ArticleBiostatistics (Oxford, England)2010
A general framework for studying genetic effects and gene-environment interactions with missing data.
Article in Biostatistics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- The genus bifidobacterium: From genomics to functionality of an important component of the mammalian gut microbiota running title: Bifidobacterial adaptation to and interaction with the host.Computational and structural biotechnology journal · 2021Review
- Accommodating missingness in environmental measurements in gene-environment interaction analysis.Genetic epidemiology · 2017Article
- Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling.Journal of the American Statistical Association · 2015Article
- A Likelihood-Based Framework for Association Analysis of Allele-Specific Copy Numbers.Journal of the American Statistical Association · 2014Article
- Survival analysis with incomplete genetic data.Lifetime data analysis · 2014Article
- A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data.Statistics in biosciences · 2013Article
- Association of germline microRNA SNPs in pre-miRNA flanking region and breast cancer risk and survival: the Carolina Breast Cancer Study.Cancer causes & control : CCC · 2013Article
- Robust estimation for homoscedastic regression in the secondary analysis of case-control data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2013Article
- Common genetic variation in adiponectin, leptin, and leptin receptor and association with breast cancer subtypes.Breast cancer research and treatment · 2011Article
- Proper Use of Allele-Specific Expression Improves Statistical Power forJournal of the American Statistical AssociationArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Missing data arise in genetic association studies when genotypes are unknown or when haplotypes are of direct interest. We provide a general likelihood-based framework for making inference on genetic effects and gene-environment interactions with such missing data. We allow genetic and environmental variables to be correlated while leaving the distribution of environmental variables completely unspecified. We consider 3 major study designs-cross-sectional, case-control, and cohort designs-and construct appropriate likelihood functions for all common phenotypes (e.g. case-control status, quantitative traits, and potentially censored ages at onset of disease). The likelihood functions involve both finite- and infinite-dimensional parameters. The maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Expectation-Maximization (EM) algorithms are developed to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed inferential and numerical methods perform well in practical settings. Illustration with a genome-wide association study of lung cancer is provided.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.