Set-Valued Analysis for Genome-Wide Association Studies of Complex Diseases

This paper proposes set-value analysis for genome-wide association studies (GWAS) of complex diseases. A FIR model is established to describe the relationship between single nucleotide polymorphism (SNP) data and complex disease phenotype. An iteration algorithm based on maximum likelihood estimation is introduced to identify the parameter of model. A numerical simulation example is constructed to demonstrate the convergence of iteration algorithm. This method can be used to GWAS of schizophrenia and the error rate of phenotype inference is shown to be under 25%. The last part briefly summarizes this article and looks forward to the related further work.