ArticleGenetica2010
Identifying disease polymorphisms from case-control genetic association data.
Article in Genetica, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- LATE-NC risk alleles (in TMEM106B, GRN, and ABCC9 genes) among persons with African ancestry.Journal of neuropathology and experimental neurology · 2023Article
- Population-specific long-range linkage disequilibrium in the human genome and its influence on identifying common disease variants.Scientific reports · 2019Article
- Rare high-impact disease variants: properties and identifications.Genetics research · 2016Article
- Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis.Nature genetics · 2012Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In case-control association studies, it is typical to observe several associated polymorphisms in a gene region. Often the most significantly associated polymorphism is considered to be the disease polymorphism; however, it is not clear whether it is the disease polymorphism or there is more than one disease polymorphism in the gene region. Currently, there is no method that can handle these problems based on the linkage disequilibrium (LD) relationship between polymorphisms. To distinguish real disease polymorphisms from markers in LD, a method that can detect disease polymorphisms in a gene region has been developed. Relying on the LD between polymorphisms in controls, the proposed method utilizes model-based likelihood ratio tests to find disease polymorphisms. This method shows reliable Type I and Type II error rates when sample sizes are large enough, and works better with re-sequenced data. Applying this method to fine mapping using re-sequencing or dense genotyping data would provide important information regarding the genetic architecture of complex traits.
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Registered trials
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