A new multipoint method for genome-wide association studies by imputation of genotypes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-06-17

AUTHORS

Jonathan Marchini, Bryan Howie, Simon Myers, Gil McVean, Peter Donnelly

ABSTRACT

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. More... »

PAGES

906-913

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ng2088

DOI

http://dx.doi.org/10.1038/ng2088

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046979341

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17572673


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