P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower  The genetics and clinical consequences of resting heart rate (RHR) remain incompletely understood. Here, the authors discover new genetic variants associated with RHR and find that higher genetically predicted RHR decreases risk of atrial fibrillation and ischemic stroke. However, RHR is influenced by disease status and a plethora of potential confounders, which could affect these associations.Higher resting heart rate (RHR) is associated with cardiovascular diseases and all-cause mortality in traditional epidemiological studies6. In our previous study, we found evidence for a positive association between genetically predicted RHR and all-cause mortaliy7. However, a higher genetically predicted RHR was not found to increase the risk of cardiovascular diseases9 and appeared to decrease the risks of atrial fibrillation and cardio-embolic stroke9. Multiple genome-wide association studies (GWAS) have identified RHR-associated genetic variants which could be used as genetic instruments in MR studies to entangle the relationship between RHR and cardiovascular disease10. The two largest GWAS to date have been performed in the UK Biobank10 but were either performed in a subcohort of individuals with available genetic data during its time of publication7 or lacked a replication cohort10. We set out to perform a genome-wide meta-analysis of RHR in the largest sample size to date with internal replication of the associated genetic variants, in order to broaden our knowledge of the biological mechanisms underlying interindividual differences in RHR and identify robustly associated RHR-associated genetic variants to assess the genetic association of RHR with cardiovascular disease and all-cause mortality.A Mendelian randomization (MR) approach, in which genetic variants associated with RHR are used as a proxy for RHR, can also be used to study the association of RHR with cardiovascular diseases and all-cause mortality. Since genetic variants are fixed from conception and then randomly assigned from parents to offspring, they are more immune to reverse causation and confoundersIn this work, a genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent RHR-associated genetic variants in 352 loci, including 68 genetic variants outside previously identified RHR-associated loci Fig. . In addi10. Out of the 425 genetic variants inside previously identified RHR-associated loci, a total of 376 were internally replicated. In addition, 332 out of the 352 loci were considered internally replicated as they showed the concordant direction of effects and nominal associations (P < 0.01) in the UK Biobank GWAS and IC-RHR meta-analysis  heritability of RHR, as calculated by LD score regression, was estimated to be 10%. A polygenic score weighted by the effect sizes of the IC-RHR explained 5.33% of the variation in RHR in the UK Biobank. A Chow test indicated the absence of strong differences between participants with a history of any cardiovascular disease or use of RHR-altering medication versus participants without such a medical history . Second, we performed a GWAS on 484,307 individuals from the UK Biobank. These large cohorts were meta-analyzed to include up to 835,464 individuals, in whom 30,458,884 directly genotyped and imputed autosomal variants were analyzed  with RHR lead variants. Functional annotation of these coding variants is provided in Supplementary Data 13. Lastly, 381 unique genes were taken forward by data-driven expression-prioritized integration for complex traits (DEPICT) analyses . The newly discovered gene clusters consisted of mostly protein–protein interaction pathways and were commonly located in the periphery of the network , ventricular depolarization  and ventricular repolarization (CACNA1C) , as shown in Fig. P-value thresholds   or in the CARDIoGRAMplusC4D cohort , in line with previous analyses9. Similarly, there was no evidence for an association between genetically predicted RHR and myocardial infarction in the UK Biobank or in the CARDIoGRAMplusC4D cohort  and in the AFgen consortium , but these results were not significant after correction for multiple testing (P < 4.17 × 10−3). MR-Lasso, which can provide evidence for potential causal associations when there is a small number of genetic variants with heterogeneous ratio estimates, indicated that genetically predicted RHR was significantly inversely associated with atrial fibrillation , while this was not true for a genetically predicted RHR of 80 bpm .Higher genetically predicted RHR was suggestively associated with a lower risk of atrial fibrillation development in the UK Biobank , ischemic stroke  and cardio-embolic stroke , suggestively associated with large artery stroke  and not with small vessel stroke  in the MEGASTROKE consortium. The results were consistent across MR methods for any, ischemic and cardioembolic stroke . We found no evidence for a non-linear association between genetically determined RHR and any ischemic stroke , but MR-Steiger sensitivity analysis filtered a large part of the genetic variants and repeating the MR on the remaining subset did not show a significant association between RHR and pulse pressure . The results were robust to MR-Lasso  and MR-contamination mixture models . We excluded 96 variants associated with the Q-R upslope at −18 ms of the R peak (P < 0.05), which has been established as a biomarker for dilated cardiomyopathy14, to investigate whether reversed causation contributed to the association with dilated cardiomyopathy. The results were similar to the main analyses  and concordantly associated with their respective outcomes in both cohorts while not reaching genome-wide significance in either one of the outcome cohorts , myocardial infarction (USP39), and atrial fibrillation (SLC35F1 and SSPN) through their effects on RHR have been associated with these cardiovascular diseases in recent studies35.We report 493 genetic variants in 352 loci associated with RHR, discovered in the largest GWAS meta-analysis of RHR to date in up to 835,465 individuals7. Conditional analyses on the tissue enrichment demonstrate that genes influencing RHR are more likely co-expressed than primarily or solely located within non-cardiovascular tissues. However, it should be noted that conditional analyses inherently attenuate tissue enrichment considering DEPICT is based on the co-regulation of gene expression36. Using cardiac single-nucleus RNA data, we demonstrate that RHR genes are mostly expressed in cardiomyocytes. We provide electrophysiological insights into the biology of the RHR-associated variants and show that they exert diverse effects on ECG morphology with the largest effect on ventricular repolarization.To obtain further biological insights into RHR, we performed pathway analyses using DEPICT and found numerous newly associated pathways. The strongest associated clusters were identified previously and their importance to RHR biology was therefore validated in the current study2 and MR studies7, we do not find evidence for an association between genetically predicted RHR and all-cause mortality. Moreover, genetically predicted RHR was not associated with parental longevity nor with any of the 35 leading causes of mortality. Lack of such associations suggests that follow-up length or large heterogenetic effects of RHR on different causes of mortality are unlikely causes for the absence of an association between genetically predicted RHR and all-cause mortality37. We demonstrate that the most likely cause of the discrepancy between current and previous results arises from false positive findings in previous one-sample MR analyses that were caused by weak-instrument bias at lower P-value thresholds38. We hypothesize that RHR is not on the causal pathway to mortality itself and that previous observational studies are more likely to reflect confounders, such as stress and socio-economic status, or reversed causation, in which an individual’s disease status increases both RHR and mortality risk40.In-depth analyses were performed to assess genetic associations of RHR with clinical outcomes. In contrast to previous observational9. We do find a significant negative exponential dose-response curve between RHR and atrial fibrillation in support of an inverse relationship, and take the non-linear MR forward as the main result considering the fractional polynomial test indicated that a non-linear model fitted the localized average causal effect estimates better than the linear model. Previous observational studies on the relationship between RHR and atrial fibrillation have shown conflicting results and have described various relationships including inverse linear43, U-shaped43, and J-shaped44 associations. All these association patterns support the hypothesis that individuals with a low RHR might exhibit a higher risk of atrial fibrillation development compared to those with an average RHR. A recent stratified Mendelian randomization showed an inverse genetic relationship between RHR and atrial fibrillation in individuals with an RHR below 65 bpm as well45. Possible mechanisms that could underly an increased risk of atrial fibrillation in individuals with a low RHR include increased left atrial stroke volume and consequent atrial remodeling due to myocyte stretching46, or an increased vagal tone promoting global disorganization in the left atrium due to increased heterogeneity of the refractory period47. In contrast to the often hypothesized U-shaped or J-shaped association44, we find a decreasing risk of atrial fibrillation development in those with a high RHR. One potential explanation is that previous observational studies were affected by collider bias through confounding factors which increase atrial fibrillation risk and typically occur in tandem with a high rather than a low RHR, such as hypertension48 and obesity49. We advocate for a cautious interpretation of current results due to the diverse biological mechanisms through which the RHR-associated genetic variants alter the risk of atrial fibrillation development8.The linear MR between RHR and atrial fibrillation provided suggestive evidence for an inverse relationship between RHR and atrial fibrillation, in line with a previous linear MR study5. We showed that atrial fibrillation attenuates the protective effect of higher genetically predicted RHR on developing cardio-embolic stroke. This indicates either biological or mediated pleiotropic effects of atrial fibrillation in the association between genetically determined RHR and cardio-embolic stroke, which cannot be distinguished based on the current results. The relationship between a low RHR and cardioembolic stroke was attenuated by only 18.4% when corrected for atrial fibrillation, which might underestimated due to atrial fibrillation being a commonly missed diagnosis. Correction for atrial fibrillation only minimally affected the association between RHR and any or ischemic stroke, despite cardio-embolic stroke accounting for a substantial amount of ischemic stroke cases50. Although hypertension is another important risk factor for stroke, it commonly occurs in tandem with a higher and not a lower RHR52 and we found that neither systolic nor diastolic blood pressure affects the association between RHR and stroke. We did find that pulse pressure attenuates the association between RHR and any, ischemic and large-artery stroke. Lower RHR has previously been demonstrated to increase pulse pressure due to a higher likelihood of pressure wave reflections during prolonged systole53 and increased pulse pressure has been established as a risk factor of stroke55. Moreover, the Conduit Artery Functional Endpoint Study (CAFE) study postulated that pulse pressure underlies the inferiority of β-blocker based treatment (which lowers RHR) to amlodipine-based treatment in the prevention of stroke, despite equal effects on peripheral blood pressure56. Our results could be considered as support for this mechanism in the scenario that the RHR-associated genetic variants only affect pulse pressure through RHR and the association with stroke is primarily driven by RHR. However, MR-Steiger sensitivity analysis indicated that the association between the RHR-associated genetic variants and pulse pressure is unlikely mediated through RHR entirely. Biological pleiotropic effects are therefore more likely to cause the attenuation of the association between RHR and stroke when correcting for pulse pressure.We found that genetically predicted RHR was inversely associated with risk of any, ischemic and cardio-embolic stroke. The results were not replicated in the UK Biobank, possibly due to the substantially lower amount of cases. The inverse association is in contrast to many observational studies and we therefore performed multivariable MR analyses to pinpoint biological mechanisms that could underly the discrepancy59. There is also evidence that ivabradine lowers cardiovascular mortality in heart failure with a reduced ejection fraction60. This protective effect is more likely due to its effect on RHR than heart rate variability as it has a larger effect on RHR61. The fact that the MR results were robust to the exclusion of SNPs associated with the −18 ms point of the R-peak, an established biomarker of dilated cardiomyopathy, supports the interpretation that current findings are driven by RHR differences that mimic pharmacological rate control14. Our MR on the compound definition of heart failure could be hampered by its phenotypical heterogeneity, as we were unable to differentiate between heart failure with reduced and preserved ejection fraction. It would be interesting to repeat the current MR analyses if more in-depth phenotyping on left ventricular ejection fraction and function becomes available, especially considering the different effects of RHR on familial dilated versus hypertrophic cardiomyopathy in the current study.Finally, our study provides evidence that higher genetically predicted RHR increases the risk of developing dilated cardiomyopathy. The importance of decreasing RHR in the treatment of heart failure with reduced ejection fraction, the clinical phenotype of dilated cardiomyopathy, has been thoroughly studied. Beta-blockers have been shown to reduce mortality in individuals with heart failure with reduced ejection fraction and form the cornerstone of pharmacological treatment7, there is still a large gap with heritability estimates from twin studies that range between 23% and 70%64. Future studies could include whole exome sequencing data to further increase our insights into the genetic architecture of RHR65. Second, individuals with cardiovascular diseases were included in the GWAS, which could potentially affect exposure-outcome associations. However, post-hoc analysis showed that UK Biobank participants with a history of cardiovascular disease or who used RHR-altering medication can be jointly analyzed with participants without such a medical profile. In addition, a two-sample MR strategy was adopted, reducing the risk that potential weak-instrument bias increases type 1 error rates through the reintroduction of confounding, population stratification, or correlated pleiotropy38. We note the broad biological nature of RHR genetic variants as illustrated by the diverse ECG patterns the genetic variants elicit on the full cardiac cycle. These broad effects should be taken into consideration for the correct interpretation of the MR results, as pleiotropy and reversed causation might be introduced in the MR. For example, some genetic variants were included in the MR analyses which could be more specific for another trait (i.e. rs2234962 near BAG3 for dilated cardiomyopathy). We believe that the influence of reversed causation on current results is minimal because we excluded variants more strongly associated with the outcome. The MR results were generally consistent across a multitude of sensitivity analyses, strengthening the interpretation of a true relationship. However, our study is not interventional in design, and a conservative interpretation of the results as generally unconfounded rather than causal estimates should be preferred. We stress that any causal claims can only be made if interventions or drugs alter RHR equal to the biological mechanisms in which RHR-associated genetic variants affect RHR.Several limitations should be considered. Although the current 493 RHR-associated genetic variants explained more than double the RHR variance compared to the 64 loci from our previous studyIn conclusion, our GWAS meta-analysis identified 493 RHR-associated genetic variants within 352 loci, to which we prioritized 670 candidate causal genes. We demonstrated cardiovascular tissues as the primary enrichment sites for RHR gene effects and showed that their gene expression is highest in cardiomyocytes. ECG signatures showed that RHR-associated genetic variants exert the largest effect on RHR through ventricular repolarization. We found no evidence for linear and non-linear associations between genetically predicted RHR and all-cause mortality across several analyses, suggesting that the well-known link between higher RHR and all-cause mortality reflects confounding factors and reversed causation. The results point towards an inverse association between genetically predicted RHR and the development of atrial fibrillation and any stroke, ischemic stroke, and cardio-embolic stroke, whereas it is positively associated with dilated cardiomyopathy development. Multivariable MR analyses showed that atrial fibrillation attenuates the protective effect of higher RHR on the development of cardio-embolic stroke. Pulse pressure attenuates the protective effects on any stroke, ischemic and large artery stroke, but this likely reflects biological pleiotropy rather than true mediation.The full RHR meta-analysis included 100 studies with data on RHR in up to 835,465 individuals. RHR was obtained from ECG in 54 studies, from pulse rate in 31 studies (of which seven were self-measured by the participants), from blood pressure monitor in nine studies, from electronic medical records in three studies, from manual measurement in one study and through a combination of multiple of the before mentioned methods in two studies. Further information on cohort characteristics is provided in Supplementary Data Genotyping and quality control before imputation were performed using different genome-wide genotyping arrays and methods, as further detailed in Supplementary Data 66.The UK Biobank was imputed to the Haplotype Reference v1.1 panel (HRC) by the Wellcome Trust Centre for Human Genetics. Analysis has been restricted to variants that are in the HRC v1.1. Quality control of samples and variants and imputation was performed by the Wellcome Trust Centre for Human Genetics, as described in more detail elsewhereThe 99 cohorts of the IC-RHR were imputed to 1000 Genomes Phase 1 and 3. For further information, please see Supplementary Data P-value based on beta and standard error versus reported P-values.On the cohort level, we performed quality control by (1) re-formatting and SNP-name harmonization; (2) checking the used reference panel by plotting effect allele frequency plots using 1000G as a reference; (3) checking for genomic inflation by plotting QQ plots; (4) checking the betas by plotting histograms of the beta, frequency and info; (5) comparison of the expected 67. Genetic correlations were considered significant if they achieved a Bonferroni-corrected significance threshold of P < 0 .05/855 = 5.85 × 10−5. The GWAS Catalog was queried to find previously established genetic variants (P < 1 × 10−5) in LD (R2 > 0.8) with all 493 RHR variants68. Summary statistics were downloaded from the NHGRI-EBI GWAS Catalog on 27/04/2020.Genetic correlation analyses with GWAS of previously investigated traits were performed using LD Hub platformR2 > 0.8; (3) eQTL genes in LD with RHR-associated variants at R2 > 0.8; and (4) DEPICT gene mapping using variants that achieved P < 1 × 10−8 (further information described below). Annotation of all identified genes was performed by querying GeneALacart69.For all independent genetic variants that were genome-wide significantly associated in the final meta-analysis, candidate causal genes were prioritized as followed: (1) by proximity, the nearest gene and any other gene within 10 kb; (2) protein-coding genes containing variants in LD with RHR-associated variants at 70. The dbNSFP database contains information on multiple prediction algorithms and conservation scores further detailed elsewhere70.The dbNSFP database (version 3a) was queried to obtain functional prediction and annotation of all potential non-synonymous genetic variants13 in data repositories from GTEx V771, GTEx brain71, Brain-eMeta eQTL72, and blood eQTL from Westra73 and CAGE74. Colocalization analyses were performed to test whether the effect size of the RHR-associated variants on the phenotype is most likely mediated by gene expression13. eQTL genes were considered as candidate causal genes if they achieved a significance after Bonferroni correction for the amount of eQTLs tested , passed the HEIDI test at P > 0.05, and if the lead variants of the eQTL genes were in LD (R2 > 0.8) with the RHR-associated genetic variants.Colocalization of multiple expression quantitative trait loci (eQTL) was performed using SMR and HEIDI analyses (version 0.710)https://data.broadinstitute.org/mpg/depict/) was used to perform integrated gene function analyses as stated above36. DEPICT was run using all genetic variants that achieved P < 1 × 10−8. Genes were considered possible candite genes if FDR < 0.05, taking into account multiple hypothesis testing.DEPICT was used to find genes associated with identified variants, enriched gene sets, and tissues in which these genes are highly expressed. DEPICT.v1.beta version rel137  was determined for every new iteration. Conditional analyses were performed up until the highest Z-value reached 2.585, which corresponds to the lowest Z-value with an FDR < 0.05.Here, 14. Detailed information on the methodology can be found in the study of Verweij et al. and is briefly discussed below. The ECGenitics browser contains genome-wide summary statistics of the complete cardiac cycle. The complete cardiac cycle was defined using two methods, including (a) the signal-averaged electrocardiographic beat surrounding the R wave at a resolution of 500 Hz resulting in 500 averaged data points and (b) R–R intervals corrected signal .The ECGenetics browser was used to gain insights into the electrophysiological effect of the RHR-associated genetic variantsP-value of 0.05/493/(500 × 2) = 1 × 10−7. Effects were aligned to the most positively associated allele across all time points. The heatmap shows a hue ranging from red (positive effect) to a blue color (negative effect) color scale, with yellow indicating no effect. Secondly, the total effect of the 493 RHR-associated genetic variants on ECG morphology was assessed using an ECG-wide MR approach (inverse variance weighted fixed-effects model) on the non-normalized and normalized association pattern.All RHR-associated genetic variants were tested for their association with both the non-normalized and normalized association patterns. A heatmap was constructed containing all associated genetic variants associated with at least one point on the ECG at a Bonferonni-corrected https://singlecell.broadinstitute.org/single_cell/study/SCP498/transcriptional-and-cellular-diversity-of-the-human-heart under study ID SCP49) to gain insights in their transcriptional and cellular diversity15. We selected the 86 genetic variants strongly associated with at least one ECG time point. We took forward the most likely candidate gene per genetic variant, based on the amount of gene identification strategies by which the gene was identified. When a genetic variant highlighted multiple genes identified by the same amount of gene identification strategies, we took forward the gene with the highest biological plausibility of involvement in RHR biology. A dotted heatmap was constructed for this subset of genes.All genes prioritized in the current study were queried in the single-cell data from the study of Tucker et al. through the Broad Institute’s Single Cell Portal  and data on the cause of death from the National Health Service (NHS) Information Centre. Prevalence of disease was also based on an interview with a trained nurse at the baseline visit (self-reported). HES data were available up to 31-03-2017 for English participants, 29-02-2016 for Welsh participants, and 31-10-2016 for Scottish. Information on cause of death was available for participants from England and Wales until 31-01-2018, and from the NHS Central Register Scotland for participants from Scotland until 30-11-2016. Definitions of all-cause mortality, 35 leading causes of mortality , coronary artery disease, myocardial infarction, atrial fibrillation, stroke , heart failure and subtypes  are provided in Supplementary Data Statistical details of the analyses on functional outcomes are provided in the “Genetics and regression analyses on functional outcomes in the UK Biobank” and “Mendelian randomization” sections.80, AFGen81, MEGASTROKE50, and ICBP-plus19 consortia and descriptions have been detailed previously. Effect sizes from the ICBP-plus consortium were obtained from the meta-analysis of the UK Biobank and ICBP-plus, after subtracting the effects from the UK Biobank . An overview of these studies is provided in Supplementary Data External cohorts included the CARDIoGRAMplusC4D7. The GWAS model was adjusted for age, age2, body mass index, sex, and study-specific covariates .All included cohorts performed genetic variant association analyses on RHR using linear regression analyses assuming an additive genetic model  < 25 for the genetic variants computed as sample size × Info × 2 × minor allele frequency (MAF)×(1−MAF). After these exclusions, a total of 19,400,415 and 27,082,649 variants remained available for the UK Biobank and IC-RHR GWAS respectively.All genetic variants were excluded if they had poor imputation quality score (Info < 0.3) and effective sample size (90. LD score regression software (v1.0.0) was used to calculate linkage disequilibrium score regression intercepts and attenuation ratios12. We corrected for genomic inflation prior to the meta-analysis by multiplying the standard errors with the square root of linkage disequilibrium score regression intercepts in the UK Biobank (1.132 ± 0.017) and the IC-RHR (1.020 ± 0.010)12.Again, a fixed-effects meta-analysis using the inverse variance method in METAL was performed to pool the data from the UK Biobank and IC-RHR up to 835,465 participants using ~30M genetic variants91. An independent genetic variant was defined as a genome-wide significant genetic variant in low LD (R2 < 0.005) with another genome-wide significant variant within a five-megabase window. A genetic locus was defined as the most significant variant in a megabase region at either side of the independent genetic variant.PLINK (version 1.9) was used to prune genetic variants in a set of independently associated variantsP < 1 × 10−8 in the discovery (UKB + IC-RHR) meta-analysis;the sentinel genetic variant has P < 0.01) in the UKB GWAS alone;the sentinel genetic variant shows support (P < 0.01) in the IC-RHR meta-analysis alone;the sentinel genetic variant shows support  provided by UK Biobank received a family ID. A Chow test was used to investigate whether there were significant differences in beta estimates in participants with and without cardiovascular disease or RHR-altering medication93. The post-hoc quality control was performed using the statistical software STATA 15 (StataCorp LP).We performed additional analyses to investigate whether individuals with a history of cardiovascular disease or those who took RHR-altering medication could influence the results of the GWAS. The UK Biobank population was stratified by a medical history of any cardiovascular disease or reported intake of RHR-altering medication. A history of any cardiovascular disease was defined according to the definition in Supplementary Data All of the outcomes assessed in the UK Biobank that are reported in this manuscript have been adjusted for age, sex, the first 30 principal components (PCs) to account for population stratification, and genotyping array (Affymetrix UK Biobank Axiom® array or Affymetrix UK BiLEVE Axiom array). The exclusions were performed according to the above-mentioned methods for the GWAS of RHR in the UK Biobank. In addition, we excluded 74,471 individuals based on familial relatedness, after which 412,481 individuals remained available for further analyses.P < 1 × 10−8 in the RHR GWAS . The associations with all-cause mortality and 35 leading causes of mortality within the UK Biobank  were obtained using a Cox proportional hazard model during a median (interquartile range) follow-up of 8.9 (8.2–9.5) years. The associations with parental longevity was assessed using linear regression analyses. The associations with both prevalent and incidence of cardiovascular diseases were assessed using logistic regression analyses. Cox and linear regressions were corrected for age at baseline, while the logistic regression analysis was corrected for age until the last date of follow-up to correctly account for both prevalent and incident disease.SNP-outcome associations for all outcomes (see the section “UK Biobank definitions”) were obtained for all 493 variants with a P-value threshold for SNP inclusion, (c) the assessment of the outcome in an independent cohort, and (d) the follow-up length7. Genetic risk scores for RHR were created following an additive model by summing the number of alleles  for each individual after multiplication with the effect size for RHR. Genetic risk scores were constructed using the 493 discovered variants within the full meta-analyses using the effect sizes of the IC-RHR, the effect sizes of the UK Biobank, and using the 73 previously discovered variants at five P-value thresholds . These were transformed to translate to a change of 5 bpm. The association with all-cause mortality was tested using Cox regression analyses in different populations of the UK Biobank. One population included all individuals . Another population included a subset of individuals which were genotyped for the UK Biobank interim release from May 2015, which included in the GWAS by Eppinga et al. . The final population consisted of a subset of individuals without genetic information at the time of the UK Biobank interim release, which was therefore not included in the GWAS by Eppinga et al., ncases = 11,336, ncontrols = 283,081). Please note that sample sizes might slightly differ from those in the previous GWAS due to updated exclusions. Lastly, point d) was taken into account by re-performing above-mentioned steps using mortality data up until the previously available follow-up . All regression analyses were performed using the statistical software STATA 15 (StataCorp LP).We performed an in-depth assessment of the association between RHR and all-cause mortality in the UK Biobank by systematically altering the differences between the current study and the previous study from Eppinga et al., which included (a) the set of SNPs, (b) the P < 1 × 10−8 in the final meta-analysis were taken forward in the MR. To minimize overlap between exposure and outcome cohorts, effect sizes were taken from the IC-RHR data to test the associations with outcomes within the UK Biobank, whereas effect sizes were taken from the UK Biobank to test the association within other independent cohorts. Proxies (LD > 0.8) were searched in case genetic variants could not be found within the UK Biobank or IC-RHR. All effect sizes were transformed to translate to a change in RHR of 5 bpm.All 493 independent genetic variants at F-statistic using the following equation:Potential weak instrument bias was assessed by calculating the n is the sample size of the exposure and R2 is the amount of variance of the exposure explained by the SNP94. R2 was calculated based on summary statistics using a previously established formula95. Genetic variants were not excluded from further analyses if the F-statistic was <10 as this can exacerbate bias by increasing the chance of winner’s curse38. Exposure and outcome summary statistics were then harmonized using the TwoSample MR package96. Forward strand alleles were inferred using allele frequency information and palindromic SNPs were removed if the MAF was above the recommended setting of 0.4296. MR-Steiger filtering was applied to explore pleiotropic effects through the assessment of potential reversed causation. R2 for both the exposure and outcome were calculated and variants were removed from further analyses if the R2 of the exposure is significantly lower  than the R2 of the tested outcome97. R2 for linear traits was calculated as described above95, R2 for binary outcomes was calculated on the liability scale98. A true causal direction was assumed if the R2 for binary outcomes was too small to be correctly estimated. Variants were excluded from further analyses in case a false causal direction was indicated.In this formula, 99. A Cochran’s QP-value of <0.05 was considered as proof of heterogeneity within the IVW estimate and, as a consequence, balanced horizontal pleiotropy. An I2 index >25% supports this conclusion100. The MR-Egger test was performed to allow SNPs to exert unbalanced horizontal pleiotropy101. The Rücker framework assesses heterogeneity within the MR-Egger regression (Rucker’s Q) and calculates the difference between heterogeneity within the IVW effect estimate (Q–Q’)99. A significant Q–Q’ (P < 0.05) in combination with a significant non-zero intercept of the MR-Egger regression (P < 0.05) was considered an indication of unbalanced horizontal pleiotropy. We then moved from an IVW model to the MR-Egger model as initial analysis, as the MR-Egger can provide causal estimates if SNPs exert unbalanced horizontal pleiotropy under the assumption that the Instrument Strength Independent of Direct Effect (InSIDE) assumption holds. Weak instrument bias in the MR-Egger regression analysis was assessed by I2GX and was considered to indicate a low risk of measurement error if larger than 95%102. The MR-Lasso method was used to find consistent estimates under the same assumptions as the IVW method, but only for the set of genetic variants not identified as outlier103. This method is most valuable in the scenario that a small proportion of the genetic variants is invalid and shows heterogeneous ratio estimates103. The weighed median approach was used to provide a consistent estimate if up to half of the variants are invalid. Finally, we performed the MR contamination mixture method to provide a consistent estimate if no larger subset of invalid genetic variants estimates the same causal association than the subset of valid genetic variants104.The linear association between genetically determined RHR on all outcomes was initially assessed using the IVW multiplicative random-effects method, which provides a consistent estimate under the assumption of balanced pleiotropy. The Rücker framework was applied to assess heterogeneity and thus potential pleiotropy within the MR effect estimates106. These associations were assessed using the UK Biobank as an outcome cohort, considering this was the largest cohort with individual-level data available to us. Consequently, we used the independent weights of the IC-RHR meta-analysis to construct a weighted polygenetic risk score of RHR by summing the number of alleles  for each individual after multiplication with the effect size between the genetic variant and RHR. We first calculate residual RHR by subtracting the results of the regression of RHR from the polygenetic score of RHR from RHR itself. Covariates of the regression included age, age2, sex, BMI, genotyping array, and PC1-PC30, analogous to the GWAS. Residual RHR, which characterizes the predicted RHR for an individual if their polygenetic score took the value zero, was then divided into 30 quantiles. Stratifying on residual RHR rather than total RHR avoids overadjustment and collider bias as residual RHR is not downstream of the effect of the genetic variants on the outcome in a causal diagram. We then calculated the genetic associations with the exposure in each stratum of residual RHR using linear regression analyses, correcting for the same covariates as described above. Two tests for non-linearity in the genetic association with the exposure (trend and Cochran’s Q tests) were performed to investigate heterogeneity in the polygenetic score of RHR on residual RHR in different strata. We then calculated the genetic associations with the outcome in each stratum. The same methodology was used as described in the section “Genetics and regression analyses”, including the same covariate model  and regression type . The outcome regression coefficient was then divided by the exposure regression coefficient as a ratio of coefficients to obtain local average causal effects (LACE) in each stratum. These localized average causal effects were meta-regressed against the mean of the exposure in each stratum in a flexible semiparametric framework, using the derivative of fractional polynomial models of degrees 1 and 2. All possible fractional polynomials of degrees 1 and 2 were fitted using the powers −2, −1, 0, 0.5, 1, 2, and 3106. The fractional polynomial of degree 1 is fit to the data if the fractional polynomial of degree 1 was as good of a fit (P > 0.05) as the degree 2 as indicated by the likelihood ratio test. Three tests for non-linearity of the association between genetically predicted RHR and the outcomes are reported: a trend test, which assesses for a linear trend among the localized average causal effect estimates, a Cochran’s Q test, and a fractional polynomial test, which assesses whether a non-linear model fits the localized average causal effect estimates better than a linear model. Please note that before fitting the fractional polynomials, we subtracted 45 from the values of RHR as the most flexible fit is achieved when the exposure is close to 0 but still positive. A reference of RHR of 70 bpm was taken as this was close to the mean RHR of 69.3 bpm. An additional 1390 individuals were dropped compared to the linear MR estimates obtained from the UK Biobank cohort due to missing BMI values necessary for the correction of the exposure regression coefficients.The non-linear associations of genetically predicted RHR with all-cause mortality and cardiovascular diseases were assessed using a fractional polynomial method81), systolic blood pressure, diastolic blood pressure, or pulse pressure  as secondary exposures to obtain insights in the direct effect of RHR on (subtypes of) stroke that are independent of this secondary exposures107. First, a multivariable MR-IVW method was used, in which for each exposure the instruments are selected and regressed together against the outcome, weighting for the inverse variance of the outcome107. Weak instrument bias for any of the exposures was assessed using Qx1 and Qx2107. When both are larger than the critical value on the χ2 distribution, there is little evidence of weak instrument bias. The critical value on the χ2 distribution was calculated by subtracting one degree of freedom from the amount of SNPs at a P-value of 0.05. Qa was considered to indicate potential pleiotropy when larger than the critical value on the χ2 distribution as calculated by the amount of SNPs minus two degrees of freedom at a P-value of 0.05107. Multivariable MR-Egger was performed to allow for unbalanced horizontal pleiotropy108. An MR-Egger intercept with a P-value < 0.05 in combination with a significant Qa was considered proof of unbalanced horizontal pleiotropy, and consequently the MR-Egger regression to provide a robust causal estimate108. Multivariable MR-Lasso analysis was performed as this method provides consistent estimates even when half of the genetic variants are invalid instruments and display unbalanced pleiotropy109. We also performed multivariable weighted median analysis as this type of analysis has been shown to perform well under higher levels of pleiotropy109. We did not search for proxies in the multivariable MR setting as this could introduce uncertainty through different LD patterns between the secondary exposure and outcome and we therefore re-estimate the univariable effect of RHR on the outcome with the eligible SNPs to allow for a better comparison of the results.A multivariable MR approach was used to gain additional insights into the relationship between RHR (effect sizes of the UK Biobank) and (subtypes of) stroke from the MEGASTROKE consortium. We used either atrial fibrillation  when the Wald estimates were significant at a Bonferonni corrected threshold of P < 1.01 × 10−4, that is, α = 0.05 with Bonferroni correction for a maximum of 493 independent tests, and (c) the genetic variant did not reach a genome-wide significant threshold of P-value < 5 × 10−8 in either one of the outcome cohorts used in the current study.We assessed whether the Wald estimates between the RHR-associated genetic variants and the cardiovascular disease outcomes could identify risk loci not previously associated with these outcomes in their respective GWASs. The genetic variants were considered associated with the outcome if (a) the Wald estimates had concordant effects within the UK Biobank as well as either the CARDIoGRAMplusC4D96, and the MR-Lasso source code103. The multivariable MR analyses were performed using the MVMR (version 0.3)107 and MendelianRandomization (version 0.5.1) packages110. Non-linear MR analyses were performed based on previously described methods105. For the MR on (subtypes of) mortality, we considered a liberal two-sided P-value of P < 0.05 significant for any of the outcomes using the IVW-MR random effects model. For the MR on cardiovascular diseases, we considered a Bonferroni corrected two-sided P-value for the amount of unique outcomes (P = 0.05/12 = 4.17 × 10−3) to be significant for the main IVW-MR random effects analyses, and a two-sided P-value between 4.17 × 10−3 and 0.05 to indicate suggestive evidence for an association. A two-sided P-value threshold of P < 0.05 was adopted for the sensitivity analyses.MR analyses were performed using R (version 3.6.3), the TwoSampleMR package (version 0.5.3)111. Effect sizes for the UK Biobank were obtained through linear regression analyses using every RHR SNP available in the UK Biobank as exposure, and systolic, diastolic, and pulse pressure as outcomes. Covariates included age, age2, sex, BMI, genchip, and PC1-PC30. Cluster-robust standard errors with genetic family IDs as clusters were used to account for the relatedness among participants. Individuals belonging together based on 3rd-degree or closer as indicated by the kinship matrix (kinship coefficient > 0.0442) provided by UK Biobank received a family ID. To keep the cohort similar to the one used in the study from Evangelou et al., we excluded those who self-reported as of non-European ancestry  and pregnant women (n = 306) from the 484,307 included in the GWAS, leaving 465,659 individuals for the analysis19. We note that we did not correct the standard errors for the genomic inflation reported for the GWASs of blood pressure traits in the UK Biobank as our linear regression estimates, while resembling the GWAS data, will not be exactly equal to BOLT-LMM estimates due to different methodologies82.We used the MetaSubtract (version 1.60) package in R to remove the effects sizes of the UK Biobank from the largest blood pressure GWAS’s to date in order to obtain the independent effect sizes of the ICBP consortiumFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-31Reporting Summary


There is an ongoing endeavor within the potato breeding sector to rapidly adapt potato from a clonal polyploid crop to a diploid hybrid potato crop. While hybrid breeding allows for the efficient generation and selection of parental lines, it also increases breeding program complexity and results in longer breeding cycles. Over the past two decades, genomic prediction has revolutionized hybrid crop breeding through shorter breeding cycles, lower phenotyping costs, and better population improvement, resulting in increased genetic gains for genetically complex traits. In order to accelerate the genetic gains in hybrid potato, the proper implementation of genomic prediction is a crucial milestone in the rapid improvement of this crop. The authors of this paper set out to test genomic prediction in hybrid potato using current genotyped material with two alternative models: one model that predicts the general combining ability effects (GCA) and another which predicts both the general and specific combining ability effects (GCA+SCA). Using a training set comprising 769 hybrids and 456 genotyped parental lines, we found that reasonable a prediction accuracy could be achieved for most phenotypes with both zero common parents ( Solanum tuberosum L.) is the most important starch source among vegetable crops. Cultivated for its edible tubers, potato’s end-use spans across multiple markets including table, chipping, frying, and industrial processing, with a global export value of USD 3.36 billion [Potato  innovations for conventional clonal breeding including the use of marker-assisted selection, progeny testing strategies, and genetic transformation  . Among tWith all of hybrid breeding’s benefits, it is also known for being more resource and time intensive in contrast to inbred systems . Hybrid Recently, GP has received much attention in tetraploid potato with encouraging results demonstrated in chipping quality ,27, dise3). All phenotyping procedures, trait definitions, and instrumentation used here are described in [Between 2017 and 2020, several crossing blocks were constructed around 712 experimental inbred lines sourced from Solynta, a Dutch hybrid potato breeder . These ln2) . Hybrid n2) , with 35ribed in .® [The parental lines used for test-crosses were genotyped using the targeted genotyping-by-sequencing (GBS) technology, SeqSNP® . These pvided by , which asvd function was applied to Using this filtered marker set, a principal component analysis was applied to the marker data to assess any evident population structure. This was achieved by scaling the SNP matrix )  via the pT(1−p)) . The dompT(1−p)) . Unless pT(1−p)) .A two-stage modeling approach was conducted to first model within each trial to produce unbiased estimates for each hybrid (BLUEs), which in turn were used in multi-year and location prediction models.y, is modeled on hybrid h (b (m (c) in the trial field. There were four residual variance structures among the several spatial models chosen. The simplest was a residual variance with an independent and identically distributed (IID) Gaussian distribution such that:For the first stage, a model selection procedure was conducted on each trial and trait to account for specific field trends and spatial dynamics. Eleven different spatial models were utilized across all 16 trait and trial combinations. From these eleven spatial models, only four were selected based upon Akaike’s information criterion (AIC); these four spatial models are described in detail here. The base model equation has the form:That is, the residual is modeled with a constant spatial variance (ed error . The lassults in:var(ε)=σey) is modeled on a fixed trial intercept  for hybrid h in location t, and an IID Gaussian error term, To evaluate the genetic variance of the hybrids across trials, raw data from each trial was used to create the following multi-trial model:Using model , across-f to approximate the full residual covariance structure  of parents i and j, a hybrid by trial interaction (t) with trial f and hybrid r. All random effects have Gaussian distributions centered about 0 with the variances of GCA and hybrid-trial interactions being structured by the genomic relationship matrices s is the specific combining ability for parents i and j. The variance of the SCA effect is defined by the dominance relationship matrix scaled by a scalar variance component ) ,47. Thesedure of  was adapA models .To test the advantage of including dominance effects in GP for hybrid potato, we examine the performance of the GCA  and GCA+est sets . This prest sets .Among all 16 trial and trait combinations, the majority were best fit with both an autoregressive trend and nugget effect . HoweverUsing the variance components from Model , multiplnd Davis ).The filtered SNP dataset was used for population analysis and the construction of both additive genomic relationship matrices  . These con error . The rooHybrid breeding has been revolutionized by GS and GP, making adoption of these techniques a key step in accelerating quantitative improvement. Of particular importance when implementing these technologies is determining the contribution and nature of the genetic effects in the relevant target and then assessing how molecular marker and pedigree information can best provide predictive power for it. This in turn provides a roadmap for breeders to place these predictive methods where they can be most efficiently applied. Additionally, understanding the presence of genotype-by-environment interactions has also received increasing attention as selection continues to shift away from general performance to adaptability to specific envirotypes. Our findings represent a first inspection of GP for multiple tuber traits in hybrid potato using conventional genetic models.GP was possible in both testing scenarios (0EP and 1EP) in both genetic models (GCA and GCA+SCA) for all tuber traits, though not equally among them. Average tuber volume had the highest prediction accuracy in both test scenarios  followed by dry matter content and total tuber yield. Looking at other available studies, dry matter content (and starch) has a consistently high prediction accuracy (0.73–0.84) in tetraploid populations ,29,33. TWe found no practical benefit in the incorporation of dominance effects (via SCA) for the GP model’s performance in any tuber phenotype. The lack of added value with SCA also appears inconsistent given the magnitude of SCA variances measured here  and in oformance ,53. Rathformance . The appWhile present, genotype-by-environment interactions did not appear to affect model performance for dry matter content and average tuber volume. This coincides with relatively consistent heritabilities between trial locations and the small  diploid  and tetr diploid ,31,33,55 diploid ,33,55, tThese results have some implications for potato breeders. First, this study gives ample evidence that stable additive genetic effects are not only able to be estimated for potato inbred lines, but can also be leveraged for the prediction of a parent’s breeding value in a dedicated cross. In particular, these results show the potential of genomic prediction in both a test cross and novel crossing set context through the use of the 1EP and 0EP test sets, respectively. Based upon these results, confident predictions can be offered in both contexts for all phenotypes studied here with the exception of total tuber number, which exhibited depressed model accuracy, making it too unreliable in a novel crossing set for breeders. The GCA variance being the largest genetic effect among all traits is also worth reiterating as it directs breeders towards prioritizing additive variance among multiple tuber traits. This directly touches upon a breeding program’s structure and how it exploits additive variation during population improvement among highly heritable traits.In this study, we have laid the groundwork for genomic prediction in hybrid potato with simple genetic models and no training set optimization. We demonstrate the GEBVs estimated from an additive model (our GCA model) suffices in predicting hybrid potato performance in dry matter content, average tuber volume, and total yield. The effect of genotype-by-environment interactions is mostly relevant for yield components such as tuber number and total yield, while dry matter content and average tuber volume were remarkably stable among target environments for genomic prediction. Principally, these results show for the first time that genomic prediction can be leveraged for inbred and hybrid selection in diploid hybrid potato. The adoption of predictive breeding in diploid potato is invaluable for the continued genetic progress of this new hybrid crop.


MUC13 gene under a long-term balancing selection, as well as NPR3 gene under positive selection for pig stature. Our study provides abundant genomic variations for robust selection-signature detection and accurate haplotypes for deciphering complex traits in pigs.High-quality whole-genome resequencing in large-scale pig populations with pedigree structure and multiple breeds would enable accurate construction of haplotype and robust selection-signature detection. Here, we sequence 740 pigs, combine with 149 of our previously published resequencing data, retrieve 207 resequencing datasets, and form a panel of worldwide distributed wild boars, aboriginal and highly selected pigs with pedigree structures, amounting to 1096 genomes from 43 breeds. Combining with their haplotype-informative reads and pedigree structure, we accurately construct a panel of 1874 haploid genomes with 41,964,356 genetic variants. We further demonstrate its valuable applications in GWAS by identifying five novel loci for intramuscular fat content, and in genomic selection by increasing the accuracy of estimated breeding value by 36.7%. In evolutionary selection, we detect  Accurate haplotypes with abundant genomic variations benefit genetic research. Here, the authors accurately construct 1,874 pig haplotypes and demonstrate their applications in genome-wide association studies, prediction of breeding values and analyses of evolutionary selection. More importantly, it delivered more than 100 million metric tons of meat to the world in 2021 (https://www.statista.com/statistics/273232/net-pork-production-worldwide-by-country/#statisticContainer), playing a crucial role in agricultural meat production for human beings. Investigating the genetic characteristics of pigs and the evolutionary mechanism of their important traits will help us make the most of its value in both biomedical model and agricultural aspects. There are numerous genome-wide association studies (GWAS) on growth, meat quality, and disease resistance by low or medium density SNP chips in large-scale pig cohorts6. Although the cost of SNP chips is less than resequencing technology, their sparse density restricts the application to fine mapping and identification of causal variants for the related traits. In this case, imputing genotypes of SNP chips into genotypes of high-density variant panels by utilizing the haplotype reference panel for the deep genetic dissection of the studied pig phenotypes has become an effective and efficient approach7. In this sense, the haplotype reference panel’s variant density, phasing accuracy, and diversity are critical for imputation. Several studies have provided pig haplotype reference panels, but they have certain limitations in sample and breed size, variant number, and phasing accuracy11. Therefore, it is essential to construct a high-quality pig haplotype reference panel with multiple breeds and plentiful individuals for genotype imputation and further genetic investigations.The pig can serve as an excellent human biomedical model and promising xenotransplantation donor due to its similarity to humans in anatomy, physiology, and genetics14. There are possibly over 730 pig breeds worldwide, of which two-thirds are distributed in China and Europe15. These breeds vary widely in stature, disease resistance, coat color, behavior, fertility, and other characteristics, which are primarily governed by genetic factors. For instance, PLAG1 and BMP2 have been identified as strong candidate genes associated with body size18. MUC13 and RNF216 were associated with disease resistance19. In domestic pigs, PLAG1 has been subjected to long-term positive selection12, and the favorable allele conferring larger stature has a higher frequency in domestic pigs than in wild boars20. In contrast, several loci in humans that should theoretically be subject to long-term positive selection did not appear to have a high frequency of advantageous alleles, possibly a consequence of adverse selection on other traits24. Simultaneously, the polymorphism around these loci is significantly higher than the genome in the remaining regions. In this sense, they can be defined as long-term balancing selection loci25. There are common genes under balancing selection that have been identified in mammals previously, such as HLA, ABO, and HBB26. Although several genes under balancing selection have been discovered in studies of pigs28, they were found in breeding populations selected by favorable traits in the short term. For example, balancing selection was identified for a 200 kb deletion affecting the BMPER and BBS9 genes in a breeding population of pigs27. However, whether genes under long-term balancing selection specific for pigs exist or not remains elusive. Particularly when utilizing genes under balancing selection in the breeding program, extra considerations should be taken into account to minimize negative impacts.As a long-domesticated species, the pig has undergone a series of selections for body size, disease resistance, meat quality, etcN = 30) in Asia and parts of the worldwide breeds32. Combining the other high-quality pig genome sequences publicly available, we accurately create a pig haplotype reference panel by taking advantage of haplotype-informative reads, pedigree structure (3 resource populations), and population Linkage Disequilibrium information, by totally using resequencing data of 43 breeds (N ≥ 3) and 1,096 pigs worldwide with an average sequencing depth of 17.1×35. The established haplotype panel contribute to an increased power of GWAS and the accuracy of genomic selection . By reanalyzing previously published SNP array data, imputation GWAS (iGWAS) detects five novel loci associated with intramuscular fat (IMF) content. Subsequently, the accuracy of the estimated breeding value (EBV) of IMF is significantly improved (R2 from 0.49 to 0.67). Furthermore, we perform positive- and balancing-selection analyses. Positive-selection analyses identify a candidate gene controlling stature variation. Balancing-selective analyses reveal another balancing selection gene associated with diarrhea- and growth-related traits. Together, the study provides rich genetic variants and high-quality haplotype resources of the pig genome, and we demonstrate their utilization by discovering novel candidate genes for IMF, body stature, and balancing selection.In this work, we sequence 740 pigs and combine 149 of our previously published pig resequencing data, covering most of the common breeds (32) and retrieved 207 resequencing data from the NCBI SRA database, representing in total 43 pig breeds (N ≥ 3) with an average sequencing depth of 17.1 × (Supplementary Data 36. 3) We also used routine filtering thresholds of variants missing rate ≥ 0.05 and individuals call rate ≤ 0.85 to obtain high-quality genotypes. Finally, 937 individuals  and 41,964,356 autosomal variants were used for further analyses  We then build haplotype scaffolds by including the pedigree structure (44 trios and 135 duos)34. (3) Finally, a high-quality haplotype reference panel with 1874 haplotypes was harvested by integrating linked information in haplotype blocks and scaffolds into LD information from populations35 , as imputation targets and the remaining 1854 haploid genomes as reference panel. We randomly selected 50k, 60k, 80k, 100k, and 300k variants at autosomes and masked the remaining unselected sites to mimic chips for target individuals. We imputed the genotypes of masked sites and estimated the correlation and concordance rate between sequenced genotypes and imputed genotypes. The results demonstrated that their accuracies (Mean R2/concordance rate) were 0.89/99.16%, 0.91/99.24%, 0.92/99.41%, 0.94/99.50%, and 0.96/99.71%, respectively  as imputation targets. The results demonstrated that their accuracies (Mean R2/concordance rate) were 0.88/98.80%, 0.90/98.94%, 0.91/99.13%, 0.93/99.25%, and 0.96/99.62%, respectively  in rare variants varied from 0.84 to 0.93. The two results implied that the haplotype reference panel has an excellent performance in both common commercial and Chinese indigenous breeds. Moreover, we estimated the imputation accuracy for each breed. The results showed that LW, WDU, DU, LR, and PT have a high imputation accuracy in 41 breeds included in the reference panel  were used for constructing a haplotype reference panel. We took the following three steps to extract the linkage and LD information for phasing haplotypes: (1) We first established haplotype blocks based on haplotype-informative readsely Fig. . The SNPely Fig. . BecauseIn order to rescue the samples being removed for low call rate, we picked up samples with at least an average sequencing depth of 4 × and randomly selected 1,000 k variants to impute missing genotypes. Ninety samples with at least a concordance rate (imputed vs. genotyped) of 97.00% between sequenced genotypes and imputed genotypes were recalled for subsequent analyses  of 1,490 Duroc pigs from a published study of IMF2. The haplotypes were firstly constructed using 50k array data of all Duroc pigs. Based on these haplotypes, we imputed missing genotypes using our haplotype reference panel, increasing the number of polymorphic sites from 44,266 (array) to 16,963,108 (imputation) after filtering with minor allele frequency ≥ 0.01. The GWASs were performed using array and imputation genotypes, respectively. Quantile-quantile plots with genome control lambda are shown in Supplementary Fig. RASGRP3 , ENSSSCG00000040980 , and UVRAG ; two leading variants resided 19.9 kb upstream of gene GDF3  and 33.5 kb upstream of ENSSSCG00000050360  (Supplementary Data P < 5 × 10-8) among haplotypes at each locus . The correlation (R2) between phenotypic value and imputation EBV (R2 = 0.67) increased by 36.7% compared with chip EBV (R2 = 0.49)  using selected imputed whole genome sequencing (WGS) variants  using imputed genotypes. Previous studies suggested that genomic prediction would benefit from pre-selecting markers49) Fig. . Further45. We used the allele frequency correlation method to scan balancing selection loci on autosomes across the following four populations: Southeastern Chinese domestic pigs , Northwestern Chinese domestic pigs , Crossbred pigs , and European domestic pigs 45. Two significant loci shared by four populations were distinctive , which has been identified as experiencing a long-term balancing selection in the vertebrates46. Another locus with the most potent signal resides at 135.4 Mb on SSC13. To further confirm its balancing selection, we calculated the nucleotide diversity (PI) by a sliding window of 1 kb in this region. The PI was elevated compared with the surrounding region , one Visayan warty pig from the Philippines (Sus cebifrons), one Javan warty pig from Indonesia (Sus verrucosus), one common warthog from Africa (Phacochoerus africanus), and three Pygmy hogs from India . The phylogenetic relationship of haplotypes was assessed by a maximum-likelihood tree. We found that all Hap2 of European and Asian pigs were clustered into a clade without outgroups  generated by White Duroc boars and Erhualian sows. There was no artificial selection for any traits during the breeding process49. The principal component analysis showed that no population stratification existed in the F2 population16  in the F2 population  and 8 growth-related traits were significantly different between Hap1 and Hap2 after Bonferroni correction with adjusted P value < 0.05 (Table P = 4.1 × 10-35), while these 8 growth-related traits have a higher value in the Hap1 group (Student’s test P = 9.1 × 10-5 ~ 1.0 × 10-8). The susceptibility towards ETEC F4ac is determined by whether the bacterium adheres to the intestinal receptor or not4. In our previous study, MUC13 was identified as a causative gene affecting the susceptibility to ETEC F4ac in pigs4, which is a major determinant of diarrhea and mortality in neonatal and young pigs. In this sense, the susceptibility towards ETEC F4ac is one of the selecting pressures on MUC13. Another study also demonstrated that MUC13 affects growth-related traits (rump width and chest width) of pigs50, consistent with the results of phenotypic associations. Collectively, the Hap1 confers diminished resistance to ETEC F4ac but is positively associated with growth-related traits.To further investigate the traits under balancing selection of N = 164; EHL: Erhualian, N = 132) and small stature pigs  to detect candidate genes controlling body size51. We determined the FST value across four pairwise designs , and the intersection of their top 1% variants revealed 139 variants under positive selection  also show evidence of positive selection at a significance level of 0.05. High EHHs of haplotypes could provide evidence of positive selection on this locus61. Remarkably, the EHHs were higher in pDLY and EHL than BMX and TBT  and the number of variants  when using randomly selected 50k target variants8. Generally, the imputed accuracy of rare variants (MAF ≤ 0.05) is low compared with common variants38. Remarkably, the accuracy of imputed rare variants in our reference haplotype panel is up to 0.83~0.92 using randomly selected 50~300k target variants, exhibiting similar performance to the human haplotype reference panel (UK10K or Haplotype Reference Consortium)38.The high-quality haplotype reference panel depends on two aspects: accurate variant genotyping and high-quality haplotype phasing. Our study shows an accurate variant genotyping with an accuracy of 98.56%, validated by both array replication and mendelian distortion analysis. To maximize the utility of linked variants information, we integrated haplotype-informative sequencing reads, linkage phases within pedigrees, and population LD information to generate high-quality haplotype phases. The integrated information guarantees a high-quality haplotype reference panel. Compared with a previously reported public database Animal-ImputedDB  for online imputation, including 28 pig breeds and 233 individuals worldwide, our reference haplotype panel largely increased the imputed genetic variants accuracy  and imputed genotypes (R2 = 0.51). However, using pre-selected significant variants from GWAS (P<0.01), the accuracy of EBV markedly increased by 36.7% based on imputed genotypes compared with array genotypes. Likewise, the phenotypic prediction ability by EBV also improves significantly by 20% using a cvBLUP method using imputed genotypes. In this sense, it would be more effective at predicting EBV using trait-related variants that have been pre-selected. There are some reasons why such a high increase in genomic prediction accuracy in the study: 1) Genotype imputation increases the number of variants in Linkage Disequilibrium with causal mutation or even the number of causal variants, resulting in identifying more novel QTLs. 2) Pre-selected variants by GWAS mitigate the effects of unrelated variants on genomic prediction. 3) The XT-50K array could be used for genomic prediction across multiple breeds, sexes, and traits, while the pre-selected variants for IMF were derived from GWAS of a single breed, sex, and trait. Except for pleiotropic variants, they probably harbor specific variants for the single breed, sex, and trait. Although these variants may not suit other traits or breeds in genomic prediction, they have a good performance on the target breed and trait.IMF is a major determinant of pork quality, and the phenotype is difficult to acquire on a large scale. With our constructed haplotype reference panel, we reanalyzed previously published low-density chip data (Porcine SNP50 Beadchip) and identified five novel loci associated with IMF. The reason why significant loci could not be identified in the chip GWAS is that GWAS statistical power is reduced due to a low Linkage Disequilibrium (LD) between SNPs and potential causative variants  under balancing selection, showing its robust detection ability46. We further demonstrated MUC13’s balancing selection by nucleotide diversity (PI) and Tajimas’D. The PI was increased compared with the surrounding region, and the selection region showed a positive Tajimas’D. The above three results confirmed that the locus is under balancing selection. The LD analysis of the selection region identified a strong LD block of ~8kb, including 240 variants located in the first two introns of MUC13. We found only two haplotypes (Hap1 or Hap2) in the populations used for scanning long-term balancing selection genes . The frequency of Hap1 stays at an intermediate level (0.58), again consistent with the characteristics of balancing selection. MUC13 exhibits a strong signature of balancing selection shared by Chinese and European pigs. Furthermore, we traced the emergence time of these two haplotypes, which suggested that the Hap1 haplogroups were shared for a long time in Suidae animals20, and the Hap2 might be introgressed from an extinct species to Sus scrofa during the formation of Sus scrofa or occur at a low frequency in other species. Our previous study demonstrated that MUC13 is a single copy gene that encodes two transcripts (MUC13A and MUC13B) with distinct PTS domains4. The expression of MUC13B in the small intestine can confer enhanced susceptibility to ETEC F4ac, resulting in diarrhea and mortality in neonatal and young pigs. There are two common mechanisms that cause balancing selection: 1) Overdominance occurs, controlled by one trait, where the fitness of the heterozygote is superior to either of the homozygotes. 2) Pleiotropy, multiple traits are governed by a single locus, gives the heterozygote the highest fitness. To inspect whether overdominance occurred in ETEC F4ac, we selected a proxy SNP 13_135417839, in complete linkage disequilibrium with the haplotype of MUC13, to estimate the overdominance of ETEC F4ac. The incidence of ETEC F4ac for genotypes AA, AG, and GG of SNP 13_135417839 are 0.78, 0.58, and 0.13, respectively, indicating that it is not the case of overdominance. Thus, we speculated the MUC13 pleiotropy might cause this balancing selection. Our further analyses indeed show that MUC13 associates with ETEC F4ac and 8 growth-related traits, confirming its pleiotropy. We then investigate how MUC13 might affect the growth-related traits. As far as we know, the small intestine at which ETEC F4ac interacts exerts an effect on nutrient absorption in mammals66, hence may potentially benefit growth-related traits in pigs owing to better nutrient absorption. A previous study also identified MUC13 as a candidate gene associated with rump width and chest width in pigs50. The above results suggested that MUC13 may play a role in nutrient absorption. Nonetheless, experimental validation is required to elucidate the molecular mechanism further.To our knowledge, ST method across four pairwise designs, our positive selection analyses identified 36 candidate genes associated with the stature of pigs. Of which six genes have been previously confirmed to affect the stature-related traits of livestock, such as limb bone lengths, rear legs side view, chest girth, hock circumference, body length, body weight, and body size67. The skeletal frame is a major determinant of stature. According to previous reports, NPR3 is associated with human height, human skeletal frame size, and mouse skeletal overgrowth60. Moreover, we checked the alleles at proxy variant 16_18869137 in the pDLY, EHL, BMX, and TBT populations. The large stature pigs  share the same the major allele, supporting its positive selection.Using the FMUC13 in pigs. Positive-selection analyses highlighted NPR3 as a candidate gene affecting the stature of pigs. The study provided a haplotype resource and insights into the genetic deciphering of complex traits in pigs.Taken together, the study provides rich genetic resources from 1,096 pigs with 43 breeds and detected 41,964,356 autosomal variants. With these genetic resources, we construct a high-quality pig haplotype reference panel and demonstrate its applications in imputation and deciphering genetic mechanisms and breeding programs. Furthermore, we deciphered a long-term balancing selection gene 32. Genomic DNA was extracted from the ear punches or muscles of pigs using a standard phenol-chloroform extraction protocol. The DNA for each sample was sheared into fragments of 200-800 bp (or 300-400 bp). Next-generation genome sequencing libraries were constructed following the standard protocol of the library preparation kit. Genome sequencing was performed to generate 150 bp  paired-end reads on the Illumina HiSeq or MGISEQ-2000 platform according to the manufacturer’s standard protocols. Using the Fastp v0.20.068, we removed reads with ≥ 10% missing (“N”) bases or quality score ≤ 20 for ≥ 50% of bases. All clean reads were aligned to the Sus scrofa reference genome 11.169 using BWA v0.7.1770 . The sequencing depth and coverage were calculated by Mosdepth v0.3.271. We sorted the bam files of mapped reads by genome position and marked PCR duplication using Picard v2.21.4 (http://broadinstitute.github.io/picard). The gvcf files were generated using the official pipeline of GATK v4.1.7.0, including program BaseRecalibrator, ApplyBQSR, and HaplotypeCaller. We then jointly genotyped all gvcf files to obtain the final vcf file using GATK GenotypeGVCFs. The variants were filtered with the following criteria: (1) SNP: QD < 2.0, QUAL < 30.0, MQ < 40.0, SOR > 3.0, FS > 60.0, MQRankSum < -12.5, ReadPosRankSum < -8.0; (2) INDEL: QD < 2.0, QUAL < 30.0, MQ < 40.0, FS > 200.0, ReadPosRankSum < -20.0. In addition, variants with reads coverage depth beyond the whole-genome average depth of 1.5 times were removed. Due to the low quality of SNP near INDEL, we used 325 individual array genotypes to evaluate the accuracy of SNPs that are within 1-50 bp of Indels  in Jiangxi Agricultural University. We collected 950 pig samples  representing 30 breeds for whole-genome sequencing and retrieved 207 resequencing data from the NCBI SRA database . We visualized the Neighbor-Joining tree with the FigTree v1.4.4 software (http://tree.bio.ed.ac.uk/software/figtree).All analyses were based on autosomal variants. The genetic distance between individual pairs was calculated using Plink v1.9 with parameter “--distance-matrix”33 for each individual. Vcf file A with haplotype blocks was obtained. Secondly, based on the pedigree information  and scaffolds (vcf B) with population LD information using SHAPEIT4 v4.4.2 with parameters “--sequencing --scaffold --use-PS 0.0001”35.Firstly, we established haplotype blocks based on sequencing reads using HapCUT2 v1.3.1 with parameters “--mbq 13 --mmq 20 --indels 1”35. We then conducted genotype imputation using IMPUTE5 v1.5.5 with default parameters74. The Porcine SNP50 Beadchip genotypes for 1,490 Duroc pigs and their IMF content phenotype were downloaded from a published GWAS study2. GWASs were performed using the Genome-wide Efficient Mixed-Model Association method  with a univariate linear mixed model75. The estimated breeding value was calculated using gcta v1.93.2Beta76. We first generated a genetic relationship matrix based on pre-selected variants (P < 0.01 in GWAS) using the parameter “--make-grm”. We then used BLUP (best linear unbiased prediction) method to estimate the breeding value using parameters “--grm --pheno --reml-pred-rand” or “--reml --grm --pheno --cvblup” .Based on the genotypic data genotyped by Porcine SNP50 Beadchip, the haplotype construction was performed using SHAPEIT4 v4.4.2 with default parameters77. The nucleotide diversity (PI) and Tajimas’D were calculated by a sliding 1kb window using VCFtools v0.1.1378. We employed LDBlockShow v1.40 to generate and visualize the LD pattern of haplotype block with parameter “-SeleVar 2”. The multiple sequence alignment for the haplotype data was conducted using Clustalo v1.2.479, and the maximum-likelihood tree was built using Fasttree v2.1.11 (http://www.microbesonline.org/fasttree)80. We annotated variants using the online Ensembl Variant Effect Predictor . The F2 population used for phenotypic analyses was constructed with two founder breeds: White Duroc and Chinese Erhualian. Briefly, two White Duroc boars were mated to 17 Erhualian sows, and 9 F1 boars were then intercrossed with 59 F1 sows avoiding full-sib mating to generate 1912 F2 animals49. A microscopic enterocyte adhesion assay was used to record in vitro ETEC F4ac adhesion phenotypes4. Animals at day 240 ± 3 were slaughtered for recording Carcass straight length, Carcass diagonal length, Carcass weight, Small intestine length, Neck Bone length, Anterior brachial bone length, and Ear area.To detect the genome traces of balancing selection, we used BetaScan2 v1.0 to calculate Beta1* Score with parameters “-w 1000 -fold”ST values of variants were calculated using software VCFtools v0.1.13 with parameter “--weir-fst-pop”78. Briefly, FST was estimated as follows:Four pairwise designs  were used for identifying candidate variants under positive selection on body size. The Weir and Cockerham weighted FMSG denotes the observed mean square errors for variants within populations. MSP represents the observed mean square errors for variants between populations. nc is the average sample size after incorporating and correcting for the variance of sample size over population,ni denotes the sample size in the ith population, pAi is the frequency of variant allele A in the ith population, and pA across populations. Moreover, XP-EHH, a cross-population statistic, was used to detect the positive selection further. It is based on extended haplotype homozygosity (EHH) statistics inside each population. We used the rehh v3.2.2 package implemented in R to calculate EHH and XP-EHH statistics81. The parameter to consider the positive selection was P Value of XP-EHH < 0.05.Where Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


To examine the causal associations of diet-derived circulating antioxidants with knee osteoarthritis (OA), hip OA, and rheumatoid arthritis (RA) within the two-sample Mendelian randomization (MR) framework.Independent single-nucleotide polymorphisms (SNPs) significantly associated with circulating levels of diet-derived antioxidants  were extracted as genetic instruments. Summary statistics of genetic instruments associated with knee OA, hip OA, and RA were obtained from corresponding genome-wide association studies (GWASs). The inverse-variance weighted (IVW) was applied as the primary analysis method, with four sensitivity analysis approaches employed to evaluate the robustness of the primary results.p = 4.43 × 10−3], while genetically determined per unit increase in absolute circulating levels of β-carotene was suggestively associated with increased risk of RA . No other causal association was found. Significant evidence for heterogeneity and pleiotropic outlier was only identified when absolute circulating vitamin C was evaluated as the exposure, whereas all sensitive analysis provided consistently non-significant results.Genetically determined per unit increment of absolute circulating levels of retinol was significantly associated with a reduced risk of hip OA [odds ratio (OR) = 0.45, 95% confidence interval (CI) 0.26–0.78, Our results demonstrated that genetically determined lifelong higher exposure to absolute circulating levels of retinol is associated with a decreased risk of hip OA. Further MR study with more genetic instruments for absolute circulating levels of antioxidants are needed to confirm our results. As the two most common forms of arthritis, osteoarthritis (OA) and rheumatoid arthritis (RA) affect over 300 million and nearly 20 million people worldwide, respectively. It is foreseeable that these figures will dramatically increase as a result of the prolonged life expectancy as well as the ageing of the global population . OA maniOf note, a growing body of evidence indicates that oxidative stress associated with the abundant production of reactive oxygen species (ROS) is involved in the onset and progression of OA and RA, suggesting that diet-derived antioxidants might possess the preventive potential against OA  and RA . OxidatiRecently, Mendelian randomization (MR), employing genetic variants such as single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) in observational settings to make causal inference between environmentally modifiable exposure and outcome, has been gaining popularity in recent years . Since gThe present two-sample MR study was conducted based on publicly available genome-wide association studies (GWASs) summary-level data, and ethical approval and informed consent had been acquired in each original study. In brief, we first extracted the summary statistics of genetic instruments for absolute circulating antioxidants and circulating antioxidant metabolites from the corresponding GWASs, then the summary statistics for these genetic instruments were obtained from GWASs of knee OA, hip OA and RA. Eventually, the causal associations of diet-derived circulating antioxidants and risk of knee OA, hip OA, and RA were evaluated within a two-sample MR framework, and the schematic overview of our present study design is shown in r2 < 0.001 within a 10,000 kb sliding window) serving as IVs significantly associated with diet-derived circulating antioxidants were extracted.In our present two-sample MR study, a total of five common diet-derived antioxidants, namely retinol, β-carotene, lycopene, vitamin C and Vitamin E (α-tocopherol and γ-tocopherol), were included. For each diet-derived antioxidant, the absolute circulating level and/or relative circulating level of the antioxidant metabolite were considered. Summary-level genetic association statistics for independent SNPs (p < 5 × 10−8) in the meta-analysis of a GWAS and further replication studies comprising 8,902 Caucasian individuals were selected (p < 5 × 10−8) in meta-analysis of GWAS and further replication samples involving 3,881 European individuals was selected (p < 5 × 10−6) were obtained from a GWAS of 441 Old Order Amish adults (p < 5 × 10−8) in a large-scale GWAS in 52,018 individuals of European ancestry were chosen (p < 5 × 10−8) in meta-analysis of one GWAS with two independent replication samples consisting of 7,781 European individuals were obtained , α-tocopherol (n = 11), and γ-tocopherol (n = 13) at suggestive genome-wide significance level (p < 1 × 10−5) were derived from a GWAS comprising 7,824 adults from 2 European population studies (p < 1 × 10−5) were derived from a recently published GWAS comprising 1,957 European participants  and hip OA  were extracted from one recent GWAS meta-analysis performed based on the United Kingdom Biobank and Arthritis Research UK Osteoarthritis Genetics (arcOGEN) resources, in which approximately 17.5 million SNPs in 455,221 individuals of European ancestry were analyzed  based on a type I error being 0.05, a statistical power of 0.8, sample size, proportion of case and variance explained by genetic instruments (2R) were estimated using an online toolR2 were either extracted from the original studies or calculated based on the following formula: R2 = 2 × EAF × (1 − EAF) × beta2/ [(2 × EAF × (1 − EAF) × beta2 + 2 × EAF × (1 − EAF) × N × se2)], where EAF, beta, se, and N denote the effect allele frequency, estimated effect and its standard error of genetic instrument on exposure, and sample size, respectively , set based on Bonferroni correction method, was considered statistically significant, while a two-sided p value less than 0.05 was regarded suggestive significance. All analysis were performed utilizing “forestplot,” “TwoSampleMR” and “MRPRESSO” packages in R version 4.2.0.The inverse-variance weighted (IVW) was employed as the primary MR analysis approach, and four sensitive analysis methods, including weighed median, weighted mode, MR-Egger and MR pleiotropy residual sum and outlier (MR-PRESSO), were applied to test the robustness of IVW estimates to horizontal pleiotropy. When all selected SNPs are valid instrumental variables, the IVW method gives the most precise estimate of the causal effect by combing the Wald ratio estimate of each genetic instrument in a meta-analysis model. When only one genetic instrument is available, the Wald ratio estimate was applied. The weighted median method could provide a consistent causal estimate if at least one half of the employed genetic instruments are valid , while tectively . The eff2R) for absolute circulating levels of retinol, β-carotene, lycopene, vitamin C, and α-tocopherol was 2.3, 2.5, 31.0, 1.7, and 1.7%, respectively. The R2 for circulating metabolites levels of retinol, vitamin C, α-tocopherol, and γ-tocopherol was 22.9, 21.7, 6.8, and 9.8%, respectively. The effect estimates for the associations of genetic instruments for absolute circulating antioxidants and circulating antioxidant metabolites with knee OA, hip OA and RA are shown in p = 4.43 × 10−3), while genetically determined per unit increase in β-carotene was associated with increased risk of RA  with suggestive significance. Since the number of genetic instruments available for retinol and β-carotene was less than 3, sensitive analysis could not be performed. As for the associations of the other three diet-derived antioxidants  with knee OA, hip OA and RA, no significant evidence was found in the main IVW analysis, and the estimates derived from all sensitive analysis methods were consistently non-significant. Significant heterogeneity across individual genetic instrument estimate was found for the association of vitamin C with knee OA (p = 2.09 × 10−3), hip OA (p = 0.03) and RA (p = 1.91 × 10−3). No significant evidence of pleiotropy was found in MR-Egger regression  study (n = 108)  . As regan = 108) , 32, whin = 108) , 21, 22.n = 108) , while an = 108) . Besidesn = 108)  as well n = 108) . It has n = 108) . Of inten = 108)  found thn = 108) , it is on = 108)  also focn = 108) . These mn = 108) , and ourThe main strength of our present study lies in the utilization of genetic instruments as proxies for diet-derived circulating antioxidants evaluated as absolute levels and relative concentrations in the form of metabolites to perform two-sample MR analysis to infer their causal effects on risk of knee OA, hip OA and RA. However, several potential limitations should be acknowledged. First, since only less than three genetic instruments are available for absolute circulating levels of retinol and β-carotene, we could not perform sensitive analysis to examine and correct for the potential pleiotropy. Nevertheless, these genetic instruments are not associated with other factors as shown in PhenoScanner database, suggesting that the possibility of pleiotropy is small. Second, non-linear causal associations of diet-derived circulating antioxidants with OA and RA could not be examined based on the summary-level data. However, it is reasonable to speculate that insufficient or excessive levels of antioxidants might have a harmful health effect. Third, our present MR analysis was underpowered to detect small causal effects due to the small proportion of variance explained by currently available genetic instruments for absolute circulating levels of all included antioxidants except for lycopene. Notwithstanding, consistently non-significant evidence was shown for circulating metabolites with sufficient statistical power. Last, although most of the causal associations detected in our present MR analysis were non-significant, we could not rule out the possibility that diet-derived antioxidants might have beneficial health effects in individuals deficient in these antioxidants. Besides, a synergistic effect between diet-derived antioxidants might exist, whereas this possibility could not be explored in our present MR study.Taken together, utilizing genetic instruments as proxies for diet-derived circulating antioxidants evaluated as absolute levels and relative levels of their metabolites, the causal associations of diet-derived circulating antioxidants with the risk of knee OA, hip OA and RA were comprehensively assessed within a two-sample MR framework. We found that genetically predicted higher absolute circulating levels of retinol are associated with a reduced risk of hip OA, while genetically predicted higher absolute circulating levels of β-carotene are associated with increased risk of RA. Further MR study with more genetic instruments for absolute circulating levels of antioxidants, followed by the findings in larger-scale GWASs in the future, are needed to confirm our results.The original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding author.Ethical approval was not provided for this study on human participants because the present two-sample MR study was conducted based on publicly available genome-wide association studies (GWASs) summary-level data, and ethical approval and informed consent had been acquired in each original study.YM, JL, and HC had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. LH, YM, JL, and HC contributed to conception and design. LH, YX, TJ, ZZ, MW, and HC were involved in the acquisition of data. LH, YM, JL, and HC carried out analysis and interpretation of data. All authors were involved in drafting the article or revising it critically for important intellectual content, and approved the final version to be submitted for publication.This work was supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. LY20H260001) and K.C. Wong Magna Fund in Ningbo University.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.