Tracing Behçet's disease origins along the Silk Road: an anthropological evolutionary genetics perspective.

OBJECTIVES: Behçet's disease is a multifactorial vasculitis that shows its highest prevalence in geographical areas historically involved in the Silk Road, suggesting that it might have originated somewhere along these ancient trade routes. This study aims to provide a first clue towards genetic evidence for this hypothesis by testing it via an anthropological evolutionary genetics approach. METHODS: Behçet's disease variation at ancestry informative mitochondrial DNA control region and haplogroup diagnostic sites was characterised in 185 disease subjects of Italian descent and set into the Eurasian mitochondrial landscape by comparison with nearly 9,000 sequences representative of diversity observable in Italy and along the main Silk Road routes. RESULTS: Dissection of the actual genetic ancestry of disease individuals by means of population structure, spatial autocorrelation and haplogroup analyses revealed their closer relationships with some Middle Eastern and Central Asian groups settled along the Silk Road than with healthy Italians. CONCLUSIONS: These findings support the hypothesis that the Behçet's disease genetic risk has migrated to western Eurasia in parallel with ancestry components typical of Silk Road-related groups. This provided new insights that are useful to improve the understanding of disease origins and diffusion, as well as to inform future association studies aimed at properly accounting for the actual genetic ancestry of the examined Behçet's disease samples in order to minimise the detection of spurious associations and to improve the identification of genetic variants with actual clinical relevance.

Imputation reliability on DNA biallelic markers for drug metabolism studies.

BACKGROUND: Imputation is a statistical process used to predict genotypes of loci not directly assayed in a sample of individuals. Our goal is to measure the performance of imputation in predicting the genotype of the best known gene polymorphisms involved in drug metabolism using a common SNP array genotyping platform generally exploited in genome wide association studies. METHODS: Thirty-nine (39) individuals were genotyped with both Affymetrix Genome Wide Human SNP 6.0 (AFFY) and Affymetrix DMET Plus (DMET) platforms. AFFY and DMET contain nearly 900000 and 1931 markers respectively. We used a 1000 Genomes Pilot + HapMap 3 reference panel. Imputation was performed using the computer program Impute, version 2. SNPs contained in DMET, but not imputed, were analysed studying markers around their chromosome regions. The efficacy of the imputation was measured evaluating the number of successfully imputed SNPs (SSNPs). RESULTS: The imputation predicted the genotypes of 654 SNPs not present in the AFFY array, but contained in the DMET array. Approximately 1000 SNPs were not annotated in the reference panel and therefore they could not be directly imputed. After testing three different imputed genotype calling threshold (IGCT), we observed that imputation performs at its best for IGCT value equal to 50%, with rate of SSNPs (MAF > 0.05) equal to 85%. CONCLUSIONS: Most of the genes involved in drug metabolism can be imputed with high efficacy using standard genome-wide genotyping platforms and imputing procedures.