Cross-species data integration to prioritize causal genes in lipid metabolism

Abstract

Purpose of review More than one hundred loci have been identified from human genome-wide association studies (GWAS) for blood lipids. Despite the success of GWAS in identifying loci, subsequent prioritization of causal genes related to these loci remains a challenge. To address this challenge, recent work suggests that candidate causal genes within loci can be prioritized through cross-species integration using genome-wide data from the mouse. Recent findings Mouse model systems provide unparalleled access to primary tissues, like the liver, that are not readily available for human studies. Given the key role the liver plays in controlling blood lipid levels and the wealth of liver genome-wide transcript and protein data available in the mouse, these data can be leveraged. Using coexpression network analysis approaches with mouse genome-wide data, coupled with cross-species analysis of human lipid GWAS, causal genes within lipid loci can be prioritized. Prioritization through both mouse and human along with biochemical validation provide a systematic and valuable method to discover lipid metabolism genes. Summary The prioritization of causal lipid genes within GWAS loci is a challenging process requiring a multidisciplinary approach. Integration of data types across species, such as the mouse, can aid in causal gene prioritization.

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure

“…Through this process, we observed agreements in 11 of 18 GWAS variants, which provided some degree of confidence in the gene prioritization. However, we acknowledge that the PoPs method will miss variants that do not act through various mechanisms captured by PoPs 13 , highlighting the challenge in assigning the gene responsible for GWAS loci [28][29][30] . Third, we provide biological credibility for most of our genetic findings through an extensive and complementary analysis covering HF risk factors, LV cardiac MRI, and -omics.…”

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure

“…Through this process, we observed agreements in 11 of 18 GWAS variants, which provided some degree of confidence in the gene-prioritization. For the remaining nine variants there is some uncertainty in the gene responsible for the association, highlighting the challenge in assigning the gene responsible for GWAS loci [28][29][30] . Third, we provide biological credibility for most of our genetic findings through extensive and complementary analysis covering HF risk factors, LV cardiac MRI, and -omics.…”

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

“…While the gene prioritization approaches are not independent of each other, integrating several prioritization predictors provides higher confidence when attempting to characterize causal genes. Others have also highlighted the importance of such frameworks in different diseases [29,54,55].…”