Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. Genetic prediction of complex traits so far has limited accuracy because of insufficient understanding of the genetic risk. Here, Maier et al. develop an improved method for trait prediction that makes use of genetic correlations between traits and apply it to summary statistics of psychiatric diseases. For common complex disorders, which are those that are underlain by multiple genetic and environmental factors2, predictive genetic testing that can discriminate individuals who are most at risk is currently limited, mainly because much of the genetic variation remains poorly understood4. The potential of genetic risk prediction to (i) inform early interventions and (ii) aid diagnosis by identifying individuals with an increased genetic risk of disease could be improved substantially by increasing the accuracy of genetic risk predictors5. While genome-wide association studies (GWASs) of increased sample size will continue to unravel the role of genetic factors for complex diseases6, improved prediction models are also required to maximise the accuracy of a risk predictor.Personalised medicine, in which genetic testing is the basis for informing future health status and determining intervention, is effectively applied for a number of monogenic disorders9. However, this approach is not optimal because it either ignores linkage disequilibrium (LD) between markers or accounts for LD by discarding potentially informative SNPs10. Prediction accuracy of complex phenotypes can be improved by methods that jointly estimate the SNP associations to obtain SNP effect estimates with best linear unbiased predictor (BLUP) properties within a linear mixed model (LMM) approach, a model termed genomic BLUP (GBLUP)12. A multi-trait extension of the LMM approach, yielding multivariate BLUP (MT-BLUP) predictors of the SNP effects, can further improve prediction accuracy when phenotypes are genetically correlated, because measurements on each trait provide information on the genetic values of the other correlated traits16. MT-BLUP has been shown to improve prediction accuracy for genetically correlated common psychiatric disorders when combining individual-level data across independent data sets17. However, the application of MT-BLUP to complex common disorders is limited as combining individual-level genotype-phenotype data across case–control studies of all complex diseases is generally not feasible due to data protection concerns and restrictions on data sharing.GWASs use linear regression to independently estimate the effects of single-nucleotide polymorphisms (SNPs) across the genome, and commonly, these estimated SNP effects are then used to create a genetic risk predictor in independent samples18 that extends a single-trait summary statistic BLUP model (SBLUP) by assuming that marker effects come from a mixture of distributions. We apply our approach to multiple phenotypes in the Psychiatric Genomics Consortium (PGC) to compare summary statistic approaches to direct estimation on individual-level data. We further apply our approach to summary statistics of several other phenotypes to create predictors that we evaluate using the UK Biobank data. We show that, for most traits, our multi-trait predictors improve prediction accuracy as compared to a single-trait predictors.Here we overcome this limitation by developing a framework that combines publically available GWAS summary statistics across multiple studies of different traits together in a weighted index to generate approximate multi-trait summary statistic BLUP (wMT-SBLUP) predictors 21 or approximate mixture model predictors (LDPred). We first extend the SBLUP approach to a multi-trait framework (MT-SBLUP) and find a computational limitation associated with the inversion of a SNP-by-SNP-by-trait matrix. To overcome this, we then derive theory to show how single-trait predictors with BLUP properties can be combined together in a weighted index to generate predictors with equivalent properties to those gained from a MT-BLUP analysis  estimates of the SNP effects and do not have optimal properties for predictioni, and focal trait of interest, f, we have a genetic prediction k, that we can combine together using the index weights, wi,k, for each k (proportion of phenotypic variance explained by genome-wide SNPs), rG is the genetic correlation between trait k and the focal trait and Meff is the effective number of chromosome segments and Nk is the sample size of trait k. These weights will ensure that the contribution of each added trait is approximately proportional to the square root of its sample size, its SNP heritability and its genetic correlation with the focal trait (trait 1), while accounting for different variances of single-trait BLUP predictors.Consider two genetically correlated traits for which we have individual-level genetic predictors with BLUP properties. For each individual, rG can be estimated from GWAS summary statistics using LD score regression23. Following20, individual-level genetic predictors with BLUP properties can also be obtained from GWAS summary statistics (18 (wMT-LDPred), therefore depending upon the genetic architecture of the trait approximate multi-trait summary statistics can be created to maximise genomic prediction accuracy.Both 18 can be combined in an approximate multi-trait weighting  that a wMT-SBLUP genetic predictor has the same expected prediction accuracy as one created from a multivariate mixed-effects model (multi-trait BLUP: MT-BLUP) if the linkage disequilibrium among SNP markers in the individual-level analysis is well approximated by a reference genotype panel (see Methods section). We demonstrate that a wMT-SBLUP predictor increases prediction accuracy over a single-trait predictor, with the magnitude of increase being proportional to the ratio of the SNP heritability of the added traits relative to that of the predicted trait, the sample size of the added traits relative to that of the predicted trait and the genetic correlation between the added traits and the predicted trait Fig.  and 2. WWe also provide a theoretical expectation for the loss in prediction accuracy that occurs when using an independent reference sample to compute SBLUP effects compared to a predictor based on BLUP effects (see Methods section), and we detail the loss of prediction accuracy in our simulation study Fig.  and 4.25 and bipolar data, two psychiatric disorders known to have a high genetic correlation26. The availability of combined individual-level data for both disorders enabled a direct comparison of the MT-BLUP16 and wMT-SBLUP approaches. We calculated all predictors for the previously used16 PGC wave 1 (PGC1) data sets24 and compared the prediction accuracy  across diseases and approaches. We find comparable but slightly lower accuracies in the wMT-SBLUP predictors as compared to the MT-BLUP predictors (0.151 vs 0.156 in bipolar disorder and 0.217 vs 0.219 in schizophrenia) and an increase in prediction accuracy as compared to the single-trait (BLUP) predictors   Fig. . Our res25 and bipolar disorder (see Methods section), which included the PGC1 data. To test whether the addition of more cohorts improved prediction accuracy, we estimate wMT-SBLUP predictors in the PGC2 data. Having shown the resemblance of wMT-SBLUP and MT-BLUP by theory, simulation and in the PGC1 data, we refrained from running a MT-BLUP model in the PGC2 data to avoid the computational burden of analysing the combined schizophrenia bipolar data set. For schizophrenia, there were 36 cohorts  and for bipolar disorder there were 23 cohorts . We conducted a cohort-wise leave-one-out cross-validation approach to examine variation in prediction accuracy across cohorts.We then applied our approach to the larger PGC wave 2 (PGC2) data sets for schizophrenia25, and (iii) that, for some test set cohorts, there is no mixing proportion that will lead to a multi-trait predictor which outperforms a single-trait predictor. The larger gain in accuracy that results from supplementing a bipolar disorder predictor with schizophrenia data compared to supplementing a schizophrenia predictor with bipolar disorder data is consistent with greater power of the schizophrenia discovery sample. We find that for both single-trait and multi-trait predictors the SBLUP predictors outperform the OLS predictors in almost all cohorts  predictor  for the ith individual at the jth SNP and pj the minor allele frequency. b are the genetic effects for each SNP, and y, W, b and k, the number of SNP markers, M, and the number of individuals, N, and are described in the sections below. We denote the distributional properties var(b) = B, var(R and var(y) = WBW′ + R.We consider a general linear mixed model:b of Eq.   7) can bk, y of Eq.  by replacing k of Eq.  by theirEquation  can thenk attributable to the SNPs The shrinkage parameter is 21 and is similar to the LDpred model presented by Vilhjálmsson et al.18 but with a few differences. The first is that it only considers the infinitesimal case, where all SNPs are considered to be causal and their effect sizes follow a normal distribution. This corresponds to the LDpred-Inf model. The second difference is that the shrinkage parameter of Vilhjálmsson et al.18 is 18 calculate BLUP effects for blocks of a certain number of SNPs following a tiling window approach giving a block diagonal structure to L, whereas our implementation within the software GCTA (see URLs) follows a sliding window approach giving a banded diagonal to L. Assuming an error variance of M dimensional SNP LD matrix, with only little loss of information (see below).This approach was implemented in ref. N = For multiple phenotypes, the elements of Eq.  become: that Eq.  can be ementclasspt{minimakk single-trait An alternative to Eq. , is to os of Eq. , using aj and focal trait f, we have k traits, and we wish to obtain the index weights, wj,k, for each For SNP 36. Utilising a selection index approach, the solution for wSBLUP of Eq.  and can be empirically obtained as an inverse of the variance of genomic relationships40. Here we use an estimate of Meff of 60,000, which is in line both with our estimates from the genomic relationships in our simulation data and with previously reported estimates41. In Eq.  = rGhkhl/M, with rG the genetic correlation, and given the squared correlation between the true genetic effects of the SNPs, bk, and Second, we consider the off-diagonal elements of n by Eq.  as \docuCSBLUP, which is composed of the covariance between the true genetic effects of the SNPs for the focal trait, bf, and k traits. The first element of CSBLUP is covariance between the true genetic effects of the SNPs for the focal trait bf and CSBLUP are bf on bk where the regression coefficient bf and Finally, we can consider the column vector ts, Eqs. , 19) an anCSBLUPVSBLUP can be well approximated by a diagonal matrix with entries wf will become 1 and wk for all other traits will be Meff is large, and thus a trait with higher SNP heritability will still have a larger contribution to the multi-trait predictor than a trait with lower SNP heritability.For traits with low power, 22 and therefore Equation  implies s of Eq.  can be amentclass2pt{minims of Eq. . Both \d2 of Eq.  can be os of Eq. , which wIn the previous section, we have shown how j and focal trait f, we have k traits, and we wish to obtain the index weights, wj,k, for each For SNP COLS is now a k × 1 column vector of the covariances of the k traits with the true genetic effects of the SNPs for the focal trait, and VOLS is a k × k variance–covariance matrix of the VOLS are:k and l areCOLS now has elementsJust like before, the optimal weights can be derived as:These weights are considerably different from the BLUP weights, which reflects the different variances of BLUP effects and OLS effects. Here we include this section for completeness but focus our analyses on multi-trait BLUP effects, because they are more accurate in expectation than multi-trait OLS effects.42. As outlined above, Eq. \documentr of Eq.  and to tL, rather than directly using the individual-level data to calculate W′W, can be approximated by considering the scenario where SNP makers are unlinked, resulting in diag[L]. The diagonal elements of j are then:W′W)(W′W)] can be approximated as N as the SNP markers are unlinked. Equation , , , 19), , , 21), , , 23), , , 25), , , 27), , , 29), , , 31), , , 33) an an33) anr in Eq.  does notMeff, of ~ 60,000 and sample size, N, of 500,000, h2 = 0.5 and N = 500,000, the accuracy of the multi-trait SBLUP predictor will also be 91% of the accuracy of the multi-trait BLUP predictor.For a phenotype of SNP heritability 0.5, with effective number of independent markers (independent genomic segments), L to be a a diagonal matrix, which will lead to a conservative estimate of the accuracy of SBLUP relative to the accuracy of BLUP, and that this estimate is in fact equivalent to the expected accuracy of a polygenic risk predictor based on marginal OLS effects28. In practice, approximating L through an external reference data set leads to SBLUP predictors, which are more accurate than predictors based on marginal OLS effects but less accurate than predictors based on BLUP effects.It should be noted that here we assume Computing weights and combing up to 930,000 SNP effects of 34 traits takes <10 min on an Intel Xeon E7-8837 processor with 2.76 GHz. Memory requirements do not extend much beyond the amount necessary to read in the summary statistics. Calculation of single-trait SBLUP effects is more computationally demanding, so we split the data by chromosome. Runtime for chromosome one was <40 min, with memory usage just under 10 GB.To compare the accuracy of single-trait and multi-trait genetic predictors created from SNP effects obtained from both individual-level and summary statistic data, we conducted a simulation study based on real genotypes from the Kaiser Permanente study  and simulated phenotypes.45). SNP QC procedures on the initial data sets consisted of per-SNP missing data rate of <0.01, minor allele frequency >0.01, per-person missing data rate <0.01 and Hardy–Weinberg disequilibrium p-value < 1 × 10−6. For the subsequent imputation, the data were first haplotyped using HAPI-UR. After that, Impute2 was used to impute the haplotypes to the 1000 genomes reference panel . Best-guess genotypes at common SNPs included in the HapMap 3 European sample were then extracted and filtered for imputation info score >0.5, missing data rate of <0.01, minor allele frequency >0.01, per-person missing data rate <0.01 and Hardy–Weinberg disequilibrium p-value < 1 × 10−6. Next, we performed principal component analysis and removed individuals with principal eigenvector values that were >7 SD from the mean of the European cluster. Lastly, we identified pairs of individuals with genetic relatedness matrix >0.05 and removed one individual from each of these pairs.From the GERA cohort, we selected 50,000 individuals of European ancestry  of the genotypic data, see ref. The Atherosclerosis Risk in Communities study (ARIC data) was used as an independent LD reference when estimating SBLUP SNP effects of Eq. . Eight te ~ N were added to this genetic value for each individual to create phenotypes with mean 0 and variance of 1. To remove any effects of population stratification, the simulated phenotypes were then regressed against the first 20 genetic principal components, and the residuals from this regression were used in all subsequent analyses.We then randomly assigned 20,000, 20,000 and 10,000 individuals from the GERA cohort to create three data sets: training set one, training set two, and a testing set. We simulated two genetically correlated traits by randomly selecting 2000 causal SNPs. Effect sizes for the causal markers were simulated from a bivariate normal distribution with mean 0, variances of In training set 1, we simulated trait 1 and we then estimated: (i) OLS SNP effects using Eq.  \documensing Eq.  \documenfrom Eq.  and the e ~ N. We also compared estimates obtained using LDPred with the proportion of SNPs in the model of 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1 or using the LDPred-Inf option.In the testing set, we then used the estimated SNP effects from the training sets to produce genetic predictors for both traits. Single-trait genetic predictors were created for both simulated traits from (i) the OLS SNP effects rG = 0.8, respectively, in the first scenario and were rG = 0.5, respectively, in the second scenario. In each set-up, six replicates were conducted, each with a different set of randomly selected causal markers. We then repeated all analyses on a permuted data set, where the values of the genotype matrix were permuted across all individuals, for each SNP. This creates a genotype matrix where the allele frequency distribution remains the same, but all LD structure is removed, allowing us to determine the degree to which differences between the simulations results are driven by the LD structure in the real genotype data. Finally, because prediction accuracy is expected to be reduced by the error introduced by using an external LD reference data set and a restricted LD window when implementing Eq. (λ when implementing Eq. (We created two simulation scenarios. Heritability of the first and second trait and genetic correlations were ting Eq. .46.We then applied our approach to the schizophrenia (SCZ) and bipolar disorder (BIP) samples from both wave 1 and wave 2 data of the PGC (PGC1 and PGC2). A description of the data collection and imputation of the SNP genotype data can be found elsewhererG between schizophrenia and bipolar disorder using ldsc: 0.72, SE: 0.03; estimated using meta-analysis of all PGC2 schizophrenia and bipolar cohorts, excluding cohorts which were used as test set in the initial PGC1 analysis), and it enabled us to draw a direct comparison between the approach described here and a previous study, which estimated multi-trait BLUP SNP effects λ was set to 1,100,000, corresponding roughly to 1,000,000 markers and an observed scale SNP heritability estimate of 0.47, and for the estimation of bipolar disorder SBLUP effects, lambda was set to 1,200,000, corresponding roughly to 1,000,000 markers and an observed scale SNP heritability estimate of 0.45. For the four SCZ testing cohorts and the three BIP testing cohorts used in the previous study, we created: (i) weighted multi-trait SBLUP predictors 16.We selected these two disorders because there is a high genetic correlation between them  and 23 cohorts for bipolar disorder  available to us. The number of SNPs used in the PGC2 analyses varied between cohorts. Summary statistics for each of the PGC2 cohorts was available to an imputed SNP set of >10,000,000 SNPs. After intersecting this set of SNPs with the HapMap3 SNPs and the ARIC SNPs, 932,344 SNPs remained that were used to create predictors.25. A cross-validation approach allowed us to get a more robust estimate of the increase of prediction accuracy achieved by our multi-trait prediction method compared to a single-trait predictor. We employed a leave-one-out cross-validation approach, where, for each test set cohort, all cohorts of the same disease without any highly related individuals were chosen to be in the training set for the single-trait predictor and all cohorts of both diseases without any highly related individuals were chosen to be in the training set for the multi-trait predictor. To identify pairs of cohorts with highly related individuals, genetic relatedness for all pairs of individuals  was calculated based on chromosome 22, and whenever at least one pair of individuals had relatedness >0.8, that pair of cohorts was not simultaneously used in the training set and the test set.We applied a cross-validation approach as we observed that prediction accuracy as well as accuracy differences between predictors can be highly dependent on the choice of the test set in the extended PGC2 data set  simply follows the equality b = log(OR). The weights derived for each trait make assumptions about the variance of SNP effects. We found that, in the summary statistics we used, the observed variance across SNP effects often departed from the expected value. To correct for that, we scaled the SNP effect estimates for each trait to have a variance of one and multiplied the weights for the unscaled SNP effects by the expected standard deviation across all SNPs.In each iteration of the cross-validation, a different cohort acts as the test set and a different set of cohorts comprises the training set. To create a predictor from a particular set of cohorts, we first had to obtain effect size estimates from this particular set of cohorts. This is achieved by performing a meta-analysis of the summary statistics of the cohorts that comprise the training set. The meta-analysed beta values λ, to 1,300,000 for schizophrenia and to 2,000,000 for bipolar disorder, corresponding to observed scale SNP heritability estimates of 0.43 and 0.33 for schizophrenia and bipolar disorder, respectively. We then used the PLINK “--score” function to turn SNP effects We created approximate SBLUP effects from Eq.  and the from Eq.  and set s of Eq.  for the To test the degree to which the choice of weights affects the accuracy of the multi-trait predictor, we compared the accuracy of multi-trait predictors based on a spectrum of other weights  had generated summary statistics for multiple similar phenotypes, so we chose only one phenotype per study, which left us with 34 phenotypes. For example, out of “Cigarettes per day” and “Smoking Ever” we only selected the latter to have only one trait for smoking. We used 112,338 unrelated individuals of European descent in the UK biobank data as the testing set. We paired 6 phenotypes out of the 34 summary statistic phenotypes to phenotypes in the UK Biobank: Height, BMI, fluid intelligence score, depression, angina, and diabetes. The first three are quantitative traits and the latter three are disease traits for which we could identify at least 1000 cases in the UK Biobank data. For details, see Supplementary Table For the disease traits, we used the self-reported diagnoses rather than ICD10 diagnoses, as they tend to have larger sample sizes. For depression, we used a more refined definition of cases and controls, where individuals were not counted as cases if they had any history of psychiatric symptoms or diagnoses other than depression or if they were prescribed drugs that are indicative of such diagnoses. Individuals were selected as controls only when there was an absence of any psychiatric symptoms or diagnoses and only when they were not prescribed any drugs that could be indicative of such diagnoses. All 6 traits in the UK Biobank were corrected for age, sex and the first 10 principal components by regressing the phenotype on these covariates and using the residuals from that regression for further analysis. For each trait, the SNPs that went into the analysis were based on the overlap between the GWAS summary statistics, the HapMap3 SNPs, the GERA data set, which was used as an LD reference in the SBLUP analysis, and the imputed SNPs from the UK Biobank.  Depending on the trait, the total number of SNPs ranged from around 660,000 to around 930,000.λ value of k, where M is assumed to be 1,000,000. As LD reference set, we used a random subset of 10,000 people of European descent from the GERA data set, and we set the LD window size to 2000 kb. We then used the PLINK “–score” function to turn SNP effects p = 0.05 with the focal trait, as well as the focal trait itself. The summary statistics based single-trait SBLUP predictors of the selected phenotypes were then combined into multi-trait SBLUP We created single-trait g to Eq. . These wrG with the focal trait (Fig. We compared the performance of the multi-trait predictors ait Fig. . This ishttps://github.com/uqrmaie1/smtpred.Code reported in this manuscript is available from http://cnsgenomics.com/software/gcta/For GCTA, see https://github.com/bulik/ldscFor LDSC, see https://sites.google.com/site/honglee0707/mtg2For MTG2, see https://github.com/bvilhjal/ldpred/For LDpred, see http://www.ukbiobank.ac.uk/For UK Biobank, see http://www.cog-genomics.org/plink2For PLINK2, see http://www.med.unc.edu/pgc/results-and-downloadsPGC summary statistics data are available from https://www.ukbiobank.ac.uk/For UK Biobank data, see Supplementary InformationDescriptions of Additional Supplementary FilesSupplementary Data 1


P=4.46E−08), where NT5DC1 and COL10A1 reside; the SEC23IP locus (P=6.47E−07); the PSD locus (P=7.58E−08) and ZCCHC4 locus (P=1.79E−06). No genome-wide significant association was observed among the common variants. The strongest signal was noted at rs9325032 in PPP2R2B . Taken together, our data add to the growing evidence of general signal transduction molecules  having an important role in the etiology of ADHD. Although the biological implications of these findings need to be further explored, they highlight the possible role of cellular communication as a potential core component in the development of both adult and childhood forms of ADHD.Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable childhood-onset neuropsychiatric condition, often persisting into adulthood. The genetic architecture of ADHD, particularly in adults, is largely unknown. We performed an exome-wide scan of adult ADHD using the Illumina Human Exome Bead Chip, which interrogates over 250 000 common and rare variants. Participants were recruited by the International Multicenter persistent ADHD CollaboraTion (IMpACT). Statistical analyses were divided into 3 steps: (1) gene-level analysis of rare variants <1%); (2) single marker association tests of common variants (MAF⩾1%), with replication of the top signals; and (3) pathway analyses. In total, 9365 individuals (1846 cases and 7519 controls) were examined. Replication of the most associated common variants was attempted in 9847 individuals (2077 cases and 7770 controls) using fixed-effects inverse variance meta-analysis. With a Bonferroni-corrected significance level of 1.82E−06, our analyses of rare coding variants revealed four study-wide significant loci: 6q22.1 locus ( Meta-analyses of some candidate genes and genome-wide association results have yielded interesting hypotheses for this disorder, with pathway analyses pointing towards neurodevelopmental networks.1ADHD in children and adolescents is highly heritable with an estimated mean heritability of ~76%.8 Phenotypic heterogeneity could be another explanation for the multifarious findings in ADHD genetics. Such heterogeneity could occur when various smaller sets are combined to increase sample size and it may lead to the loss of a real association signal.9 Examination of a refined phenotype may reduce phenotypic heterogeneity and increase the probability of detecting a true association signal, although adequately large sample size will still be required to detect associations of small effects.The mixed results of the previous association studies may be partially due to the polygenic and multifactorial nature of ADHD, with both common and rare variants likely contributing to its etiology.1 symptom persistence may represent one promising refined phenotype.10 As most molecular genetic studies on ADHD so far have been limited to its childhood form only, this might constrain the possibilities to identify genetics of symptom persistence.Given that ADHD is a developmentally heterogeneous condition that covers both a remitting and a persistent adult type of ADHD,In this study, we investigated the role of common and rare exonic polymorphisms in the persistent, adult form of ADHD (aADHD). The focus on low frequency protein-coding variants in addition to the genome-wide common polymorphisms provides an additional source of potentially relevant variation that has received little attention in aADHD genetic studies so far.11 The population in Southern Brazil where this study was performed is mainly of European descent and population stratification was not found in the European-derived population of Rio Grande do Sul.13 Individuals from Southern Brazil show predominantly European ancestry (94%) according to estimates of interethnic admixture.14All adult ADHD cases examined in this study were volunteers enrolled through the International Multicenter persistent ADHD CollaboraTion (IMpACT). All patients were diagnosed with ADHD according to DSM-IV criteria, meaning the presence of ADHD symptoms before 7 years of age. Controls were recruited either at an IMpACT site  or through population studies . All subjects were of European descent who was ensured by means of self-report together with multi-dimensional scaling . In BrazAll participants provided signed informed consent in accordance with the Declaration of Helsinki. The study was approved by ethics committees in each collaborating country at the corresponding recruitment center: in Germany, by the Ethics Committee at the University of Würzburg ; in Norway, by the regional Ethics Committee for medical and health research ethics, western Norway at the University of Bergen  and the Norwegian Data Inspectorate, the Regional and National Committees for Medical and Health Research Ethics and the Norwegian Directorate of Health at the University of Trondheim ; in Spain, by the Ethics Committee of the Hospital Universitari Vall d'Hebron  at the Department of Psychiatry from the Hospital Universitari Vall d'Hebron ; in the Netherlands, by the regional Ethics Committee , the Institutional Review Board of the Radboud University Medical Center  and by the regional Ethics Committee  at the outpatient clinic of GGZ Delfland in Delft, to Parnassia, psycho-medical centre in The Hague or at the department of Psychiatry at the Radboud University Nijmegen Medical Centre in Nijmegen, the Netherlands.; in Brazil, by the Ethics Committee of the Hospital de Clínicas de Porto Alegre  at the ADHD Outpatient Program at the Hospital de Clínicas de Porto Alegre (HCPA); in UK, by the National Research Ethics Committee at the National Adult ADHD Outpatient Clinic at the South London and Maudsley NHS Trust,  and the Wellcome Trust Case Control Consortium (WTCCC).For the discovery phase of the study, samples were available from four European IMpACT sites: Germany, The Netherlands, Norway and Spain. A replication sample, to examine the most significant SNVs from the common variant analysis identified in the discovery stage, was recruited from the following four IMpACT sites: Brazil, Germany, The Netherlands and the UK. Additional samples were recruited through the WTCCC. A detailed description of all samples is provided in the 15 Best practice guidelines were used to perform quality control (QC) of genotype calls in GenomeStudio.16 Further QC was carried out on all data sets using PLINK,17 with the following steps: (1) genotyping rate threshold was set to 98% both for individuals and for SNVs, (2) Hardy–Weinberg test threshold was set to P<1.00E−05, (3) overall heterozygosity of individuals was screened based on common ⩾1%) and rare (MAF<1%) SNVs separately, with outliers defined as those outside the range of mean±3s.d. of the total heterozygosity observed in a sample, (4) relatedness (PI_HAT) threshold was set to 10% and (5) ethnic homogeneity was ensured by means of multi-dimensional scaling with HapMap3 populations. Genotype calling and all QC steps were performed for each data set individually. Those samples that were collected in the same country  were merged in PLINK and additional QC steps were implemented. Specifically, the screening for heterozygosity, cryptic relatedness and population outliers was performed once more as described above. As the end result, four data sets were produced: a combined German sample, a combined Dutch sample, a combined Norwegian sample and a Spanish sample.All subjects included in the discovery stage were genotyped on the InfiniumHumanExome array . The German cases, the full Dutch sample and the full Spanish sample were genotyped on HumanExome-12v1-1_A, the German controls and the HUNT Norwegian sample of controls were genotyped on HumanExome-12v1_A and the UiB Norwegian sample was genotyped on HumanExome-12v1_B version of the chip. All genotypes were processed using the Illumina GenomeStudio V2011.1 software, with additional genotype assignments implemented in the zCall.For the replication stage, the most significant three common (MAF>1%) SNVs were genotyped by KASP assay  in IMpACT samples . The Dutch replication sample was also genotyped on Perlegen (Perlegen Sciences) and HumanCytoSNP-12 array (Illumina).18 Calculation of principal components was performed based only on individuals and SNVs revealing high genotyping rate (⩾99%) and common variants (MAF⩾1%) after removal of strand ambiguous SNVs and those in high LD (r2<0.2). Long stretches of LD were also removed prior to calculation of principal components.19Statistical analyses were divided into three main stages: (1) examination of rare coding SNVs (MAF<1%), (2) examination of common SNVs (MAF⩾1%) with replication in an independent sample and (3) exploration of biological pathways possibly involved in the development of adult ADHD based on the results from the previous two steps. Prior to the analyses, each subject's genetic substructure characteristics were estimated with principal components analysis implemented in EIGENSTRAT software for each data set individually.20 The software first analyzes each data set individually and then combines the generated summary statistics across all the data . The individual analyses generated single-variant score statistics as well as covariance matrices to reflect linkage disequilibrium (LD) among the variants. We performed two meta-analyses at the gene level: the MAF-weighted burden test and the variable threshold test (VT) across all data sets. Only those genes were tested in which at least two variants were observed across all data sets. All individual tests were adjusted for the number of significant principal components as calculated in EIGENSTRAT. Variants of those genes that reached a P-value below 1.00E−05 in the individual association tests were manually evaluated in GenomeStudio in the German, Dutch and Norwegian data sets. GenomeStudio data was not available for the Spanish data set. Single-nucleotide polymorphisms displaying less than optimal clustering were excluded. Bonferroni correction was applied to adjust for multiple testing in the final step of the analyses.Rare variants were defined as those with MAF<1%. The variants were combined per gene and tested for association with adult ADHD in RAREMETAL.Significant association signals, revealed by these analyses of rare variants, were also examined in the context of association among common variants in meta-analysis of this study as well as in that of Psychiatric Genomics Consortium (PGC).21 applying genomic control.22 Only those SNVs present in at least two data sets were meta-analyzed. QQ-plots were constructed to ensure proper distribution of observed statistics.Common SNVs were defined as those with MAF⩾1%. The variants were tested in PLINK software, assuming an additive model. Each data set was first analyzed separately, including significant EIGENSTRAT principal components as covariates. The results were then combined with the use of fixed-effects inverse variance meta-analysis in METAL,Replication was attempted for the top three SNVs detected in the discovery phase. In the Brazilian, Dutch and German samples, the association of each top variant was assessed in PLINK with logistic regression assuming an additive model. The association in the British sample was assessed by the chi-square test after constructing 2 × 2 contingency tables of allele counts, as we did not have access to raw genotype data of the controls. Each data set was analyzed individually. The results were combined in the fixed-effects inverse variance meta-analysis implemented in PLINK. The three top common variants were also looked up in the largest published ADHD genome-wide association meta-analysis of PGC.NT5DC1, the structural details surrounding the sites of missense mutations were investigated in Discovery studio Visualizer . In addition, SIFT and PolyPhen predictions were also carried out in Ensembl Variant Effect Predictor (VEP).We performed a comprehensive assessment of all rare SNVs contributing to the association signals that survived the study-wide correction for multiple testing to explore their potential functional impact. Such effect was evaluated in two ways: (1) influence of the genetic variation on the encoded protein and (2) regulatory DNA effects . The severity of missense SNVs on the function of the encoded proteins was assessed with sequence alignment, using protein blast and protein homology modeling. Applying homology models of human 23The possible regulatory effects were assessed in RegulomeDB, utilizing genomic annotation databases that employ the data curated by the Encyclopedia of DNA Elements (ENCODE) and provide information on functionally important elements. RegulomeDB identifies DNA features and regulatory elements of the human genome.24 First, a degree of association was calculated for each gene based on both common and rare variants by the use of principal component regression. Rare variants were defined as those with MAF<1% and a burden score was created for each gene, computed as weighted sum of all rare variants in that gene. All data sets were analyzed individually, adjusted for significant EIGENSTRAT components. Fixed effects meta-analysis was then performed, using the square root of the sample sizes as weights. To evaluate each gene's contribution to the examined gene-sets (GO term), the P-value of each gene from the meta-analysis was converted to a Z-value and used as an outcome variable for a regression model with gene-set membership as a predictor. Gene size, gene-sets' gene density as well as global LD were added as covariates to adjust for possible confounding effects and prevent spurious association. We restricted our pathway analysis only to those sets that contained SNVs in at least 10 genes in our total aADHD data . Bonferroni correction was applied to adjust for multiple testing.To evaluate whether any biological processes may be implicated by our results, we performed gene-set analyses using gene ontology (GO) terms. The analyses were performed in MAGMA.http://hbatlas.org), the Expression Atlas (https://www.ebi.ac.uk/gxa/home) and Allen Brain Atlas (http://www.brain-map.org/).Gene expression in the human brain across the lifetime was assessed for the loci surviving the Bonferroni correction. Data was accessed from the Human Brain Transcriptome project  were available for the analyses. 25 we leaned on the view of rare functional variants being under selective pressure and utilized two methods to explore such scenario: (1) the MAF-weighted burden test that defines rare variants with a fixed threshold and places weights inverse to the MAFs of the variants within a gene and (2) the VT that empirically determines the MAF threshold and makes no prior assumption about the relationship between MAFs and effect sizes of the variants within a gene. Out of 14 431 genes with observed variants, 13 715 genes contained at least two variants. We adjusted our significance level for the use of two methods and the 13 715 genes containing more than two rare SNVs , which yielded a Bonferroni-corrected study-wide significance threshold of 1.82E−06. Four loci survived this correction for multiple testing. The most significant association was observed using the VT method, present at 6q22.1 , where the 5′-nucleotidase domain containing 1 (NT5DC1) gene resides along with the collagen type X alpha 1 (COL10A1) gene. This locus was also the most significant finding using the MAF-weighted burden test (ST1). As it is still unknown what is the most powerful statistical approach for testing whether a mixture of neutral, risk and protective variants is present within a single gene,SEC23IP; P=6.47E−07, VT method), the pleckstrin and Sec7 domain containing gene  and zinc finger, CCHC domain containing 4 gene . ST1 summarizes the most significant genes (P⩽1.00E−05) observed in the performed gene-based analyses of rare coding variants.The three additional study-wide significant associations were observed for the SEC23 interacting protein gene .PPP2R2B gene . No variant reached genome-wide significance (P<5.00E−08). ST3 details the top SNVs with association P-values <1.00E−03 in the meta-analysis. SF1 depicts the QQ-plots.The most significant signal in our meta-analysis of common variants was observed at rs9325032 in the PPP2R2B gene) showed a modest trend of significance , although significant heterogeneity  and discordant direction of effect was observed among samples (ST4A). Comparisons using the data for childhood ADHD collected by the PGC26 revealed no significant findings for the top SNVs in the current study (ST4B).Replication was attempted for the top three common SNVs (ST4). No variant reached genome-wide significance (SF2). However, rs9325032 . No single common variant reached genome-wide significance and, thus, common variants were not assessed further for functionality. First, we evaluated the effect of rare missense variants on the encoded protein in the most significant locus of NT5DC1. The NT5DC1 protein contains a known 5′-nucleotidase domain (aa11–337) and a haloacid dehalogenase-like domain (aa198–324). NT5DC1 missense variants in this locus. Structural inspection of the SNVs in the NT5DC1 locus revealed their potential to form a possible phosphorylation site (rs150257749) and to induce conformational change (rs150293032)   and SF3BNT5DC1 (rs150293032), two in PSD (rs142273937 and rs200819772), two in SEC23IP (rs142665854 and rs142266445) and one in ZCCHC4 (rs151252286). Detailed features of these missense variants are presented in ST5.While examining the impact on their respective encoded proteins across all the 32 variants, six were predicted to be deleterious by both PolyPhen and SIFT: one in PSD gene  and probably affect the binding properties of polymerase (RNA) II (DNA directed) polypeptide A (POLR2A) as well as may alter CTCF regulatory element binding (rs140739855 and rs200819772) (ST6). In addition, the binding of POLR2A and CTCF may also be affected by the variants in the other two study-wide significant loci: rs73357833 in SEC23IP, rs201763036 in ZCCHC4 (ST6). No significant eQTL effects were observed. Detailed RegulomeBD features of all variants are presented in ST6.Apart from surveying the possible direct effects of the variants on their encoded proteins, we also explored their regulatory potential using RegulomeDB. These analyses revealed that the SNVs with the most likely regulatory effect reside within the P-value of 2.17E−05. The strongest association was observed for ‘mRNA 3′-end processing . ST7 summarizes the top GO terms (P⩽1.00E−03) observed in the meta-analysis of both common and rare variants.To evaluate whether certain biological processes may be implicated by our results, we performed gene-set analyses using GO terms. Among the GO terms, 1.844 terms contained data for 10 or more genes in our data set. This brings the Bonferroni-corrected significance threshold to a NT5DC1, SEC23IP, PSD and ZCCHC4) was evaluated throughout life in the Human Brain Transcriptome atlas. The transcriptional trajectories of all four loci revealed variability between pre- and post-natal stages, in line with the general patterns of gene expression in the brain across the lifetime.27The expression of the four study-wide significant loci . The other three loci showed high expression in the cortex and basal ganglia input nuclei , and throughout brain .We have also examined the expression patters of the four aforementioned loci with regards to the brain regions. NT5DC1 and COL10A1 reside, as well as the SEC23IP, PSD and ZCCHC4 loci.This study aimed to shed light to the genetic architecture of adult ADHD, the form of this disorder that has received relatively little attention so far. Our main findings are the novel, study-wide significant candidate loci for adult ADHD at 6q22.1, where NT5DC1 encodes a member of the haloacid dehalogenase superfamily of enzymes, the exact physiological role of which is still largely unknown.28 On the basis of its sequence similarity to other haloacid dehalogenases, NT5DC1 may be involved in the de-phosphorylation of intracellular signaling molecules and our in silico analyses of the SNVs contributing to its association signal suggest that these variants may alter its function . With our sample size it is not possible to statistically distinguish the effects of these two genes from each other, but the strongest association is seen when testing the larger NT5DC1 gene (P=4.48E−08).function . The NT5SEC23IP belongs to a family of enzymes as well: intracellular phospholipase A1 family that degrades phospholipids and is involved in membrane trafficking.29 Altogether with actin cytoskeleton organization, membrane trafficking is also a mechanism through which PSD-encoded protein (a guanine nucleotide exchange factor) mediates a number of neuronal functions,30 including dendritic spine formation and stabilization.31 Furthermore, ZCCHC4-encoded zinc-finger DNA-binding protein may also be linked to cell signal transduction as suggested epigenetic silencer of RAS gene family.33RAS genes reportedly inhibit apoptosis,33 a physiological cell death by which unsuccessful neurons are deleted from the central nervous system in the developing brain.34Similarly to 6q22.1 locus, the protein encoded by 51 In addition, paralogs of the NT5DC1 gene have also been reported to have role in the development of ADHD and other neuropsychiatric disorders.54 These findings fit with the higher transcript level of NT5DC1 reported in substantia nigra, a major localization of dopaminergic neurons.Thus, in this study, we noted association signals in several genes involved in signal transduction, highlighting the possible role of cellular communication in the development of adult ADHD. In line with these observations, previous studies have implicated all four of our study-wide significant loci in disorders of the central nervous system, including ADHD and related neurodevelopmental disorders, ADHD co-morbid conditions as well as syndromes where ADHD symptoms are present.55 These aggregation tests are based on various assumptions about the underlying genetic architecture and their power depends on the true, and, in case of ADHD, unknown, disease model.55 In this study, we used two methods to detect gene-based association: (1) the VT method that leans on the view of functional allelic variants being a subject to purifying selection pressure and empirically calculates a threshold below which the variants are more likely to be functional, based on the observed data; and (2) MAF-weighted burden test, which makes the same assumption of functional variants being under selective pressure as the VT method. As all the four study-wide significant loci reveal association signals with both methods (ST1B), it may suggest that the results appear to be robust with regard to different gene-based tests operating under the same hypothesis and that such applications may help us in uncovering underlying genetic architecture of such complex disorders as ADHD.Despite being the largest genetic study on adult ADHD, it is still likely to be underpowered to detect variants with very small effect sizes. Thus, no genome-wide significant signal was observed for common polymorphisms (MAF⩾1%). However, gene-based statistical methods to analyze rare variants combined could potentially improve power.56 including childhood ADHD.1 Thus, dysregulation of cellular communication could be a core component in the development of both adult and childhood forms of ADHD. Nonetheless, these findings should be subjected to further examination in larger samples before their role in ADHD can be firmly established.Taken together, all four study-wide significant loci have been implicated in the function of neural circuitry and communication, cellular mechanisms previously linked to the development of mental disorders,


We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of  related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure. The analyses were performed with full knowledge of the simulation model. Our findings demonstrate that we can significantly reduce the rate of false positive signals by incorporating the relatedness of the study participants. Current association methods for quantitative phenotypes are mostly designed for use with unrelated individuals. However, often studies include information on families or related individuals. Also, many times samples assumed to include only independent individuals may suffer from underlying cryptic relatedness among the participants. Using related individuals in statistical analyses has many advantages, such as increased ability for quality control and robustness of the results with respect to population stratification. Ignoring relatedness between study participants can have significant impact on the study results and increase false positive rates . Linear Because of the computational intensity involved in the estimation of the parameters of the LMM, most methods perform single marker analysis . As GWASth Genetic Analysis Workshop (GAW19) [We have developed a new association method that combines LMM and LASSO to capitalize on the benefits of both approaches. Our method handles multiple markers and models  common genetic background among individuals through the use of a random polygenic effect. It employs a LASSO algorithm  to estimshop GAW1 [9] to sn  related individuals. Let y =  be the vector of the phenotypic values of the quantitative trait of interest. We assume that for each person i the value of his/her phenotype is determined  by a number, say S, of  loci/SNPs, hereafter referred to as quantitative trait loci (QTLs). Furthermore, the trait is also influenced  by K known covariates Xi, i = 1,…, K, . In addition, there is some environmental effect or residual, denoted as ei. Finally, we suppose the phenotype is affected by a number of other loci whose collective (random) polygenic effect on the trait is denoted as ui. Assuming additivity across all effects, the overall phenotypic value of a person isβ =  is a vector of unknown coefficients for the covariate fixed effects, γ =  is a vector of unknown coefficients of the fixed effect of the SNPs, Zi =  a vector of 0, 1, or 2 s, denoting the number of copies of the disease (minor) allele carried by the individual at each of the loci. Note that, if a SNP does not contribute to the trait levels, we expect its corresponding coefficient γs to be equal to zero. We assume that the vector of random polygenic effects u =  follows a multivariate normal distribution with mean 0 and covariance matrix Ω, while the errors eis are independent and identically distributed coming from a normal distribution with mean 0 and variance. Finally, the two random effects, ui and ei, are considered independent of each other and the covariates. Under these assumptions, the joint distribution of the yi’s is a multivariate normal distribution with mean equal to βX + γZ and variance covariance matrixXT = , ZT = , and I is the n × n identity matrix. If there is minimal or no inbreeding among the individuals in the sample, then the matrix Ω is approximately equal toΦ and Δ are known matrices whose elements are functions of the condensed coefficients of identity between the individuals in the sample, and σap2 and σdp2 are the additive and dominance polygenic variance of the trait [Consider a sample of he trait , 10.γs are expected to be negligible, a LASSO method may be useful as it generally forces most of the parameters in the model to shrink toward zero with only a small number of the parameters remaining significantly different from zero. In our case, the penalized likelihood function that needs to be maximized to estimate the necessary parameters is given by the following equation:θ =  and λ is a nonnegative penalty parameter. Maximization of the above function can be achieved using an algorithm similar to that described by Schelldorfer et al. [Estimation of the model parameters is done by maximizing the corresponding multivariate normal likelihood. The fitting of the model, though, is computationally intense and plagued by many issues because of the high dimensionality of the parameter space and the small available sample size. However, as most r et al. .For our analyses, we used the family-simulated data sets from GAW19 [We used the SBP at the last known appointment as the phenotype of interest and the imputed genotypes provided as those were used in the generation of the simulated phenotypes. Because of the large number of SNPs available, greater than 8,000,000, we preprocessed the genotypes to reduce the number of SNPs used in the analyses to reduce the computational burden. We excluded SNPs with minor allele count less than 10 copies in the entire data set  to avoid potential problems with unstable estimates of the model parameters in equation  were used to fit the LASSO model. In all analyses in phase I or II, phenotypes were adjusted for gender, age, smoking status, and blood pressure medication.The number of SNPs on the map was prohibitively large for directly implementing our approach. So, we implemented a 2-step scheme to reduce the number of SNPs used when fitting the penalized model. Two-step methods are routinely employed when the computational intensity of a method prohibits direct use of the method on a GWAS . In phasp <1E-05. A SNP was called as “significant” if its p value was less than the given threshold  or if its coefficient was different from zero in the optimal LASSO model. We present the results for 3 LASSO analyses. The first, Add, represents an analysis in which the random polygenic effect was assumed to consist of only additive genetic variance, that is, the covariance structure was assumed to be Ω = 2Φσa2. The second model, Add-Dom, assumed that the trait had also a dominance effect and thus the matrix Ω was set to have the form Ω = 2Φσa2 + Δσd2. Finally, for comparison, we also implemented the standard LASSO method that assumes that all individuals in the study are unrelated, denoted as Ind. The standard LASSO approach is a subcase of our approach where matrix Ω is the 0 matrix.The results from the analyses of all replicates are summarized in Table The first group of columns in the table, “Significant SNPs,” summarizes the number of SNPs declared “significant” by each method. The second section, “QTLs discovered,” summarizes the number of QTLs declared “significant” and is used to gauge the TDR of the method. This column uses a very strict definition of what constitutes “true” hit. A more relaxed rule for what constitutes a true signal considered as a true hit any SNP that resided within (an arbitrary) 2 kbp from any QTL. There were 2248 SNPs on the map that were within 2 kbp from a simulated QTL. The results using this definition are summarized in the “SNPs within 2 kbp from QTL” section of the table.From Table The results from the LASSO analyses show that modeling the relatedness of the individuals in the sample pays off, regardless of whether we did (Add-Dom) or did not (Add) model the dominance effect. The LASSO method for related individuals, on average, reduced the number of SNPs with nonzero coefficients in the final model relatively to the corresponding number from the standard LASSO method (Ind). For example, when the Bayesian information criterion (BIC) was used to select the best model, the Ind model resulted in 15 significant SNPs, whereas both the Add and the Add-Dom models included, on average, approximately 7 “significant” SNPs,, marking a reduction of approximately 53 % in the number of “significant” SNPs. For this particular data, it appears that using the more complex modeling of the covariance structure of the random effect had no significant effect on the performance of the method, as the results from both the additive (Add) and the additive-dominance (Add-Dom) models were almost identical. This was expected as the particular phenotype (SBP) was generated using a model with only additive effects. The choice of the information criterion was also not surprising. BIC being stricter resulted in models that, on average, included fewer SNPs with nonzero coefficients, 7.1 from 137.1, than when Akaike information criterion (AIC) was used. The actual TDR of the method, though, appears very low (approximately 1 %). This is not surprising as the TDR of the method was affected by the low discovery rate of the PLINK analysis.p <1E-04, for declaring significant SNPs with the PLINK analysis in hopes of increasing the number of actual simulated QTLs “identified” in the phase I analyses. For the LASSO model, we opted not to fit the Add-Dom model as it significantly increases the analysis time and, based on the analysis above, we did not anticipate the results of the 2 models to significantly differ. Table To further study the TDR of the LASSO method, we reanalyzed the data using a less-strict threshold, p value threshold of 1E-04 for the GEMMA analyses.In an effort to increase the number of true QTLs identified, we repeated the analyses, using the genome-wide efficient mixed-model analysis (GEMMA) software  in phaseTable We have presented a 2-stage approach that uses a LASSO method for analyzing quantitative phenotypes of  related individuals. We first perform a GWAS to uncover SNPs potentially associated with the regulation of the phenotype. Next, a multivariate LASSO approach that accounts for relevant covariates and relatedness among the study participants is used to further weed out additional SNPs. The method models relatedness of individuals in the sample through inclusion of a random polygenic effect whose covariance structure reflects relatedness of the individuals. We used the GAW19 simulated family data to study the properties of the method and to compare its performance to the standard LASSO for unrelated individuals.In application to the GAW19 simulated data, our results indicate that incorporating the relatedness of individuals in the LASSO model can help significantly reduce the number of non–trait-regulating SNPs falsely inferred as associated with the trait relatively to the standard LASSO approach, which assumes unrelated individuals. Furthermore, for these particular data, it appears that including a dominance genetic effect in the model has no significant effect other than to increase the computational intensity of the method. This was expected as the particular data were generated without dominance effects.The power of the LASSO method appears to be small, mainly as a result of the inability of the initial GWAS scan to identify QTLs to take forward for further analysis. In an attempt to increase the number of QTLs selected for the phase II analysis, we explored 2 methods for the GWAS: (a) using PLINK on only the founders of the pedigrees, and (b) using GEMMA to allow for related individuals and use of all study participants. Unfortunately, even though the GEMMA analysis yielded a slightly larger number of true signals, neither of the 2 methods was able to advance to the next stage a significant number of true QTLs. This behavior could potentially be a result of the low contribution of the simulated QTLs to the phenotype. Perhaps using other methods in stage I, such as the transmission disequilibrium test or family-based association test, on an appropriate subset of the data, may increase the number of QTLs that are carried forward, thereby potentially increasing the TDR of the method.


Despite the increasing recognition that microbial communities within the human body are linked to health, we have an incomplete understanding of the environmental and molecular interactions that shape the composition of these communities. Although host genetic factors play a role in these interactions, these factors have remained relatively unexplored given the requirement for large population-based cohorts in which both genotyping and microbiome characterization have been performed.We performed whole-genome sequencing of 298 donors from the Human Microbiome Project (HMP) healthy cohort study to accompany existing deep characterization of their microbiomes at various body sites. This analysis yielded an average sequencing depth of 32x, with which we identified 27 million (M) single nucleotide variants and 2.3 M insertions-deletions.LCT gene, with Bifidobacterium longum abundance in stool. The associations of microbial features with both high-level genetic attributes and single variants were specific to particular body sites, highlighting the opportunity to find unique genetic mechanisms controlling microbiome properties in the microbial communities from multiple body sites.Taxonomic composition and functional potential of the microbiome covaried significantly with genetic principal components in the gastrointestinal tract and oral communities, but not in the nares or vaginal microbiota. Example associations included validation of known associations between FUT2 secretor status, as well as a variant conferring hypolactasia near the This study adds deep sequencing of host genomes to the body-wide microbiome sequences already extant from the HMP healthy cohort, creating a unique, versatile, and well-controlled reference for future studies seeking to identify host genetic modulators of the microbiome.The online version of this article (10.1186/s13073-018-0515-8) contains supplementary material, which is available to authorized users. The Human Microbiome Project (HMP) was the first population-scale, body-wide metagenomic microbiome survey, with initial results published in 2012 . ExampleIrak3 [Irak4 [In this study, we provide further data to characterize potential interactions between the microbiome and the human host, specifically, genome sequencing for the HMP cohort. While interpersonal variation in the microbiome can be considerable, the microbiome of specific individuals can conversely be remarkably stable –14, suggIrak3  and Irak3 [Irak4 .The study of genetic effects on the microbiome becomes far more complex in humans. In addition to genetics, microbiome composition is strongly influenced by environmental factors such as diet, overall health status, and medication use , 19. WitNOD2 were also associated with Enterobacteriaceae in patients with inflammatory bowel disease [LCT) gene, responsible for lactose tolerance, have been associated with abundance of Bifidobacterium [Bifidobacterium has also been associated with a loss-of-function variant in the fucosyltransferase 2 (FUT2) gene, responsible for the transfer of the terminal fucose residues to the mucosal glycans [More detailed mapping of associations between single gene variants and microbial taxa has been successful in a targeted approach of candidate genes or variants, where clear molecular mechanisms have been established. For example, inflammatory bowel disease risk loci near  disease . In addiacterium , 25, 26. glycans .Despite these examples, very few individual microbe-polymorphism associations have been identified that have reached genome-wide significance. Cohort size is certainly a main limiting factor. Studies to date have included up to 1800 individuals, a relatively small number for a successful genome-wide association study of any trait, and especially so considering that the heritability of microbial features can be low relative to many quantitative traits. Furthermore, clinically relevant associations might be discoverable only in cohorts with particular diseases, for example, in conditions such as inflammatory bowel disease or rheumatoid arthritis, which are both genetically complex and accompanied by microbial dysbioses , 29.To facilitate future microbiome-genetic association meta-analyses and to provide a baseline characterization of the HMP population, we report here on whole-genome sequencing from the blood of 298 HMP participants. The data achieve an average of 32x coverage, allowing us to discover two times as many variants as were previously identified using “contaminant” human reads from a subset of 93 whole metagenome sequencing (WMS) samples , 30. Thehttps://molgenis26.target.rug.nl/downloads/gonl_public/variants/release5/) and 1000 Genomes phase 3 (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/) and searched for the presence of all our variants in these other cohorts.Genome sequencing was performed at the Broad Institute using polymerase chain reaction (PCR)-free library preparation on Illumina HiSeq X Ten machines. Reads were mapped to the genome using Burrows-Wheeler Aligner , and varhttps://github.com/konradjk/loftee). To classify mutations into severity groups, we used the annotations provided by VEP. If one variant was located within several transcripts and therefore had multiple potential effects on the coding sequence, we annotated the variant with the most severe outcome.We estimated the impact of coding variants using Variant Effect Predictor (VEP)  with EnsFor principal component analysis (PCA) on the variants, we used 5.9 million (M) common  > 0.05, call rate > 95%) single nucleotide variants (SNVs) in PLINK 1.9 . For joiKinship analysis was performed using the Kinship-based INference for Genome-wide association studies (KING) algorithm . The ranAll metagenomic data underwent quality control according to the HMP protocol , 39. We http://huttenhower.sph.harvard.edu/humann2). For a given metagenome, HUMAnN2 constructs a sample-specific database by concatenating and indexing the pangenomes of species detected in the sample  ( species ). HUMAnN species . Finally species  to recon species .10 transformation on their relative abundances (with a pseudocount of 10–5) to stabilize the variation for linear modeling.To filter the MetaPhlAn relative abundances for further testing, we used only species-level data and required a species to be present in at least 25% of the samples in a given body site. This reduced the number of species from 567 to 119 in stool, from 428 to 119 in buccal mucosa, from 479 to 161 in supragingival plaque, from 461 to 156 in tongue dorsum, from 380 to 29 in anterior nares, and from 367 to 23 in posterior fornix. We then applied log10 transformation to the read-counts-per-million pathway abundance values returned by the HUMAnN2 pipeline.Metabolic pathway abundance was much more stable than species-level abundance: for example, more than 78% of the pathways detected were present in more than 75% of stool samples. As the number of samples where a pathway was not detected was relatively small for each pathway, we used only the abundances that were present for statistical testing, and used only pathways that were present in more than 75% of the samples per body site. This reduced the number of species from 756 to 500 in stool, from 706 to 427 in buccal mucosa, from 750 to 511 in supragingival plaque, from 744 to 509 in tongue dorsum, from 742 to 355 in anterior nares, and from 697 to 247 in posterior fornix. Finally, we applied logIn both cases, if multiple samples were collected from the same person and body site, we averaged the relative abundance values. The final number of donors was 209 for stool, 159 for buccal mucosa, 169 for supragingival plaque, 185 for tongue dorsum, 128 for anterior nares, and 80 for posterior fornix.R2). To put the calculated values into context, we shuffled the sample labels 10,000 times and calculated R2 for each of the shuffled datasets. The average R2 statistic per site was averaged over all features, and the Z-score was calculated based on the same statistic in the 10,000 permuted datasets. For single features the empirical p value was calculated as the proportion of permuted R2 scores larger than the actual score.To associate genomic PCA with the microbiome, we fit a linear model to each of the microbial features, predicting it using the first five principal components. Based on the residuals, we calculated the amount of variance explained .The HMP cohort design and sample collection has been described in depth . BrieflyTo obtain host genome information, genomic DNA from the blood of 298 of the 300 individuals was sequenced using PCR-free sequencing. The average sequencing coverage was 32.77x, with a range of 23.9× to 56.7× Fig. . ContamiThese data provide an almost complete pairing of human genome sequencing to microbial amplicons and metagenomes across the entire HMP cohort. Genetic variation in this cohort was previously inferred using “contaminant” human reads from 93 subjects’ WMS data , 30. WhiAfter filtering according to quality and location in low-complexity regions, 29 M variants remained, consisting of 26.7 M SNVs and 2.3 M insertions-deletions (indels)  tract, oral cavity, skin, nares, and vagina. In some cases replicate samples were collected over time to assess temporal stability of the microbiome. In total, more than 5000 samples were characterized using 16S rRNA gene sequencing and more than 2000 using shotgun WMS. The former approach gives a high-level overview of taxonomic composition, whereas the latter allows species-level identification and profiling of functional potential of the microbiome. We therefore used WMS data in subsequent analyses. The distribution of samples with WMS was not equivalent between body sites, with most samples drawn from six locations representing four of the major areas described above: gut (stool), oral , nares (anterior nares), and vaginal (posterior fornix); no WMS data were available from the skin samples. Within the six body sites, the number of donors ranged from 80 for vaginal posterior fornix to 209 for gut samples; the average number of reads per sample ranged from 34 M in posterior fornix to 86 M in tongue dorsum. Using the WMS data from these samples, we identified taxonomic composition using MetaPhlan2  and funcTo compare host genetic variation with microbial variation, we first assessed the degree to which high-level genetic patterns could be correlated with microbiome composition. PCA on the common SNVs (MAF > 0.05) demonstrated that the first five principal components predominantly represented the ethnic and racial ancestry of the donors. For example, host genetics of the African-American, one of two groups of Asian-American, and the Caucasian subjects showed the strongest effect Fig. . To furtR2) by the first five host-genome principal components. In stool samples, the percent of species-level variation explained by the host principal components was 3.8%, higher than expected by chance alone  of the species-level variation; in tongue dorsum, this figure was 4.1% . In an identical analysis of MetaCyc metabolic pathway abundance, we found only the pathways in the gut microbiomes to be significantly correlated with common variant principal components. In summary, the association between high-level host genetic features and microbiome properties was significant at multiple body sites.Next, for HMP300 we calculated what percentage of microbial variation in the six body sites could be explained  were all positively correlated with the first genomic principal component, demonstrating that these species have higher abundance in donors of Caucasian origin. Another significant species, Sutterella wadsworthensis, was associated with PC4, which separates donors of Asian origin into two groups. Examining other body sites, we found that Porphyromonas catoniae, Propionibacterium propionicum, and unclassified Gemella were significantly associated with host genomic variation in buccal mucosa  < 0.05 by permutation test), with 10% Fig. . Of thesA similar pathway-level analysis revealed a large number (82 of 541) of pathways significantly (FDR < 0.05 as above) correlated with genetic principal components in stool Fig. . SeveralIn other body sites, pathway-level variability was on average not correlated with genetic principal components, although some individual correlated pathways were found  for the analysis, we identified 11 pairs of first degree relatives and one pair of third degree relatives.t test between unrelated and related similarity scores. For vaginal samples the effect was also pronounced, but we did not have enough female-female pairs to achieve statistical significance.We next sought to determine whether the degree of relation was reflected in the similarity of their microbiomes. For this analysis we calculated the Bray-Curtis distance between all donor pairs and divided the pairs into three groups: same ethnicity, different ethnicity, and relatives Fig. . As coulFUT2 and LCT. FUT2 is responsible for the transfer of terminal fucose residues to mucosal glycans [B. longum is significantly lower in non-secretors  compared to secretors [To study the influence of individual genetic variants on microbiome composition, we began with known associations of  glycans . Bifidobecretors .B. longum had the strongest correlation of the 118 tested species . We used linear regression to predict the relative abundance of each individual species based on the secretor genotype dosage. LCT has been associated with increased abundance of lactose-metabolizing Bifidobacteria in the gut [LCT encodes lactase, the enzyme responsible for breaking down lactose in the upper GI tract; in tandem with increased Bifidobacteria, this suggests that more dietary lactose collects in the large intestine. The ability to produce lactase in adulthood or lactose intolerance (hypolactasia) is controlled by the presence of a homozygous G allele in rs4988235 SNV close to LCT [Similarly,  the gut , 25. LCTe to LCT  supportst test to compare the 64 donors with the homozygous G genotype to the rest of the 145 donors. After testing each of the 118 individual species abundance against the presence of this variant, we found that B. longum had the strongest effect (FDR = 0.095), thereby confirming the previously found association , which, in combination with our modest sample size, limits our discovery potential to associations with very large effect sizes.Finally, we assessed associations between host genome and microbiome variation in a non-targeted manner directly through a genome-wide association study. We performed the analysis separately for each body site, concentrating on SNVs with MAF > 0.1 and comparing them to both microbial species- and metabolic pathway-level abundances. We used ordinary linear regression models, taking into account the effects of sex, ethnicity, and sample collection location. After filtering the microbial features (see Methods), we identified 120–160 species in GI tract and oral samples and approximately 25 species in skin and vaginal samples. The number of metabolic pathways passing filtering was considerably higher, between 350 and 530 major pathways per site. Together the large numbers of SNVs, body sites, and microbial features in the analysis impose a strict significance criterion  Genome-Wide Association Studies (GWAS) Catalog , hypothep values smaller than multiple testing-corrected significance limits. However, there were a number of associations  are of course not enough for a typical association discovery study, the example applications we investigate here are representative of how this data resource might be used in the future. In combination with larger, targeted populations, for example, the HMP cohort can now be used as a baseline, comparator, or validation in microbiome-genetic association studies at most body sites of interest. The high-quality host and microbial data here are appropriate for future meta-analyses and as a methodological framework to make larger discovery efforts more efficient and less costly.Previous comparisons between monozygotic and dizygotic twins , 52 haveMicrobial communities are highly adapted to the environment of particular body locations . We founIn our genome-wide association analysis, no individual associations reached the threshold for statistical significance. This is contrast with the findings of Blekhman et al. , who repLCT variants and Bifidobacterium abundance that has also been validated in additional external studies [SULT2B1 that are relatively strongly associated with Actinomyces viscocus in skin (p = 2.4 × 10–8). This gene is involved in processing dehydroepiandrosterone, a hormone implicated in epidermal thickness and sebum production of the skin [ERAP1) gene, which is involved in antigen presentation and associated with inflammatory bowel disease [ERAP1 was associated with Actinomyces graevenitzii (p = 3.9 × 10–8), a normal member of the oral microflora that can, under some circumstances, act as an opportunistic pathogen and cause pulmonary abscesses [ERAP1 were associated with Lachnospiraceae bacteria (p = 3.2 × 10–8). Interestingly, Lachnospiraceae was the most differentially regulated microbial family in the terminal ileum of patients with ankylosing spondylitis [ERAP1 [Nevertheless, many findings between the two datasets were qualitatively similar, including an association between  studies , 25, 26.the skin . In addi disease  and a nu disease –57, appebscesses . In the ndylitis , a diseas [ERAP1 . These aNOD2-Enterobacteriaceae association found in patients with inflammatory bowel disease, it was calculated that detecting the same association in a genome-wide significant manner would require 3700 patients [In general, to reliably identify genetic influences on microbial features, it will be necessary to increase the number of samples with both host and microbial sequencing. This is particularly true when combining baseline “healthy” and disease-specific populations, genetic variants, and microbial variants. For example, based on the patients . In thispatients , 26 emphFUT2 and LCT), we showed that the HMP genetic data provide power to validate targeted hypotheses and contribute evidence to meta-analyses. Given that the HMP features broad biogeographic microbial sampling across the body, and the human sequencing data are of high quality and consistent with data from projects such as GoNL and 1000 Genomes, these data are well suited for such purposes. The comprehensive nature of whole-genome sequencing makes the data versatile, allowing the combination of HMP data with genome analysis technologies from genotyping arrays for common variants to exome sequencing for coding variation or other targeted approaches.Combining HMP data with data from other cohorts through meta-analysis or as a validation cohort is thus one way to facilitate future studies. By validating two previous genotype-microbe associations (LCT and FUT2 genes associated significantly with the abundance of B. longum in stool. In a broader, untargeted genome-wide setting, we did not identify significant associations with single variants, mainly due to stringent multiple testing criteria imposed by the numbers of microbial features and body sites in combination with a relatively modest sample size. The top associations, however, provide an initial picture of body-wide host-microbial interaction potential. In addition, the dataset as a whole, when paired with the comprehensive microbiome sequencing already performed on this cohort, constitutes an invaluable resource for further studies.Here, we present the results of whole-genome sequencing of donors from the HMP healthy cohort study, enabling the study of host genetic effects on the microbiome properties of multiple body sites in this cohort. We detected significant correlations between high-level genetic features and both microbial species and community functional profiles. Using these data, we verified that variants near the