Using genetic data to strengthen causal inference in observational research
Causal inference is essential across the biomedical, behavioural and social sciences. By
progressing from confounded statistical associations to evidence of causal relationships …
progressing from confounded statistical associations to evidence of causal relationships …
Causal inference with genetic data: Past, present, and future
JB Pingault, R Richmond… - Cold Spring …, 2022 - perspectivesinmedicine.cshlp.org
The set of methods discussed in this collection has emerged from the convergence of two
scientific fields—genetics and causal inference. In this introduction, we discuss relevant …
scientific fields—genetics and causal inference. In this introduction, we discuss relevant …
Validating, augmenting and refining genome-wide association signals
JPA Ioannidis, G Thomas, MJ Daly - Nature Reviews Genetics, 2009 - nature.com
Studies using genome-wide platforms have yielded an unprecedented number of promising
signals of association between genomic variants and human traits. This Review addresses …
signals of association between genomic variants and human traits. This Review addresses …
Orienting the causal relationship between imprecisely measured traits using GWAS summary data
Inference about the causal structure that induces correlations between two traits can be
achieved by combining genetic associations with a mediation-based approach, as is done in …
achieved by combining genetic associations with a mediation-based approach, as is done in …
Identifying causal variants at loci with multiple signals of association
Although genome-wide association studies have successfully identified thousands of risk
loci for complex traits, only a handful of the biologically causal variants, responsible for …
loci for complex traits, only a handful of the biologically causal variants, responsible for …
CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies
Genome-wide association studies (GWASs) have revolutionized the field of complex trait
genetics over the past decade, yet for most of the significant genotype-phenotype …
genetics over the past decade, yet for most of the significant genotype-phenotype …
Comparison of strategies for scalable causal discovery of latent variable models from mixed data
Modern technologies allow large, complex biomedical datasets to be collected from patient
cohorts. These datasets are comprised of both continuous and categorical data (“Mixed …
cohorts. These datasets are comprised of both continuous and categorical data (“Mixed …
Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics
Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a
wide range of traits using summary statistics from genome-wide association studies …
wide range of traits using summary statistics from genome-wide association studies …
Inferring causal relationships between risk factors and outcomes from genome-wide association study data
An observational correlation between a suspected risk factor and an outcome does not
necessarily imply that interventions on levels of the risk factor will have a causal impact on …
necessarily imply that interventions on levels of the risk factor will have a causal impact on …
Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome
A major application for genome-wide association studies (GWAS) has been the emerging
field of causal inference using Mendelian randomization (MR), where the causal effect …
field of causal inference using Mendelian randomization (MR), where the causal effect …