Genome-wide association (GWA) study is becoming a powerful tool in deciphering genetic basis of complex human diseases/traits. Currently, the univariate analysis is the most commonly used method to identify genes associated with a certain disease/phenotype under study. A major limitation with the univariate analysis is that it may not make use of the information of multiple correlated phenotypes, which are usually measured and collected in practical studies. The multivariate analysis has proven to be a powerful approach in linkage studies of complex diseases/traits, but it has received little attention in GWA. In this study, we aim to develop a bivariate analytical method for GWAS, which can be used for a complex situation that a continuous trait and a binary trait measured are under study. Based on the modified extended generalized estimating equation (EGEE) method we proposed herein, we assessed the performance of our bivariate analyses through extensive simulations as well as real data analyses. In the study, to develop an EGEE approach for bivariate genetic analyses, we combined two different generalized linear models corresponding to phenotypic variables using a Seemingly Unrelated Regression (SUR) model. The simulation results demonstrated that our EGEE-based bivariate analytical method outperforms univariate analyses in increasing statistical power under a variety of simulation scenarios. Notably, EGEE-based bivariate analyses have consistent advantages over univariate analyses whether or not there exits a phenotypic correlation between the two traits. Our study has practical importance, as one can always use multivariate analyses as a screening tool when multiple phenotypes are available, without extra costs of statistical power and false positive rate. Analyses on empirical GWA data further affirm the advantages of our bivariate analytical method.

Patients with ovarian cancer (OC) may be treated with surgery, chemotherapy and/or radiation therapy, although none of these strategies are very effective. Several plant-based natural products/dietary supplements, including extracts from Emblica officinalis (Amla), have demonstrated potent anti-neoplastic properties. In this study we determined that Amla extract (AE) has anti-proliferative effects on OC cells under both in vitro and in vivo conditions. We also determined the anti-proliferative effects one of the components of AE, quercetin, on OC cells under in vitro conditions. AE did not induce apoptotic cell death, but did significantly increase the expression of the autophagic proteins beclin1 and LC3B-II under in vitro conditions. Quercetin also increased the expression of the autophagic proteins beclin1 and LC3B-II under in vitro conditions. AE also significantly reduced the expression of several angiogenic genes, including hypoxia-inducible factor 1α (HIF-1α) in OVCAR3 cells. AE acted synergistically with cisplatin to reduce cell proliferation and increase expression of the autophagic proteins beclin1 and LC3B-II under in vitro conditions. AE also had anti-proliferative effects and induced the expression of the autophagic proteins beclin1 and LC3B-II in mouse xenograft tumors. Additionally, AE reduced endothelial cell antigen – CD31 positive blood vessels and HIF-1α expression in mouse xenograft tumors. Together, these studies indicate that AE inhibits OC cell growth both in vitro and in vivo possibly via inhibition of angiogenesis and activation of autophagy in OC. Thus AE may prove useful as an alternative or adjunct therapeutic approach in helping to fight OC.

In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.