Evidence map›Paper›PMID 39209554›Full record

ArticleGenome research2024

A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits.

Boyang Fu, Prateek Anand, Aakarsh Anand, Joel Mefford, Sriram Sankararaman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Boyang Fu *Department of Computer Science, University of California, Los Angeles, Los Angeles, California 90095, USA; boyang1995@ucla.edu sriram@cs.ucla.edu.ORCID 0000-0002-0082-8735
Prateek Anand *Department of Computer Science, University of California, Los Angeles, Los Angeles, California 90095, USA.
Aakarsh Anand *Department of Computer Science, University of California, Los Angeles, Los Angeles, California 90095, USA.
Joel MeffordSemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California 90024, USA.
Sriram SankararamanDepartment of Computer Science, University of California, Los Angeles, Los Angeles, California 90095, USA; boyang1995@ucla.edu sriram@cs.ucla.edu.ORCID 0000-0003-1586-9641

Funding

Statistical Models for Dissecting Human Population Admixture and its Role in Evolution and DiseaseR35GM125055 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SANKARARAMAN, SRIRAM · 2017 to 2021
$1.6M
Expressive and scalable statistical models for genomic and biomedical dataR35GM153406 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sriram Sankararaman · 2024 to 2026
$1.0M
NIGMS NIH HHS R35 GM125055NIGMS NIH HHS R35 GM153406
6 · The paper itself

Abstract

Our knowledge of the contribution of genetic interactions (

Indexed as

AlgorithmsEpistasis, GeneticGenome-Wide Association StudyHumansModels, GeneticMultifactorial InheritancePhenotypePolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID39209554
PMCPMC11529862

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.