Rare Variant Association Tests Research Articles

Rare variant association analyses reveal the significant contribution of carbohydrate metabolic disturbance in severe adolescent idiopathic scoliosis

BackgroundAdolescent idiopathic scoliosis (AIS), the predominant genetic-influenced scoliosis, results in spinal deformities without vertebral malformations. However, the molecular aetiology of AIS remains unclear.MethodsUsing genome/exome sequencing, we studied 368 patients with...

A novel rare variants association test for binary traits in family-based designs via copulas.

With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association test, which is a widely used variant-set test, have been proposed for unrelated samples and extended for family data. Family data have been shown to be powerful when analyzing rare variants. However, most of such methods capture familial relatedness using a random effect component within the generalized linear mixed model framework. Therefore, there is a need to develop unified and flexible methods to study the association between a set of genetic variants and a trait, especially for a binary outcome. Copulas are multivariate distribution functions with uniform margins on the interval and they provide suitable models to capture familial dependence structure. In this work, we propose a flexible family-based association test for both rare and common variants in the presence of binary traits. The method, termed novel rare variant association test (NRVAT), uses a marginal logistic model and a Gaussian Copula. The latter is employed to model the dependence between relatives. An analytic score-type test is derived. Through simulations, we show that our method can achieve greater power than existing approaches. The proposed model is applied to investigate the association between schizophrenia and bipolar disorder in a family-based cohort consisting of 17 extended families from Eastern Quebec.

Learning the kernel for rare variant genetic association test.

Introduction: Compared to Genome-Wide Association Studies (GWAS) for common variants, single-marker association analysis for rare variants is underpowered. Set-based association analyses for rare variants are powerful tools that capture some of the missing heritability in trait association studies. Methods: We extend the convex-optimized SKAT (cSKAT) test set procedure which learns from data the optimal convex combination of kernels, to the full Generalised Linear Model (GLM) setting with arbitrary non-genetic covariates. We call this extended cSKAT (ecSKAT) and show that the resulting optimization problem is a quadratic programming problem that can be solved with no additional cost compared to cSKAT. Results: We show that a modified objective is related to an upper bound for the p-value through a decreasing exponential term in the objective function, indicating that optimizing this objective function is a principled way of learning the combination of kernels. We evaluate the performance of the proposed method on continuous and binary traits using simulation studies and illustrate its application using UK Biobank Whole Exome Sequencing data on hand grip strength and systemic lupus erythematosus rare variant association analysis. Discussion: Our proposed ecSKAT method enables correcting for important confounders in association studies such as age, sex or population structure for both quantitative and binary traits. Simulation studies showed that ecSKAT can recover sensible weights and achieve higher power across different sample sizes and misspecification settings. Compared to the burden test and SKAT method, ecSKAT gives a lower p-value for the genes tested in both quantitative and binary traits in the UKBiobank cohort.

Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.

An allelic-series rare-variant association test for candidate-gene discovery

Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n= 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.

Toward Identification of Functional Sequences and Variants in Noncoding DNA.

Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power.

Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes.

Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA-FIRST, a recently developed strategy to filter and analyse genome-wide rare variants, or user-defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases. Ravages is available on the CRAN at https://cran.r-project.org/web/packages/Ravages/ and maintained on Github at https://github.com/genostats/Ravages.

Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank

The ongoing release of large-scale sequencing data in the UK Biobank allows for the identification of associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a scalable and accurate method for rare-variant association tests, POLMM-GENE, in which we used a proportional odds logistic mixed model to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole-exome-sequencing data for five ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.

Adjusting for common variant polygenic scores improves yield in rare variant association analyses.

With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α = 2.6 × 10-6), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery.

CRELD1 variants are associated with bicuspid aortic valve in Turner syndrome

Turner syndrome (TS) is a chromosomal disorder caused by complete or partial loss of the second sex chromosome and exhibits phenotypic heterogeneity, even after accounting for mosaicism and karyotypic variation. Congenital heart defects (CHD) are found in up to 45 percent of girls with TS and span a phenotypic continuum of obstructive left-sided lesions, with bicuspid aortic valve (BAV) being the most common. Several recent studies have demonstrated a genome-wide impact of X chromosome haploinsufficiency, including global hypomethylation and altered RNA expression. The presence of such broad changes to the TS epigenome and transcriptome led others to hypothesize that X chromosome haploinsufficiency sensitizes the TS genome, and several studies have demonstrated that a second genetic hit can modify disease susceptibility in TS. The objective of this study was to determine whether genetic variants in known heart developmental pathways act synergistically in this setting to increase the risk for CHD, specifically BAV, in TS. We analyzed 208 whole exomes from girls and women with TS and performed gene-based variant enrichment analysis and rare-variant association testing to identify variants associated with BAV in TS. Notably, rare variants in CRELD1 were significantly enriched in individuals with TS who had BAV compared to those with structurally normal hearts. CRELD1 is a protein that functions as a regulator of calcineurin/NFAT signaling, and rare variants in CRELD1 have been associated with both syndromic and non-syndromic CHD. This observation supports the hypothesis that genetic modifiers outside the X chromosome that lie in known heart development pathways may influence CHD risk in TS.

Investigating genotype-phenotype relationship of extreme neuropathic pain disorders in a UK national cohort.

The aims of our study were to use whole genome sequencing in a cross-sectional cohort of patients to identify new variants in genes implicated in neuropathic pain, to determine the prevalence of known pathogenic variants and to understand the relationship between pathogenic variants and clinical presentation. Patients with extreme neuropathic pain phenotypes (both sensory loss and gain) were recruited from secondary care clinics in the UK and underwent whole genome sequencing as part of the National Institute for Health and Care Research Bioresource Rare Diseases project. A multidisciplinary team assessed the pathogenicity of rare variants in genes previously known to cause neuropathic pain disorders and exploratory analysis of research candidate genes was completed. Association testing for genes carrying rare variants was completed using the gene-wise approach of the combined burden and variance-component test SKAT-O. Patch clamp analysis was performed on transfected HEK293T cells for research candidate variants of genes encoding ion channels. The results include the following: (i) Medically actionable variants were found in 12% of study participants (205 recruited), including known pathogenic variants: SCN9A(ENST00000409672.1): c.2544T>C, p.Ile848Thr that causes inherited erythromelalgia, and SPTLC1(ENST00000262554.2):c.340T>G, p.Cys133Tr variant that causes hereditary sensory neuropathy type-1. (ii) Clinically relevant variants were most common in voltage-gated sodium channels (Nav). (iii) SCN9A(ENST00000409672.1):c.554G>A, pArg185His variant was more common in non-freezing cold injury participants than controls and causes a gain of function of NaV1.7 after cooling (the environmental trigger for non-freezing cold injury). (iv) Rare variant association testing showed a significant difference in distribution for genes NGF, KIF1A, SCN8A, TRPM8, KIF1A, TRPA1 and the regulatory regions of genes SCN11A, FLVCR1, KIF1A and SCN9A between European participants with neuropathic pain and controls. (v) The TRPA1(ENST00000262209.4):c.515C>T, p.Ala172Val variant identified in participants with episodic somatic pain disorder demonstrated gain-of-channel function to agonist stimulation. Whole genome sequencing identified clinically relevant variants in over 10% of participants with extreme neuropathic pain phenotypes. The majority of these variants were found in ion channels. Combining genetic analysis with functional validation can lead to a better understanding as to how rare variants in ion channels lead to sensory neuron hyper-excitability, and how cold, as an environmental trigger, interacts with the gain-of-function NaV1.7 p.Arg185His variant. Our findings highlight the role of ion channel variants in the pathogenesis of extreme neuropathic pain disorders, likely mediated through changes in sensory neuron excitability and interaction with environmental triggers.

Inflated expectations: Rare-variant association analysis using public controls.

The use of publicly available sequencing datasets as controls (hereafter, "public controls") in studies of rare variant disease associations has great promise but can increase the risk of false-positive discovery. The specific factors that could contribute to inflated distribution of test statistics have not been systematically examined. Here, we leveraged both public controls, gnomAD v2.1 and several datasets sequenced in our laboratory to systematically investigate factors that could contribute to the false-positive discovery, as measured by λΔ95, a measure to quantify the degree of inflation in statistical significance. Analyses of datasets in this investigation found that 1) the significantly inflated distribution of test statistics decreased substantially when the same variant caller and filtering pipelines were employed, 2) differences in library prep kits and sequencers did not affect the false-positive discovery rate and, 3) joint vs. separate variant-calling of cases and controls did not contribute to the inflation of test statistics. Currently available methods do not adequately adjust for the high false-positive discovery. These results, especially if replicated, emphasize the risks of using public controls for rare-variant association tests in which individual-level data and the computational pipeline are not readily accessible, which prevents the use of the same variant-calling and filtering pipelines on both cases and controls. A plausible solution exists with the emergence of cloud-based computing, which can make it possible to bring containerized analytical pipelines to the data (rather than the data to the pipeline) and could avert or minimize these issues. It is suggested that future reports account for this issue and provide this as a limitation in reporting new findings based on studies that cannot practically analyze all data on a single pipeline.

Gene-Level Associations in Patients With and Without Pathogenic Germline Variants in CDKN2A and Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a component of familial melanoma due to germline pathogenic variants (GPVs) in CDKN2A. However, it is unclear what role this gene or other genes play in its etiology. We analyzed 189 cancer predisposition genes using parametric rare-variant association (RVA) tests and nonparametric permutation tests to identify gene-level associations in PDAC for patients with (CDKN2A+) and without (CDKN2A-) GPV. Exome sequencing was performed on 84 patients with PDAC, 47 CDKN2A+ and 37 CDKN2A-. After variant filtering, various RVA tests and permutation tests were run separately by CDKN2A status. Genes with the strongest nominal associations were evaluated in patients with PDAC from The Cancer Genome Atlas and the UK Biobank (UKB). A secondary analysis including only GPV from UKB was also performed. In RVA tests, ERCC4 and RET showed the most compelling evidence as plausible PDAC candidate genes for CDKN2A+ patients. In contrast, the findings in CDKN2A- patients provided evidence for HMBS, EPCAM, and MRE11 as potential new candidate genes and confirmed ATM, BRCA2, and PALB2 as PDAC genes, consistent with findings in The Cancer Genome Atlas and the UKB. As expected, CDKN2A- patients were more likely to harbor GPVs from the 189 genes investigated. When including only GPVs from UKB, significant associations with PDAC were seen for ATM, BRCA2, and CDKN2A. These results suggest that variants in other genes likely play a role in PDAC in all patients and that PDAC in CDKN2A+ patients has a distinct etiology from PDAC in CDKN2A- patients.

SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene–phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene–phenotype associations.

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score.

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: "RAVA-FIRST" (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the gnomAD populations, which are referred to as "CADD regions". (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 enriched for rare variants in early-onset patients. This region that was missed by standard sliding windows procedures is included in a TAD region that contains a strong candidate gene. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages.

Family-based association tests for rare variants

Next-generation sequencing has revolutionized family-based association tests for rare variants. As the lower power of genome wide association study for detecting casual rare variants, methods aggregating effects of multiple variants have been proposed, such as burden tests and variance component tests. This paper summarizes the methods of rare variants association test that can be applied for family data, introduces their principles, characteristics and applicable conditions and discusses the shortcomings and the improvement of the present methods.

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

A rare variant analysis framework using public genotype summary counts to prioritize disease-predisposition genes

Sequencing cases without matched healthy controls hinders prioritization of germline disease-predisposition genes. To circumvent this problem, genotype summary counts from public data sets can serve as controls. However, systematic inflation and false positives can arise if confounding factors are not controlled. We propose a framework, consistent summary counts based rare variant burden test (CoCoRV), to address these challenges. CoCoRV implements consistent variant quality control and filtering, ethnicity-stratified rare variant association test, accurate estimation of inflation factors, powerful FDR control, and detection of rare variant pairs in high linkage disequilibrium. When we applied CoCoRV to pediatric cancer cohorts, the top genes identified were cancer-predisposition genes. We also applied CoCoRV to identify disease-predisposition genes in adult brain tumors and amyotrophic lateral sclerosis. Given that potential confounding factors were well controlled after applying the framework, CoCoRV provides a cost-effective solution to prioritizing disease-risk genes enriched with rare pathogenic variants.

RAVAQ: An integrative pipeline from quality control to region-based rare variant association analysis.

Next-generation sequencing technologies have opened up the possibility to sequence large samples of cases and controls to test for association with rare variants. To limit cost and increase sample sizes, data from controls could be used in multiple studies and might thus be generated on different sequencing platforms. This could pose some problems of comparability between cases and controls due to batch effects that could be confounding factors, leading to false-positive association signals. To limit batch effects and ensure comparability of datasets, stringent quality controls are required. We propose an integrative five-steps pipeline, RAVAQ, that (a) performs a specific three-step quality control taking into account the case-control status to ensure data comparability, (b) selects qualifying variants as defined by the user, and (c) performs rare variant association tests per genomic region. The RAVAQ pipeline is wrapped in an R package. It is user-friendly and flexible in its arguments to adapt to the specificity of each research project. We provide examples showing how RAVAQ improves rare variant association tests. The default RAVAQ quality control outperformed the widely used Variant Quality Score Recalibration method, removing inflation due to spurious signals. RAVAQ is open source and freely available at https://gitlab.com/gmarenne/ravaq.

RAREsim: A simulation method for very rare genetic variants

Identification of rare-variant associations is crucial to full characterization of the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirror the distribution of rare variants and haplotype structure in real data. Additionally, importing real-variant annotation enables in silico comparison of methods, such as rare-variant association tests and polygenic scoring methods, that focus on putative causal variants. Existing simulation methods are either unable to employ real-variant annotation or severely under- or overestimate the number of singletons and doubletons, thereby reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare-variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real-variant annotations. We highlight RAREsim's utility across various genetic regions, sample sizes, ancestries, and variant classes.