N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R2 = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank , a method for multi-trait polygenic risk prediction using individual-level genotypes and/or summary statistics from large cohorts. PRS are usually constructed as a weighted sum of adjusted genetic effects through linkage disequilibrium (LD) information8. To boost predictive power of PRS, various approaches, including weighted multi-trait summary statistic best linear unbiased prediction method (wMT-SBLUP)9 and multi-trait analysis of GWAS (MTAG)10, incorporate information contained in related traits that share genetic architecture with a trait of interest. However, such approaches only utilize marginal single-nucleotide polymorphism (SNP) effects of multiple traits, not SNP effects conditional on other SNPs and thus may produce less accurate risk scores and limited prediction accuracy (PA) to some extent. There exists a necessity to develop multi-trait prediction methods using whole-genome individual-level genotypes.With the arrival of large-scale public biobanks harboring more than 500K samples, polygenic risk scores (PRS)-based methods11 and multi-trait genomic best linear unbiased prediction method (MTGBLUP)15, which treat the genetic effects as random to obtain individual and SNP risk predictors using one or more genetically correlated traits. However, MTGBLUP requires the estimation of genetic relationship matrix (GRM), which becomes computationally prohibitive as the sample size increases. Furthermore, it implicitly assumes the infinitesimal genetic architecture, which indicates all variants are causal with relatively small effect size, whereas in reality complex traits or diseases are estimated to have roughly only a few thousand causal variants on the genome17. It deserves to construct polygenic risk predictors to accommodate a broad range of genetic architectures for large-sample datasets. Penalized regression methods such as the least absolute shrinkage and selection operator (Lasso)19, the elastic net20, the adaptive Lasso21, the minimax concave penalty (MCP)23, or statistical learning approaches24 have previously been evaluated for genomic risk prediction26. These methods have the potential to be applied to GWAS data with large sample size via efficient penalized regression algorithm under the non-infinitesimal assumption. Furthermore, they can be extended to incorporate genetic effects for related traits via summary statistics by adding additional penalty functions28 while the multivariate linear mixed model of MTGBLUP requires individual-level genotype data for all traits12.The most commonly used whole-genome prediction methods for multiple traits are bivariate ridge regression method31  and NHS/HPFS/PHS cohort32  and perform systematic simulation studies and real GWAS analyses. Using large-scale GWAS datasets, we show that our multi-trait methods significantly increase the PA, as illustrated for human height (HGT) by incorporating information from body mass index (BMI), hip circumference (HIP), waist circumference (WST), and waist–hip ratio (WHR) compared to other single-trait approaches. We further demonstrate that our prediction methods outperform other multi-trait approaches, including MTGBLUP and the recently proposed MTAG methods.Here we develop a new statistical framework for cross-trait penalized regression (CTPR) for polygenic risk prediction of complex traits using individual-level data and/or summary statistics in large cohorts. In contrast to other methods based on multivariate modeling such as MTGBLUP and MTAG, our method attempts to optimize the PA only for the primary trait of interest. This strategy allows us to leverage information from other traits that is useful for the primary trait only, and thus additional traits with different degrees of genetic relatedness could be effectively utilized. Our approach takes advantage of genetic correlation among multiple traits to predict the primary trait of interest using penalized least-squares methods. Among many regularized methods, the Lasso and the MCP are evaluated for inducing a sparse solution. In order to incorporate the shared genetic effects across traits for improving PA, we propose a cross-trait penalty which is a smooth function of pairwise genetic effects. This penalty function utilizes not only individual-level genotypes but also summary statistics and thus the method can exploit many recent largest GWAS results. Because multiple traits are not required to be measured on the same individuals, any existing datasets of genetically correlated traits can be used to improve PA. Another important feature of our method is that our novel implementation of a parallel computing algorithm called Message Passing Interface (MPI) makes it feasible to apply our methods to large biobank-scale GWAS data. Based on the new computing algorithm, we apply our method to large-scale GWAS data (~1M) from UK Biobankn-fold cross-validation (e.g. n = 5) to select tuning parameters . The data for the primary trait are randomly partitioned into n equal-sized subsets, where n−1 subsets are used as a training set and the remaining one as a validation set. We select the optimal values for λ1 and λ2 that minimize the averaged mean squared error (MSE) of the primary trait over n folds. Because the closed form solution for coordinate descent algorithm over a single SNP coefficient exists, our method is computationally efficient and can be extended for parallel computing.The CTPR algorithm estimates all SNP effects for the primary trait of interest via a new multivariate penalized least-squares method utilizing information from individual-level data and/or summary statistics of genetically correlated secondary traits. We propose a new cross-trait quadratic penalty function with the Lasso or the MCP penalty to incorporate common genetic effects across multiple traits. This function induces the smoothness of the coefficients and can incorporate the prior knowledge on the similarity of a pair of traits at a given SNP via the adjacency coefficients. It effectively extracts information from the secondary traits that is beneficial for the primary trait but scales down information that is not. All coefficients are estimated in a computationally effective way using coordinate decent algorithm. We utilize To make it feasible to apply our methods to biobank-based GWAS data, we further develop a novel distributed memory parallel computing algorithm utilizing MPI. First, large-scale GWAS data are divided into non-overlapping subgroups containing SNPs in low LD within each subgroup and each MPI core is assigned to one of subgroups with its own memory. Next, we define another group, called core-group, each of which contains several subgroups. All MPI cores in the same core-group run simultaneously at each estimation step keeping all cores in other core-groups waiting till finish. In this way, coefficients within a core-group are simultaneously updated and eventually all coefficients are updated consecutively in core-group to improve the computational efficiency as well as to avoid convergence problem. This algorithm enables multiple subgroups of SNP coefficients updated simultaneously or sequentially at each estimation step and therefore it provides the approximate (time-saving) or the exact (time-consuming) solution for coordinate descent optimization. We observed these approaches produce similar predictive power as long as the convergence is achieved. The overview of our CTPR method regarding MPI algorithm for biobank-based GWAS data is described in Fig. N = 437K individuals and P = 1M SNPs from UK Biobank, which required ~1.7TB of memory with float data type (i.e. 437K*1M*4B = ~1.7TB). The CTPR ran on 40 cores (Intel Xeon CPU 2.1 GHz) with 48 GB of memory for each core, total of ~1.9 TB of memory, for up to 7 days to complete the analyses with 40 core-groups (exact solution). The running time of CTPR depends linearly not only on the sample size (N) and the number of SNPs (P) but also on the number of core-group (q), which represents O(NPq). With 10 core-groups (approximate solution), the running time of CTPR dropped to ~1.75 days and it still generated almost the same predictive performance as exact solution due to good convergence. Even when sample size increases, the running time is able to remain similar because larger sample size increases likelihood of convergence and therefore less number of core-groups are needed.To assess the computational feasibility of CTPR for biobank-based GWAS data, we simulated data using To evaluate the predictive power of CTPR in a practical way, we first mimicked the real GWAS data  for simulation and then changed three important parameters  one at a time. These simulations were designed to provide a clear picture on how each parameter would affect the gain in power of CTPR and hence we can obtain practical insight on how to increase PA for the primary trait of interest through collecting appropriate secondary traits and more samples. We further compared our proposed methods with the existing methods such as MTAG and MTGBLUP under the same simulation settings.33 explaining 45% of phenotypic variance for the primary trait and 25% for the secondary traits and varied genetic correlation among four traits . To simulate the phenotypes, we used the classical linear model and generated genetic effects from a multivariate normal distribution with the desired SNP-heritability level and genetic correlation among multiple traits. The random error for each individual was independently sampled from a univariate normal distribution. PA was assessed throughout the paper using squares of correlation between true and predicted phenotype values, which is equivalent to prediction R2.We sampled 30,000 (30K), 200,000 (200K), or 436,837 (437K) individuals for a training set and 20,000 (20K) individuals for a validation set with 955,842 (1M) common SNPs from UK Biobank. We simulated up to four phenotypes with 6.8K–12.6K causal SNPs33 which fitted a single SNP linear regression with top 10 genotype PCs. The implementation of a novel cross-trait penalty function enables the CTPR to exploit fixed SNP effects of the secondary trait. As shown, increase in genetic correlation between two traits resulted in a gain in PA of multi-trait methods (Lasso + CTPR or MCP + CTPR) over single-trait methods (Lasso or MCP) in terms of prediction R2. The Lasso + CTPR performed slightly better than the MCP + CTPR and the Lasso performed better than the MCP. We next examined if more secondary traits can improve PA of the primary trait , and LDpred with pruning + thresholding and then determined the risk score with the best predictive capacity. For single-trait approach, we used the unadjusted PRS and the LDpred-adjusted risk scores . For multi-trait approach, we first executed MTAG to incorporate information from summary statistics for multiple traits and then computed PA via PRS and LDpred . We used the same simulation settings as previous simulations. The overall predictive performance of our methods was uniformly better than that of summary statistics-based methods in simulations  were similar to single-trait methods  but MTAG-based methods performed increasingly better as number of traits increased and genetic correlation was getting larger  and they were excluded in the simulations for sample size , which consisted of 955,842 (1M) SNPs. We divided these genotype datasets into an adequate number of files for parallel computing. We computed summary statistics for all traits which were conducted via SNPTEST with age, sex, and top 10 genotype PCs.We evaluated the proposed prediction methods with real GWAS data by taking HGT as a primary trait and BMI, HIP, WST, and WHR as secondary traits. For our analysis, UK Biobank data35 on the GWAS summary statistics to estimate the proportion of phenotypic variance explained by all SNPs (i.e. heritability) for HGT, BMI, HIP, WST, and WHR using LDSC software37. We showed that 45.3% (s.e. = 4.1%), 24.3% (s.e. = 2.4%), 20.1% (s.e. = 2.1%), 18.7% (s.e. = 1.9%), and 14.7% (s.e. = 1.9%) of variance of HGT, BMI, HIP, WST, and WHR, respectively, can be explained by 1M SNP data in the UK Biobank. We showed that the genetic correlation between HGT and BMI was −0.130 and those between HGT and the other phenotypes were 0.304 (HIP), 0.162 (WST), and −0.112 (WHR). The phenotypic correlations between HGT and the other phenotypes were computed after adjusting for age and sex. HGT and BMI or WHR were shown to be negatively correlated , and HGT and HIP or WST were shown to be positively correlated  in the UK Biobank, respectively  using UK Biobank and 8.7% (23.5%) using NHS/HPFS/PHS data as a validation set  or 11.4% (MTGBLUP) to 14.5% (MCP + CTPR) or 14.6% (Lasso + CTPR) using UK Biobank and from R2 = 9.0% (LDpred + MTAG) or 8.5% (MTGBLUP) to 11.3%  using NHS/HPFS/PHS data  from UK Biobank can substantially improve PA compared to the smaller training samples (N = 30K). Similar to the 30K sample analyses, our multi-trait methods outperformed all summary statistics-based methods  as well as our single-trait methods  to 54% (ref. 39)) and thus 45.3% may not be the upper limit of the PA40. The PA for HGT improved from R2 = 35.8% (LDpred + MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) using UK Biobank and from R2 = 22.3% (LDpred + MTAG) to 29.2% (MCP + CTPR) or 29.8% (Lasso + CTPR) using NHS/HPFS/PHS data as a validation set  and thuThe scatter plots for actual HGT vs. predicted HGT of the 20K testing samples from NHS/HPFS/PHS or UK Biobank provided practical insight on how differenty our multi-trait method (Lasso + CTPR) and the summary statistics-based multi-trait method (LDpred + MTAG) generate the predicted outcomes, using 30K or 437K training samples, respectively  of causal SNPs. Recently, the proportion of underlying susceptibility SNPs for 32 complex traits were estimated using GWAS summary statistics and external LD information based on a two or three component normal-mixture model42, and 1.48% of SNPs from GIANT consortium (N = 253K) were expected as causal for human height. Most complex traits and disease phenotypes from a variety of sources were expected to have a smaller proportion of causal SNPs than human height (0.15–1.40%). Since the genetic architecture underlying these broad ranges of phenotypes have proportion of causal SNPs far from the infinitesimal model, we expect that our multi-trait methods will perform well for many phenotypes of interest. Also, we found Lasso-based methods perform increasingly better than Ridge/GBLUP-based methods as training sample size increases at the same proportion of causal variants in our simulation studies  or approximate mixture model predictors (LDpred), summary statistics-based methods perform worse than GBLUP-based methods with individual-level data in general27 and Kim and Xing28 proposed to use L1 norm of the difference between two coefficients28 as well as L2 norm. Indicator variables for SNP effects can be even employed. For this paper, we selected a Laplacian quadratic penalty to incorporate related traits because the proposed cross-trait penalty function is easier to obtain the close form solution for a single coefficient and computationally more efficient than other penalty forms. Furthermore, it imposes the smoothness over coefficients to remedy the disadvantage of Lasso or MCP penalties, which is one of the advantages of the elastic net20.For a cross-trait penalty, Li and LiOur cross-trait penalty term directly compares SNP effects of two phenotypes and does not scale SNP effects during the estimation step for genetic prediction. Therefore, it becomes an important issue to make all phenotypic values on the same scale and direction. Furthermore, our cross-trait penalty can utilize fixed GWAS summary statistics whose phenotypes and genotypes are rarely available. Since we generally have no information on phenotypes and genotypes for such summary statistics, it is necessary to implement an appropriate method to handle this issue. To make all phenotypic values on the same scale and direction, we first compute all marginal SNP effects via simple linear regression and then re-scale the secondary trait to make the slope for SNP effects of the primary trait regressed on SNP effects of the secondary traits equal to 1. This procedure ensures all SNP effects for all traits on the same scale as well as the same direction. For example, HGT and BMI are negatively correlated and thus the slope for beta coefficients of HGT regressed on beta coefficients of BMI is negative. The CTPR utilizes BMI multiplied by the slope for HGT~BMI regression as a secondary trait and it finally uses positively correlated BMI on the same scale. This has been implemented in the software CTPR . The complete pipeline used to install and execute the CTPR, including required computing resources, input file formats, and optional parameters, is provided on GitHub (see the URL below). On top of that, we are implementing the pipeline on public cloud platforms  such that users do not need to compile and install the pipeline by themselves.The adjacency coefficients in our cross-trait penalty function provide a convenient way to incorporate prior knowledge about the relation between the phenotypes. Here, we assumed no prior knowledge and thus set all adjacency coefficients to 1. To improve PA, we can use more appropriate values such as the cross-trait heritability estimates from literature on large-scale GWAS studies, which quantifies the genetic contribution to the covariance between two traits. Alternatively, we can consider SNP-specific adjacency measures based on their correlation coefficients with the respective phenotypes. Also, SNP-set specific adjacency measures can be considered based on cross-trait heritability computed from a specific SNP-set when functional information on SNPs are available. All SNPs are divided into multiple SNP-sets based on functional information and then each cross-trait heritability estimate for each SNP-set can be computed and used as adjacency coefficients.16.Although our prediction methods provide powerful predictive performance, they have some limitations. First, the proposed methods require more computation time than summary statistics-based methods such as LDpred and MTAG although our methods outperform them. Novel MPI algorithm were thus implemented using parallel computing to reduce computation time and utilize computing resource effectively. This algorithm can compute exact  SNP effects as well as approximate ones and both are remarkably similar once the convergence is achieved. Interestingly, the computation time of our method increases only linearly with the sample size, number of SNPs, and number of core-groups and also larger sample size enables us to specify smaller number of core-groups, which makes it feasible for use in future large-sample GWAS analyses. Second, the current models described here were designed only for continuous traits. In principle, the current methods can be directly applied to binary traits by treating them as continuous traits with values of 0 and 1 although the performance on binary traits still need to be evaluated using simulation and real GWAS data. In general, the CTPR can suffer miscalibration for binary traits and specifically miscalibration takes place when minor allele count multiplied by the proportion of cases is small. Thus, we recommend to use binary traits with a case proportion of at least 10% and SNPs with MAF > 0.1% in biobank-based GWAS dataIn conclusion, the proposed cross-trait prediction method described here is a powerful and practical tool to predict a trait of interest by incorporating other related traits using large-scale GWAS data. It can utilize GWAS summary statistics from many related secondary traits for better prediction. We expect our proposed method will become more useful as larger sample GWAS data become accessible via either individual-level data or summary statistics.29. Over 500,000 subjects with their phenotypes in the UK Biobank, ~488,377 subjects were genotyped at ~800,000 SNPs using the UK BiLEVE and UK Biobank Axiom Arrays from Affymetrix. Pre-phasing was carried out using a modified version of the SHAPEIT2 (ref. 43) and imputation was performed with the UK10K and the 1000 Genomes Phase 3 reference panels using IMPUTE2 (ref. 44), which resulted in ~93 million SNPs  > 10−10, we finally have 5,498,274 SNPs. Genotyping, quality control for SNPs and samples, and imputation procedures are described in detail here31. Individuals of non-European ancestry were excluded to be consistent with NHS/HPFS/PHS cohort data, which results in 456,837 individuals. For simulation studies and real GWAS analyses, we prepared 30,000 (30K) and 436,837 (437K) individuals for a training set and the remaining 20,000 (20K) individuals for a validation set.UK Biobank is a large-scale national health resource of over 500,000 individuals from across the United Kingdom (UK). Study participants were invited to 1 of 22 centers across the UK between 2006 and 2010, described in detail elsewhere45 (v.1.0.18.c). Imputation was performed using Minimac46 (v.2012-08-15), which finally led to ~31 million SNPs for each dataset  individuals to use an independent validation set , Health Professionals Follow-up Study (HPFS), and Physicians’ Health Study (PHS) into three compiled datasets based on their genotype platform types: Affymetrix, Illumina HumanHap, and Illumina OmniExpress. We eliminated any SNPs that were not in all studies or with high missingness (>0.05), which led to 668,283 SNPs in the Affymetrix, 459,999 SNPs in the Illumina HumanHap, and 565,810 SNPs in the Illumina OmniExpress dataset. Based on a pairwise identity by descent (IBD) analysis for each combined dataset, we removed individuals that are duplicates  or flagged for unexpected duplicates with the different cohort IDs but pairwise genotype concordance rate >0.99. Related individuals  were not removed in each dataset. This resulted in 8065 individuals in the Affymetrix dataset, 6787 individuals in the Illumina HumanHap, and 5,917 individuals in the OmniExpress, the total number of which is 20,769. For imputation, the 1000 Genomes Project Phase I Integrated Released Version 3 Haplotypes excluding monomorphic and singleton sites  were used as reference panel. Genotypes on each chromosome were first split into chunks and each chunk of chromosome was pre-phased using MACHK traits with nk  independent samples for each trait. We assume the first trait to be the primary one we wish to predict. Denote yki as the phenotypic value and xkij as the genotype for the jth  SNP of the ith sample with the kth trait. Assume αk is the intercept and βkj is the coefficient for jth SNP and kth trait. The coefficients can be estimated using the following least-squares method:Suppose we have P is larger than nk.Note the solution of  is equiv18 and the MCP22. The Lasso penalty, defined as + is a function which sets negative values to 0 and γ is an additional regularization parameter with large value providing smoother estimators but larger bias and less accurate variable selection. When γ → ∞, it converges to the Lasso. In practice, it can be set to a constant to reduce computational complexity.To avoid overfitting, we add to  a sparsiakk′j is the adjacency coefficient, which can be used for incorporating the prior knowledge on the similarity of the coefficient between trait k and k′ for SNP j. If we have M additional traits for which only summary statistics, not individual-level genotypes, are available for all coefficients, these traits can be also used through adding additional quadratic penalties. The modified cross-trait penalty is defined asIf we assume the phenotypes are correlated and affected by some common causal SNPs with similar effects, we can add to  another akk′j = 1 for all SNPs in this study, which assumes no prior knowledge. Finally, the sparse and smoothed estimate of β can be estimated bywhere 48, which optimizes over one coefficient at a time with other coefficients fixed, can be used for obtaining the solution of (3). Because there is a closed form solution for optimization over a single coefficient, the algorithm is very computationally efficient and can handle a large number of SNPs. Suppose, βk′j′. The univariate optimization over sgn(.) is the sign function. For MCP penalty, the solution is given byγ → ∞, −3. Let O(NP2). To reduce the computational complexity to O(NP), the residuals can be updated usingWe first standardize the data so that 2, we compute the solutions for a decreasing sequence of values for λ1 on the log scale, starting at the smallest value Considerable speedup can be achieved by organizing the iterations around the features with non-zero coefficients (active sets). After a complete cycle through all the coefficients, we iterate on only the active set till convergence. The process repeats until another complete cycle does not change the active set. Given a fixed value of λλ1 and λ2, we use n-fold cross-validation. Since our focus is to improve the prediction of the primary phenotype, we only divide the dataset for the primary phenotype into n folds, each fold will be used as the validation dataset and the remaining as the training dataset. The datasets for secondary phenotypes are not divided and will be fully used. However, if some individuals have data on both the primary phenotype and secondary phenotypes, the secondary ones are also divided into n folds for those individuals to avoid overfitting. We then run the penalized least-squares method on the training dataset and obtain the MSE on the validation dataset. We select the values for λ1 and λ2 that minimize the averaged MSE over the n folds. Though γ is also tunable, we could fix γ to reduce computational cost while still achieving good performance. We use γ = 3.0 following the author’s suggestion22.To select the tuning parameter Recent biobank-based GWAS data contain several millions of SNPs in hundreds of thousands of individuals. Due to limited memory and computing resources, it may not be feasible to update the coefficients for all SNPs simultaneously. Alternatively, we propose to divide SNPs into multiple subgroups and allocate each subgroup to an MPI core for parallel computing. Each core updates only parameters in the subgroup with the remaining coefficients transferred from other cores. Because MPI allows for communication between different cores, the data of all cores are synchronized at each estimation step. A detailed description of MPI algorithm is below.q subgroups where SNPs within the same subgroup are independent. Each MPI core is assigned to one of q subgroups and then all cores run simultaneously at each estimation step while the residuals of the specific subgroup are updated in a sequential way . When q = 1, we have one subgroup and the coefficients for all SNPs are updated sequentially. When q = P, the number of subgroups equals the number of SNPs and thus all SNPs are updated concurrently. In the former case, there is no gain in computation time and the latter case may lead to some convergence issues. There is a need to balance between them. We propose to use another group, i.e. s core-groups (s ≤ q), each of which contains several subgroups . Now, wq (≤P) mutually exclusive index sets {I1, I2,…,Iq} where Ih∩ Ih′=∅, h≠h′, and s (≤q) ordered index sets {A1, A2,…,As} where a1 <a2 <…<as for any element af∈Af. We partition the parameters q subvectors, hth subgroup in the fth core-group. In Eqs (4) and  and , the solhth subgroup in the fth core-group are easily estimated. When s = 1, the coefficients in each subgroup are updated sequentially and when s = q, the proposed MPI algorithm becomes the original coordinate descent algorithm but taking 1/q required memory on each core.Since the last two terms of K traits, each with nk samples . We selected P SNPs and set the number of causal SNPs to C (C ≤ P) which were randomly selected. To generate the phenotype, we used the following linear model: K traits, βj =  were generated from the following multivariate normal distribution: βj ~ NK if jth SNP is causal, βj = 0 if jth SNP is non-causal where D is a K × K covariance matrix with  element denoted by dlm. If we set var (yki) = 1, the heritability for kth trait is xkij and βkj are independent to each other and xkij are standardized, dlm(l≠m) can be calculated using ρlm is the correlation between βlj and βmj. Since var (yki)= 1, the random error is We simulated N = 13,685) instead of all testing samples and then conducted subsampling hypothesis tests49. The unrelated testing samples  have no relatives (third degree or closer) in the cohort and hence there should be no relatives between training and testing sets. With the same 437K training samples, we observed small reduction in PA using unrelated testing samples but difference was not statistically significant based subsampling p-values  from NHS/HPFS/PHS cohort and re-computed PA using only these samples. The results were still similar to our previous results and NHS/HPFS/PHS samples still showed smaller PA than UK Biobank testing, likely due to genetic heterogeneity between UK and US samples, and UK Biobank samples are likely environmentally more homogeneous than samples within NHS/HPFS/PHS cohort. Furthermore, the recent paper19 investigated the impact of relatives in UK Biobank on PA and concluded that there was no discernable difference in prediction results between using a training set drawn from the set of kinship-filtered samples and samples from the unfiltered set. Due to notably large number of related samples (>30%), removing these samples will result in a nontrivial decease in training sample size and PA. Because we found the statistically non-significant impact of related samples in UK Biobank on PA, we utilized full UK Biobank samples for our analyses.The UK Biobank data contains related samples, including twins, parent–offspring and sibling pairs. A total of 142,726 training samples (32.7%) have at least one relative (third degree or closer) in the cohort, which leads to 314,111 unrelated training samples. Also, a total of 6315 testing samples (31.6%) among 20,000 are inferred to be related with someone in the full cohort. In order to check how related samples between training and testing sets from UK Biobank have impact on potential inflation of PA, we re-computed PA using only unrelated testing samples (http://www.ukbiobank.ac.uk/, LDpred: https://github.com/bvilhjal/ldpred, MTGBLUP: https://github.com/uqrmaie1/mtgblup, MTAG: https://github.com/omeed-maghzian/mtag/.The URLs presented in the paper are as follows. UK Biobank: Further information on experimental design is available in the https://github.com/wonilchung/CTPR. Data analysis codes are available from the corresponding author upon reasonable request.The CTPR software is available at Supplementary InformationPeer Review FileReporting Summary


Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of Fb on TG levels. Here, we summarize the analyses of 8 papers whose authors had access to the GOLDN data and were grouped together because they pursued investigations into Fb treatment responses as part of GAW20. These papers report explorations of a variety of genetics, epigenetics, and study design questions. Data regarding treatment with 160 mg of micronized Fb per day for 3 weeks included pretreatment and posttreatment TG and methylation levels (ML) at approximately 450,000 epigenetic markers (cytosine-phosphate-guanine [CpG] sites). In addition, approximately 1 million single-nucleotide polymorphisms (SNPs) were genotyped or imputed in each of the study participants, drawn from 188 pedigrees.The analyses of a variety of subsets of the GOLDN data used a number of analytic approaches such as linear mixed models, a kernel score test, penalized regression, and artificial neural networks.Results indicate that (a) CpG ML are responsive to Fb; (b) CpG ML should be included in models predicting the TG level responses to Fb; (c) common and rare variants are associated with TG responses to Fb; (d) the interactions of common variants and CpG ML should be included in models predicting the TG response; and (e) sample size is a critical factor in the successful construction of predictive models representing the response to Fb. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of fenofibrate (Fb) on triglyceride (TG) levels . BecauseFb is a treatment for elevated TG levels. There are well-known outcomes, although individual responses are highly heterogeneous. Binding of Fb activates peroxisome proliferator-activated receptor-α, starting a cascade that leads to the modulation of genes that regulate lipoprotein metabolism and inflammation. Reduced levels of lipids and lipoproteins, including TGs, are observed after fasting, and even stronger effects are observed after a fatty meal. There are also many nonlipid effects, such as improvements in biomarkers of inflammation. Treatment is often administered to individuals with high lipid levels when changes to diet and exercise either cannot be followed or do not show the desired results . There aFollow-up: postTG = a + b × covariates + errorChange: postTG − preTG = a + b × covariates + errorANCOVA: postTG = a + b × covariates + c × preTG + errorwhere postTG is posttreatment TG level and preTG is pretreatment TG level. The definition of Change can be based either on a difference or a ratio, depending on whether a log-transformation is applied. A Percent Change model can also be applied, where an appropriate value is between 30 and 50%, depending on ln preTG [A number of well-established statistical approaches are available for modeling and/or predicting the treatment response, depending on the goals of the study. These include full longitudinal data analysis, pretreatment and posttreatment analysis, summary statistics, and end-point analyses, including survival analysis. For longitudinal analyses we use full information of the course of the considered outcome, and for the others we can use aggregated or partial information. Often linear mixed models (LMMs) or generalized estimating equations (GEEs) are incorporated to address the correlations within the data. There are several ways regression models can be applied to analyze pretreatment and posttreatment data. Three common models are (a) a follow-up model, (b) a change analysis, and (c) an analysis of covariance (ANCOVA) model. They are:ln preTG . A binarln preTG , who useThe focus of most of the 8 manuscripts in this group was predicting the TG level response to Fb treatment. There were several complexities in these data, including the nonindependence of subjects in the pedigrees and the large number of single-nucleotide polymorphism (SNP) markers, genome-wide. The analytic approaches were fairly broad, and the most common was LMM, which tests for associations between the markers and the trait, while adjusting for the statistical nonindependence of members of the same pedigree. An alternative approach, the kernel score test (KST), used by Yasmeen et al.  allows oTG and methylation levels (ML) at approximately 450,000 epigenetic markers were measured pretreatment and posttreatment in the study participants, allowing a wide variety to the analyses. In addition, approximately 1 million SNPs were genotyped or imputed in each of the study participants drawn from 188 pedigrees, permitting the integration of clinical, genetic, and genomic data. Each paper used the SNPs to inform their analyses, however only some used the TG data , 4, 6–9 Two contributions analyzed the simulated data , 9. BothAlthough SNP and TG level data were analyzed to identify predictors of TG level response by most of the investigators, the inclusion of longitudinal epigenetic and TG level data pretreatment and posttreatment provided an opportunity for interesting research questions, along with some analytic challenges, regarding ML under drug treatment. Research on epigenetic responses to Fb or other drugs has not been extensive, and the GOLDN study is exceptional in providing these longitudinal treatment response data. ML are highly variable and depend on factors such as age, smoking, treatment, and laboratory/batch/probe effects and cell type used. Here, they were assayed from the CD4+ T cells extracted from whole blood. Because of the widespread effects of lipids and inflammation, it is hypothesized that this cell type should be sufficient in the GOLDN study.cis-meQTL analysis identifying many more such sites, while accounting for 2 different probes.Beta scores, which correspond to the methylated proportion of total signal from the population-specific probe, were provided. Two different probes were used for pretreatment and posttreatment, which had serious implications, producing a confounding batch effect that presented analytic challenges for detecting Fb-responsive CpG sites  and inteThe most common considerations of all contributions were adjusting for family structure and use of covariates in the analyses. LMM were used by most to adjust for family structure , 5–7. OtIn the subsequent sections, we provide greater detail regarding (a) the primary research questions posed by the 8 studies; (b) the analytic approaches used to investigate them; (c) the data used in the analyses; (d) the primary findings of each study; (e) a discussion of the factors that help us interpret the primary findings; and (f) the conclusions that can be drawn from this collective work. Greater detail on the individual studies can be gleaned from the original manuscripts –9.The GOLDN study used a longitudinal design , 14 to spreTG and postTG, respectively. Because TG levels have a skewed distribution, some authors used log-transformed triglyceride (lnTG).Lipid levels, including TGs, were measured approximately 1 day prior to the drug intervention, with times 1 and 2 corresponding to measurements taken prior to treatment and times 3 and 4 corresponding to measurements taken after the treatment, within each pair  only 1 or 2 days apart. One can assume that TG1 and TG2 as well as TG3 and TG4 measure the same levels, except for random variation. The 8 papers in this group focused on treatment effects by using either T2 or T4, or the means of T1 and T2 or T3 and T4. These are denoted as Genetic markers were array-based SNPs. Epigenetic marker ML were beta-scores of CpG markers using a methylation array, based on CD4+ T cells and measured only at time points 2 and 4. The GAW20 simulations used the same data, except that posttreatment TG levels were simulated based on linear models.Our Genetics of Treatment Response groups investigated associations with and predictions of responses to Fb. There is a marked distinction in the aims of association and prediction analyses, although both can begin with the same CpG ML and TG levels data in a single study sample. Association analysis tests each CpG ML to identify those having a significant relationship with TG levels in the population under analysis. The goal is to then use bioinformatics and functional studies to reveal the biology driving these associations. Prediction analysis focuses on using the data in the study sample to develop an analytic model composed of CpG ML in order to predict TG levels. The ultimate goal is to use this prediction model and CpG ML in individuals who have not had their TG levels measured for prediction of their specific TG level. As an example, regression models are widely used for prediction.cis-meQTL around each CpG site, genome-wide. Some did more targeted analyses. Cantor et al. [Table r et al.  tested sr et al.  tested Tr et al.  tested ar et al. , 5–7, whr et al.  applied r et al.  and ANNsr et al.  with 10-y is a vector of traits; X is the SNP data coded according to the minor allele count; β is a vector of the regression coefficients for fixed effects; u is a vector of random effects, Φ is a matrix of pairwise kinship coefficients; and I is the identity matrix). The covariance matrix LMM approaches are widely used in genetic studies of pedigrees. This method assumes that the expected value of a trait is a linear combination of fixed and random effect predictors. Although genetic and covariate effects are modeled as fixed, family effects are considered random effects. LMM can be described as follows: methods .y is expressed as:X is a matrix of known fixed covariates; β is a vector of the regression coefficients; h(Z) = KaT is a nonparametric function that depends on the kernel matrix K = ZZT; and a is a vector of random effects, a~N. The matrix Z is the matrix of the markers, and τ is the genetic covariance component. KST investigates whether the genetic covariance component equals zero  [y.In KST, the trait , τ = 0) , which cLASSO  is a peny is expressed as:X is a matrix of known fixed covariates; β is a vector of the regression coefficients; and β are found by minimising the sum of the residual sum of squares and a penalty function:λ is a regularization parameter that controls the amount of shrinkage, and ℓ1-norm penalty which is a sum of the absolute values of the coefficients . One important property of the LASSO penalty is that it allows the coefficients to be set to exactly zero, thus performing variable selection.The trait ANN is a computational model based on a collection of nodes that are connected in layers, where the signal travels from the input layer to the output layer, including possible hidden layers. An ANN consists of the interconnections between different layers of nodes, the weights of the interconnections, and of the activation function for converting a node’s weighed input to its output.i represents the nodes of a layer; index j represents input nodes; wij and bj are weights; and g is an activation function [Each layer of an ANN can be described by a neural network function as follows:function . DiffereTable The investigations we report here are focused on the response to treatment with Fb. However, the study designs and analytic approaches used are quite varied, and the results are fairly broad. Table Four studies , 5, 6, 9ANAPC2 and KIAA1804, were selected, and both also had highly significant meQTL, providing support for the existence of Fb-responsive CpG sites. In the second, Wu et al. [cis-meQTL. By using LMM, they identified 229 SNPs associated with ML changes at 610 CpG sites. Among those, there were several consistent with what was reported previously. The most significant, located upstream of MGAT1, is known to be related to TG levels or lipid accumulation [RHCE, is associated with a well-known lipid SNP, rs10903129, in the gene TMEM57. These studies provide additional support for the existence of Fb-responsive CpG sites.In the first study, Cantor et al.  addresseu et al.  conductemulation . EnrichmFTO), rs2206135 (CTNNBL1), cg13438334 (DGAT1), and cg22390041 (ALDH4A1) are located in genes known to be associated with obesity risk. In the fourth study, Yasmeen et al. [p values were observed. Their results support the importance of considering the interactions of SNPs and ML when modeling the effects of Fb on TG levels, and illustrate that KST is appropriate for modeling treatment response with epigenetic data.In the third study, Xia et al.  evaluateDNMT3L, which is known to regulate DNA methylation activity and is associated with obesity [MACROD2 in the response to Fb.Two manuscripts provided support for the association of common variants with the TG-level response to Fb. Xu et al.  identify obesity , was ide obesity  conducteTwo papers focused on analytic questions regarding sample size and correction for the nonindependence of pedigree members. Cherlin et al.  exploredThis manuscript summarizes the aims, methods, study designs and results of 8 GAW20 investigations that were grouped together because they focused on the genetics of responses to treatment with Fb and the methods to examine it. The questions addressed and methods applied were derived from the longitudinal TG and ML data collected before and after Fb treatment in the GOLDN study. SNP data permitted both targeted and genome-wide assessments of genetic associations with TGs. ML data on CpG sites permitted targeted and genome-wide meQTL analyses. Genetics and genomics data on the same individuals undergoing treatment allowed an analysis of their interactions in the prediction of response. Analytic methods, where the number of genetic and genomic predictors is larger than the sample size, were applied, and findings indicate that an adequate sample size is critical. The 8 manuscripts clearly illustrate that correcting for the nonindependence of individuals within pedigrees using LMM to identify SNP associations with Fb response is straightforward, but correction when developing more complex models is not. This suggests that there is a need for the development of additional methods to accommodate such data.The variability among individuals in their responses to drug treatment is often ignored, but as medicine moves toward greater precision in caring for patients, this area of investigation will grow. Currently, the complex influences on drug responses are understudied and often unknown, although there are exceptions for those variants exhibiting a Mendelian impact. The work summarized here clearly indicates that the inclusion of genetics and genomics data in a longitudinal drug treatment study is feasible and that such study has the potential to affect the precision of prediction.The 8 summarized papers explore the genetic and genomic influences on the differences in drug responses among individuals and the appropriateness of study design elements and analytic methods to detect them. For example, the novelty of measuring pretreatment and posttreatment epigenetic ML invited questions regarding the responses of CpG sites to treatment with Fb, as well as the predictive role of ML changes in the known TG response to Fb. The former studies were hampered by a batch effect between pretreatment and posttreatment ML, and this confounding design element should provide a note of caution to future studies. However, these data and the analyses we report were successful in providing support for the notion that the ML of some CpG sites respond to Fb. Future studies can be designed to ensure there is no confounding batch effect, and the specific findings identified here can be studied for replication. In addition, given an unforeseen batch effect, a genetic approach to identify candidate CpG sites for meQTL studies, like the one used here , may be Study design remains an important issue for drug response work, and a critical issue is the development of adequate samples. As with other studies of complex traits, effect sizes of individual SNPs and CpG sites are likely to be small and difficult to detect. This is especially important when there is genome-wide multiple testing of interactions, and rare variants. In addition to sample size, the nonindependence of pedigree members was a concern. One may posit that the number of samples in the GOLDN pedigrees would provide more statistical power if they were collected on independent individuals. However, family data are likely to be more homogeneous, which can increase statistical power. The studies reported here corrected for the nonindependence of the pedigree members rather than capitalizing on the genetic transmission of information among family members. Although studying the transmission of the drug response in pedigrees is a more attractive approach, having complete data is unlikely because only some pedigree members take the treatment drug.Several conclusions are drawable from the 8 GAW20 manuscripts addressing responses to treatment with Fb summarized here. Regarding genomics, we can conclude that some CpG ML are responsive to Fb. In addition, CpG ML should be included in models predicting the TG responses to Fb. Regarding genetic contributions, both common and rare variants are associated with TG responses to Fb. Furthermore, genetics and genomics should be combined to include the interactions of common variants and CpG ML in models predicting the TG level response to Fb. Regarding study designs, multiple classes of models and statistical analyses are appropriate for these studies, and sample size is a critical factor in the successful construction of predictive models representing the response to Fb.


Exploring the natural occurring genetic variation of the wild barley genepool has become a major target of barley crop breeding programmes aiming to increase crop productivity and sustainability in global climate change scenarios. However this diversity remains unexploited and effective approaches are required to investigate the benefits that unadapted genomes could bring to crop improved resilience. In the present study, a set of Recombinant Chromosome Substitution Lines (RCSLs) derived from an elite barley cultivar ‘Harrington’ as the recurrent parent, and a wild barley accession from the Fertile Crescent ‘Caesarea 26–24’, as the donor parent  have been utilised in field and controlled conditions to examine the contribution of wild barley genome as a source of novel allelic variation for the cultivated barley genepool.Twenty-eight RCSLs which were selected to represent the entire genome of the wild barley accession, were genotyped using the 9 K iSelect SNP markers  and phenotyped for a range of morphological, developmental and agronomic traits in 2 years using a rain-out shelter with four replicates and three water treatments. Data were analysed for marker traits associations using a mixed model approach.We identified lines that differ significantly from the elite parent for both qualitative and quantitative traits across growing seasons and water regimes. The detailed genotypic characterisation of the lines for over 1800 polymorphic SNP markers and the design of a mixed model analysis identified chromosomal regions associated with yield related traits where the wild barley allele had a positive response increasing grain weight and size. In addition, variation for qualitative characters, such as the presence of cuticle waxes on the developing spikes, was associated with the wild barley introgressions. Despite the coarse location of the QTLs, interesting candidate genes for the major marker-trait associations were identified using the recently released barley genome assembly.This study has highlighted the role of exotic germplasm to contribute novel allelic variation by using an optimised experimental approach focused on an exotic genetic library. The results obtained constitute a step forward to the development of more tolerant and resilient varieties.The online version of this article (10.1186/s12870-018-1527-7) contains supplementary material, which is available to authorized users. Breeding programmes have successfully increased yield and quality traits of major cereals including barley , 2. HoweHordeum are preserved in gene banks worldwide, second only to rice  and wheat  (FAO 2010) and provide an enormous potential resource for allele mining. There have been many descriptive and comparative studies examining diversity in wild and landrace barleys, but few examples utilising the allelic diversity have been reported ) and the recurrent parent ([Hv]) alleles obtained for the significant peak region were used to calculate the relative contribution (RP) of the exotic parent alleles on the trait performance as follows:The Best Linear Unbiased Estimates (BLUEs) for each trait for the donor ([2) were used to identify possible putative candidate genes underlying the predicted phenotypic variation. In this case the number of genes in the targeted regions was first estimated by looking at the physical position of the iSelect SNP markers in the newly released barley genome assembly [The position of major determinant genes related to plant phenology and morphology was estimated from previous studies and public gene databases to support the results obtained from this analysis. Additionally, some genomic regions accounting for a significantly large proportion of a trait’s variance  of the k-th principal component axis, γik and δjk are the genotype and the environment scores (eigenvectors) for the k-th principal component axis, ρij is the interaction residual and εijk is the random error.Values for yield (DY) and grain weight (TGW) for each of the RCSLs were used to investigate the effect of the exotic genome in the performance and stability of the crop across the environmental conditions imposed in the field. Genotype by environment (GE) interaction was studied by means of an additive main effects and multiplicative interaction model (AMMI) for which six environments were defined as the combination of growing season and water treatment. Using this method, the overall variation observed for yield traits such as dry yield and thousand grain weight was partitioned into genotype main effects, environment main effects and GE interaction . The modAs a result, the interaction principal components generated (IPCA1 and IPCA2) are used to graphically summarise (biplots) the GE variation observed. The AMMI2 biplot uses the genotypes and environments scores for the first two IPCA components (IPCA1 on the X-axes and IPCA2 on the Y-axes) giving information about the GE patterns observed. Genotypes with IPCA scores close to zero are more stable or widely adapted to the tested environments whereas specific adaptation of the genotypes is determined by the length of the orthogonal projection of the genotype points onto the environmental vectors. In addition, the AMMI1 biplot tests the genotypes yield potential and stability simultaneously by plotting in the same diagram the average yields (X-axes) and the first dimension measure of GE interaction (IPCA1) for both genotypes and environments (Y-axes). AMMI model was computed using Genstat 17 .Additional file 1:Table S1. RCSLs genotypes determined for 1848 SNP markers from the 9 K SNP chip for barley [r barley .  2013 and (B) 2014. Accumulated rainfall (mm) values per week (x axes), air maximum and minimum average temperature values per week (± SE). Data obtained from James Hutton Institute weather station . Field trials were established in the week 16 both years. (DOCX 28 kb)Additional file 3:Figure S2. Volumetric water content (ml cm− 3) in the soil profile at (A) 100 mm, (B) 200 mm, (C) 300 mm and (D) 400 mm depth in the full irrigated (blue), partial irrigated (green), and drought (red) water treatment in 2013 (left) and 2014 (right) field trials. Vertical dashed lines indicate beginning and end of heading time referred to days after sowing (DAS) for year. Error bars indicate standard error of the mean. (DOCX 258 kb)Additional file 4:Table S2. Climate data before sowing, during seedling establishment, before heading and during heading in the 2013 and 2014 growing seasons. Air and soil mean temperature values (± SE) and accumulated rainfall (mm) values obtained from the James Hutton Institute weather station . (DOCX 15 kb)Additional file 5:Table S3. Genotypic mean values (± SE) for the morphological, developmental and agronomic traits registered in 2013 and 2014 field trials. Genotypes are arranged in ascending order from top to bottom of the table for collar height (COL), peduncle length (PdL), ear length (EAR), seed area (SdA), seed length (SdL), seed width (SdW), heading date (HEA), number of tillers (TILL), dry yield (DY), thousand grain weight (TGW), biomass yield (BY) and harvest index (HI). Best linear unbiased estimators (BLUEs) for genotypes labelled with different letters differ significantly . Harrington mean values highlighted in bold. (XLSX 20 kb)Additional file 6:Table S4. Significant quantitative trait locus (QTLs) detected on the RCSLs exotic genetic library associated with thirteen morphological, developmental and agronomic traits. One trait per tab. (XLSX 167 kb)


Pinus sylvestris has a long history of basic and applied research that is relevant for both forestry and evolutionary studies. Its patterns of adaptive variation and role in forest economic and ecological systems have been studied extensively for nearly 275 years, detailed demography for a 100 years and mating system more than 50 years. However, its reference genome sequence is not yet available and genomic studies have been lagging compared to, for example, Pinus taeda and Picea abies, two other economically important conifers. Despite the lack of reference genome, many modern genomic methods are applicable for a more detailed look at its biological characteristics. For example, RNA‐seq has revealed a complex transcriptional landscape and targeted DNA sequencing displays an excess of rare variants and geographically homogenously distributed molecular genetic diversity. Current DNA and RNA resources can be used as a reference for gene expression studies, SNP discovery, and further targeted sequencing. In the future, specific consequences of the large genome size, such as functional effects of regulatory open chromatin regions and transposable elements, should be investigated more carefully. For forest breeding and long‐term management purposes, genomic data can help in assessing the genetic basis of inbreeding depression and the application of genomic tools for genomic prediction and relatedness estimates. Given the challenges of breeding  and the economic importance, application of genomic tools has a potential to have a considerable impact. Here, we explore how genomic characteristics of P. sylvestris, such as rare alleles and the low extent of linkage disequilibrium, impact the applicability and power of the tools. It is also found in the mountainous areas of Mediterranean peninsulas in Southern Europe. It is especially competitive in poor soils and in dry and extremely cold environments, and compared to many other Pinus, its ecological niche is wide  in most parts of the distribution  trials in France comes from as early as 1745 , efficient gene flow, predominantly outcrossing nature, and large 22 Gbp genome, yet strong phenotypic differentiation in adaptive traits, make P. sylvestris an intriguing system to examine adaptive processes of polygenic traits. Lack of significant longitudinal structure allows replicating sampling along latitudinal transects and inspecting whether the same loci or variants are participating in the adaptive trait variation throughout the distribution or whether the adaption has emerged via different combinations of adaptive alleles. In comparison with, for example, the North American interior spruce complex , inference of genetic relatedness, and by genomic prediction  mating system and inbreeding depression, (b) genetic diversity and linkage disequilibrium, (c) population structure, history, and allele frequency distribution, (d) adaptive variation at trait and molecular level, (e) genome size and architecture, and (f) application of genomics in breeding and conservation.In this review, we consider biological, life history, and genomic characteristics that are relevant for future applications of genomic information in 2Pinus sylvestris, as many other forest trees, is wind‐pollinated, diploid, and predominantly outcrossing. The selfing rate measured in mature seeds is about 5%–10%  offspring to that of controlled outcrossed or open‐pollinated offspring. In pines, embryonic mortality can be evaluated by examining the proportion of empty seeds from different types of crosses . Furthermore, inbreeding depression likely was due to partially deleterious alleles at more than 100 loci, even if overdominance effects could not be fully excluded. Similar methods could help to identify genomic areas with lethals also in conifers. So far, various functional and evolutionary prediction models of allelic substitution have been used to identify deleterious alleles. This has then allowed comparisons of allelic and genotypic frequencies between populations or species, as in Populus , the nonrandom association of alleles generally does not extend far, and genotypes are in Hardy–Weinberg equilibrium as expected under random mating  seems to converge to 0.006/bp  is expected to equal the pairwise nucleotide diversity, θ . The mutation rates per bp per year vary from 0.22 to 1.3 × 10−9  effect of linked selection permeating most of the genic areas, thus reducing genetic diversity , which would predict local effects of linked selection nearby genic regions, as observed, for example, in maize that also has low LD  and extent of LD are critical factors in breeding as they determine how selection on a subset of loci will affect other nearby loci and essentially determines the genomic resolution of many breeding efforts. In an equilibrium situation, LD depends on both c and eN and the population‐level recombination parameter ρ = 4ecN be estimated from LD patterns. Further, c can be independently estimated with genetic mapping. In a practical context, it is good to remember that c affects the precision of, for example, QTL and other genetic mapping efforts, whereas ρ is more significant for association studies.The recombination rate per bp  that is more informative about the recombination rate suggests more extensive LD than the value from the overall r2 decay  in the more continuous part of the distribution  type. Bradburd, Coop, and Ralph  model  hardly shifts the distribution of Tajima's D from the equilibrium population expectations . SavolaP. sylvestris. In this Bayesian approach, the presence of a within‐population association signal is required across different populations to exclude spurious associations. Other new promising methods, akin to QST−FST comparisons, for identification of polygenic adaptation rely on principal components of relatedness derived from genomic data and phenotype data obtained from common garden studies  and P. lambertiana annotations (Box ) reach at most 58% gene space completeness . Current Box 11P. sylvestris  contain P. sylvestris transcriptomes are available for needles, pollen, vegetative buds, phloem, and various developmental stages of embryos and megagametophytes .De novoTreeGenes  tree genomes, annotations, transcriptomes, and other resources and tools. It also provides P. sylvestris data obtained from NCBI for ESTs, cDNA, and TSA (Transcriptome Shotgun Assembly) databases. Somewhat overlapping with ConGenie.ConGenie  allows access to multiple conifer reference sequences, annotated genes, transcriptomes, and ESTs.PIER database  contains sequence information on 19,194 repeats first identified in P. taeda.Gymno PLAZA 1.0  can be used, for example, to identify sequencing reads originating from organellar genomes.STAPLER Pipeline maker  have not been identified. Even though the genomic resources at the moment for P. sylvestris are limited, new techniques, such as ATAC‐seq, allow targeting the open chromatin and therefore access the regulatory regions of even large plant genomes  on trait and adaptive variation via regulating gene expression have become evident across a wide variety of species , it is probably the most comprehensive reference especially in the case when, for example, read data from targeted sequencing contain a lot of off‐target sequence not present in the transcriptome. Mapping to a diverged reference is bound to lead to some extent of reference bias and, for example, exclusion of highly diverged, potentially evolutionarily important genomic regions . Box is a collection of useful genomic resources and databases available for P. sylvestris at the moment.7P. sylvestris response to future environmental changes. Predicting responses to artificial or natural changes requires an understanding of both basic biological properties, evolutionary processes and genomic basis of adaptive trait variations, combined with practical aspects and reality of breeding and conservation activities. Two main methods, GWAS and genomic prediction, hold most potential to link genomic and phenotypic data together for application purposes.Combining the phenotypic information from the phenotype and theory‐based knowledge with modern genomic technologies provides an opportunity for improved breeding methods and better predictions of P. sylvestris have been based on a limited number of mostly candidate loci. Exome capture and transcriptome sequencing methods have now identified enough SNPs to enable design of efficient and affordable chip‐based genotyping methods . Also, genotyping‐by‐sequencing . HoweveNe and family structure in breeding populations can increase the extent of LD. The observed LD patterns and empirical results in other conifers suggest that genomic prediction is feasible in cases where selection takes place within families (Chen et al., P. sylvestris breeding, for example, in Finland is conducted in such a setting and allows genomic prediction to be developed.Genomic prediction is based on the idea that a limited number of genetic markers can inform about areas of the genome responsible for trait variation. It relies on extensive LD between genetic markers and the causal polymorphism, typically not observed at the population‐level samples. However, reduced P. sylvestris breeding. Recent studies have shown that rare alleles may account for a considerable proportion of phenotypic variation and that some of these rare alleles may be deleterious, maintained by mutation‐selection balance (Wainschtein et al., The low success of transferring genomic prediction models across different breeding populations is due to differences in the patterns of LD between populations. This limitation has been observed in trees Isik, , as wellP. sylvestris, each family or other subsample will have their unique set of rare variants. They are likely to be missed by, for example, general purpose SNP chips and can only be identified by sequencing‐based methods. Population stratification in rare alleles is likely to differ from the pattern observed with more common alleles as they often show more spatial clustering. Therefore, more rigorous methods to deal with the fine spatial structure are needed when working with rare variants (Mathieson & McVean, P. sylvestris, a topic not well covered so far.In P. sylvestris (Dyderski, Paź, Frelich, & Jagodziński, P. sylvestris in northern areas (Bärring, Berlin, & Andersson Gull, Warming temperatures are already causing range shifts both at the northern and southern distribution limits of species, including Along with the longer growth periods, also many forest disturbance effects are likely to take place. According to Seidl et al. , temperaP. sylvestris research and also applicable to many other forest trees. Bridging the artificial gap between quantitative, population, and evolutionary genetics is necessary for an in‐depth understanding of causes and consequences of, for example, clinal adaptive variation, inbreeding depression, large genome size, and skewed AFS in P. sylvestris.In summary, new genetic technologies and the molecular diversity they reveal—DNA polymorphisms, expression changes, structural, and repetitive element variation—are opening many venues to take a new look into questions identified by earlier None declared.


Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits these effects are smaller, with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0 and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20,000 individuals in the Generation Scotland family cohort genotyped for ~700,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWAS of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence, and education is consistent with mutation-selection balance. Generalg . All of Firstly, using a recently developed analytic design for combined pedigree and genome-wide molecular genetic data, we test whether rare genetic variants, CNVs, and structural variants make an additional contribution to the genetic variance in intelligence, neuroticism, and extraversion. Secondly, using unrelated individuals, and genotype data imputed using the Haplotype Reference Consortium , 4 (HRC)General intelligence has been found to be heritable, with twin and family studies estimating that 50 to 80% . HeritabAs directional selective pressure on a trait is expected to deplete its genetic variation, the existence of such robust heritability findings seems paradoxical when evolutionary theory is considered . HoweverPopulation genetic simulations show that very rare (MAF < 0.1%) variants explain little of the population variance in traits that are not under selection . HoweverEmpirical studies so far have failed to find evidence of a link between intelligence and rare variants . These sLess is known about the genetics of personality . As withh2g), and genetic effects associated with kinship (h2kin) . Among related individuals, LD is stronger and hence allows us to capture variation not tagged by common SNPs at the population level. This includes rare variants, CNVs, and other structural variants. As the inclusion of family members can introduce confounding between shared genetic effects and shared environmental effects [In the current study, we quantify the total genetic effect across the autosomes on intelligence  were sampled from Glasgow, Tayside, Ayrshire, Arran and North-East Scotland of whom 23,919 donated blood or saliva for DNA extraction. These samples were collected, processed, and stored using standard procedures and managed through a laboratory information management system at the Wellcome Trust Clinical Research Facility Genetics Core, Edinburgh [P-value < 10−6, and a missingness of > 5%. This left 519,729 common SNPs from 22 autosomes. Following quality control, a total of 20,032 genotyped individuals  were retained; 18,293 of these individuals were a part of 6578 nuclear or extended families [Data was used from the Generation Scotland: Scottish Family Health Study (GS:SFHS) , 45, 46.dinburgh . We thendinburgh . that infamilies . The meaThe Tayside Research Ethics Committee (reference 05/S1401/89) provided ethical approval for this study.g), years in education (Education), neuroticism, and extraversion were examined using GREML-KIN, and GREML-MS. Four cognitive tests were used to derive general intelligence; the Mill Hill Vocabulary Scale (MHVS)  [r = .90) [g) was derived by extracting the first unrotated principal component from the four cognitive tests. This single component accounted for 42.3% of the variance in the total sample and each of the individual tests used demonstrated strong loadings on the first unrotated component . Education was calculated in the GS:SFHS as the years of full time formal education, which was recoded into an ordinal scale from 0 to 10 . Education and general intelligence were positively correlated .General intelligence ( = 0.90) , 51, ther = .90) , Wechsler = .90)  and execr = .90) . The genThe other two measures examined were the personality traits of extraversion and neuroticism, which were measured using the Eysenck Personality Questionnaire Revised Short Form, a self-report questionnaire requiring a yes or no response to 24 items . Both scThe effects of age, sex and population stratification were adjusted for using regression prior to fitting the models in GREML. Supplementary Fig. h2g, the proportion of phenotypic variance explained by common SNPs, and secondly, the additional genetic effects associated with pedigree can be captured by K and used to derive h2kin, the proportion of phenotypic variance that is explained by genetic effects that are clustered within families. More recently this method has been extended by Xia and colleagues [h2g) at the population level and those associated with pedigree (h2kin), respectively. These two genetic sources of variance were quantified using a genetic relationship matrix (GRM) derived in the GCTA software [For each of the phenotypes examined here, variance was partitioned into five corresponding effects plus residual variance. This variance components analysis is based on the work of Zaitlen and colleagues  who develleagues  to adjussoftware .g) was used to derive the variance component of G in order to quantify the contribution made by common SNPs, h2g. This was derived in the manner set out by Yang and colleagues [A genomic relationship matrix  for individuals j or k at locus i is described as xji or xki. N indicates the total number of SNPs.MAF for SNP kin, (used to derive the variance component K) was derived using the method described by Zaitlen et al. [g. Here, values in the GRMg that were equal to or <0.025 were set to 0.The kinship relationship matrix, GRMn et al.  by modifN by N matrix  with diagonal entries set to 1 and non-diagonal entries set to 1 if the pair of individuals have the relationship described, or set to zero otherwise. The three SRMs derived here captured variance associated with the similarity of spouses, , siblings  and nuclear families . As discussed by Xia et al. [Couple will very likely will capture some effects of assortative mating, which can be couples who, before meeting, were more similar than chance in terms of the trait under investigation, as well as couples who become more similar due to time spent together. Dominance effects can be captured with the SRMSibling. These variance components were derived in an identical manner to Xia et al. [Three similarity matrices (SRM) were used to capture the variance associated with specific relationships between individuals. Each SRM was created by deriving an a et al. , whereasa et al. , howeverFor each trait we first fitted the two GRMs and the three SRMs simultaneously using a linear mixed model (LMM) using the GCTA software , 57. ThiY is a vector of standardised residuals derived from one of the phenotypes. Random genetic effects were explained by fitting the G and K, which captured variants in LD with common SNPs found across a population and the extra genetic effects captured by the increase in LD found between members of the same extended family, respectively. Similarity between related pairs of individuals was adjusted for by fitting the F, S and C to control for the contributions made by environmental similarities, as well as any effect of assortative mating and dominance, that could result in members of a nuclear family, siblings, and couples, being more similar. This enabled us to extract the variance associated with these matrices from our genetic matrices to ensure that the genetic estimates derived are not biased by these confounding influences.Here, h2g (common SNP-associated effects), h2kin (pedigree-associated genetic effects), ef2 (family effects), es2 (sibling effects) and ec2 (couple effects) were estimated (Table Restricted maximum likelihood (REML), implemented using the GCTA software , was useFamily matrix and GRMkin matrix is information on the ~18k 1st degree relatives. However, SRMFamily holds ~1.8k pairs of unique entries (couple pairs) and GRMkin holds ~23 k pairs of unique entries . The unique entries from both matrices result in an increase of power, which allows the disentangling of the variance from those two different sources.Despite collinearity between the five matrices, simulations conducted by Xia et al.  show thakin and SRMFamily, helps to capture the property of pedigree-associated genetic variants. This logic extends to separating the variance from each of the similarity matrices. Although SRMCouple and SRMSib are nested within the SRMFamily, there are 9853 pairs of unique entries (representing parents-offspring) within the SRMFamily, which helps to separate the similarity matrices. As shown by Xia et al. [An additional point is that the pedigree-associated genetic effects decay as the distance of the relationship increases, whereas nuclear family similarity effects do not. Thus, the fact that GS:SFHS consist of different classes of relatives, as well as the unique entries within the GRMa et al. , this meAn additional caveat of the GREML-KIN method is that it requires a cohort of related individuals where there are a sufficient number of different degrees of relatives and family members so that pedigree genetic effects can be disentangled from the family environmental influences. Due to these requirements, a simulation study should be conducted in a data set before using GREML-KIN. The goal of these simulations is to examine whether there are a suitable level of appropriate relationships, and whether the confounding environmental and genetic factors can be discomposed accurately in the population under investigation as was shown by Xia et al. in the ~20,000 members of GS:SFHS.Sib, SRMFamily, and SRMCouple to measure the similarity shared between siblings, nuclear family members, and couples, respectively. The similarity shared between siblings is a product of additive genetic effects and dominance genetic effects, in addition to any environmental influences. However, as GRMg and GRMkin capture additive genetic effects, only environmental influences and some of the total dominance genetic effect remain in SRMSib. Similarly, SRMFamily models similarity between nuclear family members, which is composed of additive genetic effects and environmental factors. In the presence of GRMg and GRMkin, what remained in SRMFamily will be variance attributable to environmental factors. The SRMCouple represents the similarity between couples, which can be due to assortative mating and shared environmental influences.In the current study, we computed SRMkin in GREML-KIN captures the contributions made by genetic variants poorly tagged by genotyped SNPs and is not confounded by the inclusion of close relatives, we replicated our results using unrelated individuals. Using genotyped data, imputed using the Haplotype Consortium (HRC) [https://imputation.sanger.ac.uk/). A quality control check was performed by checking autosomal haplotypes to ensure that strand orientation, reference allele and position matched the reference panel. Data were then pre-phased using the Shapeit2 duohmm option provided by the Shapeit2 v2r837 software [In order to show that the GRMum (HRC) , 4 data software –60, whersoftware . Finallyg, -i.e., estimated by using all genotyped common SNPs on the autosomes for the whole population. To test whether the additional variance captured by our GRMkin is due to less common variants, imputed and genotyped variants were assigned to one of six matrices describing the frequency of the minor allele. The six bins, and the matrices derived using them, were MAF = 0.001–0.01 (GRM0.001–0.01), MAF = > 0.01–0.1 (GRM0.01–0.1), MAF = > 0.1–0.2 (GRM0.1–0.2), MAF = > 0.2–0.3 (GRM0.2–0.3), > 0.3–0.4 (GRM0.3–0.4), MAF = > 0.4–0.5 (GRM0.4–0.5) [A relatedness cutoff was applied to the participants of GS:SFHS of 0.025 resulting in a sample size of 7370. Note, that relatedness was based on the GRM0.4–0.5) . These sg) the final model was the GKSC model, allowing for a significant contribution from additive common genetic effects, additive pedigree-associated genetic variants, sibling similarity and couple similarity. For g, common SNPs (h2g) explained 23% (SE = 2%) of the phenotypic variation. Pedigree-associated genetic variants (h2kin) added an additional 31% (SE = 3%) to the genetic contributions to g, yielding a total contribution of genetic effects of 54% (SE = 3%) on g. The sibling effects (es2) and couple effects (ec2), accounted for 9% (SE = 1%) and 22% (SE = 2%), respectively. As noted previously, these estimates could also include effects of dominance and assortative mating, respectively.The results of the full GKFSC models as well as the results of the selected models, can be seen in Table g and education are similar to the heritability estimates derived using the traditional pedigree study design in the same data set, which found a heritability estimate of 54% (SE = 2%) for g and 41% (SE = 2%) for education  of the variance with pedigree-associated variants explaining an additional 19% (SE = 3%). The F, S, and C components were not statistically significant and the family similarity component accounted for only 2% of the variance in the full model and 1% in a model that included only the G and the K in addition to F.For extraversion the only detectable source of genetic variation came from the G, which accounted for 13% (SE = 2%), with F explaining a further 9% (SE = 1%) of the phenotypic variation. The lack of pedigree-associated genetic effects could be due to low statistical power, as K explained 5% of the variance in the full model and 6% in a GKF model, but with a relatively large SE, estimated at 5%.https://rubenarslan.github.io/generation_scotland_pedigree_gcta/.In addition to our model selection procedure, we also fit all possible component combinations for all phenotypes, to show a more complete account of the data and to give readers the ability to explore the consequences of including different components for the results, even when some of those components were not significant. The results have been made interactively available at The results of GREML-MS are consistent with GREML-KIN. The total contribution of all SNPs resulted in a heritability estimate of 50% (SE = 10%) for intelligence and 37% (SE = 10%) for education Table  was evidWhen examining the variance explained by MAF using GREML-MS Figs.  and 4 foWe next examined if there was evidence of selective pressure acting on the cognitive and personality variables using GREML-MS. For a trait that is not under selective pressure while the majority of genetic variants will be rare, the majority of genetic variation associated with the trait is expected to be common . A traitExtraversion also demonstrated evidence that low-MAF variants made a greater contribution than more common variants. Neuroticism, however followed the model predicted under the assumption of evolutionary neutrality.This study aimed to decompose and quantify additive genetic sources of variation to intelligence and personality in novel manners, using molecular genetic and pedigree data from the same large sample. In doing so, we sought to identify reasons for the gap between pedigree-based and SNP-based estimates of heritability in samples of unrelated individuals, a difference that might be due to genetic variants in poor LD with SNPs genotyped on current platforms. A number of novel findings speak to long-standing questions in behaviour genetics and evolutionary genetics of psychological differences , 32, 66.h2kin). The pedigree-associated genetic variants accounted for over half of the genetic effects in these phenotypes. Even though GREML-MS is expected to underestimate heritability for traits where the genetic architecture includes the contribution of CNVs, structural variants and very rare variants [Firstly, using GREML-KIN we could account for the entire heritability of general intelligence and education, as estimated in twin and family studies, by adding the G and K estimates we derived directly from genome-wide molecular genetic data , 67. Secvariants , we werevariants . HoweverThe GREML-SC method of estimating heritability from unrelated individuals using common genome-wide SNPs, often produces lower heritability estimates than those derived using family-based studies because it relies on LD between genotyped SNPs and causal variants at the population level. Should LD between genotyped SNPs and causal variants be low, then the genetic similarity between a pair of individuals at the causal variant will be different to the genetic similarity at genotyped SNPs, resulting in an underestimation of heritability. In within-family and twin studies, relatedness is based on identity by decent (IBD), where segments of DNA have been inherited from a recent common ancestor. Should a region be IBD between a pair of individuals, then all variants within that segment, except de novo mutations, are shared. Population-based SNP methods are sensitive to allele frequency, whereas IBD methods are blind to such effects. Therefore, the discrepancy between heritability estimates is consistent with the idea that causal variants in low LD with genotyped SNPs account for difference between IBD methods and population-based estimates derived using molecular genetic data.kin), would be unmeasured in a GWAS on unrelated individuals using genotyped data.In the current study, we investigate if variants in poor LD with genotyped SNPs account for additional heritability by using DNA from close family members. Higher genetic relatedness within families leads to an increase in the LD between genotyped SNPs and potentially causal variants, resulting in heritability estimates in our study that are comparable to pedigree-based methods. This provides evidence that for intelligence the gap between the heritability estimates derived using IBD methods and those derived using SNP-based population methods is most likely due to causal variants in low LD with genotyped SNPs. In addition, we were able to model this missing variance and separate it from the additive common genetic effects that are estimated in a GREML-SC analysis based on unrelated individuals. The additional source of additive genetic variance from closely related family members, captured here in our kinship matrix , past environment shared by nuclear family members (SRMFamily) and assortative mating, in addition to potential current environmental factors shared by couples (SRMCouple).It should be noted that, the average age of participants in GS:SFHS is 47.4-years-old, which means that the people still cohabiting together are most likely couples whereas parents-offspring and siblings no longer live in the same household. Additionally, dominance has been shown to have little impact on complex traits . TherefoHowever, the replication of the GREML-KIN findings with GREML-MS in the subsample of unrelated individuals provides further evidence that the heritability estimates are not majorly affected by residual confounding. Indeed, for intelligence and education both of these methods provide highly similar estimates differing by <4 and 7 percentage points, respectively, well within one standard error of GREML-MS. These estimates in turn are highly similar to the estimate found using traditional pedigree-based analyses , indicatThe patterns found in our GREML-MS analyses were consistent with the findings of Evans et al.  for neurAnother noteworthy divergence occurred between GREML-KIN and GREML-MS results for the personality traits. For extraversion, SNPs with a MAF of 0.001–0.01 explained 17.0% (SE = 9.2) while the K component explained only 4.9% (SE = 5.1) and was dropped from the final selected model. However, the G plus K estimate for extraversion is 16.2%, which is not significantly different from the total heritability estimate provided by GREML-MS (20.9%). This is consistent with the interpretation that there is an effect of the K component for extraversion, which is too small to attain statistical significance in this sample. The results of neuroticism also do not match between GREML-KIN and GREML-MS. The total heritability estimate for GREML-MS was 11.4%, similar to the G estimate, but in GREML-KIN the K explained a further 19% (SE = 2.5), while almost no effect was found for SNPs with a MAF of 0.001–0.01 using GREML-MS. As the GREML-KIN estimate is closer to twin and family study estimates of the narrow-sense heritability for neuroticism, this discrepancy might mean that the causal variants involved in neuroticism are even rarer, or perhaps due to non-SNP-associated genetic variants captured by GREML-KIN, but missed in GREML-MS. Potentially, the slightly lower measurement reliabilities for our personality measures may explain why results are less consistent than for intelligence.g and the GRMkin in the current study . By varying the frequency of the causal variants in a simulated data set, Evans et al. showed that even when using only array markers, the total variance captured by these two matrices was equal to the true heritability in the data set, irrespective of the frequency of the causal variants. Consistent with the notion that the pedigree genetic effects captured by the GRMkin are due to the effect of rare variants, GRMkin captured an increasingly greater proportion of variance as the causal variant frequency fell. The reverse was true for the GRMg, which captured less variance as causal variant frequency fell.The pattern we found using GREML-KIN is consistent with rare variants explaining much of the gap between heritability estimates from pedigree and GREML-SC analyses, although CNVs, and structural variation could also play a part, because they are poorly tagged by genotyped SNPs as well. This can be seen in Evans et al. , who useWe found further, more direct support for an important role of rare variants using GREML-MS, which showed that for each of the cognitive variables examined here, a large contribution to phenotypic variance was made by SNPs with a MAF between 0.001 and 0.01. For extraversion, almost all of the heritability was tagged by low-MAF SNPs. Altogether this indicates that the genetic signal to be found in imputed GWAS is much larger than GREML-SC estimates based on genotyped unrelated individuals would suggest.In our GREML-MS results for general intelligence and extraversion the relationship between MAF and cumulative genetic variance explained was not proportionately linear, with increasing contributions being made to the genetic variance explained as MAF fell. This pattern contradicts the neutral evolutionary model  and suggThe GREML-KIN results favour the inclusion of a large K component for all traits except extraversion. This is consistent with a major contribution by rare and other poorly tagged variants. Previous work has already suggested a role for mutation-selection balance acting on harm avoidance and novelty seeking , traits A limitation of this the GREML-KIN approach is that X-specific variance will go unnoticed. This is due to males being haploid and females being diploid at these regions and so the expected relationship on X chromosome is different between pairs of individual of the same sex and pairs of individuals of different sexes, even though they share the same degree of relationship, e.g., ~0.5 for mothers-and-daughters but 0 for fathers-and-sons. As all unmodelled variance remains in the residuals, the majority of the variance due to the X chromosome will, therefore, remain in the residuals.Another limitation is that the variance analyses are blind to the direction of effects and the number of variants involved in each genetic component. If, as we would predict, future work finds that variants with the lowest minor allele frequencies tend to have larger negative effects on intelligence, it would imply a coupling between the selection coefficient of alleles and their effect on intelligence, as selective pressure would act to minimise the frequency of highly deleterious variants. If this coupling were strong , future The sibling similarity component, which was retained in all models of intelligence, tracks the meta-analytic estimate of shared environmental variance (11%) from twin studies almost exactly. However, in our study the sibling component might also include the quarter of the dominance variation that siblings share, because siblings are the only relationship in this study where dominance plays a significant role . In the The family similarity component was only retained in the model for extraversion, although the point estimate was non-zero in the full neuroticism model as well. This is consistent with meta-analytic estimates of shared environment for adults . Howevercurrent environment remains important and that the spouse is a better aggregated indicator of the current environment than the sibling with whom one usually no longer shares a home in adulthood. We find no couple similarity component for personality, which is consistent with much weaker assortative mating on personality, especially neuroticism and extraversion [The couple similarity component is somewhat complex to interpret. For intelligence and education, there is evidence of assortative mating , which waversion –88.In the current study, we were able to exploit the high LD found between members of the same family to estimate the contribution of genetic effects that are normally missed in GREML-SC analyses of GWAS data. Using GREML-KIN, we simultaneously modelled the effect of the family, sibling and couple similarity to avoid potential environmental confounds inflating our estimates of the genetic effects. For intelligence and education, we find that genetic variants poorly tagged on current genotyping platforms explained a substantial proportion of the phenotypic variance, raising our heritability estimates to match those derived using pedigree-based quantitative methods. Such variants can include CNVs, structural variants, and rare variants. We find similar effects for neuroticism. For extraversion, pedigree-associated variants appear to play a smaller role in phenotypic variation. GREML-MS analyses, used with data imputed to the HRC reference panel, allowed us to examine lower frequency variants in a sample of unrelated individuals and provides strong convergent evidence, especially for intelligence and educational attainment. These results indicate that future GWAS using HRC imputation will be successful in finding the large majority of variants associated with intelligence. However for neuroticism whole-genome sequencing is likely to be more successful as our results from GREML-KIN suggest a large contribution from non-SNP/very rare/poorly tagged genetic variants. Finally, our results suggest mutation-selection balance has maintained heritable variation in intelligence, and potentially to some degree also in neuroticism and extraversion, explaining why differences in these traits persist to this day despite selection. Future work should directly measure rare variants, as well as CNVs and structural variants, and test the direction of their effects.Supplementary Table 1Supplementary material Supplementary Tables 2 and 3