Evidence map›Paper›PMID 25297068›Full record

ArticleBioinformatics (Oxford, England)2015

Genome measures used for quality control are dependent on gene function and ancestry.

Jing Wang, Leon Raskin, David C Samuels, Yu Shyr, Yan Guo

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers.

0numbers the graph read from it
0cells of the map it votes in
104citing papers in PubMed
–field-weighted citation impact
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

104 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. CountrywideFrontiers in plant science · 2025
    Article

44 more citing papers are in PubMed but not listed here.

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

5 authors.

Jing WangCenter for Quantitative Sciences and Department of Medicine and Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37212, USA.
Leon RaskinCenter for Quantitative Sciences and Department of Medicine and Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37212, USA.
David C SamuelsCenter for Quantitative Sciences and Department of Medicine and Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37212, USA.
Yu ShyrCenter for Quantitative Sciences and Department of Medicine and Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37212, USA.
Yan GuoCenter for Quantitative Sciences and Department of Medicine and Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37212, USA.

Funding

Tumor Immunology and Microenvironment Research ProgramP30CA068485 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MATTHEW J TYSKA · 1995 to 2026
$172.8M
Genome Sequencing to identify novel genetic factors for breast cancer riskR01CA158473 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI ZHENG, WEI · 2012 to 2016
$4.3M
Consortium Study to Identify Breast Cancer Susceptibility LociR01CA148667 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI LONG, JIRONG, ZHENG, WEI · 2011 to 2015
$3.3M
Breast Cancer Survival: Lifestyle and Genetic DeterminantsR01CA118229 · NCI · VANDERBILT UNIVERSITY · PI SHU, XIAO-OU · 2007 to 2010
$2.9M
Barrett's Esophagus Translational Research Network Coordinating CenterU01CA163056 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI SHYR, YU · 2011 to 2016
$2.2M
Genetic Basis of Familial Colorectal Cancer Type XR00CA158141 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI RASKIN, LEON · 2012 to 2014
$725k
NCI NIH HHS 5R00CA158141NCI NIH HHS P30 CA068485NCI NIH HHS P30 CA68485NCI NIH HHS R00 CA158141NCI NIH HHS R01 CA118229NCI NIH HHS R01CA118229NCI NIH HHS R01CA125558NCI NIH HHS R01 CA148667NCI NIH HHS R01CA148667NCI NIH HHS R01 CA158473NCI NIH HHS R01CA158473NCI NIH HHS R01CA64266NCI NIH HHS U01 CA163056
6 · The paper itself

Abstract

motivationThe transition/transversion (Ti/Tv) ratio and heterozygous/nonreference-homozygous (het/nonref-hom) ratio have been commonly computed in genetic studies as a quality control (QC) measurement. Additionally, these two ratios are helpful in our understanding of the patterns of DNA sequence evolution.

resultsTo thoroughly understand these two genomic measures, we performed a study using 1000 Genomes Project (1000G) released genotype data (N=1092). An additional two datasets (N=581 and N=6) were used to validate our findings from the 1000G dataset. We compared the two ratios among continental ancestry, genome regions and gene functionality. We found that the Ti/Tv ratio can be used as a quality indicator for single nucleotide polymorphisms inferred from high-throughput sequencing data. The Ti/Tv ratio varies greatly by genome region and functionality, but not by ancestry. The het/nonref-hom ratio varies greatly by ancestry, but not by genome regions and functionality. Furthermore, extreme guanine + cytosine content (either high or low) is negatively associated with the Ti/Tv ratio magnitude. Thus, when performing QC assessment using these two measures, care must be taken to apply the correct thresholds based on ancestry and genome region. Failure to take these considerations into account at the QC stage will bias any following analysis. CONTACT: yan.guo@vanderbilt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Evolution, MolecularGenome, HumanQuality ControlGenome-Wide Association StudyGenotypeHigh-Throughput Nucleotide SequencingHumansPhenotypePolymorphism, Single Nucleotide

Identifiers

PMID25297068
PMCPMC4308666

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.