Each week, we publish lay abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from Statistics in Medicine with the full article now available to read here.

Majumdar, A, Pasaniuc, B. A Bayesian method for estimating gene-level polygenicity under the framework of transcriptome-wide association study. Statistics in Medicine. 2023; 1–19. doi: 10.1002/sim.9892

Complex human traits, such as body mass index (BMI), cholesterol levels, or diseases such as asthma and type 2 diabetes, are influenced by both genetic and environmental factors. BMI is an example of a quantitative trait, and asthma is an example of a disease trait. The genetic underpinnings of one trait can vary from others. For instance, more genetic factors can contribute to the variation of one quantitative trait than another. Or more genetic factors can act on the risk of a disease than another. The phenomenon that multiple genetic factors regulate a trait is known as polygenicity. Estimating polygenicity provides valuable insights into the genetic architecture of the trait. 

Genes are crucial genetic units. Instead of all genes, a small subset of them can impact a trait. This paper aims to develop a statistical and computational framework to learn about the proportion of genes that govern a trait, termed gene-level polygenicity. The method, genepoly, simultaneously opines about the subset of genes regulating the trait.

 genepoly was applied to various complex traits. Among three anthropometric traits, height appeared to be more polygenic than BMI and waist-to-heap ratio. For lipid traits, HDL seemed to be more polygenic than LDL. Asthma, a disease trait, emerged as the least polygenic in the analyses. genepoly identified the subsets of genes affecting each of these traits. The findings inform the biology of these traits in a novel manner. 

In conclusion, genepoly is a methodologically sound statistical approach to gauging the gene-level polygenicity for a complex trait and the subset of genes modulating the trait. It is computationally efficient and can be applied to other biomedically important traits to understand their genetic architecture better.