Evidence map›Paper›PMID 35456375›Full record

ArticleGenes2022

Inherited and De Novo Variation in Lithuanian Genomes: Introduction to the Analysis of the Generational Shift.

Alina Urnikyte, Laura Pranckeniene, Ingrida Domarkiene, Svetlana Dauengauer-Kirliene, Alma Molyte, Ausra Matuleviciene, Ingrida Pilypiene, Vaidutis Kučinskas

Open access · goldAbstract read
In one paragraph

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 3 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. Article
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

8 authors at 1 institution in 1 country.

Alina UrnikyteDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.ORCID 0000-0002-0764-0946
Laura PranckenieneDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.
Ingrida DomarkieneDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.ORCID 0000-0002-2088-8620
Svetlana Dauengauer-KirlieneDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.
Alma MolyteDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.
Ausra MatulevicieneDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.
Ingrida PilypieneDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.
Vaidutis KučinskasDepartment of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Santariskiu Street 2, LT-08661 Vilnius, Lithuania.ORCID 0000-0001-6433-7305
Vilnius University · LT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most genetic variants are rare and specific to the population, highlighting the importance of characterizing local population genetic diversity. Many countries have initiated population-based whole-genome sequencing (WGS) studies. Genomic variation within Lithuanian families are not available in the public databases. Here, we describe initial findings of a high-coverage (an average of 36.27×) whole genome sequencing for 25 trios of the Lithuanian population. Each genome on average carried approximately 4,701,473 (±28,255) variants, where 80.6% (3,787,626) were single nucleotide polymorphisms (SNPs), and the rest 19.4% were indels. An average of 12.45% was novel according to dbSNP (build 150). The WGS structural variation (SV) analysis identified on average 9133 (±85.10) SVs, of which 95.85% were novel. De novo single nucleotide variation (SNV) analysis identified 4417 variants, where 1.1% de novo SNVs were exonic, 43.9% intronic, 51.9% intergenic, and the rest 3.13% in UTR or downstream sequence. Three potential pathogenic de novo variants in the

Indexed as

Genome, HumanINDEL MutationHumansLithuaniaPolymorphism, Single NucleotideWhole Genome Sequencingde novo variationnewbornsSNVtrioswhole genome sequencing

Identifiers

PMID35456375
PMCPMC9028680
OpenAlexW4220876351

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.