ArticleBioinformatics (Oxford, England)2012
Analysis of case-control association studies with known risk variants.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data.Genes · 2020Pooled it
- Genome-wide association studies of polygenic risk score-derived phenotypes may lead to inflated false positive rates.Scientific reports · 2023Article
- Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Reverse GWAS: Using genetics to identify and model phenotypic subtypes.PLoS genetics · 2019Article
- A GWAS in Latin Americans highlights the convergent evolution of lighter skin pigmentation in Eurasia.Nature communications · 2019Article
- Genetic Mechanisms Leading to Sex Differences Across Common Diseases and Anthropometric Traits.Genetics · 2017Article
- Two novel loci, COBL and SLC10A2, for Alzheimer's disease in African Americans.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Article
- Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies.Genetics · 2016Article
- Article
- Mixed model with correction for case-control ascertainment increases association power.American journal of human genetics · 2015Article
- Accurate liability estimation improves power in ascertained case-control studies.Nature methods · 2015Article
- X-inactivation informs variance-based testing for X-linked association of a quantitative trait.BMC genomics · 2015Article
- Testing calibration of risk models at extremes of disease risk.Biostatistics (Oxford, England) · 2015Article
- Effective genetic-risk prediction using mixed models.American journal of human genetics · 2014Article
- Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population.JAMA · 2014Article
- Advantages and pitfalls in the application of mixed-model association methods.Nature genetics · 2014Article
- Windfalls and pitfalls: Applications of population genetics to the search for disease genes.Evolution, medicine, and public health · 2013Article
- Informed conditioning on clinical covariates increases power in case-control association studies.PLoS genetics · 2012Article
- The Covariate's Dilemma.PLoS genetics · 2012Article
- XWAS: A Software Toolset for Genetic Data Analysis and Association Studies of the X Chromosome.The Journal of heredityArticle
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Authors and funding
16 authors.
Funding
Abstract
motivationThe question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature.
resultsWe show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants. AVAILABILITY: LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/.
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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.