genetics of magnetic resonance imaging (MRI)–confirmed lacunar stroke collaboration: the genetics of MRI-confirmed lacunar stroke project contains samples derived from the National Institute of Neurological Disorders and Stroke Stroke Genetics Network (SIGN-NINDS) study, the Wellcome Trust Case Control Consortium 2 stroke study, and DNA Lacunar (UK Young Lacunar Stroke DNA Study). The Stroke Genetics Network (SiGN) study was funded by a cooperative agreement grant from the US National Institute of Neurological Disorders and Stroke, NIH (U01 NS069208). Collection of the DNA Lacunar was primarily supported by the Wellcome Trust (WT072952) with additional support from the Stroke Association (TSA 2010/01). Genotyping of the DNA Lacunar samples was supported by a Stroke Association Grant (TSA 2013/01). The principal funding for the WTCCC2 stroke study was provided by the Wellcome Trust, as part of the Wellcome Trust Case Control Consortium 2 project .H.S. Markus received personal compensation for lectures from AstraZeneca. The other authors report no disclosures.


Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific  regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up. However, for common variants identified in genome-wide association studies (GWASs), the success in locus identification is not yet matched by an understanding of their underlying basic mechanism or effect on pathophysiology. Expression quantitative trait loci (eQTL), which are responsible for a significant proportion of variation in gene expression, could serve as a link between the numerous non-coding genetic associations that have been identified in GWASs and susceptibility to common diseases directly through their association with gene expression regulation.600807), body mass index (MIM: 601665), celiac disease (MIM: 212750), and Crohn disease (MIM: 266600).8Significant advances in understanding the genetic architecture of schizophrenia MIM: 81500 havSNCA was recently shown to act through the disruption of an enhancer;Studies integrating eQTL and GWAS data have almost exclusively used marginal association statistics which typically represent the primary, or most significant, eQTL signal when assessing co-localization with GWASs, ignoring other SNPs that affect expression independently of the primary eQTL for a given gene. However, recent findings indicating that conditionally independent eQTL are widespreadHere, we leveraged genotype and dorsolateral prefrontal cortex (DLPFC) expression data provided by the CommonMind Consortium (CMC) to elucidate the role of conditional eQTL in the etiology of schizophrenia (SCZ). Currently comprising the largest existing postmortem brain genomic resource at nearly 600 samples, the CMC is generating and making publicly available an unprecedented array of functional genomic data, including gene expression (RNA sequencing), histone modification (chromatin immunoprecipitation [ChIP-seq]), and SNP genotypes, from individuals with psychiatric disorders as well as unaffected controls.−5. After QC, 668 individuals genotyped at 767,368 markers were used for imputation. Phasing was performed on each chromosome using ShapeIt v2.r790,We used pre-QC’ed genotype and expression data from the CommonMind Consortium, and detailed information on quality control, data adjustment, and normalization procedures can be found in Fromer et al.cis-eQTL, we a conducted forward stepwise conditional analysis implemented in MatrixEQTLcis-eQTL tests within each chromosome. FDR was not re-assessed at each conditional step; instead, a fixed p value threshold was used as the inclusion criteria in the stepwise model selection. For each gene with at least one cis-eQTL (gene ± 1 Mb) association at a 5% false discovery rate (FDR), the most significant SNP was added as a covariate in order to identify additional independent associations . This procedure was repeated iteratively until no further eQTL met the p value threshold criteria. We used a linear regression model, adjusting for diagnosis and five ancestry covariates inferred by GemTools. Following eQTL identification, only autosomal eQTL were retained for downstream analyses.An overview of our workflow can be found in Replication was performed in the HBCC microarray cohort  and four ancestry components.ROSMAP data were obtained from the AMP-AD Knowledge Portal see . Quantilcis-region, and genic constraint score) for our modeling analyses. Gene lengths were calculated using Ensembl gene locations. We obtained LD blocks from the LDetect Bitbucket site to tally the number of blocks overlapping each gene’s cis-region (gene ± 1 Mb). We obtained loss-of-function-based genic constraint scores from the Exome Aggregation Consortium (ExAC). A negative binomial generalized linear regression model was used to model the number of eQTL per gene based on the above variables; results were qualitatively the same using linear regression of Box-Cox transformed eQTL numbers. Backward-forward stepwise regression using the full model with interaction terms for these three variables was used to determine the relationship between genomic attributes and eQTL number. These analyses were implemented in R. cis-heritability of gene expression was estimated using the same CMC data that were used for eQTL detection, including all markers in the cis-region and implemented in GCTA.We considered three genomic features . For each subgroup of eQTL, we performed a logistic regression of status as eQTL or matched SNP on overlap with functional annotation, including the four SNP matching parameters as covariates. Enrichment was taken as the regression coefficient estimate, interpretable as the log-odds ratio for being an eQTL given a functional annotation. Functional annotations tested included: brain promoters and enhancers  and non-neuronal (NeuN−) nucleus H3K4me3 and H3K27ac ChIP-seq marks from the CMC. For each data source, active promoter and enhancer (or H3K4me3 and H3K27ac) annotations were tested for enrichment jointly. This analysis was repeated but restricting to matched SNPs located within 1 Mb of any of the 16,423 genes that were tested for eQTL, in order to determine whether the enrichment estimates were inflated due to the proximity of our primary and conditional eQTL to brain-expressed genes, which may be more likely to occur near active regulatory regions in the brain. In addition, to ensure that any enrichment patterns observed were not due to varying effect size among primary and conditional eQTL, the enrichment analyses were also carried out taking into account the variance in expression explained by each eQTL. Variance explained (R2) was estimated using the variancePartition2 bins: bin 1, 1 × 10−2 ≤ R2 ≤ 1.75 × 10−2; bin 2, 1.75 × 10−2 ≤ R2 ≤ 2.25 × 10−2; and bin 3, 2.25 × 10−2 ≤ R2 ≤ 3 × 10−2. Logistic regression of status as eQTL or matched SNP was then carried out separately for each R2 bin, within each eQTL order.We divided eQTL into separate subgroups by stepwise conditional order  and created sets of matched SNPs drawn from the SNPsnapcis-eQTL association, we carried out a series of “all-but-one” conditional analyses, implemented within MatrixEQTL,In order to isolate each conditionally independent −6 (excluding the MHC locus), defined these loci by their LD r2 ≥ 0.6 with the lead SNP, and then merged overlapping loci. GWAS and eQTL signatures were qualitatively compared using p value-p value (P-P) plots, rendered in R, and LocusZoomFor our co-localization analyses, we used summary statistics and genomic intervals from the 2014 Psychiatric Genomics Consortium (PGC) SCZ GWAS.cis-region. Second, the coloc2 model optionally incorporates changes implemented in gwas-pw.0, no association; H1, GWAS association only; H2, eQTL association only; H3, both but not co-localized; and H4, both and co-localized) from genome-wide data. Coloc2 uses these proportions as priors  in the empirical Bayesian calculation of the posterior probability of co-localization for each locus (eQTL cis-region). Coloc2 averages per-SNP Wakefield asymptotic Bayes factors (WABF)Multiple methods that aim to identify GWAS-eQTL co-localized loci are currently available.Primary and conditional eQTL were identified using genotype and RNA-seq data from the CommonMind Consortium post-mortem DLPFC samples (467 European-ancestry case and control subjects).π1) in ROSMAP was 0.57 and 0.26 for primary and conditional eQTL, respectively; in HBCC π1 was 0.46 and 0.20 for primary and conditional eQTL. Therefore, replication was stronger for primary than for conditional eQTL, as expected given their stronger effect sizes. Replication rates were somewhat higher in the RNA-seq ROSMAP data than in HBCC.We tested for replication of conditional eQTL in two independent datasets, the National Institute of Mental Health’s Human Brain Collection Core  and the Religious Orders Study/Memory and Aging Projectcis-region, genic constraint score, and genic cis-SNP-heritability. According to prior results, eQTL that are shared across tissues and cell types tend to be located closer to transcription start sites than context-specific eQTL;cis-region (±1 Mb), and Exome Aggregation Consortium (ExAC) genic constraint score,cis-region SNPs, or cis-SNP-heritability, estimated by linear mixed model variance component analysiscis-heritability on number of independent eQTL , gene length, LD blocks per genic ent eQTL . In a joWe then addressed whether genes with conditional eQTL exhibit greater context specificity as measured by the robust expression specificity metric Tau.cis-regulatory elements such as promoters and enhancers. Putative causal eSNPs have been shown to be enriched in genomic regions containing functional annotations such as DNase hypersensitive sites, transcription factor binding sites, promoters, and enhancers.+) and non-neuronal (NeuN−) ChIP-seq peaks from a subset of the CMC post-mortem DLPFC samples. The overlap of H3K4me3 and H3K27ac ChIP-seq peaks was used as a proxy for active promoters, and H3K27ac peaks that do not overlap H3K4me3 peaks were used as a (relatively non-specific) proxy for enhancers.One way in which eQTL may affect gene expression is through alteration of +) promoters and enhancers  promoters and enhancers, this trend of a marked decrease in active promoters but steady levels of enhancer enrichment with greater eQTL order was not observed for non-neuronal PFC nuclei (2) and comparing enrichment across eQTL order, within each R2 bin  schizophrenia GWAS,We performed co-localization analyses in order to evaluate the extent of overlap between eQTL and GWAS signatures in schizophrenia and to identify putative causal genes from GWAS associations. Considering 217 loci  with leacis-region. Coloc2, our modified implementation of coloc, analyzes the hierarchical model of gwas-pw,0, no association; H1, GWAS association only; H2, eQTL association only; H3, both but not co-localized; and H4, both and co-localized). We then used these probabilities as priors to calculate empirical Bayesian posterior probabilities for the five hypotheses for each locus, in particular PPH4 for co-localization.To quantitatively compare the SCZ GWAS and eQTL association signatures, we modified the R package colocIREB2  as an example . We then tested for co-localization between the GWAS and all of the eQTL summary statistics resulting from the above conditioning analysis using coloc2  was implicated as co-localized with the GWAS signal at this locus with a posterior probability for co-localization (PPH4) of 0.94. A qualitative examination of the IREB2 locus supported the coloc2 results: the correlation between the GWAS p values and conditional eQTL p values was higher than that between the GWAS and primary eQTL p values  ≥ 0.8  ≥ 0.8 . When re4) ≥ 0.8 . We acknSLC35E2, PROX1-AS1 (MIM: 601546), PPM1M (MIM: 608979), SDAD1P1, STAT6 (MIM: 601512), and IREB2. Also notable are the occurrences of complex patterns of co-localization for some loci; for example, three loci showed evidence for co-localization with a primary eQTL for one gene and a conditional eQTL for another.Importantly, for 6 of the 40 co-localizing loci, a conditional rather than primary eQTL co-localized with the GWAS with compelling qualitative support . The genSLC35E2 and IREB2) and improved detection of primary eQTL co-localization due to isolation of independent eQTL signatures and our choice of co-localization software (coloc2). Of the six co-localized loci identified in the previous but not current analysis, three resulted from differences in study design such as GWAS locus definition and eQTL overlap criteria, and two were suggestive in the current analysis (0.65 < PPH4 < 0.8). The one remaining discrepant locus (chr8:143302933–143403527) was found to co-localize with TSNARE1 eQTL previously (Sherlock p = 8.24 × 10−7) but not here . A qualitative comparison of the eQTL and GWAS data (2∼0.2–0.4). Additionally, our attempts to disentangle independent eQTL signal via conditional analysis do not reveal the GWAS index SNP to be in high LD with any of the conditionally independent eQTL peaks.In the prior CMC study, a GWAS-eQTL co-localization analysis implemented in Sherlock and using non-conditional eQTL summary statistics reported a total of 18 co-localized loci, representing 17% of the 108 genome-wide significant loci examined. Through our all-but-one conditional co-localization analysis, we replicate the majority of their findings and detect an additional 13 instances of co-localization, bringing the total number of co-localizations when considering only the genome-wide significant (and not including the MHC) loci up to 24 (representing 22% of these 108 loci) . These 1WAS data  did not 4s resulting from the same WABFs due to a higher prior probability of co-localization estimated in the non-conditional coloc2 analysis. Thirty-five loci were co-localized in both analyses; five loci that were co-localized in the non-conditional analysis only were highly suggestive in the conditional analysis (0.65 < PPH4 < 0.8), and the five loci that were co-localized only in the conditional coloc2 analysis involved conditional eQTL, emphasizing the utility of the conditional analysis. This conditional eQTL co-localization represents a substantial proportion (∼15%) of all instances of co-localization, and furthermore could reflect context-specific differential expression that has the potential to implicate cell types, tissue types, and developmental stages that are relevant to disease etiology.We also compared our conditional co-localization results with those from non-conditional eQTL analysis, using coloc2 and the same SCZ GWAS loci . Conditicis-SNP-heritability. This demonstrates that genetic variation affecting RNA abundance is incompletely characterized by focusing on only one primary eQTL per gene, which is the case currently for most eQTL studies.We utilized genotype and expression data from 467 human post-mortem brain samples from the DLPFC to conduct eQTL mapping analyses, to characterize both primary and conditional eQTL. We then identified co-localization between SCZ GWAS and eQTL association signals, comprising both primary and conditional eQTL. Our principal findings include four major observations. First, we detect that conditional eQTL are widespread in the brain tissue samples we investigated. In 63% of genes with at least one eQTL, we found multiple statistically independent eQTL . In addition, conditional eQTL make substantial contributions to regulatory genetic variation, as there is a strong association between eQTL number and gene expression CYFIP1. In our data, associations with specificity of expression across tissues, developmental periods, and cell types determined from single-cell RNA-sequencing data suggest that context specificity plays a role in the occurrence of multiple statistically independent eQTL. Cell type specificity is particularly strongly correlated with eQTL number, consistent with those cell types being present in the current tissue homogenate data. Since previous studies have shown the importance of developmentalSecond, we find the genomics of conditional eQTL and their genes are consistent with complex, context-specific regulation of gene expression, which may be conferred through overlap with distal regulatory elements. Genes with more independent eQTL tend to be larger and span multiple recombination hotspot intervals, and tend to be less constrained at the protein level. While these associations may reflect in part greater power to detect independent eQTL that are not in linkage disequilibrium and explain more phenotypic variance, they are also consistent with more complex regulation and greater potential for regulatory genetic variation. Context-specific genetic regulation of expression could manifest as conditional eQTL signal in the analysis of expression from a heterogeneous source. For example, eQTL in naive and stimulated  monocytesThis context specificity of expression regulation is potentially mediated through overlap of eSNPs with distal regulatory elements, such as enhancers. Conditional eQTL occur farther from transcription start sites than primary eQTL, consistent with effects on enhancers. In addition, while both primary and conditional eQTL are enriched in both active promoter and enhancer regions, their enrichment in active promoters diminishes with increasing conditional eQTL order. In other words, conditional eQTL show greater enrichment in enhancers relative to promoters than do primary eQTL.Third, we have identified a number of candidate genes for which genetic variation for expression co-localizes with genetic variation for schizophrenia risk , includi−8) SCZ loci and 19 genes in 17 suggestive (p < 1 × 10−6) loci. In addition to a number of previously implicated SCZ risk genes, our findings include several genes not previously considered as candidates,SLC35E2, PTPRU (MIM: 602454), LINC01792, DCLK3, PPM1M, LOC101929479—because the genes themselves do not overlap the GWAS locus regions but their eQTL do. In examining these genes for expression specificity in GTEx tissues, brain sample cell types from single-cell RNA-seq,Our analyses prioritize 27 genes within 24 genome-wide significant  is a key regulator of iron homeostasisIREB2 homolog Irp2 knockouts exhibit impairments in coordination and balance, exploration, and nociception.C4) gene as a SCZ risk geneSTAT6 expression is broadly postnatal and shows specificity for microglia , Bernie Devlin, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Keisuke Hirai, Hiroyoshi Toyoshiba , Enrico Domenici, Laurent Essioux (F. Hoffmann-La Roche Ltd), Lara Mangravite, Mette Peters (Sage Bionetworks), Thomas Lehner, and Barbara Lipska (NIMH). Additional members of CMC: A. Ercument Cicek, Cong Lu, Kathryn Roeder, Lu Xie (Carnegie Mellon Univ.); Konrad Talbot ; Scott E. Hemby (High Point Univ.); Laurent Essioux (Hoffmann-La Roche); Andrew Browne, Andrew Chess, Aaron Topol, Alexander Charney, Amanda Dobbyn, Ben Readhead, Bin Zhang, Dalila Pinto, David A. Bennett, David H. Kavanagh, Douglas M. Ruderfer, Eli A. Stahl, Eric E. Schadt, Gabriel E. Hoffman, Hardik R. Shah, Jun Zhu, Jessica S. Johnson, John F. Fullard, Joel T. Dudley, Kiran Girdhar, Kristen J. Brennand, Laura G. Sloofman, Laura M. Huckins, Menachem Fromer, Milind C. Mahajan, Panos Roussos, Schahram Akbarian, Shaun M. Purcell, Tymor Hamamsy, Towfique Raj, Vahram Haroutunian, Ying-Chih Wang, Zeynep H. Gümüş (Mount Sinai School of Med.); Geetha Senthil, Robin Kramer (NIMH); Benjamin A. Logsdon, Jonathan M.J. Derry, Kristen K. Dang, Solveig K. Sieberts, Thanneer M. Perumal (Sage Bionetworks); Roberto Visintainer ; Leslie A. Shinobu (Takeda); Patrick F. Sullivan (Univ. North Carolina); and Lambertus L. Klei (Univ. Pittsburgh School of Med.).