Genetic Basis Of Common Diseases Research Articles

Novel methods for epistasis detection in genome-wide association studies.

More and more genome-wide association studies are being designed to uncover the full genetic basis of common diseases. Nonetheless, the resulting loci are often insufficient to fully recover the observed heritability. Epistasis, or gene-gene interaction, is one of many hypotheses put forward to explain this missing heritability. In the present work, we propose epiGWAS, a new approach for epistasis detection that identifies interactions between a target SNP and the rest of the genome. This contrasts with the classical strategy of epistasis detection through exhaustive pairwise SNP testing. We draw inspiration from causal inference in randomized clinical trials, which allows us to take into account linkage disequilibrium. EpiGWAS encompasses several methods, which we compare to state-of-the-art techniques for epistasis detection on simulated and real data. The promising results demonstrate empirically the benefits of EpiGWAS to identify pairwise interactions.

Human gene and disease associations for clinical-genomics and precision medicine research.

We are entering the era of personalized medicine in which an individual's genetic makeup will eventually determine how a doctor can tailor his or her therapy. Therefore, it is becoming critical to understand the genetic basis of common diseases, for example, which genes predispose and rare genetic variants contribute to diseases, and so on. Our study focuses on helping researchers, medical practitioners, and pharmacists in having a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases. Our focus here is to create a comprehensive database with mobile access to all available, authentic and actionable genes, SNPs, and classified diseases and drugs collected from different clinical and genomics databases worldwide, including Ensembl, GenCode, ClinVar, GeneCards, DISEASES, HGMD, OMIM, GTR, CNVD, Novoseek, Swiss‐Prot, LncRNADisease, Orphanet, GWAS Catalog, SwissVar, COSMIC, WHO, and FDA. We present a new cutting‐edge gene‐SNP‐disease‐drug mobile database with a smart phone application, integrating information about classified diseases and related genes, germline and somatic mutations, and drugs. Its database includes over 59 000 protein‐coding and noncoding genes; over 67 000 germline SNPs and over a million somatic mutations reported for over 19 000 protein‐coding genes located in over 1000 regions, published with over 3000 articles in over 415 journals available at the PUBMED; over 80 000 ICDs; over 123 000 NDCs; and over 100 000 classified gene‐SNP‐disease associations. We present an application that can provide new insights into the information about genetic basis of human complex diseases and contribute to assimilating genomic with phenotypic data for the availability of gene‐based designer drugs, precise targeting of molecular fingerprints for tumor, appropriate drug therapy, predicting individual susceptibility to disease, diagnosis, and treatment of rare illnesses are all a few of the many transformations expected in the decade to come.

Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping.

As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways. To alleviate this limitation, in this chapter, we propose geQTL, a sparse regression method that can detect both group-wise and individual associations between SNPs and expression traits. geQTL can also correct the effects of potential confounders. Our method employs computationally efficient technique, thus it is able to fulfill large scale studies. Moreover, our method can automatically infer the proper number of group-wise associations. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that geQTL can effectively detect both individual and group-wise signals and outperform the state-of-the-arts by a large margin. This book chapter well illustrates that decoupling individual and group-wise associations for association mapping is able to improve eQTL mapping accuracy, and inferring individual and group-wise associations.

From Identification to Function: Current Strategies to Prioritise and Follow-Up GWAS Results.

Along with family-based studies, dozens of genome-wide association studies (GWAS) have clearly identified the genetic basis of common diseases and complex traits. There are currently hundreds of single nucleotide polymorphisms (SNP) associated with human disease as well as biochemical and physiological phenotypes. Although this is only the tip of the iceberg, we are now confronted with a general lack of understanding of how these trait-associated variants act. How do these genetic changes lead to overt clinical phenotypes? What are the molecular mechanisms? Can we harness this information to develop better preventive and curative strategies? Current efforts are shifting to focus on these questions as we move from identifying variants to understanding their effects. Here I provide a broad overview of the main technical concerns and current bottlenecks as we approach this new phase.

Sparse regression models for unraveling group and individual associations in eQTL mapping.

BackgroundAs a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways.ResultsTo alleviate this limitation, in this paper, we propose geQTL, a sparse regression method that can detect both group-wise and individual associations between SNPs and expression traits. geQTL can also correct the effects of potential confounders. Our method employs computationally efficient technique, thus it is able to fulfill large scale studies. Moreover, our method can automatically infer the proper number of group-wise associations. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that geQTL can effectively detect both individual and group-wise signals and outperforms the state-of-the-arts by a large margin.ConclusionsThis paper well illustrates that decoupling individual and group-wise associations for association mapping is able to improve eQTL mapping accuracy, and inferring individual and group-wise associations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0986-9) contains supplementary material, which is available to authorized users.

Progress and promise in understanding the genetic basis of common diseases.

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.

The genome as pharmacopeia: Association of genetic dose with phenotypic response

The majority of compounds that enter clinical trials fail to meet regulatory standards for marketed therapeutics. Success in clinical trials is increased when therapies are directed against targets for which genetic variants have been linked to disease risk or progression in humans. Here we review how genome-wide association studies (GWAS) have furthered our understanding of the genetic basis of common disease, and how GWAS findings could stimulate development of novel drugs. Genetic screens that associate variants with clinical phenotypes of interest, like pharmacological screens that associate compounds with in vitro responses of interest, establish an intervention–response relationship on which a therapeutic hypothesis can be based. We highlight the similarities between the GWAS approach and well-established principles in early drug discovery, from primary screen through lead identification. We believe that integration of genetic and pharmacological screens will considerably enhance the development of therapeutic interventions with clear benefit to patients.

Searching for missing heritability: Designing rare variant association studies

Genetic studies have revealed thousands of loci predisposing to hundreds of human diseases and traits, revealing important biological pathways and defining novel therapeutic hypotheses. However, the genes discovered to date typically explain less than half of the apparent heritability. Because efforts have largely focused on common genetic variants, one hypothesis is that much of the missing heritability is due to rare genetic variants. Studies of common variants are typically referred to as genomewide association studies, whereas studies of rare variants are often simply called sequencing studies. Because they are actually closely related, we use the terms common variant association study (CVAS) and rare variant association study (RVAS). In this paper, we outline the similarities and differences between RVAS and CVAS and describe a conceptual framework for the design of RVAS. We apply the framework to address key questions about the sample sizes needed to detect association, the relative merits of testing disruptive alleles vs. missense alleles, frequency thresholds for filtering alleles, the value of predictors of the functional impact of missense alleles, the potential utility of isolated populations, the value of gene-set analysis, and the utility of de novo mutations. The optimal design depends critically on the selection coefficient against deleterious alleles and thus varies across genes. The analysis shows that common variant and rare variant studies require similarly large sample collections. In particular, a well-powered RVAS should involve discovery sets with at least 25,000 cases, together with a substantial replication set.

The application of network label propagation to rank biomarkers in genome-wide Alzheimer's data.

BackgroundRanking and identifying biomarkers that are associated with disease from genome-wide measurements holds significant promise for understanding the genetic basis of common diseases. The large number of single nucleotide polymorphisms (SNPs) in genome-wide studies (GWAS), however, makes this task computationally challenging when the ranking is to be done in a multivariate fashion. This paper evaluates the performance of a multivariate graph-based method called label propagation (LP) that efficiently ranks SNPs in genome-wide data.ResultsThe performance of LP was evaluated on a synthetic dataset and two late onset Alzheimer’s disease (LOAD) genome-wide datasets, and the performance was compared to that of three control methods. The control methods included chi squared, which is a commonly used univariate method, as well as a Relief method called SWRF and a sparse logistic regression (SLR) method, which are both multivariate ranking methods. Performance was measured by evaluating the top-ranked SNPs in terms of classification performance, reproducibility between the two datasets, and prior evidence of being associated with LOAD.On the synthetic data LP performed comparably to the control methods. On GWAS data, LP performed significantly better than chi squared and SWRF in classification performance in the range from 10 to 1000 top-ranked SNPs for both datasets, and not significantly different from SLR. LP also had greater ranking reproducibility than chi squared, SWRF, and SLR. Among the 25 top-ranked SNPs that were identified by LP, there were 14 SNPs in one dataset that had evidence in the literature of being associated with LOAD, and 10 SNPs in the other, which was higher than for the other methods.ConclusionLP performed considerably better in ranking SNPs in two high-dimensional genome-wide datasets when compared to three control methods. It had better performance in the evaluation measures we used, and is computationally efficient to be applied practically to data from genome-wide studies. These results provide support for including LP in the methods that are used to rank SNPs in genome-wide datasets.

Evaluating empirical bounds on complex disease genetic architecture

The genetic architecture of human diseases governs the success of genetic mapping and the future of personalized medicine. Although numerous studies have queried the genetic basis of common disease, contradictory hypotheses have been advocated about features of genetic architecture (for example, the contribution of rare versus common variants). We developed an integrated simulation framework, calibrated to empirical data, to enable the systematic evaluation of such hypotheses. For type 2 diabetes (T2D), two simple parameters--(i) the target size for causal mutation and (ii) the coupling between selection and phenotypic effect--define a broad space of architectures. Whereas extreme models are excluded by the combination of epidemiology, linkage and genome-wide association studies, many models remain consistent, including those where rare variants explain either little (<25%) or most (>80%) of T2D heritability. Ongoing sequencing and genotyping studies will further constrain the space of possible architectures, but very large samples (for example, >250,000 unselected individuals) will be required to localize most of the heritability underlying T2D and other traits characterized by these models.

Exploring Predisposition and Treatment Response—the Promise of Genomics

Spurred by large-scale public and private efforts as well as technological developments, the last few years have seen a major leap forward in our understanding of the genetic basis of cardiovascular disease. This revolution is in its infancy and will continue to alter the medical landscape for years to come. There is a need within the general cardiology community to develop a better understanding about how these developments may alter routine clinical care. In this review, we will provide an overview of the current state of genetics as pertains to rare cardiovascular diseases and then review advances in the discovery of the genetic basis of common disease with the potential for improved risk assessment and drug development. We will also outline a few recent examples of pharmacogenetic advances that are already starting to become a part of clinical management and finally discuss the promise as well as the challenges in using next-generation sequencing technologies to provide personalized cardiovascular care.

Comparison of scoring methods for the detection of causal genes with or without rare variants

Rare causal variants are believed to significantly contribute to the genetic basis of common diseases or quantitative traits. Appropriate statistical methods are required to discover the highest possible number of disease-relevant variants in a genome-wide screening study. The publicly available Genetic Analysis Workshop 17 data set consists of 697 individuals and 24,487 genetic variants. It includes a simulated complex disease model with intermediate quantitative phenotypes. We compare four gene-wise scoring methods with respect to ranking of causal genes under variable allele frequency thresholds for collapsing of rare variants and considering whether or not rare variants were included. We also compare causal genes for which the ranks differ clearly between scoring methods regarding such characteristics as number and strength of causal variants. We corroborated our findings with additional simulations. We found that the maximum statistics method was superior in assigning high ranks to genes with a single strong causal variant. Hotelling’s T2 test was superior for genes with several independent causal variants. This was consistent for all phenotypes and was confirmed by single-gene analyses and additional simulations. The multivariate analysis performed similarly to Hotelling’s T2 test. The least absolute shrinkage and selection operator (LASSO) analysis was widely comparable with the maximum statistics method. We conclude that the maximum statistics method is a superior alternative to Hotelling’s T2 test if one expects only one independent causal variant per gene with a dominating effect. Such a variant could also be a supermarker derived by collapsing rare variants. Because the true nature of the genetic effect is unknown for real data, both methods need to be taken into consideration.

Genomics in Africa: Avoiding Past Pitfalls

A landmark genomics project is taking shape in Africa that shifts the power and prominence to local scientists. If successful, the program will offer valuable insights into the inheritance of common diseases and reshape the paradigm of foreign-funded research.

Genome-wide association studies and susceptibility to infectious diseases

Progress in genomics and the associated technological, statistical and bioinformatics advances have facilitated the successful implementation of genome-wide association studies (GWAS) towards understanding the genetic basis of common diseases. Infectious diseases contribute significantly to the global burden of disease and there is robust epidemiological evidence that host genetic factors are important determinants of the outcome of interactions between host and pathogen. Indeed, infectious diseases have exerted profound selective pressure on human evolution. However, the application of GWAS to infectious diseases has been relatively limited compared with non-communicable diseases. Here we review GWAS findings for important infectious diseases, including malaria, tuberculosis and HIV. We highlight some of the pitfalls recognized more generally for GWAS, as well as issues specific to infection, including the role of the pathogen which also has a genome. We also discuss the challenges encountered when studying African populations which are genetically more ancient and more diverse that other populations and disproportionately bear the main global burden of serious infectious diseases.

After GWAS: Searching for Genetic Risk for Schizophrenia and Bipolar Disorder

Ten years ago it was widely expected that the genetic basis of common disease would be resolved by genome-wide association studies (GWAS), large-scale studies in which the entire genome is covered by genetic markers. However, the bulk of heritable variance remains unexplained. The authors consider several alternative research strategies. For instance, whereas it has been hypothesized that a common disease is associated primarily with common genetic variants, it is now plausible that multiple rare variants each have a potent effect on disease risk and that they could accumulate to become a substantial component of common disease risk. This idea has become more appealing since the discovery that copy number variants (CNVs) are a substantial source of human mutations and are associated with multiple common diseases. CNVs are structural genomic variants consisting of microinsertions, microdeletions, and transpositions in the human genome. It has been argued that numerous rare CNVs are plausible causes of a substantial proportion of common disease, and rare CNVs have been found to be potent risk factors in schizophrenia and autism. Another approach is to "parse the genome," i.e., reanalyze subsets of current GWAS data, since the noise inherent in genome-wide approaches may be hiding valid associations. Lastly, technological advances and declining costs may allow large-scale genome-wide sequencing that would comprehensively identify all genetic variants. Study groups even larger than the 10,000 subjects in current meta-analyses would be required, but the outcomes may lead to resolution of our current dilemma in common diseases: Where is the missing heritability?

Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations

BackgroundGenome-wide association studies give insight into the genetic basis of common diseases. An open question is whether the allele frequency distributions and ancestral vs. derived states of disease-associated alleles differ from the rest of the genome. Characteristics of disease-associated alleles can be used to increase the yield of future studies.MethodsThe set of all common disease-associated alleles found in genome-wide association studies prior to January 2010 was analyzed and compared with HapMap and theoretical null expectations. In addition, allele frequency distributions of different disease classes were assessed. Ages of HapMap and disease-associated alleles were also estimated.ResultsThe allele frequency distribution of HapMap alleles was qualitatively similar to neutral expectations. However, disease-associated alleles were more likely to be low frequency derived alleles relative to null expectations. 43.7% of disease-associated alleles were ancestral alleles. The mean frequency of disease-associated alleles was less than randomly chosen CEU HapMap alleles (0.394 vs. 0.610, after accounting for probability of detection). Similar patterns were observed for the subset of disease-associated alleles that have been verified in multiple studies. SNPs implicated in genome-wide association studies were enriched for young SNPs compared to randomly selected HapMap loci. Odds ratios of disease-associated alleles tended to be less than 1.5 and varied by frequency, confirming previous studies.ConclusionsAlleles associated with genetic disease differ from randomly selected HapMap alleles and neutral expectations. The evolutionary history of alleles (frequency and ancestral vs. derived state) influences whether they are implicated in genome-wide assocation studies.

Deciphering the genetic basis of common diseases by integrated functional annotation of common and rare variants

Genome-wide associations studies (GWAS) have identified >400 loci associated with common human diseases. Despite the extraordinary achievement that these results represent, several questions remain unanswered: (i) What is the contribution of rare variants to common diseases not addressed by GWAS? (ii) What are the functional variants underlying the GWAS associations? To answer these questions, we first explored the role of common and rare variants in morbid obesity (BMI>40) by identifying all the variants in the intervals of two endocannabinoid metabolic genes (MGLL and FAAH) using targeted sequencing. We used two different statistical approaches (single marker tests and collapsed markers tests) to identify variants in four intervals associated with high BMI. The majority of the associated variants are located in promoter sequences or in predicted transcriptional enhancers, suggesting their regulatory role. Additionally, we demonstrate that the set of rare variants in the FAAH promoter associated with BMI are also associated with an increased level of anandamide, a FAAH substrate, further suggesting a functional role in obesity. In a second study, we examined one of the most compelling GWAS that had identified SNPs in the 9p21 gene desert associated with coronary artery disease (CAD) and Type 2 diabetes (T2D). We identified 33 enhancers in 9p21; the interval is the second most dense gene-desert for predicted enhancers and is six times denser than the whole genome. The activity of three of these enhancers is affected by regulatory variants that show allelic differences in DNA-protein affinity and are strongly associated with either CAD or T2D. Some of these variants are predicted to disrupt the transcription-binding site relevant to CAD biology. Our study shows that the 9p21 gene desert is significantly enriched in enhancer elements and suggests the associations with CAD and T2D likely result from the cumulative contributions of multiple regulatory variants. Taken together, our studies show that a comprehensive approach of human genetics that integrates rare variants and functional knowledge, such as regulatory elements or metabolic and cell-specific effects, can leverage the findings of GWAS and lead to a deeper functional understanding of the role of genetic variation in human diseases.

Gene copy number variation and common human disease

Variation in gene copy number is increasingly recognized as a common, heritable source of inter-individual differences in genomic sequence. The role of copy number variation is well established in the pathogenesis of rare genomic disorders. More recently, germline and somatic copy number variation have been shown to be important pathogenic factors in a range of common diseases, including infectious, autoimmune and neuropsychiatric diseases and cancer. In this review, we describe the range of methods available for measuring copy number variants (CNVs) in individuals and populations, including the limitations of presently available assays, and highlight some key examples of common diseases in which CNVs have been shown clearly to have a pathogenic role. Although there has been major progress in this field in the last 5 years, understanding the full contribution of CNVs to the genetic basis of common diseases will require further studies, with more accurate CNV assays and larger cohorts than have presently been completed.

Exploratory data analysis in large-scale genetic studies

Genome-wide association studies (GWAS) have become the method of choice for investigating the genetic basis of common diseases and complex traits. The immense scale of these experiments is unprecedented, involving thousands of samples and up to a million variables. The careful execution of exploratory data analysis (EDA) prior to the actual genotype-phenotype association analysis is crucial as this identifies problematic samples and poorly assayed genetic polymorphisms that, if undetected, can compromise the outcome of the experiment. EDA of such large-scale genetic data sets thus requires specialized numerical and graphical strategies, and this article provides a review of the current exploratory tools commonly used in GWAS.

Align human interactome with phenome to identify causative genes and networks underlying disease families

Understanding the complexity in gene-phenotype relationship is vital for revealing the genetic basis of common diseases. Recent studies on the basis of human interactome and phenome not only uncovers prevalent phenotypic overlap and genetic overlap between diseases, but also reveals a modular organization of the genetic landscape of human diseases, providing new opportunities to reduce the complexity in dissecting the gene-phenotype association. We provide systematic and quantitative evidence that phenotypic overlap implies genetic overlap. With these results, we perform the first heterogeneous alignment of human interactome and phenome via a network alignment technique and identify 39 disease families with corresponding causative gene networks. Finally, we propose AlignPI, an alignment-based framework to predict disease genes, and identify plausible candidates for 70 diseases. Our method scales well to the whole genome, as demonstrated by prioritizing 6154 genes across 37 chromosome regions for Crohn's disease (CD). Results are consistent with a recent meta-analysis of genome-wide association studies for CD. Bi-modules and disease gene predictions are freely available at the URL http://bioinfo.au.tsinghua.edu.cn/alignpi/