ArticleGenetic epidemiology2017
Conditional analysis of multiple quantitative traits based on marginal GWAS summary statistics.
Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Approximate conditional phenotype analysis based on genome wide association summary statistics.Scientific reports · 2021Pooled it
- Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies.Statistics in medicine · 2026Article
- A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics.Scientific reports · 2023Article
- A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.PloS one · 2022Article
- SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits.Genome biology · 2021Article
- Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics.Briefings in bioinformatics · 2021Article
- Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.PLoS computational biology · 2021Article
- Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data.NeuroImage · 2020Article
- A powerful and versatile colocalization test.PLoS computational biology · 2020Article
- Genetic correlations of polygenic disease traits: from theory to practice.Nature reviews. Genetics · 2019Review
- Varicose veins of lower extremities: Insights from the first large-scale genetic study.PLoS genetics · 2019Article
- Article
- Significance Testing for Allelic Heterogeneity.Genetics · 2018Article
- A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.Genetic epidemiology · 2018Article
- Improved Use of Small Reference Panels for Conditional and Joint Analysis with GWAS Summary Statistics.Genetics · 2018Article
- Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses.Genetics · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
There has been an increasing interest in joint association testing of multiple traits for possible pleiotropic effects. However, even in the presence of pleiotropy, most of the existing methods cannot distinguish direct and indirect effects of a genetic variant, say single-nucleotide polymorphism (SNP), on multiple traits, and a conditional analysis of a trait adjusting for other traits is perhaps the simplest and most common approach to addressing this question. However, without individual-level genotypic and phenotypic data but with only genome-wide association study (GWAS) summary statistics, as typical with most large-scale GWAS consortium studies, we are not aware of any existing method for such a conditional analysis. We propose such a conditional analysis, offering formulas of necessary calculations to fit a joint linear regression model for multiple quantitative traits. Furthermore, our method can also accommodate conditional analysis on multiple SNPs in addition to on multiple quantitative traits, which is expected to be useful for fine mapping. We provide numerical examples based on both simulated and real GWAS data to demonstrate the effectiveness of our proposed approach, and illustrate possible usefulness of conditional analysis by contrasting its result differences from those of standard marginal analyses.
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