Distinguishing correlation from causation using genome-wide association studies

11/21/2018
by   Luke J. O'Connor, et al.
0

Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships between genetically correlated traits using GWAS summary association statistics. In particular, our method quantifies what part of the genetic component of trait 1 is also causal for trait 2 using mixed fourth moments E(α_1^2α_1α_2) and E(α_2^2α_1α_2) of the bivariate effect size distribution. If trait 1 is causal for trait 2, then SNPs affecting trait 1 (large α_1^2) will have correlated effects on trait 2 (large α_1α_2), but not vice versa. We validated this approach in extensive simulations. Across 52 traits (average N=331k), we identified 30 putative genetically causal relationships, many novel, including an effect of LDL cholesterol on decreased bone mineral density. More broadly, we demonstrate that it is possible to distinguish between genetic correlation and causation using genetic association data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/04/2019

On genetic correlation estimation with summary statistics from genome-wide association studies

Genome-wide association studies (GWAS) have been widely used to examine ...
research
01/24/2019

Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data

Summary statistics of genome-wide association studies (GWAS) teach causa...
research
04/29/2019

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

Background: Many genome-wide association studies have detected genomic r...
research
03/29/2016

Locally Epistatic Models for Genome-wide Prediction and Association by Importance Sampling

In statistical genetics an important task involves building predictive m...
research
10/30/2017

Implicit Causal Models for Genome-wide Association Studies

Progress in probabilistic generative models has accelerated, developing ...
research
01/09/2019

The Mahalanobis kernel for heritability estimation in genome-wide association studies: fixed-effects and random-effects methods

Linear mixed models (LMMs) are widely used for heritability estimation i...
research
08/10/2018

Genome-Wide Association Studies: Information Theoretic Limits of Reliable Learning

In the problems of Genome-Wide Association Study (GWAS), the objective i...

Please sign up or login with your details

Forgot password? Click here to reset