Complex Disease Genes Research Articles

Phenolyzer: phenotype-based prioritization of candidate genes for human diseases.

Prior biological knowledge and phenotype information may help to identify disease genes from human whole-genome and whole-exome sequencing studies. We developed Phenolyzer (http://phenolyzer.usc.edu), a tool that uses prior information to implicate genes involved in diseases. Phenolyzer exhibits superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text.

SWGDT: A sliding window-based genotype dependence testing tool for genome-wide susceptibility gene scan

Genome-wide association studies (GWAS) are a powerful tool for pathogenetic studies of complex diseases. The rich genetic information of GWAS data is mostly not fully utilized. In this study, we developed a sliding window-based genotype dependence testing tool SWGDT. SWGDT can be applied to GWAS data for genome-wide susceptibility gene scan utilizing known causal gene information. To evaluate the performance of SWGDT, a real GWAS dataset of Kashin-Beck disease (KBD) was analyzed. Immunohistochemistry was also performed to validate the relevance of identified gene with KBD. SWGDT analysis of KBD GWAS data identified a novel candidate gene TACR1 for KBD. Immunohistochemistry observed that the expression level of TACR1 protein in KBD articular cartilage was significantly higher than that in healthy articular cartilage. The real GWAS data analysis results illustrate the performance of SWGDT for genome-wide susceptibility gene scan. SWGDT can help to identify novel disease genes that may be missed by GWAS.

Novel therapeutics for coronary artery disease from genome-wide association study data.

BackgroundCoronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD.MethodsPreviously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD.ResultsOur analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05).ConclusionsWe have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

Screening toll-like receptor markers to predict latent tuberculosis infection and subsequent tuberculosis disease in a Chinese population.

BackgroundWe investigated whether polymorphisms in the toll-like receptor genes or gene–gene interactions are associated with susceptibility to latent tuberculosis infection (LTBI) or subsequent pulmonary tuberculosis (PTB) in a Chinese population.MethodsTwo matched case–control studies were undertaken. Previously reported polymorphisms in the toll-like receptors (TLRs) were compared between 422 healthy controls (HC) and 205 LTBI patients and between 205 LTBI patients and 109 PTB patients, to assess whether these polymorphisms and their interactions are associated with LTBI or PTB. A PCR-based restriction fragment length polymorphism analysis was used to detect genetic polymorphisms in the TLR genes. Nonparametric multifactor dimensionality reduction (MDR) was used to analyze the effects of interactions between complex disease genes and other genes or environmental factors.ResultsSixteen markers in TLR1, TLR2, TLR4, TLR6, TLR8, TLR9, and TIRAP were detected. In TLR2, the frequencies of the CC genotype (OR = 2.262; 95% CI: 1.433–3.570) and C allele (OR = 1.566; 95% CI: 1.223–1.900) in single-nucleotide polymorphism (SNP) rs3804100 were significantly higher in the LTBI group than in the HC group, whereas the GA genotype of SNP rs5743708 was associated with PTB (OR = 6.087; 95% CI: 1.687–21.968). The frequencies of the GG genotype of SNP rs7873784 in TLR4 (OR = 2.136; 95% CI: 1.312–3.478) and the CC genotype of rs3764879 in TLR8 (OR = 1.982; 95% CI: 1.292-3.042) were also significantly higher in the PTB group than in the HC group. The TC genotype frequency of SNP rs5743836 in TLR9 was significantly higher in the LTBI group than in the HC group (OR = 1.664; 95% CI: 1.201–2.306). An MDR analysis of gene–gene and gene–environment interactions identified three SNPs (rs10759932, rs7873784, and rs10759931) that predicted LTBI with 84% accuracy (p = 0.0004) and three SNPs (rs3804100, rs1898830, and rs10759931) that predicted PTB with 80% accuracy (p = 0.0001).ConclusionsOur results suggest that genetic variation in TLR2, 4, 8 and 9, implicating TLR-related pathways affecting the innate immunity response, modulate LTBI and PTB susceptibility in Chinese.Electronic supplementary materialThe online version of this article (doi:10.1186/s12881-015-0166-1) contains supplementary material, which is available to authorized users.

Pinpointing disease genes through phenomic and genomic data fusion.

BackgroundPinpointing genes involved in inherited human diseases remains a great challenge in the post-genomics era. Although approaches have been proposed either based on the guilt-by-association principle or making use of disease phenotype similarities, the low coverage of both diseases and genes in existing methods has been preventing the scan of causative genes for a significant proportion of diseases at the whole-genome level.ResultsTo overcome this limitation, we proposed a rigorous statistical method called pgFusion to prioritize candidate genes by integrating one type of disease phenotype similarity derived from the Unified Medical Language System (UMLS) and seven types of gene functional similarities calculated from gene expression, gene ontology, pathway membership, protein sequence, protein domain, protein-protein interaction and regulation pattern, respectively. Our method covered a total of 7,719 diseases and 20,327 genes, achieving the highest coverage thus far for both diseases and genes. We performed leave-one-out cross-validation experiments to demonstrate the superior performance of our method and applied it to a real exome sequencing dataset of epileptic encephalopathies, showing the capability of this approach in finding causative genes for complex diseases. We further provided the standalone software and online services of pgFusion at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgfusion.ConclusionspgFusion not only provided an effective way for prioritizing candidate genes, but also demonstrated feasible solutions to two fundamental questions in the analysis of big genomic data: the comparability of heterogeneous data and the integration of multiple types of data. Applications of this method in exome or whole genome sequencing studies would accelerate the finding of causative genes for human diseases. Other research fields in genomics could also benefit from the incorporation of our data fusion methodology.

Adapting simultaneous analysis phylogenomic techniques to study complex disease gene relationships

The characterization of complex diseases remains a great challenge for biomedical researchers due to the myriad interactions of genetic and environmental factors. Network medicine approaches strive to accommodate these factors holistically. Phylogenomic techniques that can leverage available genomic data may provide an evolutionary perspective that may elucidate knowledge for gene networks of complex diseases and provide another source of information for network medicine approaches. Here, an automated method is presented that leverages publicly available genomic data and phylogenomic techniques, resulting in a gene network. The potential of approach is demonstrated based on a case study of nine genes associated with Alzheimer Disease, a complex neurodegenerative syndrome.The developed technique, which is incorporated into an update to a previously described Perl script called “ASAP,” was implemented through a suite of Ruby scripts entitled “ASAP2,” first compiles a list of sequence-similarity based orthologues using PSI-BLAST and a recursive NCBI BLAST+ search strategy, then constructs maximum parsimony phylogenetic trees for each set of nucleotide and protein sequences, and calculates phylogenetic metrics (Incongruence Length Difference between orthologue sets, partitioned Bremer support values, combined branch scores, and Robinson–Foulds distance) to provide an empirical assessment of evolutionary conservation within a given genetic network. In addition to the individual phylogenetic metrics, ASAP2 provides results in a way that can be used to generate a gene network that represents evolutionary similarity based on topological similarity (the Robinson–Foulds distance).The results of this study demonstrate the potential for using phylogenomic approaches that enable the study of multiple genes simultaneously to provide insights about potential gene relationships that can be studied within a network medicine framework that may not have been apparent using traditional, single-gene methods. Furthermore, the results provide an initial integrated evolutionary history of an Alzheimer Disease gene network and identify potentially important co-evolutionary clustering that may warrant further investigation.

CHRFAM7A, a human-specific and partially duplicated α7-nicotinic acetylcholine receptor gene with the potential to specify a human-specific inflammatory response to injury.

Conventional wisdom presumes that the α7nAChR product of CHRNA7 expression mediates the ability of the vagus nerve to regulate the inflammatory response to injury and infection. Yet, 15 years ago, a 2nd structurally distinct and human-specific α7nAChR gene was discovered that has largely escaped attention of the inflammation research community. The gene, originally called dupα7nAChR but now known as CHRFAM7A, has been studied exhaustively in psychiatric research because of its association with mental illness. However, dupα7nAChR/CHRFAM7A expression is relatively low in human brain but elevated in human leukocytes. Furthermore, α7nAChR research in human tissues has been confounded by cross-reacting antibodies and nonspecific oligonucleotide primers that crossreact in immunoblotting, immunohistochemistry, and RT-PCR. Yet, 3 independent reports show the human-specific CHRFAM7A changes cell responsiveness to the canonical α7nAChR/CHRNA7 ion-gated channel. Because of its potential for the injury research community, its possible significance to human leukocyte biology, and its relevance to human inflammation, we review the discovery and structure of the dupα7nAChR/CHRFAM7A gene, the distribution of its mRNA, and its biologic activities and then discuss its possible role(s) in specifying human inflammation and injury. In light of emerging concepts that point to a role for human-specific genes in complex human disease, the existence of a human-specific α7nAChR regulating inflammatory responses in injury underscores the need for caution in extrapolating findings in the α7nAChR literature to man. To this end, we discuss the translational implications of a uniquely human α7nAChR-like gene on new drug target discovery and therapeutics development for injury, infection, and inflammation.

Targeted exon sequencing fails to identify rare coding variants with large effect in rheumatoid arthritis.

IntroductionAlthough it has been suggested that rare coding variants could explain the substantial missing heritability, very few sequencing studies have been performed in rheumatoid arthritis (RA). We aimed to identify novel functional variants with rare to low frequency using targeted exon sequencing of RA in Korea.MethodsWe analyzed targeted exon sequencing data of 398 genes selected from a multifaceted approach in Korean RA patients (n = 1,217) and controls (n = 717). We conducted a single-marker association test and a gene-based analysis of rare variants. For meta-analysis or enrichment tests, we also used ethnically matched independent samples of Korean genome-wide association studies (GWAS) (n = 4,799) or immunochip data (n = 4,722).ResultsAfter stringent quality control, we analyzed 10,588 variants of 398 genes from 1,934 Korean RA case controls. We identified 13 nonsynonymous variants with nominal association in single-variant association tests. In a meta-analysis, we did not find any novel variant with genome-wide significance for RA risk. Using a gene-based approach, we identified 17 genes with nominal burden signals. Among them, VSTM1 showed the greatest association with RA (P = 7.80 × 10−4). In the enrichment test using Korean GWAS, although the significant signal appeared to be driven by total genic variants, we found no evidence for enriched association of coding variants only with RA.ConclusionsWe were unable to identify rare coding variants with large effect to explain the missing heritability for RA in the current targeted resequencing study. Our study raises skepticism about exon sequencing of targeted genes for complex diseases like RA.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-014-0447-7) contains supplementary material, which is available to authorized users.

Use of admixture and association for detection of quantitative trait loci in the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples (T2D-GENES) study.

Admixture mapping and association testing have been successfully applied to the detection of genes for complex diseases. Methods have also been developed to combine these approaches. As an initial step to determine the feasibility of combining admixture and association mapping in the context of whole genome sequencing, we have applied several methods to data from the Genetic Analysis Workshop 18. Here, we describe the steps necessary to carry out such a study from selection of reference populations and preprocessing of data through to the testing itself. We detected one significant result with a Bonferroni corrected p-value of 0.032 at single nucleotide polymorphism rs12639065. Computing local ancestry for Hispanic populations was challenging because there are relatively few methods by which to handle 3-way admixture, and publicly available Native American reference panels are scarce. However, combining admixture and association is a promising approach for detection of quantitative trait loci because it might be able to elevate the power of detection by combining 2 different sources of genetic signal.

The Role of Mutation Rate Variation and Genetic Diversity in the Architecture of Human Disease

BackgroundWe have investigated the role that the mutation rate and the structure of genetic variation at a locus play in determining whether a gene is involved in disease. We predict that the mutation rate and its genetic diversity should be higher in genes associated with disease, unless all genes that could cause disease have already been identified.ResultsConsistent with our predictions we find that genes associated with Mendelian and complex disease are substantially longer than non-disease genes. However, we find that both Mendelian and complex disease genes are found in regions of the genome with relatively low mutation rates, as inferred from intron divergence between humans and chimpanzees, and they are predicted to have similar rates of non-synonymous mutation as other genes. Finally, we find that disease genes are in regions of significantly elevated genetic diversity, even when variation in the rate of mutation is controlled for. The effect is small nevertheless.ConclusionsOur results suggest that gene length contributes to whether a gene is associated with disease. However, the mutation rate and the genetic architecture of the locus appear to play only a minor role in determining whether a gene is associated with disease.

QTL mapping in outbred populations: successes and challenges

Quantitative trait locus (QTL) mapping in animal populations has been a successful strategy for identifying genomic regions that play a role in complex diseases and traits. When conducted in an F2 intercross or backcross population, the resulting QTL is frequently large, often encompassing 30 Mb or more and containing hundreds of genes. To narrow the locus and identify candidate genes, additional strategies are needed. Congenic strains have proven useful but work less well when there are multiple tightly linked loci, frequently resulting in loss of phenotype. As an alternative, we discuss the use of highly recombinant outbred models for directly fine-mapping QTL to only a few megabases. We discuss the use of several currently available models such as the advanced intercross (AI), heterogeneous stocks (HS), the diversity outbred (DO), and commercially available outbred stocks (CO). Once a QTL has been fine-mapped, founder sequence and expression QTL mapping can be used to identify candidate genes. In this regard, the large number of alleles found in outbred stocks can be leveraged to identify causative genes and variants. We end this review by discussing some important statistical considerations when analyzing outbred populations. Fine-resolution mapping in outbred models, coupled with full genome sequence, has already led to the identification of several underlying causative genes for many complex traits and diseases. These resources will likely lead to additional successes in the coming years.

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.

GentrepidV2.0: a web server for candidate disease gene prediction

BackgroundCandidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required.DescriptionGentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype.ConclusionsThe system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/.

Contribution of rare and common variants determine complex diseases—Hirschsprung disease as a model

Finding genes for complex diseases has been the goal of many genetic studies. Most of these studies have been successful by searching for genes and mutations in rare familial cases, by screening candidate genes and by performing genome wide association studies. However, only a small fraction of the total genetic risk for these complex genetic diseases can be explained by the identified mutations and associated genetic loci. In this review we focus on Hirschsprung disease (HSCR) as an example of a complex genetic disorder. We describe the genes identified in this congenital malformation and postulate that both common ‘low penetrant’ variants in combination with rare or private ‘high penetrant’ variants determine the risk on HSCR, and likely, on other complex diseases. We also discuss how new technological advances can be used to gain further insights in the genetic background of complex diseases. Finally, we outline a few steps to develop functional assays in order to determine the involvement of these variants in disease development.

Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template.

MotivationComplex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns.ResultsHere we present a phase-only correlation (POC) based classification method for recognizing the type of complex diseases. First, a virtual sample template is constructed for each subclass by averaging all samples of each subclass in a training dataset. Then the label of a test sample is determined by measuring the similarity between the test sample and each template. This novel method can detect the similarity of overall patterns emerged from the differentially expressed genes or proteins while ignoring small mismatches.ConclusionsThe experimental results obtained on seven publicly available complex disease datasets including microarray and protein array data demonstrate that the proposed POC-based disease classification method is effective and robust for diagnosing complex diseases with regard to the number of initially selected features, and its recognition accuracy is better than or comparable to other state-of-the-art machine learning methods. In addition, the proposed method does not require parameter tuning and data scaling, which can effectively reduce the occurrence of over-fitting and bias.

Something about Genetics in Psychiatry

Something about Genetics in Psychiatry

Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer

BackgroundAnalysis of DNA copy number alterations and gene expression changes in human samples have been used to find potential target genes in complex diseases. Recent studies have combined these two types of data using different strategies, but focusing on finding gene-based relationships. However, it has been proposed that these data can be used to identify key genomic regions, which may enclose causal genes under the assumption that disease-associated gene expression changes are caused by genomic alterations.ResultsFollowing this proposal, we undertake a new integrative analysis of genome-wide expression and copy number datasets. The analysis is based on the combined location of both types of signals along the genome. Our approach takes into account the genomic location in the copy number (CN) analysis and also in the gene expression (GE) analysis. To achieve this we apply a segmentation algorithm to both types of data using paired samples. Then, we perform a correlation analysis and a frequency analysis of the gene loci in the segmented CN regions and the segmented GE regions; selecting in both cases the statistically significant loci. In this way, we find CN alterations that show strong correspondence with GE changes. We applied our method to a human dataset of 64 Glioblastoma Multiforme samples finding key loci and hotspots that correspond to major alterations previously described for this type of tumors.ConclusionsIdentification of key altered genomic loci constitutes a first step to find the genes that drive the alteration in a malignant state. These driver genes can be found in regions that show high correlation in copy number alterations and expression changes.

Identification of Genes for Complex Diseases Using Integrated Analysis of Multiple Types of Genomic Data

Various types of genomic data (e.g., SNPs and mRNA transcripts) have been employed to identify risk genes for complex diseases. However, the analysis of these data has largely been performed in isolation. Combining these multiple data for integrative analysis can take advantage of complementary information and thus can have higher power to identify genes (and/or their functions) that would otherwise be impossible with individual data analysis. Due to the different nature, structure, and format of diverse sets of genomic data, multiple genomic data integration is challenging. Here we address the problem by developing a sparse representation based clustering (SRC) method for integrative data analysis. As an example, we applied the SRC method to the integrative analysis of 376821 SNPs in 200 subjects (100 cases and 100 controls) and expression data for 22283 genes in 80 subjects (40 cases and 40 controls) to identify significant genes for osteoporosis (OP). Comparing our results with previous studies, we identified some genes known related to OP risk (e.g., ‘THSD4’, ‘CRHR1’, ‘HSD11B1’, ‘THSD7A’, ‘BMPR1B’ ‘ADCY10’, ‘PRL’, ‘CA8’,’ESRRA’, ‘CALM1’, ‘CALM1’, ‘SPARC’, and ‘LRP1’). Moreover, we uncovered novel osteoporosis susceptible genes (‘DICER1’, ‘PTMA’, etc.) that were not found previously but play functionally important roles in osteoporosis etiology from existing studies. In addition, the SRC method identified genes can lead to higher accuracy for the diagnosis/classification of osteoporosis subjects when compared with the traditional T-test and Fisher-exact test, which further validates the proposed SRC approach for integrative analysis.

Abstract 50: Characterization of the Gwas-nominated Agtrap-plod1 Locus Using Zfn-mutated Rats

Background: Genome-wide association studies (GWAS) are frequently used to nominate candidate genes for complex diseases, but are largely unable to identify the causative variant(s). One example: the AGTRAP-PLOD1 locus contains 6 genes in close proximity that are all associated with blood pressure (BP), making it difficult to delineate specific allele(s) that underlie human hypertension at this locus. Here we present a novel rat model that was developed to rapidly dissect GWAS loci using high-throughput gene mutation on a single hypertensive background. METHODS: Agtrap, Mthfr, Clcn6, Nppa, Nppb, and Plod1 were individually mutated in the Dahl SS rat by zinc finger nuclease (ZFN) injections. ZFN-mutated and wild type (WT) control SS rats (n=8-25 per group) were assessed for BP and renal damage after 10 days on a 4% NaCl diet. Temporal gene expression during development of hypertension was assessed by qRT-PCR and confirmed by RNA-seq. Results: Expression of all AGTRAP-PLOD1 transcripts changed significantly in response to elevated BP, indicating possible roles of the entire AGTRAP-PLOD1 locus in salt-sensitive hypertension. However, compared with WT, only NPPA mutation further increased mean BP (Δ+27mmHg, P&lt;0.001) and heart weight (1.62±0.03 vs. 1.13±0.03g, P&lt;0.001) in response to 4% NaCl diet. In contrast, CLCN6 mutation significantly decreased diastolic BP (Δ-22mmHg, P&lt;0.001) but had no effect on systolic BP compared to WT. Following salt challenge, proteinuria was elevated by mutation of PLOD1 (147±12 mg/day, P&lt;0.001) and MTHFR (132±23 mg/day, P&lt;0.05) compared with WT littermates (91±5 mg/day), whereas AGTRAP mutation decreased protein excretion (54±9 mg/day, P&lt;0.05). Analysis of haplotype blocks that are associated with gene expression and human hypertension confirmed that NPPA, CLCN6, and MTHFR are the primary mediators of BP changes linked to the AGTRAP-PLOD1 locus. Conclusions: Combined with human GWAS, our data show for the first time that NPPA and CLCN6 are divergent mediators of BP at the AGTRAP-PLOD1 locus, while MTHFR mutation directly increases susceptibility to end-stage renal disease. These novel mechanistic data provide rationale for developing haplotype-specific therapies for treating hypertension.

A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases

BackgroundIn the last years GWA studies have successfully identified common SNPs associated with complex diseases. However, most of the variants found this way account for only a small portion of the trait variance. This fact leads researchers to focus on rare-variant mapping with large scale sequencing, which can be facilitated by using linkage information. The question arises why linkage analysis often fails to identify genes when analyzing complex diseases. Using simulations we have investigated the power of parametric and nonparametric linkage statistics (KC-LOD, NPL, LOD and MOD scores), to detect the effect of genes responsible for complex diseases using different pedigree structures.ResultsAs expected, a small number of pedigrees with less than three affected individuals has low power to map disease genes with modest effect. Interestingly, the power decreases when unaffected individuals are included in the analysis, irrespective of the true mode of inheritance. Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance.ConclusionsWhen applied in a sensible way linkage is an appropriate and robust technique to map genes for complex disease. Unlike association analysis, linkage analysis is not hampered by allelic heterogeneity. So, why does linkage analysis often fail with complex diseases? Evidently, when using an insufficient number of small pedigrees, one might miss a true genetic linkage when actually a real effect exists. Furthermore, we show that the test statistic has an important effect on the power to detect linkage as well. Therefore, a linkage analysis might fail if an inadequate test statistic is employed. We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.