Integrating results from genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology. Here, we present a probabilistic Mendelian randomization (MR) method, PMR-Egger, for TWAS applications. PMR-Egger relies on a MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and is scalable to hundreds of thousands of individuals. In simulations, PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust under various types of model misspecifications, is more powerful than existing TWAS/MR approaches, and can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. Transcriptome-wide association studies integrate GWAS and transcriptome data to examine the molecular mechanisms underlying disease etiology. Here the authors present PMR-Egger, a powerful TWAS method based on probabilistic Mendelian Randomization. Integrating the existing association results from both GWASs and eQTL mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently proposed for such integrative analysis. For example, PrediXcan1 performs a weighted SNP set test in GWAS using SNP weights inferred from eQTL studies. TWAS2 infers the association between gene expression and disease trait by leveraging cis-SNP information. SMR3 or GSMR4 directly tests the causal association between gene expression and disease trait under a Mendelian randomization (MR) framework, using either a single instrument or multiple independent instruments. While each of these integrative methods was originally proposed to solve a different problem, as we will show here, all of them can be viewed as a two-sample MR method with different modeling assumptions. Because of their relationship to MR, these methods effectively attempt to identify genes causally associated with diseases or complex traits in the context of transcriptome-wide association studies (TWAS).Genome-wide association studies (GWASs) have identified many SNPs associated with common diseases and disease related traits. Parallel expression quantitative trait loci (eQTL) mapping studies have also identified many 5. MR aims to determine the causal relationship between an exposure variable  and an outcome variable  in observational studies. MR treats SNPs as instrumental variables for the exposure variable and uses these SNP instruments to estimate and test the causal effect of the exposure on the outcome. MR methods have been widely applied to investigate the causal relationship among various complex traits6, and, through a two-sample design, can be easily adapted to settings where the exposure and outcome are measured on two different sets of individuals. However, MR analysis for TWAS applications is not straightforward and requires the development of new methods that can accommodate two important features of TWAS analysis.MR analysis is a form of instrumental variable analysis for causal inference7, MR-Egger8, median-based regression9, SMR3, or GSMR4, can only make use of a single SNP instrument or multiple independent SNP instruments. Handling only independent SNPs is restrictive, as most exposure variables/molecular traits are polygenic/omnigenic in nature and are influenced by multiple SNPs that are in potential LD with each other. Consequently, incorporating multiple correlated SNPs can often help explain a greater proportion of variance in the exposure than using independent SNPs and improve MR power12. Due to the benefits of using multiple correlated instruments, most TWAS methods  rely on polygenic modeling assumptions to incorporate all cis-SNPs that are in high LD for TWAS applications. By incorporating all cis-SNPs, as we will show below, these methods can lead to substantial power improvement over standard MR approaches that use only a few independent SNPs. Unfortunately, many TWAS methods rely on a two-stage MR inference procedure: they estimate SNP effect sizes in the exposure study and plug in these estimates to the outcome study for causal effect inference. The two-stage inference procedure in MR fails to account for the uncertainty in parameter estimates in the exposure study, which can lead to biased causal effect estimates and power loss13. Therefore, it is important to incorporate multiple correlated instruments in a likelihood inference framework for MR analysis in TWAS applications.First, both GWASs and eQTL mapping studies collect SNPs that are in high linkage disequilibrium (LD) with each other. Traditional MR methods, such as the random effects version or the fixed effect version of the inverse variance weighted regression16. Horizontal pleiotropy occurs when a genetic variant affects the outcome variable through pathways other than or in addition to the exposure variable17. Horizontal pleiotropy is widely distributed across the genome, affects a wide spectrum of complex traits, and can be driven by LD and extreme polygenicity of traits18. Despite its wide prevalence, however, only a limited number of MR methods have been developed to test and control for horizontal pleiotropy; even fewer are applicable for TWAS applications. For example, some existing methods  test for horizontal pleiotropic effects without directly controlling for them. Some methods  control for horizontal pleiotropic effects without directly testing them21. Some methods  test and control for horizontal pleiotropic effects, but can only accommodate independent instruments. As far as we are aware, there is only one two-sample MR method currently developed for testing and controlling for pleiotropic effects in the presence of correlated instruments: LDA MR-Egger28. Unfortunately, as we will show below, LDA MR-Egger cannot handle realistic LD pattern among cis-SNPs for TWAS applications.Second, perhaps more importantly, SNP instruments exhibit pervasive horizontal pleiotropic effectsHere, we develop a generative two-sample MR method in a likelihood framework, which we refer to as the probabilistic two-sample Mendelian randomization (PMR), to perform MR analysis using multiple correlated instruments for TWAS applications. Within the PMR framework, we focus on a particular horizontal pleiotropy effect modeling assumption based on the burden test assumption commonly used for rare variant test. This particular horizontal pleiotropy effect effectively generalizes the Egger-regression assumption commonly used for MR analysis to correlated instruments. We refer to our method as PMR-Egger. With simulations and real data applications, we show that PMR-Egger provides calibrated type I error for causal effect testing in the presence of horizontal pleiotropic effects, is more powerful than existing MR approaches, can directly test for horizontal pleiotropy, and is scalable to hundreds of thousands of individuals.PMR-Egger is described in the Methods, with technical details provided in the α = 0). In the absence of horizontal pleiotropic effects, PMR-Egger, together with PrediXcan, TWAS, and CoMM, all provides calibrated type I error , as one would expect, PMR-Egger p-values become inflated, with genomic control factor being 1.08, 1.31, and 1.37, for settings where the ratio is 1:9, 3:7, and 5:5, respectively  of 0.1 instead of a nominal p-value threshold to allow for fair comparison among methods. When horizontal pleiotropic effects are absent or small, PMR-Egger, TWAS and CoMM have similar power, all outperforming the other three methods, highlighting the importance of making polygenic assumptions on β and modeling all cis-SNPs together . The gene expression data are obtained from the GEUVADIS study and contains 15,810 genes. The phenotypes include seven common diseases from Wellcome Trust Case Control study (WTCCC), 22 diseases from Kaiser Permanente/UCSF Genetic Epidemiology Research Study on Adult Health and Aging (GERA), and ten quantitative traits from UK Biobank. The GWAS sample size ranges from 4,686 (for Crohn’s disease (CD) in WTCCC) to 337,198 (for UK Biobank). The its Fig. , GERA trits Fig. , and UK its Fig. ; with geits Fig. . Note thp-value threshold may artificially favor those methods that have inflated type I error control. For this analysis, we excluded LDA MR-Egger for comparison, as its p-values are overly inflated. Consistent with simulations, we found that SMR can barely detect any genes significantly associated with traits across all three data, much less so than that detected by the other four methods. We found that the number of gene-trait pairs detected by CoMM and PMR-Egger is higher than that detected by TWAS and PrediXcan in all three GWASs, again consistent with simulations as well as previous observations that likelihood-based inference often achieves higher power than two-stage inference. However, we do notice that PMR-Egger detects slightly lower number of gene-trait pairs than CoMM based on the same genome-wide p-value threshold, consistent with the inflated genomic inflation factors observed for CoMM. Indeed, we found that the estimated We examined the number of associated genes detected by different methods based on a Bonferroni corrected transcriptome-wide threshold Figs. . The numLNK/SH2B3 gene is only identified by PMR-Egger to be associated with platelet count in the UK Biobank . The association between LNK and plate count is consistent with results from recent large-scale GWASs31. LNK/SH2B3 encodes the lymphocyte adaptor protein (LNK) that is primarily expressed in hematopoietic and endothelial cells32. In hematopoietic cells, LNK functions as a negative regulator of cell proliferation and the thrombopoietin-mediated cytokine signaling pathway, which is a key signaling pathway that promotes megakaryocytes to form platelets32. Indeed, platelets are overproduced and accumulated in Lnk knockdown cells as well as Lnkknockout mouse33, supporting a causal role of LNK in platelets production. As the second example, the NOD2 gene is identified by PMR-Egger to be associated with Crohn’s disease , and, with a slightly less significance, also by CoMM (p = 7.8 × 10−15). The association between NOD2 and CD was not identified by the other methods . NOD2 encodes a cytosolic pattern recognition receptor that acts both as a cytoplasmic sensor of microbial products and as an important mediator of innate immunity and inflammatory response34. The NOD2 gene is a well-known susceptible gene for CD and is perhaps one of the first genes ever implied for CD. Multiple SNPs in NOD2 have been found to be associated with CD in both early linkage studies35 and many recent GWASs36. NOD2 variants associated with CD often reside in the ligand recognition domain of NOD2 and can lead to aberrant bacterial handling and antigen presentation37. Indeed, Nod2-deficient mice displays dysregulated bacterial community in the ileum and Nod2-deficient ileal epithelia exhibit impaired ability of inducing immune responses for bacteria elimination38. It is thus hypothesized that mis-regulation of NOD2 can causally lead to altered interactions between ileal microbiota and mucosal immunity, resulting in increased disease susceptibility to CD38. As a third example, the TFRC gene is identified by PMR-Egger to be associated with red blood cell distribution width (RDW) in the UK Biobank (p = 3.3 × 10−17). Such association is not identified by the other methods . TFRC encodes the classical transferrin receptor that is involved in cellular iron uptake39. Multiple SNPs in TFRC have been established to be associated with various erythrocyte phenotypes in GWASs40. These associated erythrocyte phenotypes include the mean corpuscular hemoglobin (MCH) and mean corpuscular volume  which is directly related to RDW40. The variants in TFRC likely lead to decreased iron availability for red cell precursors, as has been observed in mice deficient in Tfrc, thus resulting in a compensatory increase of red blood cell size as measured by RDW41. The regional association plots for these three genes are presented in Supplementary Figs. Overall, by controlling for horizontal pleiotropic effects, PMR-Egger detected many likely causal genes that other methods failed to detect. For example, the 42 . Briefly, we follow42 and focused on independent and non-overlapping genomic regions that harbor at least one genome-wide significant SNP and at least one significant TWAS gene  that is measured on n1 individuals in the gene expression study and denote Zx as an n1 by p matrix of genotypes for p instruments  in the same study. Note that, unlike standard MR methods that select independent instruments, we follow existing TWAS approaches and use all cis-SNPs that are in LD as instruments. We denote y as an n2-vector of outcome variable  that is measured on n2 individuals in the GWAS and denote Zy as an n2 by p matrix of genotypes for the same p instruments there. For both Zx and Zy, we coded their genotypes based on minor allele count, where the minor allele is defined in the GWAS data. We examined the robustness of such genotype coding in simulations through genotype flipping. We consider three linear regressions to model the two studies separatelyμx and μy are the intercepts; n2-vector of exposure variable on the n2 individuals in the GWAS; β is a p-vector of instrumental effect sizes on the exposure variable; α is a scalar that represents the causal effect of the exposure variable on the outcome variable; γ is a p-vector of horizontal pleiotropic effect sizes of p instruments on the outcome variable; εx is an n1-vector of residual error with each element independently and identically distributed from a normal distribution n2-vector of residual error with each element independently and identically distributed from the same normal distribution ϵ is an n2-vector of residual error with each element independently and identically distributed from a normal distribution β and the unobserved gene expression measurements We denote α. The causal interpretation of α requires two assumptions of MR analysis to hold: (i) instruments are associated with the exposure; (ii) instruments are not associated with any other confounders that may be associated with both exposure and outcome. Note that our model no longer requires the general exclusion restriction condition of traditional MR , as we make explicite modeling assumptions on the horizontal pleiotropy effects γ. Certainly, PMR-Egger still need to satisfy the InSIDE assumption that the instrument-exposure effects and instrument-outcome effects are independent of each other, which is sometimes refered to as the weak exclusion restriction condition8. In our model, we derive the causal interpretation and identification of α under the decision-theoretic framework of causal inference51  algorithm for parameter inference by maximizing the joint likelihood defined based on Eqs. , which estimates 13.In the above model, we are interested in estimating the causal effect  on Eqs. , 4 . As explained above, we use “probabilistic” to refer to both the data generative model and the maximum likelihood inference procedure. We use “Egger” to refer to the horizontal pleiotropic assumption on  in Eqs. , 4 is a es Table . Comparecis-SNPs for the gene BACE1 on chromosome 11 from the GEUVADIS data52 (data processing details in the next section) and simulated gene expression values. We used the gene BACE1 because the number of cis-SNPs in this gene represents the median of all genes. With the scaled genotype data Zx, we simulated SNP effect sizes β from a normal distribution N, where the scalar PVEzx represents the proportion of gene expression variance explained by genetic effects. We summed the genetic effects across all cis-SNPs as Zxβ. In addition, we simulated residual errors εx from a normal distribution We performed simulations to assess the performance of PMR-Egger and compare it with existing approaches. To do so, we first obtained 556 54 and simulated a quantitative trait. Here, we directly used β from the gene expression data, which, when paired with the causal effect α, yielded the vertical pleiotropic effects αβ. We set εy from a normal distribution zy represents the proportion of phenotypic variance explained by vertical pleiotropic effects in the absence of horizontal pleiotropic effects. Afterwards, we simulated horizontal pleiotropic effects γ for these SNPs (more details below). We summed the horizontal pleiotropic effects, vertical pleiotropic effects, and residual errors to yield the simulated trait.Next, we obtained genotypes for the same 556 SNPs from 2000 randomly selected control individuals in the Kaiser Permanente/UCSF Genetic Epidemiology Research Study on Adult Health and Aging (GERA)zx = 10%, PVEzy = 0, with all γj = 0. On top of the baseline setting, we varied one parameter at a time to examine the influence of various parameters. For PVEzx, we set it to be either 1%, 5%, or 10%, close to the median gene expression heritability estimates across genes56. For β, we examined alternative SNP effect size distributions that deviate from the polygenic assumption in the baseline setting. Specifically, we randomly selected either 1 SNP, 1%, 10%, or 100% of the SNPs to have non-zero effect, and simulated their effects from a normal distribution to explain a fixed PVEzx in total. In addition, we examined the case of correlated β, where the SNP effects on gene expression is generated from a multivariate normal distribution with the covariance matrix w∑. Here, ∑ is the LD matrix among SNPs and w is a scalar that is chosen to ensure that PVEzx equal to 10%. For PVEzy, we varied its value to be either 0% (for null simulations), 0.2%, 0.4%, or 0.6% (for power simulations). For the horizontal pleiotropy effects γ, we randomly assigned a fixed proportion of γj to be non-zero . Afterwards, we set the absolute value of non-zero γj to be the same value of γ. As a sensitivity analysis, we also randomly assigned some of their signs to be positive and some of their signs to be negative, with the ratio of positive effects to negative effects being either 1:9, 3:7, or 5:5. Here, we set γ to be 1 × 10−4, 5 × 10−4, 1 × 10−3, or 2 × 10−3, which corresponds to the 50%, 70%, 90%, 95% quantiles of horizontal pleiotropic effect estimates across all genes and all traits in the WTCCC data (more details below), respectively. While genotype coding is based on allele frequency in both simulations and analysis, we also examined cases where we randomly flipped the genotypes of a fraction of SNPs before analysis so that genotype coding does not match between simulation and analysis. The faction of flipped genotype SNPs is set to be either 10%, 30%, or 50%. In addition, we conducted analysis by orienting SNP genotypes based on its estimated effect sign on the gene expression, to examine whether such “positive orientation” strategy can improve the performance of PMR-Egger. For null simulations and type I error control examination, we performed 10,000 simulation replicates for each simulation scenario described above. For power calculation, for each scenario, we performed 1000 alternative simulations together with 9000 null simulations and calculated power based on false discovery rate (FDR).In the simulations, we first examined a baseline simulation setting where we set PVEn1 = 465, n2 = 2000), different causal effect sizes (PVEzy = 0 or 0.6%) and different pleiotropy effect sizes (γ = 0 or 0.0005). In the analysis, we calculated the LD matrix in the eQTL data using the observed individual-level genotypes in the eQTL study. We calculated the LD matrix in the GWAS data from a reference panel. The reference panel is constructed in four different ways, by using individual-level genotypes from either all individuals in the GWAS (n = 2000), 10% of randomly selected individuals from the GWAS (n = 200), individuals with European ancestry (n = 503) or individuals with African ancestry (n = 611) from the 1000 Genomes project phase 3. Note that the African ancestry panel includes 99 Esan in Nigeria (ESN), 113 Gambian in Western Division, Mandinka (GWD), 99 Luhya in Webuye, Kenya (LWK), 85 Mende in Sierra Leone (MSL), 108 Yoruba in Ibadan, Nigeria (YRI), 96 African Caribbean in Barbados (ACB), and 61 people with African Ancestry in Southwest USA (ASW).While we applied PMR-Egger to analyze individual-level data from all simulations, we also applied PMR-Egger to analyze summary statistics in a subset of simulations to validate the implementation of the summary statistics-based PMR-Egger algorithm. These results are presented in the Discussion section. Here, we considered the simulation settings with a fixed sample size . For each gene in turn, we used its cis-SNPs to simulate its gene expression level as described above. Afterwards, we applied different methods to analyze simulated data. The cross-gene-based simulations reflect the varying LD pattern and the varying number of cis-SNPs across genes that we observe in real data, and thus are likely to be more realistic than the single gene-based simulations. We performed cross-gene simulations under all simulation settings described above, including settings with varying gene expression heritability, varying genetic architectures underlying gene expression, as well as varying causal and horizontal pleiotropy effects.Besides the single gene-based simulations, we also conducted cross-gene simulations. Specifically, we randomly selected 10,000 genes from GEUVADIS. We extracted 52 as the gene expression data and examined 39 phenotypes from three GWASs. The three GWASs include the Wellcome Trust Case Control study (WTCCC)57, the Kaiser Permanente/UCSF Genetic Epidemiology Research Study on Adult Health and Aging (GERA)54, and the UK Biobank58.We applied our method to perform TWAS by integrating gene expression data with several GWASs. Specifically, we obtained GEUVADIS data52 contains gene expression measurements for 465 individuals collected from five different populations that include CEPH (CEU), Finns (FIN), British (GBR), Toscani (TSI) and Yoruba (YRI). In the expression data, we only focused on protein coding genes and lincRNAs that are annotated in GENCODE (release 12)60. Among these genes, we removed lowly expressed genes that have zero counts in at least half of the individuals to obtain a final set of 15,810 genes. We performed PEER normalization to remove confounding effects and unwanted variations following previous studies61. Afterwards, following14, to remove remaining population stratification, we quantile normalized the gene expression measurements across individuals in each population to a standard normal distribution, and then further quantile normalized the gene expression measurements to a standard normal distribution across individuals from all five populations. Besides expression data, all individuals in GEUVADIS also have their genotypes sequenced in the 1000 Genomes Project. We obtained genotype data from the 1000 Genomes Project phase 3. We filtered out SNPs that have a Hardy-Weinberg equilibrium (HWE) p-value < 10−4, a genotype call rate <95%, or a minor allele frequency (MAF) <0.01. We retained a total of 7,072,917 SNPs for analysis.The GEUVADIS data57. The diseases include type 1 diabetes , Crohn’s disease , rheumatoid arthritis , bipolar disorder , type 2 diabetes , coronary artery disease , and hypertension . We obtained quality controlled genotypes from WTCCC and initially imputed missing genotypes using BIMBAM62 to arrive at a total of 458,868 SNPs shared across all individuals. Afterwards, we further imputed SNPs using the 1000 Genomes as the reference panel using SHAPEIT and IMPUTE263. We filtered out SNPs that have an HWE p-value < 10−4, a genotype call rate <95%, or an MAF < 0.01 to obtain a total of 2,793,818 imputed SNPs. For each trait in turn, we first regressed the phenotype on the top 10 genotype principal components (PCs) and obtained phenotype residuals. We then scaled the phenotype residuals to have a mean of zero and standard deviation of one and used these phenotype residuals for TWAS analysis. In addition to the main analysis that uses phenotype residuals, we also performed parallel analysis with PMR-Egger where we used the original phenotype as the outcome variable and the top 10 genotype PCs as covariates.The WTCCC data consists of about 14,000 cases from seven common diseases and 2938 shared controlsp-value < 10−4 to yield a total of 487,609 SNPs. We phased genotypes using SHAPEIT64 and imputed SNPs based on the Haplotype Reference Consortium (HRC version r1.1) reference panel65 on the Michigan Imputation Server using Minimac366. Afterwards, we further filtered out SNPs that have a HWE p-value < 10−4, a genotype call rate <95%, an MAF < 0.01, or an imputation score < 0.30 to arrive at a total of 8,385,867 SNPs that are shared across 61,953 individuals. We examined 22 diseases in GERA that include asthma , allergic rhinitis , cardiovascular disease , cancers , depressive disorder (n = 7900), dermatophytosis (n = 8443), type 2 diabetes , dyslipidemia , hypertension , hemorrhoids (n = 9922), abdominal hernia (n = 6876), insomnia (n = 4357), iron deficiency (n = 2706), irritable bowel syndrome (n = 3367), macular degeneration (n = 4031), osteoarthritis , osteoporosis (n = 5909), peripheral vascular disease , peptic ulcer (n = 1007), psychiatric disorders (n = 9408), stress disorders (n = 4706), and varicose veins (n = 2714). For each trait in turn, we first regressed the phenotype on the top 10 genotype principal components (PCs) and obtained phenotype residuals. We then scaled the phenotype residuals to have a mean of zero and standard deviation of one and used these phenotype residuals for TWAS analysis. In addition to the main analysis that uses phenotype residuals, we also performed parallel analysis with PMR-Egger where we used the original phenotype as the outcome and the top 10 genotype PCs as covariates.The GERA study consists of 61,953 individuals and 675,367genotyped SNPs. We filtered out SNPs that had a genotype calling rate below 0.95, MAF < 0.01, or HWE 58. We followed the same sample QC procedure in Neale lab  to retain a total of 337,198 individuals of European ancestry. We filtered out SNPs with an HWE p-value < 10−7, a genotype call rate <95%, or an MAF < 0.001 to obtain a total of 13,876,958 SNPs. We selected 10 UK Biobank quantitative traits that have a phenotyping rate >80%, a SNP heritability > 0.2 and a low correlation among them following a previous study67. The 10 traits include height , Platelet count (h2 = 0.404), bone mineral density (h2 = 0.401), red blood cell count (h2 = 0.324), FEV1–FVC ratio (h2 = 0.313), body mass index , RBC distribution width (h2 = 0.288), Eosinophils count (h2 = 0.277), forced vital capacity (h2 = 0.277), white blood cell count (h2 = 0.272). For each trait in turn, we regressed the resulting standardized phenotypes on sex and top 10 genotype principal components (PCs) to obtain the residuals, standardized the residuals to have a mean of zero and a standard deviation of one, and finally used these scaled residuals to conduct TWAS analysis. We also performed parallel analysis with PMR-Egger by using the original phenotype and including the top 10 genotype PCs as covariates.The UK Biobank data consists of 487,409 individuals and 92,693,895 imputed SNPscis-SNPs that are within either 100 kb upstream of the transcription start site (TSS) or 100 kb downstream of the transcription end site (TES). We overlapped these SNPs in GEUVADIS with the SNPs obtained from each of the three GWASs to obtain common sets of SNPs. The median number of the overlapped cis-SNPs between GEUVADIS and WTCCC, GERA or UK Biobank are 200, 556, or 500, respectively. Afterwards, for each pair of gene (from GEUVADIS) and trait (from GWAS) in turn, we examined the causal relationship between gene expression and trait of interest while testing and controlling for potential horizontal pleiotropic effects.We combined the GEUVADIS data with each of the three GWASs for TWAS analysis. To do so, in the GEUVADIS data, for each gene in turn, we extracted cis-SNP to be the instrumental variable. (2) PrediXcan, which uses multiple correlated instruments but does not control for horizontal pleiotropy. For PrediXcan, we used all cis-SNPs for the model and used ElasticNet implemented in the R package glmnet to obtain the coefficient estimates for the cis-SNPs. (3) TWAS, which uses multiple correlated instruments but does not control for horizontal pleiotropy. For TWAS, we used all cis-SNPs for the model and used BSLMM46 implemented in the GEMMA software68 to obtain coefficient estimates for the cis-SNPs. (4) CoMM, which uses multiple correlated instruments but does not control for horizontal pleiotropy. We used all cis-SNPs for the model and used the R package CoMM for model fitting. (5) LDA MR-Egger, which uses multiple correlated instruments and controls for horizontal pleiotropy. We used all cis-SNPs for the model and contacted the authors of LDA MR-Egger to obtain the method source code. All these methods are suitable for two-sample design and yield p-values for testing the causal effect α. Note that PrediXcan, TWAS and CoMM are not originally described as an MR method but conceptually rely on the same joint MR model based on Eqs.  SMR, which uses a single instrument and does not control for horizontal pleiotropy. For SMR, we first performed a linear regression to choose the top associated  SMR, whicis-SNPs for methods that can make use of multiple correlated instruments . We performed a linear regression to select the top associated cis-SNP as the instrumental variable for SMR, as it can only use a single instrument. In all simulations and real data applications, methods that can use either individual-level data or summary statistics  are applied using individual-level data as input to ensure their optimal performance. Methods that can only use individual-level data (CoMM) are applied using individual-level data as input. Methods that can only use summary statistics (SMR and LDA MR-Egger) are applied using summary data as input. For PMR-Egger, we used individual-level data for all main analyses and used summary data for a subset of analyses that are described in the Discussion section.Again, we used all 42. In the FOCUS analysis, we followed42 and obtained a set of independent non-overlapping genomic regions termed as LD blocks from LDetect69. We removed genomic regions that overlap with the MHC region due to the extensive LD structure there. Following42, we also focus our analysis on a subset of regions that harbor at least one genome-wide-significant SNP , and for each TWAS/MR method , also harbor at least one TWAS gene that is declared significant by the given method. We then applied FOCUS to analyze these remaining regions and identify genes that are in the 90% credible set.Besides the above methods, we also compared different methods to a recently published fine-mapping TWAS method, FOCUSp-value for testing horizontal pleiotropic effects. Note that, unlike PMR-Egger and LDA MR-Egger, MR-PRESSO requires independent instruments and uses permutation to obtain the empirical p-values. Due to the heavy computational burden resulting from permutations, we restricted the number of permutations in MR-PRESSO to 10,000  and were only able to apply MR-PRESSO to a subset of simulation scenarios.For testing horizontal pleiotropic effect, we compared the performance of PMR-Egger with two existing methods that include (1) LDA MR-Egger; and (2) the global test in MR-PRESSO, which is implemented as an R package. Both these methods examine one gene at a time and output a Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


P-value threshold of 1×10−5. After imputation, we found eight SNPs, one for both BMI-SBP and SBP-DBP, and eight for SBP-DBP, exceed significant statistic level. Expression quantitative trait loci analysis identified rs4794029 as new significant eQTL in tissues related to BMI and SBP. Also, we found 6 new significant eQTLs  in tissues were related to SBP and DBP. Gene-based analysis identified nominally associated genes (P < 0.05) with BMI-SBP, BMI-DBP, and SBP-DBP, respectively, such as PHOSPHO1, GNGT2, KEAP1, and S1PR5. In the pathway analysis, we found some pathways associated with BMI-SBP, BMI-DBP and SBP-DBP, such as prion diseases, IL5 pathway, cyclin E associated events during G1/S transition, TGF beta signaling pathway, G βγ signaling through PI3Kγ, prolactin receptor signaling etc. These findings may enrich the results of genetic variants related to BMI and BP traits, and provide some evidences to future study the pathogenesis of hypertension and obesity in the northern Chinese population.Recently, new loci related to body mass index (BMI) or blood pressure (BP) have been identified respectively in genome-wide association studies (GWAS). However, limited studies focused on jointly associated genetic variance between systolic pressure (SBP), diastolic pressure (DBP) and BMI. Therefore, a bivariate twin study was performed to explore the genetic variants associated with BMI-SBP, BMI-DBP and SBP-DBP. A total of 380 twin pairs (137 dizygotic pairs and 243 monozygotic pairs) recruited from Qingdao Twin Registry system were used to access the genetic correlations  by bivariate Cholesky decomposition model. Bivariate GWAS in 137 dizygotic pairs nominated 27 single identified 27 quantitative trait nucleotides (QTNs) for BMI and SBP, 27 QTNs for BMI and DBP, and 25 QTNs for SBP and DBP with the suggestive  Hypertension and obesity are public health challenges worldwide , 2. HypeGenome-wide association studies (GWAS) are used to nominate loci for complex diseases . RecentlThe participants were recruited from Qingdao twin registry system by internet, newspaper and local community hospital. More details of recruitment have described in literatures , 35. Que2). Blood pressure was taken by a standard procedure using mercurial table stand model sphygmomanometer. Systolic blood pressure was defined as Korotkoff of phase I (appearance of sound), and diastolic blood pressure was defined as Korotkoff phase V (disappearance of sound). Average values were used after three measurement.BMI was defined as an individual’s body weight in kilograms divided by the square of his/her height in meters  [137 DZ pairs was genotyped using Infinium Omni2.5Exome-8v1.2 BeadChip  on autosomes. Ultimately, 1,338,796 SNPs from 274 samples were analyzed in this bivariate GWAS according to inclusion criteria  .r2 >0.6), MAF>0.05, and Hwe>1×10−4. Finally, 7,401,356 SNPs were included in this study.To identify new risk variants, IMPUTE 2  softwarehttp://www.vcu.edu/mx) with age and sex adjusted. Due to the deviation of the distribution, the Blom’s formula was used to guarantee the normal distribution or approximate normal distribution. Bivariate Cholesky model decomposed variations for both phenotype into additive genetic (A), common (shared) environmental (C), and unique /non-shared environmental (E) variance. Nested models were conducted by dropping A (CE model) or C (AE model) after fitting the full model (ACE). The likelihood ratio χ2 tests compared whether the full model and nested model has significant difference [Bivariate Cholesky decomposition model  was usedfference , 39. Thefference .P < 5 × 10−8 was adopted as significant threshold [P < 1 × 10−5 as a suggestive evidence level [P- values of SNPs against the expected P- values. The genomic inflation factor λ revealed if there was evidence of inflation of test statistics due to population stratification. Manhattan plots provided a compact visual way of demonstrating P- values of SNPs ordered by chromosome on the x axis. PLINK software was used to perform conditional analysis on GWAS data to identify independently associated variants [P-value of 0.001 was adopted as significant threshold.The genome-wide efficient mixed-model association (GEMMA)  was applhreshold , 44, andce level , 46. Whevariants . The reghttp://pubs.broadinstitute.org/mammals/haploreg/haploreg.php)  [https://www.gtexportal.org/home/)  [cis-eQTLs were identified in adipose-subcutaneous, artery-aorta, artery-coronary, artery-tibial, brain-cerebellar-hemisphere, heart-atrial appendage, and heart-left ventricle tissue.Expression quantitative trait loci has been widely used to uncover effect of genetic variants on phenotypes , 49. To r, 2015)  and publase: V8) . cis-eQTP- value below 2.63×10−6 (0.05/19001) was considered to be genome-wide significant. P- value below 0.05 was considered as nominal significance threshold [We used VEGAS 2 , 53 to dhreshold .PASCAL programme was used to calculate pathway-scored . The gen2, and mean SBP and DBP are 130.8±20.1mmHg and 83.1±10.9mmHg, respectively. 137 DZ pairs were used in GWAS with mean age of 51.0±7.2 years, mean BMI of 24.6±3.1 kg/m2, and mean SBP and DBP of 132.5±20.2mmHg and 84.1±10.5mmHg, respectively.The characteristics of eligible participants are shown in The phenotypic correlation of BMI-SBP, BMI-DBP, and SBP-DBP are 0.214, 0.256, and 0.684, respectively under the best fitting model AE . GeneticP < 1 × 10−5), although none of the SNPs exceeded statistically significant level (P<5×10−8).A total of 1,338,796 eligible SNPs from 137 DZ twin pairs are included in bivariate GWA analysis. Q-Q plot  indicateP = 2.94×10−7). Ten SNPs near LINC00343 gene located on chromosome 13q33.2 (P = 3.41×10−7). Eleven SNPs near LINC00343 gene were located on chromosome 13q33.2. Two SNPs at SLC39A11 gene located on chromosome 17q24.3-q25.1 (P = 5.14×10−7). Three SNPs near LINC00624 gene were located on chromosome 1q21.1-q21.2. And two SNPs at TRAPPC9 gene were located on chromosome 8q24.3 , Heart-Atrial Appendage (P = 1×10−6), and Heart-Left Ventricle (P = 2.5× 10−5)) related to BMI and SBP. As for SBP and DBP, we found significant eQTLs located on 17q21.32 and 8q12.1 that related to blood pressure, such as rs4400367, the significant eQTL in several tissues , and Heart-Left Ventricle(P = 7.3×10−12), Artery-Aorta, and Artery-Tibial (P = 1.7× 10−5)). However, none significant eQTL was found in tissues related to BMI and DBP (We queried SNPs from  and DBP .−6). However, 1043 genes of BMI-SBP, 1103 genes of BMI-DBP, and 1100 genes of SBP-DBP reached suggestive evidence level (P < 0.05). PHOSPHO1 is related to both BMI-SBP and BMI-DBP with smallest P- value. Top 10 genes are displayed in P- value.We found no genes exceeding the statistically significant level , such as prion diseases, IL5 pathway, and Cyclin E associated events during G1/S transition, respectively. TGF beta signaling pathway, G βγ signaling through PI3Kγ, prolactin receptor signaling, ADP signaling through P2RY1 pathway etc. More details of top 20 pathways are displayed in 628 pathways were significantly associated with BMI-SBP, 599 with BMI-DBP, and 766 with SBP-DBP  delivering immune signals [CYP7A1 encodes a member of the cytochrome P450 superfamily of enzymes. Cytochrome (CYP) P450 metabolites of arachidonic acid contribute to the control of BP [CYP17A1-CNNM2, CYP17A1, CYP1A2 genes associated with BP in Japanese and European population [This current GWAS identified 6 potential causal SNPs. rs4794029 may affect the gene expression level of tivation , 58. We  signals . Also, l signals . CYP7A1 ol of BP . Previoupulation , 62.PHOSPHO1, GNGT2, KEAP1, S1PR5 etc.) were associated with both BMI-SBP and BMI-DBP. PHOSPHO1 up-regulated express in rabbit models with hyperlipidemia and atherosclerosis [PHOSPHO1 in blood associated with type 2 diabetes risk [S1PR5 encodes one of receptors of sphingosine 1-phosphate (S1P), which activate the endothelial isoform of nitric-oxide synthase (eNOS) [In gene-based analysis, nine genes  . As a kee (eNOS) .In pathways enrichment analysis, 628 pathways were identified significant association with BMI-SBP, 599 pathways with BMI-DBP, and 766 pathways with SBP-DBP. Some of them were biologically plausible. G βγ signaling through PI3Kγ was found associated with BMI-DBP. The lipid kinase PI3Kγ, as a central proinflammatory signal transducer, proposed a role in obesity-induced inflammation and insulin resistance in hematopoietic cells , 71. PreP value in bivariate GWAS, 5 × 10−8 and 1 ×10−5, which based on Bonferroni correction, were still common used in current GWAS to solve multiple testing problem [P <1 ×10−5 as the suggestive association in the discovery stage. However, low P-value threshold might lead to less significant genetic variants caught, thus, more further researches are required to explore the appropriate threshold. Thirdly, since different genetic background and sample size in limited researches, most of our result cannot be verified by other studies. Currently, many risk variants has been revealed associated with obesity and hypertension, however, most of them are lack of direct biological relationship with obesity and hypertension in pathophysiology.To our best knowledge, this is the first GWAS about BMI and BP phenotypes focusing on northern Chinese population. Compared with univariate GWAS, bivariate design could identify more pleiotropic genes to reveal biological mechanisms related to obesity and hypertension. However, there are still some limitations existing. Firstly, the small twin sample due to difficulty in recruitment and restriction in inclusion criteria might be the obstacle to find more significant SNPs, genes and pathways. Secondly, due to lack of standard approach to define the cutoff  problem . Since BIn conclusion, present GWAS identified several SNPs, genes and pathways associated with both BMI and BP. Given there are still uncovered common genetic mechanism potentially related to obesity and hypertension, more studies need to be performed.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.


In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta = 1.865) has a greater effect on yield than KGW (Beta = 1.016) and GPP (Beta = 0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential.Rice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575 + 1495 F Therefore, selecting the component traits of yield was proposed as a complementary route for further improving rice production, which also has been emphasized by national and international rice breeding programs2. Studying the genetic relationship between rice yield and component traits, and selecting the component traits to improve rice yield, will provide new clues for enhancing rice yield potential.Rice is a staple food crop for about half of the world. Improving rice productivity has been the main goal of rice breeding research since the growth of population and the loss of arable land. However, rice yield per plant has a complex genetic architecture, which is determined by various physiological processes changing temporally during the growing period. These processes often matched the yield component traits that are genetically less complex than yield3. With the development of high-throughput technology, a large number of genes/quantitative trait loci (QTLs) of the three component traits were identified using QTL mapping and genome-wide association study (GWAS) methods5. At the end of 2019, 209, 223, and 239 genes/QTLs for GPP (TO: 0000445), KGW (TO: 0000382), and TP (TO: 0000152) were identified respectively (http://www.gramene.org/), which densely distributed across the 12 chromosomes. Some of them have been applied in the rational design of super rice by marker-assisted selection (MAS) breeding, in which multiple defined genes with superior alleles pyramided to increase rice yield6. Liu et al. introduced the DEP1 and Gn1 genes introduced into the restorer line 93–11, then the yield of the DEP1 / Gn1-9311 line was significantly improved, due to resource allocation improved7. In 2020, Wang et al. compared the transgenic lines with GNP1 or NAL1 to the transgenic lines with both genes. They found the latter had a significantly higher yield, which indicated the two gene combinations may enhance the source-sink relationship8. In the above researches, only a small number of genes combined for super rice breeding, if more genes are selected for pyramiding, the trade-offs between different traits need to carefully consider9. Therefore, understanding the nature and strength of the relationship between yield per plant and its components will be helpful for efficient gene selection in MAS breeding10.Rice yield per plant is a very complex agronomic trait mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW) and tillers per plant (TP), which are typical quantitative traits that are affected by multiple genes and the environment, with low heritability11. Path analyses were performed by Oladosu et al. on rice yield and component traits revealed that three component traits possessed a positive effect with yield12. Xu et al. conducted a correlation analysis between yield and its components of 300 rice germplasms. Their result indicated that yield was significantly correlated with GPP or KGW, but non-significant correlations of yield were found with TP13. One possible explanation for the conflicting results is that the bias caused by the small sample size and lack of proper control for potential unmeasured confounders. For allowing the synthesis of results from different studies to estimate a common summary effect, the meta-analysis was recognized as the appropriate method to achieve adequate sample sizes and optimal power14. Meta-analysis of GWAS is powerful in dissecting complex human diseases. It is the statistical synthesis of information from multiple cohorts independent GWAS studies, which increases power and reduces false-positive findings15. Compared to humans, plants were planted in multiple years, environments, and locations. Meta-analysis is a useful way to narrowing down confidence intervals of QTL by compiling QTL information from multiple years and locations16. A recent meta-analysis of GWAS in tomato demonstrated the benefits obtained from meta-analysis in plants. Meta-analysis can assess the heterogeneity of studies, which can be caused by many factors, such as phenotypic structure, genetic structure, linkage disequilibrium, imputation accuracies or interaction between genotype and environment18.The relationship between rice yield per plant and its components was investigated by various researchers with different materials and methods, but they were inconsistent. In Huang et al.’s study, the superior alleles of grain number generally had a positive effect on yield, while the superior alleles of grain weight generally have a negative effect on yield19. MR method was used to investigate the role of ATP citrate lyase inhibitors in cardiovascular disease20, in which the potential unmeasured confounders could be well protected from the observed association. In the MR approach, genetic variants were used as instrumental variables to avoid the possibility of confounding, because the genetic variants are randomly allocated at meiosis21. Thus, combine meta-analysis and MR for complex traits will help researchers to obtain a more reliable conclusion of their genetic relationship and further understand the genetic basis of rice yield.Recently, the Mendelian randomization (MR) approach is a popular technique to assess the causal relationship between disease and environmental risk factors within a meta-analysis framework in epidemiology22. Most GWAS studies focused on dissecting the genetic basis of single yield traits24, but the study on clarifying the genetic basis of the relationship between the yield and component traits of rice is few. Here we carried out meta-analyses of GWAS results from two populations (575 + 1495 F1) in different environments and adopted an MR design to further estimated the genetic relationship between yield per plant (YD) and component traits of rice. We aimed to detect significant single-nucleotide polymorphisms (SNPs) associated with yield or component traits, to analyze the genetic bases contributing to the relationship between them, and to investigate possible utilization pattern for selecting the component traits of yield in breeding practice to further understand the genetic basis of yield and improve the rice production. The study will provide theoretical guidelines for enhancing rice yield potential.GWAS has been proved to be a new strategy for explaining the genetic basis of complex traits, which has the advantage of improving the efficiency of detecting natural variationshttp://rapdb.dna.affrc.go.jp/), candidate genes were searched in a genomic region of 200 kb around the associated SNPs . Many embryo defective genes were identified in Arabidopsis27 , among which EMB1507 caused the embryo lethal phenotype28. Both OsBZR1 and OsSPL14 were detected in KGW and GPP, Os02g0106966 was detected in KGW and TP. In this study, only 3 significant loci for YD were detected, but 3589 significant loci for the component traits were detected. It may be because rice yield has a low heritability which is mainly affected by many minor-effect genes, the low heritability of rice yield is also showed in our previous study29. These results suggested that selecting the component traits of yield as a complementary route to improve rice production is recommended.Meta-analyses of GWAS were performed based on four datasets’ (two locations for each population) GWAS results  in meta-analyses of GWAS, among which 16 SNPs were not associated with KGW or TP (P > 0.05). We calculated the r2 of all pairs between the 16 SNPs, and then discarded SNPs in LD (r2 > 0.01) based on larger P-value. The remaining six SNPs were selected as instrumental variables to estimate the genetic relationship between GPP and YD (Table P = 0.003). In sensitivity analyses, Cochran's Q-test illustrated no obvious heterogeneity . The weighted median method also showed GPP had a positive effect on YD . MR-Egger regression indicated no evidence of directional pleiotropy for the associations of GPP with YD . It is worth noting that some cloned genes were detected in the meta-GWAS on GPP, the phenotype of transgenic plants with these genes had a similar phenomenon. For example, the OsSPL14 mutant produced more grain number per panicle, enhanced rice yield26. Compared with the control non-transgenic plants, the over-expression of OsBZR1 plants showed the 1000-grain weight was increased by about 3.4% and the spikelet number per panicle was increased 21.9%, that resulting in enhanced yield25. The cd1 mutant exhibited a variety of phenotypic traits, such as a reduction in grain number and panicle length, the biomass was lower than that of the wild type30.As required for MR analysis, a total of 2450 SNPs reached genome-wide significance for GPP P < E−06 in mP < 1E−06) in meta-analyses of GWAS, among which 395 SNPs were not associated with GPP or TP (P > 0.05), we calculated the r2 of all pairs between the 395 SNPs, and discarded SNPs in LD (r2 > 0.01) based on larger P-value. The remaining eleven SNPs were selected as instrumental variables to estimate the genetic relationship between KGW and YD (Table I2 = 0%). The weighted median method also confirmed the results of the IVW method. MR-Egger regression indicated no evidence of pleiotropy for the associations of KGW with YD  in meta-analyses of GWAS, among which four SNPs were not associated with KGW or GPP (P > 0.05), we calculated the r2 of all pairs between the four SNPs, and discarded SNPs in LD (r2 > 0.01) based on larger P-value. The remaining three SNPs were selected as instrumental variables to estimate the genetic relationship between TP and YD (Table P < 0.0001). Compared with KGW (Beta = 1.016) and GPP (Beta = 0.086), TP (Beta = 1.865) has a greater effect on yield. In sensitivity analyses, Cochran's Q-test illustrated no obvious heterogeneity . The weighted median method also showed TP had a positive effect on YD . MR-Egger regression indicated no evidence of directional pleiotropy for the associations of TP with YD . The cloned gene OsPIN2 was detected in the meta-GWAS on TP. Chen et al. found that the OsPIN2 transgenic plants had a more effective tiller number, lower 1000-grain weight, and higher yield32.As required for MR analysis, a total of 23 SNPs reached genome-wide significance for TP . Li et al. found that the tiller number of D62 mutant rice was less than that of the wild type35. The SNPs chr02_21604477 and chr11_26492375 for TP also had indirect effects on yield . This gene was annotated as a gene similar to H0818E04.14 protein and involved in nucleic acid binding (GO:0003676) and nucleotide binding (GO:0000166). The SNP chr11_26492375 is closest to Os11g0660000 (12 bp from it). This gene was annotated as sodium/calcium exchanger membrane region domain containing protein, which regulated magnesium/proton exchanger (K03452) and also related to transmembrane transport (GO:0055085) and is integral to the membrane (GO:0016021). These findings provided new information for further improve rice yield potential.We identified five significant loci that had an indirect effect on yield by MR analyses Fig. . Among t865 Fig. b for KGW700 Fig. c for TP eld Fig. , which w1 lines with different superior allele numbers of significant loci with direct effect, indirect effect, and direct plus indirect effect was shown in Fig. 1 lines with 0 to 4 superior alleles was: 42.22 g, 42.75 g, 42.76 g, 44.54 g, 47.49 g, respectively. In general, the yield of F1 hybrids rises with increases of the superior alleles  and indirect loci (5 loci) not be observed in this study. Our results indicated rice production improved with the increase of the superior alleles, it is possible that a combination of direct and indirect effects will better contribute to the yield potential of rice.The average yield performance of Fles Fig. . A similles Fig. . Other r36.In this study, a total of 3592 significant SNPs were detected in meta-GWAS on yield or its component traits, which provide more information for rice agronomic traits breeding. It is worth noting that only 3 loci were detected in meta-GWAS on yield, this may be the results from that rice yield has a low heritability and minor-effect loci hardly to be detected. For a low-heritability trait (such as yield), highly correlated auxiliary traits  will help improve the selection of traits with low heritability since they reflecting a shared biological basis32. The loci for KGW had a different direction of effects (positive effect or negative effect) on yield. Huang et al. conducted GWAS on 1495 hybrid rice lines and found the superior alleles of grain weight generally had a negative effect on yield11, but some genes that regulate KGW have been reported to have the potential to enhance rice yield, such as GW737 and GS538. To some extent, our results confirmed both of their findings, which indicated our study had greater power and more comprehensive by synthesizes different data. Then these loci were used to explore the relationship between rice yield and its component traits through these loci by MR method. The results of MR provided some evidence that selecting the component traits of yield to improve rice production, which is consistent with the improvement of rice production achieved by genetic manipulation of the component traits in previous studies39. The MR analyses provided a rationale for using component traits as indirect indices to enhanced rice yield.MR model was carried out to investigate the genetic effects of loci on yield through component traits. The loci for GPP or TP mainly had a positive genetic effect on yield, which was consistent with previous studies11. Our results suggested the improvement of yield also by pyramiding the superior alleles of loci with an indirect effect on yield. In this study, due to the limitation of sample size, hybrid lines pyramiding all the superior alleles of direct (3 loci) and indirect loci (5 loci) not be observed. However, it is interesting to note that the average yield performance of hybrid lines with 1 to 3 superior alleles of indirect loci improved with an increase of the superior alleles when adding superior alleles of the direct locus to the pyramid, the average yield performance of hybrid lines was increased. Similar to the performance of the superior alleles of four indirect loci, the yield of the superior alleles of five direct plus indirect loci also be improved  The MR approach could less prone to confounders since the genetic variants used as instrumental variables21; (iii) Using MR method to analyze the genetic relationship between quantitative traits in this study, which weighted the effects of multiple independent SNPs into a summary effect, for quantitative traits, most of them are affected by multiple genes or the interaction of genes, while the individual SNP only explain a small fraction of the variation in the quantitative traits. Since the MR analysis may be biased by the possibility of invalid instrumental variables, it is difficult to completely exclude type I error and the potential influence of pleiotropy since the instrumental variables derived from the meta-analysis of GWAS in the study. Thus, we conducted a weighted median method and the MR-Egger method to do sensitivity analysis. Compared to the IVW method40, the weighted median method showed better finite-sample Type I error rates. The estimator was consistent even if up to 50% of the information comes from invalid instrumental variables41. The results of the MR-Egger and heterogeneity test indicated the genetic variants had no pleiotropic effects on yield to some extent42. These results strengthened our confidence in the validity of assumptions.The strengths of the study are: (i) a meta-analysis of GWAS data from multiple population and environments to estimate a summary effect provided greater statistical powerIn conclusion, we analyzed the genetic basis of the relationship between yield and its component traits by GWAS and MR methods, providing genetic insights for further improving rice yield potential. Our results suggested the improvement of rice production by pyramiding the superior alleles of genes regulating component traits, and a combination of direct and indirect effects may better contribute to the yield potential of rice in breeding practice. These findings will provide theoretical guidelines for the rational design of rice by MAS breeding.1 hybrid rice lines, which produced by 115 varieties (restorer lines of 29 three-line wild-deficient hybrid rice and 86 accessions of micro-core germplasm) as male parents were crossed with 5 sterile lines (4 two-line sterile lines and 1 three-line sterile line) as female parents. The 575 hybrid lines were grown both in Huazhong Agricultural University and Wuhan University in 2012. The other population from the national center for gene research of Chinese academy of sciences, which including 1,170 lines were bred from the three-line system and 325 lines were generated from the two-line system. The 1495 hybrid lines were grown in Hangzhou and Sanya respectively11. Genotypic and phenotypic data of the 1495 lines were downloaded for subsequent analysis in this study. A total of four agronomic traits including GPP, KGW, TP, and YD were recorded in both populations. The phenotyping standards for these agronomic traits are the same in both populations, which were measured for at least three samples of each accession, and the average measurement was taken as the phenotypic value for GWAS analysis.Two populations of rice hybrid varieties were used in our study. One of the populations consists of 575 Fhttp://www.ncgr.ac.cn/RiceHap4). The genomes of 1495 hybrid lines were sequenced on the Illumina HiSeq2000 at twofold genome coverage, and 1,531,463 SNPs passing quality control (MAF > 1%).The population of 575 hybrid rice lines was sequenced on the Illumina HiSeq2500 platform at 11 × genome coverage on average. By quality control, we obtained 1,894,012 high quality SNPs with minor allele frequency (MAF) > 5% and missing rate < 20% across the 575 accessions. The high diversity SNP maps of 1495 hybrid rice varieties are publicly available (https://snp-seek.irri.org/download.zul) as the reference panel was used to perform SNP imputation in the genotype data of 575 and 1495 hybrid rice lines by using beagle software (version 5.0)43, and all imputed SNPs with MAF < 1% were filtered. Among the 3000 rice genomes project44, the 4.8mio filtered SNP dataset is used as the reference panel in the study, with an average sequencing depth of 14 × and all SNP passed the quality control . Then conducting separate GWAS for two populations in two different environments using mixed-linear-model association (MLMA) in GCTA software45 and collecting the summary statistics to run a meta-GWAS.3000 rice genomes project  from each GWAS results. Imputation increased the genome-wide SNP densities, a total of 1,838,525 common SNPs from four GWAS datasets were used for meta-analysis. We used the fixed-effect model in METAL as the primary approach to conduct the meta-analyses46. The fixed-effect model adopts the inverse variance weighting method, which weighted each study according to the inverse of its squared standard error. Then the Cochran's Q-test was performed to heterogeneity test47. For those SNPs where heterogeneity occurs (I2 >  = 50%), the random effect model in METASOFT was adopted48 . The genome-wide significant P-value for meta-GWAS was set as P < 1E−06 (− log10P = 6). According to the information of RAP-DB (http://rapdb.dna.affrc.go.jp/), candidate genes were searched in a genomic region of 200 KB around the associated SNPs. If there was a cloned gene reported to be related to yield-traits in a 200 kb genomic region, the cloned gene would be selected as the candidate gene; if not, the gene closest to the significant SNP would be selected as the candidate gene.Meta-GWAS is a meta-analysis of summary data  the genetic variants were obtained from the results of meta-GWAS that associated with the single component trait at a genome-wide significant level (P < 1E−06); (ii) the genetic variants are not associated with any confounders; (iii) the genetic variants only affect yield through the single component trait, not through other component traits (P > 0.05). Since the selected SNPs in linkage disequilibrium (LD) may result in confounding21, we calculated the r2 (LD) of all pairs between all selected SNPs using plink (version 1.90)49 and discarded SNPs in LD (r2 > 0.01) based on larger P-value.For the genetic effect of rice yield and each component trait to be consistently estimated, the genetic variants were selected according to the three assumptions in MR analysis38. In sensitivity analyses, the weighted median method39 and MR-Egger method40 are used for MR analysis, which is more robust due to pleiotropic or invalid instruments involved.The IVW method was conducted for MR analysis to assess the effect of component traits on yield, which by summarizing the effects of multiple independent SNPs11, the genotype of SNP with the highest-level yield or component trait was set to be the superior allele . Calculated the number of superior alleles in each hybrid rice line and recorded their corresponding average yield measurements. Omitted the number of superior alleles with less than 3 hybrid lines.Calculated the average phenotypic measurement corresponding to genotypes of each significant SNP, and the least significant difference method was used for multiple comparisons. Following Huang et al.’s methodSupplementary Information 1.Supplementary Information 2.


Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health. The choice of food intake is at least partially influenced by genetics, even though the effect sizes appear rather modest. Here, Cole et al. perform GWAS for food intake  and test potential causal relationships with cardiometabolic traits using Mendelian randomization. Overall, incidence rates of these dietary risk factors and their related diseases, like obesity and type 2 diabetes (T2D), are rising in parallel worldwide4, causing global epidemics that require our urgent attention. Describing a biological basis for unhealthful dietary preferences could guide more effective dietary recommendations.Unhealthful dietary habits are thought to be the leading risk factor for mortality both globally9. Five genome-wide association studies (GWAS) of macronutrient intake have been conducted to date; the most recent multi-trait analysis identified 96 independent genetic loci by combining summary statistics of individual macronutrient GWAS from 24-h diet recall questionnaires in 283K individuals in UK Biobank (UKB)9. In addition, the Neale Lab conducted GWAS  across thousands of mostly binary traits analyzed primarily as dichotomous outcomes  in 361K unrelated individuals in UKB. The recent GeneAtlas10 improved power by using linear mixed models in 450K individuals in UKB, but analyzed a smaller set of dietary variables.There is a clear, albeit modest, genetic component to diet, such as traditional measures of macronutrient intake , as demonstrated by significant heritability and individual genetic associations12 and clinical trials13. Thus, with the recent advent of large biobank-sized population cohorts with dietary data, we can now perform GWAS with multiple complementary phenotyping approaches to examine a wide array of dietary habits, including previously unstudied single food comparisons  and DPs. Here, we report heritability and GWAS analysis (using linear mixed models) of both single FI, analyzed as curated single FI quantitative traits (FI-QTs), and of PC-derived DPs (PC-DPs) using Food Frequency Questionnaire (FFQ) data in up to 449,210 Europeans from UKB; we highlight association at biologically interesting loci (olfactory receptor loci associated with tea and fruit consumption) and use Mendelian randomization (MR) analyses to elucidate potentially causal relationships pertaining to specific dietary habits.Additional measures of dietary intake, including both curated measures of single food intake (FI) and multivariate dietary patterns (DPs), such as those described by principal component (PC) analysis, have also shown significant associations with health outcomes in both epidemiological studiesP < 0.05/85 = 5.88 × 10−4; Supplementary Fig. We derived 85 curated single FI-QTs from FFQ administered in UKB, using 35 nested and complementary questions , including bread type  were significantly heritable . Overall, the FI-QTs that have significant positive loadings for PC1 include wholemeal/wholegrain bread consumption (two correlated FI-QTs contributing 15.4–15.8%), increased fruit and vegetable intake (four correlated FI-QTs contributing 1.2–2.6%), increased oily fish intake (1.8%), and increased water intake (1.2%). The FI-QTs that have significant negative loadings include white bread consumption (two correlated FI-QTs contributing 12.7–12.8%), butter and oil spread consumption (seven correlated FI-QTs contributing 1.5–2.2%), increased processed meat intake (1.9%), and consumption of milk with higher fat content . Of these, 309 also surpass a more conservative Bonferroni-corrected study-wide significance threshold  that together explain the largest amount of variance of any dietary habit analyzed (6.65%).GWAS on the 143 significantly heritable dietary habits, using linear mixed models in up to 449,210 individuals, identified 814 independent loci (defined as >500 kb apart) surpassing genome-wide significance  score regression (LDSC) intercepts : 1.011–1.030) and ratios . Credible set analysis found that more than one-third of our lead signals (289/814) were driven by 10 or fewer SNPs, 124 had 2–5 credible set SNPs and 54 had a single 95% credible set SNP (see Methods). Of the credible sets with single SNPs, 65.2% are intronic or intergenic, while 13.0% are missense variants and the remainder largely regulatory . We investigated whether any of our loci were previously reported at genome-wide significance for any trait in either the GWAS catalog18 or the comprehensive Neale Lab GWAS  of 4358 traits in 361,194 unrelated individuals in UKB. Of the 814 dietary habit GWAS loci, 205 have never been previously reported. This can largely be explained by our use of previously unstudied curated FI-QTs and PC-DPs and/or statistical power gained from linear mixed models in nearly 450K individuals.Of the 814 index SNPs, 767 (94%) are common with minor allele frequencies ≥5%; similar to previous GWAS findingsP = 0.419), the association of olfactory receptors with specific FI-QTs supports the well-known link between smell and taste. The top signal associated with “pieces of fresh fruit eaten per day” is a region on chromosome 7q35  and “pieces of dried fruit per day” (rs35260863 P = 6.4 × 10−18). The only two SNPs contained in both 95% credible sets cover a 5.8 kb region  containing olfactory receptor gene OR10G3  that contains six different olfactory receptor genes, including OR5AN1, OR5A2, OR5A1, OR4D6, OR4D10, and OR4D11  in a dense cluster of olfactory receptor genes containing OR8U8 and OR5M3 , OR4K17 with “tablespoons of raw vegetables per day” , and OR10A3 and OR10A6 with “overall cheese intake” , the GWAS results provide distinct sets of significant associations. Together, PC1 and its 19 contributing traits are associated with a total of 387 independent genome-wide significant loci, falling into one of four categories  have been associated with diseaseNEGR1; Supplementary Fig. β = −0.017, P = 2.80 × 10−48) and has been previously reported as associated with a decrease in intelligence, educational attainment, and, perhaps surprisingly, body mass index (BMI)26. We were struck by the observation that 122 PC1-associated loci were already present in the GWAS catalog18 or Neale Lab GWAS , and of these, 83 were associated with intelligence or cognitive ability, educational attainment, or obesity-related anthropometric traits, including rs1421085 in FTO and rs429358 in APOE . Both inverse variance weighted (IVW) random-effects and weighted median (WM) MR provided significant evidence of causal effects of educational attainment and fluid intelligence on PC1 , and splitting UKB into training and testing datasets also yielded similar and significant effect estimates . The smaller effects suggest that while there could be pleiotropy undetected by the Egger and WM approaches, or true bidirectional effects, the causal influences are more likely to be in the direction of educational attainment to wholemeal bread consumption and other healthful foods. Importantly, because the instrumental variables used for educational attainment are not mechanistically linked directly to educational attainment/intelligence, it remains possible that causal influences on PC1 could be due to unmeasured heritable factor(s) that are themselves causal for educational attainment/intelligence. Furthermore, because PC1 is highly correlated with hundreds of additional outcomes, educational attainment  could be influencing a larger complex phenotype made of many lifestyle factors that is captured here by PC1 dietary preferences. In contrast to the educational attainment analyses, we were unable to provide robust evidence of a causal relationship in either direction between BMI and PC1 due to significant heterogeneous pleiotropic effects leading to inconsistent causal effect estimates , we postulate that BMI and our non-isocaloric PC1 have a highly complex multi-pathway relationship for which there may be overlapping genetic etiology, but a mostly distinct underlying genetic architecture.The reverse analysis shows significant but smaller estimates of causal influences of PC1 on educational attainment and fluid intelligence  from the mostly European CARDIoGRAMplusC4D GWAS and T2D from the DIAGRAM consortium 2017 GWAS30). We therefore performed an MR analysis using rs1453548 on the complete set of traits in UKB using the Neale Lab GWAS . Using a strict Bonferroni-corrected significance threshold (P < 0.05/4358 traits = 1.15 × 10−5), we identified a significant causal effect of “cups of tea per day” on smoking status, for which increases in the minor allele T (MAF = 34%) that cause an increase in “cups of tea per day” cause a decrease in smoking status , the 51-SNP “cups of tea per day” genetic instrument excluding rs1453548 has no significant causal effect on smoking status (IVW P = 0.20), and β-ionone is also found in tobacco31, suggesting the effects of rs1453548 on odor perception of β-ionone may have pleiotropic effects on both smoking status and tea drinking. Together with a lack of additional significant causal relationships between rs1453548 and health outcomes in UKB, our results indicate that drinking more tea does not have clear effects on health outcomes in UKB, and it is possible that some previous reports on the health benefits of drinking more tea are a result of confounding with smoking status.In contrast to the associations with PC1, a single SNP, rs1453548, strongly influences “cups of tea per day” and has a plausible biological mechanism  nor will it exclude questions that significantly contribute to the variance in the total FFQ dataset. This also suggests that some PC-DPs will simply capture correlation between FI-QTs from the same FFQ question. For example, PC2 is made up of 11 significant FI-QTs based on a single FFQ question relating to the use of butter- and oil-based spreads , the relative strengths of these causal estimates suggests that higher educational attainment and/or correlated phenotypes  either directly or indirectly shift towards wholemeal bread vs. white bread consumption, and to a much lesser extent other foods described as healthful. While previous observational studies have shown that Western and prudent DPs are associated with CAD and T2D23, our MR analysis of PC1, driven by bread type, on CAD or T2D did not demonstrate a causal effect from diet to disease, but rather a small suggestion of a reverse causal relationship between CAD and diet .Of note, the interpretation of the genetic component of predominantly environmental traits, such as dietary intake, can be complicated. As we have shown, dietary habits are highly correlated both with each other and with non-dietary traits, suggesting that any single dietary phenotype may represent a broader diet and lifestyle. For instance, DP PC1 with both high genetic correlation with educational attainment and large overlap in genome-wide significant loci with BMI, is capturing variance in both obesity-related traits and measures of socioeconomic status. Additionally, similar to most nutritional epidemiology studies, measures of dietary intake in UKB are based on self-reported questionnaire data, which intrinsically suffers biases based on memory and favorable reporting39, UKB’s large sample size provides robust genetic instruments, and the strength of our causal associations, combined with our validation analysis with an independently ascertained set of instruments for educational attainment, suggesting that our results are likely not influenced by weak instrument bias. Furthermore, although we did not detect evidence of pleiotropy, it remains possible that pleiotropic effects of some of the variants associated with PC1 masked a causal effect on cardiometabolic disease risk. Finally, PC1 may not capture the causal protective features of a prudent DP as it is largely driven by bread type. However, it remains possible that there is a stronger correlative than causal relationship between PC1 or the previously described Western DP and increased risk of cardiometabolic disease.The conclusion that our PC1 DP does not appear to be a causal risk factor for disease must be viewed in the context of several potential limitations of our study. Genetic instruments derived from genome-wide significant variants tend to explain a small fraction of phenotypic variance, which can lead to lack of power to detect potentially true causal effects of diet on outcomes, although this is mitigated by the large sample size of the UKB cohort. Additionally, while the use of overlapping samples in MR could in theory lead to inflated causal estimates30, described as smelling of cedar wood but upon dilution  a more floral aroma40, has pleiotropic effects that both reduce the chances of ever smoking and increase tea intake. While SNPs at chr11p15 have already been shown to be associated with food choice with and without added β-ionone30, we highlight here for the first time a link between β-ionone odor perception with smoking status, with potential significant implications for smoking-related health problems. This result also highlights the importance of understanding the pleiotropic consequences of variants used as genetic instruments in MR.We also find several interesting associations between specific FI-QTs  and olfactory receptors. The chr11p15 locus controlling odor perception of β-ionone41; as such, our dietary habits represent potentially non-isocaloric variations in dietary intake. However, we found minimal phenotypic correlation between any of our FFQ-derived phenotypes and 24-h recall questionnaire-derived total energy intake (maximum correlation r = 0.037). Furthermore, none of our lead 814 SNPs were nominally significant in the Neale Lab total energy intake GWAS (P > 0.05/814). Nine of our phenotypes, including “slices of bread per week,” “overall cheese intake,” and “glasses of water per day” did show significant genetic correlations with total energy intake, suggesting that the genetic architecture of these traits could be shared with traits that reflect more global lifestyle and DPs 45, under UKB application 11898. We have complied with all relevant ethical regulations for work with UKB and all participants provided informed consent.UKB is a large prospective cohort with both deep phenotyping and molecular data, including genome-wide genotyping, on over 500,000 individuals aged 40–69 years living throughout the UK between 2006 and 2010N = 455,146), as determined by: (1) projection on to 1KGP phase 3 PCA space, (2) outlier detection to identify the largest cluster of individuals using Aberrant R package46, selecting the λ in which all clustered individuals fell within 1KGP EUR PC1 and PC2 limits (λ = 4.5), (3) removed individuals who did not self-report as “British,” “Irish,” “Any other white background,” “White,” “Do not know,” or “Prefer not to answer,” as self-identified non-EUR ancestry could confound dietary habits.All phenotype derivation and genomic analysis was conducted on a homogenous population of individuals of European (EUR) ancestry (http://biobank.ndph.ox.ac.uk/showcase/). Ordinal variables were ranked and set to quantitative values, while food types or foods never eaten were converted into a series of binary variables. Variables relating to alcoholic drinks per month were derived from a conglomeration of drinks per month and drinks per week questions answered by different individuals depending on their response to overall alcohol frequency (field 1558). All 85 single FI dietary phenotypes were then adjusted for age in months and sex, followed by inverse rank normal transformation on continuous FI-QTs. For individuals with repeated FFQ responses, both the dietary variable and the age in months covariate were averaged over all repeated measures. PCs were then derived from all 85 FI-QTs after filling in missing data with the median using the prcomp base function in R. FI-QTs with percent contribution (squared coordinates) greater than expected under a uniform distribution [1/85 × 100 = 1.18%] were included in Fig. 47. Phenotype correlation between all 170 dietary habits was estimated using Pearson’s pair-wise correlation on complete observations in R. All correlations (phenotypic and genetic) with P > 0.05/85 were set to 0. The significance threshold here was selected based on a Bonferroni correction for 85 total FI-QTs to maintain stringency for multiple testing and consistency across phenotype and genetic correlation analyses, while allowing for the nested and non-independent nature of the FFQ questions and derived FI-QTs.Prior to phenotype derivation, we removed individuals who were pregnant, had kidney disease as defined by ICD10 codes, or a cancer diagnosis within the last year field 40005). The UKB FFQ consists of quantitative continuous variables , ordinal non-quantitative variables depending on overall daily/weekly frequency , food types , or foods never eaten . Supplementary Data 0005. The49 pseudo-heritability measurement representing the fraction of phenotypic variance explained by the estimated relatedness matrix50. These estimates were highly correlated with 51  and effect allele frequency (f) as follows: β2 × (1 − f) × 2f. Pair-wise rg of all significantly heritable dietary habits were estimated using LDSC53. Again, all correlations (phenotypic and genetic) with P > 0.05/85 were set to 0. Index SNP annotations were evaluated using Neale Lab UKB GWAS consequence annotations based on Ensembl’s Variant Effect Predictor54. We performed DEPICT gene set and tissue enrichment analysis, which relies on reconstituted gene sets from publicly available gene sets and pathways and gene expression data to evaluate enrichment in PC1 GWAS loci55.Measures of heritability were obtained from BOLT-lmm software (v.2.3.2)mentclass2pt{minim56 using shotgun stochastic search in 500 kb windows. We then used LDstore v1.157 to calculate LD and identify SNPs in high LD (r2 ≥ 0.80) with any of the 77,229 95% credible set SNPs. The GWAS catalog18 (downloaded on 13 November 2018) and Neale Lab GWAS v2 conducted in up to 361,194 unrelated individuals  was then searched for this list of 339,832 unique SNPs in either any of the 95% credible sets or in high LD with any SNP in the 95% credible sets for any of the 2515 significant GWAS signals in 814 independent loci. We considered any locus as being previously reported if any SNP in its 95% credible set or in high LD (r2 ≥ 0.80) with any SNP in the 95% credible SNP was associated with any trait at genome-wide significance (P < 5.0 × 10−8). LocusZoom plots were made using the stand-alone package58 to include LD information from UKB as determined by LDstore and 95% credible sets from FINEMAP57.Ninety-five percent credible sets were estimated for all 2515 genome-wide significant associations using posterior probabilities as calculated by FINEMAP v1.3rg was again performed on 143 significantly heritable dietary habits with 4336 Neale Lab UKB GWAS traits, 3219 of which had at least one non-missing rg estimate from LDSC. Using a strict Bonferroni correction threshold for all pair-wise tests between 143 dietary habits and 3219 highly correlated and even overlapping Neale Lab GWAS traits , we set all nonsignificant rg to 0. Supplementary Data rg matrix.LDSC 59. We used Fisher’s test for enrichment of olfactory receptor genes using SNPsnap’s nearest gene annotation for 647 dietary habit GWAS index SNPs and for 1000 sets of null matched SNPs (167 of our index SNPs were excluded for being in the human leukocyte antigen region or had insufficient matches). We based the enrichment analysis contingency table on 842 annotated olfactory receptor genes among 48,903 genes in SNPsnap. Of the 1000 null enrichment analyses, 419 had a Fisher’s test estimate equal to or greater than our real data’s enrichment estimate.Enrichment for olfactory receptor genes among dietary habits was evaluated using 1000 sets of null matched SNPs based on MAF, number of SNPs in LD at various LD thresholds, distance to nearest gene, and gene density using the SNPsnap webtoolM = 140), fluid intelligence scores (M = 184), educational attainment (M = 309), and BMI (M = 1165). Fluid intelligence scores for GWAS in EUR  was derived from both in person and online cognitive tests. Assessment center fluid intelligence scores (field 20016) were averaged for up to three visits and adjusted for average age in months, sex, and assessment center. Online fluid intelligence scores (field 20191) were adjusted for age in months, sex, and townsend deprivation index. The final fluid intelligence score was first set to average assessment center fluid intelligence score, and when missing was filled in with the online fluid intelligence score, for which the combination of these scores were then adjusted for collection method, followed by inverse normal transformation. Educational attainment for GWAS in EUR  was derived using a previously published method based on mapping UKB qualifications field 6138 to US years of schooling60, followed by adjusted for age in months, sex, and assessment center, and inverse normal transformation. BMI was calculated from weight (field 21002) and standing height (field 50) and averaged from up to three assessment center visits. Average BMI was adjusted for average age in months, average age in months squared, assessment center, and average measurement year, followed by inverse normal transformation conducted in males and females separately. The combined male and female BMI Z-scores were then used together for genetic association testing. All GWAS were run in BOLT-lmm adjusted for 10 genetic PCs  and genotyping array.Bidirectional MR was conducted using genome-wide significant index SNPs clumped by 500 kb windows from GWAS in UKB on PC1  genome-wide significant index SNP summary statistics were published online26. After removing missing and ambiguous SNPs, the external genetic instrument contained 66 variants. Second, we split the UKB sample into two subsets and conducted GWAS for PC1 and educational attainment as described above in each subset: 1/3  of the EUR sample was used for genetic instrument variable identification and 2/3  of the EUR sample was used for testing. MR of the tea intake SNP rs1453548 was conducted using the Wald ratio estimate as calculated by the MendelianRandomization R package62 using effect size and standard errors from our “cups of tea per day” GWAS and effect size and standard errors from 4358 traits in the Neale Lab GWAS.Genetic instruments for each of the three traits consisted of the complete set of index SNPs for each independent genome-wide significant GWAS locus defined by clumping signals in 500 kb windows. In addition to IVW MR, we also conducted MR Egger to detect pleiotropic effects and WM MR to allow for the inclusion of up to 50% invalid genetic instruments. To test the robustness of any causal association, we repeated MR with filtered genetic instruments using Steiger filtering to remove variants likely influenced by reverse causation and Cook’s distance filtering to remove outlying heterogeneous variantsFurther information on research design is available in the Supplementary InformationSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Peer Review FileDescription of Additional Supplementary FilesReporting Summary