Evidence map›Paper›PMID 29902512›Full record

ArticleGenomics2019

Quality and concordance of genotyping array data of 12,064 samples from 5840 cancer patients.

Mingsheng Guo, Wei Yue, David C Samuels, Hui Yu, Jing He, Ying-Yong Zhao, Yan Guo

Abstract readEvaluation Study
In one paragraph

Article in Genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Integration of DNA Microarray with Clinical and Genomic Data.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  4. Article
  5. Article
  6. 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

7 authors.

Mingsheng GuoDepartment of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: minguo@salud.unm.edu.
Wei YueDepartment of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA.
David C SamuelsVanderbilt Genetics Institute, Dept. of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232, USA.
Hui YuDepartment of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA.
Jing HeDivision of Epidemiology, Vanderbilt University Medical Center, Nashville, TN 37212, USA.
Ying-Yong ZhaoKey Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.
Yan GuoDepartment of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: yan.guo@vanderbilt.edu.

Funding

WOMEN'S CANCERS RESEARCH PROGRAMP30CA118100 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Yolanda Sanchez · 2005 to 2026
$57.1M
NCI NIH HHS P30 CA118100
6 · The paper itself

Abstract

Genotyping arrays characterize genome-wide SNPs for a study cohort and were the primary technology behind genome wide association studies over the last decade. The Cancer Genome Atlas (TCGA) is one of the largest cancer consortium studies, and it collected genotyping data for all of its participants. Using TCGA SNP data genotyped using the Affymetrix 6.0 SNP array from 12,064 samples, we conducted a comprehensive comparisons across DNA sources (tumor tissue, normal tissue, and blood) and sample storage protocols (formalin-fixed paraffin-embedded (FFPE) vs. freshly frozen (FF)), examining genotypes, transition/transversion ratios, and mutation catalogues. During the analysis, we made important observations in relevance to the data quality issues. SNP concordance was excellent between blood and normal tissues, and slightly lower between blood and tumor tissue due to potential somatic mutations in the tumors. The observed poor SNP concordance between FFPE and FF samples suggested a batch effect. The transition/transversion ratio, a metric commonly used for quality control purpose in exome sequencing projects, appeared less applicable for genotyping array data due to the whole-genome coverage built into the array design. Moreover, there were substantially more loss of heterozygosity events than gain of heterozygosity when comparing tumors relative to normal tissues and blood. This might be a consequence of extensive copy number deletions in tumors. In summary, our thorough evaluation calls for more adequate quality control practices and provides guidelines for improved application of TCGA genotyping data.

Indexed as

Databases, GeneticGenotyping TechniquesHumansNeoplasmsPolymorphism, Single NucleotideTissue Array Analysis

Identifiers

PMID29902512
PMCPMC6546549

What OpenQuestion holds

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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.