ArticleFrontiers in genetics2019
Identifying Multi-Omics Causers and Causal Pathways for Complex Traits.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- A transcriptomic atlas of the human brain reveals genetically determined aspects of neuropsychiatric health.American journal of human genetics · 2024Article
- Selection for stress tolerance and longevity in Drosophila melanogaster have strong impacts on microbiome profiles.Scientific reports · 2024Article
- Harnessing the Transcriptional Signatures of CAR-T-Cells and Leukemia/Lymphoma Using Single-Cell Sequencing Technologies.International journal of molecular sciences · 2024Review
- Transcriptional and post-transcriptional regulation of checkpoint genes on the tumour side of the immunological synapse.Heredity · 2022Article
- Data integration and mechanistic modelling for breast cancer biology: Current state and future directions.Current opinion in endocrine and metabolic research · 2022Review
- Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine.International journal of molecular sciences · 2021Review
- Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.Computational and structural biotechnology journal · 2021Review
- Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis inGenome research · 2020Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.
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Registered trials
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.