Polycystic ovary syndrome (PCOS) is a common endocrine disorder of women, characterized by excessive ovarian androgen production, amenorrhea, oligomenorrhea, and subfecundity. Genetic variation in susceptibility probably contributes to the risk of PCOS, but no genes have been identified with certainty. We have generated strong evidence for the presence of a genetic determinant influencing PCOS susceptibility, located near the marker D19S884, on chromosome 19p13.2. The evidence comes from consistent findings in our 3 independent studies of a total of 465 families. D19S884 is located in an intron of the fibrillin 3 gene (FBN3). Fibrillin 3-containing microfibrils are present in the ovary in a geographical location to influence follicular function. It is likely that there are other susceptibility genes, unlinked to FBN3/D19S884. Our long-term goals are to find all the relevant genes, determine how they contribute to susceptibility individually and jointly, and sequence and characterize the susceptibility alleles themselves.
The aims of this project are: 1. Find the determinants and identify effects associated with D19S884, the genetic marker we have already identified: what are the """"""""downstream"""""""" effects of variation at D19S884 and neighboring sequences? 2. Investigate other candidate genes (unlinked to D19S884/FBN3) including PDE8A, by genetic and molecular methods. 3. Extend the genetic analysis from PCOS (a qualitative trait) to PCOS-associated quantitative traits. To accomplish these aims, we will use family-based genetic approaches, association studies, and in vitro studies to probe gene function and identify new candidate genes. We will continue to evaluate genetic variation in the D19S884 region of FBN3, as well as additional candidate genes, in an independent collection of PCOS families recruited as part of this project. The findings from these studies will enhance our understanding of the genetics of PCOS, a major contributor to female infertility. Better knowledge of """"""""PCOS genes"""""""" will also help in predicting responses to treatments for infertility, insulin resistance, and other aspects of the disease.
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