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Charcot-Marie-Tooth (CMT) neuropathy is inherited with genetic and clinical heterogeneity. The X-linked form (CMTX) is linked to mutations in the GJB1 gene. However, the genotype-phenotype correlation between variants in the non-coding region of GJB1 gene and CMTX is unclear. We found two structural variants (−459C>T and −713G>A) in the 5′ non-coding region of a transcript (Ref seq ID: NM_000166) of the GJB1 gene and explored its association with CMTX in two Chinese families. All family members who carried the −459C>T variant either were symptomatic or had abnormal electrophysiological studies compatible with CMTX, whereas all the non-symptomatic family members who had normal electrophysiological studies and 10 healthy unrelated controls did not have this variant. The other variant in the 5′-flanking region of the gene was found to be a benign polymorphism, although it had been earlier reported to be associated with CMTX in a Taiwanese family. Secondary structure prediction analysis of mutant mRNA using Mfold and RNAstructure softwares indicates that the −459C>T mutation may reduce translation efficiency of the GJB1 gene by changing its 5′-untranslated region secondary structure and abolishing the internal ribosome entry site at the initialization of its translation in Schwann cells. Our study can help clarify the causal mutations of CMTX in the non–protein coding region of GJB1. [ABSTRACT FROM AUTHOR]

Secret key leakage in wireless sensor networks (WSNs) is a high security risk especially when sensor nodes are deployed in hostile environment and physically accessible to attackers. With nowadays semi/fully-invasive attack techniques attackers can directly derive the cryptographic key from non-volatile memory (NVM) storage. Physically Unclonable Function (PUF) is a promising technology to resist node capture attacks, and it also provides a low cost and tamper-resistant key provisioning solution. In this paper, we designed a PUF based on double-data-rate SDRAM Type 3 (DDR3) memory by exploring its memory decay characteristics. We also described a prototype of 128-bit key generation based on DDR3 PUF with integrated fuzzy extractor. Due to the wide adoption of DDR3 memory in WSN, our proposed DDR3 PUF technology with high security levels and no required hardware changes is suitable for a wide range of WSN applications. [ABSTRACT FROM AUTHOR]

Linkage disequilibrium and disease-associated variants in the non-coding regions make it difficult to distinguish the truly associated genes from the redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called eDESE that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. eDESE initially performed the association analysis by mapping variants to genes according to their physical distance. We further demonstrated that the isoform-level eQTLs could be more powerful than the gene-level eQTLs in the association analysis using a simulation study. Then the eQTL-guided strategies, that is, mapping variants to genes according to their gene/isoform-level variant-gene cis-eQTLs associations, were also integrated with eDESE. We then applied eDESE to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the druggene interaction database (DGIdb). More importantly, seven potential susceptibility genes identified by eDESE were the target genes of multiple antipsychotics in DrugBank. Comparing the potential susceptibility genes identified by eDESE and other benchmark approaches (i.e., MAGMA and S-PrediXcan) implied that strategy based on the isoform-level eQTLs could be an important supplement for the other two strategies (physical distance and gene-level eQTLs). We have implemented eDESE in our integrative platform KGGSEE (http://pmglab.top/kggsee/#/) and hope that eDESE can facilitate the prediction of candidate susceptibility genes and isoforms for complex diseases in a multi-tissue context. [ABSTRACT FROM AUTHOR]

The clinical diagnosis of neurodegenerative disorders based on phenotype is difficult in heterogeneous conditions with overlapping symptoms. It does not take into account the disease etiology or the highly variable clinical course even amongst patients diagnosed with the same disorder. The advent of next generation sequencing (NGS) has allowed for a system-wide, unbiased approach to identify all gene variants in the genome simultaneously. With the plethora of new genes being identified, genetic rather than phenotype-based classification of Mendelian diseases such as spinocerebellar ataxia (SCA), hereditary spastic paraplegia (HSP) and Charcot-Marie-Tooth disease (CMT) has become widely accepted. It has also become clear that gene variants play a role in common and predominantly sporadic neurodegenerative diseases such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). The observation of pleiotropy has emerged, with mutations in the same gene giving rise to diverse phenotypes, which further increases the complexity of phenotype-genotype correlation. Possible mechanisms of pleiotropy include different downstream effects of different mutations in the same gene, presence of modifier genes and oligogenic inheritance. Future directions include development of bioinformatics tools and establishment of more extensive public genotype/phenotype databases to better distinguish deleterious gene variants from benign polymorphisms, translation of genetic findings into pathogenic mechanisms through in-vitro and in-vivo studies and ultimately finding disease-modifying therapies for neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Evidence from genome-wide association studies (GWAS) suggest that pleiotropic effects on human complex phenotypes are very common. Recently, an atlas of genetic correlations among complex phenotypes has broadened our understanding of human diseases and traits. Here, we examine genetic overlap, from a gene-centric perspective, among the same 24 phenotypes previously investigated for genetic correlations. After adopting the multilevel pipeline (freely available at http://grass.cgs.hku.hk/limx/kgg/), which includes intragenic single nucleotide polymorphisms (SNPs), genes, and gene-sets, to estimate genetic similarities across phenotypes, a large amount of sharing of several biologically related phenotypes was confirmed. In addition, significant genetic overlaps were also found among phenotype pairs that were previously unidentified by SNP-level approaches. All these pairs with new genetic links are supported by earlier epidemiological evidence, although only a few of them have pleiotropic genes in the GWAS Catalog. Hence, our gene and gene-set analyses are able to provide new insights into cross-phenotype connections. The investigation on genetic sharing at three different levels presents a complementary picture of how common DNA sequence variations contribute to disease comorbidities and trait manifestations. [ABSTRACT FROM AUTHOR]

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 (N¼18 859) three pathogenic scores according to the inheritance mode (AD, AR and XL). This inheritance mode-specific framework achieved higher accuracy (area under curve ¼0.84) 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]