Complex genetics of CAD

Coronary artery disease (CAD) is a complex disease, reflecting the interplay of genetic and environmental factors. The identification of genetic risk loci strongly increases with new technologies like sequencing or large-scale genome-wide association studies (GWAS). Most identified variants in established CAD susceptibility loci are in non-coding regions, including intergenic or intronic variations. 

Due to methodological limitations, the true causal variants and genes associated with the traits are still difficult to identify. New algorithms like “finemapping” and gene prioritization tools are used to identify causal variants.

The overall goal is to find the true disease-causing genes, which are involved in the pathogenesis of CAD.

In addition, we aim to develop new methods to help to understand functional effects of non-coding variants in regulatory domains of the genome such as expression quantitative trait locus (eQTL).

Dr. rer. nat. Sören Mucha

Syed M. Ijlal Haider, MSc


Syed M. Ijlal Haider, MSc

The candidate gene studies focus on associations between genetic variation within pre-defined gene of interest and phenotypes. This approach is in contrast to GWAS, which basically scan the entire genome for common genetic variation.

Apart from GWAS, we are performing a candidate gene-based approach to provide a better insight into the pathobiology of CAD by exposing novel CAD genes and its contributing effects. For this purpose, we have selected 107 established CAD loci and sequenced more than 18,000 cases and controls in German population. In addition, we further have included 50,000 whole exome sequencing data from UK Biobank in order to further increase the statistical power. Careful bioinformatics analysis and strict quality control is being done to get reliable results.

The aim of this project is to identify rare (Minor allele frequency < 0.01) and highly deleterious variants associated with coronary artery disease (CAD) and myocardial infarction (MI) by using different statistical approaches like gene-based aggregation analysis and meta-analysis.

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