Anticipation in multiple-case lymphoid cancer families after controlling for ascertainment biases

Jones, Samantha; Brooks‐Wilson, Angela

doi:10.1080/10428194.2021.1948026

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The “Super Seniors” are defined as those who are 85 or older and have no history of being diagnosed with the following 5 types of diseases: cardiovascular disease, cancer, diabetes, major pulmonary disease, or dementia. In this study, 1,162 samples of 4,559,465 markers were genotyped using a custom Infinium Omni5Exome-4 v1.3 BeadChip (Illumina, San Diego, CA, USA) at the McGill University/Genome Quebec Innovation Centre (Montreal, Quebec, Canada) [33]. The data underwent extensive quality control after genotyping, including re-clustering, removal of replicate and tri-allelic SNPs, and checking for sex discrepancies and relatedness.…”

Section: Data Applicationmentioning

confidence: 99%

Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes

Chen

Jones

et al. 2022

Hum Hered

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Introduction: Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. Sparseness leads to maximum likelihood estimators (MLEs) of log-OR parameters that are biased away from their null value of zero and tests with inflated type 1 errors. Different penalized-likelihood methods have been developed to mitigate sparse-data bias. We study penalized logistic regression using a class of log-F priors indexed by a shrinkage parameter m to shrink the biased MLE towards zero. For a given m, log-F-penalized logistic regression may be easily implemented using data augmentation and standard software. Method: We propose a two-step approach to the analysis of a genetic association study: first, a set of variants that show evidence of association with the trait is used to estimate m; and second, the estimated m is used for log-F-penalized logistic regression analyses of all variants using data augmentation with standard software. Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. Results: We evaluate the statistical properties of our proposed two-step method and compared its performance to other shrinkage methods by a simulation study. Our simulation studies suggest that the proposed log-F-penalized approach has lower bias and mean squared error than other methods considered. We also illustrate the approach on data from a study of genetic associations with "super senior" cases and middle aged controls. Discussion/Conclusion: We have proposed a method for single rare variant analysis with binary phenotypes by logistic regression penalized by log-F priors. Our method has the advantage of being easily extended to correct for confounding due to population structure and genetic relatedness through a data augmentation approach.

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Section: Data Applicationmentioning

confidence: 99%