title: Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants.
abstract: In genome-wide association studies of binary traits, investigators typically use logistic regression to test common variants for disease association within studies, and combine association results across studies using meta-analysis. For common variants, logistic regression tests are well calibrated, and meta-analysis of study-specific association results is only slightly less powerful than joint analysis of the combined individual-level data. In recent sequencing and dense chip based association studies, investigators increasingly test low-frequency variants for disease association. In this paper, we seek to (1) identify the association test with maximal power among tests with well controlled type I error rate and (2) compare the relative power of joint and meta-analysis tests. We use analytic calculation and simulation to compare the empirical type I error rate and power of four logistic regression based tests: Wald, score, likelihood ratio, and Firth bias-corrected. We demonstrate for low-count variants (roughly minor allele count [MAC] < 400) that: (1) for joint analysis, the Firth test has the best combination of type I error and power; (2) for meta-analysis of balanced studies (equal numbers of cases and controls), the score test is best, but is less powerful than Firth test based joint analysis; and (3) for meta-analysis of sufficiently unbalanced studies, all four tests can be anti-conservative, particularly the score test. We also establish MAC as the key parameter determining test calibration for joint and meta-analysis.
authors.affiliation: Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.
authors.forename: Clement
authors.initials: C
authors.lastname: Ma
authors.forename: Tom
authors.initials: T
authors.lastname: Blackwell
authors.forename: Michael
authors.initials: M
authors.lastname: Boehnke
authors.forename: Laura J
authors.initials: LJ
authors.lastname: Scott
mesh_terms: D002138:Calibration; D016022:Case-Control Studies; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D005787:Gene Frequency; D014644:Genetic Variation; D006801:Humans; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D008957:Models, Genetic
keywords: joint analysis; low-frequency variants; meta-analysis; single nucleotide polymorphisms; single variant tests