The determination of gene-by-gene and gene-by-environment interactions has long been one of the greatest challenges in genetics. The traditional methods are typically inadequate because of the problem referred to as the "curse of dimensionality." Recent combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, the combinatorial partitioning method, and the restricted partition method, have a straightforward correspondence to the concept of the phenotypic landscape that unifies biological, statistical genetics, and evolutionary theories. However, the existing approaches have several limitations, such as not allowing for covariates, that restrict their practical use. In this study, we report a generalized MDR (GMDR) method that permits adjustment for discrete and quantitative covariates and is applicable to both dichotomous and continuous phenotypes in various population-based study designs. Computer simulations indicated that the GMDR method has superior performance in its ability to identify epistatic loci, compared with current methods in the literature. We applied our proposed method to a genetics study of four genes that were reported to be associated with nicotine dependence and found significant joint action between CHRNB4 and NTRK2. Moreover, our example illustrates that the newly proposed GMDR approach can increase prediction ability, suggesting that its use is justified in practice. In summary, GMDR serves the purpose of identifying contributors to population variation better than do the other existing methods.

We tested six single nucleotide polymorphisms (SNPs) in the alpha4 subunit gene (CHRNA4) and four SNPs in the beta2 subunit gene (CHRNB2) of nicotinic acetylcholine receptors (nAChRs) for association with nicotine dependence (ND), which was assessed by smoking quantity (SQ), the heaviness of smoking index (HSI) and the Fagerstrom test for ND (FTND) in 2037 subjects from 602 nuclear families of either European-American (EA) or African-American (AA) ancestry. Analysis of the six SNPs within CHRNA4 demonstrated that in the EA sample SNPs rs2273504 and rs1044396 are significantly associated with the adjusted SQ and FTND score, respectively. In the AA samples, SNPs rs3787137 and rs2236196 are each significantly associated with at least two adjusted ND measures. Association of rs2236196 with the adjusted HSI and FTND scores in the AA samples remained significant after correction for multiple testing. Furthermore, analysis revealed gender- and ethnic-specific associations for several SNPs with ND measures in both ethnic samples; however, only the association of SNP rs2236196 with the three adjusted ND measures remained significant after correcting for multiple testing in the AA female samples. Haplotype analysis of rs2273505-rs2273504-rs2236196 showed significant association after Bonferroni correction of a C-G-G haplotype (53.4%) with three adjusted ND measures in samples from the AA females. A similar analysis for the four SNPs within CHRNB2 did not reveal significant association with the three ND measures. In summary, our findings provide convincing evidence for the involvement of the nAChR alpha4 subunit, but not of the nAChR beta2 subunit, in nicotine addiction.

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G x G) and gene-environment (G x E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G x G and G x E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G x G and G x E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence.

Epidemiological studies have demonstrated that genetic factors account for at least 50% of the liability for nicotine dependence (ND). Although several linkage studies have been conducted, all samples to date were primarily of European origin. In this study, we conducted a genomewide scan of 1,261 individuals, representing 402 nuclear families, of African American (AA) origin. We examined 385 autosomal microsatellite markers for ND, which was assessed by smoking quantity (SQ), the Heaviness of Smoking Index (HSI), and the Fagerstrom Test for ND (FTND). After performing linkage analyses using various methods implemented in the GENEHUNTER and S.A.G.E. programs, we found a region near marker D10S1432 on chromosome 10q22 that showed a significant linkage to indexed SQ, with a maximum LOD score of 4.17 at 92 cM and suggestive linkage to HSI, SQ, and log-transformed SQ. Additionally, we identified three regions that met the criteria for suggestive linkage to at least one ND measure: on chromosomes 9q31 at marker D9S1825, 11p11 between markers D11S1993 and D11S1344, and 13q13 between markers D13S325 and D13S788. Other locations on chromosomes 15p11, 17q25, and 18q12 exhibited some evidence of linkage for ND (LOD >1.44). The four regions with significant or suggestive linkage were positive for multiple ND measures by multiple statistical methods. Some of these regions have been linked to smoking behavior at nominally significant levels in other studies, which provides independent replication of the regions for ND in different cohorts. In summary, we found significant linkage on chromosome 10q22 and suggestive linkage on chromosomes 9, 11, and 13 for major genetic determinants of ND in an AA sample. Further analysis of these positive regions by fine mapping and/or association analysis is thus warranted. To our knowledge, this study represents the first genomewide linkage scan of ND in an AA sample.

Artesunate (ART) is a semi-synthetic derivative of artemisinin extracted from the plant Artemisia annua is a safe and effective antimalarial drug. In the present investigation, ART was found also to inhibit angiogenesis in vivo and in vitro. The anti-angiogenic effect in vivo was evaluated in nude mice by means of human ovarian cancer HO-8910 implantation and immunohistochemical stainings for microvessel (CD₃₁), vascular endothelial growth factor (VEGF) and VEGF receptor KDR/flk-1. Tumor growth was decreased and microvessel density was reduced following drug treatment with no apparent toxicity to the animals. ART also remarkably lowered VEGF expression on tumor cells and KDR/flk-1 expression on endothelial cells as well as tumor cells. The in vitro effect of ART was tested on models of angiogenesis, namely, proliferation, migration and tube formation of human umbilical vein endothelial cells (HUVEC). The results showed that ART significantly inhibited angiogenesis in a dose-dependent form in the range of 0.5∼50 µmol/l. Additionally, the inhibitory effect of ART on HVUEC proliferation was stronger than that on Hela, JAR, HO-8910 cancer cells, NIH-3T3 fibroblast cells and human endometrial cells, indicating that ART was selectively against HUVEC. These findings and the known low toxicity of ART are clues that ART may be a promising angiogenesis inhibitor.

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Dihydroartemisinin is a potent compound against LLC cell line in vitro. In vivo, the combination strategy of DHA and chemotherapeutics holds promise for the treatment of relatively large and rapidly growing lung cancers.

Our study provides first evidence on the presence of gene-gene interaction among the four genes in affecting ND. Although CHRNB2 alone was not significantly associated with ND in several previously reported association studies on ND, we found it affects ND through interactions with CHRNA4 and NTRK2.