ArticleBMC bioinformatics2017
SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
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- The Estonian Biobank's journey from biobanking to personalized medicine.Nature communications · 2025Review
- Multivariate genome-wide association analysis by iterative hard thresholding.Bioinformatics (Oxford, England) · 2023Article
- A Meta-Analysis of the Genome-Wide Association Studies on Two Genetically Correlated Phenotypes Suggests Four New Risk Loci for Headaches.Phenomics (Cham, Switzerland) · 2023Article
- Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.Human molecular genetics · 2021Article
- Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities.Journal of genetics and genomics = Yi chuan xue bao · 2021Review
- Heterogeneity in Obesity: Genetic Basis and Metabolic Consequences.Current diabetes reports · 2020Review
- Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy.American journal of human genetics · 2019Article
- Neuropathological correlates and genetic architecture of microglial activation in elderly human brain.Nature communications · 2019Article
- Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships.Frontiers in genetics · 2019Article
- Article
- MARV: a tool for genome-wide multi-phenotype analysis of rare variants.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
Abstract
backgroundGenome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits.
resultsWe have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10
conclusionsThe SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.
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