DETECTION POWER OF RANDOM, CASE-CONTROL, AND CASE-PARENT CONTROL DESIGNS FOR ASSOCIATION TESTS AND GENETIC MAPPING OF COMPLEX TRAITS

We compared the relative detection power of random, case-control, and case-parent control (TDT) study designs by computer simulation of five parameters: Mode of inheritance (MOl), magnitude of genetic effect (y ), disease susceptibility allele frequency in the founder population ( P I), population age (t ), and the genetic distance (ᶿ ) between disease susceptibility locus ( D), and marker locus (M). Our results show that none of the three study designs can be claimed to be the most powerful (requiring the smallest sample size) constantly under every different genetic context (parameter combination). Our analysis indicates that both case-parent control and case-control designs have more power than the random sampling design in most genetic contexts. But the relative power between case-parent and case control depends on the specific parameter combinations. Random sampling has more power than case-parent control (although less power than case-control design) under some high genetic effect (Y ) and initial allele frequency (PI) combinations. All the three study designs show the most power under additive models of inheritance and least power under recessive mode of inheritance

IMPACT OF DATA TRANSFORMATION ON THE PERFORMANCE OF DIFFERENT CLUSTERING METHODS AND CLUSTER NUMBER DETERMINATION STATISTICS FOR ANALYZING GENE EXPRESSION PROFILE DATA

We have assessed the impact of 13 different data transformation methods on the performance of four types of clustering methods (partitioning (K-mean), hierarchical distance (Average Linkage), multivariate normal mixture, and non-parametric kernel density) and four cluster number determination statistics (CNDS) (Pseudo F, Pseudo t2, Cubic Clustering Criterion (CCC), and Bayesian Information Criterion (BIC), using both simulated and real gene expression profile data. We found that Square Root, Cubic Root, and Spacing transformations have mostly positive impacts on the performance of the four types of clustering methods whereas Tukey\u27s Bisquare and Interquantile Range have mostly negative impacts. The impacts from other transformation methods are clustering method-specific and data type-specific. The performance of CNDS improves with appropriately transformed data. Multivariate Mixture Clustering and Kernel Density Clustering perform better than K-mean and Average Linkage in grouping both simulated and real gene expression profile data