New Advances in Disease Risk Prediction

Objectives:

The primary goal was to identify genetic variants linked to Alzheimer’s disease (AD) using advanced genomic techniques. The study aimed to enhance understanding of the genetic underpinnings of AD by employing sophisticated methods to analyse large-scale genomic data. Key objectives included pinpointing novel genetic variants associated with AD, elucidating their functional roles, and exploring their potential impact on disease mechanisms.

By utilising innovative tools and methodologies, the research sought to pave the way for new insights into AD pathology and potential therapeutic targets.

Solutions:

The study combined data from genome-wide association studies (GWAS) with expression quantitative trait loci (eQTL) datasets to enhance the analysis of genetic variants. Meta-analysis and colocalisation techniques were employed to pinpoint and validate genetic variants associated with AD, providing a detailed view of the genetic landscape.

We Identified variants were functionally annotated and linked to specific biological pathways, helping to elucidate their role in AD.

Challenges:

1. Alzheimer’s disease has a complex genetic background, making it difficult to identify significant variants.
2. Merging and analysing large genomic datasets from various sources required careful handling and sophisticated methods.
3. Interpreting how genetic variants affect biological pathways and disease mechanisms posed a significant challenge.

Impact:

1. The study identified several novel variants associated with Alzheimer’s disease, expanding the understanding of its genetic basis.
2. Linking these variants to biological pathways provided new insights into how they may contribute to AD.
3. The findings offer potential targets for future research and therapeutic development, advancing the field of Alzheimer’s research.