Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes.

Family-based sequencing studies are increasingly used to find rare genetic variants of high risk for disease traits with familial clustering. In some studies, families with multiple disease subtypes are collected and the exomes of affected relatives are sequenced for shared rare variants (RVs). Since different families can harbor different causal variants and each family harbors many RVs, tests to detect causal variants can have low power in this study design. Our goal is rather to prioritize shared variants for further investigation by, for example, pathway analyses or functional studies. The transmission-disequilibrium test prioritizes variants based on departures from Mendelian transmission in parent-child trios. Extending this idea to families, we propose methods to prioritize RVs shared in affected relatives with two disease subtypes, with one subtype more heritable than the other. Global approaches condition on a variant being observed in the study and assume a known probability of carrying a causal variant. In contrast, local approaches condition on a variant being observed in specific families to eliminate the carrier probability. Our simulation results indicate that global approaches are robust to misspecification of the carrier probability and prioritize more effectively than local approaches even when the carrier probability is misspecified.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Markov chain Monte Carlo sampling of gene genealogies conditional on unphased SNP genotype data.

The gene genealogy is a tree describing the ancestral relationships among genes sampled from unrelated individuals. Knowledge of the tree is useful for inference of population-genetic parameters and has potential application in gene-mapping. Markov chain Monte Carlo approaches that sample genealogies conditional on observed genetic data typically assume that haplotype data are observed even though commonly-used genotyping technologies provide only unphased genotype data. We have extended our haplotype-based genealogy sampler, sampletrees, to handle unphased genotype data. We use the sampled haplotype configurations as a diagnostic for adequate sampling of the tree space based on the reasoning that if haplotype sampling is restricted, sampling from the tree space will also be restricted. We compare the distributions of sampled haplotypes across multiple runs of sampletrees, and to those estimated by the phase inference program, PHASE. Performance was excellent for the majority of individuals as shown by the consistency of results across multiple runs. However, for some individuals in some datasets, sampletrees had problems sampling haplotype configurations; longer run lengths would be required for these datasets. For many datasets though, we expect that sampletrees will be useful for sampling from the posterior distribution of gene genealogies given unphased genotype data.

Secondary analysis of publicly available data reveals superoxide and oxygen radical pathways are enriched for associations between type 2 diabetes and low-frequency variants.

Genome-wide association studies explain at most 5%-10% of the heritable components of type 2 diabetes. Some of the "missing type 2 diabetes heritability" could be explained by low-frequency variants. We examined the associations between low-frequency variants and type 2 diabetes, using data from 2538 diabetic and 2977 nondiabetic subjects in the publicly available database of Genotypes and Phenotypes. We applied two approaches. First, we combined information from all low-frequency (1%-5%) variants at a locus in a gene-centric analysis of associations with diabetes. Next, we searched for gene ontology (GO) biological processes that were enriched for gene-centric associations, after correcting for multiple testing to control the false discovery rate (FDR). We found three GO biological processes that were significantly enriched for associations to diabetes: "response to superoxide" (FDR-adjusted p=2.7×10(-3)), "response to oxygen radical" (FDR-adjusted p=2.7×10(-3)), and "heart contraction" (FDR-adjusted p=2.6×10(-2)). There were three genes that contributed to "response to superoxide" and "oxygen radical" pathways, including the SOD1 gene. Gene-centric tests of association with low-frequency variants, followed by analysis to evaluate which biological pathways are enriched for these associations has the potential to recover, at least some proportion of, the "missing heritability" of type 2 diabetes.

Adjusting for spurious gene-by-environment interaction using case-parent triads.

In the case-parent trio design, unrelated children affected with a disease are genotyped along with their parents. Information may also be collected on environmental factors in the children. The design permits estimation and testing of genetic effects and gene-by-environment interaction. Recently, it has been demonstrated that when genotypes are measured at a non-causal test locus, population stratification can create spurious interaction. That is, the environmental factor can appear to modify the disease risk associated with genotypes at the test locus without modifying the disease risk of genotypes at the causal locus. One design-based approach that is robust to spurious interaction requires the environmental factor to also be available on an unaffected sibling of the affected child. We explore the source of spurious interaction and suggest an alternate approach that mitigates its effects using case-parent triads. Our approach is based on adjusting the risk model using ancestry informative markers or random markers measured on the affected child and does not require data on unaffected siblings. We apply an approach to generating case-parent data, implemented in a freely-available R package soon to be released on the Comprehensive R Archive Network (CRAN).

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.

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.

On the use of allelic transmission rates for assessing gene-by-environment interaction in case-parent trios.

Allelic transmission rates from parents to cases are frequently stratified by an environmental risk factor E and compared, with heterogeneity interpreted as gene-environment interaction or GxE. Though generally invalid, such analyses continue to appear. We revisit why heterogeneity is not equivalent to GxE in a range of settings not considered previously. The objective is a fuller understanding of the bias in transmission rates and what is driving it. Extending previously published findings, we derive parental mating-type probabilities in cases and use them to obtain transmission rates, which we then compare to GxE. Through simulation, we investigate the practical implications of the bias for a transmission-based test of GxE. We find that general population characteristics distort the picture of GxE obtained from transmission rates: the stratum-specific mating-type probabilities under G - E dependence and the allele frequency under independence. Furthermore, the transmission-based test has inflated error rates relative to a likelihood-based test. Our investigation provides further insight into how and why transmission-based tests and descriptive summaries can mislead about GxE. For exploring GxE, we suggest graphical displays of the transmission rates within parental mating types, as they are robust to population stratification and the penetrance model.

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.