Literature DB >> 31038695

Family-based quantitative trait meta-analysis implicates rare noncoding variants in DENND1A in polycystic ovary syndrome.

Matthew Dapas1, Ryan Sisk1, Richard S Legro2, Margrit Urbanek1,3,4, Andrea Dunaif5, M Geoffrey Hayes1,3,6.   

Abstract

CONTEXT: Polycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5-15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date.
OBJECTIVE: The objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis.Design, Patients, and
Methods: We performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis.
RESULTS: We found rare variants in DENND1A (P=5.31×10-5, Padj=0.039) that were significantly associated with reproductive and metabolic traits in PCOS families.
CONCLUSIONS: Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.
Copyright © 2019 Endocrine Society.

Entities:  

Year:  2019        PMID: 31038695      PMCID: PMC6660913          DOI: 10.1210/jc.2018-02496

Source DB:  PubMed          Journal:  J Clin Endocrinol Metab        ISSN: 0021-972X            Impact factor:   5.958


  113 in total

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Authors:  Susan Sam; Richard S Legro; Paulina A Essah; Teimuraz Apridonidze; Andrea Dunaif
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3.  Candidate gene region for polycystic ovary syndrome on chromosome 19p13.2.

Authors:  M Urbanek; A Woodroffe; K G Ewens; E Diamanti-Kandarakis; R S Legro; J F Strauss; A Dunaif; R S Spielman
Journal:  J Clin Endocrinol Metab       Date:  2005-08-09       Impact factor: 5.958

4.  Heritability of polycystic ovary syndrome in a Dutch twin-family study.

Authors:  J M Vink; S Sadrzadeh; C B Lambalk; D I Boomsma
Journal:  J Clin Endocrinol Metab       Date:  2005-10-11       Impact factor: 5.958

5.  Thirty-seven candidate genes for polycystic ovary syndrome: strongest evidence for linkage is with follistatin.

Authors:  M Urbanek; R S Legro; D A Driscoll; R Azziz; D A Ehrmann; R J Norman; J F Strauss; R S Spielman; A Dunaif
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7.  Testosterone and visceral fat in midlife women: the Study of Women's Health Across the Nation (SWAN) fat patterning study.

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2.  The contribution of rare genetic variants to the pathogenesis of polycystic ovary syndrome.

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Review 3.  Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome.

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4.  Further delineation of familial polycystic ovary syndrome (PCOS) via whole-exome sequencing: PCOS-related rare FBN3 and FN1 gene variants are identified.

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5.  Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis.

Authors:  Matthew Dapas; Frederick T J Lin; Girish N Nadkarni; Ryan Sisk; Richard S Legro; Margrit Urbanek; M Geoffrey Hayes; Andrea Dunaif
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6.  PCOS-GWAS Susceptibility Variants in THADA, INSR, TOX3, and DENND1A Are Associated With Metabolic Syndrome or Insulin Resistance in Women With PCOS.

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Review 7.  Genetic Susceptibility to Joint Occurrence of Polycystic Ovary Syndrome and Hashimoto's Thyroiditis: How Far Is Our Understanding?

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Review 9.  The Genetics of Polycystic Ovary Syndrome: An Overview of Candidate Gene Systematic Reviews and Genome-Wide Association Studies.

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Review 10.  Naturally Occurring and Experimentally Induced Rhesus Macaque Models for Polycystic Ovary Syndrome: Translational Gateways to Clinical Application.

Authors:  David H Abbott; Jeffrey Rogers; Daniel A Dumesic; Jon E Levine
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