Rank-Based Tests for Identifying Multiple Genetic Variants Associated with Quantitative Traits

Abstract

SummaryWe consider the analysis of multiple genetic variants within a gene or a region that are expected to confer risks to human complex diseases with quantitative traits, where the trait values do not follow the normal distribution even after some transformations. We rank the phenotypic values, calculate a score to measure the trend effect of a particular allele for each marker, and then construct three statistics based on the quadratic frameworks of methods Hotelling T 2 , the summation of squared univariate statistic and the inverse of the square root weighted statistics to combine the scores for different marker loci. Simulation results show that the above three test statistics can control the type I error rate well and are more robust than standard tests constructed based on linear regression. Application to GAW16 data for rheumatoid arthritis successfully detects the association between the HLA-DRB1 gene and anticyclic citrullinated protein measure, while the standard methods based on normal assumption cannot detect this association.

Brain Arterial Diameters and Cognitive Performance: The Northern Manhattan Study

“…For arteries not visualized in axial source MRA images, we assigned a diameter of zero. We obtained the average of the 13 arterial segments and transformed each distribution to normal using a rank-based Blom method to be able to weight equally the 13 brain arteries with naturally occurring various diameters (Li, Yuan, Han, Gao, and Li, 2014). We summed and averaged the 13 normally distributed brain arterial diameters to obtain the “global Brain Arterial Remodeling (BAR) score.” We also used regional arterial scores to relate to cognitive function localized downstream from that artery (or arteries).…”

Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies

“…Compared with single-marker analysis, which tests every marker individually and is commonly employed in genome-wide association study, multiple-marker test has been well appreciated because of its potentially improved statistical power. Statistical methods for multiple-marker analysis can be summarized as synthesizing single-marker test statistics such as Hotelling’s T 2 test 1 2 3 and summation of squared univariate test 4 5 , weighted Fourier transformation 6 , variance-components score test 7 , principal components regression method 8 9 10 , and Kernel-machine-based test 11 . Performances of these methods have been explored by intensive computer simulations 1 12 13 .…”