The Cochran-Armitage trend test is commonly used as a genotype-based test for candidate gene association. Corresponding to each underlying genetic model there is a particular set of scores assigned to the genotypes that maximizes its power. When the variance of the test statistic is known, the formulas for approximate power and associated sample size are readily obtained. In practice, however, the variance of the test statistic needs to be estimated. We present formulas for the required sample size to achieve a prespecified power that account for the need to estimate the variance of the test statistic. When the underlying genetic model is unknown one can incur a substantial loss of power when a test suitable for one mode of inheritance is used where another mode is the true one. Thus, tests having good power properties relative to the optimal tests for each model are useful. These tests are called efficiency robust and we study two of them: the maximin efficiency robust test is a linear combination of the standardized optimal tests that has high efficiency and the MAX test, the maximum of the standardized optimal tests. Simulation results of the robustness of these two tests indicate that the more computationally involved MAX test is preferable.

Erythrocyte measures are heritable and have important health implications, yet their genetic determinants are largely unknown. We performed genome-wide association analyses in 24,167 European-ancestry individuals for six erythrocyte traits and identified associations at 23 loci (P values 5×10-8 to 1×10-57). Replication testing in an independent set of 9,456 European-ancestry individuals showed strong evidence of association in all 23 loci in meta-analysis of the discovery and replication cohorts. Our findings include previously identified loci (HBS1L/MYB, HFE, TMPRSS6, TFR2, SPTA1) and novel associations (EPO, TFRC, SH2B3, and 15 other loci). This study has identified novel determinants of erythrocyte traits, offering insight into common variants underlying variation in erythrocyte measures.

Test statistics for association between markers on autosomal chromosomes and a disease have been extensively studied. No research has been reported on performance of such test statistics for association on the X chromosome. With 100,000 or more single-nucleotide polymorphisms (SNPs) available for genome-wide association studies, thousands of them come from the X chromosome. The X chromosome contains rich information about population history and linkage disequilibrium. To identify X-linked marker susceptibility to a disease, it is important to study properties of various statistics that can be used to test for association on the X chromosome. In this article, we compare performance of several approaches for testing association on the X chromosome, and examine how departure from Hardy-Weinberg equilibrium would affect type I error and power of these association tests using X-linked SNPs. The results are applied to the X chromosome of Klein et al. [2005], a genome-wide association study with 100K SNPs for age-related macular degeneration. We found that a SNP (rs10521496) covered by DIAPH2, known to cause premature ovarian failure (POF) in females, is associated with age-related macular degeneration.

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The Cochran-Armitage trend test (CATT) is well suited for testing association between a marker and a disease in case-control studies. When the underlying genetic model for the disease is known, the CATT optimal for the genetic model is used. For complex diseases, however, the genetic models of the true disease loci are unknown. In this situation, robust tests are preferable. We propose a two-phase analysis with model selection for the case-control design. In the first phase, we use the difference of Hardy-Weinberg disequilibrium coefficients between the cases and the controls for model selection. Then, an optimal CATT corresponding to the selected model is used for testing association. The correlation of the statistics used for selection and the test for association is derived to adjust the two-phase analysis with control of the Type-I error rate. The simulation studies show that this new approach has greater efficiency robustness than the existing methods.