ArticlePloS one2013
Single nucleotide polymorphism network: a combinatorial paradigm for risk prediction.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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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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
2 citing papers in PubMed, 3 citations in OpenAlex.
- Strong Cumulative Evidence of Associations of 6 Single Nucleotide Polymorphisms with Ovarian Cancer Risk: An Umbrella Review.Journal of clinical medicine · 2023Review
- Single nucleotide polymorphisms as markers of genetic susceptibility for oral potentially malignant disorders risk: Review of evidence to date.Oral oncology · 2016Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.
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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 Socratic graph.