ArticleBiostatistics (Oxford, England)2021
A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses.Nature genetics · 2020Pooled it
- Improving isoform-level eQTL and integrative genetic analyses of breast cancer risk with long-read RNA transcript assemblies.bioRxiv : the preprint server for biology · 2026Article
- Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia.American journal of human genetics · 2024Article
- Article
- Nitrated Polycyclic Aromatic Hydrocarbon (Nitro-PAH) Signatures and Somatic Mutations in Diesel Exhaust-Exposed Bladder Tumors.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023Article
- Common variants in breast cancer risk loci predispose to distinct tumor subtypes.Breast cancer research : BCR · 2022Article
- TP53 Pathway Function, Estrogen Receptor Status, and Breast Cancer Risk Factors in the Carolina Breast Cancer Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- Random effect based tests for multinomial logistic regression in genetic association studies.Genetic epidemiology · 2021Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- Diesel exhaust and bladder cancer risk by pathologic stage and grade subtypes.Environment international · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose to test for genetic associations using a mixed-effect two-stage polytomous model score test (MTOP). In the first stage, a standard polytomous model is used to specify all possible subtypes defined by the cross-classification of the tumor characteristics. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization algorithm to account for missing data on tumor markers. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. The proposed methods have been implemented in a user-friendly and high-speed R statistical package called TOP (https://github.com/andrewhaoyu/TOP).
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