Evidence map›Paper›PMID 39053457›Full record

ArticleAmerican journal of human genetics2024

Inferring causal direction between two traits using R

Huiling Liao, Haoran Xue, Wei Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Huiling LiaoDivision of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Haoran XueDepartment of Biostatistics, City University of Hong Kong, Kowloon, Hong Kong.
Wei PanDivision of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA. Electronic address: panxx014@umn.edu.

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
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
Integrating Alzheimer's disease GWAS with proteomic and metabolomic QTL dataRF1AG067924 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2020
$1.9M
NIA NIH HHS R01 AG065636NIA NIH HHS R01 AG069895NIA NIH HHS RF1 AG067924NIA NIH HHS U01 AG073079
6 · The paper itself

Abstract

In Mendelian randomization, two single SNP-trait correlation-based methods have been developed to infer the causal direction between an exposure (e.g., a gene) and an outcome (e.g., a trait), called MR Steiger's method and its recent extension called Causal Direction-Ratio (CD-Ratio). Here we propose an approach based on R

Indexed as

Genome-Wide Association StudyPolymorphism, Single NucleotideTranscriptomeCholesterol, LDLHumansMendelian Randomization AnalysisModels, GeneticPhenotypeCholesterol, LDLcoefficient of determinationGWASinstrument variableIVMendelian randomizationTWAS

Identifiers

PMID39053457
PMCPMC11339628

What OpenQuestion holds

Textmetadata
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Registered trials

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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.