Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the signals identified by association analysis may not have specific pathological relevance to diseases so that a large fraction of disease-causing genetic variants is still hidden. Association is used to measure dependence between two variables or two sets of variables. GWAS test association between a disease and single-nucleotide polymorphisms (SNPs) (or other genetic variants) across the genome. Association analysis may detect superficial patterns between disease and genetic variants. Association signals provide limited information on the causal mechanism of diseases. The use of association analysis as a major analytical platform for genetic studies of complex diseases is a key issue that may hamper discovery of disease mechanisms, calling into the questions the ability of GWAS to identify loci-underlying diseases. It is time to move beyond association analysis toward techniques, which enables the discovery of the underlying causal genetic structures of complex diseases. To achieve this, we propose the concept of genome-wide causation studies (GWCS) as an alternative to GWAS and develop additive noise models (ANMs) for genetic causation analysis. Type 1 error rates and power of the ANMs in testing causation are presented. We conducted GWCS of schizophrenia. Both simulation and real data analysis show that the proportion of the overlapped association and causation signals is small. Thus, we anticipate that our analysis will stimulate serious discussion of the applicability of GWAS and GWCS.
Keywords: GWAS; GWCS; additive noise models; causal inference; linkage disequilibrium; prediction.