Family history aggregation unit-based tests to detect rare genetic variant associations with application to the Framingham Heart Study.
Am J Hum Genet
; 109(4): 738-749, 2022 04 07.
Article
in En
| MEDLINE
| ID: mdl-35316615
ABSTRACT
A challenge in standard genetic studies is maintaining good power to detect associations, especially for low prevalent diseases and rare variants. The traditional methods are most powerful when evaluating the association between variants in balanced study designs. Without accounting for family correlation and unbalanced case-control ratio, these analyses could result in inflated type I error. One cost-effective solution to increase statistical power is exploitation of available family history (FH) that contains valuable information about disease heritability. Here, we develop methods to address the aforementioned type I error issues while providing optimal power to analyze aggregates of rare variants by incorporating additional information from FH. With enhanced power in these methods exploiting FH and accounting for relatedness and unbalanced designs, we successfully detect genes with suggestive associations with Alzheimer disease, dementia, and type 2 diabetes by using the exome chip data from the Framingham Heart Study.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Diabetes Mellitus, Type 2
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Year:
2022
Type:
Article