Ovarian Physiology and GWAS: Biobanks, Biology, and Beyond. (July 2016)

• Record Type: Journal Article
• Authors: Laisk-Podar, Triin; Lindgren, Cecilia M.; Peters, Maire; Tapanainen, Juha S.; Lambalk, Cornelis B.; Salumets, Andres; Mägi, Reedik
• Is Part Of: Trends in endocrinology and metabolism. Volume 27:Number 7(2016)
• Page Start: 516
• Page End: 528
• Publication Date: 2016-07
• Subjects: ovarian reserve -- menopause -- polycystic ovary syndrome -- premature ovarian insufficiency; Endocrinology -- Periodicals; Metabolism -- Periodicals; Metabolism; 616.4
• DOI: 10.1016/j.tem.2016.04.011
• Languages: English
• ISSNs: 1043-2760

Abstract

Abstract : Ovarian function is central to female fertility, and several genome-wide association studies (GWAS) have been carried out to elucidate the genetic background of traits and disorders that reflect and affect ovarian physiology. While GWAS have been successful in reporting numerous genetic associations and highlighting involved pathways relevant to reproductive aging, for ovarian disorders, such as premature ovarian insufficiency and polycystic ovary syndrome, research has lagged behind due to insufficient study sample size. Novel approaches to study design and analysis methods that help to fit GWAS findings into biological context will improve our knowledge about genetics governing ovarian function in fertility and disease, and provide input for clinical tools and better patient management. Trends: Large population-based biobanks can be harnessed for genetic studies in ovary-related phenotypes to take research efforts to the next level. New analytical methods that use GWAS summary statistics can be used to identify the most likely causal genes, pathways, underlying mechanisms, genetic correlations, and causal relationships between phenotypes. There is significant genetic overlap between traits and disorders reflecting ovarian function, such as age at natural menopause, polycystic ovary syndrome, and premature ovarian insufficiency. Results from large-scale genetic association studies can provide information for more personalized patient management.