Prospective Cohort Study of Uterine Fibroids and Miscarriage Risk

We sought to determine the relationship of fibroids to pregnancy loss in a prospective cohort in which fibroid status was uniformly documented in early pregnancy. Participants had an intake interview, transvaginal ultrasonography, computer-assisted telephone interview, and follow-up assessment of outcomes. We recruited diverse participants for the Right From the Start study from 8 metropolitan areas in 3 states in the United States during 2000-2012. Participants were at least 18 years of age, trying to become pregnant or at less than 12 weeks' gestation, not using fertility treatments, fluent in English or Spanish, and available for telephone interviews. Miscarriage was defined as loss before 20 weeks' gestation. Fibroid presence, number, type, and volume were assessed using standardized ultrasonography methods. We used proportional hazards models to estimate associations. Among 5,512 participants, 10.4% had at least 1 fibroid, and 10.8% experienced a miscarriage. Twenty-three percent had experienced a prior miscarriage and 52% prior births. Presence of fibroids was associated with miscarriage in models without adjustments. Adjusting for key confounders indicated no increase in risk (adjusted hazard ratio = 0.83, 95% confidence interval: 0.63, 1.08). No characteristic of fibroids was associated with risk. Prior evidence attributing miscarriage to fibroids is potentially biased. These findings imply that surgical removal of fibroids to reduce risk of miscarriage deserves careful scrutiny

A genome-wide association study meta-analysis of clinical fracture in 10,012 African American women

Background Osteoporosis is a major public health problem associated with excess disability and mortality. It is estimated that 50–70% of the variation in osteoporotic fracture risk is attributable to genetic factors. The purpose of this hypothesis-generating study was to identify possible genetic determinants of fracture among African American (AA) women in a GWAS meta-analysis. Methods Data on clinical fractures (all fractures except fingers, toes, face, skull or sternum) were analyzed among AA female participants in the Women's Health Initiative (WHI) (N = 8155), Cardiovascular Health Study (CHS) (N = 504), BioVU (N = 704), Health ABC (N = 651), and the Johnston County Osteoarthritis Project (JoCoOA) (N = 291). Affymetrix (WHI) and Illumina (Health ABC, JoCoOA, BioVU, CHS) GWAS panels were used for genotyping, and a 1:1 ratio of YRI:CEU HapMap haplotypes was used as an imputation reference panel. We used Cox proportional hazard models or logistic regression to evaluate the association of ~ 2.5 million SNPs with fracture risk, adjusting for ancestry, age, and geographic region where applicable. We conducted a fixed-effects, inverse variance-weighted meta-analysis. Genome-wide significance was set at P < 5 × 10− 8. Results One SNP, rs12775980 in an intron of SVIL on chromosome 10p11.2, reached genome-wide significance (P = 4.0 × 10− 8). Although this SNP has a low minor allele frequency (0.03), there was no evidence for heterogeneity of effects across the studies (I2 = 0). This locus was not reported in any previous osteoporosis-related GWA studies. We also interrogated previously reported GWA-significant loci associated with fracture or bone mineral density in our data. One locus (SMOC1) generalized, but overall there was not substantial evidence of generalization. Possible reasons for the lack of generalization are discussed. Conclusion This GWAS meta-analysis of fractures in African American women identified a potentially novel locus in the supervillin gene, which encodes a platelet-associated factor and was previously associated with platelet thrombus formation in African Americans. If validated in other populations of African descent, these findings suggest potential new mechanisms involved in fracture that may be particularly important among African Americans

A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores(PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.Results: The integrated polygenic prediction improved the average performance (pseudo-R2)for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity","type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension",and "sleep apnea" reaching phenome-wide significance.Conclusions: Our study has expanded the methodological utility of PRS in patient stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICDCM, International Classification of Diseases, Clinical Modification; LD, linkage disequilibrium;MA, multi-ancestry; MAF, minor allele frequency; NIH, National Institutes of Health; PCA,principal component analysis; PheWAS, phenome-wide association study; PCOS, polycysticovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk sc

Insights from the largest diverse ancestry sex-specific disease map for genetically predicted height

We performed ancestry and sex specific Phenome Wide Association Studies (PheWAS) to explore disease related outcomes associated with genetically predicted height. This is the largest PheWAS on genetically predicted height involving up to 840,000 individuals of diverse ancestry. We explored European, African, East Asian ancestries and Hispanic population groups. Increased genetically predicted height is associated with hyperpotassemia and autism in the male cross-ancestry analysis. We report male-only European ancestry associations with anxiety disorders, post-traumatic stress and substance addiction and disorders. We identify a signal with benign neoplasm of other parts of digestive system in females. We report associations with a series of disorders, several with no prior evidence of association with height, involving mental disorders and the endocrine system. Our study suggests that increased genetically predicted height is associated with higher prevalence of many clinically relevant traits which has important implications for epidemiological and clinical disease surveillance and risk stratification

Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity (Nature Genetics, (2018), 50, 1, (26-41), 10.1038/s41588-017-0011-x)

In the HTML version of this article initially published, the author groups ‘CHD Exome+ Consortium’, ‘EPIC-CVD Consortium’, ‘ExomeBP Consortium’, ‘Global Lipids Genetic Consortium’, ‘GoT2D Genes Consortium’, ‘EPIC InterAct Consortium’, ‘INTERVAL Study’, ‘ReproGen Consortium’, ‘T2D-Genes Consortium’, ‘The MAGIC Investigators’ and ‘Understanding Society Scientific Group’ appeared at the end of the author list but should have appeared earlier in the list, after author Krina T. Zondervan. The errors have been corrected in the HTML version of the article. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.</p