Comparison of Six Statistics of Genetic Association Regarding Their Ability to Discriminate between Causal Variants and Genetically Linked Markers | Zendy

Justo Lorenzo Bermejo | Zendy; Alfonso GarcíaPérez | Zendy; Andreas Brandt | Zendy; Kari Hemminki | Zendy; Abigail G. Matthews | Zendy

Open Access

Comparison of Six Statistics of Genetic Association Regarding Their Ability to Discriminate between Causal Variants and Genetically Linked Markers

Author(s) -

Justo Lorenzo Bermejo,

Alfonso GarcíaPérez,

Andreas Brandt,

Kari Hemminki,

Abigail G. Matthews

Publication year - 2011

Publication title -

human heredity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.423

H-Index - 62

eISSN - 1423-0062

pISSN - 0001-5652

DOI - 10.1159/000332006

Subject(s) - genome wide association study , genetic association , biology , international hapmap project , genetics , bayes' theorem , imputation (statistics) , bayesian probability , statistics , allele , single nucleotide polymorphism , genotype , missing data , haplotype , mathematics , gene

Genome-wide association (GWA) studies still rely on the common-disease common-variant hypothesis since the assumption is associated with increased power. In GWA studies, polymorphisms are genotyped and their association with disease is investigated. Most of the identified associations are indirect and reflect a shared inheritance of the genotyped markers and genetically linked causal variants. We have compared six statistics of genetic association regarding their ability to discriminate between markers and causal susceptibility variants, including a probability value (Pval) and a Bayes Factor (BF) based on logistic regression, and the attributable familial relative risk (FRR).

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