CNV analysis in the Lithuanian population

• Published in: BMC genetics Vol. 17; no. 1; p. 64
• Main Authors: Urnikyte, A., Domarkiene, I., Stoma, S., Ambrozaityte, L., Uktveryte, I., Meskiene, R., Kasiulevičius, V., Burokiene, N., Kučinskas, V.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 04.05.2016; BioMed Central Ltd
• Subjects: Algorithms; Analysis; Animal Genetics and Genomics; Biomedical and Life Sciences; Care and treatment; Copy number variations; DNA Copy Number Variations; European Continental Ancestry Group - ethnology; European Continental Ancestry Group - genetics; Female; genes; Genetics and Population Dynamics; Genetics, Population; genotyping; Human population genetics; Humans; Life Sciences; Lithuania - ethnology; Male; Mental retardation; Microbial Genetics and Genomics; Oligonucleotide Array Sequence Analysis - methods; Plant Genetics and Genomics; Research Article; Risk factors; Single nucleotide polymorphisms; CNVRs; CNV; Copy number variation; LITGEN project
• ISSN: 1471-2156; 1471-2156
• DOI: 10.1186/s12863-016-0373-6

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 (Aukštaičiai and Žemaičiai) (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 Aukštaičiai and Žemaičiai, 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.