A New Functional F-Statistic for Gene-Based Inference Involving Multiple Phenotypes
Dugan, A. J.; Fardo, D. W.; Zaykin, D. V.; Vsevolozhskaya, O. A.
Show abstract
Genetic pleiotropy is the phenomenon where a single gene or genetic variant influences multiple traits. Numerous statistical methods exist for testing for genetic pleiotropy at the variant level, but fewer methods are available for testing genetic pleiotropy at the gene-level. In the current study, we derive an exact alternative to the Shen and Faraway functional F-statistic for functional-on-scalar regression models. Through extensive simulation studies, we show that this exact alternative performs similarly to the Shen and Faraway F-statistic in gene-based, multi-phenotype analyses and both F-statistics perform better than existing methods in small sample, modest effect size situations. We then apply all methods to real-world, neurodegenerative disease data and identify novel associations.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies 96%
- A robust association test leveraging unknown genetic interactions: Application to cystic brosis lung disease 95%
- Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes 95%
Similar papers in this journal
- Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity 96%
- Joint Modeling of Effect Sizes for Two Correlated Traits: Characterizing Trait Properties to Enhance Polygenic Risk Prediction 95%
- Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes. 95%
Similar papers in this journal
- A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically-determined protein-protein networks 95%
- BinomiRare: A carriers-only test for association of rare genetic variants with a binary outcome for mixed models and any case-control proportion 94%
- Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations 92%
Similar papers in this journal
- A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes 96%
- An exact, unifying framework for region-based association testing in family-based designs, including higher criticism approaches, SKATs, multivariate and burden tests 95%
- CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies 95%
Similar papers in this journal
- The PPLD has advantages over conventional regression methods in application to moderately sized genome-wide association studies 96%
- Assessing the performance of genome-wide association studies for predicting disease risk 95%
- HCLC-FC: a novel statistical method for phenome-wide association studies 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.