Common Disease-common Variant Research Articles

Assembly of Inflammation-Related Genes for Pathway-Focused Genetic Analysis

Recent identifications of associations between novel variants in inflammation-related genes and several common diseases emphasize the need for systematic evaluations of these genes in disease susceptibility. Considering that many genes are involved in the complex inflammation responses and many genetic variants in these genes have the potential to alter the functions and expression of these genes, we assembled a list of key inflammation-related genes to facilitate the identification of genetic associations of diseases with an inflammation-related etiology. We first reviewed various phases of inflammation responses, including the development of immune cells, sensing of danger, influx of cells to sites of insult, activation and functional responses of immune and non-immune cells, and resolution of the immune response. Assisted by the Ingenuity Pathway Analysis, we then identified 17 functional sub-pathways that are involved in one or multiple phases. This organization would greatly increase the chance of detecting gene-gene interactions by hierarchical clustering of genes with their functional closeness in a pathway. Finally, as an example application, we have developed tagging single nucleotide polymorphism (tSNP) arrays for populations of European and African descent to capture all the common variants of these key inflammation-related genes. Assays of these tSNPs have been designed and assembled into two Affymetrix ParAllele customized chips, one each for European (12,011 SNPs) and African (21,542 SNPs) populations. These tSNPs have greater coverage for these inflammation-related genes compared to the existing genome-wide arrays, particularly in the African population. These tSNP arrays can facilitate systematic evaluation of inflammation pathways in disease susceptibility. For additional applications, other genotyping platforms could also be employed. For existing genome-wide association data, this list of key inflammation-related genes and associated subpathways can facilitate comprehensive inflammation pathway- focused association analyses.

GPDTI: A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases

The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities. We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors. Information on the generated data sets and executable code is available upon request.

Progress in unraveling the genetics of coronary artery disease and myocardial infarction

Atherosclerosis is a leading cause of morbidity and mortality. Coronary artery disease (CAD) and its clinical manifestation, myocardial infarction (MI), are equally determined by interacting environmental and (largely unknown) multigenic factors. Genome-wide and candidate gene searches for single nucleotide polymorphism (SNP) associations with CAD/MI (ie, coronary heart disease) often have resulted in nonreproducible or weak associations. Associations with intermediate surrogates have not ensured clinical event predictions. Linked SNP groups (haplotypes) can be more informative than individual SNPs unless the functional gene variant is known with certainty. Recent results of genetic association studies challenge the "common disease-common variant" hypothesis and suggest that multiple, relatively uncommon alleles often determine variation in coronary risk factors (eg, low high-density lipoprotein). Genetic risk scores, generated by considering several functional SNPs or haplotypes in multiple genes within a biologic pathway implicated in coronary heart disease, could improve predictive ability. Despite the complexity of coronary heart disease genetics, steady progress can be expected.

Commentary: Rare alleles, modest genetic effects and the need for collaboration

of the complex issues facing those trying to identify genetic variants underlying common complex disease. They focus on the common disease—common variant model where effect sizes associated with individual genetic variants are small. Undoubtedly this will be the case for most, but not all, variants. An L-shaped or exponential distribution of mutation effect sizes has wide support 2–4 with many variants with small effects, a smaller number with intermediate effects and relatively few with large effects. It could be argued that the genetic variants related to human disease that have been identified to date primarily reflect the study designs used to identify them. Linkage studies conducted among families with multiple cases of disease were successful in identifying highly penetrant variants with large effects. Association studies conducted in general population samples using common genetic markers typically find low penetrance variants with (very) small effects, as noted by Khoury. This is not unexpected given that these common genetic variants are ancient and will have been subject to some selective pressure over time. 3 We can predict that re-sequencing studies in the near future which study rarer variants (say 0.05–5%) will identify many variants of intermediate effect associated with common complex disease. This paradigm shift has already begun with the seminal work of Cohen, who compared non-synonymous sequence variations in individuals at the extremes of the population distribution of LDL-cholesterol levels, and determined that a significant fraction of genetic variance is due to multiple alleles with intermediate effects that are present at low frequencies (0.05–5%) in the population, particularly persons of African ancestry. 5 Until many such studies are reported it will be premature to decide on the relative importance of the common variant—common disease model and the alternative rare variant—common disease model which states that disease susceptibility to common diseases is the result of multiple low frequency/rare variants with larger phenotypic effects. As Cohen notes, although individually rare, these variants may be collectively common in the population. This has important consequences since the issues of causal inference and clinical application, described well by Khoury, maybe somewhat different for these variants. Our view of the genetic contribution to common complex disease is becoming clearer but is still biased as it is highly determined by available genetic technology and its cost. 6 Nevertheless, the questions raised by Khoury with respect to the common variants currently employed in genetic association studies are important to consider. The most immediate questions are perhaps how to interpret the findings of the increasing number of genome-wide association (GWA) studies which are now being conducted and how this global research effort could be most efficiently marshalled.

Genetic factors in human obesity

This paper was commissioned by the Foresight programme of the Office of Science and Innovation, Department of Trade and Industry © 2007 Queen’s Printer and Controller of HMSO; published with permission Journal compilation © 2007 The International Association for the Study of Obesity. obesity reviews 8 (Suppl. 1) , 37–40 37 obesity reviews (2007) 8 (Suppl. 1) , 37–40 OnlineOpen: This article is available free online at www.blackwell-synergy.com Blackwell Publishing LtdOxford, UKOBRObesity Reviews1467-7881© 2007 Queen’s Printer and Controller of HMSO; published with permission; Journal compilation © 2007 The International Association for the Study of Obesity ? 20078 ••3740Review Article Genetic factors in human obesity I. S. Farooqi & S. O’Rahilly

Variants in common diseases

Most common diseases arise from interaction between multiple genetic variations and factors such as diet. Studies of such diseases that exploit the rich data on variation in the human genome are just beginning. Overeating and physical inactivity are major causes of type 2 diabetes mellitus, but they affect only genetically susceptible individuals and the genetic basis of the disease is notoriously complex. Recent research has suggested that specific genes may be associated with the risk of developing the disease, however. Now a genome-wide search using high-density genotyping arrays has identified four previously unknown genes as diabetes risk factors, and confirmed a known association with the TCF7L2 gene. Together these five genes may contribute a sizeable fraction of the disease risk in type 2 diabetes, and analysis of their function should clarify the pathogenesis of diabetes and point to new drug targets. In addition, individuals shown to have these mutations could minimize their risk by adjusting diet.

Response to Correspondence: Loss-of-Function Mutation in Tryptophan Hydroxylase-2 Identified in Unipolar Major Depression

Response to Correspondence: Loss-of-Function Mutation in Tryptophan Hydroxylase-2 Identified in Unipolar Major Depression

Approaching Inherited Disease on a Genomic Scale

We review current approaches that can extend our understanding of monogenic disease towards complex disease. Recent studies showed that currently established disease genes differ in their protein size, tissue specificity and the phylogenetic distribution of homologs. These characteristics can be explained by the fact that monogenic disease mutations must be sufficiently deleterious to produce a clearly recognizable phenotype, but also must not be lethal in an early embryonic stage. On the other hand, deletion of each gene in the human genome must be evolutionarily disadvantageous. For most genes, this disadvantage might manifest as an increased susceptibility to complex disease. Accordingly, mildly deleterious variants can be observed in a wide spectrum of genes. The phenotypic manifestation of these mildly deleterious variants might depend on somatic mutations, which cause the breakdown of compensating mechanisms in individual cells. At present, association studies are the most promising strategy for mapping complex disease phenotypes. However, these are restricted to the identification of common disease variants and often provide only marginally convincing statistical evidence. Novel computational strategies, which take prior biological knowledge into account, therefore might play a major role in the design and interpretation of large-scale association studies. Keywords: human genes, phenotype, genome sequence, HapMap project, genetic variants

Focus on Rare genetic variants in bipolar disorder: how outliers help understand complex disorders

There is a long-standing debate between those who support the Common Disease Common Variant (CDCV) approach to the study of disorders with complex inheritance and those who argue in favour of a search for rare variants [Common Disease Rare Variant (CDRV); Doris, 2002]. The CDCV approach is based on the well-accepted assumption that for most complex disorders, including schizophrenia and bipolar disorder, inheritance is multifactorial with several genetic variants interacting with each other and with the environment to cause the disease. Because these disorders are relatively common, it is assumed that causative variants should be common too. The high prevalence of these variants and the fact that each one of them is neither sufficient nor obligatory by itself to cause the disease makes them difficult to identify. Indeed, until today not one common genetic variant has been shown to be associated with psychiatric disease beyond doubt, at least to the same extent that ApoE4 is associated with Alzheimer's disease. Supporters of the CDRV approach are not necessarily opposed to the idea that most cases of complex disorders are caused by common genetic variants. Rather, they suggest a ‘shortcut’ to the identification of the involved genes by the use of special outlier cases - extreme phenotypes, patients with chromosomal aberrations, or the study of large multiplex pedigrees. In all of these instances the hope is that inheritance is more Mendelian in nature, with a variant of major effect contributing to a large extent to the aetiology of the disorder.

Gain-of-Function Mutations and Therapeutic Implications

Somatic gene transfer has been extensively used to express genes of interest and probe the molecular physiology of lipoprotein metabolism and many other processes in animal models.1 Among its advantages over germ-line transgenic animals are the ability to rapidly express genes on selected genetic backgrounds without time-intensive backcrossing, the greater ability to control expression level through dosing of the gene transfer vector, and the greater ability to directly compare 2 genes, or 2 variants of the same gene, with regard to their effects in vivo. The latter advantage has not been fully exploited in the investigation of the functional consequences of naturally occurring human mutations. See page 2143 In this issue of Arteriosclerosis, Thrombosis, and Vascular Biology is a report by Ross and colleagues of the rescue of lipoprotein lipase (LPL)-deficient mice from lethality using neonatal intramuscular injection of an adenoviral vector to achieve somatic gene transfer and expression of a naturally occurring gain-of-function mutant of LPL.2 This report presents a variety of important issues for investigators interested in lipoprotein metabolism as well as those generally interested in somatic gene transfer and therapy. Unlike in humans, LPL deficiency in mice is lethal within the first 48 hours of birth.3,4 Whereas a previous study demonstrated that neonatal intraperitoneal administration of an adenoviral vector encoding wild-type LPL rescued 3% of the mice to adulthood,5 the current study achieved 95% rescue to adulthood. The major difference appears to be that Ross et al used a naturally-occurring variant of LPL, known …

Ethnic differences in allele frequency of autoimmune-disease-associated SNPs

Several multiple, large-scale, genetic studies on autoimmune-disease-associated SNPs have been reported recently: peptidylarginine deiminase type 4 (PADI4) in rheumatoid arthritis (RA); solute carrier family 22 members 4 and 5 (SLC22A4 and 5) in RA and Crohn's disease (CD); programmed cell death 1 (PDCD1) in systemic lupus erythematosus (SLE), type 1 diabetes mellitus (T1D), and RA; and protein tyrosine phosphatase nonreceptor type 22 (PTPN22) in T1D, RA, and SLE. Because these reports on association were not always evaluated in multiple ethnic groups and because ethnic difference in allele frequency of the variants has been also reported, we investigated allele frequencies of nine SNPs in four autoimmune-disease-associated loci in Caucasian, African-descent, and Japanese populations. Although SNPs in PADI4 had similar allele frequency among three groups [maximal difference 11%; (P >0.05)], the other three loci revealed statistically significant allele frequency differences (maximal difference 39% (P <0.00001), 13% (P <0.00001), and 8% (P <0.00001) in SLC22A4, PDCD1, and PTPN22, respectively). Of note, three SNPs in the three loci that had allele frequency more than 8% in the Caucasian population were either not polymorphic at all or extremely rare in the Japanese population. Our data suggest that ethnic variations of polymorphisms should be evaluated in detail, and differences should be incorporated into investigations of susceptibility variants for common diseases.

Detection and frequency estimation of rare variants in pools of genomic DNA from large populations using mutational spectrometry

DNA variants underlying the inheritance of risk for common diseases are expected to have a wide range of population allele frequencies. The detection and scoring of the rare alleles (at frequencies of <0.01) presents significant practical problems, including the requirement for large sample sizes and the limitations inherent in current methodologies for allele discrimination. In the present report, we have applied mutational spectrometry based on constant denaturing capillary electrophoresis (CDCE) to DNA pools from large populations in order to improve the prospects of testing the role of rare variants in common diseases on a large scale. We conducted a pilot study of the cytotoxic T lymphocyte-associated antigen-4 gene ( CTLA4) in type 1 diabetes (T1D). A total of 1228 bp, comprising 98% of the CTLA4 coding sequence, all adjacent intronic mRNA splice sites, and a 3′ UTR sequence were scanned for unknown point mutations in pools of genomic DNA from a control population of 10,464 young American adults and two T1D populations, one American (1799 individuals) and one from the United Kingdom (2102 individuals). The data suggest that it is unlikely that rare variants in the scanned regions of CTLA4 represent a significant proportion of T1D risk and illustrate that CDCE-based mutational spectrometry of DNA pools offers a feasible and cost-effective means of testing the role of rare variants in susceptibility to common diseases.

Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies

Genome-wide disease-association mapping has been heralded as the study design of the next generation, but the lack of analytical methods to use genotype data fully is a large stumbling block. Here we describe an algorithm and statistical method that efficiently and exhaustively exploits haplotype information by subjecting alleles (a marker or contiguous sets of markers) from sliding windows of all sizes to transmission disequilibrium tests. By applying our method to simulated data and to Hirschsprung disease, we show that it can detect both common and rare disease variants of small effect. These results show that the theoretical benefits of genome-wide association studies are at last realizable.

Native state stabilization by NSAIDs inhibits transthyretin amyloidogenesis from the most common familial disease variants

Transthyretin (TTR) tetramer dissociation and misfolding affords a monomeric amyloidogenic intermediate that misassembles into aggregates including amyloid fibrils. Amyloidogenesis of wild-type (WT) TTR causes senile systemic amyloidosis (SSA), whereas fibril formation from one of the more than 80 TTR variants leads to familial amyloidosis, typically with earlier onset than SSA. Several nonsteroidal anti-inflammatory drugs (NSAIDs) stabilize the native tetramer, strongly inhibiting TTR amyloid fibril formation in vitro. Structure-based designed NSAID analogs are even more potent amyloid inhibitors. The effectiveness of several NSAIDs, including diclofenac, diflunisal, and flufenamic acid, as well as the diclofenac analog, 2–[(3,5-dichlorophenyl) amino] benzoic acid (inhibitor 1), has been demonstrated against WT TTR amyloidogenesis. Herein, the efficacy of these compounds at preventing acid-induced fibril formation and urea-induced tetramer dissociation of the most common disease-associated TTR variants (V30M, V122I, T60A, L58H, and I84S) was evaluated. Homotetramers of these variants were employed for the studies within, realizing that the tetramers in compound heterozygote patients are normally composed of a mixture of WT and variant subunits. The most common familial TTR variants were stabilized substantially by flufenamic acid and inhibitor 1, and to a lesser extent by diflunisal, against acid-mediated fibril formation and chaotrope denaturation, suggesting that this chemotherapeutic option is viable for patients with familial transthyretin amyloidosis.

Linkage of bipolar affective disorder on chromosome 8q24: follow-up and parametric analysis.

Our group first reported a linkage finding for bipolar (BP) disorder on chromosome 8q24 in a study of 50 multiplex pedigrees, with an HLOD score reaching 2.39. Recently, Cichon et al reported an LOD score of 3.62 in the same region using two-point parametric analysis. Subsequently, we published the results of a genome scan for linkage to BP disorder using a sample extended to 65 pedigrees in which chromosome 8q24 provided the best finding, an NPL score of 3.13, approaching the accepted score for suggestive linkage. We have now fine mapped this region of chromosome 8 in our 65 pedigrees by the addition of 19 microsatellite markers reaching a marker density of 0.8 cM and an information content of 0.84. After the addition of the new data, the original NPL score slightly increased to 3.25. Two-point parametric analysis using the model employed by Cichon et al obtained an LOD score of 3.32 for marker D8S256 at theta=0.14 exceeding the proposed threshold for genomewide significance. After adjusting the parameters in accordance with the 'common disease-common variant' hypothesis, multipoint parametric analysis resulted in an HLOD of 2.49 (alpha=0.78) between D8S529 and D8S256, and defined a 1-LOD interval corresponding to a 2.3 Mb region. No allelic association with the disease was observed for our set of microsatellite markers. Biologically, plausible candidate genes in this region include thyroglobulin, KCNQ3 coding for a voltage-gated potassium channel and the gene for brain adenyl-cyclase (ADCY8).

The allelic architecture of human disease genes: common disease-common variant...or not?

Linkage disequilibrium (LD) plays a central role in current and proposed methods for mapping complex disease genes. LD-based methods work best when there is a single susceptibility allele at any given disease locus, and generally perform very poorly if there is substantial allelic heterogeneity. The extent of allelic heterogeneity at typical complex disease loci is not yet known, but predictions about allelic heterogeneity have important implications for the design of future mapping studies, including the proposed genome-wide association studies. In this article, we review the available data and models relating to the number and frequencies of susceptibility alleles at complex disease loci-the 'allelic architecture' of human disease genes. We also show that the predicted frequency spectrum of disease variants at a gene depends crucially on the method of ascertainment, for example from prior linkage scans or from surveys of functional candidate loci.