Rare Variant Association Analysis 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...

Ultra-rare genetic variation in relapsing polychondritis: a whole-exome sequencing study

ObjectiveRelapsing polychondritis (RP) is a systemic inflammatory disease of unknown aetiology. The objective of this study was to examine the contribution of rare genetic variations to RP.MethodsWe performed a case–control...

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.

VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants

Rare or de novo variants have substantial contribution to human diseases, but the statistical power to identify risk genes by rare variants is generally low due to rarity of genotype data. Previous studies have shown that risk genes usually have high expression in relevant cell types, although for many conditions the identity of these cell types are largely unknown. Recent efforts in single cell atlas in human and model organisms produced large amount of gene expression data. Here we present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Gamma-Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts. VBASS can be generalized to integrate other types of functional genomics data in statistical genetics analysis.

Genetic analysis of transcription factors in dopaminergic neuronal development in Parkinson's disease.

Genetic variants of dopaminergic transcription factor-encoding genes are suggested to be Parkinson's disease (PD) risk factors; however, no comprehensive analyses of these genes in patients with PD have been undertaken. Therefore, we aimed to genetically analyze 16 dopaminergic transcription factor genes in Chinese patients with PD. Whole-exome sequencing (WES) was performed using a Chinese cohort comprising 1917 unrelated patients with familial or sporadic early-onset PD and 1652 controls. Additionally, whole-genome sequencing (WGS) was performed using another Chinese cohort comprising 1962 unrelated patients with sporadic late-onset PD and 1279 controls. We detected 308 rare and 208 rare protein-altering variants in the WES and WGS cohorts, respectively. Gene-based association analyses of rare variants suggested that MSX1 is enriched in sporadic late-onset PD. However, the significance did not pass the Bonferroni correction. Meanwhile, 72 and 1730 common variants were found in the WES and WGS cohorts, respectively. Unfortunately, single-variant logistic association analyses did not identify significant associations between common variants and PD. Variants of 16 typical dopaminergic transcription factors might not be major genetic risk factors for PD in Chinese patients. However, we highlight the complexity of PD and the need for extensive research elucidating its etiology.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

Family-base rare variant association analysis in Saudi Arabian hydrocephalus subjects using whole exome sequencing.

Hydrocephalus is a highly heterogeneous multifactorial disease that arises from genetic and environmental factors. Familial genetic studies of hydrocephalus have elucidated four robustly associated hydrocephalus associated loci. This study aims to identify potential genetic causation in cases of hydrocephalus, with or without spina bifida and Dandy Walker Syndrome (DWS), using family-based rare variant association analysis of whole exome sequencing. We performed whole exome sequencing in 143 individuals across 48 families where at least one offspring was affected with hydrocephalus (N.=27), with hydrocephalus with spina bifida (N.=21) and with DWS (N.=3), using Illumina HiSeq 2500 instrument. No pathogenic or putative pathogenic single-nucleotide variants were evident in the four known hydrocephalus loci in our subjects. However, after examining 73 known hydrocephalus genes previously identified from literature, we identified three potentially impactful variants from the cohort. Using a gene panel comprising variants in known neural tube defects loci, we identified a total of 1024 potentially deleterious variants, of which 797 were missense variants and 191 were frameshift variants, 36 were stop gain/loss variants. A small portion of our family pedigree analyses yielded putative genetic signals which may be responsible for hydrocephaly elated phenotypes, however the low diagnostic yield may be due to lack of capture of genetic variants in the exonic regions i.e. structural variants may only be evident from whole genome sequencing. We identified three potentially impactful variants from our cohort in 73 known hydrocephalus genes previously identified in literature.

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.

Rare-variant association analysis reveals known and new age-related hearing loss genes.

Age-related (AR) hearing loss (HL) is a prevalent sensory deficit in the elderly population. Several studies showed that common variants increase ARHL susceptibility. Here, we demonstrate that rare-variants play a crucial role in ARHL etiology. We analyzed exome and imputed data from white-European UK Biobank volunteers, performing both single-variant and rare-variant aggregate association analyses using self-reported ARHL phenotypes. We identified and replicated associations between ARHL and rare-variants in KLHDC7B, PDCD6, MYO6, SYNJ2, and TECTA. PUS7L and EYA4 also revealed rare-variant associations with ARHL. EYA4, MYO6, and TECTA are all known to underline Mendelian nonsyndromic HL. PDCD6, a new HL gene, plays an important role in apoptosis and has widespread inner ear expression, particularly in the inner hair cells. An unreplicated common variant association was previously observed for KHLDC7B, here we demonstrate that rare-variants in this gene also play a role in ARHL etiology. Additionally, the first replicated association between SYNJ2 and ARHL was detected. Analysis of common variants revealed several previously reported, i.e., ARHGEF28, and new, i.e., PIK3R3, ARHL associations, as well as ones we replicate here for the first time, i.e., BAIAP2L2, CRIP3, KLHDC7B, MAST2, and SLC22A7. It was also observed that the odds ratios for rare-variant ARHL associations, were higher than those for common variants. In conclusion, we demonstrate the vital role rare-variants, including those in Mendelian nonsyndromic HL genes, play in the etiology of ARHL.

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.

FGF20 and PGM2 variants are associated with childhood asthma in family-based whole-genome sequencing studies.

Asthma is a heterogeneous common respiratory disease that remains poorly understood. The established genetic associations fail to explain the high estimated heritability, and the prevalence of asthma differs between populations and geographic regions. Robust association analyses incorporating different genetic ancestries and whole-genome sequencing data may identify novel genetic associations. We performed family-based genome-wide association analyses of childhood-onset asthma based on whole-genome sequencing (WGS) data for the 'The Genetic Epidemiology of Asthma in Costa Rica' study (GACRS) and the Childhood Asthma Management Program (CAMP). Based on parent-child trios with children diagnosed with asthma, we performed a single variant analysis using an additive and a recessive genetic model and a region-based association analysis of low-frequency and rare variants. Based on 1180 asthmatic trios (894 GACRS trios and 286 CAMP trios, a total of 3540 samples with WGS data), we identified three novel genetic loci associated with childhood-onset asthma: rs4832738 on 4p14 ($P=1.72\ast{10}^{-9}$, recessive model), rs1581479 on 8p22 ($P=1.47\ast{10}^{-8}$, additive model) and rs73367537 on 10q26 ($P=1.21\ast{10}^{-8}$, additive model in GACRS only). Integrative analyses suggested potential novel candidate genes underlying these associations: PGM2 on 4p14 and FGF20 on 8p22. Our family-based whole-genome sequencing analysis identified three novel genetic loci for childhood-onset asthma. Gene expression data and integrative analyses point to PGM2 on 4p14 and FGF20 on 8p22 as linked genes. Furthermore, region-based analyses suggest independent potential low-frequency/rare variant associations on 8p22. Follow-up analyses are needed to understand the functional mechanisms and generalizability of these associations.

Recent advances and challenges of rare variant association analysis in the biobank sequencing era.

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

The Potential Connection between Molecular Changes and Biomarkers Related to ALS and the Development and Regeneration of CNS.

Neurodegenerative diseases are one of the greatest medical burdens of the modern age, being mostly incurable and with limited prognostic and diagnostic tools. Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease characterized by the loss of motoneurons, with a complex etiology, combining genetic, epigenetic, and environmental causes. The neuroprotective therapeutic approaches are very limited, while the diagnostics rely on clinical examination and the exclusion of other diseases. The recent advancement in the discovery of molecular pathways and gene mutations involved in ALS has deepened the understanding of the disease pathology and opened the possibility for new treatments and diagnostic procedures. Recently, 15 risk loci with distinct genetic architectures and neuron-specific biology were identified as linked to ALS through common and rare variant association analyses. Interestingly, the quantity of related proteins to these genes has been found to change during early postnatal development in mammalian spinal cord tissue (opossum Monodelphis domestica) at the particular time when neuroregeneration stops being possible. Here, we discuss the possibility that the ALS-related genes/proteins could be connected to neuroregeneration and development. Moreover, since the regulation of gene expression in developmental checkpoints is frequently regulated by non-coding RNAs, we propose that studying the changes in the composition and quantity of non-coding RNA molecules, both in ALS patients and in the developing central nervous (CNS) system of the opossum at the time when neuroregeneration ceases, could reveal potential biomarkers useful in ALS prognosis and diagnosis.

Association analyses of rare variants identify two genes associated with refractive error.

PurposeGenetic variants identified through population-based genome-wide studies are generally of high frequency, exerting their action in the central part of the refractive error spectrum. However, the power to identify associations with variants of lower minor allele frequency is greatly reduced, requiring considerable sample sizes. Here we aim to assess the impact of rare variants on genetic variation of refractive errors in a very large general population cohort.MethodsGenetic association analyses of non-cyclopaedic autorefraction calculated as mean spherical equivalent (SPHE) used whole-exome sequence genotypic information from 50,893 unrelated participants in the UK Biobank of European ancestry. Gene-based analyses tested for association with SPHE using an optimised SNP-set kernel association test (SKAT-O) restricted to rare variants (minor allele frequency < 1%) within protein-coding regions of the genome. All models were adjusted for age, sex and common lead variants within the same locus reported by previous genome-wide association studies. Potentially causal markers driving association at significant loci were elucidated using sensitivity analyses by sequentially dropping the most associated variants from gene-based analyses.ResultsWe found strong statistical evidence for association of SPHE with the SIX6 (p-value = 2.15 x 10−10, or Bonferroni-Corrected p = 4.41x10-06) and the CRX gene (p-value = 6.65 x 10−08, or Bonferroni-Corrected p = 0.001). The SIX6 gene codes for a transcription factor believed to be critical to the eye, retina and optic disc development and morphology, while CRX regulates photoreceptor specification and expression of over 700 genes in the retina. These novel associations suggest an important role of genes involved in eye morphogenesis in refractive error.ConclusionThe results of our study support previous research highlighting the importance of rare variants to the genetic risk of refractive error. We explain some of the origins of the genetic signals seen in GWAS but also report for the first time a completely novel association with the CRX gene.

Dissection of mendelian predisposition and complex genetic architecture of craniovertebral junction malformation.

The craniovertebral junction (CVJ) is an anatomically complex region of the axial skeleton that provides protection of the brainstem and the upper cervical spinal cord. Structural malformation of the CVJ gives rise to life-threatening neurological deficits, such as quadriplegia and dyspnea. Unfortunately, genetic studies on human subjects with CVJ malformation are limited and the pathogenesis remains largely elusive. In this study, we recruited 93 individuals with CVJ malformation and performed exome sequencing. Manual interpretation of the data identified three pathogenic variants in genes associated with Mendelian diseases, including CSNK2A1, MSX2, and DDX3X. In addition, the contribution of copy number variations (CNVs) to CVJ malformation was investigated and three pathogenic CNVs were identified in three affected individuals. To further dissect the complex mutational architecture of CVJ malformation, we performed a gene-based rare variant association analysis utilizing 4371 in-house exomes as control. Rare variants in LGI4 (carrier rate = 3.26%, p = 3.3 × 10-5) and BEST1 (carrier rate = 5.43%, p = 5.77 × 10-6) were identified to be associated with CVJ malformation. Furthermore, gene set analyses revealed that extracellular matrix- and RHO GTPase-associated biological pathways were found to be involved in the etiology of CVJ malformation. Overall, we comprehensively dissected the genetic underpinnings of CVJ malformation and identified several novel disease-associated genes and biological pathways.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BackgroundMultilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.ResultsWe herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.ConclusionIn conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/.

Genetics of progressive supranuclear palsy in a Chinese population

BackgroundGenetics plays an important role in progressive supranuclear palsy (PSP) and remains poorly understood. A detailed literature search identified 19 PSP-associated genes: MAPT, LRRK2, LRP10, DCTN1, GRN, NPC1, PARK, TARDBP, TBK1, BSN, GBA, STX6, EIF2AK3, MOBP, DUSP10, SLCO1A2, RUNX2, CXCR4, and APOE. To date, genetic studies on PSP have focused on Caucasian population. The gaps in PSP genetic study on East Asian populations need to be filled. MethodsExon and flanking regions of the PSP-associated genes were sequenced in 104 patients with PSP and 488 healthy controls. Common variant-based association analysis and gene-based association tests of rare variants were performed using PLINK 1.9 and the sequence kernel association test-optimal, respectively. Additionally, the association of APOE and MAPT genotypes with PSP was evaluated. The above association analyses were repeated among probable PSP patients. Finally, PLINK 1.9 was used to test variants associated with the onset age of PSP. ResultsA rare non-pathogenic variant of MAPT (c.425C > T,p.A142V) was detected in a PSP patient. No common variants were significantly associated with PSP. In both the rare-variant and the rare-damaging-variant groups, the combined effect for GBA reached statistical significance (p = 1.43 × 10−3, p = 4.98 × 10−4). The result between APOE, MAPT genotypes and PSP risk were inconsistent across all PSP group and probably PSP group. ConclusionsThe pathogenic variant in MAPT were uncommon in PSP patients. Moreover, GBA gene was likely to increase the risk of PSP, and GBA-associated diseases were beyond α-synucleinopathies. The association between APOE, MAPT and PSP is still unclear among the non-Caucasian population.

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.

Examination of Genetic Susceptibility in Radiation-Associated Meningioma.

Previous epidemiological studies have demonstrated elevated susceptibility to ionizing radiation in some families, thus suggesting the presence of genetic components that conferred increased rate of radiation-associated meningioma (RAM). In this study, we exome-sequenced and investigated the segregation pattern of rare deleterious variants in 11 RAM pedigrees. In addition, we performed a rare-variant association analysis in 92 unrelated familial cases of RAM that were ancestry-matched with 88 meningioma-free controls. In the pedigree analysis, we found that each family carried mostly a unique set of rare deleterious variants. A follow-up pathway analysis of the union of the genes that segregated within each of the 11 pedigrees identified a single statistically significant (q value = 7.90E-04) "ECM receptor interaction" set. In the case-control association analysis, we observed no statistically significant variants or genes after multiple testing correction; however, examination of ontological categories of the genes that associated with RAM at nominal P values <0.01 identified biologically relevant pathways such as DNA repair, cell cycle and apoptosis. These results suggest that it is unlikely that a small number of highly penetrant genes are involved in the pathogenesis of RAM. Substantially larger studies are needed to identify genetic risk variants and genes in RAM.

Protein interaction network analysis reveals genetic enrichment of immune system genes in frontotemporal dementia

Interactors of protein products of known genes for frontotemporal dementia (FTD) are likely to be involved in the molecular pathways towards disease. We therefore applied protein interaction network (PIN) analysis to prioritize candidate genes for rare variant association analysis. We created an FTD-PIN starting from known FTD genes downloading their physical interactors and performed functional enrichment analyses. We identified overrepresented processes in FTD and selected genes (n = 440) belonging to these processes for rare variant analysis in a Belgian cohort of 228 FTD patients and 345 controls. SKAT-O analysis suggested TNFAIP3 as the top gene (p = 0.7 × 10−3) reaching near test-wide significance (p = 2.5 × 10−4). We then analyzed the TNFAIP3-subnetwork within the FTD-PIN which indicated enrichment of several immune signaling networks, suggesting that disrupted immune signaling may be implicated in TNFAIP3-related FTD. Our study demonstrates that integration of PINs with genetic data is a useful approach to increase the power for rare variant association analysis. Furthermore, we present a computational pipeline for identifying potential novel therapeutic targets and risk-modifying variants.