Evidence map›Paper›PMID 39421855›Full record

ArticleThe annals of applied statistics2024

A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation.

Mykhaylo M Malakhov, Ben Dai, Xiaotong T Shen, Wei Pan

Abstract read
In one paragraph

Article in The annals of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Mykhaylo M MalakhovDivision of Biostatistics and Health Data Science, University of Minnesota.ORCID 0000-0002-6856-3913
Ben DaiDepartment of Statistics, The Chinese University of Hong Kong.ORCID 0000-0002-1620-1021
Xiaotong T ShenSchool of Statistics, University of Minnesota.ORCID 0000-0003-1300-1451
Wei PanDivision of Biostatistics and Health Data Science, University of Minnesota.ORCID 0000-0002-1159-0582

Funding

Causal and integrative deep learning for Alzheimer's disease geneticsU01AG073079 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2021 to 2025
$3.5M
Deep Learning with Neuroimaging Genetic Data for Alzheimer's DiseaseR01AG069895 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2020 to 2024
$3.4M
Estimation and inference in directed acyclic graphical models for biological networksR01AG074858 · NIA · UNIVERSITY OF MINNESOTA · PI Wei Pan, XIAOTONG Tom SHEN · 2022 to 2026
$3.2M
Discovering causal genes, brain regions and other risk factors for Alzheimer's DiseaseR01AG065636 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2024
$3.1M
Estimation and Inference of Gene Regulatory NetworksR01GM126002 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2017 to 2020
$1.4M
Biostatistics in Genetics and Genomics Training ProgramT32GM132063 · NIGMS · UNIVERSITY OF MINNESOTA · PI BASU, SAONLI, PAN, WEI · 2020 to 2024
$1.3M
NIA NIH HHS R01 AG065636NIA NIH HHS R01 AG069895NIA NIH HHS R01 AG074858NIA NIH HHS U01 AG073079NIGMS NIH HHS R01 GM126002NIGMS NIH HHS T32 GM132063
6 · The paper itself

Abstract

Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (differential regulation analysis by bootstrapping), a gene-based method for testing whether patterns of genetic regulation are significantly different between tissues or other biological contexts. DRAB first leverages the elastic net to learn context-specific models of local genetic regulation and then applies a novel bootstrap-based model comparison test to check their equivalency. Unlike previous model comparison tests, our proposed approach can determine whether population-level models have equal predictive performance by accounting for the variability of feature selection and model training. We validated DRAB on mRNA expression data from a variety of human tissues in the Genotype-Tissue Expression (GTEx) Project. DRAB yielded biologically reasonable results and had sufficient power to detect genes with tissue-specific regulatory profiles while effectively controlling false positives. By providing a framework that facilitates the prioritization of differentially regulated genes, our study enables future discoveries on the genetic architecture of molecular phenotypes.

Indexed as

Differential regulationgene expressionmodel selection testpenalized regressiontranscriptome prediction

Identifiers

PMID39421855
PMCPMC11484521

What Socratic 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 Socratic graph.