BackgroundHaplotype-based approaches have been extensively studied for case-control association mapping in recent years. It has been shown that haplotype methods can provide more consistent results comparing to single-locus based approaches, especially in cases where causal variants are not typed. Improved power has been observed by clustering similar or rare haplotypes into groups to reduce the degrees of freedom of association tests. For family-based association studies, one commonly used strategy is Transmission Disequilibrium Tests (TDT), which examine the imbalanced transmission of alleles/haplotypes to affected and normal children. Many extensions have been developed to deal with general pedigrees and continuous traits.ResultsIn this paper, we propose a new haplotype-based association method for family data that is different from the TDT framework. Our approach (termed F_HapMiner) is based on our previous successful experiences on haplotype inference from pedigree data and haplotype-based association mapping. It first infers diplotype pairs of each individual in each pedigree assuming no recombination within a family. A phenotype score is then defined for each founder haplotype. Finally, F_HapMiner applies a clustering algorithm on those founder haplotypes based on their similarities and identifies haplotype clusters that show significant associations with diseases/traits. We have performed extensive simulations based on realistic assumptions to evaluate the effectiveness of the proposed approach by considering different factors such as allele frequency, linkage disequilibrium (LD) structure, disease model and sample size. Comparisons with single-locus and haplotype-based TDT methods demonstrate that our approach consistently outperforms the TDT-based approaches regardless of disease models, local LD structures or allele/haplotype frequencies.ConclusionWe present a novel haplotype-based association approach using family data. Experiment results demonstrate that it achieves significantly higher power than TDT-based approaches.

BackgroundMicroRNAs (miRNAs) are a new class of naturally occurring, small, non-coding RNAs that regulate protein-coding mRNAs by causing mRNA degradation or repressing translation. The roles of miRNAs in lineage determination and proliferation, as well as the localization of several miRNA genes at sites of translocation breakpoints or deletions, have led to speculation that miRNAs could be important factors in the development or maintenance of the neoplastic state.ResultsWe showed that miR-9 was downregulated in human gastric adenocarcinoma. Overexpression of miR-9 suppressed the growth of human gastric adenocarcinoma cell line MGC803 cell as well as xenograft tumors derived from them in SCID mice. Bioinformatics analysis indicated a putative miR-9 binding site in the 3'-untranslated region (3'UTR) of the tumor-related gene NF-κB1 mRNA. In an EGFP reporter system, overexpression of miR-9 downregulated EGFP intensity, and mutation of the miR-9 binding site abolished the effect of miR-9 on EGFP intensity. Furthermore, both the NF-κB1 mRNA and protein levels were affected by miR-9. Finally, knockdown of NF-κB1 inhibited MGC803 cell growth in a time-dependent manner, while ectopic expression of NF-κB1 could rescue MGC803 cell from growth inhibition caused by miR-9.ConclusionThese findings indicate that miR-9 targets NF-κB1 and regulates gastric cancer cell growth, suggesting that miR-9 shows tumor suppressive activity in human gastric cancer pathogenesis.