Evidence map›Paper›PMID 26478762›Full record

ArticleThe annals of applied statistics2015

BAYESIAN GROUP LASSO FOR NONPARAMETRIC VARYING-COEFFICIENT MODELS WITH APPLICATION TO FUNCTIONAL GENOME-WIDE ASSOCIATION STUDIES.

Jiahan Li, Zhong Wang, Runze Li, Rongling Wu

Open access · greenAbstract read
In one paragraph

Article in The annals of applied statistics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
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

38 citing papers in PubMed, 86 citations in OpenAlex.

  1. Article
  2. Robust sparse Bayesian regression for longitudinal gene-environment interactions.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025
    Article
  3. Article
  4. Article
  5. Bayesian Data Sketching for Varying Coefficient Regression Models.Journal of machine learning research : JMLR · 2025
    Article
  6. Review
  7. Article
  8. Article
  9. The Bayesian Regularized Quantile Varying Coefficient Model.Computational statistics & data analysis · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  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

4 authors at 1 institution in 1 country.

Jiahan LiDepartment of Applied and Computational, Mathematics and Statistics, The University of Notre Dame, Notre Dame, IN 46556. jli7@nd.edu.
Zhong WangCenter for Computational Biology, Beijing Forestry University, Beijing, China 100083. zhongwang@bjfu.edu.cn.
Runze LiDepartment of Statistics, The Methodology Center, The Pennsylvania State University, University Park, PA 16802. rli@stat.psu.edu.
Rongling WuCenter for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033. rwu@hes.hmc.psu.edu.
Beijing Forestry University · CN

Funding

Data Coordinating Center for the NHLBI Asthma Network (AsthmaNet)U10HL098115 · NHLBI · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI MAUGER, DAVID T. · 2009 to 2015
$57.6M
THE REFINEMENT &APPLICATION OF MEDICAL DECISION MAKING IN PREVENTION SCIENCESP50DA010075 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI LANZA, STEPHANIE T · 1996 to 2014
$24.5M
Penn State Clinical and Translational Science InstituteUL1TR000127 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI SINOWAY, LAWRENCE I · 2012 to 2015
$17.1M
Constitutive and Agent-Based Multiscale Models to Improve Vein Graft SurvivalU01HL119178 · NHLBI · UNIVERSITY OF FLORIDA · PI BERCELI, SCOTT A, GARBEY, MARC · 2013 to 2018
$2.7M
NCATS NIH HHS UL1 TR000127NHLBI NIH HHS U01 HL119178NHLBI NIH HHS U10 HL098115NIDA NIH HHS P50 DA010075
6 · The paper itself

Abstract

Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS or

Indexed as

Bayesian approachGroup variable selectionGWASLongitudinal data

Identifiers

PMID26478762
PMCPMC4605444
OpenAlexW1889787453

What OpenQuestion holds

Textmetadata
LicenceTDM
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.