Genome-wide Causation Studies of Complex Diseases

TL;DR

This paper introduces genome-wide causation studies (GWCS) as a new approach to uncover causal genetic structures of complex diseases, moving beyond traditional association analysis used in GWAS.

Contribution

The paper proposes GWCS and additive noise models (ANMs) as novel methods to identify causal genetic variants, addressing limitations of association signals in GWAS.

Findings

ANMs effectively test for causation with controlled error rates

GWCS reveals distinct causal signals from association signals in schizophrenia

Simulation and real data show low overlap between association and causation signals

Abstract

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.