Indirect effect inference and application to GAW20 data

Genetic and Epigenetic Signatures Associated with the Divergence of Aquilegia Species

Widely grown in the Northern Hemisphere, the genus Aquilegia (columbine) is a model system in adaptive radiation research. While morphological variations between species have been associated with environmental factors, such as pollinators, how genetic and epigenetic factors are involved in the rapid divergence in this genus remains under investigated. In this study, we surveyed the genomes and DNA methylomes of ten Aquilegia species, representative of the Asian, European and North American lineages. Our analyses of the phylogeny and population structure revealed high genetic and DNA methylomic divergence across these three lineages. By multi-level genome-wide scanning, we identified candidate genes exhibiting lineage-specific genetic or epigenetic variation patterns that were signatures of inter-specific divergence. We demonstrated that these species-specific genetic variations and epigenetic variabilities are partially independent and are both functionally related to various biological processes vital to adaptation, including stress tolerance, cell reproduction and DNA repair. Our study provides an exploratory overview of how genetic and epigenetic signatures are associated with the diversification of the Aquilegia species.

Causal modeling in a multi-omic setting: insights from GAW20

BACKGROUND

Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disease phenotypes. The GAW20 Causal Modeling Working Group has applied complementary approaches (eg, Mendelian randomization, structural equations modeling, Bayesian networks) to discover novel causal effects of genomic and epigenomic variation on lipid phenotypes, as well as to validate prior findings from observational studies.

RESULTS

Two Mendelian randomization studies have applied novel approaches to instrumental variable selection in methylation data, identifying bidirectional causal effects of CPT1A and triglycerides, as well as of RNMT and C6orf42, on high-density lipoprotein cholesterol response to fenofibrate. The CPT1A finding also emerged in a Bayesian network study. The Mendelian randomization studies have implemented both existing and novel steps to account for pleiotropic effects, which were independently detected in the GAW20 data via a structural equation modeling approach. Two studies estimated indirect effects of genomic variation (via DNA methylation and/or correlated phenotypes) on lipid outcomes of interest. Finally, a novel weighted R2 measure was proposed to complement other causal inference efforts by controlling for the influence of outlying observations.

CONCLUSIONS

The GAW20 contributions illustrate the diversity of possible approaches to causal inference in the multi-omic context, highlighting the promises and assumptions of each method and the benefits of integrating both across methods and across omics layers for the most robust and comprehensive insights into disease processes.