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FMR1 Genotype with Autoimmunity-Associated Polycystic Ovary-Like Phenotype and Decreased Pregnancy Chance

Overview of attention for article published in PLOS ONE, December 2010
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Title
FMR1 Genotype with Autoimmunity-Associated Polycystic Ovary-Like Phenotype and Decreased Pregnancy Chance
Published in
PLOS ONE, December 2010
DOI 10.1371/journal.pone.0015303
Pubmed ID
Authors

Norbert Gleicher, Andrea Weghofer, Irene H. Lee, David H. Barad

Abstract

The FMR1 gene partially appears to control ovarian reserve, with a specific ovarian sub-genotype statistically associated with a polycystic ovary (PCO)- like phenotype. Some forms of PCO have been associated with autoimmunity. We, therefore, investigated in multiple regression analyses associations of ovary-specific FMR1 genotypes with autoimmunity and pregnancy chances (with in vitro fertilization, IVF) in 339 consecutive infertile women (455 IVF cycles), 75 with PCO-like phenotype, adjusted for age, race/ethnicity, medication dosage and number of oocytes retrieved. Patients included 183 (54.0%) with normal (norm) and 156 (46%) with heterozygous (het) FMR1 genotypes; 133 (39.2%) demonstrated laboratory evidence of autoimmunity: 51.1% of het-norm/low, 38.3% of norm and 24.2% het-norm/high genotype and sub-genotypes demonstrated autoimmunity (p=0.003). Prevalence of autoimmunity increased further in PCO-like phenotype patients with het-norm/low genotype (83.3%), remained unchanged with norm (34.0%) and decreased in het-norm/high women (10.0%; P<0.0001). Pregnancy rates were significantly higher with norm (38.6%) than het-norm/low (22.2%, p=0.001). FMR1 sub-genotype het-norm/low is strongly associated with autoimmunity and decreased pregnancy chances in IVF, reaffirming the importance of the distal long arm of the X chromosome (FMR1 maps at Xq27.3) for autoimmunity, ovarian function and, likely, pregnancy chance with IVF.

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Geographical breakdown

Country Count As %
Poland 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 15%
Student > Bachelor 6 15%
Student > Master 4 10%
Student > Doctoral Student 3 8%
Professor 3 8%
Other 11 28%
Unknown 6 15%
Readers by discipline Count As %
Medicine and Dentistry 14 36%
Agricultural and Biological Sciences 8 21%
Biochemistry, Genetics and Molecular Biology 6 15%
Engineering 2 5%
Neuroscience 1 3%
Other 1 3%
Unknown 7 18%