ArticleGenetic epidemiology2015
Approximate score-based testing with application to multivariate trait association analysis.
Article in Genetic epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multivariate generalized linear model for genetic pleiotropy.Biostatistics (Oxford, England) · 2019Article
- Inference on phenotype-specific effects of genes using multivariate kernel machine regression.Genetic epidemiology · 2018Article
- Adaptive SNP-Set Association Testing in Generalized Linear Mixed Models with Application to Family Studies.Behavior genetics · 2018Article
- A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data.PLoS computational biology · 2017Article
- Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Review
- Statistical Methods for Testing Genetic Pleiotropy.Genetics · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
For genome-wide association studies and DNA sequencing studies, several powerful score-based tests, such as kernel machine regression and sum of powered score tests, have been proposed in the last few years. However, extensions of these score-based tests to more complex models, such as mixed-effects models for analysis of multiple and correlated traits, have been hindered by the unavailability of the score vector, due to either no output from statistical software or no closed-form solution at all. We propose a simple and general method to asymptotically approximate the score vector based on an asymptotically normal and consistent estimate of a parameter vector to be tested and its (consistent) covariance matrix. The proposed method is applicable to both maximum-likelihood estimation and estimating function-based approaches. We use the derived approximate score vector to extend several score-based tests to mixed-effects models. We demonstrate the feasibility and possible power gains of these tests in association analysis of multiple and correlated quantitative or binary traits with both real and simulated data. The proposed method is easy to implement with a wide applicability.
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Registered trials
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