Rare Variant Association Methods Research Articles

Bayesian Inference Associates Rare KDR Variants with Specific Phenotypes in Pulmonary Arterial Hypertension.

Background - Approximately 25% of patients with pulmonary arterial hypertension (PAH) have been found to harbor rare mutations in disease-causing genes. To identify missing heritability in PAH we integrated deep phenotyping with whole-genome sequencing data using Bayesian statistics. Methods - We analyzed 13,037 participants enrolled in the NIHR BioResource - Rare Diseases (NBR) study, of which 1,148 were recruited to the PAH domain. To test for genetic associations between genes and selected phenotypes of pulmonary hypertension (PH), we used the Bayesian rare-variant association method BeviMed. Results - Heterozygous, high impact, likely loss-of-function variants in the Kinase Insert Domain Receptor (KDR) gene were strongly associated with significantly reduced transfer coefficient for carbon monoxide (KCO, posterior probability (PP)=0.989) and older age at diagnosis (PP=0.912). We also provide evidence for familial segregation of a rare nonsense KDR variant with these phenotypes. On computed tomographic imaging of the lungs, a range of parenchymal abnormalities were observed in the five patients harboring these predicted deleterious variants in KDR. Four additional PAH cases with rare likely loss-of-function variants in KDR were independently identified in the US PAH Biobank cohort with similar phenotypic characteristics. Conclusions - The Bayesian inference approach allowed us to independently validate KDR, which encodes for the Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), as a novel PAH candidate gene. Furthermore, this approach specifically associated high impact likely loss-of-function variants in the genetically constrained gene with distinct phenotypes. These findings provide evidence for KDR being a clinically actionable PAH gene and further support the central role of the vascular endothelium in the pathobiology of PAH.

Leveraging Family History in Case-Control Analyses of Rare Variation.

Standard methods for case-control association studies of rare variation often treat disease outcome as a dichotomous phenotype. However, both theoretical and experimental studies have demonstrated that subjects with a family history of disease can be enriched for risk variation relative to subjects without such history. Assuming family history information is available, this observation motivates the idea of replacing the standard dichotomous outcome variable used in case-control studies with a more informative ordinal outcome variable that distinguishes controls (0), sporadic cases (1), and cases with a family history (2), with the expectation that we should observe increasing number of risk variants with increasing category of the ordinal variable. To leverage this expectation, we propose a novel rare-variant association test that incorporates family history information based on our previous GAMuT framework for rare-variant association testing of multivariate phenotypes. We use simulated data to show that, when family history information is available, our new method outperforms standard rare-variant association methods, like burden and SKAT tests, that ignore family history. We further illustrate our method using a rare-variant study of cleft lip and palate.

Detecting Clustered Independent Rare Variant Associations Using Genetic Algorithms.

The availability of an increasing collection of sequencing data provides the opportunity to study genetic variation with an unprecedented level of detail. There is much interest in uncovering the role of rare variants and their contribution to disease. However, detecting associations of rare variants with small minor allele frequencies (MAF) and modest effects remains a challenge for rare variant association methods. Due to this low signal-to-noise ratio, most methods are underpowered to detect associations even when conducting rare variant association tests at the gene level. We present a new method for detecting rare variant associations. The algorithm consists of two steps. In the first step, a genetic algorithm searches for a promising genomic region containing a collection of genes with causal rare variants. In the second step, a genetic algorithm aims at removing false positives from the located genomic region. We tested the proposed method with a collection of datasets obtained from real exome data. The proposed method possesses sufficient power for detecting associations of rare variants with complex phenotypes. This method can be used for studying the contribution of rare variants with complex disease, particularly in cases where single-variant or gene-based tests are underpowered.

Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico

BackgroundThe development of sequencing techniques and statistical methods provides great opportunities for identifying the impact of rare genetic variation on complex traits. However, there is a lack of knowledge on the impact of sample size, case numbers, the balance of cases vs controls for both burden and dispersion based rare variant association methods. For example, Phenome-Wide Association Studies may have a wide range of case and control sample sizes across hundreds of diagnoses and traits, and with the application of statistical methods to rare variants, it is important to understand the strengths and limitations of the analyses.ResultsWe conducted a large-scale simulation of randomly selected low-frequency protein-coding regions using twelve different balanced samples with an equal number of cases and controls as well as twenty-one unbalanced sample scenarios. We further explored statistical performance of different minor allele frequency thresholds and a range of genetic effect sizes. Our simulation results demonstrate that using an unbalanced study design has an overall higher type I error rate for both burden and dispersion tests compared with a balanced study design. Regression has an overall higher type I error with balanced cases and controls, while SKAT has higher type I error for unbalanced case-control scenarios. We also found that both type I error and power were driven by the number of cases in addition to the case to control ratio under large control group scenarios. Based on our power simulations, we observed that a SKAT analysis with case numbers larger than 200 for unbalanced case-control models yielded over 90% power with relatively well controlled type I error. To achieve similar power in regression, over 500 cases are needed. Moreover, SKAT showed higher power to detect associations in unbalanced case-control scenarios than regression.ConclusionsOur results provide important insights into rare variant association study designs by providing a landscape of type I error and statistical power for a wide range of sample sizes. These results can serve as a benchmark for making decisions about study design for rare variant analyses.

Enrichment of colorectal cancer associations in functional regions: Insight for using epigenomics data in the analysis of whole genome sequence-imputed GWAS data.

BackgroundThe evaluation of less frequent genetic variants and their effect on complex disease pose new challenges for genomic research. To investigate whether epigenetic data can be used to inform aggregate rare-variant association methods (RVAM), we assessed whether variants more significantly associated with colorectal cancer (CRC) were preferentially located in non-coding regulatory regions, and whether enrichment was specific to colorectal tissues.MethodsActive regulatory elements (ARE) were mapped using data from 127 tissues and cell-types from NIH Roadmap Epigenomics and Encyclopedia of DNA Elements (ENCODE) projects. We investigated whether CRC association p-values were more significant for common variants inside versus outside AREs, or 2) inside colorectal (CR) AREs versus AREs of other tissues and cell-types. We employed an integrative epigenomic RVAM for variants with allele frequency <1%. Gene sets were defined as ARE variants within 200 kilobases of a transcription start site (TSS) using either CR ARE or ARE from non-digestive tissues. CRC-set association p-values were used to evaluate enrichment of less frequent variant associations in CR ARE versus non-digestive ARE.ResultsARE from 126/127 tissues and cell-types were significantly enriched for stronger CRC-variant associations. Strongest enrichment was observed for digestive tissues and immune cell types. CR-specific ARE were also enriched for stronger CRC-variant associations compared to ARE combined across non-digestive tissues (p-value = 9.6 × 10−4). Additionally, we found enrichment of stronger CRC association p-values for rare variant sets of CR ARE compared to non-digestive ARE (p-value = 0.029).ConclusionsIntegrative epigenomic RVAM may enable discovery of less frequent variants associated with CRC, and ARE of digestive and immune tissues are most informative. Although distance-based aggregation of less frequent variants in CR ARE surrounding TSS showed modest enrichment, future association studies would likely benefit from joint analysis of transcriptomes and epigenomes to better link regulatory variation with target genes.

A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases

We present a rapid and powerful inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison. Under a baseline model, disease risk is fixed across all individuals in a study. Under an association model, disease risk depends on a latent bipartition of rare variants into pathogenic and non-pathogenic variants, the number of pathogenic alleles that each individual carries, and the mode of inheritance. A parameter indicating presence of an association and the parameters representing the pathogenicity of each variant and the mode of inheritance can be inferred in a Bayesian framework. Variant-specific prior information derived from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integrated into the inference. Association models can be fitted to different subsets of variants in a locus and compared using a model selection procedure. This procedure can improve inference if only a particular class of variants confers disease risk and can suggest particular disease etiologies related to that class. We show that our method, called BeviMed, is more powerful and informative than existing rare variant association methods in the context of dominant and recessive disorders. The high computational efficiency of our algorithm makes it feasible to test for associations in the large non-coding fraction of the genome. We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare diseases. We show that it can identify multiple loci involved in rare diseases, while correctly inferring the modes of inheritance, the likely pathogenic variants, and the variant classes responsible.

Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project.

Waist-to-hip ratio (WHR), a relative comparison of waist and hip circumferences, is an easily accessible measurement of body fat distribution, in particular central abdominal fat. A high WHR indicates more intra-abdominal fat deposition and is an established risk factor for cardiovascular disease and type 2 diabetes. Recent genome-wide association studies have identified numerous common genetic loci influencing WHR, but the contributions of rare variants have not been previously reported. We investigated rare variant associations with WHR in 1510 European-American and 1186 African-American women from the National Heart, Lung, and Blood Institute-Exome Sequencing Project. Association analysis was performed on the gene level using several rare variant association methods. The strongest association was observed for rare variants in IKBKB (P=4.0 × 10(-8)) in European-Americans, where rare variants in this gene are predicted to decrease WHRs. The activation of the IKBKB gene is involved in inflammatory processes and insulin resistance, which may affect normal food intake and body weight and shape. Meanwhile, aggregation of rare variants in COBLL1, previously found to harbor common variants associated with WHR and fasting insulin, were nominally associated (P=2.23 × 10(-4)) with higher WHR in European-Americans. However, these significant results are not shared between African-Americans and European-Americans that may be due to differences in the allelic architecture of the two populations and the small sample sizes. Our study indicates that the combined effect of rare variants contribute to the inter-individual variation in fat distribution through the regulation of insulin response.

Literature Reviews on Methods for Rare Variant Association Studies

The widespread availability of genome sequencing data has yielded different rare variant association methods in population-based or family-based designs. However, it is challenging to know which method is appropriate in practice. Our purpose of this paper is to provide a general review of the literature for rare variant association studies and suggestions on future research directions. This paper discusses methods for recent rare variant association studies in three categories. The first two categories are for population-based designs, with/without considering the direction of the effects of causal rare variants. In the third category, methods for family-based designs are concluded.

Detecting associations of rare variants with common diseases: collapsing or haplotyping?

In recent years, a myriad of new statistical methods have been proposed for detecting associations of rare single-nucleotide variants (SNVs) with common diseases. These methods can be generally classified as 'collapsing' or 'haplotyping' based. The former is the predominant class, composed of most of the rare variant association methods proposed to date. However, recent works have suggested that haplotyping-based methods may offer advantages and can even be more powerful than collapsing methods in certain situations. In this article, we review and compare collapsing- versus haplotyping-based methods/software in terms of both power and type I error. For collapsing methods, we consider three approaches: Combined Multivariate and Collapsing, Sequence Kernel Association Test and Family-Based Association Test (FBAT): the first two are population based and are among the most popular; the last test is family based, a modification from the popular FBAT to accommodate rare SNVs. For haplotyping-based methods, we include Logistic Bayesian Lasso (LBL) for population data and family-based LBL (famLBL) for family (trio) data. These two methods are selected, as they can be used to test association for specific rare and common haplotypes. Our results show that haplotype methods can be more powerful than collapsing methods if there are interacting SNVs leading to larger haplotype effects. Even if only common SNVs are genotyped, haplotype methods can still detect specific rare haplotypes that tag rare causal SNVs. As expected, family-based methods are robust, whereas population-based methods are susceptible, to population substructure. However, the population-based haplotype approach appears to have smaller inflation of type I error than its collapsing counterparts.

Variant Association Tools for Quality Control and Analysis of Large-Scale Sequence and Genotyping Array Data

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT's data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

Fine-Scale Patterns of Population Stratification Confound Rare Variant Association Tests

Advances in next-generation sequencing technology have enabled systematic exploration of the contribution of rare variation to Mendelian and complex diseases. Although it is well known that population stratification can generate spurious associations with common alleles, its impact on rare variant association methods remains poorly understood. Here, we performed exhaustive coalescent simulations with demographic parameters calibrated from exome sequence data to evaluate the performance of nine rare variant association methods in the presence of fine-scale population structure. We find that all methods have an inflated spurious association rate for parameter values that are consistent with levels of differentiation typical of European populations. For example, at a nominal significance level of 5%, some test statistics have a spurious association rate as high as 40%. Finally, we empirically assess the impact of population stratification in a large data set of 4,298 European American exomes. Our results have important implications for the design, analysis, and interpretation of rare variant genome-wide association studies.

Testing for Rare Variant Associations in the Presence of Missing Data

For studies of genetically complex diseases, many association methods have been developed to analyze rare variants. When variant calls are missing, naïve implementation of rare variant association (RVA) methods may lead to inflated type I error rates as well as a reduction in power. To overcome these problems, we developed extensions for four commonly used RVA tests. Data from the National Heart Lung and Blood Institute-Exome Sequencing Project were used to demonstrate that missing variant calls can lead to increased false-positive rates and that the extended RVA methods control type I error without reducing power. We suggest a combined strategy of data filtering based on variant and sample level missing genotypes along with implementation of these extended RVA tests.

Pathway‐Based Approaches for Sequencing‐Based Genome‐Wide Association Studies

For analyzing complex trait association with sequencing data, most current studies test aggregated effects of variants in a gene or genomic region. Although gene-based tests have insufficient power even for moderately sized samples, pathway-based analyses combine information across multiple genes in biological pathways and may offer additional insight. However, most existing pathway association methods are originally designed for genome-wide association studies, and are not comprehensively evaluated for sequencing data. Moreover, region-based rare variant association methods, although potentially applicable to pathway-based analysis by extending their region definition to gene sets, have never been rigorously tested. In the context of exome-based studies, we use simulated and real datasets to evaluate pathway-based association tests. Our simulation strategy adopts a genome-wide genetic model that distributes total genetic effects hierarchically into pathways, genes, and individual variants, allowing the evaluation of pathway-based methods with realistic quantifiable assumptions on the underlying genetic architectures. The results show that, although no single pathway-based association method offers superior performance in all simulated scenarios, a modification of Gene Set Enrichment Analysis approach using statistics from single-marker tests without gene-level collapsing (weighted Kolmogrov-Smirnov [WKS]-Variant method) is consistently powerful. Interestingly, directly applying rare variant association tests (e.g., sequence kernel association test) to pathway analysis offers a similar power, but its results are sensitive to assumptions of genetic architecture. We applied pathway association analysis to an exome-sequencing data of the chronic obstructive pulmonary disease, and found that the WKS-Variant method confirms associated genes previously published.

SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits

Currently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models. SimRare is an interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case-control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically.

A Fast and Noise‐Resilient Approach to Detect Rare‐Variant Associations With Deep Sequencing Data for Complex Disorders

Next generation sequencing technology has enabled the paradigm shift in genetic association studies from the common disease/common variant to common disease/rare-variant hypothesis. Analyzing individual rare variants is known to be underpowered; therefore association methods have been developed that aggregate variants across a genetic region, which for exome sequencing is usually a gene. The foreseeable widespread use of whole genome sequencing poses new challenges in statistical analysis. It calls for new rare-variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of noncausal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease-associated P-values of individual variants using a weight that is the inverse of the expected standard deviation of the allele frequencies under the null. This approach, dubbed as Sigma-P method, is extremely robust to the inclusion of a high proportion of noncausal variants and is also powerful when both detrimental and protective variants are present within a genetic region. The performance of the Sigma-P method was tested using simulated data based on realistic population demographic and disease models and its power was compared to several previously published methods. The results demonstrate that this method generally outperforms other rare-variant association methods over a wide range of models. Additionally, sequence data on the ANGPTL family of genes from the Dallas Heart Study were tested for associations with nine metabolic traits and both known and novel putative associations were uncovered using the Sigma-P method.

The empirical power of rare variant association methods: results from sanger sequencing in 1,998 individuals.

The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations (deleterious, protective, or both) in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases.