Inference of causal relationships between genetic risk factors for cardiometabolic phenotypes and female-specific health conditions

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Using electronic health record data from 71,008 ancestrally diverse females, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index [BMI], coronary artery disease [CAD], type 2 diabetes [T2D], and hypertension) using cross-trait genetic correlation analyses, polygenic risk scores (PRSs), Mendelian randomization (MR), and chronology analyses. We observed 29 significant associations between cardiometabolic PRSs and obstetrical/gynecological conditions (BMI and endometrial cancer; BMI and polycystic ovarian syndrome [PCOS)]; T2D and gestational diabetes; T2D and PCOS). MR analysis provided additional evidence of independent causal effects. We also identified an inverse association between CAD and breast cancer. High cardiometabolic PRSs were associated with early development of PCOS and gestational hypertension. We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female-specific health conditions. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiometabolic diseases such as coronary artery disease (CAD), obesity, hypertension, and type 2 diabetes (T2D) are among the leading causes of death in the world and are highly comorbid 5 , 6 , 7 . Many studies have shown that the pathophysiology of cardiometabolic diseases affects males and females differently 8 . Although cardiometabolic diseases have many sequelae that affect both sexes, including depression, anxiety, chronic obstructive pulmonary disease (COPD), and cancer 9 , 10 , they disproportionately affect females because of their links to female-specific health conditions, such as preeclampsia, gestational diabetes, stillbirth, and pregnancy loss 11 , 12 . Multiple lines of evidence link female health conditions to cardiometabolic diseases. Individuals who develop preeclampsia during pregnancy are more likely to develop cardiovascular diseases and hypertension after pregnancy 13 , 14 . Obesity is closely linked to polycystic ovarian syndrome (PCOS), and individuals with PCOS are at high risk of developing T2D 15 – 17 . Individuals with endometriosis are at high risk of developing various cancers and cardiovascular diseases such as myocardial infarction and ischemic heart disease 18 , 19 . Despite this evidence, the relationship between female-specific health conditions and cardiometabolic phenotypes, and particularly the potential for shared genetic risk, is understudied. Investigation of shared genetic risks between cardiometabolic diseases and female-specific health conditions has the potential to reveal genetic cluster-based risk factors that can be used to improve screening practices for many diseases in high-risk patients. Genome-wide association studies (GWAS) have exposed common genetic etiologies among diseases such as T2D, obesity, sleep apnea, hypertension, Alzheimer’s disease, and many types of cancer 1 – 4 . There is evidence of impacts on cardiometabolic phenotypes for more than 1,000 different loci, but the effect size of any single variant is generally minimal. To more accurately estimate an individual’s overall risk of disease, researchers have used GWAS results to calculate polygenic risk scores (PRSs), which sum the effects of common single-nucleotide polymorphisms (SNPs) on a given phenotype 25 and can be used to predict the risks of many cardiometabolic diseases and comorbidities 26 , 27 . To investigate the effects of genetic risk of cardiometabolic phenotypes on female health conditions, we measured PRSs for cardiometabolic phenotypes and then determined their correlations with female-specific health conditions documented in electronic health records (EHRs). Prior studies successfully used PRSs as genetic risk factors linking multiple adverse phenotypes 29 , 30 . PRS-based association tests have the advantage that they are based on unvarying risk factors (i.e., inherited genetic burden), and they make fewer assumptions than association tests based on individual genetic factors. We hypothesized that the genetic risks of adverse cardiometabolic phenotypes can explain a significant portion of phenotypic variance in female-specific health conditions. To test this hypothesis, we obtained genotypic and phenotypic data on a wide range of female-specific health conditions from the Penn Medicine BioBank (PMBB) and the Electronic Medical Records and Genomics (eMERGE) network. We first measured pairwise genetic correlations between cardiometabolic phenotypes and female-specific health conditions to determine whether there was a common genetic basis. We then estimated the significance of associations between the genetic risks of adverse cardiometabolic phenotypes and female-specific health conditions. In addition, we evaluated potential causal relationships between genetic risk scores for cardiometabolic phenotypes and female-specific health conditions using Mendelian randomization (MR) approaches. Furthermore, because EHR data provide an opportunity to map disease prevalence by age, we generated a chronological map of female-specific health conditions in individuals with high and low genetic risk scores to understand age-specific prevalence in high-risk and low-risk individuals. Methods The authors have individual-level access to genotype data and medical records from the PMBB and the eMERGE network datasets. Study populations Penn Medicine BioBank The PMBB is a University of Pennsylvania academic biobank that recruits patient participants from the University of Pennsylvania Health System surrounding the greater Philadelphia area in the United States. PMBB links patient genotype data with detailed EHR information. Currently, PMBB consists of genome-wide and whole-exome sequence data of approximately 45,000 samples. The PMBB cohort is diverse, with over 25% of its participants being of African ancestry. PMBB genotype data were imputed to the TOPMED Reference panel using the Michigan Imputation server. We included 21,837 female participants from PMBB in this study (Table 1 ). The stratified analyses in this study included only participants of European or African ancestry, while excluding those of Asian or Hispanic ancestry because of sample size limitations. Table 1 Sample sizes in the PMBB and eMERGE datasets overall and stratified by ancestry. Phenotype PMBB Sample Size (N Cases) eMERGE Sample Size (N Cases) All European African All European African Cardiometabolic Phenotypes BMI 20,209 12,344 6,744 39,403 32,880 5,555 CAD 21,837 (3,002) 13,515 (1,956) 7,039 (955) 49,171 (9,597) 38,918 (7,953) 9,067 (1,515) DBP 21,612 13,343 6,994 NA NA NA Hypertension 21,837 (10,278) 13,515 (5,640) 7,039 (4,286) 49,171 (27,685) 38,918 (21,883) 9,067 (5,265) T2D 21,837 (4,388) 13,515 (1,969) 7,039 (2,221) 49,171 (12,403) 38,918 (8,999) 9,067 (3,090) Female Health Phenotypes Breast cancer 21,837 (1,621) 13,515 (1,621) 7,039 (415) 49,171 (4,148) 38,918 (3,639) 9,067 (443) Cervical cancer 21,837 (105 13,515 (53) 7,039 (50) 49,171 (332) 38,918 (263) 9,067 (60) Ectopic pregnancy 2,808 (1,779) 1,201 (827) 1,319 (757) 3,078 (823) 1,975 (570) 641 (172) Endometrial cancer 21,837 (286) 13,515 (183) 7,039 (90) 49,171 (771) 38,918 (680) 9,067 (83) Endometriosis 21,837 (701) 13,515 (320) 7,039 (340) 49,171 (2,314) 38,918 (1,891) 9,067 (354) Excessive fetal growth 693 (85) 293 (37) 329 (42) 2,433 (627) 1,618 (422) 437 (162) Gestational diabetes 2,655 (523) 1,111 (193) 1,262 (248) 3,174 (762) 2,005 (445) 719 (251) Gestational hypertension 2,666 (631) 1,118 (256) 1,275 (324) 3,135 (703) 1,980 (411) 711 (253) Intrauterine death 685 (57) 289 (23) 324 (27) 2,156 (64) 1,438 (44) 358 (19) Miscarriage 2,934 (389) 1,273 (199) 1,360 (151) 3,568 (823) 2,323 (577) 740 (200) Ovarian cancer 21,837 (305) 13,515 (191) 7,039 (89) 49,171 (1,589) 38,918 (1,359) 9,067 (197) Placenta abruption/previa 1,192 (396) 482 (157) 586 (195) 2,674 (997) 1,697 (672) 583 (262) Polycystic ovarian syndrome 21,837 (736) 13,515 (387) 7,039 (272) 49,171 (1,006) 38,918 (750) 9,067 (209) Poor fetal growth 792 (202) 325 (78) 388 (107) 2,209 (167) 1,478 (115) 360 (27) Postpartum depression 924 (384) 374 (136) 466 (226) 2,417 (555) 1,579 (349) 457 (177) Postpartum hemorrhage 846 (283) 350 (108) 408 (146) 2,320 (401) 1,544 (261) 408 (109) Preeclampsia 2,631 (452) 1,100 (149) 1,264 (272) 3,132 (702) 1,974 (406) 713 (249) Preterm birth 687 (66) 284 (21) 332 (40) 2,144 (57) 1,433 (35) 350 (16) Stillbirth 649 (17) 270 (3) 311 (12) 2,123 (8) 1,417 (7) 347 (0) Uterine cancer 21,837 (113) 13,515 (66) 7,039 (43) 49,171 (418) 38,918 (348) 9,067 (64) Uterine fibroid 21,837 (1,570) 13,515 (516) 7,039 (984) 49,171 (5,711) 38,918 (4,103) 9,067 (1,455) Vaginal cancer 21,837 (23) 13,515 (13) 7,039 (9) 49,171 (109) 38,918 (89) 9,067 (18) Vulvar cancer 21,837 (41) 13,515 (25) 7,039 (14) 49,171 (120) 38,918 (97) 9,067 (21) Electronic Medical Records and Genomics The eMERGE network is a nationwide consortium that combines genome-wide sequence data with EHRs from several health systems across the United States, with most participants coming from the Geisinger Health System and Vanderbilt University. eMERGE data were imputed to the Haplotype Reference Consortium panel using the Michigan Imputation Server. Like the PMBB cohort, the eMERGE cohort is diverse across ancestries (~ 20% samples of non-European ancestry) and ages. This study included data from 49,171 female patients in the eMERGE network born after 2001(Table 1 ). Low sample sizes of participants of Asian and Hispanic ancestry also limited ancestry-stratified analyses to include only those of European and African ancestry. GWAS of cardiometabolic phenotypes To determine genetic correlations and calculate PRSs, we collected quantitative data on the effects of genetic variants on cardiometabolic phenotypes from several large GWAS. Given the diverse nature of our study population, we obtained the largest publicly available multi-ancestry GWAS summary statistics for four cardiometabolic phenotypes: obesity (measured as body mass index [BMI]), CAD, hypertension (measured as diastolic blood pressure [DBP], systolic blood pressure [SBP], and pulse pressure [PP]), and T2D. Summary statistics and source studies for each phenotype are shown in Table 2 . Table 2 GWAS datasets used to calculate genetic correlations and polygenic risk scores and the number of SNPs used from each GWAS to calculate the corresponding PRS. Phenotype Ancestries Included Source Sample Size (N Cases) PMID N SNPs included in PRS Type 2 Diabetes EUR, AFR, EAS, SAS, HIS Vujkovic et al., Nat Gen, 2020, Million Veterans Program 1,407,282 (228,499) 32541925 PMBB: 1,023,697 eMERGE: 716,330 Body Mass Index EUR, AFR, EAS, SAS, HIS Justice et al., Nat Com, 2017, GIANT 241,258 28443625 PMBB: 885,143 eMERGE: 614,668 Hypertension (DBP, SBP, PP) EUR, AFR, EAS, SAS, HIS Giri et al., Nat Gen, 2019, Million Veterans Program 318,891 30578418 PMBB: 1,024,567 eMERGE: 715,471 Coronary Artery Disease EUR, EAS, SAS, HIS, AFR van der Harst et al., Circ Res, 2018, CARDIoGRAMplusC4D + UKBB 547,261 (122,733) 29212778 ​​ PMBB: 981,480 eMERGE: 681,029 **DBP = Diastolic Blood Pressure; SBP = Systolic Blood Pressure; PP = Pulse Pressure; EUR = European ancestry; EAS = East Asian ancestry; SAS = South Asian ancestry; HIS = Hispanic ancestry; AFR = African ancestry; MVP = Million Veterans Program; GIANT = The Genetic Investigation of ANthropometric Traits; UKB = UK BioBank Genome-wide associations of female-specific health conditions Large multi-ancestry GWAS are not available for most of the female-specific health conditions evaluated in this study. Therefore, we used PLINK version 1.90 to conduct GWAS of female-specific health conditions documented in the PMBB and eMERGE datasets 31 . We filtered the genetic variants in the PMBB and eMERGE imputed datasets to include only variants with imputation quality R 2 > 0.3 and minor allele frequency > 0.01. We then conducted a meta-analysis of the GWAS of the two cohorts using PLINK. Genetic correlation calculation We calculated pairwise genetic correlations between cardiometabolic phenotypes and female-specific health conditions using linkage disequilibrium score regression (LDSC) 32 . LDSC accounts for linkage disequilibrium (LD) among SNPs by using an external reference panel that should match the ancestry distribution of the corresponding GWAS. We generated a multi-ancestry LD reference panel using the HapMap3 SNPs (~ 1 M common variants) from the entire 1000 Genomes population 33 . Polygenic risk scores PRSs calculated using a GWAS of one particular ancestry group tend to perform poorly when applied to individuals from a different ancestry group 25 , 34 , 35 . To accurately calculate PRSs for our diverse cohorts, we calculated PRSs for the cardiometabolic phenotypes using the same publicly available GWAS used in the genetic correlation analyses. Weights for each SNP were calculated using PRS-CS (version from Apr 24, 2020) 37 . Like LDSC, PRS-CS requires a reference panel that matches the ancestry distribution of the target dataset. We generated a multi-ancestry LD reference panel using the HapMap SNPs from the 1000 Genomes populations. We then used PLINK (v1.90) to identify LD blocks and calculate the LD between the SNPs in each block. For PRS-CS, the global shrinkage parameter phi was fixed to 0.01, and default values were selected for all other parameters. PRSs were then calculated using the weights with PLINK. Only the SNPs in the target dataset, summary statistics, and LD reference panel were included in the PRSs. The numbers of SNPs used for each PRS calculation are listed in Table 2 . The scores were then normalized to obtain meaningful beta coefficients. To evaluate the power of the PRSs, we tested their performance in prediction of the corresponding primary phenotypes in the summary statistics. We could not obtain quantitative measurements of blood pressure for participants in the eMERGE cohort, and PP and SBP measurements were not curated in the PMBB cohort. Therefore, we evaluated the performance of the PRSs for blood pressure traits (SP, DBP, PP) in the eMERGE cohort based on hypertension case-control phenotypes, and we evaluated the performance of the PRS blood pressure traits in the PMBB cohort based on DBP or hypertension (for SBP and PP) as outcomes. We constructed logistic regression models for binary phenotypes (CAD, T2D, and hypertension) and evaluated PRS performance by the area under the receiver-operator curves (AUCs) using the pROC package in R. Similarly, we constructed linear regression models for continuous phenotypes (BMI and DBP) and evaluated them by the R 2 using the glm function in R. The regression models used birth year and the first five principal components (PCs) as covariates. We tested PRS performance overall as well as for subgroups of European or African ancestry. Phenotype data Cases and controls for each phenotype were defined using International Classification of Disease (ICD) diagnosis codes. Participants were coded as cases of a given phenotype if their records contained at least one of the corresponding ICD codes. For pregnancy-related phenotypes, participants were only considered controls if their records had at least one pregnancy-related ICD code and no ICD codes for relevant complications (such as miscarriage) during pregnancy. Participants were counted as controls for cardiometabolic phenotypes and all non-pregnancy related health conditions if their records did not contain any relevant ICD code. The complete list of ICD codes used to include or exclude participants as cases and controls can be found in Supplementary Table 1 . Using these definitions, we determined the sample size for each phenotype in the eMERGE and PMBB cohorts (Table 1 ). PRS association analyses We tested the association between each cardiometabolic PRS and female-specific health condition by fitting separate logistic regression models adjusted by birth year and the first five PCs. We conducted this analysis for all participants and for subsets of participants of European or African ancestry. To account for biases from multiple hypothesis testing, we determined if associations passed a false discovery rate significance threshold of 0.05, adjusting the p values with the number of female-specific health conditions (23) tested. The logistic regressions were performed using the glm function in R. The results from the PMBB and eMERGE cohorts were combined using the rma function from the metafor R package under the restricted maximum-likelihood estimator model 36 . We used PheWAS-View to visualize the results 38 . We then created prevalence plots for each significant association. The participants were divided into quintiles based on the PRS for each condition, and the percentage of cases was calculated for each quintile. Mendelian randomization To identify evidence of causality between cardiometabolic phenotypes and female-specific health conditions, we performed one-sample MR for 29 significant associations from the PRS analyses using the ivreg function in the ivpack R package. Cardiometabolic PRSs were used as genetic instruments, with birth year and the first five PCs included as covariates. The results were then combined in a meta-analysis using the metafor R package. Because the small sample size for some of the health conditions could limit the power of the analysis, we also performed two-sample MR for the same associations using the inverse variance weighted (IVW) method in the twoSampleMR package in R 39 . MR sensitivity analyses were conducted using the weighted median and MR Egger methods in the same package. The genetic instruments for the cardiometabolic phenotypes in the two-sample MR were the significant SNPs in the GWAS summary statistics used to calculate the PRSs. The SNPs were pruned according to LD patterns among the 1000 Genomes HapMap SNPs, and the remaining representative SNPs were included in the analysis. Matching genetic instruments for the female-specific health conditions were obtained from publicly available GWAS through the twoSampleMR package ( Supplementary Table 2 ). Since these GWAS for female-specific health conditions were conducted in only European ancestry populations, we performed two-sample MR for only associations that were significant in all participants or participants of European ancestry. Chronology analyses We divided the participants into high-risk and low-risk groups according to each cardiometabolic PRS. High PRS was defined as the top quintile (> 80th percentile), and low PRS was defined as the bottom quintile (< 20th percentile). We considered age at the first occurrence of each female-specific health condition from the ICD records. For pregnancy-related conditions, the participants were split into three age groups: <25, 25–40, and 40–55 years. We excluded participants who were over 55 years of age at the first occurrence of a pregnancy-related condition because of low sample sizes and potential errors in diagnosis coding. For all other conditions, the participants were split into five age groups: 70 years. We then examined the combined case prevalence of each health condition within the high and low PRS groups in each cohort across all age groups. Results Genetic correlations among cardiometabolic phenotypes and female-specific health conditions We found thirteen statistically significant correlations between the cardiometabolic phenotypes and six female-specific health conditions (Fig. 1 A). BMI was correlated with breast cancer (R g =-0.179, p = 0.011) and PCOS (R g =0.4, p = 0.007); CAD with breast cancer (R g =-0.199, p = 0.011), PCOS (R g =0.216, p = 0.04), and postpartum depression (R g =0.204, p = 0.025); PP with gestational hypertension (R g =0.481, p = 0.0025); SBP with breast cancer (R g =-0.153, p = 0.041) and gestational hypertension (R g =0.523, p = 0.0017); and T2D with breast cancer (R g =-0.196, p = 0.0001), excessive fetal growth (R g =0.126, p = 0.044), gestational diabetes (R g =0.529, p = 0.011), gestational hypertension (R g =0.237, p = 0.028), and PCOS (R g =0.316, p = 0.0093). PRS performance for predicting primary phenotypes We calculated a PRS for each cardiometabolic phenotype and confirmed the distributions of the raw and normalized scores ( Supplementary Figs. 1, 2, 3, 4, 5, and 6 ). The full model for all PRSs generally performed well in predicting the corresponding primary phenotypes across ancestry groups (Table 3 ). The covariate-only (null) model performed better for participants of African ancestry than for participants of European ancestry, whereas the PRS-only model performed better for participants of European ancestry than for participants of African ancestry. We calculated the difference between the full and null models for all PRSs in each cohort and found that the PRSs improved the predictive performance more for participants of European ancestry than for participants of African ancestry (p = 0.000488, Wilcoxon signed rank test). Thus, the cardiometabolic PRSs were more accurate in individuals with European ancestry than in individuals with African ancestry, but they improved predictive models in both groups. Table 3 Effect estimates for tests of association of PRS with primary phenotype in the eMERGE and PMBB datasets Phenotype Ancestry N Total N Cases R 2 or AUC of Full Model Beta SE P-value R 2 or AUC of Null Model R 2 or AUC of PRS-only Model eMERGE BMI All 39,338 0.08494 2.378 0.05044 0 0.0334 0.0593 EUR 32,880 0.07283 2.334 0.05075 0 0.0132 0.0595 AFR 5,555 0.0697 2.772 0.2457 3.42E-29 0.0485 0.0229 CAD All 49,171 9,597 0.784 0.3091 0.01318 1.62E-121 0.7745 0.5474 EUR 38,918 7,953 0.7733 0.3039 0.01391 7.65E-106 0.7624 0.5629 AFR 9,067 1,515 0.8228 0.3 0.04424 1.23E-11 0.8197 0.5462 DBP (Hypertension) All 49,171 27,685 0.8109 0.1387 0.01347 7.17E-25 0.8099 0.529 EUR 38,918 21,883 0.7994 0.1504 0.01459 6.34E-25 0.798 0.5355 AFR 9,067 5,265 0.8619 0.1709 0.03923 1.32E-05 0.8612 0.5068 PP (Hypertension) All 49,171 27,685 0.8115 0.1644 0.01344 2.15E-34 0.8099 0.526 EUR 38,918 21,883 0.8001 0.1673 0.01413 2.23E-32 0.798 0.5276 AFR 9,067 5,265 0.8613 0.0974 0.04696 0.0381 0.8612 0.5044 SBP (Hypertension) All 49,171 27,685 0.8131 0.2977 0.01665 1.86E-71 0.8099 0.5352 EUR 38,918 21,883 0.8024 0.3143 0.01756 1.1E-71 0.798 0.5446 AFR 9,067 5,265 0.8622 0.3127 0.06055 2.42E-07 0.8612 0.5031 T2D All 49,171 12,403 0.7193 0.6334 0.01914 3.02E-240 0.691 0.6163 EUR 38,918 8,999 0.6963 0.6275 0.0202 9.05E-212 0.655 0.6145 AFR 9,067 3,090 0.7794 0.6444 0.06331 2.49E-24 0.7724 0.5483 PMBB BMI All 20,209 0.1542 2.658 0.08244 2.05E-222 0.1108 0.1417 EUR 12,344 0.07473 2.621 0.08679 2.66E-193 0.006422 0.06782 AFR 6,744 0.03203 2.708 0.2089 5.75E-38 0.008037 0.02711 CAD All 21,837 3,002 0.8235 0.3571 0.02354 5.73E-52 0.8148 0.5619 EUR 13,515 1,956 0.8303 0.3894 0.02732 4.44E-46 0.8185 0.5929 AFR 7,039 955 0.799 0.2566 0.05214 8.60E-07 0.7955 0.5389 DBP All 21,612 0.0425 1.036 0.06661 3.05E-54 0.03183 0.02905 EUR 13,343 0.01534 0.9964 0.08064 7.01E-35 0.004144 0.01213 AFR 6,994 0.04559 1.104 0.129 1.35E-17 0.03571 0.01375 PP (Hypertension) All 21,837 10,278 0.8125 0.1942 0.02328 7.18E-17 0.8112 0.602 EUR 13,515 5,640 0.7797 0.1918 0.02638 3.53E-13 0.7778 0.5272 AFR 7,039 4,286 0.8389 0.1886 0.05376 0.00045 0.8383 0.5391 SBP (Hypertension) All 21,837 10,278 0.8144 0.327 0.02523 2.08E-38 0.8112 0.6097 EUR 13,515 5,640 0.7813 0.279 0.02846 1.11E-22 0.7778 0.5397 AFR 7,039 4,286 0.8415 0.4505 0.05923 2.81E-14 0.8383 0.5649 T2D All 21,837 4,388 0.754 0.8136 0.03523 5.49E-118 0.7287 0.6713 EUR 13,515 1,969 0.712 0.8418 0.04533 5.55E-77 0.6634 0.6323 AFR 7,039 2,221 0.7342 0.7896 0.05947 3.11E-40 0.7168 0.5919 **BMI = Body Mass Index; CAD = Coronary artery disease, T2D = Type 2 Diabetes; DBP = Diastolic Blood Pressure; SBP = Systolic Blood Pressure; PP = Pulse Pressure; EUR = European ancestry; AFR = African ancestry; Beta coefficients and standard errors are per SD PRS in the full model. Covariates included: birth year and the first five PCs. Associations between cardiometabolic risks and female-specific health conditions We detected numerous associations between cardiometabolic PRSs and female health conditions in the meta-analysis of both cohorts (Fig. 1 B, 1 C, and 1 D). Twenty-nine associations were statistically significant after correction for multiple hypothesis testing (Table 4 ). In the meta-analysis, for all participants and participants of European ancestry, PRS BMI was associated with endometrial cancer (beta all =0.24, se all =0.046, p all =9.4⋅10 − 8 ; beta eur =0.28, se eur =0.083, p eur =0.00075), gestational diabetes (beta all =0.23, se all =0.051, p all =6⋅10 − 6 ; beta eur =0.29, se eur =0.061, p eur =2.6⋅10 − 6 ), and PCOS (beta all =0.27, se all =0.039, p all =2.4⋅10 − 12 ; beta eur =0.28, se eur =0.044, p eur =2.1⋅10 − 10 ). PRS BMI was also associated with breast cancer in all participants (beta all =-0.071, se all =0.022, p all =0.0016). These results suggest that an increased genetic risk of obesity heightens the risks of endometrial cancer, gestational diabetes, and PCOS but decreases the risk of breast cancer. PRS CAD was negatively associated with breast cancer for all participants and participants of European ancestry (beta all =-0.072, se all =0.015, p all =1⋅10 − 6 ; beta eur =-0.071, se all =0.016, p eur =5.3⋅10 − 6 ). This finding suggests that individuals, particularly those of European ancestry, with high PRS CAD are at relatively low risk of breast cancer compared with individuals with low PRS CAD . PRS T2D was significantly associated with gestational diabetes in all participants and participants of European ancestry (beta all =0.59, se all =0.063, p all =1.2⋅10 − 20 ; beta eur =0.67, se eur =0.075, p eur =2.1⋅10 − 19 ) and with PCOS in all participants and participants of European ancestry (beta all =0.22, se all =0.043, p all =1.9⋅10 − 7 ; beta eur =0.21, se eur =0.048, p eur =1.3⋅10 − 5 ) but not with PCOS in participants of African ancestry. These results support a potential genetic basis for the well-known associations between T2D and both gestational diabetes and PCOS 16 , 17 . In addition, PRS T2D was associated with gestational hypertension in all participants (beta all =0.18, se all =0.064, p all =0.0041), breast cancer in all participants (beta all =-0.073, se all =0.021, p all =0.00066), and postpartum depression in all participants (beta all =0.18, se all =0.07, p all =0.01). Table 4 Significant (FDR P-value < 0.05) associations between cardiometabolic PRS and female health conditions identified in the PMBB and eMERGE meta-analysis PRS Association Ancestry Beta SE OR 95% CI P-value BMI Breast cancer All -0.071 0.0225 0.93 0.891–0.973 0.00159 Endometrial cancer All 0.244 0.0458 1.28 1.17–1.4 9.4⋅10 − 8 EUR 0.278 0.0825 1.32 1.12–1.55 0.000754 Gestational diabetes All 0.23 0.0508 1.26 1.14–1.39 6⋅10 − 6 EUR 0.288 0.0613 1.33 1.18–1.5 2.59⋅10 − 6 PCOS All 0.272 0.0387 1.31 1.22–1.42 2.37⋅10 − 12 EUR 0.281 0.0442 1.32 1.21–1.45 2.09⋅10 − 11 CAD Breast cancer All -0.0718 0.0147 0.931 0.904–0.958 9.96⋅10 − 7 EUR -0.0708 0.0156 0.932 0.904–0.961 5.32⋅10 − 6 DBP Excessive fetal growth AFR -0.416 0.13 0.66 0.511–0.851 0.00136 Gestational hypertension AFR 0.273 0.0768 1.31 1.13–1.53 0.000372 Preeclampsia AFR 0.221 0.0771 1.25 1.07–1.45 0.00421 PP Gestational hypertension All 0.218 0.0443 1.24 1.14–1.36 8.51⋅10 − 7 EUR 0.248 0.0549 1.28 1.15–1.43 6.6⋅10 − 6 Preeclampsia All 0.196 0.0581 1.22 1.09–1.36 0.000762 EUR 0.193 0.0582 1.21 1.08–1.36 0.000914 SBP Gestational hypertension All 0.332 0.0825 1.39 1.19–1.64 5.61⋅10 − 5 EUR 0.31 0.065 1.36 1.2–1.55 1.77⋅10 − 6 AFR 0.416 0.099 1.52 1.24–1.84 2.77⋅10 − 5 Preeclampsia All 0.275 0.0799 1.32 1.13–1.54 0.000567 EUR 0.234 0.0695 1.26 1.1–1.45 0.000767 AFR 0.368 0.103 1.44 1.18–1.77 0.00038 T2D Breast cancer All -0.0726 0.0213 0.93 0.892–0.97 0.000657 Gestational diabetes All 0.587 0.063 1.8 1.59–2.03 1.19⋅10 − 20 EUR 0.675 0.0749 1.96 1.7–2.28 2.13⋅10 − 19 Gestational hypertension All 0.184 0.0642 1.2 1.06–1.36 0.00414 PCOS All 0.223 0.0428 1.25 1.15–1.36 1.93⋅10 − 7 EUR 0.21 0.048 1.23 1.12–1.36 1.26⋅10 − 5 Postpartum depression All 0.18 0.07 1.2 1.04–1.37 0.0101 **BMI = Body Mass Index; CAD = Coronary artery disease, T2D = Type 2 Diabetes; PCOS = Polycystic ovarian syndrome; EUR = European ancestry; P = PMBB; E = eMERGE; Beta coefficients are per SD PRS. Results were adjusted for birth year and the first five PCs. The three PRSs for blood pressure traits (PRS DBP , PRS SBP , and PRS PP ) showed varying associations with gestational hypertension and preeclampsia. In the meta-analysis of both cohorts, PRS DBP was significantly associated with gestational hypertension and preeclampsia in participants of African ancestry (gestational hypertension: beta afr =0.27, se afr =0.077, p afr =0.00037; preeclampsia: beta afr =0.22, se afr =0.077, p afr =0.0042). PRS DBP was also significantly associated with excessive fetal growth in participants of African ancestry (beta afr =-0.42, se afr =0.13, p afr =0.0014). PRS PP was significantly associated with gestational hypertension and preeclampsia in all participants and participants of European ancestry (gestational hypertension: beta all =0.22, se all =0.044, p all =8.5⋅10 − 7 ; beta eur =0.25, se eur =0.055, p eur =6.6⋅10 − 6 ; preeclampsia: beta all =0.2, se all =0.058, p all =0.00076; beta eur =0.19, se eur =0.058, p eur =0.00091). PRS SBP was associated with gestational hypertension and preeclampsia in all participants, participants of European ancestry, and participants of African ancestry in the meta-analysis (gestational hypertension: beta all =0.33, se all =0.083, p all =5.6⋅10 − 5 ; beta eur =0.31, se eur =0.065, p eur =1.8⋅10 − 6 ; beta afr =0.42, se afr =0.099, p afr =2.8⋅10 − 5 ; preeclampsia: beta all =0.28, se all =0.08, p all =0.00057; beta eur =0.23, se eur =0.07, p eur =0.00077; beta afr =0.37, se afr =0.1, p afr =0.00038). For each significant association, we explored the case prevalence of female-specific health condition per the associated PRS quintile in each cohort (Fig. 2 B and Supplementary Figs. 7, 8, 9, 10, 11, 12, 13, 14, and 15 ). The trends matched the results of the association analyses across ancestry groups and in both cohorts. For the positive associations, such as that between PCOS and PRS BMI , the number of cases increased as the PRS percentile increased, whereas the opposite was true for the negative association between breast cancer and PRS CAD (Fig. 2 B). Association between PRS CAD and breast cancer To validate the inverse association between PRS CAD and breast cancer, we examined the genetic correlation between CAD and breast cancer using the UKBB Genetic Correlation Browser (Fig. 2 A). The genetic correlation between I9_CHD (Major coronary heart disease event) and C50 (Diagnoses-main ICD10: C50 Malignant neoplasm of breast) was significantly negative (R g =-0.24, p = 0.0325). We also compared the effects of SNPs in the CAD GWAS and a large publicly available breast cancer GWAS ( Supplementary Table 2 ). Among the 220 SNPs that had a significant effect in the CAD GWAS after pruning, 125 had an opposite effect on breast cancer, and the beta estimates of the SNPs were negatively correlated between the two GWASs (Fig. 2 C). Additionally, to evaluate the risk of ascertainment biases and reporting of comorbidities in the EHR, we assessed the association between PRS CAD and breast cancer in females who were not diagnosed with CAD (n = 61,201). In these individuals, we observed a slightly less significant association between breast cancer and CAD (beta all =-0.0484, p all =0.0045; beta eur =-0.0457, p eur =0.0115) than in the full dataset. The overlap between the participants diagnosed with CAD and those diagnosed with breast cancer in our cohorts suggests that females with a diagnosis of CAD were less likely to be diagnosed with breast cancer (Fig. 2 D). Mendelian randomization We performed one-sample MR in the PMBB and eMERGE cohorts for the 29 significant associations and combined them in a meta-analysis (Fig. 3 A). The majority of associations remained significant (p < 0.05), and the direction of the estimated beta coefficients aligned with the effect directions in the PRS association analysis. The MR results supported causal relationships between many of the cardiometabolic phenotypes and female health conditions, with the most significant associations being between T2D and gestational diabetes (All: p = 1.5⋅10 − 11 , beta = 1.52, se = 0.23; EUR: p = 2.9⋅10 − 10 , beta = 1.76, se = 0.28) and between BMI and PCOS (All: p = 1.1⋅10 − 8 , beta = 0.0021, se = 0.00037; EUR: p = 6.1⋅10 − 8 , beta = 0.0021, se = 0.0038). We also performed two-sample MR to leverage the power of larger GWAS of female-specific health conditions (Fig. 3 B). Most of the associations remained significant when using the IVW method but became less significant when using the MR Egger and weighted median methods. Some associations were significant by all three methods, however, particularly those between CAD and breast cancer (IVW: beta=-0.0587, se = 0.021, p = 0.00463; MR Egger: beta=-0.102, se = 0.049, p = 0.04; Weighted median: beta=-0.0642, se = 0.028, p = 0.021) and between T2D and gestational diabetes (IVW: beta = 0.613, se = 0.044, p = 1.73⋅10 − 43 ; MR Egger: beta = 0.656, se = 0.11, p = 8.8⋅10 − 10 ; Weighted median: beta = 0.635, se = 0.066, p = 8.12⋅10 − 22 ). The role of population stratification Population stratification can confound the results of PRS association and MR analyses. Therefore, we tested the PRS associations with PCs ( Supplementary Table 3 ). The high R 2 values for most cardiometabolic PRSs indicated that much of the variance in the PRSs could be explained by the PCs, although the R 2 values were smaller when European and African ancestry groups were considered separately. The PCs explained more of the PRS variance for participants of African ancestry than for participants of European ancestry, which suggests that there was more population stratification in the former subpopulation than in the latter, and that there may be confounding factors influencing some results. To overcome these biases, we accounted for PCs in both the PRS association analyses and the one-sample MR analyses. Additionally, we performed one-sample MR without adjusting for covariates (PCs and birth year) and compared the results with those of the adjusted MR analysis ( Supplementary Table 4 ). Chronology analyses We next asked whether genetic risk of cardiometabolic phenotypes affected the time at which female-specific health conditions developed. We found that participants in the younger age groups with high-risk cardiometabolic PRSs tended to have more health conditions than those with low-risk cardiometabolic PRSs (Fig. 4 and Supplementary Figs. 16, 17, 18, 19, and 20 ). In particular, pregnancy-related conditions were relatively more prevalent in participants < 25 years of age with high PRSs than low PRSs for all cardiometabolic PRSs. This difference was less pronounced in participants 25–40 years of age. Although many of the cardiometabolic PRSs did not affect most pregnancy-related conditions, high-risk PRSs that had an effect on pregnancy-related conditions tended to have larger effects in participants < 25 years of age than in participants 25–40 years of age. In addition, PRSs for high blood pressure tended to have greater effects than other PRSs on gestational hypertension and preeclampsia in the younger age groups (< 25 and 25–40 years). We observed similar trends for PRS T2D and gestational diabetes and for PRS BMI and PCOS. We also observed high prevalence of endometriosis and uterine fibrosis in participants with high-risk PRSs, particularly those 25–40 years of age. Discussion We observed genetic correlations between cardiometabolic traits and a variety of obstetric and gynecological disorders. Additionally, we showed that genome-wide PRSs for cardiometabolic traits have potential translational value for individuals with female-specific health conditions. The genetic correlations between cardiometabolic phenotypes and female-specific health conditions suggest a high degree of shared genetic etiology among all the phenotypes tested in this study. We investigated the effects of this shared genetic etiology by generating PRSs for cardiometabolic phenotypes and testing their associations with various female-specific health conditions. The PRSs were generally predictive of their corresponding cardiometabolic phenotypes as well as several female-specific health conditions. Prior research based mainly on non-genetic factors provided evidence for some of these associations, such those between BMI and PCOS and between T2D and gestational diabetes 15 , 16 . Epidemiological studies have also provided evidence of a positive correlation between obesity and endometrial cancer 40 . Our results suggest that certain relationships between cardiometabolic phenotypes and female-specific health conditions have a genetic basis. Furthermore, our MR results suggest that some of these relationships are causal. PRS CAD was inversely associated with breast cancer in individuals of European ancestry. CAD and breast cancer share many common risk factors, such as smoking and poor diet 41 ; however, our results indicated that a high genetic risk of CAD was protective against breast cancer, as participants with high PRS CAD had lower incidence of breast cancer than participants with low PRS CAD . When we considered only participants with high PRS CAD but no CAD diagnosis, we still saw a moderately reduced risk of breast cancer. Several factors might contribute to these results. First, the patterns might reflect ascertainment biases and competing risks. Individuals with high risk of CAD likely live shorter lives than individuals with low risk of CAD and are therefore less likely to be diagnosed with breast cancer during their lifetime. Second, treatments for CAD might protect against breast cancer. Third, genetic mechanisms that predispose individuals to CAD might have protective effects against breast cancer. Our results, together with those of another recent study that showed a protective effect of PRS CAD on breast cancer, suggest that more research should be undertaken to uncover the mechanisms behind this association 42 . The differences among the three-blood pressure related PRSs suggests that analysis of the genetic risk of all three blood pressure traits might improve the understanding of hypertension during pregnancy and elucidate interesting differences due to genetic burden in individuals of different ancestries. PRS DBP was significantly associated with gestational hypertension and preeclampsia in only participants of African ancestry, while only PRS SBP was significantly associated with gestational hypertension and preeclampsia in all participants and participants of European and African ancestry. Prior studies identified variability in predictive performance when different blood pressure measurements were used to predict hypertension and other diseases. Our results warrant further studies to replicate this association in other large multi-ancestry datasets to reach a more definite conclusion on the role blood pressure measurements play in predicting hypertensive diseases during pregnancy 43 , 44 . High genetic risks of most of the cardiometabolic phenotypes tended to increase the risk of certain health conditions at a young age, even in the absence of an overall association. For example, PRS BMI was not associated with gestational hypertension and ectopic pregnancy overall; however, in the youngest age group (< 25 years), these complications were more prevalent in participants with high PRS BMI than in participants with low PRS BMI . Similarly, there was no overall association between PRS SBP and PCOS or endometriosis, but participants who developed PCOS before 25 years of age or endometriosis at 25–40 years of age were more likely to have high PRS SBP than low PRS SBP . The difference in the prevalence of these conditions between participants with high-risk PRS and participants with low-risk PRS became smaller with increasing age. Patients with high genetic risk of cardiometabolic phenotypes may therefore have an elevated risk of early development of female-specific health conditions. These findings may help to prioritize patients for early screening. Our study establishes a link between the genetic risks of cardiometabolic phenotypes and several diseases that are unique to females of European and African ancestry; however, we estimated the genetic risk using PRS alone, which is based on common variants and does not include the effects of other rare variants and copy-number variations. In addition, we did not consider clinical or environmental factors, such as education level and socioeconomic status. We found that population stratification was potentially present in our cohorts, so we accounted for PCs in our analyses accordingly. Our focus on PRSs reflects the limitations of methods for multi-modal risk prediction. Current interest in building integrative risk models suggests that this gap can be closed in the near future 25 , 45 . Furthermore, the weaker performance of PRSs in individuals of African ancestry contributed to a lack of power to identify associations specific to those individuals and suggests a need for future studies to include more racially and ethnically diverse cohorts. The small sample size for some conditions may have limited the power of our one-sample MR analysis. Although our two-sample MR used GWASs with larger cohorts, these cohorts were primarily of European ancestry and therefore lacked the power to reveal causality in individuals of non-European ancestry. Furthermore, the causal effects identified in our MR analyses might be biased because of horizontal pleiotropy, in which genetic variants associated with cardiometabolic phenotypes affect other traits that in turn influence female-specific health conditions. We included methods such as MR Egger that account for pleiotropy and other confounding factors but may not have captured all their effects. EHRs are particularly advantageous for investigating disease trajectories and progression. Our analysis provided a visualization of the burden of early diagnosis of female-specific health conditions in individuals with high genetic risk of cardiometabolic phenotypes. However, these results might have been affected by ascertainment biases of EHRs and inclusion of confounding factors. This study illustrates the influence of genetic risk of cardiometabolic phenotypes on female-specific health conditions and highlight differences in the genetic predisposition among individuals of European and African ancestries. Our findings suggest that cardiometabolic PRSs may have clinical utility as non-modifiable risk factors for screening and early diagnosis of a variety of obstetric and gynecological conditions. To improve the power of cardiometabolic PRSs to predict risks of female-specific health conditions, future studies should incorporate PRSs with other genetic and non-genetic risk factors and study their effects on larger and more diverse multi-ancestry populations. Declarations Acknowledgements: We acknowledge the Penn Medicine BioBank (PMBB) for providing data and thank the patient-participants of Penn Medicine who consented to participate in this research program. We would also like to thank the Penn Medicine BioBank team and Regeneron Genetics Center for providing genetic variant data for analysis. The PMBB is approved under IRB protocol# 813913 and supported by Perelman School of Medicine at University of Pennsylvania. Sources of Funding: The eMERGE Network was initiated and funded by NHGRI through the following grants: Phase IV: U01HG011172 (Cincinnati Children’s Hospital Medical Center); U01HG011175 (Children’s Hospital of Philadelphia); U01HG008680 (Columbia University); U01HG011176 (Icahn School of Medicine at Mount Sinai); U01HG008685 (Mass General Brigham); U01HG006379 (Mayo Clinic); U01HG011169 (Northwestern University); U01HG011167 (University of Alabama at Birmingham); U01HG008657 (University of Washington Medical Center, Seattle); U01HG011181 (Vanderbilt University Medical Center); U01HG011166 (Vanderbilt University Medical Center serving as the Coordinating Center) Phase III: U01HG8657 (Kaiser Permanente Washington/University of Washington Medical Center); U01HG8685 (Brigham and Women’s Hospital); U01HG8672 (Vanderbilt University Medical Center); U01HG8666 (Cincinnati Children’s Hospital Medical Center); U01HG6379 (Mayo Clinic); U01HG8679 (Geisinger Clinic); U01HG8680 (Columbia University Health Sciences); U01HG8684 (Children’s Hospital of Philadelphia); U01HG8673 (Northwestern University); U01HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG8676 (Partners Healthcare/Broad Institute); and U01HG8664 (Baylor College of Medicine). 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Supplementary Files NatureCommunicationsSupplementaryTables.xlsx Supplementary Tables 1-4 NatureCommunicationsSupplementaryFigures.pdf Supplementary Figures 1-20 rs.pdf Reporting Summary Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2023 Read the published version in Journal of the American Heart Association → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1386628","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":90185200,"identity":"5d25c5f8-a502-4c36-ba14-95689cc09aa3","order_by":0,"name":"Brenda Xiao","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Brenda","middleName":"","lastName":"Xiao","suffix":""},{"id":90185201,"identity":"1ef426fa-5076-4ff5-884b-5f99938c848e","order_by":1,"name":"Digna Velez Edwards","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Digna","middleName":"Velez","lastName":"Edwards","suffix":""},{"id":90185202,"identity":"f1ae4142-4bff-4078-941a-828fa6e7833d","order_by":2,"name":"Anastasia Lucas","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anastasia","middleName":"","lastName":"Lucas","suffix":""},{"id":90185203,"identity":"ead7029f-df05-4677-817b-746be232f93d","order_by":3,"name":"Theodore Drivas","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Theodore","middleName":"","lastName":"Drivas","suffix":""},{"id":90185204,"identity":"3e6767b0-3400-4dca-a4fe-e163f4d17072","order_by":4,"name":"Kathryn Gray","email":"","orcid":"","institution":"Brigham and Women's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kathryn","middleName":"","lastName":"Gray","suffix":""},{"id":90185205,"identity":"58cb5291-7974-48b7-a686-c24ab4e8cf96","order_by":5,"name":"Brendan Keating","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Brendan","middleName":"","lastName":"Keating","suffix":""},{"id":90185206,"identity":"292350dd-5bff-407c-a98c-445dd1f4868d","order_by":6,"name":"Chunhua Weng","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Weng","suffix":""},{"id":90185207,"identity":"7b55c41d-f7b1-4b78-a136-ab6c01c56a93","order_by":7,"name":"Gail Jarvik","email":"","orcid":"https://orcid.org/0000-0002-6710-8708","institution":"University of Washington","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gail","middleName":"","lastName":"Jarvik","suffix":""},{"id":90185208,"identity":"987ec84d-c71b-44d5-8563-40ce4ca57f74","order_by":8,"name":"Hakon Hakonarson","email":"","orcid":"","institution":"Center for Applied Genomics, Children's Hospital of Philadelphia, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvana","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hakon","middleName":"","lastName":"Hakonarson","suffix":""},{"id":90185209,"identity":"32bc9916-2a26-40c9-968c-8b9981b85ec2","order_by":9,"name":"Leah Kottyan","email":"","orcid":"https://orcid.org/0000-0003-3979-2220","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Leah","middleName":"","lastName":"Kottyan","suffix":""},{"id":90185210,"identity":"d1fe8ceb-52a0-40b8-b95b-f3eb16c50e65","order_by":10,"name":"Noémie Elhadad","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Noémie","middleName":"","lastName":"Elhadad","suffix":""},{"id":90185211,"identity":"fd30d790-a2d0-4cc3-b6a5-bf2dbbcb679e","order_by":11,"name":"Wei-Qi Wei","email":"","orcid":"https://orcid.org/0000-0003-4985-056X","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei-Qi","middleName":"","lastName":"Wei","suffix":""},{"id":90185212,"identity":"fb2edd88-b1de-4915-9d12-103306bb5c36","order_by":12,"name":"Yuan Luo","email":"","orcid":"https://orcid.org/0000-0003-0195-7456","institution":"Northwestern University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Luo","suffix":""},{"id":90185213,"identity":"36be4699-c97c-4d85-97d3-12a12845f314","order_by":13,"name":"Dokyoon Kim","email":"","orcid":"https://orcid.org/0000-0002-4592-9564","institution":"University of Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dokyoon","middleName":"","lastName":"Kim","suffix":""},{"id":90185214,"identity":"e86a561b-e1e3-4b8a-87e5-495e9a238f19","order_by":14,"name":"Marylyn Ritchie","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marylyn","middleName":"","lastName":"Ritchie","suffix":""},{"id":90185215,"identity":"49c65873-2084-4b3d-98dd-07b7612baa96","order_by":15,"name":"Shefali Verma","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5216-4670","institution":"University of Pennsylvania","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shefali","middleName":"","lastName":"Verma","suffix":""}],"badges":[],"createdAt":"2022-02-22 22:10:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1386628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1386628/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1161/JAHA.121.026561","type":"published","date":"2023-03-07T11:45:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":19262235,"identity":"bd7285b8-9714-4310-b0f8-002793cb99c7","added_by":"auto","created_at":"2022-03-15 20:03:17","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4205845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic correlation and the influence of shared genetic burden of cardiometabolic traits and health conditions unique to females. \u003c/strong\u003ePanel A shows a heatmap of genetic correlations from cross-trait LDSC analyses: Blue represents negative correlation and red represents positive correlation. The asterisk in each box indicates statistical significance of results. Panels B, C, and D show results of PRS-based meta-analyses between cardiometabolic PRSs and case/control status of female-specific diseases overall (Panel B) and in participants of European (Panel C) and African (Panel D) ancestry, respectively. The first panel in these plots corresponds to raw, unadjusted p-values, and the second panel shows beta estimates. The color of each point refers to the PRS for cardiometabolic traits. The red line corresponds to the p = 0.01 threshold.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/4059847d65170acb8693ec57.jpeg"},{"id":19262231,"identity":"5111ca0f-f875-4c4c-b9b6-aa780db399f0","added_by":"auto","created_at":"2022-03-15 20:03:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2141934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInverse relationship between coronary artery disease (CAD) and breast cancer. \u003c/strong\u003ePanel A shows a heatmap of negative genetic correlations between CAD and breast cancer from the UKBB Genetic Correlation Browser dataset. The gradient of color shows positive (red) to negative (blue) correlations, and the text in each box gives the genetic correlation coefficient. Panel B shows the distribution of breast cancer per each PRS\u003csub\u003eCAD\u003c/sub\u003e quintile. The x-axis represents each PRS quintile, and the y-axis represents the disease prevalence. The color of each point refers to the ancestry group (All, European, and African), and the shape indicates the target dataset (eMERGE [circle] or PMBB [triangle]). Panel C shows the effect estimates of the genome-wide significant GWAS SNPs in the CAD GWAS and the corresponding effect estimates of the same SNPs in the breast cancer GWAS. The blue line is the line of best fit from linear regression, and the shaded area around the line is the 95% confidence interval. Panel D is a Venn diagram representing the overlap of CAD and breast cancer cases in the PMBB and eMERGE datasets.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/b94bef0cfa0673f937113cb6.jpeg"},{"id":19262236,"identity":"abcd685e-8c8c-4a19-958c-2b59e4178306","added_by":"auto","created_at":"2022-03-15 20:03:17","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3724799,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendelian randomization (MR) results for 30 significant PRS-based associations. \u003c/strong\u003eForest plots show associations between\u003cstrong\u003e \u003c/strong\u003efemale health conditions as outcomes and cardiometabolic phenotypes as exposures in a one-sample (A) and two-sample (B) MR analyses. Genetic instruments are the cardiometabolic PRS in the one-sample MR and genome-wide significant SNPs from GWAS in the two-sample MR. In panel A, each point refers to beta outcome/SD exposure for PRS\u003csub\u003eBMI\u003c/sub\u003e and log(OR) outcome for all other exposures for each test performed as separated by ancestry. In panel B, each point refers to beta outcome/SD exposure for BMI and beta outcome/SD log(OR) exposure for all other variables across all methods used in sensitivity analyses for two-sample MR. P-values are reported in the last column in both panels.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/890e32c2d11d547793739e9a.jpeg"},{"id":19262616,"identity":"cc3fa448-1252-412a-8a61-ccbfead163b8","added_by":"auto","created_at":"2022-03-15 20:06:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":481097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChronology analyses of events from the electronic health records. \u003c/strong\u003eA circular plot shows disease prevalence among participans with high PRS\u003csub\u003eBMI\u003c/sub\u003e (in yellow) and low PRS\u003csub\u003eBMI\u003c/sub\u003e (in blue). General female health conditions are shown in panel A, and pregnancy and childbirth-related phenotypes are shown in panel B. The circular plots are divided into five age categories (\u0026lt;25, 25–40, 40–55, 55–70, and \u0026gt;70) for general female health conditions and three age categories (\u0026lt;25, 25–40, and 40–55) for pregnancy-related phenotypes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/8632d739c38e75837b97918c.png"},{"id":56376912,"identity":"61e584a7-bb5b-41d0-b740-0b66cde4a904","added_by":"auto","created_at":"2024-05-13 11:48:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2023840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/e3a173e3-b130-4109-b2e1-7167aa7f1607.pdf"},{"id":19262232,"identity":"df79a760-1816-461d-a412-7c441e62b9b2","added_by":"auto","created_at":"2022-03-15 20:03:17","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":52597,"visible":true,"origin":"","legend":"Supplementary Tables 1-4","description":"","filename":"NatureCommunicationsSupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/f696965af2aee99d2e0f6030.xlsx"},{"id":19262237,"identity":"1bd3e5fd-6ef2-4bd9-986e-5a618b48c010","added_by":"auto","created_at":"2022-03-15 20:03:18","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20300023,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figures 1-20\u003c/p\u003e","description":"","filename":"NatureCommunicationsSupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/2e7442f1469cf7ab1557848a.pdf"},{"id":19262234,"identity":"304b5cc3-abf9-4f05-9a58-0d10353bb584","added_by":"auto","created_at":"2022-03-15 20:03:17","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":333926,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"rs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1386628/v1/25ae3698c10122e7bfc4d302.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Inference of causal relationships between genetic risk factors for cardiometabolic phenotypes and female-specific health conditions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiometabolic diseases such as coronary artery disease (CAD), obesity, hypertension, and type 2 diabetes (T2D) are among the leading causes of death in the world and are highly comorbid\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Many studies have shown that the pathophysiology of cardiometabolic diseases affects males and females differently\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Although cardiometabolic diseases have many sequelae that affect both sexes, including depression, anxiety, chronic obstructive pulmonary disease (COPD), and cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, they disproportionately affect females because of their links to female-specific health conditions, such as preeclampsia, gestational diabetes, stillbirth, and pregnancy loss\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Multiple lines of evidence link female health conditions to cardiometabolic diseases. Individuals who develop preeclampsia during pregnancy are more likely to develop cardiovascular diseases and hypertension after pregnancy\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Obesity is closely linked to polycystic ovarian syndrome (PCOS), and individuals with PCOS are at high risk of developing T2D\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Individuals with endometriosis are at high risk of developing various cancers and cardiovascular diseases such as myocardial infarction and ischemic heart disease\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Despite this evidence, the relationship between female-specific health conditions and cardiometabolic phenotypes, and particularly the potential for shared genetic risk, is understudied. Investigation of shared genetic risks between cardiometabolic diseases and female-specific health conditions has the potential to reveal genetic cluster-based risk factors that can be used to improve screening practices for many diseases in high-risk patients.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) have exposed common genetic etiologies among diseases such as T2D, obesity, sleep apnea, hypertension, Alzheimer\u0026rsquo;s disease, and many types of cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. There is evidence of impacts on cardiometabolic phenotypes for more than 1,000 different loci, but the effect size of any single variant is generally minimal. To more accurately estimate an individual\u0026rsquo;s overall risk of disease, researchers have used GWAS results to calculate polygenic risk scores (PRSs), which sum the effects of common single-nucleotide polymorphisms (SNPs) on a given phenotype\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and can be used to predict the risks of many cardiometabolic diseases and comorbidities\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo investigate the effects of genetic risk of cardiometabolic phenotypes on female health conditions, we measured PRSs for cardiometabolic phenotypes and then determined their correlations with female-specific health conditions documented in electronic health records (EHRs). Prior studies successfully used PRSs as genetic risk factors linking multiple adverse phenotypes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. PRS-based association tests have the advantage that they are based on unvarying risk factors (i.e., inherited genetic burden), and they make fewer assumptions than association tests based on individual genetic factors. We hypothesized that the genetic risks of adverse cardiometabolic phenotypes can explain a significant portion of phenotypic variance in female-specific health conditions. To test this hypothesis, we obtained genotypic and phenotypic data on a wide range of female-specific health conditions from the Penn Medicine BioBank (PMBB) and the Electronic Medical Records and Genomics (eMERGE) network. We first measured pairwise genetic correlations between cardiometabolic phenotypes and female-specific health conditions to determine whether there was a common genetic basis. We then estimated the significance of associations between the genetic risks of adverse cardiometabolic phenotypes and female-specific health conditions. In addition, we evaluated potential causal relationships between genetic risk scores for cardiometabolic phenotypes and female-specific health conditions using Mendelian randomization (MR) approaches. Furthermore, because EHR data provide an opportunity to map disease prevalence by age, we generated a chronological map of female-specific health conditions in individuals with high and low genetic risk scores to understand age-specific prevalence in high-risk and low-risk individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe authors have individual-level access to genotype data and medical records from the PMBB and the eMERGE network datasets.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy populations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003ePenn Medicine BioBank\u003c/strong\u003e \u003cp\u003e The PMBB is a University of Pennsylvania academic biobank that recruits patient participants from the University of Pennsylvania Health System surrounding the greater Philadelphia area in the United States. PMBB links patient genotype data with detailed EHR information. Currently, PMBB consists of genome-wide and whole-exome sequence data of approximately 45,000 samples. The PMBB cohort is diverse, with over 25% of its participants being of African ancestry. PMBB genotype data were imputed to the TOPMED Reference panel using the Michigan Imputation server. We included 21,837 female participants from PMBB in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The stratified analyses in this study included only participants of European or African ancestry, while excluding those of Asian or Hispanic ancestry because of sample size limitations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample sizes in the PMBB and eMERGE datasets overall and stratified by ancestry.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePMBB Sample Size\u003c/p\u003e \u003cp\u003e(N Cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eeMERGE Sample Size\u003c/p\u003e \u003cp\u003e(N Cases)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAfrican\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAfrican\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCardiometabolic Phenotypes\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(3,002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(1,956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(9,597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(7,953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(1,515)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(10,278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(5,640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(4,286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(27,685)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(21,883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(5,265)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(4,388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(1,969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(2,221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(12,403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(8,999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(3,090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eFemale Health Phenotypes\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(1,621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(1,621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(4,148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(3,639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(443)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEctopic\u003c/p\u003e \u003cp\u003epregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,808\u003c/p\u003e \u003cp\u003e(1,779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,201\u003c/p\u003e \u003cp\u003e(827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,319\u003c/p\u003e \u003cp\u003e(757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,078\u003c/p\u003e \u003cp\u003e(823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,975\u003c/p\u003e \u003cp\u003e(570)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e641\u003c/p\u003e \u003cp\u003e(172)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial\u003c/p\u003e \u003cp\u003ecancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(701)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(2,314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(1,891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(354)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive fetal\u003c/p\u003e \u003cp\u003egrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e693\u003c/p\u003e \u003cp\u003e(85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003cp\u003e(37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e329\u003c/p\u003e \u003cp\u003e(42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,433\u003c/p\u003e \u003cp\u003e(627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,618\u003c/p\u003e \u003cp\u003e(422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e437\u003c/p\u003e \u003cp\u003e(162)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational\u003c/p\u003e \u003cp\u003ediabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,655\u003c/p\u003e \u003cp\u003e(523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,111\u003c/p\u003e \u003cp\u003e(193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,262\u003c/p\u003e \u003cp\u003e(248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,174\u003c/p\u003e \u003cp\u003e(762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,005\u003c/p\u003e \u003cp\u003e(445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e719\u003c/p\u003e \u003cp\u003e(251)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational\u003c/p\u003e \u003cp\u003ehypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,666\u003c/p\u003e \u003cp\u003e(631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,118\u003c/p\u003e \u003cp\u003e(256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,275\u003c/p\u003e \u003cp\u003e(324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,135\u003c/p\u003e \u003cp\u003e(703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,980\u003c/p\u003e \u003cp\u003e(411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e711\u003c/p\u003e \u003cp\u003e(253)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrauterine\u003c/p\u003e \u003cp\u003edeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e685\u003c/p\u003e \u003cp\u003e(57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289\u003c/p\u003e \u003cp\u003e(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324\u003c/p\u003e \u003cp\u003e(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,156\u003c/p\u003e \u003cp\u003e(64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,438\u003c/p\u003e \u003cp\u003e(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e358\u003c/p\u003e \u003cp\u003e(19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiscarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,934\u003c/p\u003e \u003cp\u003e(389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,273\u003c/p\u003e \u003cp\u003e(199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,360\u003c/p\u003e \u003cp\u003e(151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,568\u003c/p\u003e \u003cp\u003e(823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,323\u003c/p\u003e \u003cp\u003e(577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e740\u003c/p\u003e \u003cp\u003e(200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(1,589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(1,359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(197)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003cp\u003eabruption/previa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,192\u003c/p\u003e \u003cp\u003e(396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e482\u003c/p\u003e \u003cp\u003e(157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e586\u003c/p\u003e \u003cp\u003e(195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,674\u003c/p\u003e \u003cp\u003e(997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,697\u003c/p\u003e \u003cp\u003e(672)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e583\u003c/p\u003e \u003cp\u003e(262)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolycystic\u003c/p\u003e \u003cp\u003eovarian\u003c/p\u003e \u003cp\u003esyndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(1,006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(209)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor fetal\u003c/p\u003e \u003cp\u003egrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e792\u003c/p\u003e \u003cp\u003e(202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325\u003c/p\u003e \u003cp\u003e(78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e388\u003c/p\u003e \u003cp\u003e(107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,209\u003c/p\u003e \u003cp\u003e(167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,478\u003c/p\u003e \u003cp\u003e(115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e360\u003c/p\u003e \u003cp\u003e(27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostpartum\u003c/p\u003e \u003cp\u003edepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e924\u003c/p\u003e \u003cp\u003e(384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e374\u003c/p\u003e \u003cp\u003e(136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e466\u003c/p\u003e \u003cp\u003e(226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,417\u003c/p\u003e \u003cp\u003e(555)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,579\u003c/p\u003e \u003cp\u003e(349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e457\u003c/p\u003e \u003cp\u003e(177)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostpartum\u003c/p\u003e \u003cp\u003ehemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e846\u003c/p\u003e \u003cp\u003e(283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003cp\u003e(108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408\u003c/p\u003e \u003cp\u003e(146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,320\u003c/p\u003e \u003cp\u003e(401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,544\u003c/p\u003e \u003cp\u003e(261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e408\u003c/p\u003e \u003cp\u003e(109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,631\u003c/p\u003e \u003cp\u003e(452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,100\u003c/p\u003e \u003cp\u003e(149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,264\u003c/p\u003e \u003cp\u003e(272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,132\u003c/p\u003e \u003cp\u003e(702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,974\u003c/p\u003e \u003cp\u003e(406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e713\u003c/p\u003e \u003cp\u003e(249)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreterm birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e687\u003c/p\u003e \u003cp\u003e(66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284\u003c/p\u003e \u003cp\u003e(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332\u003c/p\u003e \u003cp\u003e(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,144\u003c/p\u003e \u003cp\u003e(57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,433\u003c/p\u003e \u003cp\u003e(35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e350\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e649\u003c/p\u003e \u003cp\u003e(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e311\u003c/p\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,123\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,417\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e347\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine fibroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(1,570)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(5,711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(4,103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(1,455)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVulvar cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003cp\u003e(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003cp\u003e(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003cp\u003e(120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003cp\u003e(97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003cp\u003e(21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eElectronic Medical Records and Genomics\u003c/strong\u003e \u003cp\u003eThe eMERGE network is a nationwide consortium that combines genome-wide sequence data with EHRs from several health systems across the United States, with most participants coming from the Geisinger Health System and Vanderbilt University. eMERGE data were imputed to the Haplotype Reference Consortium panel using the Michigan Imputation Server. Like the PMBB cohort, the eMERGE cohort is diverse across ancestries (~\u0026thinsp;20% samples of non-European ancestry) and ages. This study included data from 49,171 female patients in the eMERGE network born after 2001(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Low sample sizes of participants of Asian and Hispanic ancestry also limited ancestry-stratified analyses to include only those of European and African ancestry.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGWAS of cardiometabolic phenotypes\u003c/h2\u003e \u003cp\u003eTo determine genetic correlations and calculate PRSs, we collected quantitative data on the effects of genetic variants on cardiometabolic phenotypes from several large GWAS. Given the diverse nature of our study population, we obtained the largest publicly available multi-ancestry GWAS summary statistics for four cardiometabolic phenotypes: obesity (measured as body mass index [BMI]), CAD, hypertension (measured as diastolic blood pressure [DBP], systolic blood pressure [SBP], and pulse pressure [PP]), and T2D. Summary statistics and source studies for each phenotype are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGWAS datasets used to calculate genetic correlations and polygenic risk scores and the number of SNPs used from each GWAS to calculate the corresponding PRS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAncestries Included\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample Size (N Cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN SNPs included in PRS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR, AFR, EAS, SAS, HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVujkovic et al., Nat Gen, 2020, Million Veterans Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,407,282 (228,499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32541925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePMBB: 1,023,697\u003c/p\u003e \u003cp\u003eeMERGE: 716,330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR, AFR, EAS, SAS, HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJustice et al., Nat Com, 2017, GIANT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241,258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28443625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePMBB: 885,143\u003c/p\u003e \u003cp\u003eeMERGE: 614,668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (DBP, SBP, PP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR, AFR, EAS, SAS, HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGiri et al., Nat Gen, 2019, Million Veterans Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318,891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30578418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePMBB: 1,024,567\u003c/p\u003e \u003cp\u003eeMERGE: 715,471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary Artery Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR, EAS, SAS, HIS, AFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evan der Harst et al., Circ Res, 2018, CARDIoGRAMplusC4D\u0026thinsp;+\u0026thinsp;UKBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e547,261 (122,733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29212778 ​​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePMBB: 981,480\u003c/p\u003e \u003cp\u003eeMERGE: 681,029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**DBP\u0026thinsp;=\u0026thinsp;Diastolic Blood Pressure; SBP\u0026thinsp;=\u0026thinsp;Systolic Blood Pressure; PP\u0026thinsp;=\u0026thinsp;Pulse Pressure; EUR\u0026thinsp;=\u0026thinsp;European ancestry; EAS\u0026thinsp;=\u0026thinsp;East Asian ancestry; SAS\u0026thinsp;=\u0026thinsp;South Asian ancestry; HIS\u0026thinsp;=\u0026thinsp;Hispanic ancestry; AFR\u0026thinsp;=\u0026thinsp;African ancestry; MVP\u0026thinsp;=\u0026thinsp;Million Veterans Program; GIANT\u0026thinsp;=\u0026thinsp;The Genetic Investigation of ANthropometric Traits; UKB\u0026thinsp;=\u0026thinsp;UK BioBank\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eGenome-wide associations of female-specific health conditions\u003c/h2\u003e \u003cp\u003eLarge multi-ancestry GWAS are not available for most of the female-specific health conditions evaluated in this study. Therefore, we used PLINK version 1.90 to conduct GWAS of female-specific health conditions documented in the PMBB and eMERGE datasets\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. We filtered the genetic variants in the PMBB and eMERGE imputed datasets to include only variants with imputation quality R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.3 and minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.01. We then conducted a meta-analysis of the GWAS of the two cohorts using PLINK.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlation calculation\u003c/h2\u003e \u003cp\u003eWe calculated pairwise genetic correlations between cardiometabolic phenotypes and female-specific health conditions using linkage disequilibrium score regression (LDSC)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. LDSC accounts for linkage disequilibrium (LD) among SNPs by using an external reference panel that should match the ancestry distribution of the corresponding GWAS. We generated a multi-ancestry LD reference panel using the HapMap3 SNPs (~\u0026thinsp;1 M common variants) from the entire 1000 Genomes population\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePolygenic risk scores\u003c/h2\u003e \u003cp\u003ePRSs calculated using a GWAS of one particular ancestry group tend to perform poorly when applied to individuals from a different ancestry group\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. To accurately calculate PRSs for our diverse cohorts, we calculated PRSs for the cardiometabolic phenotypes using the same publicly available GWAS used in the genetic correlation analyses. Weights for each SNP were calculated using PRS-CS (version from Apr 24, 2020)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Like LDSC, PRS-CS requires a reference panel that matches the ancestry distribution of the target dataset. We generated a multi-ancestry LD reference panel using the HapMap SNPs from the 1000 Genomes populations. We then used PLINK (v1.90) to identify LD blocks and calculate the LD between the SNPs in each block. For PRS-CS, the global shrinkage parameter phi was fixed to 0.01, and default values were selected for all other parameters. PRSs were then calculated using the weights with PLINK. Only the SNPs in the target dataset, summary statistics, and LD reference panel were included in the PRSs. The numbers of SNPs used for each PRS calculation are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The scores were then normalized to obtain meaningful beta coefficients.\u003c/p\u003e \u003cp\u003eTo evaluate the power of the PRSs, we tested their performance in prediction of the corresponding primary phenotypes in the summary statistics. We could not obtain quantitative measurements of blood pressure for participants in the eMERGE cohort, and PP and SBP measurements were not curated in the PMBB cohort. Therefore, we evaluated the performance of the PRSs for blood pressure traits (SP, DBP, PP) in the eMERGE cohort based on hypertension case-control phenotypes, and we evaluated the performance of the PRS blood pressure traits in the PMBB cohort based on DBP or hypertension (for SBP and PP) as outcomes. We constructed logistic regression models for binary phenotypes (CAD, T2D, and hypertension) and evaluated PRS performance by the area under the receiver-operator curves (AUCs) using the \u003cem\u003epROC\u003c/em\u003e package in R. Similarly, we constructed linear regression models for continuous phenotypes (BMI and DBP) and evaluated them by the R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e using the \u003cem\u003eglm\u003c/em\u003e function in R. The regression models used birth year and the first five principal components (PCs) as covariates. We tested PRS performance overall as well as for subgroups of European or African ancestry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhenotype data\u003c/h2\u003e \u003cp\u003eCases and controls for each phenotype were defined using International Classification of Disease (ICD) diagnosis codes. Participants were coded as cases of a given phenotype if their records contained at least one of the corresponding ICD codes. For pregnancy-related phenotypes, participants were only considered controls if their records had at least one pregnancy-related ICD code and no ICD codes for relevant complications (such as miscarriage) during pregnancy. Participants were counted as controls for cardiometabolic phenotypes and all non-pregnancy related health conditions if their records did not contain any relevant ICD code. The complete list of ICD codes used to include or exclude participants as cases and controls can be found in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Using these definitions, we determined the sample size for each phenotype in the eMERGE and PMBB cohorts (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePRS association analyses\u003c/h2\u003e \u003cp\u003eWe tested the association between each cardiometabolic PRS and female-specific health condition by fitting separate logistic regression models adjusted by birth year and the first five PCs. We conducted this analysis for all participants and for subsets of participants of European or African ancestry. To account for biases from multiple hypothesis testing, we determined if associations passed a false discovery rate significance threshold of 0.05, adjusting the p values with the number of female-specific health conditions (23) tested. The logistic regressions were performed using the \u003cem\u003eglm\u003c/em\u003e function in R. The results from the PMBB and eMERGE cohorts were combined using the \u003cem\u003erma\u003c/em\u003e function from the \u003cem\u003emetafor\u003c/em\u003e R package under the restricted maximum-likelihood estimator model\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. We used PheWAS-View to visualize the results\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. We then created prevalence plots for each significant association. The participants were divided into quintiles based on the PRS for each condition, and the percentage of cases was calculated for each quintile.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization\u003c/h2\u003e \u003cp\u003eTo identify evidence of causality between cardiometabolic phenotypes and female-specific health conditions, we performed one-sample MR for 29 significant associations from the PRS analyses using the \u003cem\u003eivreg\u003c/em\u003e function in the \u003cem\u003eivpack\u003c/em\u003e R package. Cardiometabolic PRSs were used as genetic instruments, with birth year and the first five PCs included as covariates. The results were then combined in a meta-analysis using the \u003cem\u003emetafor\u003c/em\u003e R package. Because the small sample size for some of the health conditions could limit the power of the analysis, we also performed two-sample MR for the same associations using the inverse variance weighted (IVW) method in the \u003cem\u003etwoSampleMR\u003c/em\u003e package in R\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. MR sensitivity analyses were conducted using the weighted median and MR Egger methods in the same package. The genetic instruments for the cardiometabolic phenotypes in the two-sample MR were the significant SNPs in the GWAS summary statistics used to calculate the PRSs. The SNPs were pruned according to LD patterns among the 1000 Genomes HapMap SNPs, and the remaining representative SNPs were included in the analysis. Matching genetic instruments for the female-specific health conditions were obtained from publicly available GWAS through the \u003cem\u003etwoSampleMR\u003c/em\u003e package (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Since these GWAS for female-specific health conditions were conducted in only European ancestry populations, we performed two-sample MR for only associations that were significant in all participants or participants of European ancestry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eChronology analyses\u003c/h2\u003e \u003cp\u003e We divided the participants into high-risk and low-risk groups according to each cardiometabolic PRS. High PRS was defined as the top quintile (\u0026gt;\u0026thinsp;80th percentile), and low PRS was defined as the bottom quintile (\u0026lt;\u0026thinsp;20th percentile). We considered age at the first occurrence of each female-specific health condition from the ICD records. For pregnancy-related conditions, the participants were split into three age groups: \u0026lt;25, 25\u0026ndash;40, and 40\u0026ndash;55 years. We excluded participants who were over 55 years of age at the first occurrence of a pregnancy-related condition because of low sample sizes and potential errors in diagnosis coding. For all other conditions, the participants were split into five age groups: \u0026lt;25, 25\u0026ndash;40, 40\u0026ndash;55, 55\u0026ndash;70, and \u0026gt;\u0026thinsp;70 years. We then examined the combined case prevalence of each health condition within the high and low PRS groups in each cohort across all age groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlations among cardiometabolic phenotypes and female-specific health conditions\u003c/h2\u003e \u003cp\u003eWe found thirteen statistically significant correlations between the cardiometabolic phenotypes and six female-specific health conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). BMI was correlated with breast cancer (R\u003csub\u003eg\u003c/sub\u003e=-0.179, p\u0026thinsp;=\u0026thinsp;0.011) and PCOS (R\u003csub\u003eg\u003c/sub\u003e=0.4, p\u0026thinsp;=\u0026thinsp;0.007); CAD with breast cancer (R\u003csub\u003eg\u003c/sub\u003e=-0.199, p\u0026thinsp;=\u0026thinsp;0.011), PCOS (R\u003csub\u003eg\u003c/sub\u003e=0.216, p\u0026thinsp;=\u0026thinsp;0.04), and postpartum depression (R\u003csub\u003eg\u003c/sub\u003e=0.204, p\u0026thinsp;=\u0026thinsp;0.025); PP with gestational hypertension (R\u003csub\u003eg\u003c/sub\u003e=0.481, p\u0026thinsp;=\u0026thinsp;0.0025); SBP with breast cancer (R\u003csub\u003eg\u003c/sub\u003e=-0.153, p\u0026thinsp;=\u0026thinsp;0.041) and gestational hypertension (R\u003csub\u003eg\u003c/sub\u003e=0.523, p\u0026thinsp;=\u0026thinsp;0.0017); and T2D with breast cancer (R\u003csub\u003eg\u003c/sub\u003e=-0.196, p\u0026thinsp;=\u0026thinsp;0.0001), excessive fetal growth (R\u003csub\u003eg\u003c/sub\u003e=0.126, p\u0026thinsp;=\u0026thinsp;0.044), gestational diabetes (R\u003csub\u003eg\u003c/sub\u003e=0.529, p\u0026thinsp;=\u0026thinsp;0.011), gestational hypertension (R\u003csub\u003eg\u003c/sub\u003e=0.237, p\u0026thinsp;=\u0026thinsp;0.028), and PCOS (R\u003csub\u003eg\u003c/sub\u003e=0.316, p\u0026thinsp;=\u0026thinsp;0.0093).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePRS performance for predicting primary phenotypes\u003c/h2\u003e \u003cp\u003eWe calculated a PRS for each cardiometabolic phenotype and confirmed the distributions of the raw and normalized scores (\u003cb\u003eSupplementary Figs.\u0026nbsp;1, 2, 3, 4, 5, and 6\u003c/b\u003e). The full model for all PRSs generally performed well in predicting the corresponding primary phenotypes across ancestry groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The covariate-only (null) model performed better for participants of African ancestry than for participants of European ancestry, whereas the PRS-only model performed better for participants of European ancestry than for participants of African ancestry. We calculated the difference between the full and null models for all PRSs in each cohort and found that the PRSs improved the predictive performance more for participants of European ancestry than for participants of African ancestry (p\u0026thinsp;=\u0026thinsp;0.000488, Wilcoxon signed rank test). Thus, the cardiometabolic PRSs were more accurate in individuals with European ancestry than in individuals with African ancestry, but they improved predictive models in both groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect estimates for tests of association of PRS with primary phenotype in the eMERGE and PMBB datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAncestry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e or AUC of Full Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e or AUC of Null Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e or AUC of PRS-only Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eeMERGE\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39,338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.42E-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.62E-121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.65E-106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (Hypertension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.17E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21,883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.34E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP (Hypertension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.15E-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21,883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.23E-32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (Hypertension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.86E-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21,883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1E-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.42E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.02E-240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.05E-212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.49E-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePMBB\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.05E-222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.1417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.66E-193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.75E-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.73E-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.44E-46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.60E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.05E-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.01E-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP (Hypertension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.18E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.53E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (Hypertension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.08E-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11E-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.81E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.49E-118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.55E-77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.11E-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e**BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; CAD\u0026thinsp;=\u0026thinsp;Coronary artery disease, T2D\u0026thinsp;=\u0026thinsp;Type 2 Diabetes; DBP\u0026thinsp;=\u0026thinsp;Diastolic Blood Pressure; SBP\u0026thinsp;=\u0026thinsp;Systolic Blood Pressure; PP\u0026thinsp;=\u0026thinsp;Pulse Pressure; EUR\u0026thinsp;=\u0026thinsp;European ancestry; AFR\u0026thinsp;=\u0026thinsp;African ancestry; Beta coefficients and standard errors are per SD PRS in the full model. Covariates included: birth year and the first five PCs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between cardiometabolic risks and female-specific health conditions\u003c/h2\u003e \u003cp\u003eWe detected numerous associations between cardiometabolic PRSs and female health conditions in the meta-analysis of both cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Twenty-nine associations were statistically significant after correction for multiple hypothesis testing (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the meta-analysis, for all participants and participants of European ancestry, PRS\u003csub\u003eBMI\u003c/sub\u003e was associated with endometrial cancer (beta\u003csub\u003eall\u003c/sub\u003e=0.24, se\u003csub\u003eall\u003c/sub\u003e=0.046, p\u003csub\u003eall\u003c/sub\u003e=9.4\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=0.28, se\u003csub\u003eeur\u003c/sub\u003e=0.083, p\u003csub\u003eeur\u003c/sub\u003e=0.00075), gestational diabetes (beta\u003csub\u003eall\u003c/sub\u003e=0.23, se\u003csub\u003eall\u003c/sub\u003e=0.051, p\u003csub\u003eall\u003c/sub\u003e=6\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=0.29, se\u003csub\u003eeur\u003c/sub\u003e=0.061, p\u003csub\u003eeur\u003c/sub\u003e=2.6\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and PCOS (beta\u003csub\u003eall\u003c/sub\u003e=0.27, se\u003csub\u003eall\u003c/sub\u003e=0.039, p\u003csub\u003eall\u003c/sub\u003e=2.4\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=0.28, se\u003csub\u003eeur\u003c/sub\u003e=0.044, p\u003csub\u003eeur\u003c/sub\u003e=2.1\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e). PRS\u003csub\u003eBMI\u003c/sub\u003e was also associated with breast cancer in all participants (beta\u003csub\u003eall\u003c/sub\u003e=-0.071, se\u003csub\u003eall\u003c/sub\u003e=0.022, p\u003csub\u003eall\u003c/sub\u003e=0.0016). These results suggest that an increased genetic risk of obesity heightens the risks of endometrial cancer, gestational diabetes, and PCOS but decreases the risk of breast cancer. PRS\u003csub\u003eCAD\u003c/sub\u003e was negatively associated with breast cancer for all participants and participants of European ancestry (beta\u003csub\u003eall\u003c/sub\u003e=-0.072, se\u003csub\u003eall\u003c/sub\u003e=0.015, p\u003csub\u003eall\u003c/sub\u003e=1\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=-0.071, se\u003csub\u003eall\u003c/sub\u003e=0.016, p\u003csub\u003eeur\u003c/sub\u003e=5.3\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). This finding suggests that individuals, particularly those of European ancestry, with high PRS\u003csub\u003eCAD\u003c/sub\u003e are at relatively low risk of breast cancer compared with individuals with low PRS\u003csub\u003eCAD\u003c/sub\u003e. PRS\u003csub\u003eT2D\u003c/sub\u003e was significantly associated with gestational diabetes in all participants and participants of European ancestry (beta\u003csub\u003eall\u003c/sub\u003e=0.59, se\u003csub\u003eall\u003c/sub\u003e=0.063, p\u003csub\u003eall\u003c/sub\u003e=1.2\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=0.67, se\u003csub\u003eeur\u003c/sub\u003e=0.075, p\u003csub\u003eeur\u003c/sub\u003e=2.1\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e) and with PCOS in all participants and participants of European ancestry (beta\u003csub\u003eall\u003c/sub\u003e=0.22, se\u003csub\u003eall\u003c/sub\u003e=0.043, p\u003csub\u003eall\u003c/sub\u003e=1.9\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=0.21, se\u003csub\u003eeur\u003c/sub\u003e=0.048, p\u003csub\u003eeur\u003c/sub\u003e=1.3\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) but not with PCOS in participants of African ancestry. These results support a potential genetic basis for the well-known associations between T2D and both gestational diabetes and PCOS\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, PRS\u003csub\u003eT2D\u003c/sub\u003e was associated with gestational hypertension in all participants (beta\u003csub\u003eall\u003c/sub\u003e=0.18, se\u003csub\u003eall\u003c/sub\u003e=0.064, p\u003csub\u003eall\u003c/sub\u003e=0.0041), breast cancer in all participants (beta\u003csub\u003eall\u003c/sub\u003e=-0.073, se\u003csub\u003eall\u003c/sub\u003e=0.021, p\u003csub\u003eall\u003c/sub\u003e=0.00066), and postpartum depression in all participants (beta\u003csub\u003eall\u003c/sub\u003e=0.18, se\u003csub\u003eall\u003c/sub\u003e=0.07, p\u003csub\u003eall\u003c/sub\u003e=0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant (FDR P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) associations between cardiometabolic PRS and female health conditions identified in the PMBB and eMERGE meta-analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAncestry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.891\u0026ndash;0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEndometrial cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17\u0026ndash;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.4\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGestational diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.18\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.59\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePCOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.22\u0026ndash;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.37\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.21\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.09\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.904\u0026ndash;0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.96\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.904\u0026ndash;0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.32\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcessive fetal growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.511\u0026ndash;0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGestational hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.13\u0026ndash;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGestational hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.51\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15\u0026ndash;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.6\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.08\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGestational hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.19\u0026ndash;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.61\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.77\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.24\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.77\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.13\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.18\u0026ndash;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eT2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.892\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGestational diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.59\u0026ndash;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.13\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGestational hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePCOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.93\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.26\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostpartum depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.04\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e**BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; CAD\u0026thinsp;=\u0026thinsp;Coronary artery disease, T2D\u0026thinsp;=\u0026thinsp;Type 2 Diabetes; PCOS\u0026thinsp;=\u0026thinsp;Polycystic ovarian syndrome; EUR\u0026thinsp;=\u0026thinsp;European ancestry; P\u0026thinsp;=\u0026thinsp;PMBB; E\u0026thinsp;=\u0026thinsp;eMERGE; Beta coefficients are per SD PRS. Results were adjusted for birth year and the first five PCs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe three PRSs for blood pressure traits (PRS\u003csub\u003eDBP\u003c/sub\u003e, PRS\u003csub\u003eSBP\u003c/sub\u003e, and PRS\u003csub\u003ePP\u003c/sub\u003e) showed varying associations with gestational hypertension and preeclampsia. In the meta-analysis of both cohorts, PRS\u003csub\u003eDBP\u003c/sub\u003e was significantly associated with gestational hypertension and preeclampsia in participants of African ancestry (gestational hypertension: beta\u003csub\u003eafr\u003c/sub\u003e=0.27, se\u003csub\u003eafr\u003c/sub\u003e=0.077, p\u003csub\u003eafr\u003c/sub\u003e=0.00037; preeclampsia: beta\u003csub\u003eafr\u003c/sub\u003e=0.22, se\u003csub\u003eafr\u003c/sub\u003e=0.077, p\u003csub\u003eafr\u003c/sub\u003e=0.0042). PRS\u003csub\u003eDBP\u003c/sub\u003e was also significantly associated with excessive fetal growth in participants of African ancestry (beta\u003csub\u003eafr\u003c/sub\u003e=-0.42, se\u003csub\u003eafr\u003c/sub\u003e=0.13, p\u003csub\u003eafr\u003c/sub\u003e=0.0014). PRS\u003csub\u003ePP\u003c/sub\u003e was significantly associated with gestational hypertension and preeclampsia in all participants and participants of European ancestry (gestational hypertension: beta\u003csub\u003eall\u003c/sub\u003e=0.22, se\u003csub\u003eall\u003c/sub\u003e=0.044, p\u003csub\u003eall\u003c/sub\u003e=8.5\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=0.25, se\u003csub\u003eeur\u003c/sub\u003e=0.055, p\u003csub\u003eeur\u003c/sub\u003e=6.6\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; preeclampsia: beta\u003csub\u003eall\u003c/sub\u003e=0.2, se\u003csub\u003eall\u003c/sub\u003e=0.058, p\u003csub\u003eall\u003c/sub\u003e=0.00076; beta\u003csub\u003eeur\u003c/sub\u003e=0.19, se\u003csub\u003eeur\u003c/sub\u003e=0.058, p\u003csub\u003eeur\u003c/sub\u003e=0.00091). PRS\u003csub\u003eSBP\u003c/sub\u003e was associated with gestational hypertension and preeclampsia in all participants, participants of European ancestry, and participants of African ancestry in the meta-analysis (gestational hypertension: beta\u003csub\u003eall\u003c/sub\u003e=0.33, se\u003csub\u003eall\u003c/sub\u003e=0.083, p\u003csub\u003eall\u003c/sub\u003e=5.6\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; beta\u003csub\u003eeur\u003c/sub\u003e=0.31, se\u003csub\u003eeur\u003c/sub\u003e=0.065, p\u003csub\u003eeur\u003c/sub\u003e=1.8\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; beta\u003csub\u003eafr\u003c/sub\u003e=0.42, se\u003csub\u003eafr\u003c/sub\u003e=0.099, p\u003csub\u003eafr\u003c/sub\u003e=2.8\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; preeclampsia: beta\u003csub\u003eall\u003c/sub\u003e=0.28, se\u003csub\u003eall\u003c/sub\u003e=0.08, p\u003csub\u003eall\u003c/sub\u003e=0.00057; beta\u003csub\u003eeur\u003c/sub\u003e=0.23, se\u003csub\u003eeur\u003c/sub\u003e=0.07, p\u003csub\u003eeur\u003c/sub\u003e=0.00077; beta\u003csub\u003eafr\u003c/sub\u003e=0.37, se\u003csub\u003eafr\u003c/sub\u003e=0.1, p\u003csub\u003eafr\u003c/sub\u003e=0.00038).\u003c/p\u003e \u003cp\u003eFor each significant association, we explored the case prevalence of female-specific health condition per the associated PRS quintile in each cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB \u003cb\u003eand Supplementary Figs.\u0026nbsp;7, 8, 9, 10, 11, 12, 13, 14, and 15\u003c/b\u003e). The trends matched the results of the association analyses across ancestry groups and in both cohorts. For the positive associations, such as that between PCOS and PRS\u003csub\u003eBMI\u003c/sub\u003e, the number of cases increased as the PRS percentile increased, whereas the opposite was true for the negative association between breast cancer and PRS\u003csub\u003eCAD\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eAssociation between PRS\u003csub\u003eCAD\u003c/sub\u003e and breast cancer\u003c/h2\u003e \u003cp\u003eTo validate the inverse association between PRS\u003csub\u003eCAD\u003c/sub\u003e and breast cancer, we examined the genetic correlation between CAD and breast cancer using the UKBB Genetic Correlation Browser (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The genetic correlation between I9_CHD (Major coronary heart disease event) and C50 (Diagnoses-main ICD10: C50 Malignant neoplasm of breast) was significantly negative (R\u003csub\u003eg\u003c/sub\u003e=-0.24, p\u0026thinsp;=\u0026thinsp;0.0325). We also compared the effects of SNPs in the CAD GWAS and a large publicly available breast cancer GWAS (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Among the 220 SNPs that had a significant effect in the CAD GWAS after pruning, 125 had an opposite effect on breast cancer, and the beta estimates of the SNPs were negatively correlated between the two GWASs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eAdditionally, to evaluate the risk of ascertainment biases and reporting of comorbidities in the EHR, we assessed the association between PRS\u003csub\u003eCAD\u003c/sub\u003e and breast cancer in females who were not diagnosed with CAD (n\u0026thinsp;=\u0026thinsp;61,201). In these individuals, we observed a slightly less significant association between breast cancer and CAD (beta\u003csub\u003eall\u003c/sub\u003e=-0.0484, p\u003csub\u003eall\u003c/sub\u003e=0.0045; beta\u003csub\u003eeur\u003c/sub\u003e=-0.0457, p\u003csub\u003eeur\u003c/sub\u003e=0.0115) than in the full dataset. The overlap between the participants diagnosed with CAD and those diagnosed with breast cancer in our cohorts suggests that females with a diagnosis of CAD were less likely to be diagnosed with breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eMendelian randomization\u003c/h2\u003e \u003cp\u003eWe performed one-sample MR in the PMBB and eMERGE cohorts for the 29 significant associations and combined them in a meta-analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The majority of associations remained significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the direction of the estimated beta coefficients aligned with the effect directions in the PRS association analysis. The MR results supported causal relationships between many of the cardiometabolic phenotypes and female health conditions, with the most significant associations being between T2D and gestational diabetes (All: p\u0026thinsp;=\u0026thinsp;1.5\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e, beta\u0026thinsp;=\u0026thinsp;1.52, se\u0026thinsp;=\u0026thinsp;0.23; EUR: p\u0026thinsp;=\u0026thinsp;2.9\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e, beta\u0026thinsp;=\u0026thinsp;1.76, se\u0026thinsp;=\u0026thinsp;0.28) and between BMI and PCOS (All: p\u0026thinsp;=\u0026thinsp;1.1\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, beta\u0026thinsp;=\u0026thinsp;0.0021, se\u0026thinsp;=\u0026thinsp;0.00037; EUR: p\u0026thinsp;=\u0026thinsp;6.1\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, beta\u0026thinsp;=\u0026thinsp;0.0021, se\u0026thinsp;=\u0026thinsp;0.0038).\u003c/p\u003e \u003cp\u003eWe also performed two-sample MR to leverage the power of larger GWAS of female-specific health conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Most of the associations remained significant when using the IVW method but became less significant when using the MR Egger and weighted median methods. Some associations were significant by all three methods, however, particularly those between CAD and breast cancer (IVW: beta=-0.0587, se\u0026thinsp;=\u0026thinsp;0.021, p\u0026thinsp;=\u0026thinsp;0.00463; MR Egger: beta=-0.102, se\u0026thinsp;=\u0026thinsp;0.049, p\u0026thinsp;=\u0026thinsp;0.04; Weighted median: beta=-0.0642, se\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;=\u0026thinsp;0.021) and between T2D and gestational diabetes (IVW: beta\u0026thinsp;=\u0026thinsp;0.613, se\u0026thinsp;=\u0026thinsp;0.044, p\u0026thinsp;=\u0026thinsp;1.73\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;43\u003c/sup\u003e; MR Egger: beta\u0026thinsp;=\u0026thinsp;0.656, se\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;8.8\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e; Weighted median: beta\u0026thinsp;=\u0026thinsp;0.635, se\u0026thinsp;=\u0026thinsp;0.066, p\u0026thinsp;=\u0026thinsp;8.12\u0026sdot;10\u003csup\u003e\u0026minus;\u0026thinsp;22\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eThe role of population stratification\u003c/h2\u003e \u003cp\u003ePopulation stratification can confound the results of PRS association and MR analyses. Therefore, we tested the PRS associations with PCs (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). The high R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e values for most cardiometabolic PRSs indicated that much of the variance in the PRSs could be explained by the PCs, although the R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e values were smaller when European and African ancestry groups were considered separately. The PCs explained more of the PRS variance for participants of African ancestry than for participants of European ancestry, which suggests that there was more population stratification in the former subpopulation than in the latter, and that there may be confounding factors influencing some results. To overcome these biases, we accounted for PCs in both the PRS association analyses and the one-sample MR analyses. Additionally, we performed one-sample MR without adjusting for covariates (PCs and birth year) and compared the results with those of the adjusted MR analysis (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eChronology analyses\u003c/h2\u003e \u003cp\u003eWe next asked whether genetic risk of cardiometabolic phenotypes affected the time at which female-specific health conditions developed. We found that participants in the younger age groups with high-risk cardiometabolic PRSs tended to have more health conditions than those with low-risk cardiometabolic PRSs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand Supplementary Figs.\u0026nbsp;16, 17, 18, 19, and 20\u003c/b\u003e). In particular, pregnancy-related conditions were relatively more prevalent in participants\u0026thinsp;\u0026lt;\u0026thinsp;25 years of age with high PRSs than low PRSs for all cardiometabolic PRSs. This difference was less pronounced in participants 25\u0026ndash;40 years of age. Although many of the cardiometabolic PRSs did not affect most pregnancy-related conditions, high-risk PRSs that had an effect on pregnancy-related conditions tended to have larger effects in participants\u0026thinsp;\u0026lt;\u0026thinsp;25 years of age than in participants 25\u0026ndash;40 years of age. In addition, PRSs for high blood pressure tended to have greater effects than other PRSs on gestational hypertension and preeclampsia in the younger age groups (\u0026lt;\u0026thinsp;25 and 25\u0026ndash;40 years). We observed similar trends for PRS\u003csub\u003eT2D\u003c/sub\u003e and gestational diabetes and for PRS\u003csub\u003eBMI\u003c/sub\u003e and PCOS. We also observed high prevalence of endometriosis and uterine fibrosis in participants with high-risk PRSs, particularly those 25\u0026ndash;40 years of age.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe observed genetic correlations between cardiometabolic traits and a variety of obstetric and gynecological disorders. Additionally, we showed that genome-wide PRSs for cardiometabolic traits have potential translational value for individuals with female-specific health conditions. The genetic correlations between cardiometabolic phenotypes and female-specific health conditions suggest a high degree of shared genetic etiology among all the phenotypes tested in this study. We investigated the effects of this shared genetic etiology by generating PRSs for cardiometabolic phenotypes and testing their associations with various female-specific health conditions. The PRSs were generally predictive of their corresponding cardiometabolic phenotypes as well as several female-specific health conditions. Prior research based mainly on non-genetic factors provided evidence for some of these associations, such those between BMI and PCOS and between T2D and gestational diabetes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Epidemiological studies have also provided evidence of a positive correlation between obesity and endometrial cancer\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our results suggest that certain relationships between cardiometabolic phenotypes and female-specific health conditions have a genetic basis. Furthermore, our MR results suggest that some of these relationships are causal.\u003c/p\u003e \u003cp\u003ePRS\u003csub\u003eCAD\u003c/sub\u003e was inversely associated with breast cancer in individuals of European ancestry. CAD and breast cancer share many common risk factors, such as smoking and poor diet\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e; however, our results indicated that a high genetic risk of CAD was protective against breast cancer, as participants with high PRS\u003csub\u003eCAD\u003c/sub\u003e had lower incidence of breast cancer than participants with low PRS\u003csub\u003eCAD\u003c/sub\u003e. When we considered only participants with high PRS\u003csub\u003eCAD\u003c/sub\u003e but no CAD diagnosis, we still saw a moderately reduced risk of breast cancer. Several factors might contribute to these results. First, the patterns might reflect ascertainment biases and competing risks. Individuals with high risk of CAD likely live shorter lives than individuals with low risk of CAD and are therefore less likely to be diagnosed with breast cancer during their lifetime. Second, treatments for CAD might protect against breast cancer. Third, genetic mechanisms that predispose individuals to CAD might have protective effects against breast cancer. Our results, together with those of another recent study that showed a protective effect of PRS\u003csub\u003eCAD\u003c/sub\u003e on breast cancer, suggest that more research should be undertaken to uncover the mechanisms behind this association\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe differences among the three-blood pressure related PRSs suggests that analysis of the genetic risk of all three blood pressure traits might improve the understanding of hypertension during pregnancy and elucidate interesting differences due to genetic burden in individuals of different ancestries. PRS\u003csub\u003eDBP\u003c/sub\u003e was significantly associated with gestational hypertension and preeclampsia in only participants of African ancestry, while only PRS\u003csub\u003eSBP\u003c/sub\u003e was significantly associated with gestational hypertension and preeclampsia in all participants and participants of European and African ancestry. Prior studies identified variability in predictive performance when different blood pressure measurements were used to predict hypertension and other diseases. Our results warrant further studies to replicate this association in other large multi-ancestry datasets to reach a more definite conclusion on the role blood pressure measurements play in predicting hypertensive diseases during pregnancy\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHigh genetic risks of most of the cardiometabolic phenotypes tended to increase the risk of certain health conditions at a young age, even in the absence of an overall association. For example, PRS\u003csub\u003eBMI\u003c/sub\u003e was not associated with gestational hypertension and ectopic pregnancy overall; however, in the youngest age group (\u0026lt;\u0026thinsp;25 years), these complications were more prevalent in participants with high PRS\u003csub\u003eBMI\u003c/sub\u003e than in participants with low PRS\u003csub\u003eBMI\u003c/sub\u003e. Similarly, there was no overall association between PRS\u003csub\u003eSBP\u003c/sub\u003e and PCOS or endometriosis, but participants who developed PCOS before 25 years of age or endometriosis at 25\u0026ndash;40 years of age were more likely to have high PRS\u003csub\u003eSBP\u003c/sub\u003e than low PRS\u003csub\u003eSBP\u003c/sub\u003e. The difference in the prevalence of these conditions between participants with high-risk PRS and participants with low-risk PRS became smaller with increasing age. Patients with high genetic risk of cardiometabolic phenotypes may therefore have an elevated risk of early development of female-specific health conditions. These findings may help to prioritize patients for early screening.\u003c/p\u003e \u003cp\u003eOur study establishes a link between the genetic risks of cardiometabolic phenotypes and several diseases that are unique to females of European and African ancestry; however, we estimated the genetic risk using PRS alone, which is based on common variants and does not include the effects of other rare variants and copy-number variations. In addition, we did not consider clinical or environmental factors, such as education level and socioeconomic status. We found that population stratification was potentially present in our cohorts, so we accounted for PCs in our analyses accordingly. Our focus on PRSs reflects the limitations of methods for multi-modal risk prediction. Current interest in building integrative risk models suggests that this gap can be closed in the near future\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Furthermore, the weaker performance of PRSs in individuals of African ancestry contributed to a lack of power to identify associations specific to those individuals and suggests a need for future studies to include more racially and ethnically diverse cohorts.\u003c/p\u003e \u003cp\u003eThe small sample size for some conditions may have limited the power of our one-sample MR analysis. Although our two-sample MR used GWASs with larger cohorts, these cohorts were primarily of European ancestry and therefore lacked the power to reveal causality in individuals of non-European ancestry. Furthermore, the causal effects identified in our MR analyses might be biased because of horizontal pleiotropy, in which genetic variants associated with cardiometabolic phenotypes affect other traits that in turn influence female-specific health conditions. We included methods such as MR Egger that account for pleiotropy and other confounding factors but may not have captured all their effects.\u003c/p\u003e \u003cp\u003eEHRs are particularly advantageous for investigating disease trajectories and progression. Our analysis provided a visualization of the burden of early diagnosis of female-specific health conditions in individuals with high genetic risk of cardiometabolic phenotypes. However, these results might have been affected by ascertainment biases of EHRs and inclusion of confounding factors.\u003c/p\u003e \u003cp\u003eThis study illustrates the influence of genetic risk of cardiometabolic phenotypes on female-specific health conditions and highlight differences in the genetic predisposition among individuals of European and African ancestries. Our findings suggest that cardiometabolic PRSs may have clinical utility as non-modifiable risk factors for screening and early diagnosis of a variety of obstetric and gynecological conditions. To improve the power of cardiometabolic PRSs to predict risks of female-specific health conditions, future studies should incorporate PRSs with other genetic and non-genetic risk factors and study their effects on larger and more diverse multi-ancestry populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe acknowledge the Penn Medicine BioBank (PMBB) for providing data and thank the patient-participants of Penn Medicine who consented to participate in this research program. We would also like to thank the Penn Medicine BioBank team and Regeneron Genetics Center for providing genetic variant data for analysis. The PMBB is approved under IRB protocol# 813913 and supported by Perelman School of Medicine at University of Pennsylvania.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eSources of Funding:\u0026nbsp;\u003c/strong\u003eThe eMERGE Network was initiated and funded by NHGRI through the following grants:\u0026nbsp;\u003cbr\u003e\u0026nbsp;Phase IV: U01HG011172 (Cincinnati Children\u0026rsquo;s Hospital Medical Center); U01HG011175 (Children\u0026rsquo;s Hospital of Philadelphia); U01HG008680 (Columbia University); U01HG011176 (Icahn School of Medicine at Mount Sinai); U01HG008685 (Mass General Brigham); U01HG006379 (Mayo Clinic); U01HG011169 (Northwestern University); U01HG011167 (University of Alabama at Birmingham); U01HG008657 (University of Washington Medical Center, Seattle); U01HG011181 (Vanderbilt University Medical Center); U01HG011166 (Vanderbilt University Medical Center serving as the Coordinating Center)\u0026nbsp;\u003cbr\u003ePhase III: U01HG8657 (Kaiser Permanente Washington/University of Washington Medical Center); U01HG8685 (Brigham and Women\u0026rsquo;s Hospital); U01HG8672 (Vanderbilt University Medical Center); U01HG8666 (Cincinnati Children\u0026rsquo;s Hospital Medical Center); U01HG6379 (Mayo Clinic); U01HG8679 (Geisinger Clinic); U01HG8680 (Columbia University Health Sciences); U01HG8684 (Children\u0026rsquo;s Hospital of Philadelphia); U01HG8673 (Northwestern University); U01HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG8676 (Partners Healthcare/Broad Institute); and U01HG8664 (Baylor College of Medicine).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e B.X. performed the analysis. S.S.V designed the study. B.X. and S.S.V wrote the manuscript. T.D, A.L helped with data extraction and analyses. H.H, L.L, N.E, W.Q.W, Y.L helped with data generation. B.X, D.V.E, T.D, K.G. B.L, C.W. G.P.J, D.K, M.D.R, S.S.V critically reviewed and approved the manuscript. Regeneron Genetics Center genotyped the Penn Medicine BioBank dataset.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChang C, Zhang K, Veluchamy A, et al. A Genome-Wide Association Study Provides New Evidence That CACNA1C Gene is Associated With Diabetic Cataract. Invest Ophthalmol Vis Sci. 2016;57(4):2246\u0026ndash;2250.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel SR. Shared genetic risk factors for obstructive sleep apnea and obesity. J Appl Physiol (1985). 2005;99(4):1600\u0026ndash;1606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuang QS, Zheng H, Gu XD, Shen L, Ji HF. 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Nat Commun. 2020;11(1):6084.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1386628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1386628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiometabolic diseases are highly comorbid, but their relationship with female-specific health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. Using electronic health record data from 71,008 ancestrally diverse females, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index [BMI], coronary artery disease [CAD], type 2 diabetes [T2D], and hypertension) using cross-trait genetic correlation analyses, polygenic risk scores (PRSs), Mendelian randomization (MR), and chronology analyses. We observed 29 significant associations between cardiometabolic PRSs and obstetrical/gynecological conditions (BMI and endometrial cancer; BMI and polycystic ovarian syndrome [PCOS)]; T2D and gestational diabetes; T2D and PCOS). MR analysis provided additional evidence of independent causal effects. We also identified an inverse association between CAD and breast cancer. High cardiometabolic PRSs were associated with early development of PCOS and gestational hypertension. We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female-specific health conditions.\u003c/p\u003e","manuscriptTitle":"Inference of causal relationships between genetic risk factors for cardiometabolic phenotypes and female-specific health conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-15 20:03:15","doi":"10.21203/rs.3.rs-1386628/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"729cff5e-bbb3-498e-b9d4-7dc1469ef81b","owner":[],"postedDate":"March 15th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-13T11:45:36+00:00","versionOfRecord":{"articleIdentity":"rs-1386628","link":"https://doi.org/10.1161/JAHA.121.026561","journal":{"identity":"journal-of-the-american-heart-association","isVorOnly":true,"title":"Journal of the American Heart Association"},"publishedOn":"2023-03-07 11:45:35","publishedOnDateReadable":"March 7th, 2023"},"versionCreatedAt":"2022-03-15 20:03:15","video":"","vorDoi":"10.1161/JAHA.121.026561","vorDoiUrl":"https://doi.org/10.1161/JAHA.121.026561","workflowStages":[]},"version":"v1","identity":"rs-1386628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1386628","identity":"rs-1386628","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}