Abstract Number:
3392
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Yu-Jyun Huang (1), Nuzulul Kurniansyah (2), Daniel F Levey (3), Joel Gelernter (3), Jennifer Huffman (4), Kelly Cho (4), Peter Wilson (5), Daniel Gottlieb (6), Kenneth Rice (7), Tamar Sofer (1)
Institutions:
(1) Beth Israel Deaconess Medical Center, Boston, MA, (2) Department of Medicine, Brigham and Women’s Hospital, Boston, MA, (3) Department of Psychiatry, Yale University School of Medicine, New Haven, CT, (4) MAVERIC, VA Boston Healthcare System, Boston, MA, (5) Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, (6) Division of Sleep Medicine, Harvard Medical School, Boston, MA, (7) University of Washington, Seattle, WA
Co-Author(s):
Daniel F Levey
Department of Psychiatry, Yale University School of Medicine
Joel Gelernter
Department of Psychiatry, Yale University School of Medicine
Kelly Cho
MAVERIC, VA Boston Healthcare System
Peter Wilson
Division of Cardiology, Department of Medicine, Emory University School of Medicine
First Author:
Presenting Author:
Abstract Text:
Mendelian randomization (MR) analysis is widely used in genetic epidemiology to estimate the causal effect of a risk factor on an outcome of interest. Increasing evidence shows the importance of sex differences in health and disease mechanisms. However, research on sex-specific causal effects is lacking due to limited sex-specific GWASs. Motivated by GWASs from the Million Veteran Program, in which only 10% of individuals are female, a major limitation to MR analyses is weak IVs, which manifest as poor variant-exposure effect estimates that lead to unstable causal effect estimates. We propose a Bayesian framework to stabilize female exposure GWAS effect sizes by borrowing information from the male population. By specifying a particular prior distribution on female exposure GWAS effect sizes, we demonstrate two special cases of posterior means, including the inverse variance-weighted meta-analysis and the adaptive weight approach. We perform a series of simulation studies to examine the performance of our proposed Bayesian approach in MR analysis. Finally, we apply the proposed method to estimate the causal effects of sleep phenotypes on cardiovascular-related diseases
Keywords:
MR analysis|Bayesian framework|Sex-specific causal effect| | |
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand