Interpreting disease genetics using functional genomics

Het is overduidelijk dat veel complexe ziekten een genetische achtergrond hebben. Door de genoom-wijde associatie studies die over de afgelopen vijf jaar zijn uitgevoerd, zijn er veel genetische varianten gevonden die bijdragen aan deze genetische achtergrond. Voor veel van deze varianten is het echter niet duidelijk hoe zij werken en uiteindelijk ziekte veroorzaken. Voor het ontwikkelen van therapieën voor deze ziekten is het echter van groot belang dat deze functie word onderzocht. In dit proefschrift wordt aangetoond dat veel met ziekte geassocieerde varianten de activiteit (transcriptie) van genen op verschillende manieren beïnvloeden, wat aantoont dat een groot deel van deze varianten een regulatoire functie heeft. Daarnaast tonen we aan dat de genen die beïnvloed worden door deze ziekte varianten niet altijd dichtbij de ziekte variant liggen. Sommige varianten beïnvloeden genen die op grote genetische afstanden liggen van de variant, of soms op volledig andere chromosomen. We laten zien dat deze effecten functionele netwerken kunnen vormen die samen specifieke ziekteprocessen beschrijven. Als een gevolg hiervan wordt een belangrijk deel van de functie van de met ziekte geassocieerde varianten blootgelegd, Daarnaast presenteren we met dit proefschrift verschillende computationele methodes om de statistische power te vergroten om de associatie tussen genetische variatie en transcriptie te bepalen

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Background: Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Methods: Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Results: Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate <0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. Conclusions: To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response

Contains fulltext : 220669.pdf (publisher's version ) (Open Access)Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure

Not just differential gene expression but also differential gene regulation in immune cells account for individual differences in the immune response. Authors show here by single-cell RNA-sequencing of peripheral blood mononuclear cells from a large cohort of genetically diverse individuals that gene expression and regulatory changes in these cells depend on the context of and interactions between cell types, genetics, type of pathogen and time after exposure. The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease

Correction for both common and rare cell types in blood is important to identify genes that correlate with age

Background Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. Results Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model;

Genotype harmonizer:automatic strand alignment and format conversion for genotype data integration

BACKGROUND: To gain statistical power or to allow fine mapping, researchers typically want to pool data before meta-analyses or genotype imputation. However, the necessary harmonization of genetic datasets is currently error-prone because of many different file formats and lack of clarity about which genomic strand is used as reference. FINDINGS: Genotype Harmonizer (GH) is a command-line tool to harmonize genetic datasets by automatically solving issues concerning genomic strand and file format. GH solves the unknown strand issue by aligning ambiguous A/T and G/C SNPs to a specified reference, using linkage disequilibrium patterns without prior knowledge of the used strands. GH supports many common GWAS/NGS genotype formats including PLINK, binary PLINK, VCF, SHAPEIT2 & Oxford GEN. GH is implemented in Java and a large part of the functionality can also be used as Java 'Genotype-IO' API. All software is open source under license LGPLv3 and available from http://www.molgenis.org/systemsgenetics. CONCLUSIONS: GH can be used to harmonize genetic datasets across different file formats and can be easily integrated as a step in routine meta-analysis and imputation pipelines