Using methylation data for autism in peripheral tissues, we did not identify a significant concordance in effect direction of CpGs with p value < 10−4 in the SCDC MWAS . In contrast, using methylation data for autism from post-mortem brain tissues, we identify a significant concordance in effect direction of CpGs with a p value < 10−4 in the SCDC MWAS . Supporting this, we observe an enrichment for genes that are dysregulated in the post-mortem autism brain . Finally, integrating genome-wide association study (GWAS) data for autism  with mQTL maps from cord-blood (n = 771), we demonstrate that mQTLs of CpGs associated with SCDC scores at p value thresholds of 0.01 and 0.005 are significantly shifted toward lower p values in the GWAS for autism (p < 5 × 10−3). We provide additional support for this using a GWAS of SCDC, and demonstrate a lack of enrichment in a GWAS of Alzheimer’s disease. Our results highlight the shared cross-tissue methylation architecture of autism and autistic traits, and demonstrate that mQTLs associated with differences in DNA methylation associated with childhood autistic traits are enriched for common genetic variants associated with autism and autistic traits.Previous studies have identified differences in DNA methylation in autistic individuals compared to neurotypical individuals. Yet, it is unclear if this extends to autistic traits—subclinical manifestation of autism features in the general population. Here, we investigate the association between DNA methylation at birth (cord blood), and scores on the Social and Communication Disorders Checklist (SCDC), a measure of autistic traits, in 701 8-year-olds, by conducting a methylome-wide association study (MWAS). We did not identify significant CpGs associated with SCDC. The most significant CpG site was cg14379490, on chromosome 9  contains supplementary material, which is available to authorized users. Imv2.r644) . Imputedv2.r644)  that conh2 = 0.12 ± 0.05. The LDSR intercept (0.99) suggested that there was no inflation in GWAS estimates due to population stratification. The λGC was 1.013. We replicated the previously identified genetic correlation (constrained intercept) [rg = 0.46 ± 0.20, p value = 0.019; iPSYCH-autism: rg = 0.45 ± 0.18, p value = 0.01).As reported previously , 22, 31,https://www.dropbox.com/sh/8za5xspmbjydpst/AAA_ZGmMLOE8Ql7egi5Mcu8Ha?dl=0 . These summary statistics are also provided as RData files with this manuscript.The summary statistics for the MWAS (SCDC and CCC) can be downloaded from here: https://molecularautism.biomedcentral.com/articles/10.1186/s13229-018-0224-6 .Summary statistics for the SEED and the SSC MWAS can be obtained from here: Summary statistics for the MINERvA cohort can be obtained by contacting Jonas Bybjerg-Grauholm.MWAS summary statistics:http://www.med.unc.edu/pgc/results-and-downloads (iPSYCH-PGC GWAS-2017).The summary statistics for the autism GWAS (iPSYCH) can be downloaded from http://web.pasteur-lille.fr/en/recherche/u744/igap/igap_download.php.The Alzheimer’s GWAS can be downloaded from https://www.dropbox.com/sh/8za5xspmbjydpst/AAA_ZGmMLOE8Ql7egi5Mcu8Ha?dl=0.The summary statistics for the SCDC GWAS can be obtained from GWAS summary statistics:https://github.com/autism-research-centre/MWAS_autistictraitsScripts for running the two regression models for the MWAS and running the enrichment analyses with the mQTL data are available here: http://www.mqtldb.org/mQTL data used in this (coord blood) is a part of the ARIES cohort, and can be downloaded here: http://zzz.bwh.harvard.edu/plink/); IlluminaHumanMethylation450k.db (http://www.bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450kprobe.html); MASS (https://cran.r-project.org/web/packages/MASS/index.html); LDSC (https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation).We used the following software/packages: Plink . The top CpG site was cg14379490, on chromosome 9 , which is equivalent to a 0.51 standard deviation unit decrease in SCDC scores. This CpG site is an “Open Sea” CpG site, whose closest gene is FAM120A, which encodes a scaffold protein that is expressed in a wide number of human tissues. We identified 19 CpG sites with suggestive p values  , possibly because of the relatively small sample size and the regression model used. Further, gene-set analysis also did not identify significant association after correcting for multiple testing . There was a high correlation in Z-scores  between the two models. Subsequent analyses were conducted using model 1 MWAS results as these are easier to interpret.Methylome-wide association analysis  did not identify any significant loci after Bonferroni correction , equivalent to a 0.55 standard deviation unit decrease in CCC scores. The Manhattan plot and QQ plot are included in Additional file p value < 1 × 10−4), the effect direction was concordant for 18 of them in the CCC MWAS . Similarly, of the 32 CpGs with p value < 1 × 10−4 in the CCC MWAS, 28 had concordant effect direction in the SCDC MWAS . Scores on the CCC and the SCDC were phenotypically correlated  in the participants who were included in the MWAS (n = 666), and both questionnaires measure difficulties in pragmatic communication. Given that we were testing correlated phenotypes in the same cohort using CpG methylation measured in the same tissue, we hypothesized that the MWAS for the two phenotypes will be positively correlated. The Z-scores for the MWAS of the two phenotypes were significantly correlated , which increased if we considered only CpGs with p value < 0.01 in either one of the phenotypes .To provide confidence to our primary analyses, we conducted an MWAS of scores on the CCC, which was reversed scored to identify difficulties in communication (Methods). The most significant CpG was cg13711424 . For all of them, we first investigated concordance in effect direction of all CpG sites with p value < 0.01) in the three autism MWAS have a shift toward lower p values in the SCDC MWAS using a one-sided Wilcoxon-rank sum test. This tests more CpGs than an effect direction concordance test and is agnostic to effect direction which may be discordant in different peripheral tissues measured at different developmental stages. After Bonferroni correction , we did not identify a significant shift toward lower p values for the nominally significant CpGs from any of the three datasets , though we note a nominally significant shift in the SEED dataset. This lack of overlap may be due to the low statistical power of the MWAS of SSC and the three autism MWAS, none of which have identified significantly differentially methylated CpGs.Given that there was limited evidence for concordance in effect direction between the datasets, we next tested if nominally significant CpGs . At a more stringent p value threshold of p value < 1 × 10−5, 88 of the 133 probes had concordant effect directions in the two datasets . In contrast, Wilcoxon rank-sum test of all CpGs with p value < 0.01 in the post-mortem MWAS did not identify a significant shift toward lower p values . We next tested if the results were supported in a different dataset. A previous study investigated differential methylation in post-mortem neurons from the frontal lobe (identified using FACS sorting) in autism [p value < 1 × 10−4 had concordant effect direction in the two datasets . However, we identified a significant shift toward lower p values  of all CpGs with p value < 0.01 in the SCDC MWAS.Methylation signatures in post-mortem brain tissues are more relevant to neurodevelopmental phenotypes than methylation signatures in peripheral tissue, and, for autism, are statistically better powered than MWAS in peripheral tissues, as these have identified more differentially methylated loci compared to peripheral tissue analyses , 20. Conp values in the SCDC MWAS when compared to the other genes. We identified a significant shift toward lower p values for the transcriptionally dysregulated genes , but did not identify a significant enrichment for any of the modules .A few studies have identified consistent sets of dysregulated genes in autism, and co-expression modules enriched for these dysregulated genes –31. Prevcis and, to a smaller extent, trans genetic control. We identified mQTLS associated with SCDC CpGs below four p value thresholds , and compared the distribution of p value of these mQTLS in the autism GWAS against the p value distributions of mQTLs above the PSCDC (Methods). After multiple testing correction, mQTLS of CpGs with PSCDC = 0.01, and 0.005 have significantly lower p values in the autism GWAS   and at PSCDC 0.001 . In contrast, we did not identify an enrichment for mQTLs in the Alzheimer’s GWAS  Table , Fig. 2.This study investigated the shared biology of autism and autistic traits by integrating genetic, methylation, and post-mortem gene expression data. We first investigated the validity of considering autistic traits for methylation studies. Considering autistic traits over a case-control design is useful in that (1) it captures greater variance across the underlying liability spectrum, (2) it can be used to increase sample sizes by phenotyping individuals for whom methylation data is available, and (3) it can be used to link methylation signatures from tissues collected in early life to the phenotype, as this can be more difficult for autism.We conducted a prospective MWAS of autistic traits (SCDC) by measuring methylation signatures in the cord blood and linking it to autistic traits measured later in life. While we did not identify a significant CpG association with autistic traits after multiple testing correction, we were able to confirm that this analysis produced biologically meaningful signals by identifying significant correlation with an MWAS of a similar childhood phenotype (CCC) measured in the same cohort. Notably, the correlation in methylation values mirrored the phenotypic correlation. This, in principle, is similar to the idea of genetic correlation analysis of related phenotypes measured in the same cohort, though the methods used are very different.Despite this, we did not identify a significant overlap between the MWAS of SCDC and MWAS of autism conducted using peripheral tissues , 16. We p values. In contrast, using a neuron-specific MWAS generated using a different post-mortem tissue dataset, we identified a significant overlap using a Wilcoxon-rank sum test of p values but not a significant sign-concordance. Additionally, using an RNA sequencing dataset of autism and neurotypical post-mortem brains [In contrast to the results from the peripheral tissues, we observed some degree of overlap between MWAS conducted in post-mortem brain tissues , 20 and m brains , we idenm brains , 16. Thup value thresholds providing confidence in our results. We did not test this in other peripheral tissue MWAS for which we had access to summary statistics given the lack of overlap between these and the SCDC MWAS.Given the highly polygenic nature of autism , it is lOur study does not investigate causality. While methods such as Mendelian randomization can investigate causality , 49, thiA few caveats must be borne in mind while interpreting the results of this analysis. First, the current array-based method interrogates only a small proportion of all CpG sites in the genome. Thus, significant loci associated with autistic traits may lie outside of the regions interrogated. Second, due to the nature of the assay, the methylation values may also capture hydroxymethylation. We cannot exclude the possibility of signal attenuation due to assaying both hydroxymethylation and methylation in the current study, and the correlation between hydroxymethylation between blood and brain is low . Third, Our study demonstrates a degree of methylomic overlap between autism and autistic traits, but we are limited in making further conclusions. Two factors—sample size and heterogeneity between the various samples—limit our understanding of methylation in autism. Future meta-analyses of both autism and autistic traits may help improve the statistical power of both the MWAS and aid in better understanding the shared etiology between the two phenotypes. We identified an enrichment for autism and autistic traits GWAS signals in the top CpG loci for autistic traits, but these must be replicated in independent MWAS of autistic traits.Additional file 1:Supplementary Note. (DOCX 485 kb)


Mendelian randomization (MR) has been increasingly used to strengthen causal inference in observational epidemiology. Methodological developments in the field allow detecting and/or adjusting for different potential sources of bias, mainly bias due to horizontal pleiotropy (or “off‐target” genetic effects). Another potential source of bias is nonrandom matching between spouses . In this study, we performed simulations to investigate the bias caused in MR by assortative mating. We found that bias can arise due to either cross‐trait assortative mating  or single‐trait assortative mating , even if the exposure and outcome phenotypes are not the phenotypes under assortment. The simulations also indicate that bias due to assortative mating accumulates over generations and that MR methods robust to horizontal pleiotropy are also affected by this bias. Finally, we show that genetic data from mother–father–offspring trios can be used to detect and correct for this bias. We also evaluate how methods robust to horizontal pleiotropy perform in the presence of assortative mating, and present approaches to detect and correct for this bias.22.1GX and GY denote the genetic influences on the exposure (X) and outcome (Y) phenotypes. It is assumed that there is no horizontal pleiotropy between X and Y, so that all genetic variants that belong to GX are valid genetic instruments of X. This will facilitate detection of assortative mating bias using d‐separation rules. The collective effect of unmeasured common causes of X and Y is represented by U. We will interpret the causal diagrams assuming faithfulness, according to which d‐connection  implies statistical association.Figure GX is only related to Y via the path GX→X→Y. Therefore, GX will associate with Y only if the path X→Y exists , and therefore is a valid instrument to assess the causal effect of X on Y.The top left panel depicts a situation with no assortative mating. It can be seen that X. We used nondirected lines rather than arrows to denote assortative mating. Single‐trait assortative mating induces an association between mother's GX and father's GX. This is because if people with high values of X tend to select partners with high values of X , then, by consequence of assortment at the phenotypic level, mother–father pairs will also be positively genetically correlated for X. However, the association of mother's X and father's X does not open any backdoor paths between GX and Y, so GX is a valid genetic instrument even in the presence of single‐trait assortative mating on X. The same reasoning applies to single‐trait assortative mating on Y. However, in some situations single‐trait assortative mating can render GX and Y associated in the absence of a causal effect of X on Y, as we show next using simulations.The top right panel depicts a situation with single‐trait assortative mating (indicated by the thick line) on X and Y by two thick lines: from mother's X to father's Y, and from mother's Y to father's X. In this situation, if X does not cause Y, the mother's GX and father's GY, and mother's GY and father's GX will be associated. If X does cause Y, then all parental genetic variables will associate. Therefore, cross‐trait assortative mating can induce associations between GX and Y even in the absence of a causal effect of X on Y. This invalidates the MR assumptions.The bottom‐left panel illustrates cross‐trait assortative mating on GX→ mother's X↔ father's X← father's GX is not open, because both mother's X and father's X are colliders  on the path. Therefore, mother's GX and father's GX are not associated in this graph. Therefore, attempting to graphically represent assortment using bidirected arrows between parents’ phenotypes would imply in saying that assortment at the phenotypic level does not result in a genetic correlation between spouses, which is implausible.The bottom right panel provides a justification for representing assortment using nondirected lines rather than bidirected arrows . The path mother's 2.2X and Y induces associations between GX and Y. This means GX is an invalid instrument to assess the causal effect of X on Y, leading to bias in MR analyses. However, this bias can be counteracted by conditioning on measured variables that block such bias sources (without creating new ones).The bottom left panel in Figure GY would control for bias due to assortative mating, since all open backdoor paths from GX to Y are mediated by GY, and conditioning on GYdoes not create any new open backdoor path. However, it is important to remember that GY represents all genetic influences on GY, not all of which will be known. This can be seen more clearly in Supporting Information Figure GXM and GXU represent measured and unmeasured genetic influences on X, respectively, such that GX={GXM,GXU}; GYM and GYU are defined analogously, with respect to Y. Conditioning on the measured genotypes (GYM) does not control for unmeasured genetic differences (GYU). Therefore, unless all genetic influences on Y are known, measured and properly modelled, adjustment for genetic influences on Y may mitigate, but it is unlikely to eliminate, assortative mating bias.Figure GX block the open backdoor paths between GX and Y without creating new ones. Supporting Information Figure X are measured—that is, conditioning on mother's and father's GXM blocks all backdoor paths (due to assortative mating) between GXM and Y. Given that genetic instruments of X are a subset of GX, this approach can be used to control for assortative mating bias without measuring all genetic influences on X, and requires measuring the genetic instruments in individuals  and their parents (to adjust for bias).An alternative way to overcome this bias is to adjust for parental genotype. Figure 2.3X and Y leads to bias in MR and (b) assess the strategy of using parent's GXM to control for assortative mating bias. Additional simulations  demonstrate that cross‐trait assortative mating on X , which equals the broad‐sense heritability of X (due to the absence of nonadditive components of genetic variance) in our simulations. In scenarios where X has no causal effect on Y, the same interpretation holds for hY2 with respect to Y.A detailed description of the simulation model is provided in the Supporting Information Methods. We simulated mother–father–offspring trios as depicted in Figure GX were combined into an additive allele score ZX, and the direct effect of ZX on X was controlled by the δX parameter; ZY and δY are defined analogously with respect to Y (see the Supporting Information Methods for details). Therefore, hX2=(δX2 var (ZX))/ var (X) and  hY2=(δY2 var (ZY))/ var (Y). Given that in our model assortative mating model leads to changes in genetic and phenotypic variances while δX and δY are structural parameters, the actual values of hX2 and hY2 were higher in simulations with positive assortment than in simulations without assortment when keeping δX and δY constant. However, in our simulations such differences were very small, so for simplicity we will ignore that hX2 and hY2 are affected by assortative mating when presenting and discussing the results.All genetic variants in X and Y was induced by pairing women and men according to XP and YP . These variables were equal to X and Y, respectively, plus random error terms, such that cor= cor =P∈. Small values of P imply that people weakly assort on X and Y, whereas high values of P imply that they strongly assort on X and Y. All other factors influencing partnering preferences are embedded in the error terms.Positive assortment, which leads to a positive correlation between parents, was simulated using proxies of the phenotypes of interest, so as to allow control over the strength of the assortment. Cross‐trait assortative mating on YP, and women were sorted in ascending order of XP; in the other set of women–men pairs, men were sorted in ascending order of XP, and women were sorted in ascending order of YP. The two sorted sets of women–men pairs were then combined together, preserving the order resulting from the sorting steps above, generating the full dataset of mother–father pairs.To mimic a bidirectional process, we initially paired women and men at random (so as to not induce assortment), and randomly divided the resulting women–men pairs into two sets. In one set of women–men pairs, men were sorted in ascending order of X on Y. This scenario was used to evaluate the performance of selected MR methods to detect a causal effect.We also simulated scenarios with no assortative mating and a nonzero causal effect of 2.4X on Y was estimated using two‐stage least squares regression (TSLS). Unless stated otherwise, all 50 genetic variants in GX were combined in an weighted additive allele score, which was used as the instrumental variable (IV) for X. The weights (ω={ωk}k=150) were obtained by regressing X on each genetic variant in GX in one random half of the simulated dataset. To avoid overfitting, those weights were used to construct the IV in the other half of the data.We investigated the bias and the coverage of different MR estimators across these scenarios. The causal effect of TSLS (1): estimating the causal effect of the exposure on the outcome with no covariates. The causal effect estimate was obtained by fitting the following two linear regression models:where X^ is the value of X predicted by the model (this is because the error term is omitted), β^0 is the intercept estimate, and β^1 is the estimate of the change in X associated with a unit increment in S .Y^ is the value of Y predicted by the model (this is because the error term is omitted), γ^0 is the intercept estimate, and γ^1 is the estimate of the change in Y associated with a unit increment in X^—that is, the estimate of the causal effect of the exposure X on the outcome Y.TSLS (2): estimating the causal effect of the exposure on the outcome adjusting for parental allele scores. The causal effect estimate was obtained by fitting the following two linear regression models:where Sm and Sf denote mother's and father's respective allele scores.γ^1∗ is the estimate of the causal effect of the exposure X∗ on the outcome Y∗.Three versions of the TSLS method were performed:TSLS (3): jointly estimating the causal effect of the exposure on the outcome, and the direct effects of parent's exposure phenotypes on (offspring's) outcome using paternal nontransmitted allele scores as instruments of parent's exposure phenotype; therefore, this analysis has three exposure variables: offspring's, mother's, and father's exposure phenotypes, three IVs: offspring's allele score, mother's nontransmitted allele score, and father's nontransmitted allele score, respectively, and one outcome variable: offspring's outcome phenotype. The causal effect estimate was obtained by fitting the following four linear regression models:where Wm and Wf denote mother's and father's, respectively, nontransmitted allele scores, and X^m′ and X^f′ denote mother's and father's, respectively, predicted exposure phenotype.γ^1′ is the estimate of the causal effect of the individual's exposure X′ on the individual's outcome Y′, γ^2′ is an estimate of the direct effect of mother's exposure Xm′ on the individual's outcome Y′, and γ^3′ is an estimate of the direct effect of father's exposure Xf′ on the individual's outcome Y′.For the next method, we constructed allele scores using nontransmitted alleles  and TSLS (3) aim at providing both a causal effect estimate that is robust to assortative mating and a test of whether or not assortative mating bias is present. Of note, the equations above are for explanation only, since the estimates were based on TSLS (as mentioned above), which takes account of the estimation error in the first‐stage .For each of those methods, the causal effect estimate and false rejection rate of the 95% confidence intervals were calculated. Additional analyses (described in the Supporting Information Methods) were performed to evaluate the performance of summary data MR methods robust to horizontal pleiotropy, and of tests commonly used to detect horizontal pleiotropy in the summary data setting.2.5Previous studies have used MR in samples of unrelated individuals to investigate the causal effect of height on educational attainment   and TSLS (3)  were unbiased with false discovery rates close to 5%. Figure Y was highly heritable, while changing the heritability of X had virtually no effect on bias . The bias was also invariant to whether all or subset of variants in GX are used to construct the IV and to the total number of variants in GY, thus corroborating the notion that the bias depends mainly on the degree of assortment and heritability of Y  are measured in study individuals and their parents.Figure oth TSLS   and TSLS (3) were unbiased, regardless of the number of generations  with X and Y. Supporting Information Table X and Y, either through horizontal or through vertical pleiotropy. If X and Y are not genetically correlated, then single‐trait assortative mating does not bias MR . Supporting Information Table In analyses including summary data MR methods, it was observed that those methods were similarly biased to one another and to the conventional TSLS method, with false rejection rates varying according to the precision of each method , but the data were insufficient to differentiate between assortative mating and other potential sources of between‐spouses correlation  twin model, which has been recently developed with the goal of testing for horizontal pleiotropy, could in principle be used to test and correct for assortative mating bias . This could be exploited to detect and possibly correct for assortative mating bias, but further methodological work is required on this topic. Although comparing methods to detect and adjust for assortative mating will be useful, it is likely that the methods are complementary to each other, and choosing one over the other will depend on study‐specific factors such as the data available and the research question.X and father's Y (and vice‐verse) is from 0.1 to 0.3, then  to 0.022 (for P=0.7) when setting the heritability of Y(hY2) to 10%; when setting hY2 = 50%, the bias ranged from 0.036 (for P=0.4) to 0.110 (for P=0.7). Given that  var (X)≈1 and  var (Y)≈1 in our simulations , they were assigned to all genetic variants under consideration. This simplified the simulation model while allowing the main conclusions to be drawn. However, doing so prevented us from exploring more refined issues. For example, if some, but not all, of the genetic variants influence the variable(s) under assortment through horizontal pleiotropy, there will be between‐instrument heterogeneity, unlike in our simulations. This suggests that some of the robust MR methods, such as the median and the MBE, may be robust to assortative mating bias in those particular cases (provided that their assumptions hold). Therefore, it is possible that heterogeneity tests detect and some MR methods correct for assortative mating bias in some circumstances, but more firm conclusions require further methodological work. Therefore, our findings should be interpreted only as general indications on how assortative mating can influence MR, and extrapolating our conclusions to scenarios not covered in our simulations should be avoided.GX and GY. However, there are other forms that assortment can bias MR findings. For the sake of illustration, assume that intelligence is not heritable. Nevertheless, if more intelligent women tend to partner with taller men (and vice‐versa), a MR analysis assessing the causal effect of height on family earnings would be biased because partner's intelligence is likely to have a causal effect on family earnings. Further methodological development on how to detect and control for bias in cases such as this is required.We focused on how assortment on heritable phenotypes may lead to bias in MR by inducing a correlation between We demonstrated that there was little evidence of an effect of height on educational attainment after adjustment for parental genotype. This suggests that effects of height on educational attainment may be due to assortative mating or dynastic effects. In this sample, the biggest impact came from adjusting for father's allele score. However, our empirical results are imprecise and are provided for illustration. Future work should combine larger samples of related individuals to precisely estimate the effect of height on educational outcomes while controlling for assortative mating and dynastic effects.It is possible in principle to combine the simple assortative mating bias adjustment approach presented here  with methods that offer robustness to other biases, such as horizontal pleiotropy. For example, assortative mating bias adjustment could be combined with horizontal pleiotropy robust methods that require individual‐level data, such as Linear Slichter Regression (Spiller, Slichter, Bowden, & Davey Smith, Our study reenforces assortative mating as a potential bias source in MR, and the utility of trio data to detect and adjust for this bias. Whenever possible, and especially when the phenotypes under consideration are likely to be under assortment, we recommend researchers to perform sensitivity analysis using trio data to test if assortative mating is present and, if so, to obtain causal effect estimates more robust to this bias.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6-associated conduction changes.Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the The online version of this article (10.1186/s13059-018-1457-6) contains supplementary material, which is available to authorized users. In the heart, the ventricular conduction system propagates the electrical impulses that coordinate ventricular chamber contraction. The QRS interval on an electrocardiogram (ECG) is used clinically to quantify duration of ventricular depolarization in the heart. Prolonged QRS duration is an independent predictor of mortality in both the general population –4 and inQRS interval duration is a quantitative trait influenced by multiple genetic and environmental factors and is known to be influenced by both age and gender , 12. TheWe previously performed a genome-wide association meta-analysis in 40,407 individuals and identified 22 genetic loci associated with QRS duration . The QRSP < 6.17 × 10−8 for single variants ) and CASC20 —the former a pseudogene and the latter a non-protein-coding gene, both with currently uncharacterized function.We further identified an intronic variant in the t portal  for bloofibrosis ; the sigfibrosis . The neaP < 5.18 × 10−7) was identified in ADAMTS6   Table .Table 2GADAMTS6 gene-based signal is driven by two rare non-synonymous variants: rs61736454 (p.Ser90Leu) and rs114007286 (p.Arg603Trp), which have allele frequencies of 0.0018 and 0.0021, respectively  = 3.01(0.58)], MAF = 0.002, N = 59,903), and was extremely well imputed (info score = 0.995). Importantly, after meta-analysis with discovery exome summary statistics, the signal reached exome-wide significance  = 2.75(0.38)], N = 145,496), underscoring the robustness of our initial discovery signal driver. Data for rs114007286 were not available. ADAMTS6 is a highly constrained gene, with a probability of loss of function intolerance score of 1.0 (pLI = 1.0) . The p.Ser90Leu variant lies within the ADAMTS6 propeptide, which is predicted to be important for initiation of folding, because the homologous ADAMTS9 propeptide is an intramolecular chaperone essential for its secretion [O-fucosylation, which is a QC signal that prevents secretion of ADAMTS proteins that are improperly folded [The y folded .Adamts6 is expressed in the atrioventricular and septal cushions and myocardium of the embryonic heart, with expression persisting into adult ventricular, trabecular, and septal myocardium  components and other secreted molecules. ADAMTS6 is closely related to ADAMTS10, which interacts with and accelerates assembly of fibrillin-1, mutations in which cause Marfan syndrome . This suium Fig. –d.Fig. 1Adamts6 mutations were recovered in a forward genetic screen [Mice with recessive c screen   knockout mice exhibit slow conduction, QRS prolongation, and increased susceptibility to ventricular arrhythmias [Adamts6 heterozygous (Adamts6m/+) adult mice are viable and without structural heart defects  expression  being the predominant myocardial gap junction protein in the human and mouse myocardium. hythmias –43, consium Fig.  and d. Iion Fig. , suggestium Fig.  and b. TADAMTS6 coding variants from the exome-chip analysis (p.Ser90Leu and p.Arg603Trp), myc-tagged ADAMTS6 constructs with the variants introduced by site-directed mutagenesis were expressed in HEK293F cells. Western blotting was used to compare the levels of mutant and wild type (WT) myc-tagged ADAMTS6 in the transfected cell lysates and medium. As positive and negative controls, respectively, we transfected the known pathogenic murine variant (p.Ser149Arg) and two rare non-synonymous human ADAMTS6 variants predicted to be benign . Western blotting confirmed that the Adamts6 p.Ser149Arg variant was not secreted . Novel loci include genes involved in cardiac development and dysfunction, some of which are highly expressed in skeletal and/or cardiac tissue. To establish further evidence for these novel loci and mechanisms underlying each association, future functional experiments are essential.In a meta-analysis of data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Exome-Chip ECG consortium, we identified 28 loci associated with QRS duration. With the addition of 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts, all 34 variants across the 28 loci passed the exome-chip-wide significance threshold, indicating our results are robust. Furthermore, effect size directions between discovery and replication remained consistent and ADAMTS6, a gene not previously implicated in cardiac conduction. We chose to focus on this novel locus and seek functional validation as the association was driven by multiple rare coding variants that were predicted to be damaging by in silico tools. The coding variants driving the association in the population study and the mutations identified in the mouse forward genetic screen all impair ADAMTS6 secretion, indicating reduction/loss of function. Significantly, although heterozygosity of the variants in mice is not associated with structural heart defects, we detected reduction of Cx43 gap junctions in the ventricular myocardium. Homozygous Adamts6 mutants show complete loss of Cx43 gap junctions as well as structural heart defects, implying a dosage effect. Together, these findings indicate that ADAMTS6 has a novel role in regulating gap junction-mediated ventricular depolarization, with quantitative reduction in ADAMTS6 causing cardiac conduction perturbation. While our study focuses on cardiac conduction, the findings support the potential broad utility of large-scale exome-chip analysis for interrogating coding variants associated with other physiological or clinical parameters.The present study also highlights the efficacy of large-scale population-based exome-chip analysis for discovery of non-synonymous coding variants with significant functional effects. In gene-based tests, we identified an association between ventricular depolarization and rare non-synonymous variants in All participating studies formed the CHARGE EKG exome-chip consortium, including those belonging to the CHARGE consortium and external studies to investigate the role of functional variation in electrocardiographic traits. Twenty-two cohorts participated in the QRS duration analysis effort representing a maximum total sample size of 85,593 samples, consisting of 77,898 participants of European ancestry 91%) and 7695 of African descent. Individual study details and characteristics are summarized in Additional file % and 769http://www.whocc.no/atcddd/) [We analyzed QRS duration measured in milliseconds. In each study, individuals were excluded from the analyses if these had a QRS duration of > 120 ms, atrial fibrillation (AF) on baseline electrocardiogram, a history of myocardial infarction or heart failure, had Wolff–Parkinson–White syndrome (WPW), a pacemaker, or used Class I and class III blocking medications  Classification System, atcddd/) . For cohP value (pHWE), call rate, and filtering removing poorly clustering variants. Each study aligned their data reference strand to the Illumina forward strand using a central SNP allele reference and annotation file (SNP info file) [Each participating study performed genotyping using the Illumina HumanExome BeadChip / HumanCoreExome platforms. Owing to the difficulty of accurately detecting and assign genotype calls for rare variants MAF < 1%), an initial core set of CHARGE cohorts, comprising approximately 62,000 samples, assembled intensity data into a single project for a joint improved calling. The quality of the joint calling was assessed through investigating the concordance of genotypes in samples having both exome-chip and exome-sequence data, described extensively elsewhere , 47. Usi%, an inifo file)  for the All association analyses were carried out using the R-package seqMeta . Each stP value of α=0.05 / 162,199 variants / 5 analyses = 6.17 × 10−8.For single variant meta-analyses, we included all variants with a MAC ≥ 10 in order to have well-calibrated type I error rates . StatistP value threshold of exome-wide significance (P < 6.17 × 10−8) in one sex and above nominal significance in the other (P > 0.05).Suggestive sexually dimorphic associations were identified by performing sex-stratified meta-analyses, totaling 39,907 women and 31,702 men, including only from cohorts that had both male and female samples. Variants were deemed to be suggestive sex-specific when reaching below a P value significance threshold of α=0.05 / 16,085 genes / 2 different tests / 3 functional variant classes = 5.18 × 10−7.For gene-based tests, also performed using seqMeta using the “prepScores” objects from individual cohorts, we assigned variants to genes by annotating all variants on the Exome Chip using ANNOVAR  followinWe define a physically independent locus as the genomic region that contains variants within 250 kb on either side of LD-independent lead SNPs (exome-wide significant variants with r2 < 0.1), where LD calculations were based on European ancestry. Following this definition, in certain cases LD-independent lead variants are present in overlapping regions, complicating the definition and reporting of associated genetic loci and harbored genes. Therefore, we annealed loci if LD-independent exome-wide significant variants were < 250 kb from each other. Where lead SNPs from previous analyses were not contained in these regions, we considered these as novel. LD calculations were performed on the Illumina Exome Chip genotype data from the TwinsUK cohort  (n = 119www.ukbiobank.ac.uk) is a prospective study of 500,000 volunteers, comprising relatively even numbers of men and women aged 40–69 years old at recruitment, with extensive baseline, and follow-up clinical, biochemical, genetic, and outcome measures. Approximately 95,000 individuals were recruited for a Cardio test using a stationary bicycle in conjunction with a four-lead electrocardiograph device at the initial assessment (2006–2008) and ~ 20,000 individuals performed the test again (the first repeat assessment: 2011–2013). The Cardio test, thereafter known as the exercise test, started with 15 s of rest (pre-test), followed by 6 min of exercise (cycling) with an increasing workload, and a 1-min recovery period without exercise. To improve accuracy, we calculated an average QRS waveform by aligning all QRS complexes present in a window of 15 s from the resting stage. Ectopic beats and artifacts were removed. Then, we calculated the correlation between each individual QRS complex and the average QRS waveform and removed those with a correlation coefficient < 0.8. Finally, we repeated the calculation of the average QRS waveform by only considering those highly correlated individual QRS complexes. The QRS width was measured from the average QRS waveform as the interval between the onset of the Q wave and the end of the S wave. Genotyping was performed by UKB using the Applied Biosystems UK BiLEVE Axiom Array or the UKB AxiomTM Array. Single Nucleotide Variants (SNVs) were imputed centrally by UKB using a merged UK10K sequencing + 1000 Genomes imputation reference panel (https://www.biorxiv.org/content/early/2017/07/20/166298). Following phenotype and genotype QC, a total of 51,971 unrelated individuals of European ancestry remained for analysis. Thirty-four QRS discovery lead variants selected for replication were extracted from UKB imputed files, all being of high quality (Hardy-Weinberg P > 1 × 10−4 and an info score > 0.5) using QCTOOL v2 and the association analysis was performed using SNPTEST v2.5.4 assuming an additive genetic model.UK Biobank  with subsequent imputation into 160,000 chip-typed individuals and their close relatives . SelecteWe first performed a fixed-effects inverse variance weighted meta-analysis combining the summary statistics data from the UKB and deCODE analyses, followed by a combined analysis of the discovery and replication summary statistics using GWAMA v2.2.2 .Adamts6 open reading frame was generated via PCR using Phusion High-Fidelity DNA Polymerase  and embryonic mouse heart complementary DNA (cDNA) as the template and inserted into PSecTag2B . ADAMTS6 variants p.Ser90Leu and p.Arg603Trp were created in the Adamts6 cDNA using Q5 Site-Directed Mutagenesis Kit . Primer sequences used for cloning and mutagenesis are available upon request. Each plasmid insert was verified by sequencing. Human embryonic kidney (HEK293) cells obtained from ATCC were maintained in medium supplemented with 10% fetal bovine serum and 100 U/mL penicillin and 100 μg/mL streptomycin. The constructs were transfected with Lipofectamine 3000 Transfection Kit  following manufacturer’s instructions. After 72 h in serum-free medium, cell lysates were collected in lysis buffer . Extracts were electrophoresed by reducing SDS-PAGE on 10% Tris-Glycine gels. Proteins were electroblotted to Immobilon-FL membranes , incubated with primary antibody anti-myc , anti-GAPDH , and anti-Cx43 , overnight at 4 °C, followed by IRDye secondary antibodies goat anti-mouse or anti-rabbit  for 1 h at room temperature and visualized by Odyssey CLx (LI-COR). Band intensity was measured using ImageJ .A plasmid vector for expression of the full-length All values are expressed as mean ± SEM. A paired two-tailed Student’s t-test was used to assess statistical significance.Adamts6 mutant mice were recovered from a recessive ethynitrosourea (ENU) mouse mutagenesis screen conducted using non-invasive in utero fetal echocardiography [Adamts6 mutant lines were recovered, all exhibiting the identical phenotype. Mouse histology, immunostaining and RT-PCR experiments were approved by the Cleveland Clinic Institutional Animal Care and Use Committee .iography . MutantsAdamts6 mutation was consistently homozygous across all mutants recovered in the line, the pathogenic identifying it as mutation. Of the ten mutant lines, nine were identified to have the same missense mutation (c.C447G: p.S149R), while one mutant line exhibited loss of the start codon (c.G3A: p.M1I) and was confirmed to be null with no Adamts6 transcripts detected with transcript analysis. The Adamts6 missense mutation was subsequently identified as a spontaneous mutation in the C57BL/6 J production colony at the Jackson Laboratory.Mutation recovery was conducted by whole-exome capture using SureSelect Mouse All Exon kit V1, with sequencing carried out using Illumina HiSeq 2000 with minimum 50X average coverage (BGI Americas). Sequence reads were aligned to the C57BL/6 J mouse reference genome (mm9) and analyzed using CLCBio Genomic Workbench and GATK software. All homozygous mutations were genotyped across all mutants recovered in the mutant line and only the v/v Tween-20, pH 6.2) and microwaving for 1.5 min at 50% power four times in a microwave oven with 30-s intervals intervening was used before immunofluorescence. Immunofluorescence was quantified by the ratio of Cx43 signal to DAPI-positive cell nuclei integrated density . Adamts6 RNA in situ hybridization was performed using RNAScope (Advanced Cell Diagnostics) following the manufacturer’s protocol. Briefly, 7-μm sections were deparaffinized and hybridized to a mouse Adamts6 probe set  using a HybEZ™ oven (Advanced Cell Diagnostics) and the RNAScope 2.5 HD Detection Reagent Kit .Tissues were fixed in 4% paraformaldehyde in PBS at 4 °C overnight followed by paraffin embedding. Sections of 7 μm were used for hematoxylin and eosin staining, picrosirius red staining, and immunofluorescence for Cx43  followed by secondary goat anti-rabbit antibody . Antigen retrieval, i.e. immersion of slides in citrate-EDTA buffer  and 1 μg of RNA was reverse-transcribed into cDNA with SuperScript III Cells Direct cDNA synthesis system . qPCR was performed with Bullseye EvaGreen qPCR MasterMix  using an Applied Biosystems 7500 instrument. The experiments were performed with three independent samples and confirmed reproducibility. DatabasesGenotype-Tissue Expression (GTEx) Portal database: http://www.gtexportal.orgSoftwareseqMeta: http://cran.r-project.org/web/packages/seqMeta/EasyStrata: https://cran.r-project.org/web/packages/EasyStrata/PLINK 1.9: https://www.cog-genomics.org/plinkSNPTEST v2.5.4: https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.htmlGWAMA v.2.2.2: https://www.geenivaramu.ee/en/tools/gwamaAdditional file 1:Table S1. Cohort characteristics. Table S2. Single SNP meta-analyses. Table S3. Sex-stratified analyses. Table S4. SKAT analyses. Table S5. T1-burden analyses. Table S6.ADAMTS6 variant details. Table S7. Cardiac phenotype distribution in Adamts6 mutant mice. (XLSX 475 kb)Additional file 2:Figure S1. Manhattan plot for European and African-American ancestry single variant analysis. Figure S2. Quantile-quantile plot for European and African-American ancestry single variant analysis. Figure S3. Manhattan plot for EA single variant analysis. Figure S4. QQ plot for EA single variant analysis. Figure S5. Manhattan plot for AA single variant analysis. Figure S6. Quantile-quantile plot for AA single variant analysis. Figure S7. Miami plot European and African-American ancestry sex-stratified single variant analysis. Figure S8. Quantile-quantile plots for European and African-American ancestry sex-stratified single variant analyses. Figure S9. Normal morphology of adult Adamts6 heterozygous hearts. (DOCX 4290 kb)Additional file 3:Video S1. (Quicktime) Video to illustrate the DORV phenotype finding in an Adamts6 mutant heart. (MOV 1983 kb)


Cognitive behavioural therapy is a leading evidence-based treatment but has variable outcomes. Currently, there are no strong predictors of outcome. Therapygenetics research aims to identify genetic predictors of prognosis following therapy. We performed genome-wide association meta-analyses of symptoms following cognitive behavioural therapy in adults with anxiety disorders (n = 972), adults with major depressive disorder (n = 832) and children with anxiety disorders . We estimated the variance in therapy outcomes that could be explained by common genetic variants (h2SNP) and polygenic scoring was used to examine genetic associations between therapy outcomes and psychopathology, personality and learning. No single nucleotide polymorphisms were strongly associated with treatment outcomes. No significant estimate of h2SNP could be obtained, suggesting the heritability of therapy outcome is smaller than our analysis was powered to detect. Polygenic scoring failed to detect genetic overlap between therapy outcome and psychopathology, personality or learning. This study is the largest therapygenetics study to date. Results are consistent with previous, similarly powered genome-wide association studies of complex traits.Major depressive disorder and the anxiety disorders are highly prevalent, disabling and moderately heritable. Depression and anxiety are also highly comorbid and have a strong genetic correlation ( These common mental disorders account for more than 100 million disability-adjusted life years globally, and cost the UK National Health Service ~£30 billion per year4. MDD and the anxiety disorders share several features. They are more common in females, have an early age of onset in adolescence or early adulthood, and can persist throughout life, predicting further emotional difficulties and considerable impairment6. For example, at 10 years of follow up, only ~10% of adolescents diagnosed with an anxiety disorder were disorder free, ~40% still suffered with the same disorder and more than 60% reported a second diagnosis, most commonly MDD8. MDD and the anxiety disorders are heritable. Twin heritability ranges from 30 to 60%10, and SNP heritability (h2SNP) ranges from 15 to 30%13. The comorbidity between MDD and the anxiety disorders is primarily explained by shared genetic vulnerability. Twin and molecular genetic studies consistently show that MDD and the anxiety disorders have a genetic correlation (rg) that is not significantly different from one16.Major depressive disorder (MDD) and the anxiety disorders are the most prevalent psychiatric disorders 17, and is generally successful  in ~50% of patients with anxiety or depression19. The treatment components of CBT vary and are tailored to reflect disorder-specific symptoms or problems. They typically involve psychoeducation, cognitive restructuring, behavioural modification (including exposure to feared stimuli) and relaxation and/or coping strategies. These processes aim to teach the individual to challenge maladaptive responses by modifying negative and anxiety-driven thoughts and behaviours20. Studies examining outcomes following different CBT modalities report comparable effect sizes27. Nonetheless, substantial heterogeneity in outcomes indicates that the efficacy of psychological therapy can vary considerably for different people.Cognitive behavioural therapy (CBT) is an evidence-based psychological therapy used to treat anxiety and depressive disorders. It is a structured, goal-oriented, skills-based treatment that has moderate-large effect sizes  of outcomes following psychological therapy was in children with anxiety disorders (n = 939 at post-treatment)40. No significant genetic associations with therapy outcome were observed, although three independent loci were of suggestive significance (P < 5 × 10−6). A second therapy outcome GWAS, which was part of a broader gene expression analysis (n = 182) also detected several loci that were also of suggestive significance (P < 5 × 10−6)41. Such analyses require large samples to detect small genetic effects at genome-wide significance.Therapygenetics is a relatively new field, which investigates the relationship between genetic variation and outcomes following psychological therapy42. While progress has been slow to begin with, collaborative efforts have led to larger samples, numerous genomic discoveries and remarkable success for psychiatric genomics43. Studies examining genetic effects on outcomes following antidepressant medication are beginning to catch up. A meta-analysis of 2897 individuals was sufficient to detect a significant heritability estimate for remission following antidepressants  and this was the first evidence of a genetic component for treatments outcome of any kind44. A more recent study utilised family data and clinical records (n = 4213) to examine treatment resistant depression (or poor outcomes following antidepressant medication)45. They estimated a pedigree-based heritability for treatment resistant depression to be 0.6 (SE = 0.19) and subsequently detected significant genetic correlations with neuroticism , mood disorder traits  and general psychopathology . This suggests that lighter phenotyping in population-based observational studies can be a valuable approach for increasing sample size and holds promise for genomic studies of treatment outcomes. It is important to build a cohort sufficiently sized to obtain an estimate for the genome-wide common variant heritability of therapeutic outcome . This also provides more robust evidence that a genetic component exists and that genome-wide approaches hold potential as prognostic predictors of symptoms following therapy.As seen in early GWAS of psychiatric disorders and pharmacogenomics, available samples were often small and underpowered to detect genetic associationsR2 = 1.62%, P = 0.009), but poorer outcomes from low-intensity therapies 46. Recently, a polygenic score study of internet-delivered CBT (iCBT) outcomes in adults with major depression (n = 894) detected an interaction effect of a polygenic score for autism spectrum disorder on symptomatic change over time 47. This work suggests that greater genetic predisposition for autistic traits may be associated with poorer prognosis following treatment.Polygenic score analysis is one approach to improve statistical power in small samples with genetic data. No significant polygenic score associations were detected with treatment outcome in the original child study. However, a second study in the same sample found that higher polygenic predisposition for environmental sensitivity predicted better outcomes from high-intensity therapies (n = 972). We then meta-analysed the results from the adult anxiety-CBT sample with a sample of adults who completed a course of iCBT for major depression (n = 832)47, and also with the child anxiety-CBT sample (n = 920)40. This was done in order to maximise our sample size (n = 2724) and power to detect genetic effects.Here, we build on previous work, first combining several samples of individuals who have undergone a course of CBT for an anxiety disorder , personality  and learning .There is evidence to suggest that outcomes following psychological therapy are associated with three main groups of variables. First is the general level of psychopathology, for example, greater baseline severity and higher comorbidity are both associated with poorer treatment outcomesn = 972; 66.3% female; aged: 18–72, mean = 36.3, SD = 11.3) were drawn from one of three broad studies of CBT. Diagnoses were made according to DSM-IV62 criteria using the the Mini-International Neuropsychiatric Interview (MINI 5.0 or 6.0)63, the Diagnostisches Interview bei Psychischen Störungen (DIPS or MINI-DIPS)65 or the Composite International Diagnostic Interview (CIDI)66. The three predominant disorders were panic disorder (PD = 37%), panic disorder with agoraphobia (PD/AG = 42%), and specific phobia (SP = 19%). These disorders share the common components of excessive fear, anxiety, and avoidance behaviours. All participants received CBT for an anxiety disorder. The mode of treatment and the level of cognitive to behavioural focus varied between clinics and treatment types. All treatment programmes achieved comparable effect sizes68. All participants were of White Western European ancestry. Participants were not excluded for taking psychotropic medications, but this was controlled for in the subsequent analyses. The three primary recruitment sites are each briefly described below (see Table 68).Participants (Cohort 1. Bochum and Braunschweig: Participants completed one of four exposure-based CBT (eCBT) treatment programmes at the Mental Health Research and Treatment Centre, Ruhr-Universität Bochum, the Dental Clinic Bochum or at the Technische Universität Braunschweig, Germany . Treatment details for each group are reported elsewhere68. In short, Ruhr-University and Braunschweig patients received eCBT for specific phobia, agoraphobia or panic disorder. Dental Clinic patients received a shorter, dental-phobia specific exposure treatment programme. The primary outcome measure used to assess symptom severity was the clinician-rated severity scale, Clinical Global Impression-Severity (CGI-S). The CGI-S is an overall rating of anxiety symptom severity and ranges from 1 to 7, whereby a score of 1 indicates that the patient is healthy and a score of 7 is indicative of severe illness69. The CGI-S was chosen due to the range of anxiety disorders included, as it reflects symptom severity in a disorder-independent fashion.Cohort 2. Karolinska Institutet Panic Disorder iCBT: Participants with panic disorder, were collected from the university hospital psychiatric clinic in Stockholm, Sweden 26. A subset included participants from a randomised controlled trial of internet delivered CBT 25. The majority of the participants (n = 286) were drawn from routine clinical care and had received iCBT. The treatment content was identical in these groups. The self-rated version of the Panic Disorder Severity Scale (PDSS-SR)70 was used as the primary outcome measure. The PDSS-SR has seven items, each with a 5-point scale, ranging from 0–4, giving a total score range of 0–28. Cut-off scores ≥9 suggest clinical levels of panic disorder71. The scale assesses frequency and severity of panic attacks, anticipatory anxiety, phobic avoidance and occupational and social impairment.Cohort 3. Panic-Net Consortium: Participants with panic disorder and agoraphobia were enrolled from two subsequent multicentre, randomised controlled trials of eCBT 72.The self-rated Panic Agoraphobia Scale  was used in the present analysis73. The scale includes 14 items, the first of which is a screening item, 13 of which are used to determine symptom severity. Each item has a scale of 0–4, with a maximum total score of 52. The scale measures frequency, severity and duration of panic attacks, agoraphobic avoidance, anticipatory anxiety, impairment and worries about health.n = 832; 65.5% female; aged:18–75, mean = 37.9, SD = 11.8) were drawn from routine clinical care, or from an online self-referral system and received psychologist guided iCBT at the Internet Psychiatry Clinic in Stockholm (see Table 47). Diagnoses were made according to DSM-IV criteria62 using the Mini-International Neuropsychiatric Interview63. The primary outcome measure assessed was the Montgomery Åsberg Depression Rating Scale-Self report (MADRS-S)74. The MADRS-S total score, which ranges from 0 to 54, measures nine clinical characteristics of depression.Adults with MDD  with DSM-IV criteria anxiety disorder diagnoses, received individual CBT (n = 251), group CBT (n = 484) or guided self-help/parent led CBT (n = 204) at one of eleven sites36. Primary diagnoses included generalised anxiety disorder , separation anxiety disorder , social phobia , specific phobia  or other anxiety disorders . Output from the Anxiety Disorders Interview Schedule (ADIS) was converted into Clinical Severity Ratings (CSR) on a scale of 0–8 .Children , DNA was obtained from saliva samples. Genotyping of the Bochum participants was performed using the Illumina PsychChip microarray  at the Institute of Psychiatry, Psychology and Neuroscience, King’s College London41. Karolinska PD-iCBT samples were genotyped on Illumina HumanOmniExpress BeadChips  at the Department of Genomics, Life and Brain Centre, University of Bonn, Germany. The Panic-Net samples were genotyped using Illumina Human660W-Quad BeadChips  and Sentrix BeadChip Array HumanHap300 Genotyping BeadChips  at the Department of Genomics, Life & Brain Centre, University of Bonn, Germany76.DNA extraction and genotyping processes are described elsewheren = 832), DNA was extracted from blood. Genotyping was performed at LIFE and BRAIN GmbH  using the Infinium Global Screening Array 1.0 BeadArray 47. For the child anxiety study (n = 920) DNA was extracted from buccal swabs and saliva kits . Genotyping was performed on Illumina HumanCoreExome-12v1.0 microarrays (described in full40).For the adult depression samples , the adult depression sample, and child anxiety sample following a previously published protocol79. Variants were excluded if they were rare , deviated substantially from Hardy–Weinberg equilibrium (P < 10−5) or were missing in >99% of participants. Participants were removed if they had genotype calls for < 99% of variants, were phenotype-genotype sex discordant , showed signs of cryptic relatedness or duplication . The ancestry of participants was estimated from their genotypes using principal component analysis performed in EIGENSOFT80. Outliers were removed if they were >6 SD from the mean on the first three principal components. Quality controlled data were phased using SHAPEIT81 and imputed to the Haplotype Reference Consortium reference panel82 using EAGLE 283, implemented on the Sanger Imputation server. Genetic variants imputed with an info metric of <0.75, a MAF of <0.05 or which were not present in >98% of the sample were removed. Genotype data for the adult anxiety sites was then merged using PLINK 1.978 and genetic variants with MAF <0.05 or not present in >98% of the sample were removed.Quality control, implemented in PLINK 1.9All participants provided informed consent. This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. All trials and collection of samples were approved by site-specific human ethics and biosafety committees. Ethics approval for the Bochum and Braunschweig studies was received from King’s College London Psychiatry, Nursing and Midwifery Research Ethics Sub-Committee and the Ethics Committee at the Faculty of Psychology, Ruhr-Universität Bochum. The BMBF “PanicNet” RCT project was approved by the Ethics Committees of the Medical Faculty of the Technische Universität Dresden (EK 164082006) and the German Psychological Society (AH11.2009) for wave I and II, respectively. The Karolinska studies were approved by the Regional Ethics Board in Stockholm, Sweden . The storage and analysis of DNA was approved by the King’s College London Psychiatry, Nursing and Midwifery Research Ethics Sub-Committee.85.Outcome analyses examined change in symptom severity from start-of-treatment (baseline) to end-of-treatment (post-treatment). As continuous outcome measures differed between cohorts, they were standardised. Raw scores at baseline and at post-treatment were divided by the cohort specific standard deviations of baseline scores. Where there were no data at post-treatment, data were imputed using the last observation recorded. Dichotomised treatment outcomes and percent change from baseline are often used in clinical decision making in studies of treatment outcomes. However, both of these approaches have been shown to attenuate statistical power87.The effects of clinical covariates on symptom severity at baseline and post-treatment, and therapy outcomes were assessed. Linear mixed models were used to control for the random effects of cohort and site to account for between-cohort and between-trial differences in outcomes. All of the covariates, including age, sex, number of comorbidities, number of therapy sessions, psychotropic medication status, primary diagnosis and baseline severity, were entered concurrently. Thus any significant associations are controlled for all other covariates in the model. These analyses were performed using the lme4 package in R.3.4.388. A genetic relationship matrix (GRM) was included to control for the random effects of genetic similarity. Residualised therapy outcome was regressed on the number of reference allele copies , weighted by the additive effect of the allele. Effect sizes and standard errors from the individual sample level analyses were then combined in an inverse-variance weighted GWA meta-analysis (GWAMA) in METAL89. The statistical power of these analyses was estimated using the Genetic Power Calculator90. The meta-analysis sample (n = 2724) has 100% power to detect a variant explaining 2.4% of variance, 80% power to detect variants explaining 1.5% of the variance and 42% power to detect variants explaining 1% of the variance. To test for genetic heterogeneity between the meta-analysis samples sign tests were performed on the GWAS summary statistics. Pairs of summary statistics were examined using the SignTest package (see 91).All subsequent genomic analyses were performed on the imputed, quality controlled genotype data in each sample separately , prior to meta-analyses. Therapy outcome phenotypes for each sample were derived from the residuals of linear regressions of the standardised post-treatment scores on the independent covariates . Principal component analysis of the genetic data was performed and twelve genomic principal components were associated with the phenotype and included in the adult anxiety sample analyses, three principal components in the adult depression sample and one principal component in the child sample to control for population stratification. Mixed linear model association (MLMA) analyses were performed in each of the samples using GCTA MLMA-LOCOh2SNP) was assessed with univariate genomic-relationship-matrix restricted maximum likelihood (GREML), performed in GCTA88. The GCTA-GREML h2SNP estimates for treatment outcomes in the adult anxiety, adult depression and child anxiety samples were also combined in an inverse-variance weighted meta-analysis92. Linkage disequilibrium score regression of the GWAMA summary statistics was performed to provide a second estimate93 . Effect size estimates and P-values for SNPs were drawn from GWAS summary statistics. For each GWAS phenotype, five polygenic scores were computed, gradually incorporating more SNP effects, determined by the discovery phenotype GWAS P-value, using thresholds of: p ≤ 0.01, p ≤ 0.05, p ≤ 0.1, p ≤ 0.5, p ≤ 1). An individual's polygenic score is the sum of the GWAS effect alleles that they carry in their genome, each weighted by its effect size. Polygenic scores were standardised  and regressed on residualised treatment outcomes to test for an association. For each polygenic score analysis 10,000 permutations were performed to assess statistical significance. As such, we tested for associations between therapy outcomes and polygenic scores for psychopathology , personality  and learning . An estimate of the statistical power for each polygenic score analysis was computed using AVENGEME96. We assumed that 95% of SNPs had null effects and calculated power at five theoretical genetic covariances . Power calculations indicate that on average, polygenic score analyses have 80 to100% power to detect significant associations, if the discovery polygenic score trait has a genetic covariance between 0.2 and 0.3 with the treatment outcome phenotype . Each additional comorbidity is equivalent to a 0.14 unit increase in baseline symptom severity. Of note, compared with having a primary diagnosis of panic disorder, having panic disorder with agoraphobia is associated with a 0.91 unit increase in baseline severity .An overview of the clinical and demographic characteristics of each cohort are detailed in Table P < 0.05) on post-treatment scores . Here, higher comorbidity is associated with higher symptom severity and attending more sessions is associated with lower severity post-treatment. Higher baseline severity is associated with a 0.39 unit increase in post-therapy outcome.Number of comorbidities and number of treatment sessions have nominally significant effects  and post-treatment outcome  in the adult depression cohort . Number of comorbidities was associated with higher baseline severity in the child anxiety sample . A primary diagnosis of social anxiety  and baseline severity were also associated with higher symptom severity at post-treatment β = 1.34, SE = 0.18; β = 0.44, SE = 0.07, respectively—as reported in the original paper35.Notably, the effects of clinical covariates were largely consistent across the three meta-analysis samples. Number of comorbidities was associated with baseline severity (88). No individual genetic variant was associated with treatment outcomes in any of the individual samples, after correction for multiple testing (P < 5 × 10−8). However, several genetic loci surpassed a P-value threshold suggestive of association (P < 10−5) and are presented in the Phenotype and good quality genotype data were available for 972 individuals from the adult anxiety sample, 832 from the adult depression sample and 920 from the child anxiety sample. After genotype quality control and imputation there were a total of 4.71 million genetic variants shared between the datasets that were included in the analyses. We performed genome-wide mixed linear model association analyses in each sample . No genetic variant was associated with therapy outcome after correction for multiple testing. However, four independent genetic loci on chromosomes 17, 3, 13 and 5 surpassed a P-value threshold suggestive of association  had a P-value <10−5 in both the full meta-analysis and the adult anxiety sample. The quantile–quantile plot of association P-values show no departure from a chi-squared distribution expected under the null hypothesis , which suggests that there is unlikely to be underlying inflation of the association statistics due to population stratification.Summary statistics from each sample analysis were then meta-analysed in METALP < 5 × 10−5). There is some indication of shared effects at this P-value threshold between the child anxiety cohort and each of the adult anxiety and MDD cohorts . However, this level of sharing is not statistically significant.To determine whether genetic effects were shared between the cohorts, sign tests of the genetic variant association effects were performed. Here, for each pair of GWA summary statistics, we examined whether more genetic variants were acting in the same direction than one would expect by chance, using a binomial test .The proportion of variance in therapy outcomes accounted for by all assayed SNPs was assessed using GCTA-GREMLhort see . The h2S53, anxiety disorders11, autism spectrum disorder54, MDD55 and schizophrenia56), personality  and learning  , and the ASD polygenic score was nominally associated with therapy outcome in the adult depression sample .We tested for associations between therapy outcomes and polygenic scores across three domains: psychopathology  and the MDD polygenic score analyses   see .P < 2.5 x 10−5; pathway Bonferroni P < 10−5). The top genes (P < 10−4) and pathways (P < 10−3) are detailed in the No genes or pathways were associated with therapy outcomes after corrections for multiple testing (gene-wise Bonferroni n = 972) and the largest genome-wide association meta-analyses of prognostic outcomes following psychological therapy .This study presents a new adult anxiety outcome sample with genetic data . The only consistent predictor of poorer outcome was higher baseline severity . However, there was suggestive evidence of an association between higher comorbidity and worse therapy outcomes . Better adherence to treatment, indicated by number of treatment sessions completed, was also associated with favourable outcomes in the depression sample , with weaker evidence of this relationship in the adult anxiety sample . These analyses suggest that, consistent with previous findings, higher baseline symptom severity, higher comorbidity and poor adherence to treatment are associated with poorer therapy outcomes. Notably, a primary diagnosis of PD/AG or SP was associated with more severe symptoms at baseline , but this did not impact symptoms post treatment. This suggests that therapy outcome is independent of primary diagnosis and that genomic studies of therapy outcome could combine treatment samples of adults with varied primary diagnoses to increase power.First, we examined the effects of clinical covariates on baseline symptom severity and prognostic outcome following CBT, using linear mixed models. As would be expected, number of comorbidities was associated with baseline severity in all three cohorts (adult anxiety: n = 2724). No genetic effects were detected in these analyses. This result is consistent with the previous genome-wide study of therapy outcomes in the child sample40 and with other small GWAS of psychiatric traits98. The meta-analysis sample (n = 2724) had 80% power to detect variants explaining 1.5% of the variance and 42% power to detect variants explaining 1% of the variance. Therefore, it is not especially surprising that we do not detect any variants at genome-wide significance. Typically, GWAS of psychological traits have required tens of thousands of participants to detect SNPs at genome-wide significance99.We performed genome-wide linear mixed model association analyses in three independent studies of prognostic outcome following CBT, and then meta-analysed the results . A sample size of 2724 has 80% and 99% power to detect a SNP-heritability of 33% and 50%, respectively94. To achieve 80% power to detect a heritability of 20%, a sample of 4500 individuals will be required. A meta-analysis of 2 799 individuals was sufficient to detect a significant heritability estimate for therapy outcome to antidepressant drugs  and this was the first evidence of a genetic component for treatments outcome of any kind44.Our primary aim was to generate a cohort large enough to examine the heritability of prognostic therapy outcomes. However, the meta-analysis estimate of SNP heritability was low and non-significant . In the adult depression sample, increased genetic risk for ASD was nominally associated with worse treatment outcomes . Here we reproduce the finding from the original adult depression study47. It is worth noting, however, that for this current analysis, only two time-points were used (pre-treatment and post-treatment) to compute the therapy outcome phenotype, as these time-points were consistent across our cohorts. The previous study47 modelled 12 time-points, increasing sample size and power to detect the effect .Genetic associations between therapy outcome and other relevant phenotypes were investigated via polygenic score analyses. There were no significant associations in the adult anxiety cohort. There were, however, nominal associations between the Subjective Well-Being (SWB) polygenic score and therapy outcomes in children, and also between the Autism Spectrum Disorder (ASD) polygenic score and treatment outcomes in the adult depression sample. In the child sample, greater genetic propensity for SWB was associated with lower symptoms at post-treatment (Despite the availability of large GWAS samples, the polygenic scores used here were largely insufficient to capture polygenic variation associated with therapy outcome in the samples examined, after corrections for multiple tests. This could indicate that power is attenuated because of sample heterogeneity, or perhaps none of the polygenic scores examined are close enough to our treatment outcome phenotype. Power calculations indicate that analyses were well powered to detect polygenic associations if the genetic covariance between each of the polygenic score traits and treatment outcomes in each sample is more than 0.2. This suggests that the genetic covariance between treatment outcome in these samples and traits of interest is low.100. As such, genotypes associated with onset of depression following a stressful life event, would be associated with favourable outcomes following psychological therapy. However, we find no evidence of such here.There are additional factors that might explain the lack of associations. Therapy outcome phenotypes were adjusted for baseline severity, which might account for the lack of association with the psychiatric disorder polygenic scores. Such analyses explore whether genetic variants associated with disorders predict therapy outcome above and beyond initial disorder severity. However, the differential susceptibility hypothesis posits that genotypes moderate the effects of both positive and negative environmentsTaken together, findings from SNP heritability and polygenic score analyses suggest that there is likely to be genetic heterogeneity between the samples examined here. We tested for evidence of genetic heterogeneity using two approaches. First performing sign tests for consistency of the association statistics and also random-effects meta-analyses of polygenic score statistics. However, these analyses were underpowered. Therefore, we are unable to make any strong conclusions as to whether substantial heterogeneity exists, or whether disparate findings are driven by noise, attributable to low sample size.102. The strong genetic correlation between the anxiety disorders, depressive disorders and psychiatric disorders in general suggests that meta-analyses of therapy outcome could include treatment samples with variable primary diagnoses to further increase power. Combining clinical samples in meta-analyses has been a successful approach towards understanding the genetic architecture of psychiatric traits56. Larger meta-analyses, complemented by large, population-based initiatives, which collect broad clinical, demographic, outcome and genetic data, will be required to provide insights about the genetic architecture underlying therapy outcome.Therapygenetics studies will require much larger samples than presently available to detect genetic effects. This study brings together clinical samples with sufficient data for genomic analysis. Such data are scarce, and the individual cohorts studied are small for genetic analyses. Even combined, they remain underpowered. Heterogeneity between the samples and uncontrolled confounds compromise statistical power to detect genetic effects. However, there is a tradeoff between sample size and heterogeneity, and we argue we are justified in that the combined sample has considerably more power than any of the individual samples alone. The aims of this study were to build a cohort of sufficient size to estimate the SNP-heritability of psychological therapy outcomes. A significant estimate of heritability would allow for genetic correlations with therapy outcomes to be examined. The detection of significant genetic correlations allows for the joint analysis of traits, which can also boost statistical powerSupplementary Material


Arabidopsis. The results showed that, the distinction between QTNs and unrelated SNPs was more significant than the other methods. The new method detected 60 genes confirmed to be related to the target trait, which was significantly higher than the other methods, and simultaneously detected multiple gene clusters associated with the target trait.One of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods are difficult to analyze the corresponding high-dimensional massive data or SNPs. Recently, machine learning methods have become more popular in high-dimensional genetic data analysis for their fast computation speed. However, most of machine learning methods have several drawbacks, such as poor generalization ability, over-fitting, unsatisfactory classification and low detection accuracy. This study proposed a two-stage algorithm based on least angle regression and random forest (TSLRF), which firstly considered the control of population structure and polygenic effects, then selected the SNPs that were potentially related to target traits by using least angle regression (LARS), furtherly analyzed this variable subset using random forest (RF) to detect quantitative trait nucleotides (QTNs) associated with target traits. The new method has more powerful detection in simulation experiments and real data analyses. The results of simulation experiments showed that, compared with the existing approaches, the new method effectively improved the detection ability of QTNs and model fitting degree, and required less calculation time. In addition, the new method significantly distinguished QTNs and other SNPs. Subsequently, the new method was applied to analyze five flowering-related traits in  How to analysis these kind datasets is a hot topic. The pervasive feature of genetic data is hundreds of thousands single-nucleotide polymorphisms (SNPs) along with a few hundred or thousand samples, that is, the “big P, small N” problem, which brings challenges to data mining and statistical analysis6. Variable selection is the process of detecting the subset of potential variables associating with the phenotype, thus avoiding intractable problems of high-dimensional datasets analysis. There are many variable selection methods proposed for “big P, small N” datasets. The least absolute shrinkage and selection operator7 (LASSO) method minimizes the residual sum of squares, subject to the sum of the absolute value of the coefficients being less than a constant, which has the advantages of subset selection and ridge regression. Since LASSO is hard to implement and often has a high false positive rate in the detection8, many extension methods were continuously proposed under the framework of variable selection, such as, least angle regression9 (LARS), the smoothly clipped absolute deviation (SCAD) penalized10 method, the elastic net11 method and the adaptive LASSO12 method. Although most of the mythologies are successfully applied to genome-wide association analysis (GWAS) analysis16, they still suffer from the high false positive rate in detection, which hard to provide theoretical basis for the following work, say, fine mapping and gene cloning.However, a number of studies illustrate that most quantitative traits are controlled by a small portion of genetic markers among all SNPs17 (SVM) is applied to reprioritize GWAS results5; artificial neural network18 (ANN) is utilized to classify and select quantitative trait nucleotides (QTNs) associating with complex traits in genome-wide association data4.Machine learning methods are alternative to classical statistical approaches of mining “big P, small N” genetic datasets by optimizing the classification. Support vector machine19 (RF) has become a popular machine learning method in recent years. In RF, multiple decision trees are constructed by autonomous sampling (Bagging) and node random splitting technique, the final classification or regression results are obtained by voting or averaging. RF is easy to implement and has relatively fast learning speed. Recently, the improved RF methods have been proposed and applied to GWAS, Botta et al.20 and Nguyen et al.21 respectively proposed T-Trees method and ts-RF method, both of them optimize the split rules of RF decision tree nodes; Szymczak et al.22 redefined the calculation method of importance scores; Elyan & Gaber23 optimized the setting of ntree and mtry; Stephan et al.24 proposed a hybrid RF algorithm. However, most of the existing methods do not consider the population structure and polygenetic effect background, and the importance scores of all variables are maintained at the same level, so it is hard to distinguish whether the variable is related to the target trait or not.Random forest25 and its improved method EMMA eXpedited (EMMAX)26, which reduces the computational time for analyzing large GWAS datasets from years to hours. On the other hand, multi-locus GWAS analysis methods have also been proposed, such as fast multi-locus random-SNP-effect EMMA (FASTmrEMMA)16, which is more powerful in QTN detection and model fit.In real data analysis at present, the methodologies for genome-wide single-marker scan under polygenic background and population structure controls are widely used to conduct GWAS, such as efficient mixed model association (EMMA)16 method to control the population structure and correct the polygenic background, secondly applied LARS to select the subset of variables, which were potentially related to the target traits, finally utilized RF to calculate the importance scores and rankings of selected SNPs from LARS. The new method could flexibly scan each SNPs from the high dimensional datasets by constructing a fast and new matrix transformation. Furtherly, TSLRF improved the importance scores of QTNs, which were associated with target traits, thus boosted the signals of related SNPs.In this study, we proposed a two-stage association analysis method by combining variable selection with machine learning, two-stage algorithm based on least angle regression and random forest (TSLRF). TSLRF firstly adopted the model transformation of FASTmrEMMAArabidopsis thaliana data analysis  to validate the reliability of the new method. All the results showed that, TSLRF is relatively superior to the other five methods including classical RF in model fitting degree, prediction accuracy and computing time, it can effectively increase the discrimination between QTNs and SNPs. In addition, for real Arabidopsis data, the number of confirmed genes which are detected by TSLRF is significantly more than the other methods.We conducted the simulation experiments  and real yi be the phenotypic observation value of the ith individual in a natural population with n samples. The genetic model can be described as:α is a c × 1 vectorthe intercept, population structure effect and so on; p is the number of putative QTNs; W and Z are the corresponding designed matrices for α and γ; K is a known In is an n-dimensional mean vector u and 16 is adapted to whiten the covariance matrix of the polygenic matrix K and residual noise. By decomposing the variance of y in the model (1) and standardizing designed matrices for fixed and random effects (Supplementary methods), model (1) can be rewritten as:Y is phenotype after corrected; X is marker genotype after standardized, where y, say xj1, and performed simple linear regression between them to calculate residual vector which was orthogonal to Let n-1 steps, where n−1 variables have entered the active set, a LARS fit always has no more than n−1 variables with nonzero coefficients9. Efron et al.9 provided a Cp-type risk estimation formula of a s-step LARS estimator As LARS terminates at the saturated least squares fit after Cp indicates the better LARS estimation. We applied LARS to analyzing one simulated dataset which were randomly selected from 1,000 replication simulated datasets, and obtained the Cp values and residual sum of squares (RSS) values of Y and Cp and RSS decrease. At the maximum iteration step, Cp, 196, and the smallest RSS, 0.0137. For the other replications, the results have the same trend. Therefore, we set the maximum iteration step s as n-1 in simulation experiments and real Arabidopsis data analyses, then at most n-1 variables which were most likely to be associated with the target traits could be selected to construct a RF model. In the following section, we recorded the number of variables selected by LARS as k.The smaller ntree (default is 500 in RF) new self-sampling sample sets and ntree out of bag (OOB) datasets from the original training dataset. Then, a RF model was constructed by ntree CART trees generated by node random splitting technique, which randomly extracted mtry variables from the k variables at each node of each tree. Here, we set mtry be the value that could make the RF model get the best model fitting degree. RF took the average output of all CART trees as the prediction results in regression. The details of conducting RF are shown in Supplementary methods. Finally, we calculated the importance scores of the k selected variables by the variable importance measure (VIM) method in RF. VIM calculated the importance scores by OOB error rate: %IncMSE, which was obtained by randomly replacing the value of the calculated variable to form new OOB test data and calculating its prediction error. The larger %IncMSE indicates the variable more important. The details of calculating variable importance scores are shown in Supplementary methods. According to the importance scores, we could rank k variables and select the set of potentially significant variables furtherly.In order to select potentially significant variable set, the selected potential variable set lars and randomForest, respectively, in R program.TSLRF is an R-based implementation, where achieved LARS and RF using the package MAE), mean absolute percentage error (MAPE) and Pearson correlation coefficient r were selected to evaluate the performance of the new method.In this study, mean absolute error  with 199 individuals and 10,000 SNPs derived from Arabidopsis lines (http://www.arabidopsis.usc.edu/) with 216,130 SNPs and 107 traits27. Among these traits, we analyzed five traits related to flowering time, including LD: days to flowering under long days; LDV: days to flowering under long days with vernalization; SD: days to flowering under short days; FT16: days to flowering at 16C; FT22: days to flowering at 22C. These data were downloaded from the following website: http://www.arabidopsis.usc.edu/.The real dataset included 199 Arabidopsis inbreed population, and simulated six QTNs, all of which were placed at SNP positions. All the simulation information is listed on Table 30(TSRF), RF, support vector regression (SVR), ANN and EMMAX, respectively. Subsequently, we used three statistical indicators mentioned above: MAE, MAPE and r to evaluate the prediction accuracy and model fitting of the above six approaches. Meanwhile, we also compared the importance scores of three RF methods  and the computing time of six methods. All the six methods are R-based implementation, where three RF framework methods are implemented using randomForest package; SVR and ANN are implemented using e1071 and nnet packages, respectively; EMMAX is implemented using Genomic Association and Prediction Integrated Tool (GAPIT) software.To validate the performance of TSLRF, the simulation experiment was implemented, firstly. We generated 1,000 datasets (replications) from MAE and MAPE of 1,000 repeated simulated data analyses by six methods are shown on Supplementary Table MAE of the TSLRF, TSRF, RF, SVR, ANN and EMMAX, as it shows, TSLRF has the smallest MAE, 2.0997, which indicates that TSLRF is more accurate among them; the MAE of TSRF, RF and SVR are similar to each other, they are 2.3745, 2.5236 and 2.5345, respectively; the MAE of EMMAX and ANN are 3.4358 and 3.6041, larger than the other four methods. Obviously, ANN has the lowest model accuracy among these five methods.The model accuracy of the six methods were evaluated by the ten-fold cross validation. The MAPE of TSLRF, TSRF, RF, SVR and ANN  under the RF framework, we detected whether SNPs were related to the target traits through ranking their importance scores. We randomly selected the result of one simulation dataset  for analysis, the importance scores obtained from the above three methods are shown in Fig. Meanwhile, we calculated the average importance scores of 1,000 repeated simulated analyses for three RF methods. As shown on Table In addition, Table We also analyzed the simulation datasets using SVR, ANN and EMMAX. For SVR and ANN, we obtained the weight of each SNP; for EMMAX, the effect of each SNP. Based on weights or effects, the importance rankings of all SNPs can be easily obtained. However, the weights, effects and the importance scores are not under the unified scale, so we did not list the weights or effects obtained by SVR, ANN and EMMAX here.r between the true phenotypic value and the predicted value, which was calculated by the predict function in R program. The average r for 1,000 replications calculated by TSLRF, TSRF, RF, SVR, ANN and EMMAX are shown on Supplementary Table r, TSLRF follows it, which has the higher r among the three RF methods.We measured the model fitting degree by calculating Pearson correlation coefficient We compared the computing time of 1,000 repeated simulated analyses using six approaches. Figure Arabidopsis datasets in the next section.For the model accuracy and the computing time, the performance of ANN was unsatisfied, thus, the ANN method was not used for analyzing the Arabidopsis data27 includes 199 diverse inbred lines, each of which has 216,130 SNPs and 107 traits. To verify the performance of TSLRF, we re-analyzed five traits related to flowering time, including LD, LDV, SD, FT16 and FT22, and compared the result of TSLRF with that of TSRF, RF, SVR and EMMAX. The SNPs with minor allele frequency (MAF) less than 10% were deleted, the population structure and polygenic background were controlled by the model transformation of FASTmrEMMA16 (in the package mrMLM in R program). All the SNPs within 20 KB of each putative QTN were used to mine the candidate genes by TAIR (https://www.arabidopsis.org/) for these five traits. The detected putative QTNs associated with interested traits were used to fit the regression for each trait and model fitness was assessed by the indicators as above.The well-known Table Interestingly, the new method dissected several clusters of genes, which were associated with the same trait  detected by TSLRF obviously owned higher rankings compared with the other four methods (Supplementary Table MAE and MAPE) is similar to that of TSRF, which is more accurate than RF and SVR, EMMAX performs well than TSLRF in terms of MAPE. Moreover, Supplementary Table We implemented ten-fold cross validation to evaluate the model accuracy. Figure Arabidopsis genetic datasets  using five methods indicates that, TSLRF, RF and SVR have similar computing time, and the data analysis time of each trait is about 2 minutes. EMMAX takes about 15 minutes to analyze a trait. Unlike simulation experiment, TSRF spend more than 60 times longer than TSLRF.The computing time Table  for high32. In this paper, we proposed a new method called two-stage algorithm based on least angle regression and random forest (TSLRF), which adopted the model transformation of FASTmrEMMA16, this background control algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. All the results in this paper show that, population structure and polygenic background control are crucial for TSLRF in GWAS, particularly for the importance rankings and scores of SNPs.In recent years, GWAS has become an important method to dissect complexity traits. A number of studies demonstrate that controlling population structure and kinship indeed improves the power of detection and decreases the false discoverCp and RSS. Then, we used at most Variable selection plays a critical role in TSLRF, here LARS was applied in TSLRF to select the markers most likely associated with the target trait. To obtain accurate results, we using a simulated dataset which were randomly selected from 1,000 replication simulated datasets to compare different variable selection cases, setting the maximum iteration step of LARS from 1 to Arabidopsis real datasets, large-scale datasets resulted in computing time increasing exponentially. Therefore, although TSRF increases the distinction of QTNs and SNPs, the proposed new method, TSLRF, is recommended from the point of computing time.For the computing time, the new method TSLRF and classical RF both perform better and have faster speed than the other four methods , that is the reason of RF so popular in machine learning. TSRF, as a method under the RF framework, takes quite expensive computing time (more than one hour) in real data analysis Table , which iSupplementary Information