Evidence map›Paper›PMID 31062262›Full record

ArticleGenes & genomics2019

SNP genotype calling and quality control for multi-batch-based studies.

Sujin Seo, Kyungtaek Park, Jang Jae Lee, Kyu Yeong Choi, Kun Ho Lee, Sungho Won

Abstract read
PubMed Publisher
In one paragraph

Article in Genes & genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
–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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study.Hypertension research : official journal of the Japanese Society of Hypertension · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. rs2671655 single nucleotide polymorphism modulates the risk for gastric cancer in Helicobacter pylori-infected individuals: a genome-wide association study in the Korean population.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2022
    Article
  12. Article
  13. Article
  14. 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

6 authors.

Sujin SeoDepartment of Public Health Science, Graduate School of Public Health, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul, 151-742, Republic of Korea.
Kyungtaek ParkInterdisciplinary Program of Bioinformatics, College of National Sciences, Seoul National University, Seoul, 151-742, Republic of Korea.
Jang Jae LeeNational Research Center for Dementia, Chosun University, Gwangju, 61452, Republic of Korea.
Kyu Yeong ChoiNational Research Center for Dementia, Chosun University, Gwangju, 61452, Republic of Korea.
Kun Ho LeeNational Research Center for Dementia, Chosun University, Gwangju, 61452, Republic of Korea. leekho@chosun.ac.kr.
Sungho WonDepartment of Public Health Science, Graduate School of Public Health, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul, 151-742, Republic of Korea. sunghow@gmail.com.ORCID 0000-0001-5751-5089

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn genetic analyses, the term 'batch effect' refers to systematic differences caused by batch heterogeneity. Controlling this unintended effect is the most important step in quality control (QC) processes that precede analyses. Currently, batch effects are not appropriately controlled by statistics, and newer approaches are required.

methodsIn this report, we propose a new method to detect the heterogeneity of probe intensities among different batches and a procedure for calling genotypes and QC in the presence of a batch effect. First, we conducted a multivariate analysis of variance (MANOVA) to test the differences in probe intensities among batches. If heterogeneity is detected, subjects should be clustered using a K-medoid algorithm using the averages of the probe intensity measurements for each batch and the genotypes of subjects in different clusters should be called separately.

resultsThe proposed method was used to assess genotyping data of 3619 subjects consisting of 1074 patients with Alzheimer's disease, 296 with mild cognitive impairment (MCI), and 1153 controls. The proposed method improves the accuracy of called genotypes without the need to filter a lot of subjects and SNPs, and therefore is a reasonable approach for controlling batch effects.

conclusionsWe proposed a new strategy that detects batch effects with probe intensity measurement and calls genotypes in the presence of batch effects. The application of the proposed method to real data shows that it produces a balanced approach. Furthermore, the proposed method can be extended to various scenarios with a simple modification.

Indexed as

Polymorphism, Single NucleotideAlzheimer DiseaseAnalysis of VarianceCognitive DysfunctionGenetic HeterogeneityGenome-Wide Association StudyGenotyping TechniquesHumansBatch effectCallingGenome-wide association analysisK-medoid clusteringQuality control

Identifiers

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.