ArticleMethods in molecular biology (Clifton, N.J.)2017
Cross-Phenotype Association Analysis Using Summary Statistics from GWAS.
Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 6 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Mapping Gastroesophageal Reflux Disease and Coronary Artery Disease: A Comprehensive Analysis of Multivariable Mendelian Randomization and Shared Genetic Etiology.Clinical cardiology · 2025Pooled it
- Genetic overlap and causality between depression and preterm birth: a large-scale genome-wide cross-trait analysis.Psychological medicine · 2025Pooled it
- Novel Insights Into the Causal Effects and Shared Genetics Between Body Fat and Parkinson Disease.CNS neuroscience & therapeutics · 2024Pooled it
- Impact of gallstone disease on the risk of stroke and coronary artery disease: evidence from prospective observational studies and genetic analyses.BMC medicine · 2023Pooled it
- Meta-Analysis and Multivariate GWAS Analyses in 80,950 Individuals of African Ancestry Identify Novel Variants Associated with Blood Pressure Traits.International journal of molecular sciences · 2023Pooled it
- Identification of shared genetic architecture between non-alcoholic fatty liver disease and type 2 diabetes: A genome-wide analysis.Frontiers in endocrinology · 2023Pooled it
- Genetic interconnections between personality-related phenotypes and psychiatric disorders.European archives of psychiatry and clinical neuroscience · 2026Article
- Multilevel genome-wide analysis reveals shared genetic architecture between acute pancreatitis and body fat distribution.World journal of emergency medicine · 2026Article
- Shared genetic link and causal inference between blood lipids, lipid-lowering drugs and amyotrophic lateral sclerosis.Neural regeneration research · 2026Article
- A cross-ancestry genetic atlas of shared susceptibility between infectious diseases and cancer.Functional & integrative genomics · 2026Article
- A Shared Genetic Basis Underlying Myopia-Exotropia Comorbidity.Investigative ophthalmology & visual science · 2026Article
- Genetic Correlation and Causal Inference Between Female Fat Distribution and Preeclampsia: An Integrative Genomic Study.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.The journal of headache and pain · 2026Article
- Article
- Genome-Wide Discovery Reveals Adipose-Specific and Systemic Regulators of Insulin Resistance.medRxiv : the preprint server for health sciences · 2026Article
- Genetic evidence supporting obesity as a risk factor for lung squamous cell carcinoma and the identification of MFAP1 as a shared genetic target.Discover oncology · 2026Article
- An integrative association analysis for complex diseases in underrepresented groups by leveraging the trans-ethnic genetic similarity.Briefings in bioinformatics · 2026Article
- Multi-organ network of cardiometabolic disease-depression multimorbidity revealed by phenotypic and genetic analyses of MR images.Nature communications · 2026Article
- Multi-trait analysis of pigmentation traits in relation to skin and ocular cancers: Relevance for evolutionary medicine.Evolution, medicine, and public health · 2026Article
- Transcript Imbalance from TENM4 Exon Skipping: Effects on Epilepsy and Genetic Pleiotropy.Molecular neurobiology · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice.
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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.