  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. Genome-wide association studies test association
between a disease and 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 hampers
discovery of the mechanism of diseases, calling into question the ability of
GWAS to identify loci underlying diseases. It is time to move beyond
association analysis toward techniques enabling the discovery of the underlying
causal genetic strctures of complex diseases. To achieve this, we propose a
concept of a genome-wide causation studies (GWCS) as an alternative to GWAS and
develop additive noise models (ANMs) for genetic causation analysis. Type I
error rates and power of the ANMs to test for causation are presented. We
conduct GWCS of schizophrenia. Both simulation and real data analysis show that
the proportion of the overlapped association and causation signals is small.
Thus, we hope that our analysis will stimulate discussion of GWAS and GWCS.
