View Program - 2022 Population, Evolutionary, and Quantitative Genetics Conference

A common goal in genome-wide association (GWA) studies is to characterize the relationship between genotypic and phenotypic variation. Linear models are widely used tools in GWA analyses, in part, because they provide significance measures which detail how individual single nucleotide polymorphisms (SNPs) are statistically associated with a trait or disease of interest. However, traditional linear regression largely ignores non-additive genetic variation, and the univariate SNP-level mapping approach has been shown to be underpowered and challenging to interpret for certain trait architectures. While machine learning methods such as neural networks are well known to account for complex data structures, these same algorithms have also been criticized as “black box” since they do not naturally carry out statistical hypothesis testing like classic linear models. This limitation has prevented machine learning approaches from being used for association mapping tasks in GWA applications. In this talk, we present flexible and scalable classes of Bayesian multi-layer perceptron models which provide interpretable probabilistic summaries such as posterior inclusion probabilities and credible sets for association and fine-mapping tasks in high-dimensional GWA studies. We illustrate the benefits of our methods over state-of-the-art linear approaches using extensive simulations. We also demonstrate the ability of these methods to recover both novel and previously discovered genomic associations using traits from the Wellcome Trust Case Control Consortium (WTCCC), the Framingham Heart Study, and the UK Biobank.

Association and Fine-Mapping with Bayesian Machine Learning Methods