Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation

A major challenge in biomedical research is to identify causal relationships among genotypes, phenotypes, and clinical outcomes from high-dimensional measurements. Causal networks have been widely used in systems genetics for modeling gene regulatory systems and for identifying causes and risk factors of diseases. In this chapter, we describe fundamental concepts and algorithms for constructing causal networks from observational data. In biological context, causal inferences can be drawn from the natural experimental setting provided by Mendelian randomization, a term that refers to the random assignment of genotypes at meiosis. We show that genetic variants may serve as instrumental variables, improving estimation accuracy of the causal effects. In addition, identifiability issues that commonly arise when learning network structures may be overcome by using prior information on genotype–phenotype relations. We discuss four recent algorithms for genotype–phenotype network structure learning, namely (1) QTL-directed dependency graph, (2) QTL+Phenotype supervised orientation, (3) QTL-driven phenotype network, and (4) sparsity-aware maximum likelihood (SML).