Using volcano plots and regularized-chi statistics in genetic association studies

Labor intensive experiments are typically required to identify the causal disease variants from a list of disease associated variants in the genome. For designing such experiments, candidate variants are ranked by their strength of genetic association with the disease. However, the two commonly used measures of genetic association, the odds-ratio (OR) and p-value may rank variants in different order. To integrate these two measures into a single analysis, here we transfer the volcano plot methodology from gene expression analysis to genetic association studies. In its original setting, volcano plots are scatter plots of fold-change and t-test statistic (or −log of the p-value), with the latter being more sensitive to sample size. In genetic association studies, the OR and Pearson's chi-square statistic (or equivalently its square root, chi; or the standardized log(OR)) can be analogously used in a volcano plot, allowing for their visual inspection. Moreover, the geometric interpretation of these plots leads to an intuitive method for filtering results by a combination of both OR and chi-square statistic, which we term “regularized-chi”. This method selects associated markers by a smooth curve in the volcano plot instead of the right-angled lines which corresponds to independent cutoffs for OR and chi-square statistic. The regularized-chi incorporates relatively more signals from variants with lower minor-allele-frequencies than chi-square test statistic. As rare variants tend to have stronger functional effects, regularized-chi is better suited to the task of prioritization of candidate genes.     

Candidate gene polymorphisms for diabetes mellitus, cardiovascular disease and cancer are associated with longevity in Koreans

Long-lived people may have a unique genetic makeup that makes them more resistant than the general population to prevalent age-related diseases; however, not much is known about genes involved in the longevity. To identify susceptibility variants controlling longevity, we performed a high-throughput candidate gene study using 137 Koreans over 90 yr old and 213 young healthy Koreans. We evaluated 463 informative markers located in 176 candidate genes mostly for diabetes mellitus, cardiovascular disease and cancer under five genetic models. We estimated the odds ratios for each allele, genotype, haplotype, and gene-gene interaction using logistic regression analysis. Associations between 13 genes and longevity were detected at a P-value less than 0.01. Particularly, the rs671 (A) allele of the aldehyde dehydrogenase 2 family (mitochondrial) (ALDH2) gene was associated with longevity only in men (OR 2.11, P = 0.008). Four genes, proprotein convertase subtilisin/kexin type 1 (PCSK1, P = 0.008), epidermal growth factor receptor (EGFR, P = 0.003), paired box 4 (PAX4, P = 0.008), and V-yes-1 Yamaguchi sarcoma viral related oncogene homolog (LYN, P = 0.002) consistently yielded statistical evidence for association with longevity. The findings of the current study may provide a starting point for future studies to unravel genetic factors controlling longevity in Koreans.     

Principles for the organization of gene-sets

A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs.     

Identification of a genetic locus on chromosome 4q34-35 for type 2 diabetes with overweight

The incidence of type 2 diabetes is rising rapidly because of an increase in the incidence of being overweight and obesity. Identification of genetic determinants for complex diseases, such as type 2 diabetes, may provide insight into disease pathogenesis. The aim of the study was to investigate the shared genetic factors that predispose individuals to being overweight and developing type 2 diabetes. We conducted genome-wide linkage analyses for type 2 diabetes in 386 affected individuals (269 sibpairs) from 171 Korean families and association analyses with single-nucleotide polymorphisms of candidate genes within linkage regions to identify genetic variants that predispose individuals to being overweight and developing type 2 diabetes. Through fine-mapping analysis of chromosome 4q34-35, we detected a locus potentially linked (nonparametric linkage 2.81, logarithm of odds 2.27, P=6 × 10⁻⁴) to type 2 diabetes in overweight or obese individuals (body mass index, BMI⩾23 kg m⁻²). Multiple regression analysis with type 2 diabetes-related phenotypes revealed a significant association (false discovery rate (FDR) P=0.006 for rs13144140; FDR P=0.002 for rs6830266) between GPM6A (rs13144140) and BMI and waist–hip ratio, and between NEIL3 (rs6830266) and insulin level from 1314 normal individuals. Our systematic search of genome-wide linkage and association studies, demonstrate that a linkage peak for type 2 diabetes on chromosome 4q34-35 contains two type 2 diabetes-related genes, GPM6A and NEIL3.     

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BackgroundThe underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).Material and methodsWe obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the “limma” package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.ResultsThe preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.ConclusionsWe identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.     

CDH1/E-cadherin and solid tumors. An updated gene-disease association analysis using bioinformatics tools

Cancer is a group of diseases that causes millions of deaths worldwide. Among cancers, Solid Tumors (ST) stand-out due to their high incidence and mortality rates. Disruption of cell–cell adhesion is highly relevant during tumor progression. Epithelial-cadherin (protein: E-cadherin, gene: CDH1) is a key molecule in cell–cell adhesion and an abnormal expression or/and function(s) contributes to tumor progression and is altered in ST. A systematic study was carried out to gather and summarize current knowledge on CDH1/E-cadherin and ST using bioinformatics resources. The DisGeNET database was exploited to survey CDH1-associated diseases. Reported mutations in specific ST were obtained by interrogating COSMIC and IntOGen tools. CDH1 Single Nucleotide Polymorphisms (SNP) were retrieved from the dbSNP database.DisGeNET analysis identified 609 genes annotated to ST, among which CDH1 was listed. Using CDH1 as query term, 26 disease concepts were found, 21 of which were neoplasms-related terms. Using DisGeNET ALL Databases, 172 disease concepts were identified. Of those, 80 ST disease-related terms were subjected to manual curation and 75/80 (93.75%) associations were validated. On selected ST, 489 CDH1 somatic mutations were listed in COSMIC and IntOGen databases. Breast neoplasms had the highest CDH1-mutation rate. CDH1 was positioned among the 20 genes with highest mutation frequency and was confirmed as driver gene in breast cancer. Over 14,000 SNP for CDH1 were found in the dbSNP database.This report used DisGeNET to gather/compile current knowledge on gene-disease association for CDH1/E-cadherin and ST; data curation expanded the number of terms that relate them. An updated list of CDH1 somatic mutations was obtained with COSMIC and IntOGen databases and of SNP from dbSNP. This information can be used to further understand the role of CDH1/E-cadherin in health and disease.     

Genetic association of angiogenesis- and hypoxia-related gene polymorphisms with osteonecrosis of the femoral head

Multiple factors have been implicated in the development of osteonecrosis of the femoral head (ONFH). In particular, non-traumatic ONFH is directly or indirectly related to injury of the vascular supply to the femoral head. Thus, hypoxia in the femoral head caused by impaired blood flow may be an important risk factor for ONFH. In this study, we investigated whether genetic variations of angiogenesis- and hypoxia-related genes contribute to an increased risk for the development of ONFH. Candidate genes were selected based on known hypoxia and angiogenesis pathways. An association study was performed using an Affymetrix Targeted Genotyping 3K Chip array with 460 ONFH patients and 300 control subjects. We showed that single nucleotide polymorphisms (SNPs) in the genes TF, VEGFC, IGFBP3, and ACE were associated with an increased risk of ONFH. On the other hand, SNPs in the KDR and NRP1 genes were associated with protection against ONFH. The most important finding was that one SNP (rs2453839) in the IGFBP3 gene was significantly associated with a higher risk of ONFH (P = 0.0061, OR 7.74). In subgroup analysis, most candidate gene variations that were associated with ONFH occurred in the idiopathic subgroup. Among other SNPs, ACE SNPs were associated with steroid-induced ONFH (P = 0.0018-0.0037, OR > 3). Collectively, our findings suggest that genetic variations in angiogenesis- and hypoxia-related genes may help to identify susceptibility factors for the development of ONFH in the Korean population.     

In silico analysis of proteins and microRNAs related to human African trypanosomiasis in tsetse fly

Human African trypanosomiasis (HAT), also known as sleeping sickness, causes millions of deaths worldwide. HAT is primarily transmitted by the vector tsetse fly (Glossina morsitans). Early diagnosis remains a key objective for treating this disease. MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs that play key roles in vector-borne diseases. To date, the roles of proteins and miRNAs in HAT disease have not been thoroughly elucidated. In this study, we have re-annotated the function of protein-coding genes and identified several miRNAs based on a series of bioinformatics tools. A batch of 81.1 % of tsetse fly proteins could be determined homology in mosquito genome, suggesting their probable similar mechanisms in vector-borne diseases. A set of 11 novel salivary proteins and 14 midgut proteins were observed in the tsetse fly, which could be applied to the development of vaccine candidates for the control of HAT disease. In addition, 35 novel miRNAs were identified, among which 10 miRNAs were found to be unique in tsetse fly. Pathway analysis of these 10 miRNAs indicated that targets of miR-15a-5p were significantly enriched in the HAT-related neurotrophin signaling pathway. Besides, topological analysis of the miRNA-gene network indicated that miR-619-5p and miR-2490-3p targeted several genes that respond to trypanosome infection, including thioester-containing protein Tep1 and heat shock protein Hsp60a. In conclusion, our work helps to elucidate the function of miRNAs in tsetse fly and establishes a foundation for further investigations into the molecular regulatory mechanisms of HAT disease.     

Conditional GWAS revealing genetic impacts of lifestyle behaviors on low-density lipoprotein (LDL)

BackgroundAccumulation of LDL cholesterol (LDL-c) within artery walls is strongly associated with the initiation and progression of atherosclerosis development. This complex trait is affected by multifactor involving polygenes, environments, and their interactions. Uncovering genetic architecture of LDL may help to increase the understanding of the genetic mechanism of cardiovascular diseases.MethodsWe used a genetic model to analyze genetic effects including additive, dominance, epistasis, and ethnic interactions for data from the Multi-Ethnic Study of Atherosclerosis (MESA). Three lifestyle behaviors (reading, intentional exercising, smoking) were used as cofactor in conditional models.ResultsWe identified 156 genetic effects of 10 quantitative trait SNPs (QTSs) in base model and three conditional models. The total estimated heritability of these genetic effects was approximately 72.88% in the base model. Five genes (CELSR2, MARK2, ADAMTS12, PFDN4, and MAGI2) have biological functions related to LDL.ConclusionsCompared with the based model LDL, the results in three conditional models revealed that intentional exercising and smoking could have impacts for causing and suppressing some of genetic effects and influence the levels of LDL. Furthermore, these two lifestyles could have different genetic effects for each ethnic group on a specific QTS. As most of the heritability in based model LDL and conditional model LDL|Smk was contributed from epistasis effects, our result indicated that epistasis effects played important roles in determining LDL levels. Our study provided useful insight into the biological mechanisms underlying regulation of LDL and might help in the discovery of novel therapeutic targets for cardiovascular disease.     

Using propensity score adjustment method in genetic association studies

BackgroundThe statistical tests for single locus disease association are mostly under-powered. If a disease associated causal single nucleotide polymorphism (SNP) operates essentially through a complex mechanism that involves multiple SNPs or possible environmental factors, its effect might be missed if the causal SNP is studied in isolation without accounting for these unknown genetic influences. In this study, we attempt to address the issue of reduced power that is inherent in single point association studies by accounting for genetic influences that negatively impact the detection of causal variant in single point association analysis. In our method we use propensity score (PS) to adjust for the effect of SNPs that influence the marginal association of a candidate marker. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. We therefore propose a propensity score adjustment method (PSAM) as a tool for dimension reduction to improve the power for single locus studies through an estimated PS to adjust for influence from these SNPs while regressing disease status on the target-genetic locus. The degree of freedom of such a test is therefore always restricted to 1.ResultsWe assess PSAM under the null hypothesis of no disease association to affirm that it correctly controls for the type-I-error rate (<0.05). PSAM displays reasonable power (>70%) and shows an average of 15% improvement in power as compared with commonly-used logistic regression method and PLINK under most simulated scenarios. Using the open-access multifactor dimensionality reduction dataset, PSAM displays improved significance for all disease loci. Through a whole genome study, PSAM was able to identify 21 SNPs from the GAW16 NARAC dataset by reducing their original trend-test p-values from within 0.001 and 0.05 to p-values less than 0.0009, and among which 6 SNPs were further found to be associated with immunity and inflammation.ConclusionsPSAM improves the significance of single-locus association of causal SNPs which have had marginal single point association by adjusting for influence from other SNPs in a dataset. This would explain part of the missing heritability without increasing the complexity of the model due to huge multiple testing scenarios. The newly reported SNPs from GAW16 data would provide evidences for further research to elucidate the etiology of rheumatoid arthritis. PSAM is proposed as an exploratory tool that would be complementary to other existing methods. A downloadable user friendly program, PSAM, written in SAS, is available for public use.     

Chemical genetics: budding yeast as a platform for drug discovery and mapping of genetic pathways

The budding yeast Saccharomyces cerevisiae is a widely used model organism, and yeast genetic methods are powerful tools for discovery of novel functions of genes. Recent advancements in chemical-genetics and chemical-genomics have opened new avenues for development of clinically relevant drug treatments. Systematic mapping of genetic networks by high-throughput chemical-genetic screens have given extensive insight in connections between genetic pathways. Here, I review some of the recent developments in chemical-genetic techniques in budding yeast.     

Effect of graphitic carbon nitride powders on adsorption removal of antibiotic resistance genes from water

There is a growing need to eliminate antibiotic resistance genes (ARGs) in the environment and mitigate widespread antibiotic resistance. Graphitic carbon nitride (g-C₃N₄) was successfully synthesized via facile thermal polymerization approach and its potential for adsorption treatment of ARGs in water was examined. Batch adsorption experimental results revealed that g-C₃N₄ powders had robust adsorption activity for the gene ampC and ermB. Adsorption kinetics and isotherms were systematically investigated to explain the adsorption mechanism. The apparent adsorption equilibrium could be reached within 180 min. The adsorption process effectively removed ARGs (ampC and ermB) from water with 3.2 log and 4.2 log reductions, respectively. In addition, experimental data were analyzed by several models and simulated well with Langmuir isotherm and pseudo-second-order model. It indicated that adsorption process might be dominated by the chemical rate-limiting step. Moreover, the effects of temperature and pH on the removal of ARGs were conducted and the isoelectric point (IEP) was obtained. Finally, we have demonstrated that the g-C₃N₄ is a novel adsorbent and can be used as column packing to remove ARGs by filtration.     

A network-based pharmacology study of active compounds and targets of Fritillaria thunbergii against influenza

Seasonal and pandemic influenza infections are serious threats to public health and the global economy. Since antigenic drift reduces the effectiveness of conventional therapies against the virus, herbal medicine has been proposed as an alternative. Fritillaria thunbergii (FT) have been traditionally used to treat airway inflammatory diseases such as coughs, bronchitis, pneumonia, and fever-based illnesses. Herein, we used a network pharmacology-based strategy to predict potential compounds from Fritillaria thunbergii (FT), target genes, and cellular pathways to better combat influenza and influenza-associated diseases. We identified five compounds, and 47 target genes using a compound-target network (C-T). Two compounds (beta-sitosterol and pelargonidin) and nine target genes (BCL2, CASP3, HSP90AA1, ICAM1, JUN, NOS2, PPARG, PTGS1, PTGS2) were identified using a compound-influenza disease target network (C-D). Protein-protein interaction (PPI) network was constructed and we identified eight proteins from nine target genes formed a network. The compound-disease-pathway network (C-D-P) revealed three classes of pathways linked to influenza: cancer, viral diseases, and inflammation. Taken together, our systems biology data from C-T, C-D, PPI and C-D-P networks predicted potent compounds from FT and new therapeutic targets and pathways involved in influenza.     

Rare variants analysis by risk-based variable-threshold method

Genome-wide association studies, as a powerful approach for detecting common variants associated with diseases, have revealed many disease-associated loci. However, the traditional association analysis methods do not have enough power for detecting the effects of rare variants with limited sample size. As a solution to this problem, pooling rare variants by their functions into a composite variant provides an alternative way for identifying susceptible genes. In this paper, we propose a new pooling method to test the variant–disease association and to identify the functional rare variants related with the disease. Variants with smaller and larger risk measures defined as the ratio of allele frequencies between cases and controls are pooled and a chi-square test of the resultant pooled table is calculated. We vary the threshold of pooling over all possible values and use the maximal chi-square as test statistic. The maximal chi-square is in fact the global maximum over all possible poolings. Our approach is similar to the existing variable-threshold method, but we threshold on the risk measure instead of allele frequencies of controls. Simulation results show that our method performs better in both association testing and variant selection.