Evidence map›Paper›PMID 28875524›Full record

ArticleGenetic epidemiology2017

Iterative hard thresholding for model selection in genome-wide association studies.

Kevin L Keys, Gary K Chen, Kenneth Lange

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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

3 authors.

Kevin L KeysDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.ORCID 0000-0003-4886-6666
Gary K ChenDivision of Biostatistics, University of Southern California, Los Angeles, California, United States of America.
Kenneth LangeDepartments of Biomathematics, Human Genetics, and Statistics, University of California, Los Angeles, California, United States of America.

Funding

STATISTICAL METHODS FOR GENE MAPPINGR01GM053275 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANGE, KENNETH L · 1995 to 2019
$9.3M
Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
Genomics, GPUs, and Next Generation Computational StatisticsR01HG006139 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SOBEL, ERIC · 2011 to 2023
$5.1M
NHGRI NIH HHS R01 HG006139NHGRI NIH HHS T32 HG002536NIGMS NIH HHS R01 GM053275
6 · The paper itself

Abstract

A genome-wide association study (GWAS) correlates marker and trait variation in a study sample. Each subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here, we assume that subjects are randomly collected unrelateds and that trait values are normally distributed or can be transformed to normality. Over the past decade, geneticists have been remarkably successful in applying GWAS analysis to hundreds of traits. The massive amount of data produced in these studies present unique computational challenges. Penalized regression with the ℓ AVAILABILITY: Source code is freely available at https://github.com/klkeys/IHT.jl.

Indexed as

Genome-Wide Association StudyModels, GeneticAlgorithmsBody Mass IndexCholesterol, HDLCholesterol, LDLHumansPhenotypePolymorphism, Single NucleotideTriglyceridesCholesterol, HDLCholesterol, LDLTriglyceridesgenetic association studiesgreedy algorithmparallel computingsparse regression

Identifiers

PMID28875524
PMCPMC5696071

What OpenQuestion holds

Textmetadata
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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.