A systematic method for mapping multiple loci: an application to construct a genetic network for rheumatoid arthritis

The advent of high-throughput single nucleotide polymorphisms (SNPs) omics technologies has brought tremendous genetic data. Systematic evaluation of the genome-wide SNPs is expected to provide breakthroughs in the understanding of complex diseases. In this study, we developed a new systematic method for mapping multiple loci and applied the proposed method to construct a genetic network for rheumatoid arthritis (RA) via analysis of 746 multiplex families genotyped with more than five thousands of genome-wide SNPs. We successfully identified 41 significant SNPs relevant to RA, 25 associated genes and a number of important SNP-SNP interactions (SNP patterns). Many findings (loci, genes and interactions) have experimental support from previous studies while novel findings may define unknown genetic pathways for this complex disease. Finally, we constructed a genetic network by integrating the results from this analysis with the rapidly accumulated knowledge in biomedical domains, which gave us a more detailed insight onto the RA etiology. The results suggest that the proposed systematic method is powerful when applied to genome-wide association studies. Integrating the analysis of high-throughput SNP data with knowledge-based SNP functional annotation offers a promising way to reversely engineer the underlying genetic networks for complex human diseases.     

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.     

Biological implications of SNPs in signal peptide domains of human proteins

Proteins destined for secretion or membrane compartments possess signal peptides for insertion into the membrane. The signal peptide is therefore critical for localization and function of cell surface receptors and ligands that mediate cell-cell communication. About 4% of all human proteins listed in UniProt database have signal peptide domains in their N terminals. A comprehensive literature survey was performed to retrieve functional and disease associated genetic variants in the signal peptide domains of human proteins. In 21 human proteins we have identified 26 disease associated mutations within their signal peptide domains, 14 mutations of which have been experimentally shown to impair the signal peptide function and thus influence protein transportation. We took advantage of SignalP 3.0 predictions to characterize the signal peptide prediction score differences between the mutant and the wild-type alleles of each mutation, as well as 189 previously uncharacterized single nucleotide polymorphisms (SNPs) found to be located in the signal peptide domains of 165 human proteins. Comparisons of signal peptide prediction outcomes of mutations and SNPs, have implicated SNPs potentially impacting the signal peptide function, and thus the cellular localization of the human proteins. The majority of the top candidate proteins represented membrane and secreted proteins that are associated with molecular transport, cell signaling and cell to cell interaction processes of the cell. This is the first study that systematically characterizes genetic variation occurring in the signal peptides of all human proteins. This study represents a useful strategy for prioritization of SNPs occurring within the signal peptide domains of human proteins. Functional evaluation of candidates identified herein may reveal effects on major cellular processes including immune cell function, cell recognition and adhesion, and signal transduction.     

Synapse development in health and disease

Recent insights into the genetic basis of neurological disease have led to the hypothesis that molecular pathways involved in synaptic growth, development, and stability are perturbed in a variety of mental disorders. Formation of a functional synapse is a complex process requiring stabilization of initial synaptic contacts by adhesive protein interactions, organization of presynaptic and postsynaptic specializations by scaffolding proteins, regulation of growth by intercellular signaling pathways, reorganization of the actin cytoskeleton, and proper endosomal trafficking of synaptic growth signaling complexes. Many neuropsychiatric disorders, including autism, schizophrenia, and intellectual disability, have been linked to inherited mutations which perturb these processes. Our understanding of the basic biology of synaptogenesis is therefore critical to unraveling the pathogenesis of neuropsychiatric disorders.     

Comparison of Family History and SNPs for Predicting Risk of Complex Disease

The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)–based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%–30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history–based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP–based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.     

Association of Single Nucleotide Polymorphisms in Wnt Signaling Pathway Genes with Breast Cancer in Saudi Patients

Breast cancer is a complex heterogeneous disease involving genetic and epigenetic alterations in genes encoding proteins that are components of various signaling pathways. Candidate gene approach have identified association of genetic variants in the Wnt signaling pathway genes and increased susceptibility to several diseases including breast cancer. Due to the rarity of somatic mutations in key genes of Wnt pathway, we investigated the association of genetic variants in these genes with predisposition to breast cancers. We performed a case-control study to identify risk variants by examining 15 SNPs located in 8 genes associated with Wnt signaling. Genotypic analysis of individual locus showed statistically significant association of five SNPs located in β-catenin, AXIN2, DKK3, SFRP3 and TCF7L2 with breast cancers. Increased risk was observed only with the SNP in β-catenin while the other four SNPs conferred protection against breast cancers. Majority of these associations persisted after stratification of the cases based on estrogen receptor status and age of on-set of breast cancer. The rs7775 SNP in exon 6 of SFRP3 gene that codes for either arginine or glycine exhibited very strong association with breast cancer, even after Bonferroni''s correction. Apart from these five variants, rs3923086 in AXIN2 and rs3763511 in DKK4 that did not show any association in the overall population were significantly associated with early on-set and estrogen receptor negative breast cancers, respectively. This is the first study to utilize pathway based approach to identify association of risk variants in the Wnt signaling pathway genes with breast cancers. Confirmation of our findings in larger populations of different ethnicities would provide evidence for the role of Wnt pathway as well as screening markers for early detection of breast carcinomas.     

Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data

Background

High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.

Results

We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.

Conclusions

We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.

     

A new methodology to associate SNPs with human diseases according to their pathway related context

Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP.     

Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes

Complex diseases are often highly heritable. However, for many complex traits only a small proportion of the heritability can be explained by observed genetic variants in traditional genome-wide association (GWA) studies. Moreover, for some of those traits few significant SNPs have been identified. Single SNP association methods test for association at a single SNP, ignoring the effect of other SNPs. We show using a simple multi-locus odds model of complex disease that moderate to large effect sizes of causal variants may be estimated as relatively small effect sizes in single SNP association testing. This underestimation effect is most severe for diseases influenced by numerous risk variants. We relate the underestimation effect to the concept of non-collapsibility found in the statistics literature. As described, continuous phenotypes generated with linear genetic models are not affected by this underestimation effect. Since many GWA studies apply single SNP analysis to dichotomous phenotypes, previously reported results potentially underestimate true effect sizes, thereby impeding identification of true effect SNPs. Therefore, when a multi-locus model of disease risk is assumed, a multi SNP analysis may be more appropriate.     

In silico search for single nucleotide polymorphisms in genes important in vitamin E homeostasis

Large inter-individual variation exists in the response to vitamin E supplementation, and this may influence the outcome of human studies. It is our hypothesis that genetic heterogeneity is an important determinant of vitamin E homeostasis. Therefore we have performed an in silico search for single nucleotide polymorphisms (SNPs) associated with genes involved in vitamin E homeostasis. Based on function, the following genes were considered as candidates for vitamin E heterogeneity: c-tocopherol transfer protein (TTPA), tocopherol associated protein (TAP), lipoprotein lipase (LPL), multidrug resistance protein 2 (MDR-2), pregnane X receptor (PXR) and members of the cytochrome P450 family (CYP). Searches for coding SNPs were initiated from web based programs of the National Center for Biotechnology Information (NCBI). SNP frequencies were calculated by dividing the number of annotated coding SNPs by the number of base pairs in the open reading frame. Genes for TTPA, TAP and CYP3A5 had calculated SNP frequencies between 503 and 837 base pairs per coding SNP (bp/cSNP) and so are not highly polymorphic. In contrast, cSNP frequencies in LPL, MRP2, PXR, CYP3A4 and CYP4F2 were in the range of 100 bp/cSNP and so are highly polymorphic. Thus proteins involved in specific vitamin E binding are not highly polymorphic, may not influence inter-individual variation and so may not be good candidates for population studies. Proteins involved in drug/lipid metabolism which indirectly influence vitamin E status are highly polymorphic, are likely to influence inter-individual variation and so are good candidates for population studies. We suggest that future studies are aimed at addressing the role of such SNPs in vitamin E homeostasis.     

Medline search engine for finding genetic markers with biological significance

MOTIVATION: Genome-wide high density SNP association studies are expected to identify various SNP alleles associated with different complex disorders. Understanding the biological significance of these SNP alleles in the context of existing literature is a major challenge since existing search engines are not designed to search literature for SNPs or other genetic markers. The literature mining of gene and protein functions has received significant attention and effort while similar work on genetic markers and their related diseases is still in its infancy. Our goal is to develop a web-based tool that facilitates the mining of Medline literature related to genetic studies and gene/protein function studies. Our solution consists of four main function modules for (1) identification of different types of genetic markers or genetic variations in Medline records (2) distinguishing positive versus negative linkage or association between genetic markers and diseases (3) integrating marker genomic location data from different databases to enable the retrieval of Medline records related to markers in the same linkage disequilibrium region (4) and a web interface called MarkerInfoFinder to search, display, sort and download Medline citation results. Tests using published data suggest MarkerInfoFinder can significantly increase the efficiency of finding genetic disorders and their underlying molecular mechanisms. The functions we developed will also be used to build a knowledge base for genetic markers and diseases. AVAILABILITY: The MarkerInfoFinder is publicly available at: http://brainarray.mbni.med.umich.edu/brainarray/datamining/MarkerInfoFinder.     

Effects of natural selection on interpopulation divergence at polymorphic sites in human protein-coding Loci

To develop new strategies for searching for genetic associations with complex human diseases, we analyzed 2784 single-nucleotide polymorphisms (SNPs) in 396 protein-coding genes involved in biological processes relevant to cancer and other complex diseases, with respect to gene diversity within samples of individuals representing the three major historic human populations (African, European, and Asian) and with respect to interpopulation genetic distance. Reduced levels of both intrapopulation gene diversity and interpopulation genetic distance were seen in the case of SNPs located within the 5'-UTR and at nonsynonymous SNPs, causing radical changes to protein structure. Reduction of gene diversity at SNP loci in these categories was evidence of purifying selection acting at these sites, which in turn causes a reduction in interpopulation divergence. By contrast, a small number of SNP sites in these categories revealed unusually high genetic distances between the two most diverged populations (African and Asian); these loci may have historically been subject to divergent selection pressures.     

Interrelation Between Protein Synthesis,Proteostasis and Life Span

The production of newly synthesized proteins is a key process of protein homeostasis that initiates the biosynthetic flux of proteins and thereby determines the composition, stability and functionality of the proteome. Protein synthesis is highly regulated on multiple levels to adapt the proteome to environmental and physiological challenges such as aging and proteotoxic conditions. Imbalances of protein folding conditions are sensed by the cell that then trigger a cascade of signaling pathways aiming to restore the protein folding equilibrium. One regulatory node to rebalance proteostasis upon stress is the control of protein synthesis itself. Translation is reduced as an immediate response to perturbations of the protein folding equilibrium that can be observed in the cytosol as well as in the organelles such as the endoplasmatic reticulum and mitochondria. As reduction of protein synthesis is linked to life span increase, the signaling pathways regu-lating protein synthesis might be putative targets for treatments of age-related diseases. Eukaryotic cells have evolved a complex system for protein synthesis regulation and this review will summarize cellular strategies to regulate mRNA translation upon stress and its impact on longevity.     

p62蛋白的分子功能及其在疾病中的研究进展

螯合体1(SQSTM1/p62)是一种选择性自噬接头蛋白,在清除待降解蛋白、维持细胞内蛋白质稳态中发挥重要的调控作用。p62蛋白具有多个功能结构域,介导与多种蛋白质发生相互作用进而精确调节特定的信号通路,从而将p62蛋白与氧化防御系统、炎症反应和营养感知等重要生命过程联系起来。研究表明p62的突变或者表达异常与多种疾病的发生发展过程密切相关,包括神经退行性疾病、肿瘤、感染性疾病、遗传性疾病以及慢性疾病等。本文综述了p62蛋白的结构特征、分子功能,并系统介绍其在蛋白质稳态和信号通路调控中的多种功能,总结了p62在疾病发生发展中的复杂性与多面性,以期为p62蛋白的功能与相关疾病研究提供参考。     

Applications of computational algorithm tools to identify functional SNPs in cytokine genes

Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. However, how to identify functional SNPs from a pool containing both functional and neutral SNPs is challenging. In this study, we analyzed the genetic variations that can alter the expression and function of a group of cytokine proteins using computational tools. As a result, we extracted 4552 SNPs from 45 cytokine proteins from SNPper database. Of particular interest, 828 SNPs were in the 5'UTR region, 961 SNPs were in the 3' UTR region, and 85 SNPs were non-synonymous SNPs (nsSNPs), which cause amino acid change. Evolutionary conservation analysis using the SIFT tool suggested that 8 nsSNPs may disrupt the protein function. Protein structure analysis using the PolyPhen tool suggested that 5 nsSNPs might alter protein structure. Binding motif analysis using the UTResource tool suggested that 27 SNPs in 5' or 3'UTR might change protein expression levels. Our study demonstrates the presence of naturally occurring genetic variations in the cytokine proteins that may affect their expressions and functions with possible roles in complex human disease, such as immune diseases.     

A typical N-terminal extensions confer novel regulatory properties on GTP cyclohydrolase isoforms in Drosophila melanogaster

The cofactor tetrahydrobiopterin plays critical roles in the modulation of the signaling molecules dopamine, serotonin, and nitric oxide. Deficits in cofactor synthesis have been associated with several human hereditary diseases. Responsibility for the regulation of cofactor pools resides with the first enzyme in its biosynthetic pathway, GTP cyclohydrolase I. Because organisms must be able to rapidly respond to environmental and developmental cues to adjust output of these signaling molecules, complex regulatory mechanisms are vital for signal modulation. Mammalian GTP cyclohydrolase is subject to end-product inhibition via an associated regulatory protein and to positive regulation via phosphorylation, although target residues are unknown. GTP cyclohydrolase is composed of a highly conserved homodecameric catalytic core and non-conserved N-terminal domains proposed to be regulatory sites. We demonstrate for the first time in any organism that the N-terminal arms of the protein serve regulatory functions. We identify two different modes of regulation of the enzyme mediated through the N-terminal domains. The first is end-product feedback inhibition, catalytically similar to that of the mammalian enzyme, except that feedback inhibition by the cofactor requires sequences in the N-terminal arms rather than a separate regulatory protein. The second is a novel inhibitory interaction between the N-terminal arms and the active sites, which can be alleviated through the phosphorylation of serine residues within the N termini. Both mechanisms allow for acute and highly responsive regulation of cofactor production as required by downstream signaling pathways.