| Title: | Set-Based Tests for Genetic Association Studies with Interval-Censored Competing Risks Outcomes |
| Journal: | Statistics in Biosciences |
| Published: | 13 Jul 2024 |
| DOI: | https://doi.org/10.1007/s12561-024-09448-3 |
Publication 12839
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Abstract
Over the past decade, massive genetic compendiums such as the UK Biobank have gathered extensive genetic and phenotypic data that hold the potential to provide unparalleled insight into the genetic etiologies of various complex diseases. However, much of the disease information is collected as time-to-event outcomes in interval-censored form, and conventional tools for genetic association analysis are often not available for this type of data. For example, set-based inference for common and rare variants analysis is a fundamental investigation in germline genetics studies, but there is a lack of approaches that can perform set-based testing when the interval-censored outcome of interest is subject to the competing risk of another event. To address the need, this work proposes two set-based inference procedures for interval-censored data with competing risks, applicable to rare variants and general genotype sets as well. The interval-censored competing risks sequence kernel association test (crSKAT) is a variance components approach that is powerful when genetic variants in a set demonstrate heterogeneous signals. The interval-censored competing risks Burden (crBurden) test is more powerful when variant signals are homogeneous. Simulation studies show the superiority of the newly developed methods in comparison to ad-hoc alternatives, as evidenced by their ability to control the type I error rate and to improve power. The proposed tests are applied to the UK Biobank to search for genes associated with fracture risk while accounting for death as a competing outcome.</p>
3 Authors
- Zhichao Xu
- Jaihee Choi
- Ryan Sun
2 Applications
| Application ID | Title |
|---|---|
| 52008 | Development of statistical methods to discover novel genetic associations, explain underlying biological mechanisms, and develop risk prediction models across varied complex diseases. |
| 73569 | Development of statistical methods to identify and characterize genetic variants linked to complex diseases under the time-to-event outcome modeling framework. |
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