title: Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations.
abstract: There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but it is also potentially more robust to the commonly adopted additive inheritance mode. More importantly, we develop an adaptive test in a POM so that it can maintain high power across many possible situations. Compared to the existing methods treating multiple traits as responses, e.g., in a generalized estimating equation (GEE) approach, the proposed method can be applied to a high dimensional setting where the number of phenotypes (p) can be larger than the sample size (n), in addition to a usual small P setting. The promising performance of the proposed method was demonstrated with applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which either structural MRI driven phenotypes or resting-state functional MRI (rs-fMRI) derived brain functional connectivity measures were used as phenotypes. The applications led to the identification of several top SNPs of biological interest. Furthermore, simulation studies showed competitive performance of the new method, especially for p>n.
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
authors.forename: Junghi
authors.initials: J
authors.lastname: Kim
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
authors.forename: Wei
authors.initials: W
authors.lastname: Pan
authors.affiliation: Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http: //adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf.
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000235:genetics*; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008957:Models, Genetic; D009415:Nerve Net / Q000502:physiology*; D059906:Neuroimaging; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D040641:Quantitative Trait Loci / Q000235:genetics*; D018401:Sample Size
keywords: ADNI; GWAS; MRI; aSPU; default mode network (DMN); functional connectivity; high dimensional phenotypes; rs-fMRI