CNV analysis in the Lithuanian population.

BACKGROUND: Although copy number variation (CNV) has received much attention, knowledge about the characteristics of CNVs such as occurrence rate and distribution in the genome between populations and within the same population is still insufficient. In this study, Illumina 770 K HumanOmniExpress-12 v1.0 (and v1.1) arrays were used to examine the diversity and distribution of CNVs in 286 unrelated individuals from the two main ethnolinguistic groups of the Lithuanian population (Aukstaiciai and Zemaiciai) (see Additional file 3). For primary data analysis, the Illumina GenomeStudio™ Genotyping Module v1.9 and two algorithms, cnvPartition 3.2.0 and QuantiSNP 2.0, were used to identify high-confidence CNVs. RESULTS: A total of 478 autosomal CNVs were detected by both algorithms, and those were clustered in 87 copy number variation regions (CNVRs), spanning ~12.5 Mb of the genome (see Table 1). At least 8.6 % of the CNVRs were unique and had not been reported in the Database of Genomic Variants. Most CNVRs (57.5 %) were rare, with a frequency of <1 %, whereas common CNVRs with at least 5 % frequency made up only 1.1 % of all CNVRs identified. About 49 % of non-singleton CNVRs were shared between Aukstaiciai and Zemaiciai, and the remaining CNVRs were specific to each group. Many of the CNVs detected (66 %) overlapped with known UCSC gene regions. CONCLUSIONS: The ethnolinguistic groups of the Lithuanian population could not be differentiated based on CNV profiles, which may reflect their geographical proximity and suggest the homogeneity of the Lithuanian population. In addition, putative novel CNVs unique to the Lithuanian population were identified. The results of our study enhance the CNV map of the Lithuanian population.

RTN4 and FBXL17 Genes are Associated with Coronary Heart Disease in Genome-Wide Association Analysis of Lithuanian Families.

Coronary heart disease (CHD) is a complex and heterogeneous cardiovascular disease. There are many genome-wide association studies (GWAS) performed worldwide to extract the causative genetic factors. Moreover, each population may have some exceptional genetic characteristic. Thus, the background of our study is from the previous Lithuanian studies (the LiVicordia Project), which demonstrated the differences of the atherosclerosis process between Lithuanian and Swedish male individuals. In this study we performed GWAS of 32 families of Lithuanian origin in search of significant candidate genetic markers [single nucleotide polymorphisms (SNPs)] of CHD in this population. After careful clinical and biochemical phenotype evaluation, the ∼770K SNPs genotyping (Illumina HumanOmniExpress-12 v1.0 array) and familial GWAS analyses were performed. Twelve SNPs were found to be significantly associated with the CHD phenotype (p value <0.0001; the power >0.65). The odds ratio (OR) values were calculated. Two SNPs (rs17046570 in the RTN4 gene and rs11743737 in the FBXL17 gene) stood out and may prove to be important genetic factors for CHD risk. Our results correspond with the findings in other studies, and these two SNPs may be the susceptibility loci for CHD.