Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach
Gu, J.; Fuller, C. K.; Zheng, J.; Li, H.
Show abstract
Phenotypic correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. Here we developed a gene-based approach to measure genetic overlap between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to gene-phenotype association profile by integrating GWAS with eQTL data using a newly developed algorithm called Sherlock-II; 2) measuring the genetic overlap between a pair of traits by a normalized distance and the associated p value between the two gene-phenotype association profiles; 3) delineating genes/pathways involved. Application of this approach to a set of GWAS data covering 59 human traits detected significant overlap between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based genetic similarity measures. Examples include Cancer and Alzheimers Disease (AD), Rheumatoid Arthritis and Crohns disease, and Longevity and Fasting glucose. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Cancer and AD are co-associated with genes involved in hypoxia response and P53/apoptosis pathways, suggesting specific mechanisms underlying the inverse correlation between them. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery 97%
- Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies 97%
- Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model 96%
Similar papers in this journal
- A novel Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis 96%
- An allelic series rare variant association test for candidate gene discovery 96%
- Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization 96%
Similar papers in this journal
- Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits 96%
- Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing 95%
- Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation 95%
Similar papers in this journal
- Co-expression-wide association studies link genetically regulated interactions with complex traits 97%
- SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification 97%
- Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.