Evidence map›Paper›PMID 28158525›Full record

ArticleBriefings in bioinformatics2018

Methodological implementation of mixed linear models in multi-locus genome-wide association studies.

Yang-Jun Wen, Hanwen Zhang, Yuan-Li Ni, Bo Huang, Jin Zhang, Jian-Ying Feng, Shi-Bo Wang, Jim M Dunwell, Yuan-Ming Zhang, Rongling Wu

Erratum issuedAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 239 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
239citing papers in PubMed, 1 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

239 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Identification of septoria nodorum blotch susceptibility genes in hard winter wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  17. Genome-wide and co-expression network dissection ofFrontiers in plant science · 2026
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179 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Yang-Jun WenState Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.
Hanwen ZhangApplied Science, University of British Columbia, Columbia, Canada.
Yuan-Li NiState Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.
Bo HuangState Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.
Jin ZhangState Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.
Jian-Ying FengState Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.
Shi-Bo WangCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.
Jim M DunwellSchool of Agriculture, Policy and Development, University of Reading, Berkshire, UK.
Yuan-Ming ZhangState Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.
Rongling WuPublic Health Sciences and Statistics and Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with ≤ 0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.

Indexed as

AlgorithmsPolymorphism, Single NucleotideArabidopsisArabidopsis ProteinsBayes TheoremComputer SimulationGenome-Wide Association StudyLinear ModelsModels, GeneticMultifactorial InheritancePhenotypeArabidopsis Proteins

Identifiers

PMID28158525
PMCPMC6054291

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.