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Approved research

Evaluating the Genetic Determinants of Uterine Fibroids in African and European Ancestry Individuals

Principal Investigator: Professor Digna Velez Edwards
Approved Research ID: 13869
Approval date: October 1st 2015

Lay summary

Uterine fibroids, benign tumors of the human uterus, affect 77% of women by menopause. Fibroids negatively impact reproductive health causing heavy and painful menses, pelvic pain and pressure, pregnancy complications, and surgery. As much as 69% of risk is explained by genetic factors. We have identified 4,197 fibroid cases and 6,030 controls identified from the electronic medical record network (eMERGE) from both European and African ancestry subjects, and completed analyses of these data. With this proposal we seek to combine our data with the UK Biobank to ask whether gene variants associate with fibroids in a large cohort. Fibroids have a significant negative impact on the gynecologic health of a large proportion of women in the world with up to 77% of women being impacted and yet we have very limited understanding on the etiological risk factors. We will identify novel genetic risk factors for uterine fibroids in European and African ancestry subjects. This work will fundamentally change knowledge about fibroids and lay the ground work for breakthroughs in understanding mechanisms of fibroid formation and in identifying novel therapeutics. We propose to identify genetic markers associated with risk of fibroids through a GWAS of eligible African and European ancestry women from both eMERGE and the UK Biobank, using a multistage design. We have an existing and published phenotyping algorithm of fibroid using electronic medical record data. Additionally, we have existing funds to validate our findings in the Black Women?s Health Study and other cohort from the eMERGE network, as well as to fine map candidate regions. We will include a subset of the cohort, consisting of all eligible fibroid cases and controls. We have identified 7,499 cases with diagnosis of fibroids in the UK BioBank. We have an existing and published phenotyping algorithm for identifying fibroid cases and controls using electronic medical record data. We will apply key elements of our algorithm that are comparable with the structure of the UK Biobank. Cases will be patients who were diagnosed with fibroids or underwent a surgery to remove fibroids. Controls will be those subjects who underwent a pelvic imaging procedure but where no fibroids were identified.