# From genes to trajectories: mapping genetic influences on Huntington’s disease progression

## TL;DR

This paper introduces a model to study how genetic factors influence the progression of Huntington’s disease, revealing new SNPs that affect disease stages.

## Contribution

A novel probabilistic model identifies previously unreported genetic effects on Huntington’s disease progression.

## Key findings

- The model identified SNPs with unreported effects on HD progression at distinct stages.
- These findings suggest new genes may influence HD progression and clinical outcomes.
- The framework is expected to be applicable to other genetically influenced diseases.

## Abstract

There are many diseases with established genetic factors, such as Huntington’s disease (HD), that are characterized by variable rates of progression. However, beyond the contribution of the known genetic factors - in this case the Huntingtin (HTT) gene - the impact of the full human genome on the natural progression of such diseases throughout a patient’s life remains largely unknown. The increased availability of genome wide association (GWA) data in HD gene expansion carriers (HDGECs), combined with the clinical assessment scores on the same set of patients, has provided a perfect opportunity to assess the potentially broader genetic impact on the natural progression of HD.

We present a genetics-driven, probabilistic disease progression model designed to identify and investigate the ways in which a range of genetic factors affect the natural progression of HD. When applied to a clinico-genomic HD dataset, our model identified several single nucleotide polymorphisms (SNPs) with previously unreported effects on disease progression that act at distinct stages and with varying magnitudes. This discovery may shed light on the potential mechanistic impact of previously unidentified genes on HD that may have implications for clinical management. As increasing amounts of GWA data become available more generally, we anticipate that this modeling framework will be broadly applicable to other diseases with strong genetic components.

The source code for IHDPM is available at https://github.com/BiomedSciAI/IHDPM