A comparison in association and linkage genome-wide scans for alcoholism susceptibility genes using single-nucleotide polymorphisms

We conducted genome-wide linkage scans using both microsatellite and single-nucleotide polymorphism (SNP) markers. Regions showing the strongest evidence of linkage to alcoholism susceptibility genes were identified. Haplotype analyses using a sliding-window approach for SNPs in these regions were performed. In addition, we performed a genome-wide association scan using SNP data. SNPs in these regions with evidence of association (P 相似文献   

Identification of novel therapeutics for complex diseases from genome-wide association data

Background

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

Methods

We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level.

Results

Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets.

Conclusions

By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments.

     

Estimating the total number of susceptibility variants underlying complex diseases from genome-wide association studies

Recently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution. The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore the power was taken into account in fitting. Besides considering the most significant variants, we also tried to relax the significance threshold, allowing more markers to be fitted. The effects of false positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the winner's curse effect was corrected analytically. Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner's curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits. Finally we applied the methods to a few real disease examples (lipid traits, type 2 diabetes and Crohn's disease) and estimated that hundreds to nearly a thousand variants underlie these traits.     

Multimarker analysis and imputation of multiple platform pooling-based genome-wide association studies

For many genome-wide association (GWA) studies individually genotyping one million or more SNPs provides a marginal increase in coverage at a substantial cost. Much of the information gained is redundant due to the correlation structure inherent in the human genome. Pooling-based GWA studies could benefit significantly by utilizing this redundancy to reduce noise, improve the accuracy of the observations and increase genomic coverage. We introduce a measure of correlation between individual genotyping and pooling, under the same framework that r(2) provides a measure of linkage disequilibrium (LD) between pairs of SNPs. We then report a new non-haplotype multimarker multi-loci method that leverages the correlation structure between SNPs in the human genome to increase the efficacy of pooling-based GWA studies. We first give a theoretical framework and derivation of our multimarker method. Next, we evaluate simulations using this multimarker approach in comparison to single marker analysis. Finally, we experimentally evaluate our method using different pools of HapMap individuals on the Illumina 450S Duo, Illumina 550K and Affymetrix 5.0 platforms for a combined total of 1 333 631 SNPs. Our results show that use of multimarker analysis reduces noise specific to pooling-based studies, allows for efficient integration of multiple microarray platforms and provides more accurate measures of significance than single marker analysis. Additionally, this approach can be extended to allow for imputing the association significance for SNPs not directly observed using neighboring SNPs in LD. This multimarker method can now be used to cost-effectively complete pooling-based GWA studies with multiple platforms across over one million SNPs and to impute neighboring SNPs weighted for the loss of information due to pooling.     

Identification of the genetic basis for complex disorders by use of pooling-based genomewide single-nucleotide-polymorphism association studies

We report the development and validation of experimental methods, study designs, and analysis software for pooling-based genomewide association (GWA) studies that use high-throughput single-nucleotide-polymorphism (SNP) genotyping microarrays. We first describe a theoretical framework for establishing the effectiveness of pooling genomic DNA as a low-cost alternative to individually genotyping thousands of samples on high-density SNP microarrays. Next, we describe software called "GenePool," which directly analyzes SNP microarray probe intensity data and ranks SNPs by increased likelihood of being genetically associated with a trait or disorder. Finally, we apply these methods to experimental case-control data and demonstrate successful identification of published genetic susceptibility loci for a rare monogenic disease (sudden infant death with dysgenesis of the testes syndrome), a rare complex disease (progressive supranuclear palsy), and a common complex disease (Alzheimer disease) across multiple SNP genotyping platforms. On the basis of these theoretical calculations and their experimental validation, our results suggest that pooling-based GWA studies are a logical first step for determining whether major genetic associations exist in diseases with high heritability.     

Identification of susceptibility loci for complex diseases in a case-control association study using the Genetic Analysis Workshop 14 dataset

Although current methods in genetic epidemiology have been extremely successful in identifying genetic loci responsible for Mendelian traits, most common diseases do not follow simple Mendelian modes of inheritance. It is important to consider how our current methodologies function in the realm of complex diseases. The aim of this study was to determine the ability of conventional association methods to fine map a locus of interest. Six study populations were selected from 10 replicates (New York) from the Genetic Analysis Workshop 14 simulated dataset and analyzed for association between the disease trait and locus D2. Genotypes from 45 single-nucleotide polymorphisms in the telomeric region of chromosome 3 were analyzed by Pearson's chi-square tests for independence to test for association with the disease trait of interest. A significant association was detected within the region; however, it was found 3 cM from the documented location of the D2 disease locus. This result was most likely due to the method used for data simulation. In general, this study showed that conventional case-control association methods could detect disease loci responsible for the development of complex traits.     

Identification of neural outgrowth genes using genome-wide RNAi

While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system.     

DNA microarray analysis of rheumatoid arthritis susceptibility genes identified by genome-wide association studies

In a recent interesting review, Alex Clarke and Timothy Vyse described the genetics of rheumatic disease [1]. In the past several years, genome-wide association studies (GWAS) have led to the identification of six high-risk rheumatoid arthritis (RA) susceptibility genes - namely, CD244, PADI4, SLC22A2, PTPN22, CTLA4, and STAT4 (summarized in [2]). In vitro studies using mutant alleles and cultured cells have revealed the individual upregulation of CD244, PADI4, SLC22A2, and PTPN22 [2-6]; however, studies on the expression of RA susceptibility genes in RA patients are rare. We therefore investigated the expression of the above-mentioned six RA susceptibility genes in 112 RA patients using DNA microarray analysis. This study aims to clarify whether DNA microarray analysis and GWAS produce comparable results with respect to RA susceptibility genes.Total RNA extracted from total peripheral blood cells obtained from 112 RA patients and 45 healthy individuals was used to prepare aminoallyl RNA. As a reference, mixed RNA from 45 healthy individuals was used. The aminoallyl RNA of each individual and the reference was subjected to Cy3 and Cy5 labeling, respectively, and was hybridized with an oligonucleotide-based DNA microarray. The data obtained were analyzed by nonparametric statistical group comparison. The intensities of the noprobe spots were used as the background. The median and standard deviation of the background intensity were calculated. The genes with an intensity value that was less than the median plus 2 standard deviation of the background intensity were identified as null. The Cy3/Cy5 ratios of all spots on the DNA microarray were normalized using the global ratio median. Only gene expression data that were collected from at least 80% of samples from each group were selected for further analysis. The unpaired Mann-Whitney test was used to determine statistically significant differences in the mRNA expression levels between the RA and healthy groups. Statistical significance was set at P < 0.05.The results of our DNA microarray analysis showed that the expressions of four out of the six RA susceptibility genes were significantly higher in RA patients than in healthy individuals (1.0 × 10^-16 to 2.32 × 10^-5) (Table ​(Table1).1). As described above, the upregulation of these four genes (CD244, PADI4, SLC22A2, and PTPN22) has been previously confirmed in in vitro studies. We found, however, that CTLA4 expression levels were similar between the RA and control groups, whereas STAT4 expression was significantly downregulated in the RA group (1.38 × 10^-8). We investigated the expression of other RA susceptibility genes - namely, TRF1/C5 [7], CD40 [8], and CCL21 [8] - and found that their expressions were similar in both groups. The genetic risk factors for RA were recently reported to differ between Caucasian and Asian (Korean) populations [9]. The samples used in our microarray analysis were derived from the same Asian (Japanese) cohort. The expression profiles for these three genes may therefore not be consistent with the profiles determined by GWAS.

Table 1

Candidate genes identified from rheumatoid arthritis genome-wide association studies

Identification of potential mechanism and hub genes for neuropathic pain by expression-based genome-wide association study

Neuropathic pain (NP) is a common pathological pain state with limited effective treatments. This study was designed to identify potential mechanisms and candidate genes using gene expression–based genome-wide association study (eGWAS). All NP-related microarray experiments were obtained from Gene Expression Omnibus and ArrayExpress. Significantly dysregulated genes were identified between experimental and untreated groups, and the number of microarray experiments in which each gene was dysregulated was calculated. Significantly dysregulated genes were ranked according to P values of the chi-square test. Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database, we performed functional and pathway enrichment analysis. Protein-protein interaction (PPI) network and module analysis was performed using Cytoscape software. A total of 115 candidate genes were identified from 19 independent microarray experiments by eGWAS based on the Bonferroni threshold ( P < 2.97 × 10 ⁻⁶). Immune and inflammatory responses, and complement and coagulation cascades, were respectively the most enriched biological process and pathways for candidate genes. The hub genes with highest connectivity in PPI network and two modules Ccl2 and Jun, and Ctss application of the eGWAS methodology can identify mechanisms and candidate genes associated with NP. Our results support the validity and prevalence of inflammatory and immune mechanisms across different NP models, and Ccl2, Jun, and Ctss may be the hub genes for NP.     

Identification of genes for complex disease using longitudinal phenotypes

Using the simulated data set from Genetic Analysis Workshop 13, we explored the advantages of using longitudinal data in genetic analyses. The weighted average of the longitudinal data for each of seven quantitative phenotypes were computed and analyzed. Genome screen results were then compared for these longitudinal phenotypes and the results obtained using two cross-sectional designs: data collected near a single age (45 years) and data collected at a single time point. Significant linkage was obtained for nine regions (LOD scores ranging from 5.5 to 34.6) for six of the phenotypes. Using cross-sectional data, LOD scores were slightly lower for the same chromosomal regions, with two regions becoming nonsignificant and one additional region being identified. The magnitude of the LOD score was highly correlated with the heritability of each phenotype as well as the proportion of phenotypic variance due to that locus. There were no false-positive linkage results using the longitudinal data and three false-positive findings using the cross-sectional data. The three false positive results appear to be due to the kurtosis in the trait distribution, even after removing extreme outliers. Our analyses demonstrated that the use of simple longitudinal phenotypes was a powerful means to detect genes of major to moderate effect on trait variability. In only one instance was the power and heritability of the trait increased by using data from one examination. Power to detect linkage can be improved by identifying the most heritable phenotype, ensuring normality of the trait distribution and maximizing the information utilized through novel longitudinal designs for genetic analysis.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

Adjusted P values for genome-wide scans

Genome-wide scans for quantitative trait loci (QTL) have traditionally been summarized with plots of logarithm of odds (LOD) scores. A valuable modification is to supplement such plots with an additional vertical axis displaying quantiles of adjusted P values and labeling local maxima of the LOD scores with location-specific adjusted P values. This provides a visible gradation of genome-wide significance for the LOD score curve, instead of the stark dichotomy that a single threshold yields. Adjusted P values give genome-wide significance of individual LOD scores and are obtained through a straightforward modification of the familiar algorithm for generating permutation-based thresholds.     

Identification of novel susceptibility Loci for kawasaki disease in a Han chinese population by a genome-wide association study

Kawasaki disease (KD) is an acute systemic vasculitis syndrome that primarily affects infants and young children. Its etiology is unknown; however, epidemiological findings suggest that genetic predisposition underlies disease susceptibility. Taiwan has the third-highest incidence of KD in the world, after Japan and Korea. To investigate novel mechanisms that might predispose individuals to KD, we conducted a genome-wide association study (GWAS) in 250 KD patients and 446 controls in a Han Chinese population residing in Taiwan, and further validated our findings in an independent Han Chinese cohort of 208 cases and 366 controls. The most strongly associated single-nucleotide polymorphisms (SNPs) detected in the joint analysis corresponded to three novel loci. Among these KD-associated SNPs three were close to the COPB2 (coatomer protein complex beta-2 subunit) gene: rs1873668 (p = 9.52×10⁻⁵), rs4243399 (p = 9.93×10⁻⁵), and rs16849083 (p = 9.93×10⁻⁵). We also identified a SNP in the intronic region of the ERAP1 (endoplasmic reticulum amino peptidase 1) gene (rs149481, p_best = 4.61×10⁻⁵). Six SNPs (rs17113284, rs8005468, rs10129255, rs2007467, rs10150241, and rs12590667) clustered in an area containing immunoglobulin heavy chain variable regions genes, with p_best-values between 2.08×10⁻⁵ and 8.93×10⁻⁶, were also identified. This is the first KD GWAS performed in a Han Chinese population. The novel KD candidates we identified have been implicated in T cell receptor signaling, regulation of proinflammatory cytokines, as well as antibody-mediated immune responses. These findings may lead to a better understanding of the underlying molecular pathogenesis of KD.     

Identification of susceptibility genes for cancer in a genome-wide scan: results from the colon neoplasia sibling study

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in Americans and is the second leading cause of cancer mortality. Only a minority ( approximately 5%) of familial CRC can be explained by known genetic variants. To identify susceptibility genes for familial colorectal neoplasia, the colon neoplasia sibling study conducted a comprehensive, genome-wide linkage scan of 194 kindreds. Clinical information (histopathology, size and number of polyps, and other primary cancers) was used in conjunction with age at onset and family history for classification of the families into five phenotypic subgroups (severe histopathology, oligopolyposis, young, colon/breast, and multiple cancer) prior to analysis. By expanding the traditional affected-sib-pair design to include unaffected and discordant sib pairs, analytical power and robustness to type I error were increased. Sib-pair linkage statistics and Haseman-Elston regression identified 19 linkage peaks, with interesting results for chromosomes 1p31.1, 15q14-q22, 17p13.3, and 21. At marker D1S1665 (1p31.1), there was strong evidence for linkage in the multiple-cancer subgroup (p = 0.00007). For chromosome 15q14-q22, a linkage peak was identified in the full-sample (p = 0.018), oligopolyposis (p = 0.003), and young (p = 0.0009) phenotypes. This region includes the HMPS/CRAC1 locus associated with hereditary mixed polyposis syndrome (HMPS) in families of Ashkenazi descent. We provide compelling evidence linking this region in families of European descent with oligopolyposis and/or young age at onset (相似文献   

Candidate genes for non-diabetic ESRD in African Americans: a genome-wide association study using pooled DNA

African Americans have increased susceptibility to non-diabetic (non-DM) forms of end-stage renal disease (ESRD) and extensive evidence supports a genetic contribution. A genome-wide association study (GWAS) using pooled DNA was performed in 1,000 African Americans to detect associated genes. DNA from 500 non-DM ESRD cases and 500 non-nephropathy controls was quantified using gel electrophoresis and spectrophotometric analysis and pools of 50 case and 50 control DNA samples were created. DNA pools were genotyped in duplicate on the Illumina HumanHap550-Duo BeadChip. Normalization methods were developed and applied to array intensity values to reduce inter-array variance. Allele frequencies were calculated from normalized channel intensities and compared between case and control pools. Three SNPs had p values of <1.0E−6: rs4462445 (ch 13), rs4821469 (ch 22) and rs8077346 (ch 17). After normalization, top scoring SNPs (n = 65) were genotyped individually in 464 of the original cases and 478 of the controls, with replication in 336 non-DM ESRD cases and 363 non-nephropathy controls. Sixteen SNPs were associated with non-DM ESRD (p < 7.7E−4, Bonferroni corrected). Twelve of these SNPs are in or near the MYH9 gene. The four non-MYH9 SNPs that were associated with non-DM ESRD in the pooled samples were not associated in the replication set. Five SNPs that were modestly associated in the pooled samples were more strongly associated in the replication and/or combined samples. This is the first GWAS for non-DM ESRD in African Americans using pooled DNA. We demonstrate strong association between non-DM ESRD in African Americans with MYH9, and have identified additional candidate loci.