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In genome-wide association (GWA) studies the goal is to detect associationsbetween genetic markers and a given phenotype. The number of genetic markerscan be large and effective methods for control of the overall error rate is acentral topic when analyzing GWA data. The Bonferroni method is known to beconservative when the tests are dependent. Permutation methods give exactcontrol of the overall error rate when the assumption of exchangeability issatisfied, but are computationally intensive for large datasets. For regressionmodels the exchangeability assumption is in general not satisfied and there isno standard solution on how to do permutation testing, except some approximatemethods. In this paper we will discuss permutation methods for control of thefamilywise error rate in genetic association studies and present an approximatesolution. These methods will be compared using simulated data.

Permutation in genetic association studies with covariates: controlling the familywise error rate with score tests in generalized linear models