Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies.

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Data from current gene-disease association studies motivate changes to existing haplotype inference methodologies. Many datasets are now comprised of both pedigree and population data so it is desirable to incorporate both sources of information when inferring haplotypes. The availability of high-density SNP data also makes it possible to determine and use the precise locations of recombination events. Our proposed method reconstructs haplotype structure on a genome-wide level by jointly using the information from the Mendelian law of inheritance and local population structure. The method combines in one framework new techniques of recombination event detection, maximum likelihood optimization of population haplotype diversity and our previous algorithm of zero-recombinant haplotype reconstruction. Experiments on both real and simulated datasets prove the efficiency and accuracy of our approach in reconstructing the haplotype structure. Our method makes it possible to reveal the haplotypic variation on a genome-wide level.

Complex diseases, by definition, involve multiple factors, including gene-gene interactions and gene-environment interactions. Researchers commonly rely on simulated data to evaluate their approaches for detecting high-order interactions in disease gene mapping. A publicly available simulation program to generate samples involving complex genetic and environmental interactions is of great interest to the community. We have developed a software package named gs1.0, which has been widely used since its publication. In this article, we present an upgraded version gs2.0, which not only inherits its capacity to generate realistic genotype data but also provides great functionality and flexibility to simulate various interaction models. In addition to a standalone version, a user-friendly web server (http://cbc.case.edu/gs) has been set up to help users to build complex interaction models. Furthermore, by utilizing three three-locus models as an example, we have shown how realistic model parameters can be chosen in generating simulated data.

BACKGROUND: Adequate calcium intake is necessary to prevent osteoporosis, which poses significant public health challenges. The natural bioactive peptide calcium chelates have been regarded as superior calcium supplements. Microalgae peptides are regarded as potential candidates for protection from bone loss in osteoporosis. This study aimed to prepare microalgae calcium-chelating peptides from four microalgae proteins and assess their osteogenic activities in osteoporosis-like zebrafish. RESULTS: After in vitro gastrointestinal digestion, 4.42% Chlorella pyrenoidosa protein, 2.74% Nannochloropsis oceanica protein, 6.07% Arthospira platensis protein and 10.47% Dunaliella salina protein were retained. The calcium-chelating capacities of four microalgae protein hydrolysates (MPHs) ranged from 14.10 ± 7.16% to 34.11 ± 9.34%. CaCl 2 addition increased the maximum absorption peaks, absorption intensities and particle sizes of MPHs. Calcium-chelating MPHs showed stronger osteogenic activities than MPHs in the osteoporosis-like zebrafish model, with significantly increased mineralized tissue area and integrated optical density.CONCLUSION: Microalgae proteins have favorable digestibilities. Among the four MPHs, Nannochloropsis oceanica protein hydrolysates showed the highest calcium-chelating capacity, which might be due to its high degree of hydrolysis after in vitro digestion and high content of Ser, Tyr, Thr, Asp and Glu. The absorption intensities and particle sizes of MPHs both increased after calcium addition. MPH treatment could reverse dexamethasone-induced osteoporosis of zebrafish, and MPHs-Ca chelates showed higher osteogenic activities in osteoporosis-like phenotype zebrafish.