ReviewCell cycle (Georgetown, Tex.)2016
Reducing GWAS Complexity.
Review in Cell cycle (Georgetown, Tex.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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.
Who cites it
16 citing papers in PubMed.
- Integrating Genome-Wide Association Study (GWAS) and Marker-Assisted Selection for Enhanced Predictive Performance of Soybean Cold Tolerance.International journal of molecular sciences · 2025Article
- Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.Bioinformatics (Oxford, England) · 2025Article
- Genome-wide association studies are enriched for interacting genes.BioData mining · 2025Article
- Genome wide association studies are enriched for interacting genes.Research square · 2024Article
- Article
- Investigating the genetic determination of duration-of-fertility trait in breeding hens.Scientific reports · 2024Article
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- A Single-Step Genome Wide Association Study on Body Size Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs.Frontiers in genetics · 2021Article
- A genome-wide single nucleotide polymorphism scan reveals genetic markers associated with fertility rate in Chinese Jing Hong chicken.Poultry science · 2020Article
- Whole genome sequencing of orofacial cleft trios from the Gabriella Miller Kids First Pediatric Research Consortium identifies a new locus on chromosome 21.Human genetics · 2020Article
- Post-GWAS knowledge gap: the how, where, and when.NPJ Parkinson's disease · 2020Review
- Novel Polymorphisms inGenes · 2019Article
- Single nucleotide polymorphisms in candidate genes are significantly associated with resistance toJournal of animal science and biotechnology · 2019Article
- CRISPR-mediated deletion of prostate cancer risk-associated CTCF loop anchors identifies repressive chromatin loops.Genome biology · 2018Article
- Article
- Insertion/Deletion Within the KDM6A Gene Is Significantly Associated With Litter Size in Goat.Frontiers in genetics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015 (1,2) from our groups.
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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.