Identifying Inheritance Patterns of Allelic Imbalance, using Integrative Modeling and Bayesian Inference

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Interpreting the impact of novel mutations on phenotypic traits, especially cis-regulatory variants, is complex.
• Allelic imbalance, common in rare variants, offers a way to infer inheritance patterns in genetic studies.
• Existing allele-specific expression (ASE) methods often focus on single individuals, limiting insights.

Purpose of the Study:

• To develop a joint inference method for analyzing allele-specific expression (ASE) and chromatin accessibility across multiple individuals in a trio.
• To improve the estimation of ASE and identify its mode of inheritance simultaneously.
• To enhance statistical power and quantify uncertainty in genetic analyses of phenotypic traits.

Main Methods:

• A Bayesian approach for joint inference across multiple individuals in a trio.
• Simultaneous estimation of ASE and identification of inheritance modes.
• Application to ATAC-seq and RNA-seq data from several trios.

Main Results:

• The joint inference method improves statistical power by aggregating information across individuals.
• The model successfully estimates uncertainty and ranks inheritance modes by posterior probability.
• Demonstrated applicability to allele-specific chromatin accessibility, linking ASE to chromatin state and potential causal variants.

Conclusions:

• Joint analysis of related individuals enhances the understanding of genetic effects on gene regulation.
• This methodology facilitates the routine collection of family trio data for genetic studies.
• Improved interpretation of cis-regulatory variants and their contribution to phenotypic variation.