ArticlePloS one2016
A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci.
Article in PloS one, 2016. 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, 7 citations in OpenAlex.
- Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies.Statistical communications in infectious diseases · 2020Article
- Association of the Lactase Persistence Haplotype Block With Disease Risk in Populations of European Descent.Frontiers in genetics · 2020Article
- Into the Wild: GWAS Exploration of Non-coding RNAs.Frontiers in cardiovascular medicine · 2018Review
- Deep RNA Sequencing Uncovers a Repertoire of Human Macrophage Long Intergenic Noncoding RNAs Modulated by Macrophage Activation and Associated With Cardiometabolic Diseases.Journal of the American Heart Association · 2017Article
- A score test for genetic class-level association with nonlinear biomarker trajectories.Statistics in medicine · 2017Article
- Genome-wide interrogation reveals hundreds of long intergenic noncoding RNAs that associate with cardiometabolic traits.Human molecular genetics · 2016Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundCharacterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs.
methodsWe apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1.
resultsWe demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes.
conclusionsWe conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.
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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.