The genetic structure of the Belgian population.

BACKGROUND: National and international efforts like the 1000 Genomes Project are leading to increasing insights in the genetic structure of populations worldwide. Variation between different populations necessitates access to population-based genetic reference datasets. These data, which are important not only in clinical settings but also to potentiate future transitions towards a more personalized public health approach, are currently not available for the Belgian population. RESULTS: To obtain a representative genetic dataset of the Belgian population, participants in the 2013 National Health Interview Survey (NHIS) were invited to donate saliva samples for DNA analysis. DNA was isolated and single nucleotide polymorphisms (SNPs) were determined using a genome-wide SNP array of around 300,000 sites, resulting in a high-quality dataset of 189 samples that was used for further analysis. A principal component analysis demonstrated the typical European genetic constitution of the Belgian population, as compared to other continents. Within Europe, the Belgian population could be clearly distinguished from other European populations. Furthermore, obvious signs from recent migration were found, mainly from Southern Europe and Africa, corresponding with migration trends from the past decades. Within Belgium, a small north-west to south-east gradient in genetic variability was noted, with differences between Flanders and Wallonia. CONCLUSIONS: This is the first study on the genetic structure of the Belgian population and its regional variation. The Belgian genetic structure mirrors its geographic location in Europe with regional differences and clear signs of recent migration.

Modification of citrulline residues with 2,3-butanedione facilitates their detection by liquid chromatography/mass spectrometry.

Citrullination is a post-translational modification (PTM) that results from the deimination of the amino acid arginine into citrulline by Peptidyl Arginine Deiminase enzymes and occurs in a wide range of proteins in health and disease. This modification causes a 1 Da mass shift, which can be used to identify citrullination sites in proteins by the use of mass spectrometry. However, other PTMs, such as deamidation from asparagine to aspartic acid or from glutamine to glutamic acid, can also cause a 1 Da mass shift, making correct interpretation of the data more difficult. We developed a chemical tagging strategy which, combined with an open source search application, allowed us to selectively pinpoint citrullinated peptides in a complex mixture after liquid chromatography/mass spectrometry (LC/MS) analysis. After incubation of a peptide mixture with 2,3 butanedione, citrulline residues were covalently modified which resulted in a 50 Da shift in singly charged mass. By comparison of the peptide mass fingerprint from a modified and an unmodified version of the same sample, our in-house search application was able to identify the citrullinated peptides in the mixture. This strategy was optimized on synthetic peptides and validated on a digest of in vitro citrullinated fibrinogen, where different proteolytic enzymes were used to augment the protein coverage. This new method results in easy detection of citrullinated residues, without the need for complex mass spectrometry equipment.

FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals.

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.