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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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A Bayesian method for estimating gene-level polygenicity under the framework of transcriptome-wide association study.

Arunabha Majumdar¹, Bogdan Pasaniuc²

¹Department of Mathematics, Indian Institute of Technology Hyderabad, Kandi, Telangana, India.

Statistics in Medicine

|August 29, 2023

Summary

We introduce genepoly, a Bayesian method to estimate gene-level polygenicity, revealing the proportion of genes affecting complex traits. This approach enhances understanding of genetic architecture, with height being most polygenic and asthma least.

Keywords:

MCMC complex trait genomics gene-level association hierarchical models spike and slab prior

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Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Polygenicity, the influence of multiple genetic variants on complex traits, is typically assessed at the single nucleotide polymorphism (SNP) level.
Estimating polygenicity at the gene level offers greater biological relevance for understanding trait architecture.
Transcriptome-Wide Association Studies (TWAS) provide a framework for gene-level genetic analyses.

Purpose of the Study:

To introduce the concept of gene-level polygenicity, defined as the proportion of genes with a non-zero effect on a complex trait.
To develop and validate a Bayesian statistical method, named genepoly, for estimating gene-level polygenicity.
To identify the subset of genes significantly contributing to complex traits using a gene-centric approach.

Main Methods:

Developed a Bayesian approach (genepoly) utilizing spike and slab priors to estimate gene-level polygenicity.
Simulated data to evaluate the performance of genepoly in estimating polygenicity and identifying non-null genes.
Applied genepoly to UK Biobank data for seven phenotypes, integrating gene expression data.

Main Results:

genepoly efficiently estimates gene-level polygenicity and identifies non-null genes with high specificity and good sensitivity.
The method's accuracy improves with larger Genome-Wide Association Study (GWAS) sample sizes and reference expression panel sizes.
Height was identified as the most polygenic trait, while asthma was found to be the least polygenic among the analyzed phenotypes.

Conclusions:

Gene-level polygenicity provides a biologically meaningful measure of genetic architecture for complex traits.
The genepoly method offers a robust tool for estimating gene-level polygenicity and discovering trait-associated genes.
Findings highlight differential polygenicity across traits, with height exhibiting extensive genetic influence and asthma showing more limited genetic contribution at the gene level.