Inferring causal relationships among intermediate phenotypes and biomarkers: a case study of rheumatoid arthritis | Zendy

Wentian Li | Zendy; Mingyi Wang | Zendy; Patricia Irigoyen | Zendy; Peter K. Gregersen | Zendy

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Inferring causal relationships among intermediate phenotypes and biomarkers: a case study of rheumatoid arthritis

Author(s) -

Wentian Li,

Mingyi Wang,

Patricia Irigoyen,

Peter K. Gregersen

Publication year - 2006

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btl100

Subject(s) - causal inference , confounding , inference , rheumatoid arthritis , phenotype , causal model , computational biology , rheumatoid factor , genetic association , genotype , bioinformatics , medicine , genetics , biology , econometrics , gene , computer science , statistics , immunology , mathematics , artificial intelligence , single nucleotide polymorphism

Genetic association analysis is based on statistical correlations which do not assign any cause-to-effect arrows between the two correlated variables. Normally, such assignment of cause and effect label is not necessary in genetic analysis since genes are always the cause and phenotypes are always the effect. However, among intermediate phenotypes and biomarkers, assigning cause and effect becomes meaningful, and causal inference can be useful.

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