Evidence map›Paper›PMID 27142071›Full record

ArticleBMC genetics2016

CNV analysis in the Lithuanian population.

A Urnikyte, I Domarkiene, S Stoma, L Ambrozaityte, I Uktveryte, R Meskiene, V Kasiulevičius, N Burokiene, V Kučinskas

Open access · diamondAbstract read
In one paragraph

Article in BMC genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact, top 97% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 2 countries.

A UrnikyteDepartment of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania. alina.urnikyte@mf.vu.lt.
I DomarkieneDepartment of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania.
S StomaMaster of Science (MSc), Bioinformatics student, VU University Amsterdam, Amsterdam, Netherlands.
L AmbrozaityteDepartment of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania.
I UktveryteDepartment of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania.
R MeskieneDepartment of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania.
V KasiulevičiusClinics of Internal Diseases, Family Medicine and Oncology, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania.
N BurokieneClinics of Internal Diseases, Family Medicine and Oncology, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania.
V KučinskasDepartment of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu St. 2, LT-08661, Vilnius, Lithuania.
Vilnius University · LTVrije Universiteit Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough 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.

resultsA 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.

conclusionsThe 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.

Indexed as

DNA Copy Number VariationsAlgorithmsFemaleGenetics, PopulationHumansLithuaniaMaleOligonucleotide Array Sequence AnalysisWhite PeopleCNVCNVRsCopy number variationLITGEN project

Identifiers

PMID27142071
PMCPMC4855864
OpenAlexW2346078994

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.