Association Between HLA-DPB1 and Antineutrophil Cytoplasmic Antibody-Associated Vasculitis in Children.

OBJECTIVE: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a rare, life-threatening inflammation of blood vessels that can affect both adults and children. Compared to adult-onset disease, AAV is especially rare in children, with an annual prevalence of 0.5-6.4 cases per million children. The etiology of AAV remains largely unknown, and both environmental and genetic factors are likely involved. The present study was undertaken to explore the genetic susceptibility factors recently identified in adult patients, including HLA-DP and HLA-DQ, in pediatric patients. METHODS: We performed a genome-wide association study of pediatric AAV in patients of European ancestry (n = 63 AAV cases, n = 315 population-matched controls). RESULTS: We identified a significant genetic association between pediatric AAV and the HLA-DPB1*04:01 allele (P = 1.5 × 10-8 , odds ratio [OR] 3.5), with a stronger association observed in children with proteinase 3-ANCA positivity than in children with myeloperoxidase-ANCA positivity. Among the HLA alleles, the HLA-DPB1*04:01 allele was the most highly associated with AAV, although not significantly, in a follow-up adult AAV cohort (P = 2.6 × 10-4 , OR 0.4). T cell receptor and interferon signaling pathways were also shown to be enriched in the pediatric AAV cohort. CONCLUSION: The HLA-DPB1 locus showed an association with pediatric AAV, as similarly shown previously in adult AAV. Despite the difference in the age of onset, these findings suggest that childhood- and adult-onset vasculitis share a common genetic predisposition. The identification of genetic variants contributing to AAV is an important step to improved classification tools and treatment strategies.

An exploration of linkage fine-mapping on sequences from case-control studies.

Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis. Here, we focus on linkage analysis in a population sample, but use sequences rather than individuals as our unit of observation. Earlier investigations of sequence-based linkage mapping relied on known sequence relatedness, whereas we infer relatedness from the sequence data. We propose two ways to associate similarity in relatedness of sequences with similarity in their trait values and compare the resulting linkage methods to two genotypic-association methods. We also introduce a procedure to label case sequences as potential carriers or noncarriers of causal variants after an association has been found. This post hoc labeling of case sequences is based on inferred relatedness to other case sequences. Our simulation results indicate that methods based on sequence relatedness improve localization and perform as well as genotypic-association methods for detecting rare causal variants. Sequence-based linkage analysis therefore has potential to fine-map allelically heterogeneous disease traits.

Datasets for a simulated family-based exome-sequencing study.

We present simulated exome-sequencing data for 150 families from a North American admixed population, ascertained to contain at least four members affected with lymphoid cancer. These data include information on the ascertained families as well as single-nucleotide variants on the exome of affected family members. We provide a brief overview of the simulation steps and links to the associated software scripts. The resulting data are useful to identify genomic patterns and disease inheritance in families with multiple disease-affected members.

Inference of gene-environment interaction from heterogeneous case-parent trios.

Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.

Different Disease Endotypes in Phenotypically Similar Vasculitides Affecting Small-to-Medium Sized Blood Vessels.

Objectives: Chronic primary vasculitis describes a group of complex and rare diseases that are characterized by blood vessel inflammation. Classification of vasculitis subtypes is based predominantly on the size of the involved vessels and clinical phenotype. There is a recognized need to improve classification, especially for small-to-medium sized vessel vasculitides, that, ideally, is based on the underlying biology with a view to informing treatment. Methods: We performed RNA-Seq on blood samples from children (n = 41) and from adults (n = 11) with small-to-medium sized vessel vasculitis, and used unsupervised hierarchical clustering of gene expression patterns in combination with clinical metadata to define disease subtypes. Results: Differential gene expression at the time of diagnosis separated patients into two primary endotypes that differed in the expression of ~3,800 genes in children, and ~1,600 genes in adults. These endotypes were also present during disease flares, and both adult and pediatric endotypes could be discriminated based on the expression of just 20 differentially expressed genes. Endotypes were associated with distinct biological processes, namely neutrophil degranulation and T cell receptor signaling. Conclusions: Phenotypically similar subsets of small-to-medium sized vessel vasculitis may have different mechanistic drivers involving innate vs. adaptive immune processes. Discovery of these differentiating immune features provides a mechanistic-based alternative for subclassification of vasculitis.

SimRVSequences: an R package to simulate genetic sequence data for pedigrees.

SUMMARY: We present the R package SimRVSequences to simulate sequence data for pedigrees. SimRVSequences allows for simulations of large numbers of single-nucleotide variants (SNVs) and scales well with increasing numbers of pedigrees. Users provide a sample of pedigrees and SNV data from a sample of unrelated individuals. AVAILABILITY AND IMPLEMENTATION: SimRVSequences is publicly-available on CRAN https://cran.r-project.org/web/packages/SimRVSequences/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

perfectphyloR: An R package for reconstructing perfect phylogenies.

BACKGROUND: A perfect phylogeny is a rooted binary tree that recursively partitions sequences. The nested partitions of a perfect phylogeny provide insight into the pattern of ancestry of genetic sequence data. For example, sequences may cluster together in a partition indicating that they arise from a common ancestral haplotype. RESULTS: We present an R package perfectphyloR to reconstruct the local perfect phylogenies underlying a sample of binary sequences. The package enables users to associate the reconstructed partitions with a user-defined partition. We describe and demonstrate the major functionality of the package. CONCLUSION: The perfectphyloR package should be of use to researchers seeking insight into the ancestral structure of their sequence data. The reconstructed partitions have many applications, including the mapping of trait-influencing variants.

The Contribution Plot: Decomposition and Graphical Display of the RV Coefficient, with Application to Genetic and Brain Imaging Biomarkers of Alzheimer's Disease.

BACKGROUND/AIMS: Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically. METHODS: We investigate the properties of such a "contribution plot" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse. RESULTS: The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). CONCLUSIONS: The contribution plot with shrinkage estimation can reveal truly associated explanatory variables.

Identification of Novel Adenosine Deaminase 2 Gene Variants and Varied Clinical Phenotype in Pediatric Vasculitis.

OBJECTIVE: Individuals with deficiency of adenosine deaminase 2 (DADA2), a recently recognized autosomal recessive disease, present with various systemic vascular and inflammatory manifestations, often with young age at disease onset or with early onset of recurrent strokes. Their clinical features and histologic findings overlap with those of childhood-onset polyarteritis nodosa (PAN), a primary "idiopathic" systemic vasculitis. Despite similar clinical presentation, individuals with DADA2 may respond better to biologic therapy than to traditional immunosuppression. The aim of this study was to screen an international registry of children with systemic primary vasculitis for variants in ADA2. METHODS: The coding exons of ADA2 were sequenced in 60 children and adolescents with a diagnosis of PAN, cutaneous PAN, or unclassifiable vasculitis (UCV), any chronic vasculitis with onset at age 5 years or younger, or history of stroke. The functional consequences of the identified variants were assessed by ADA2 enzyme assay and immunoblotting. RESULTS: Nine children with DADA2 (5 with PAN, 3 with UCV, and 1 with antineutrophil cytoplasmic antibody-associated vasculitis) were identified. Among them, 1 patient had no rare variants in the coding region of ADA2 and 8 had biallelic, rare variants (minor allele frequency <0.01) with a known association with DADA2 (p.Gly47Arg and p.Gly47Ala) or a novel association (p.Arg9Trp, p.Leu351Gln, and p.Ala357Thr). The clinical phenotype varied widely. CONCLUSION: These findings support previous observations indicating that DADA2 has extensive genotypic and phenotypic variability. Thus, screening ADA2 among children with vasculitic rash, UCV, PAN, or unexplained, early-onset central nervous system disease with systemic inflammation may enable an earlier diagnosis of DADA2.

S100A12 Serum Levels and PMN Counts Are Elevated in Childhood Systemic Vasculitides Especially Involving Proteinase 3 Specific Anti-neutrophil Cytoplasmic Antibodies.

Objectives: Chronic primary systemic vasculitidies (CPV) are a collection of rare diseases involving inflammation in blood vessels, often in multiple organs. CPV can affect adults and children and may be life- or organ-threatening. Treatments for adult CPV, although effective, have known severe potential toxicities; safety and efficacy of these drugs in pediatric patients is not fully understood. There is an unmet need for biologic measures to assess the level of disease activity and, in turn, inform treatment choices for stopping, starting, or modifying therapy. This observational study determines if S100 calcium-binding protein A12 (S100A12) and common inflammatory indicators are sensitive markers of disease activity in children and adolescents with CPV that could be used to inform a minimal effective dose of therapy. Methods: Clinical data and sera were collected from 56 participants with CPV at study visits from diagnosis to remission. Serum concentrations of S100A12, C-reactive protein (CRP) and hemoglobin (Hb) as well as whole blood cell counts and erythrocyte sedimentation rate (ESR) were measured. Disease activity was inferred by physician's global assessment (PGA) and the pediatric vasculitis activity score (PVAS). Results: Serum concentrations of standard markers of inflammation (ESR, CRP, Hb, absolute blood neutrophil count), and S100A12 track with clinically assessed disease activity. These measures-particularly neutrophil counts and sera concentrations of S100A12-had the most significant correlation with clinical scores of disease activity in those children with vasculitis that is associated with anti-neutrophil cytoplasmic antibodies (ANCA) against proteinase 3. Conclusions: S100A12 and neutrophil counts should be considered in the assessment of disease activity in children with CPV particularly the most common forms of the disease that involve proteinase 3 ANCA. Key messages: - In children with chronic primary systemic vasculitis (CPV), classical measures of inflammation are not formally considered in scoring of disease activity. - Inflammatory markers-specifically S100A12 and neutrophil count-track preferentially with the most common forms of childhood CPV which affect small to medium sized vessels and involve anti neutrophil cytoplasmic antibodies (ANCA) against proteinase-3.

Simulating pedigrees ascertained for multiple disease-affected relatives.

BACKGROUND: Studies that ascertain families containing multiple relatives affected by disease can be useful for identification of causal, rare variants from next-generation sequencing data. RESULTS: We present the R package SimRVPedigree, which allows researchers to simulate pedigrees ascertained on the basis of multiple, affected relatives. By incorporating the ascertainment process in the simulation, SimRVPedigree allows researchers to better understand the within-family patterns of relationship amongst affected individuals and ages of disease onset. CONCLUSIONS: Through simulation, we show that affected members of a family segregating a rare disease variant tend to be more numerous and cluster in relationships more closely than those for sporadic disease. We also show that the family ascertainment process can lead to apparent anticipation in the age of onset. Finally, we use simulation to gain insight into the limit on the proportion of ascertained families segregating a causal variant. SimRVPedigree should be useful to investigators seeking insight into the family-based study design through simulation.

Using Gene Genealogies to Localize Rare Variants Associated with Complex Traits in Diploid Populations.

BACKGROUND AND AIMS: Many methods can detect trait association with causal variants in candidate genomic regions; however, a comparison of their ability to localize causal variants is lacking. We extend a previous study of the detection abilities of these methods to a comparison of their localization abilities. METHODS: Through coalescent simulation, we compare several popular association methods. Cases and controls are sampled from a diploid population to mimic human studies. As benchmarks for comparison, we include two methods that cluster phenotypes on the true genealogical trees: a naive Mantel test considered previously in haploid populations and an extension that takes into account whether case haplotypes carry a causal variant. We first work through a simulated dataset to illustrate the methods. We then perform a simulation study to score the localization and detection properties. RESULTS: In our simulations, the association signal was localized least precisely by the naive Mantel test and most precisely by its extension. Most other approaches had intermediate performance similar to the single-variant Fisher exact test. CONCLUSIONS: Our results confirm earlier findings in haploid populations about potential gains in performance from genealogy-based approaches. They also highlight differences between haploid and diploid populations when localizing and detecting causal variants.

Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation.

Using publicly-available data from the Alzheimer's Disease Neuroimaging Initiative, we investigate the joint association between single-nucleotide polymorphisms (SNPs) in previously established linkage regions for Alzheimer's disease (AD) and rates of decline in brain structure. In an initial, discovery stage of analysis, we applied a weighted RV test to assess the association between 75,845 SNPs in the Alzgene linkage regions and rates of change in structural MRI measurements for 56 brain regions affected by AD, in 632 subjects. After confirming association, we selected refined lists of 1694 and 22 SNPs via a bootstrap-enhanced sparse canonical correlation analysis. In a final, validation stage, we confirmed association between the refined list of 1694 SNPs and the imaging phenotypes in an independent data set. Genes corresponding to priority SNPs having the highest contribution in the validation data have previously been implicated or hypothesized to be implicated in AD, including GCLC, IDE, and STAMBP1andFAS. Though the effect sizes of the 1694 SNPs in the priority set are likely small, further investigation within this set may advance understanding of the missing heritability in AD. Our analysis addresses challenges in current imaging-genetics studies such as biased sampling designs and high-dimensional data with low association signal.

A Bayesian group sparse multi-task regression model for imaging genetics.

MOTIVATION: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. have developed an approach for the analysis of imaging genomic studies using penalized multi-task regression with regularization based on a novel group l2,1-norm penalty which encourages structured sparsity at both the gene level and SNP level. While incorporating a number of useful features, the proposed method only furnishes a point estimate of the regression coefficients; techniques for conducting statistical inference are not provided. A new Bayesian method is proposed here to overcome this limitation. RESULTS: We develop a Bayesian hierarchical modeling formulation where the posterior mode corresponds to the estimator proposed by Wang et al. and an approach that allows for full posterior inference including the construction of interval estimates for the regression parameters. We show that the proposed hierarchical model can be expressed as a three-level Gaussian scale mixture and this representation facilitates the use of a Gibbs sampling algorithm for posterior simulation. Simulation studies demonstrate that the interval estimates obtained using our approach achieve adequate coverage probabilities that outperform those obtained from the nonparametric bootstrap. Our proposed methodology is applied to the analysis of neuroimaging and genetic data collected as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI), and this analysis of the ADNI cohort demonstrates clearly the value added of incorporating interval estimation beyond only point estimation when relating SNPs to brain imaging endophenotypes. AVAILABILITY AND IMPLEMENTATION: Software and sample data is available as an R package 'bgsmtr' that can be downloaded from The Comprehensive R Archive Network (CRAN). CONTACT: nathoo@uvic.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nonrandom occurrence of lymphoid cancer types in 140 families.

We studied 140 families with two or more lymphoid cancers, including non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), chronic lymphocytic leukemia (CLL), and multiple myeloma (MM), for deviation from the population age of onset and lymphoid cancer co-occurrence patterns. Median familial NHL, HL, CLL and MM ages of onset are substantially earlier than comparable population data. NHL, HL and CLL (but not MM) also show earlier age of onset in later generations, known as anticipation. The co-occurrence of lymphoid cancers is significantly different from that expected based on population frequencies (p < .0001), and the pattern differs more in families with more affected members (p < .0001), suggesting specific lymphoid cancer combinations have a shared genetic basis. These families provide evidence for inherited factors that increase the risk of multiple lymphoid cancers. This study was approved by the BC Cancer Agency - University of British Columbia Clinical Research Ethics Board.

Sampletrees and Rsampletrees: sampling gene genealogies conditional on SNP genotype data.

UNLABELLED: : The program sampletrees is a Markov chain Monte Carlo sampler of gene genealogies conditional on either phased or unphased SNP genotype data. The companion program Rsampletrees is for pre- and post-processing of sampletrees files, including setting up the files for sampletrees and storing and plotting the output of a sampletrees run. AVAILABILITY AND IMPLEMENTATION: sampletrees is implemented in C ++. The source code, documentation and test files are available at http://stat.sfu.ca/statgen/research/sampletrees.html The R package Rsampletrees is available on CRAN http://cran.r-project.org/web/packages/Rsampletrees/index.html CONTACT: : kburkett@uottawa.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Using gene genealogies to detect rare variants associated with complex traits.

BACKGROUND AND OBJECTIVE: Standard population genetic theory says that deleterious genetic variants are likely rare and fairly recently introduced. However, can this expectation lead to more powerful tests of association between diseases and rare genetic variation? The gene genealogy describes the relationships between haplotypes sampled from the general population. Although ancestral tree-based methods, inspired by the gene genealogy concept, have been developed for finding associations with common genetic variants, here we ask whether gene genealogies can help in identifying genomic regions containing multiple rare causal variants. METHODS: With data simulated under several demographic models and using known gene genealogies, we developed and compared several tree-based statistics to determine which, if any, could detect the type of clustering expected with rare causal variants and whether the genealogic tree provides additional information about disease associations. RESULTS AND CONCLUSIONS: We found that a novel statistic based on the scaled distance between the tips of a tree performed better than other tree-based statistics. When data were simulated with mild population growth, this statistic outperformed two standard non-tree-based methods, showing that an ancestral tree-based approach has potential for rare variant discovery.

A data-smoothing approach to explore and test gene-environment interaction in case-parent trios.

Complex traits result from an interplay between genes and environment. A better understanding of their joint effects can help refine understanding of the epidemiology of the trait. Various tests have been proposed to assess the statistical interaction between genes and the environment (G×E) in case-parent trio data. However, these tests can lose power when the form of G×E departs from that for which the test was developed. To address this limitation, we propose a data-smoothing approach to estimate and test G×E between a single nucleotide polymorphism and a continuous environmental covariate. For estimating G×E, we fit a generalized additive model using penalized likelihood. The resulting point- and interval-estimates of G×E lead to a graphical display, which can serve as a visualization tool for exploring the form of interaction. For testing G×E, we propose a permutation approach, which accounts for the extra uncertainty introduced by the smoothing process. We investigate the statistical properties of the proposed methods through simulation. We also illustrate the use of the approach with an example data set. We conclude that the approach is useful for exploring novel interactions in data-rich settings.

Gene genealogies for genetic association mapping, with application to Crohn's disease.

A gene genealogy describes relationships among haplotypes sampled from a population. Knowledge of the gene genealogy for a set of haplotypes is useful for estimation of population genetic parameters and it also has potential application in finding disease-predisposing genetic variants. As the true gene genealogy is unknown, Markov chain Monte Carlo (MCMC) approaches have been used to sample genealogies conditional on data at multiple genetic markers. We previously implemented an MCMC algorithm to sample from an approximation to the distribution of the gene genealogy conditional on haplotype data. Our approach samples ancestral trees, recombination and mutation rates at a genomic focal point. In this work, we describe how our sampler can be used to find disease-predisposing genetic variants in samples of cases and controls. We use a tree-based association statistic that quantifies the degree to which case haplotypes are more closely related to each other around the focal point than control haplotypes, without relying on a disease model. As the ancestral tree is a latent variable, so is the tree-based association statistic. We show how the sampler can be used to estimate the posterior distribution of the latent test statistic and corresponding latent p-values, which together comprise a fuzzy p-value. We illustrate the approach on a publicly-available dataset from a study of Crohn's disease that consists of genotypes at multiple SNP markers in a small genomic region. We estimate the posterior distribution of the tree-based association statistic and the recombination rate at multiple focal points in the region. Reassuringly, the posterior mean recombination rates estimated at the different focal points are consistent with previously published estimates. The tree-based association approach finds multiple sub-regions where the case haplotypes are more genetically related than the control haplotypes, and that there may be one or multiple disease-predisposing loci.

A discovery study of daunorubicin induced cardiotoxicity in a sample of acute myeloid leukemia patients prioritizes P450 oxidoreductase polymorphisms as a potential risk factor.

Anthracyclines are very effective chemotherapeutic agents; however, their use is hampered by the treatment-induced cardiotoxicity. Genetic variants that help define patient's sensitivity to anthracyclines will greatly improve the design of optimal chemotherapeutic regimens. However, identification of such variants is hampered by the lack of analytical approaches that address the complex, multi-genic character of anthracycline induced cardiotoxicity (AIC). Here, using a multi-SNP based approach, we examined 60 genes coding for proteins involved in drug metabolism and efflux and identified the P450 oxidoreductase (POR) gene to be most strongly associated with daunorubicin induced cardiotoxicity in a population of acute myeloid leukemia (AML) patients (FDR adjusted p-value of 0.15). In this sample of cancer patients, variation in the POR gene is estimated to account for some 11.6% of the variability in the drop of left ventricular ejection fraction (LVEF) after daunorubicin treatment, compared to the estimated 13.2% accounted for by the cumulative dose and ethnicity. In post-hoc analysis, this association was driven by 3 SNPs-the rs2868177, rs13240755, and rs4732513-through their linear interaction with cumulative daunorubicin dose. The unadjusted odds ratios (ORs) and confidence intervals (CIs) for rs2868177 and rs13240755 were estimated to be 1.89 (95% CI: 0.7435-4.819; p = 0.1756) and 3.18 (95% CI: 1.223-8.27; p = 0.01376), respectively. Although the contribution of POR variants is expected to be overestimated due to the multiple testing performed in this small pilot study, given that cumulative anthracycline dose is virtually the only factor used clinically to predict the risk of cardiotoxicity, the contribution that genetic analyses of POR can make to the assessment of this risk is worthy of follow up in future investigations.