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Motivation It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes. Results We first proposed a novel conditional gene-based association test which can use only summary statistics to isolate independently associated genes of a disease. Applying this method, we detected 185 genes of independent association with schizophrenia. We then designed an in-silico experiment based on expression/co-expression to systematically validate pathogenic potential of these genes. We found that genes of independent association with schizophrenia formed more co-expression pairs in normal post-natal but not pre-natal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophrenia patients. The genes with independent association also had more significant P-values for differential expression between schizophrenia patients and controls in the brain regions. In contrast, indirectly associated genes or associated genes by other widely-used gene-based tests had no such differential expression and co-expression patterns. In summary, this conditional gene-based association test is effective for isolating directly associated genes from indirectly associated genes, and the results insightfully suggest that common variants might contribute to schizophrenia largely by distorting expression and co-expression in post-natal brains. Availability and implementation The conditional gene-based association test has been implemented in a platform ‘KGG’ in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: Exome sequencing studies have facilitated the detection of causal genetic variants in yet-unsolved Mendelian diseases. However, the identification of disease causal genes among a list of candidates in an exome sequencing study is still not fully settled, and it is often difficult to prioritize candidate genes for follow-up studies. The inheritance mode provides crucial information for understanding Mendelian diseases, but none of the existing gene prioritization tools fully utilize this information. Results: We examined the characteristics of Mendelian disease genes under different inheritance modes. The results suggest that Mendelian disease genes with autosomal dominant (AD) inheritance mode are more haploinsufficiency and de novo mutation sensitive, whereas those autosomal recessive (AR) genes have significantly more non-synonymous variants and regulatory transcript isoforms. In addition, the X-linked (XL) Mendelian disease genes have fewer non-synonymous and synonymous variants. As a result, we derived a new scoring system for prioritizing candidate genes for Mendelian diseases according to the inheritance mode. Our scoring system assigned to each annotated protein-coding gene (N = 18 859) three pathogenic scores according to the inheritance mode (AD, AR and XL). This inheritance mode-specific framework achieved higher accuracy (area under curve = 0.84) in XL mode. Conclusion: The inheritance-mode specific pathogenicity prioritization (ISPP) outperformed other well-known methods including Haploinsufficiency, Recessive, Network centrality, Genic Intolerance, Gene Damage Index and Gene Constraint scores. This systematic study suggests that genes manifesting disease inheritance modes tend to have unique characteristics. Availability and implementation: ISPP is included in KGGSeq v1.0 (http://grass.cgs.hku.hk/limx/kggseq/), and source code is available from (https://github.com/jacobhsu35/ISPP.git). Contact: mxli@hku.hk Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS. Contact: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: The characterization of deleterious genomic mutation (DGM) is of central significance for evolutionary biology and genetic studies. Fitness moment method has been developed to efficiently characterize DGM from natural population directly. In order to enable researchers to employ this method for theoretical and empirical research on characterizing DGM, we here present a distributed Java Application for DGM Estimation (JADE). Availability: Contact: Jinlong.Li@sbri.org

Summary: The Differentially Expressed Protein Database was designed to store the output of comparative proteomics studies and provides a publicly available query and analysis platform for data mining. The database contains information about more than 3000 differentially expressed proteins (DEPs) manually extracted from the published literature, including relevant biological, experimental and methodological elements. Tools for visualization and functional analysis of DEPs are provided via a user-friendly webinterface. Availability: http://protchem.hunnu.edu.cn/depd/ Contact: liangsp@hunnu.edu.cn

Summary: To manage high-throughput single nucleotide polymorphism (SNP) genotyping data efficiently, we developed a dynamic general database management system—SNPP (SNP Processor). It provides several functions, including data importing with comparison, Mendelian inheritance check within pedigrees, data compiling and exporting. Furthermore, SNPP may generate files for repeat genotyping and transform them into files that can be executed by a liquid handling system. Availability: http://orclinux.creighton.edu/snpp/ Contact: lanjuanzhao@creighton.edu