Comparison of type I error for multiple test corrections in large single-nucleotide polymorphism studies using principal components versus haplotype blocking algorithms.
Although permutation testing has been the gold standard for assessing significance levels in studies using multiple markers, it is time-consuming. A Bonferroni correction to the nominal p-value that uses the underlying pair-wise linkage disequilibrium (LD) structure among the markers to determine the number of effectively independent tests has recently been proposed. We propose using the number of independent LD blocks plus the number of independent single-nucleotide polymorphisms for correction. Using the Collaborative Study on the Genetics of Alcoholism LD data for chromosome 21, we simulated 1,000 replicates of parent-child trio data under the null hypothesis with two levels of LD: moderate and high. Assuming haplotype blocks were independent, we calculated the number of independent statistical tests using 3 haplotype blocking algorithms. We then compared the type I error rates using a principal components-based method, the three blocking methods, a traditional Bonferroni correction, and the unadjusted p-values obtained from FBAT. Under high LD conditions, the PC method and one of the blocking methods were slightly conservative, whereas the 2 other blocking methods exceeded the target type I error rate. Under conditions of moderate LD, we show that the blocking algorithm corrections are closest to the desired type I error, although still slightly conservative, with the principal components-based method being almost as conservative as the traditional Bonferroni correction.
Nicodemus
Kristin K
KK
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. knicodem@jhsph.edu
Liu
Wenlei
W
Chase
Gary A
GA
Tsai
Ya-Yu
YY
Fallin
M Daniele
MD
D000465:Algorithms; D005820:Genetic Testing; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012107:Research Design