ArticleGenetics2018
A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer's Disease.
Article in Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed.
- Association of environmental exposome and cognitive function among older adults with and without preclinical Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Ptprd deficiency promotes tau hyperphosphorylation and impairs cognitive function in aged mice.Biological research · 2025Article
- An Extended Application of the Fast Multi-Locus Ridge Regression Algorithm in Genome-Wide Association Studies of Categorical Phenotypes.Plants (Basel, Switzerland) · 2024Article
- Protein Kinase A in neurological disorders.Journal of neurodevelopmental disorders · 2024Review
- Cerebrovascular miRNAs Track Early Development of Alzheimer's Disease and Target Molecular Markers of Angiogenesis and Blood Flow Regulation.Journal of Alzheimer's disease : JAD · 2024Article
- Gene-level association analysis of bivariate ordinal traits with functional regressions.Genetic epidemiology · 2023Article
- Improving power of genome-wide association studies via transforming ordinal phenotypes into continuous phenotypes.Frontiers in plant science · 2023Article
- Data-driven causal model discovery and personalized prediction in Alzheimer's disease.NPJ digital medicine · 2022Article
- Gene-level association analysis of ordinal traits with functional ordinal logistic regressions.Genetic epidemiology · 2022Article
- Genetic association tests in family samples for multi-category phenotypes.BMC genomics · 2021Article
- A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies.Biometrical journal. Biometrische Zeitschrift · 2020Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Recent technical and methodological advances have greatly enhanced genome-wide association studies (GWAS). The advent of low-cost, whole-genome sequencing facilitates high-resolution variant identification, and the development of linear mixed models (LMM) allows improved identification of putatively causal variants. While essential for correcting false positive associations due to sample relatedness and population stratification, LMMs have commonly been restricted to quantitative variables. However, phenotypic traits in association studies are often categorical, coded as binary case-control or ordered variables describing disease stages. To address these issues, we have devised a method for genomic association studies that implements a generalized LMM (GLMM) in a Bayesian framework, called
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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.