Increasing power of groupwise association test with likelihood ratio test

Sequencing studies have been discovering a numerous number of rare variants, allowing the identification of the effects of rare variants on disease susceptibility. As a method to increase the statistical power of studies on rare variants, several groupwise association tests that group rare variants in genes and detect associations between genes and diseases have been proposed. One major challenge in these methods is to determine which variants are causal in a group, and to overcome this challenge, previous methods used prior information that specifies how likely each variant is causal. Another source of information that can be used to determine causal variants is the observed data because case individuals are likely to have more causal variants than control individuals. In this article, we introduce a likelihood ratio test (LRT) that uses both data and prior information to infer which variants are causal and uses this finding to determine whether a group of variants is involved in a disease. We demonstrate through simulations that LRT achieves higher power than previous methods. We also evaluate our method on mutation screening data of the susceptibility gene for ataxia telangiectasia, and show that LRT can detect an association in real data. To increase the computational speed of our method, we show how we can decompose the computation of LRT, and propose an efficient permutation test. With this optimization, we can efficiently compute an LRT statistic and its significance at a genome-wide level. The software for our method is publicly available at http://genetics.cs.ucla.edu/rarevariants .     

Fit-climbing test: a field test for indoor rock climbing

The aim of this study was to develop an indoor rock-climbing test on an artificial wall (Fit-climbing test). Thirteen climbers (elite group [EG] = 6; recreational group [RG] = 7) performed the following tests: (a) familiarization in the Fit-climbing test, (b) the Fit-climbing test, and (c) a retest to evaluate the Fit-climbing test's reliability. Gas exchange, blood lactate concentration, handgrip strength, and heart rate were measured during the test. Oxygen uptake during the Fit-climbing test was not different between groups (EG = 8.4 ± 1.1 L; RG = 7.9 ± 1.5 L, p > 0.05). The EG performance (120 ± 7 movements) was statistically higher than the RG climbers' performance (78 ± 13 movements) during the Fit-climbing test. Consequently, the oxygen cost per movement during the Fit-climbing test of the EG was significantly lower than that of the RG (p < 0.05). Handgrip strength was higher in the EG when compared with that in the RG in both pre-Fit- and post-Fit-climbing test (p < 0.05). There were no significant differences in any other variables analyzed during the Fit-climbing test (p > 0.05). Furthermore, the performance in the Fit-climbing test presented high reliability (intraclass correlation coefficient = 0.97). Therefore, the performance during the Fit-climbing test may be an alternative to evaluate rock climbers because of its specificity and relation to oxygen cost per movement during climbing.     

Kernel-based association test

Association mapping (i.e., linkage disequilibrium mapping) is a powerful tool for positional cloning of disease genes. We propose a kernel-based association test (KBAT), which is a composite function of "P-values of single-locus association tests" and "kernel weights related to intermarker distances and/or linkage disequilibria." The KBAT is a general form of some current test statistics. This method can be applied to the study of candidate genes and can scan each chromosome using a moving average procedure. We evaluated the performance of the KBAT through simulation studies that considered evolutionary parameters, disease models, sample sizes, kernel functions, test statistics, window attributes, empirical P-value estimations, and genetic/physical maps. The results showed that the KBAT had a well-controlled false positive rate and high power compared to existing methods. In addition, the KBAT was also applied to analyze a genomewide data set from the Collaborative Study on the Genetics of Alcoholism. Important genes associated with alcoholism dependence were identified. In summary, the merits of the KBAT are multifold: the KBAT is robust against the inclusion of nuisance markers, is invariant to the map scale, and accommodates different types of genomic data, study designs, and study purposes. The proposed methods are packaged in the user-friendly software, KBAT, available at http://www.stat.sinica.edu.tw/hsinchou/genetics/association/KBAT.htm.