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We began analyzing https://www.nature.com/articles/ng2088, but it redirected us to https://www.nature.com/articles/ng2088. The analysis below is for the second page.

Title[redir]:
A new multipoint method for genome-wide association studies by imputation of genotypes | Nature Genetics
Description:
Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.

Matching Content Categories {๐Ÿ“š}

  • Education
  • Science
  • Technology & Computing

Content Management System {๐Ÿ“}

What CMS is doi.org built with?

Custom-built

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Traffic Estimate {๐Ÿ“ˆ}

What is the average monthly size of doi.org audience?

๐ŸŒ  Phenomenal Traffic: 5M - 10M visitors per month


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Keywords {๐Ÿ”}

pubmed, article, google, scholar, nature, cas, association, genet, data, studies, central, genetic, genomewide, access, nat, genetics, content, donnelly, human, cookies, method, marchini, methods, power, mapping, haplotype, population, stephens, research, privacy, analysis, genotypes, diseases, study, pdf, genome, supplementary, information, multipoint, imputation, howie, myers, snps, snp, open, genotype, statistical, hum, variation, tcfl,

Topics {โœ’๏ธ}

nature portfolio permissions reprints privacy policy advertising genome-wide association study social media genome-wide association studies nature 437 nature 447 nature 444 nature genome-wide association testing development case-control association study gil mcveanย &ย peter donnelly coalescent-based association mapping author correspondence content conjuring snps genome-wide scans genome-wide strategies springerlink instant access permissions substantial fine-scale variation model-based imputation method multilocus genotype data genetic association studies matched association studies population association studies case-control studies exploratory study jurriaan detecting multiple loci privacy future association studies competing financial interests detect associations andrew article marchini genotypes jonathan marchini model-based approach multipoint association mapping international hapmap consortium de bakker institutional subscriptions read polygenic scores improves bayesian partition modelling gene confers risk correlated allele frequencies influence complex diseases maximum bayes factors region bayes factors cyp2c19 metabolizer status

Schema {๐Ÿ—บ๏ธ}

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         description:Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.
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