As of February 6th, 2017, a catalog of published Genome-Wide Association Studies (GWAS) had reported significant association of 26,791 SNPs with more than 1704 traits in 2,337 publications. Despite significant progress in dissecting the genetic architecture of complex diseases by association analysis, understanding the etiology and mechanism of complex diseases remains elusive. It is known that significant findings of association analysis have been lack of consistency and often proved to be controversial. The current approach to genomic analysis lacks breadth (number of variables analyzed at a time) and depth (the number of steps which are taken by the genetic variants to reach the clinical outcomes, across genomic and molecular levels) and its paradigm of analysis is association and correlation analysis. Association analyses are unable to discover mechanisms of disease etiology and pathology and provide useful tools for precision medicine. To facilitate the advancement of genetic studies of complex diseases, we must change the paradigm from association to causal inference. Causal inference from observational and interventional data is a cornerstone of scientific discovery and an essential component for discovery of mechanism of diseases. In many cases, performing interventional experiments is unethical or infeasible. In most genetic studies inferring causal relations must be from observational data alone.
To shift the current paradigm of genetic studies of complex diseases from association to causation, this Research Topic will attempt to address six key challenges:
(1) shift the paradigm of genetic studies of complex traits from association to causation,
(2) develop statistical methods and computational algorithms for genome-wide causal studies with qualitative and quantitative phenotypes,
(3) develop algorithms for causal inference with confounding variables
(4) develop algorithms for multilevel causal trans-omics network analysis,
(5) develop a general framework for integrated machine learning and causal inferences and
(6) unify causal inference for family, population and cells.
We encourage researchers with varying backgrounds to submit work that develops or applies causal inference theory and tools for genomic research and make paradigm shift from association to causation practically feasible.
As of February 6th, 2017, a catalog of published Genome-Wide Association Studies (GWAS) had reported significant association of 26,791 SNPs with more than 1704 traits in 2,337 publications. Despite significant progress in dissecting the genetic architecture of complex diseases by association analysis, understanding the etiology and mechanism of complex diseases remains elusive. It is known that significant findings of association analysis have been lack of consistency and often proved to be controversial. The current approach to genomic analysis lacks breadth (number of variables analyzed at a time) and depth (the number of steps which are taken by the genetic variants to reach the clinical outcomes, across genomic and molecular levels) and its paradigm of analysis is association and correlation analysis. Association analyses are unable to discover mechanisms of disease etiology and pathology and provide useful tools for precision medicine. To facilitate the advancement of genetic studies of complex diseases, we must change the paradigm from association to causal inference. Causal inference from observational and interventional data is a cornerstone of scientific discovery and an essential component for discovery of mechanism of diseases. In many cases, performing interventional experiments is unethical or infeasible. In most genetic studies inferring causal relations must be from observational data alone.
To shift the current paradigm of genetic studies of complex diseases from association to causation, this Research Topic will attempt to address six key challenges:
(1) shift the paradigm of genetic studies of complex traits from association to causation,
(2) develop statistical methods and computational algorithms for genome-wide causal studies with qualitative and quantitative phenotypes,
(3) develop algorithms for causal inference with confounding variables
(4) develop algorithms for multilevel causal trans-omics network analysis,
(5) develop a general framework for integrated machine learning and causal inferences and
(6) unify causal inference for family, population and cells.
We encourage researchers with varying backgrounds to submit work that develops or applies causal inference theory and tools for genomic research and make paradigm shift from association to causation practically feasible.