ArticleAmerican journal of epidemiology2019
Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.
Article in American journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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.
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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.Nature communications · 2022Pooled it
- Large-scale assessment of socioeconomic, demographic and health system structures with US county excess mortality, 2020-2024.medRxiv : the preprint server for health sciences · 2026Article
- Maternal antenatal depression and offspring DNA methylation.Journal of affective disorders · 2026Article
- An atlas of exposome-phenome associations in health and disease risk.Nature medicine · 2026Article
- Big Data in the Assessment of Medication Safety in Pregnancy: Opportunities and Challenges.Paediatric drugs · 2025Article
- Human Plasma Proteomics Links Modifiable Lifestyle Exposome to Disease Risk.medRxiv : the preprint server for health sciences · 2025Article
- Article
- A fast non-parametric test of association for multiple traits.Genome biology · 2023Article
- Proteomic Analysis Identifies Circulating Proteins Associated With Plasma Amyloid-β and Incident Dementia.Biological psychiatry global open science · 2023Article
- Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery.Environmental health perspectives · 2021Article
- The promise and pitfalls of precision medicine to resolve black-white racial disparities in preterm birth.Pediatric research · 2020Review
- Tackling the Complexity of the Exposome: Considerations from the Gunma University Initiative for Advanced Research (GIAR) Exposome Symposium.Metabolites · 2019Article
- Epidemiology: Back to the Future.American journal of epidemiology · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or "XWAS" have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous "X-wide inflation factor," denoted λX, can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals.
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