Leveraging Family History in Genetic Association Analyses of Binary Traits

Authors

Yixin Zhang
James B Meigs
Ching-Ti Liu
Josée Dupuis
Chloé Sarnowski

Language

English

Publication Date

10-1-2022

Journal

BMC Genomics

DOI

10.1186/s12864-022-08897-8

PMID

36182916

PMCID

PMC9526325

PubMedCentral® Posted Date

October 2022

PubMedCentral® Full Text Version

Post-print

Abstract

BACKGROUND: Considering relatives' health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS.

RESULTS: In our simulation scenario of trait with modest T2D heritability (h

CONCLUSIONS: Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies.

Keywords

Diabetes Mellitus, Type 2, Genetic Association Studies, Genome-Wide Association Study, Humans, Phenotype, Polymorphism, Single Nucleotide

Recommended Citation

Zhang, Yixin; Meigs, James B; Liu, Ching-Ti; et al., "Leveraging Family History in Genetic Association Analyses of Binary Traits" (2022). Faculty, Staff and Student Publications. 540.
https://digitalcommons.library.tmc.edu/uthsph_docs/540