ArticleAmerican journal of human genetics2015
Mixed model with correction for case-control ascertainment increases association power.
Article in American journal of human genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- Population genetic considerations for using biobanks as international resources in the pandemic era and beyond.BMC genomics · 2021Pooled it
- Case-control association mapping by proxy using family history of disease.Nature genetics · 2017Pooled it
- Liability threshold model-based disease risk prediction based on electronic health record phenotypes.Nature genetics · 2025Article
- SPAmix: a scalable, accurate, and universal analysis framework for large-scale genetic association studies in admixed populations.Genome biology · 2025Article
- FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.Briefings in bioinformatics · 2024Article
- RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.The annals of applied statistics · 2024Article
- Incorporating family history of disease improves polygenic risk scores in diverse populations.Cell genomics · 2022Article
- Accounting for age of onset and family history improves power in genome-wide association studies.American journal of human genetics · 2022Article
- Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.BMC genomics · 2022Article
- Genome-wide hierarchical mixed model association analysis.Briefings in bioinformatics · 2021Article
- The impact of age on genetic risk for common diseases.PLoS genetics · 2021Article
- Multi-scale inference of genetic trait architecture using biologically annotated neural networks.PLoS genetics · 2021Article
- PCAmatchR: a flexible R package for optimal case-control matching using weighted principal components.Bioinformatics (Oxford, England) · 2021Article
- Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration.Journal of the American Statistical Association · 2021Article
- Liability threshold modeling of case-control status and family history of disease increases association power.Nature genetics · 2020Article
- Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies.BMC bioinformatics · 2019Article
- Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use.Genetics · 2019Article
- Improved polygenic prediction by Bayesian multiple regression on summary statistics.Nature communications · 2019Article
- OSCA: a tool for omic-data-based complex trait analysis.Genome biology · 2019Article
- A comparison of popular TDT-generalizations for family-based association analysis.Genetic epidemiology · 2019Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
We introduce a liability-threshold mixed linear model (LTMLM) association statistic for case-control studies and show that it has a well-controlled false-positive rate and more power than existing mixed-model methods for diseases with low prevalence. Existing mixed-model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem by using a χ(2) score statistic computed from posterior mean liabilities (PMLs) under the liability-threshold model. Each individual's PML is conditional not only on that individual's case-control status but also on every individual's case-control status and the genetic relationship matrix (GRM) obtained from the data. The PMLs are estimated with a multivariate Gibbs sampler; the liability-scale phenotypic covariance matrix is based on the GRM, and a heritability parameter is estimated via Haseman-Elston regression on case-control phenotypes and then transformed to the liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed-model methods for diseases with low prevalence, and the magnitude of the improvement depended on sample size and severity of case-control ascertainment. In a Wellcome Trust Case Control Consortium 2 multiple sclerosis dataset with >10,000 samples, LTMLM was correctly calibrated and attained a 4.3% improvement (p = 0.005) in χ(2) statistics over existing mixed-model methods at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, case-control studies of diseases with low prevalence can achieve power higher than that in existing mixed-model methods.
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