Evidence map›Paper›PMID 37038201›Full record

ArticleBioData mining2023

Automated quantitative trait locus analysis (AutoQTL).

Philip J Freda, Attri Ghosh, Elizabeth Zhang, Tianhao Luo, Apurva S Chitre, Oksana Polesskaya, Celine L St Pierre, Jianjun Gao, Connor D Martin, Hao Chen and 10 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioData mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
17.8field-weighted citation impact, top 1% 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

6 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors at 4 institutions in 1 country.

Philip J Freda *Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
Attri Ghosh *Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
Elizabeth ZhangDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
Tianhao LuoDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
Apurva S ChitreDepartment of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
Oksana PolesskayaDepartment of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
Celine L St PierreDepartment of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
Jianjun GaoDepartment of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
Connor D MartinDepartment of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 955 Main Street, Suite 3102, Buffalo, NY, 14203, USA.
Hao ChenDepartment of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
Angel G Garcia-MartinezDepartment of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
Tengfei WangDepartment of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
Wenyan HanDepartment of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
Keita IshiwariDepartment of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 955 Main Street, Suite 3102, Buffalo, NY, 14203, USA.
Paul MeyerDepartment of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA.
Alexander LamparelliDepartment of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA.
Christopher P KingDepartment of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA.
Abraham A PalmerDepartment of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
Ruowang LiDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
Jason H MooreDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA. jason.moore@csmc.edu.
Cedars-Sinai Medical Center · USUniversity at Buffalo, State University of New York · USUniversity of California San Diego · USUniversity of Tennessee Health Science Center · US

Funding

Sequencing CoreP50DA037844 · NIDA · UNIVERSITY OF CHICAGO · PI PALMER, ABRAHAM A · 2014 to 2023
$27.4M
Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
NIDA NIH HHS P50 DA037844NIH HHS R01 LM010098
6 · The paper itself

Abstract

backgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.

resultsUsing a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.

conclusionsThis proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.

Indexed as

AutomatedDominanceEpistasisEvolutionary algorithmsGenetic programmingGWASInheritanceMachine learningQTL

Identifiers

PMID37038201
PMCPMC10088184
OpenAlexW4363673433

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.