Exploring the genetic basis of chronic periodontitis: a genome-wide association study
TL;DR: A genome-wide association study of CP was carried out in a cohort of 4504 European Americans participating in the Atherosclerosis Risk in Communities Study, finding suggestive evidence of association for six loci, including NIN, NPY, WNT5A for severe CP and NCR2, EMR1, 10p15 for moderate CP.
Abstract: Chronic periodontitis (CP) is a common oral disease that confers substantial systemic inflammatory and microbial burden and is a major cause of tooth loss. Here, we present the results of a genome-wide association study of CP that was carried out in a cohort of 4504 European Americans (EA) participating in the Atherosclerosis Risk in Communities (ARIC) Study (mean age-62 years, moderate CP-43% and severe CP-17%). We detected no genome-wide significant association signals for CP; however, we found suggestive evidence of association (P < 5 × 10(-6)) for six loci, including NIN, NPY, WNT5A for severe CP and NCR2, EMR1, 10p15 for moderate CP. Three of these loci had concordant effect size and direction in an independent sample of 656 adult EA participants of the Health, Aging, and Body Composition (Health ABC) Study. Meta-analysis pooled estimates were severe CP (n = 958 versus health: n = 1909)-NPY, rs2521634 [G]: odds ratio [OR = 1.49 (95% confidence interval (CI = 1.28-1.73, P = 3.5 × 10(-7)))]; moderate CP (n = 2293)-NCR2, rs7762544 [G]: OR = 1.40 (95% CI = 1.24-1.59, P = 7.5 × 10(-8)), EMR1, rs3826782 [A]: OR = 2.01 (95% CI = 1.52-2.65, P = 8.2 × 10(-7)). Canonical pathway analysis indicated significant enrichment of nervous system signaling, cellular immune response and cytokine signaling pathways. A significant interaction of NUAK1 (rs11112872, interaction P = 2.9 × 10(-9)) with smoking in ARIC was not replicated in Health ABC, although estimates of heritable variance in severe CP explained by all single nucleotide polymorphisms increased from 18 to 52% with the inclusion of a genome-wide interaction term with smoking. These genome-wide association results provide information on multiple candidate regions and pathways for interrogation in future genetic studies of CP.
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TL;DR: The mechanisms of microbial immune subversion that tip the balance from homeostasis to disease in oral or extra-oral sites are discussed.
Abstract: Periodontitis is a dysbiotic inflammatory disease with an adverse impact on systemic health. Recent studies have provided insights into the emergence and persistence of dysbiotic oral microbial communities that can mediate inflammatory pathology at local as well as distant sites. This Review discusses the mechanisms of microbial immune subversion that tip the balance from homeostasis to disease in oral or extra-oral sites.
1,621 citations
TL;DR: New paradigms in the understanding of periodontitis are discussed, which may shed light into other polymicrobial inflammatory disorders and highlight gaps in knowledge required for an integrated picture of the interplay between microbes and innate and adaptive immune elements that initiate and propagate chronic periodontal inflammation.
733 citations
TL;DR: Evidence is discussed that periodontitis-associated communities are 'inflammo-philic' (=loving or attracted to inflammation) in that they have evolved to not only endure inflammation but also to take advantage of it, likely to control both dysbiosis and disease progression.
Abstract: In periodontitis, dysbiotic microbial communities exhibit synergistic interactions for enhanced protection from host defenses, nutrient acquisition, and persistence in an inflammatory environment. This review discusses evidence that periodontitis-associated communities are 'inflammo-philic' (=loving or attracted to inflammation) in that they have evolved to not only endure inflammation but also to take advantage of it. In this regard, inflammation can drive the selection and enrichment of these pathogenic communities by providing a source of nutrients in the form of tissue breakdown products (e.g. degraded collagen peptides and heme-containing compounds). In contrast, those species that cannot benefit from the altered ecological conditions of the inflammatory environment, or for which host inflammation is detrimental, are likely to be outcompeted. Consistent with the concept that inflammation fosters the growth of dysbiotic microbial communities, the bacterial biomass of human periodontitis-associated biofilms was shown to increase with increasing periodontal inflammation. Conversely, anti-inflammatory treatments in animal models of periodontitis were shown to diminish the periodontal bacterial load, in addition to protecting from bone loss. The selective flourishing of inflammophilic bacteria can perpetuate inflammatory tissue destruction by setting off a 'vicious cycle' for disease progression, in which dysbiosis and inflammation reinforce each other. Therefore, the control of inflammation appears to be central to the treatment of periodontitis, as it is likely to control both dysbiosis and disease progression.
272 citations
Cites background from "Exploring the genetic basis of chro..."
...…changes, and the presence of keystone pathogens that can transform a symbiotic microbiota into a dysbiotic one (Stabholz et al., 2010; Zhou et al., 2011; Eskan et al., 2012; Hajishengallis et al., 2012; Laine et al., 2012; Divaris et al., 2013; Lindroth & Park, 2013; Hajishengallis, 2014a)....
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...response to environmental changes, and the presence of keystone pathogens that can transform a symbiotic microbiota into a dysbiotic one (Stabholz et al., 2010; Zhou et al., 2011; Eskan et al., 2012; Hajishengallis et al., 2012; Laine et al., 2012; Divaris et al., 2013; Lindroth & Park, 2013; Hajishengallis, 2014a)....
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TL;DR: In this paper, the authors present a review of established mechanisms by which Porphyromonas gingivalis interferes with host immunity and enables the emergence of dysbiotic communities.
Abstract: Recent metagenomic and mechanistic studies are consistent with a new model of periodontal pathogenesis. This model proposes that periodontal disease is initiated by a synergistic and dysbiotic microbial community rather than by a select few bacteria traditionally known as “periopathogens.” Low-abundance bacteria with community-wide effects that are critical for the development of dysbiosis are now known as keystone pathogens, the best-documented example of which is Porphyromonas gingivalis. Here, we review established mechanisms by which P. gingivalis interferes with host immunity and enables the emergence of dysbiotic communities. We integrate the role of P. gingivalis with that of other bacteria acting upstream and downstream in pathogenesis. Accessory pathogens act upstream to facilitate P. gingivalis colonization and co-ordinate metabolic activities, whereas commensals-turned pathobionts act downstream and contribute to destructive inflammation. The recent concepts of keystone pathogens, along with polymicrobial synergy and dysbiosis, have profound implications for the development of therapeutic options for periodontal disease.
267 citations
TL;DR: It is highlighted how NCR biology is being harnessed for novel therapeutic interventions particularly for enhanced tumor surveillance and how this important class of receptors regulates the functions of tissue NK cells.
Abstract: The Natural Cytotoxicity Receptors (NCRs), NKp46, NKp44, and NKp30, were some of the first human activating Natural Killer (NK) cell receptors involved in the non-MHC-restricted recognition of tumor cells to be cloned over 20 years ago. Since this time many host- and pathogen-encoded ligands have been proposed to bind the NCRs and regulate the cytotoxic and cytokine-secreting functions of tissue NK cells. This diverse set of NCR ligands can manifest on the surface of tumor or virus-infected cells or can be secreted extracellularly, suggesting a remarkable NCR polyfunctionality that regulates the activity of NK cells in different tissue compartments during steady state or inflammation. Moreover, the NCRs can also be expressed by other innate and adaptive immune cell subsets under certain tissue conditions potentially conferring NK recognition programs to these cells. Here we review NCR biology in health and disease with particular reference to how this important class of receptors regulates the functions of tissue NK cells as well as confer NK cell recognition patterns to other innate and adaptive lymphocyte subsets. Finally, we highlight how NCR biology is being harnessed for novel therapeutic interventions particularly for enhanced tumor surveillance.
206 citations
References
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TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Abstract: Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
26,280 citations
"Exploring the genetic basis of chro..." refers methods in this paper
...Using these SNPs, identity-by-state (IBS) allele sharing distance (DST values) was computed using PLINK (49), as such: DST ¼ IBS distance (IBS2 + 0.5 × IBS1)/(n SNP pairs)....
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...Using these SNPs, identity-by-state (IBS) allele sharing distance (DST values) was computed using PLINK (49), as such: DST 1⁄4 IBS distance (IBS2 + 0....
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...This step was implemented with PLINK, filtering SNPs that were correlated (r2 ≥ 0.1) with the index SNP (lowest P-value) in each locus and within a distance of 400 Kb....
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TL;DR: A new method and the corresponding software tool, PolyPhen-2, which is different from the early tool polyPhen1 in the set of predictive features, alignment pipeline, and the method of classification is presented and performance, as presented by its receiver operating characteristic curves, was consistently superior.
Abstract: To the Editor:
Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow.
Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naive Bayes classifier (Supplementary Methods).
Figure 1
PolyPhen-2 pipeline and prediction accuracy. (a) Overview of the algorithm. (b) Receiver operating characteristic (ROC) curves for predictions made by PolyPhen-2 using five-fold cross-validation on HumDiv (red) and HumVar3 (light green). UniRef100 (solid ...
We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging.
We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2).
One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations.
For a mutation, PolyPhen-2 calculates Naive Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods).
The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging.
11,571 citations
"Exploring the genetic basis of chro..." refers methods in this paper
...We used PolyPhen-2 for the prediction of potentially damaging missense changes (54) and METAL (55) for the summarization and meta-analysis of estimates derived from the two cohorts....
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TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
Abstract: Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker’s variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers. Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies 1‐8 . Because the effects of stratification vary in proportion to the number of samples 9 , stratification will be an increasing problem in the large-scale association studies of the future, which will analyze thousands of samples in an effort to detect common genetic variants of weak effect. The two prevailing methods for dealing with stratification are genomic control and structured association 9‐14 . Although genomic control and structured association have proven useful in a variety of contexts, they have limitations. Genomic control corrects for stratification by adjusting association statistics at each marker by a uniform overall inflation factor. However, some markers differ in their allele frequencies across ancestral populations more than others. Thus, the uniform adjustment applied by genomic control may be insufficient at markers having unusually strong differentiation across ancestral populations and may be superfluous at markers devoid of such differentiation, leading to a loss in power. Structured association uses a program such as STRUCTURE 15 to assign the samples to discrete subpopulation clusters and then aggregates evidence of association within each cluster. If fractional membership in more than one cluster is allowed, the method cannot currently be applied to genome-wide association studies because of its intensive computational cost on large data sets. Furthermore, assignments of individuals to clusters are highly sensitive to the number of clusters, which is not well defined 14,16 .
9,387 citations
"Exploring the genetic basis of chro..." refers methods in this paper
...Population stratification was further evaluated with principal component (PC) analysis using the EIGENSTRAT program (50)....
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National Institutes of Health1, University of Chicago2, Duke University3, Harvard University4, University of Oxford5, GlaxoSmithKline6, Johns Hopkins University7, Yale University8, deCODE genetics9, Howard Hughes Medical Institute10, Princeton University11, Washington University in St. Louis12, University of California, Berkeley13, Stanford University14, University of Michigan15, Cornell University16, University of Washington17, University of Queensland18, Vanderbilt University19, North Carolina State University20, QIMR Berghofer Medical Research Institute21
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
7,797 citations
TL;DR: The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets and focuses on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation.
Abstract: For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the “missing heritability” problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
5,867 citations
"Exploring the genetic basis of chro..." refers background or methods in this paper
...The estimation of heritable variance (h2) in the two periodontitis traits explained by all available SNPs in the present GWAS was performed with the use of GCTA (56)....
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...(56)], among the European American participants of the Dental ARIC study (n 1⁄4 4504)...
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...The estimation of heritable variance (h(2)) in the two periodontitis traits explained by all available SNPs in the present GWAS was performed with the use of GCTA (56)....
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...GCTA is based on a two-step method, where in the first step, all available SNPs are used to estimate a genetic relationship matrix (GRM) among all study participants....
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