ArticleScientific reports2015
Nonparametric Risk and Nonparametric Odds in Quantitative Genetic Association Studies.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- Association of folate metabolism gene polymorphisms with autism susceptibility and symptom severity in the Chinese population.World journal of psychiatry · 2025Article
- A Powerful Method To Test Associations Between Ordinal Traits and Genotypes.G3 (Bethesda, Md.) · 2019Article
- Pathogenetic basis of Takenouchi-Kosaki syndrome: Electron microscopy study using platelets in patients and functional studies in a Caenorhabditis elegans model.Scientific reports · 2019Article
- Group-combined P-values with applications to genetic association studies.Bioinformatics (Oxford, England) · 2016Article
- Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies.Scientific reports · 2016Article
- A two-phase procedure for non-normal quantitative trait genetic association study.BMC bioinformatics · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coefficient in a linear regression model is commonly employed to evaluate the genetic effect of a single nucleotide polymorphism associated with a quantitative trait under the assumption that the trait value follows a normal distribution or is appropriately normally distributed after a certain transformation. When this assumption is violated, the distribution-free tests are preferred. In this work, we propose the nonparametric risk (NR) and nonparametric odds (NO), obtain the asymptotic normal distribution of estimated NR and then construct the confidence intervals. We also define the genetic models using NR, construct the test statistic under a given genetic model and a robust test, which are free of the genetic uncertainty. Simulation studies show that the proposed confidence intervals have satisfactory cover probabilities and the proposed test can control the type I error rates and is more powerful than the exiting ones under most of the considered scenarios. Application to gene of PTPN22 and genomic region of 6p21.33 from the Genetic Analysis Workshop 16 for association with the anticyclic citrullinated protein antibody further show their performances.
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