Alzheimer’s disease (AD) is a neurodegenerative disease affecting roughly 40 million people. 70% of the heritability of AD is expected to be explained by Structural Variants (SVs), however these have been scarcely studied in the context of AD. This study aims to find SVs associated with AD and to investigate the properties of these correlated SVs. To this end, a dataset created using Third Generation Sequencing of Single Nucleotide Polymorphisms (SNPs) and their correlation with SVs was utilised and combined with the full summary statistics and fine-mapped results of a large AD Genome Wide Association Study. This resulted in 85 unique SVs with significant correlations to known AD associated SNPs, of which 5 were also discovered in previous research, and 80 were novel. SVs were then associated with their nearest genes, however this resulted in a relatively low overlap with known AD genes and few to no results when applied to Gene Set Enrichment Analysis. Additionally, the data was tested for enrichment of Tandem Repeats (TRs), Transposable Elements, regulatory elements and mechanisms of gene expression, which found enrichment of TRs and heterochromatin areas and a depletion of deletions, weak enhancers, and transcription elongation areas.

Coronary artery disease (CAD) is a condition characterized by the narrowing or blockage of the arteries that supply blood to the heart. It is a major global health burden and is known to be correlated with genetics, but the details of the genetic contribution remain unclear. In this study, we explored the associations between structural variants (SVs) and single nucleotide polymorphisms (SNPs) identified through previous CAD genome-wide association studies (GWAS). We identified the most relevant associations by ranking them using a proposed composite score, combining effect sizes and p-values. Filtering was applied to keep the most significant associations within a 500kb genomic window, resulting in 968 SNP-SV associations. Cross-referencing with tissue-specific eQTL data from the GTEx Portal indicated 321 SNP-SV associations that impact gene expression in coronary artery tissue. GSEA identified associated pathways involving dopachrome isomerase, phenylpyruvate tautomerase, and
catalytic activity. The results suggest that the SVs make an important contribution to the regulation of CAD-related gene expression and the overall risk of CAD.

Genome-wide association studies (GWAS) are commonly used to identify genetic variants associated with human traits by comparing genetic differences between diseased and healthy individuals. One way to gain insights into the biological consequences of these variants is to use quantitative trait locus (QTL) analysis. These connect SNP with variations in gene expression levels among individuals. QTL studies are mostly done on single nucleotide changes, but as SVs are bigger and have greater impact on traits, SV-QTL connections are of great interest. Using Gosling.js, a tool was developed to easily display the links and significance between SNPs, associated eQTLs, and SVs. The main purpose of this tool is to provide clear visualizations, while also offering options for further exploration of the chromosome. The existing search functionalities from snpXplorer have been integrated and enhanced. Users can define a variable window size before querying, allowing for flexible data examination. Additionally, the tool supports requests for data across multiple tissues. For improved performance and usability, the option to select which data tracks are shown were added.

This paper describes ZygosDB, a novel and efficient read-only database designed specifically for querying positional genomic data required for Genome-Wide Association Studies (GWAS). ZygosDB addresses limitations of existing solutions like Tabix by offering optimized data structures, compression techniques, and parallel query execution.

Our evaluation shows that ZygosDB achieves a significant speedup of 2-5 times in query throughput compared to Tabix. This improvement comes from our focus on efficient data storage and retrieval tailored to the specific needs of GWAS.

The paper also explores the impact of multi-threading on query performance and the role of compression algorithms in optimizing query throughput. We identify a decrease in performance beyond a certain number of threads and discuss the influence of compression algorithms like Gzip and LZ4.

While ZygosDB offers substantial performance gains, future work should explore avenues for further optimization, such as measuring query latency, refining memory usage, and investigating specialized column support. Overall, ZygosDB establishes itself as a powerful tool for efficient querying of large genomic datasets, facilitating more effective GWAS.

in bio-informatics visualisations are often used to relay the results of genome-wide association studies (GWAS), which can be used to get a better inside into the genetics of diseases. Over the years many websites have been developed, which can create visualisations for a variety of GWAS, with one of the most famous being locuszoom. Recent advancements in technology have allowed for collection of data which wasn't previously possible and which no visualisation tool currently supports. For the first time it is now possible to collect data on the interactions between two different types of genetic mutations: Single Nucleotide Polymorphisms, which consist of a single change in one nucleotide (one letter in a string of DNA) and Structural Variants, which consist of a change in a series of nucleotides. This paper builds upon an already existing visualisation tool snpxplorer and the goal is to design a visualisation, which captures thesse interactions and to add a new, easily integratable section to snpxplorer containing this new visualisation.

Understanding the role of genes and genetic variants is a key challenge in unraveling the driving mechanisms of Alzheimer's disease (AD). Single-cell RNA sequencing is a technique that quantifies gene expression at the cell (type) level enabling investigation of the roles of different cell types in disease. We analyzed changes in gene (co-)expression associated with genetic variants using single-cell RNA sequencing data (>1.3 million cells) from the dorsolateral prefrontal cortex (DLPFC) of 379 individuals of the ROSMAP cohort. Our single cell expression quantitative trait loci (sc-eQTL) analysis determined 3,337,065 sc-eQTLs, linking 1,882,645 SNPs to changes in expression of 8,057 genes in 7 major cell types. Next, we investigated the association of genetic variants with changes in co-expression for gene pairs (co-eQTLs), focusing on a set of variants and genes relevant to AD. Our novel non-parametric method for co-eQTL analysis compares gene co-expression distributions between SNP genotypes. We found 6,878 cell type specific co-eQTLs (variant-gene-gene combinations) relating to 18 AD variants. Although a substantial proportion of the findings is driven by eQTL effects, our method identified co-eQTLs that would not have been discovered in a correlation-based analysis. Most notable, we found variant rs13237518 (located in the TMEM106B gene) to associate with expression changes in a subset of 25 genes in excitatory neurons which is possibly indicative of higher-level disruptions related to the variant. Overall, we show that exploring genetic variant-associated changes in gene (co-)expression is a promising approach in finding cell type specific mechanisms that may be altered in AD.

Motivation: Alzheimer’s disease (AD) is a highly prevalent disease whose genetic risk factors remain largely unknown. One potential genetic risk factor is tandem repeat expansions, which have been associated with over 40 diseases, most of which affect the nervous system. Detecting VNTRs from short-read data is a challenging task, leaving many VNTRs unidentified. To date only one variable number tandem repeat (VNTR) expansion (in the ABCA7 gene) has been linked to AD. We hypothesize there are many more VNTR expansions to be discovered that associate with an increased risk of AD.
Results: We created a pipeline with which we overcame the common limitations of VNTR detection (namely, the need for a predefined set of repeats and limited detectable VNTR sizes due to read length). We performed a genome-wide search for VNTRs with a motifsize ≥ 7 bp that show repeat size variations associated with AD. We detected 71 VNTR expansions and 1242 contractions, including expansions in genes ADAMTSL3, ARHGEF10, DIP2C, EVC2, GRM8, MPPED1, PID1 and an expansion in the SCIMP gene close to a well-known AD single nucleotide polymorphism (SNP). Our pipeline is, to our knowledge, one of the very few to detect VNTRs exceeding read length without a predefined set of repeats. It is able to detect both previously reported and novel VNTRs, resulting in a promising set of VNTRs showing an association with AD.