Susceptibility Genes For Complex Diseases Research Articles

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer–related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP–genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.

A powerful conditional gene-based association approach implicated functionally important genes for schizophrenia

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. 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. 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 data are available at Bioinformatics online.

China's "Gene War of the Century" and Its Aftermath: The Contest Goes On.

Following the successful cloning of genes for mostly rare genetic diseases in the early 1990s, there was a nearly universal enthusiasm that similar approaches could be employed to hunt down genes predisposing people to complex diseases. Around 1996, several well-funded international gene-hunting teams, enticed by the low cost of collecting biological samples and China’s enormous population, and ushered in by some well-connected Chinese intermediaries, came to China to hunt down disease susceptibility genes. This alarmed and, in some cases, enraged many poorly funded Chinese scientists, who perceived them as formidable competitors. Some depicted foreign gene-hunters as greedy pilferers of the vast Chinese genetic gold mine, comparing it to the plundering of national treasures from China by invaders in the past, and called upon the government and their fellow countrymen to rise up and protect China’s genetic gold mine. Media uproar ensued, proclaiming the imminent “gene war of the century.” This article chronicles the key events surrounding this “war” and its aftermath, exposes some inherent complexities in identifying susceptibility genes for complex diseases, highlights some issues obscured or completely overlooked in the passionate and patriotic rhetoric, and debunks some misconceptions embedded in this conflict. In addition, it argues that during the entire course of this “war,” the public’s interest went conspicuously unmentioned. Finally, it articulates several lessons that can be learned from this conflict, and outlines challenges facing human genetics researchers.

The mouse QTL map helps interpret human genome-wide association studies for HDL cholesterol

Genome-wide association (GWA) studies represent a powerful strategy for identifying susceptibility genes for complex diseases in human populations but results must be confirmed and replicated. Because of the close homology between mouse and human genomes, the mouse can be used to add evidence to genes suggested by human studies. We used the mouse quantitative trait loci (QTL) map to interpret results from a GWA study for genes associated with plasma HDL cholesterol levels. We first positioned single nucleotide polymorphisms (SNPs) from a human GWA study on the genomic map for mouse HDL QTL. We then used mouse bioinformatics, sequencing, and expression studies to add evidence for one well-known HDL gene (Abca1) and three newly identified genes (Galnt2, Wwox, and Cdh13), thus supporting the results of the human study. For GWA peaks that occur in human haplotype blocks with multiple genes, we examined the homologous regions in the mouse to prioritize the genes using expression, sequencing, and bioinformatics from the mouse model, showing that some genes were unlikely candidates and adding evidence for candidate genes Mvk and Mmab in one haplotype block and Fads1 and Fads2 in the second haplotype block. Our study highlights the value of mouse genetics for evaluating genes found in human GWA studies.

Identification of Novel Schizophrenia Loci by Homozygosity Mapping Using DNA Microarray Analysis

The recent development of high-resolution DNA microarrays, in which hundreds of thousands of single nucleotide polymorphisms (SNPs) are genotyped, enables the rapid identification of susceptibility genes for complex diseases. Clusters of these SNPs may show runs of homozygosity (ROHs) that can be analyzed for association with disease. An analysis of patients whose parents were first cousins enables the search for autozygous segments in their offspring. Here, using the Affymetrix® Genome-Wide Human SNP Array 5.0 to determine ROHs, we genotyped 9 individuals with schizophrenia (SCZ) whose parents were first cousins. We identified overlapping ROHs on chromosomes 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 20, and 21 in at least 3 individuals. Only the locus on chromosome 5 has been reported previously. The ROHs on chromosome 5q23.3–q31.1 include the candidate genes histidine triad nucleotide binding protein 1 (HINT1) and acyl-CoA synthetase long-chain family member 6 (ACSL6). Other overlapping ROHs may contain novel rare recessive variants that affect SCZ specifically in our samples, given the highly heterozygous nature of SCZ. Analysis of patients whose parents are first cousins may provide new insights for the genetic analysis of psychiatric diseases.

Genome wide association study identifies many novel susceptibility genes for illumne related skin complex diseases (47.11)

Abstract With the completion of HGP and HapMap, the genome wide association study (GWAS) has become feasible and been proven to be a very useful and efficient approach to identify the susceptibility genes for complex diseases as compared to previous method in terms of linkage study. Psoriasis, vitiligo,SLE and atopic dermatitis are common immune related skin complex diseases with unknown pathogenesis. To identify the susceptibility genes predispoing to these dieases in Chinese Han populations, we carried out a seris GWAS for psoriasis, vitiligo, SLE and atopic dermatitis in Chinese populations and identified many novel genetic factors influencing disease susceiptibilty as well as found the genetic heterogenetity of these complex diseases within different ethinic populations(Nat Genet 2009, 2010). Our reserach should provide novel insights into the disease pathoghensis and lay a solid fundation for further functional research.

SNPTransformer: A Lightweight Toolkit for Genome-Wide Association Studies

High-throughput genotyping chips have produced huge datasets for genome-wide association studies (GWAS) that have contributed greatly to discovering susceptibility genes for complex diseases. There are two strategies for performing data analysis for GWAS. One strategy is to use open-source or commercial packages that are designed for GWAS. The other is to take advantage of classic genetic programs with specific functions, such as linkage disequilibrium mapping, haplotype inference and transmission disequilibrium tests. However, most classic programs that are available are not suitable for analyzing chip data directly and require custom-made input, which results in the inconvenience of converting raw genotyping files into various data formats. We developed a powerful, user-friendly, lightweight program named SNPTransformer for GWAS that includes five major modules (Transformer, Operator, Previewer, Coder and Simulator). The toolkit not only works for transforming the genotyping files into ten input formats for use with classic genetics packages, but also carries out useful functions such as relational operations on IDs, previewing data files, recoding data formats and simulating marker files, among other functions. It bridges upstream raw genotyping data with downstream genetic programs, and can act as an in-hand toolkit for human geneticists, especially for non-programmers. SNPTransformer is freely available at http://snptransformer.sourceforge.net.

Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans

The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases.

Using prior knowledge and genome-wide association to identify pathways involved in multiple sclerosis

The efforts of the Human Genome Project are beginning to provide important findings for human health. Technological advances in the laboratory, particularly in characterizing human genomic variation, have created new approaches for studying the human genome - genome-wide association studies (GWAS). However, current statistical and computational strategies are taking only partial advantage of this wealth of information. In the quest for susceptibility genes for complex diseases in GWAS data, several different analytic strategies are being pursued. In a recent report, Baranzini and colleagues used a pathway- and network-based analysis to explore potentially interesting single locus association signals in a GWAS of multiple sclerosis. This and other pathway-based approaches are likely to continue to emerge in the GWAS literature, as they provide a powerful strategy to detect important modest single-locus effects and gene-gene interaction effects.

Using disease symptoms to improve detection of linkage under genetic heterogeneity

A major reason for the slow progress in identifying susceptibility genes for complex diseases may be that the clinical diagnoses used as phenotypes are genetically heterogeneous. This has led researchers to collect various phenotypes related to the diagnosis, such as detailed symptoms, in the hope that these measurements define more homogeneous disease sub-types, influenced by a smaller number of genes that will thus be more easily detectable. Latent class analysis can be used to define disease sub-types from multivariate symptoms under the assumption that the subjects are independent, an assumption that does not hold between members of the same family. We have recently developed a latent class model allowing dependence between the latent disease class status of relatives within nuclear families. In this paper, we propose approaches to use the resulting latent class probabilities in linkage analysis. We present results from a simulation study showing that the latent class approach can provide a substantial gain in power to detect disease genes over the standard heterogeneity approach of Smith and identity-by-descent sharing methods applied to the disease diagnosis. Taking into account familial dependence in the latent class model generally provides greater power than assuming independence. In an analysis of autism symptoms in families from the Autism Genetics Research Exchange, linkage signals obtained with latent class-derived phenotypes were stronger than those obtained using the original autism spectrum disorder diagnosis.

Genome-wide association study for Crohn's disease in the Quebec Founder Population identifies multiple validated disease loci.

Genome-wide association (GWA) studies offer a powerful unbiased method for the identification of multiple susceptibility genes for complex diseases. Here we report the results of a GWA study for Crohn's disease (CD) using family trios from the Quebec Founder Population (QFP). Haplotype-based association analyses identified multiple regions associated with the disease that met the criteria for genome-wide significance, with many containing a gene whose function appears relevant to CD. A proportion of these were replicated in two independent German Caucasian samples, including the established CD loci NOD2 and IBD5. The recently described IL23R locus was also identified and replicated. For this region, multiple individuals with all major haplotypes in the QFP were sequenced and extensive fine mapping performed to identify risk and protective alleles. Several additional loci, including a region on 3p21 containing several plausible candidate genes, a region near JAKMIP1 on 4p16.1, and two larger regions on chromosome 17 were replicated. Together with previously published loci, the spectrum of CD genes identified to date involves biochemical networks that affect epithelial defense mechanisms, innate and adaptive immune response, and the repair or remodeling of tissue.

Association analysis of genetic variants in IL23R, ATG16L1 and 5p13.1 loci with Crohn’s disease in Japanese patients

Inflammatory bowel diseases, Crohn's disease (CD) and ulcerative colitis are characterised by chronic transmural, segmental and typically granulomatous inflammation of the gut. Each has a peak age of onset in the second to fourth decades of life and prevalence has been increasing significantly in both Western countries and Japan over the last decade, while their pathogenesis remains largely unknown. Recently, positive association of CD with the variants in interleukin 23 receptor (IL23R), autophagy-related 16-like 1 (ATG16L1) genes and chromosome 5p13.1 locus was reported through genome-wide association studies which are now recognised as a robust tool for the identification of susceptibility genes for complex diseases. To examine an association of reported susceptible variants in the three loci with Japanese CD patients, a total of 484 CD patients and 439 controls were genotyped. No evidence of positive association for any of these loci with CD was found in the Japanese population, even after clinically stratified subgroups of CD were used. Our result revealed a distinct ethnic difference of genetic background of CD that we reported previously in other genes between Japanese and Caucasian populations. Further genetic studies are required to confirm our findings with ethnically divergent populations.

Genome-wide linkage scan of schizophrenia: A cross-isolate study

Genetic isolates are exceptional resources for the detection of susceptibility genes for complex diseases because of the potential reduction in genetic and clinical heterogeneity. However, the outcome of these mapping efforts is dependent upon the demographic history of a given isolated population, with the most significant factors being a constant population size, the number of generations since founding, and the pathogenic loci and their allele frequencies among founders. Here we employed a cross-isolate genome-wide multipoint linkage study design using uniform genetic and clinical methods in four Daghestan ethnically and demographically diverse isolates with an aggregation of schizophrenia. Our previous population-genetics study showed that Daghestan has an extremely high genetic diversity between ethnic populations and a low genetic diversity within them. The isolates selected for this study include some with more than 200 and some with fewer than 100 generations of demographical history since their founding. Updated clinical data using DSM-IV criteria showed between-isolate differences in aggregation of distinct types of schizophrenia: one of the isolates had a predominant aggregation of disorganized schizophrenia, while the other three had predominantly paranoid schizophrenia. The summarized cross-isolate results indicated prominent within and between-isolate differences in clinical and genetic heterogeneity: the most ancient isolates have roughly twofold fewer incidences of distinct clinical phenotypes and fewer linked genomic regions compared to the demographically younger isolates, which exhibit higher clinical and genetic heterogeneity. Affected individuals in the demographically ancient isolate of ethnic Dargins (No. 6022) who suffered from disorganized schizophrenia showed the highest linkage evidence at 17p11–p12 (LOD = 3.73), while isolates with a predominant aggregation of paranoid schizophrenia (Nos. 6005, 6011, and 6034) showed the highest linkage evidence at 22q11 (LOD = 3.0 and 4.4). The unified clinical, genomic, and statistical design we used enabled us to separate the linked and unlinked pedigrees in an unbiased fashion for each genomic location. Overall maximized heterogeneity lod scores for the combined pedigrees ranging from 3.5 to 8.7 were found at 2p24, 10q26, 11q23, 12q24, 17p11–p12, 22q11, and 22q13. The cross-isolate homogeneity in linkage patterns may be ascribed to an identical-by-descent “metahaplotype” block with pathogenic loci derived from the Daghestan ethnic groups’ common ancestral metapopulation, while the cross-isolate differences may reflect differences in gene drift and recombination events in the history of local isolates. The results obtained support the notion that mapping genes of any complex disease (e.g., schizophrenia) in demographically older genetic isolates may be more time and cost effective in comparison with demographically younger isolates, especially in genetically heterogeneous outbred populations, due to higher clinical and genetic homogeneity of the primary isolates. A study at higher genotyping density across the regions of interest and fluorescence in situ hybridization analyses are currently underway.

Interpretation of simultaneous linkage and family‐based association tests in genome screens

Linkage and association analyses have played important roles in identifying susceptibility genes for complex diseases. Linkage tests and family-based tests of association are often applied in the same data to help fine-map disease loci or validate results. This paradigm increases efficiency by making maximal use of family data sets. However, it is not intuitively clear under what conditions association and linkage tests performed in the same data set may be correlated. Understanding this relationship is important for interpreting the combined results of both tests. We used computer simulations and theoretical statements to estimate the correlation between linkage statistics (affected sib pair maximum LOD scores) and family-based association statistics (pedigree disequilibrium test (PDT) and association in the pressure of linkage (APL)) under various hypotheses. Different types of pedigrees were studied: nuclear families with affected sib pairs, extended pedigrees and incomplete pedigrees. Both simulation and theoretical results showed that when there is no linkage or no association, the linkage and association tests are not correlated. When there is linkage and association in the data, the two tests have a positive correlation. We concluded that when linkage and association tests are applied in the same data, the type I error rate of neither test will be affected and that power can be increased by applying tests conditionally.

Gearing up for genome-wide gene-association studies

One of the grand challenges of human genetics to systematically map by gene-association susceptibility genes for complex diseases is underway. High-throughput genotyping platforms have been developed; a comprehensive map of human genetic variation (HapMap) to guide efficient marker selection is imminent and many researchers have assembled suitable cohorts of patients. Expectations are understandably high and it is timely to review the promise and pitfalls of this strategy.

Mapping and identifying genes for asthma and psoriasis

Susceptibility genes for complex diseases are characterized by reduced penetrance, caused by the influence of other genes, the environment or stochastic events. Recently, positional cloning efforts have yielded several candidate susceptibility genes in different complex disorders such as Crohn's disease and asthma. Within a genetic locus, however, the identification of the effector gene may pose further challenges and require functional studies. I review two examples of such challenges: the cloning of GPR154 (GPRA) and AAA1 on chromosome 7p14 at a susceptibility locus for atopy and asthma, and the study of HLA-Cw6, CCHCR1 (HCR) and CDSN on chromosome 6p21 at PSORS1, the major susceptibility locus for psoriasis. The susceptibility locus for atopy and asthma contains two genes and only one of them is protein coding. We studied its isoform-specific expression in bronchial biopsies and in a mouse model of ovalbumin-induced inflammation of bronchial epithelia. In the PSORS1 locus, strong linkage disequilibrium between genes has made it difficult to distinguish the effects of the three nearby genes. We engineered transgenic mice with either a HCR non-risk allele or the HCR*WWCC risk allele controlled by the cytokeratin-14 promoter. The results suggested that the overexpression of HCR in mouse skin was insufficient to induce a psoriasiform phenotype, but it appeared to induce allele-specific gene expression changes that were similar to those observed in psoriatic skin.

Genomic convergence to identify candidate genes for Parkinson disease: SAGE analysis of the substantia nigra

Genomic convergence is a multistep approach that combines gene expression with genomic linkage to identify and prioritize susceptibility genes for complex disease. As a first step, we previously performed linkage analysis on 174 multiplex Parkinson's disease (PD) families, identifying five peaks for PD risk and two for genes affecting age at onset (AAO) in PD [Hauser et al., Hum Mol Genet 2003;12:671-677]. We report here the next step: serial analysis of gene expression [SAGE; Scott et al., JAMA 2001;286:2239-2242] to analyze substantia nigra tissue from three PD patients and two age-matched controls. We find 933 differentially expressed genes (P<0.05) between PD and controls, but of these, only 50 genes represented by unique SAGE tags map within our previously described PD linkage regions. Furthermore, genes encoded by mitochondrial DNA are expressed 1.5-fold higher in PD patients versus controls, without an increase in the corresponding nuclear-encoded mitochondrial components, suggesting an increase in mtDNA genomes in PD or a disjunction with nuclear expression. The next step in the genomic convergence process will be to screen these 50 high-quality candidate genes for association with PD risk susceptibility and genetic effects on AAO.

A DNA Pooling Strategy for Family-Based Association Studies

Genome-wide association scans for disease susceptibility genes of complex diseases require genotyping on a massive scale. A DNA pooling strategy for family-based association studies is described, which is robust to population stratification biases and to errors in pooling. It can achieve a statistical efficiency of 0.95 with approximately 1 of 8 or fewer genotyping efforts, and an efficiency of 0.90 with approximately 1 of 16 or fewer efforts compared with individual genotyping. The pooling method described in this article provides a tradeoff between genotyping efforts and subject recruitment efforts.

SNPHunter: a bioinformatic software for single nucleotide polymorphism data acquisition and management

BackgroundSingle nucleotide polymorphisms (SNPs) provide an important tool in pinpointing susceptibility genes for complex diseases and in unveiling human molecular evolution. Selection and retrieval of an optimal SNP set from publicly available databases have emerged as the foremost bottlenecks in designing large-scale linkage disequilibrium studies, particularly in case-control settings.ResultsWe describe the architectural structure and implementations of a novel software program, SNPHunter, which allows for both ad hoc-mode and batch-mode SNP search, automatic SNP filtering, and retrieval of SNP data, including physical position, function class, flanking sequences at user-defined lengths, and heterozygosity from NCBI dbSNP. The SNP data extracted from dbSNP via SNPHunter can be exported and saved in plain text format for further down-stream analyses. As an illustration, we applied SNPHunter for selecting SNPs for 10 major candidate genes for type 2 diabetes, including CAPN10, FABP4, IL6, NOS3, PPARG, TNF, UCP2, CRP, ESR1, and AR.ConclusionSNPHunter constitutes an efficient and user-friendly tool for SNP screening, selection, and acquisition. The executable and user's manual are available at .

Evaluation of microsatellite markers in association studies: a search for an immune-related susceptibility gene in sarcoidosis

Association studies using linkage disequilibrium (LD) between candidate loci and nearby markers have been proposed to identify susceptibility genes for complex diseases. We analyzed polymorphisms of microsatellites (MSs) and LD patterns of the regions in which candidate genes related to the Th1 immune response have been annotated and attempted to identify a susceptibility gene for sarcoidosis in a marker-based association study. Nineteen MSs were identified in six Th1-related genes (IFNGR1, IFNGR2, IL12RB1, IL12RB2, STAT1 and STAT4) and then eight were further characterized as useful polymorphic markers. Most of these MSs showed LD with single nucleotide polymorphisms (SNPs) on both 5' and 3' ends of these candidate genes, in which r(2) values between at least one of the MS marker alleles and the SNPs were higher than 0.1. A significant association with one MS allele near STAT4 was shown and a cluster of SNPs in LD with the MS marker was associated with sarcoidosis. These results suggest that association studies using not only SNPs but also multi-allelic MS within or near candidate loci would be useful markers to search for a disease susceptibility gene, especially in populations with unknown LD structure.