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Unifying genetic association tests via regression: Prospective and retrospective, parametric and nonparametric, and genotype‐ and allele‐based tests
Author(s) -
Zhang Lin,
Sun Lei
Publication year - 2022
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11729
Subject(s) - nonparametric statistics , genetic association , association (psychology) , allele , parametric statistics , genotype , regression , regression analysis , population , statistics , computer science , genetics , biology , medicine , psychology , mathematics , single nucleotide polymorphism , gene , psychotherapist , environmental health
Genetic association analysis, which evaluates relationships between genetic markers and complex, heritable traits, is the basis of genome‐wide association studies. The many association tests that have been developed can generally be classified as prospective versus retrospective, parametric versus nonparametric, and genotype‐ versus allele‐based. While method classifications are useful, it can be confusing and challenging for practitioners to decide on the “optimal” test to use for their data. We go beyond known differences between some popular association tests and provide new results that show analytical connections between tests, for both population‐ and family‐based study designs.