Evidence map›Paper›PMID 19270271›Full record

ArticleGenetics2009

Expression quantitative trait loci mapping with multivariate sparse partial least squares regression.

Hyonho Chun, Sündüz Keles

Abstract read
In one paragraph

Article in Genetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026
    Article
  2. Article
  3. Integrative sparse partial least squares.Statistics in medicine · 2021
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Fast eQTL Analysis for Twin Studies.Genetic epidemiology · 2015
    Article
  12. Article
  13. Bayesian Partition Models for Identifying Expression Quantitative Trait Loci.Journal of the American Statistical Association · 2015
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits.Mammalian genome : official journal of the International Mammalian Genome Society · 2014
    Review
  20. 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

2 authors.

Hyonho ChunDepartment of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53705, USA.
Sündüz Keles

Funding

Statistical Methods for the Analysis of ChlP-chip DataR01HG003747 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI KELES, SUNDUZ · 2007 to 2024
$4.8M
NHGRI NIH HHS HG003747NHGRI NIH HHS R01 HG003747
6 · The paper itself

Abstract

Expression quantitative trait loci (eQTL) mapping concerns finding genomic variation to elucidate variation of expression traits. This problem poses significant challenges due to high dimensionality of both the gene expression and the genomic marker data. We propose a multivariate response regression approach with simultaneous variable selection and dimension reduction for the eQTL mapping problem. Transcripts with similar expression are clustered into groups, and their expression profiles are viewed as a multivariate response. Then, we employ our recently developed sparse partial least-squares regression methodology to select markers associated with each cluster of genes. We demonstrate with extensive simulations that our eQTL mapping with multivariate response sparse partial least-squares regression (M-SPLS eQTL) method overcomes the issue of multiple transcript- or marker-specific analyses, thereby avoiding potential elevation of type I error. Additionally, joint analysis of multiple transcripts by multivariate response regression increases power for detecting weak linkages. We illustrate that M-SPLS eQTL compares competitively with other approaches and has a number of significant advantages, including the ability to handle highly correlated genotype data and computational efficiency. We provide an application of this methodology to a mouse data set concerning obesity and diabetes.

Indexed as

Gene ExpressionModels, StatisticalAnimalsBayes TheoremChromosomes, MammalianComputer SimulationDiabetes MellitusGene Expression ProfilingGenetic LinkageGenetic MarkersLeast-Squares AnalysisMiceMice, ObeseOligonucleotide Array Sequence AnalysisQuantitative Trait LociGenetic Markers

Identifiers

PMID19270271
PMCPMC2674843

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.