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Inference for set‐based effects in genetic association studies with interval‐censored outcomes

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  • Ryan Sun
  • Liang Zhu
  • Yimei Li
  • Yutaka Yasui
  • Leslie Robison

Abstract

The rapid acceleration of genetic data collection in biomedical settings has recently resulted in the rise of genetic compendiums filled with rich longitudinal disease data. One common feature of these data sets is their plethora of interval‐censored outcomes. However, very few tools are available for the analysis of genetic data sets with interval‐censored outcomes, and in particular, there is a lack of methodology available for set‐based inference. Set‐based inference is used to associate a gene, biological pathway, or other genetic construct with outcomes and is one of the most popular strategies in genetics research. This work develops three such tests for interval‐censored settings beginning with a variance components test for interval‐censored outcomes, the interval‐censored sequence kernel association test (ICSKAT). We also provide the interval‐censored version of the Burden test, and then we integrate ICSKAT and Burden to construct the interval censored sequence kernel association test—optimal (ICSKATO) combination. These tests unlock set‐based analysis of interval‐censored data sets with analogs of three highly popular set‐based tools commonly applied to continuous and binary outcomes. Simulation studies illustrate the advantages of the developed methods over ad hoc alternatives, including protection of the type I error rate at very low levels and increased power. The proposed approaches are applied to the investigation that motivated this study, an examination of the genes associated with bone mineral density deficiency and fracture risk.

Suggested Citation

  • Ryan Sun & Liang Zhu & Yimei Li & Yutaka Yasui & Leslie Robison, 2023. "Inference for set‐based effects in genetic association studies with interval‐censored outcomes," Biometrics, The International Biometric Society, vol. 79(2), pages 1573-1585, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1573-1585
    DOI: 10.1111/biom.13636
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    References listed on IDEAS

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    1. C. P. Farrington, 2000. "Residuals for Proportional Hazards Models with Interval-Censored Survival Data," Biometrics, The International Biometric Society, vol. 56(2), pages 473-482, June.
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    3. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
    4. Michael G. Hudgens & Chenxi Li & Jason P. Fine, 2014. "Parametric likelihood inference for interval censored competing risks data," Biometrics, The International Biometric Society, vol. 70(1), pages 1-9, March.
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