Literature DB >> 36107328

Genetic Variation and Mendelian Randomization Approaches.

Mojgan Yazdanpanah1, Nahid Yazdanpanah1, Despoina Manousaki2,3.   

Abstract

While genome-wide association studies (GWAS) on levels of nuclear receptors are sparse, the genetics of ligands of these receptors (steroid hormones, thyroid hormones, and liposoluble vitamins) have been extensively studied in GWAS of predominantly European populations. Hundreds of genetic variants across the genome have been associated with serum levels of nuclear receptor ligands, shedding light on the physiology of hormone metabolism. These GWAS findings have been used to explore causal associations of these hormones with complex human traits and diseases in Mendelian randomization (MR) studies, and in studies using polygenic risk scores to quantify the genetic predisposition to higher/lower hormone levels. As such, besides providing insights into hormonal pathophysiology and its causal relationship with clinical complications, GWAS-identified genetic markers could ultimately play an important role in the daily clinical management of patients. As large trans-ethnic GWAS on levels of nuclear receptor ligands emerge, and with the fast advances in genotyping techniques and constant decrease of the genotyping costs, studying an individual's genetically predicted hormonal profile could be the next step in personalizing the management of patients with pathologies related to nuclear receptors and their ligands.
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.

Entities:  

Keywords:  GWAS; Ligands; Mendelian randomization; Nuclear receptors; Polygenic risk scores

Mesh:

Substances:

Year:  2022        PMID: 36107328     DOI: 10.1007/978-3-031-11836-4_19

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   3.650


  138 in total

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Journal:  JAMA       Date:  2019-05-14       Impact factor: 56.272

Review 3.  Mining electronic health records: towards better research applications and clinical care.

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Authors:  M K Shea; E J Benjamin; J Dupuis; J M Massaro; P F Jacques; R B D'Agostino; J M Ordovas; C J O'Donnell; B Dawson-Hughes; R S Vasan; S L Booth
Journal:  Eur J Clin Nutr       Date:  2007-11-21       Impact factor: 4.016

5.  Mendelian randomization: using genes as instruments for making causal inferences in epidemiology.

Authors:  Debbie A Lawlor; Roger M Harbord; Jonathan A C Sterne; Nic Timpson; George Davey Smith
Journal:  Stat Med       Date:  2008-04-15       Impact factor: 2.373

6.  Mendelian randomization analysis with multiple genetic variants using summarized data.

Authors:  Stephen Burgess; Adam Butterworth; Simon G Thompson
Journal:  Genet Epidemiol       Date:  2013-09-20       Impact factor: 2.135

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Journal:  Nat Commun       Date:  2017-02-27       Impact factor: 14.919

8.  Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers.

Authors:  Julia Höglund; Nima Rafati; Mathias Rask-Andersen; Stefan Enroth; Torgny Karlsson; Weronica E Ek; Åsa Johansson
Journal:  Sci Rep       Date:  2019-11-14       Impact factor: 4.379

9.  Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations.

Authors:  Amit V Khera; Mark Chaffin; Krishna G Aragam; Mary E Haas; Carolina Roselli; Seung Hoan Choi; Pradeep Natarajan; Eric S Lander; Steven A Lubitz; Patrick T Ellinor; Sekar Kathiresan
Journal:  Nat Genet       Date:  2018-08-13       Impact factor: 38.330

10.  A machine-learning heuristic to improve gene score prediction of polygenic traits.

Authors:  Guillaume Paré; Shihong Mao; Wei Q Deng
Journal:  Sci Rep       Date:  2017-10-04       Impact factor: 4.379

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