scholarly journals Exautomate: A user-friendly tool for region-based rare variant association analysis (RVAA)

2019 ◽  
Author(s):  
Brent D. Davis ◽  
Jacqueline S. Dron ◽  
John F. Robinson ◽  
Robert A. Hegele ◽  
Dan J. Lizotte
Keyword(s):  
Association Analysis ◽  
Rare Variant ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Rare Variants ◽  
Great Promise ◽  
Rare Variant Association ◽  
Complex Phenotypes ◽  
Rare Genetic Variation ◽  
User Friendly

AbstractRegion-based rare variant association analysis (RVAA) is a popular method to study rare genetic variation in large datasets, especially in the context of complex traits and diseases. Although this method shows great promise in increasing our understanding of the genetic architecture of complex phenotypes, performing a region-based RVAA can be challenging. The sequence kernel association test (SKAT) can be used to perform this analysis, but its inputs and modifiable parameters can be extremely overwhelming and may lead to results that are difficult to reproduce. We have developed a software package called “Exautomate” that contains the tools necessary to run a region-based RVAA using SKAT and is easy-to-use for any researcher, regardless of their previous bioinformatic experiences. In this report, we discuss the utilities of Exautomate and provide detailed examples of implementing our package. Importantly, we demonstrate a proof-of-principle analysis using a previously studied cohort of 313 familial hypercholesterolemia (FH) patients. Our results show an increased burden of rare variants in genes known to cause FH, thereby demonstrating a successful region-based RVAA using Exautomate. With our easy-to-use package, we hope researchers will be able to perform reproducible region-based RVAA to further our collective understanding behind the genetics of complex traits and diseases.

scholarly journals Selection and explosive growth may hamper the performance of rare variant association tests

2015 ◽  
Author(s):  
Lawrence H. Uricchio ◽  
John S. Witte ◽  
Ryan D. Hernandez
Keyword(s):  
Natural Selection ◽  
Rare Variant ◽  
Complex Traits ◽  
Statistical Power ◽  
Rare Variants ◽  
Model Parameters ◽  
Phenotypic Variance ◽  
Additive Variance ◽  
Rare Variant Association ◽  
Association Tests

Much recent debate has focused on the role of rare variants in complex phenotypes. However, it is well known that rare alleles can only contribute a substantial proportion of the phenotypic variance when they have much larger effect sizes than common variants, which is most easily explained by natural selection constraining trait-altering alleles to low frequency. It is also plausible that demographic events will influence the genetic architecture of complex traits. Unfortunately, most rare variant association tests do not explicitly model natural selection or non-equilibrium demography. Here, we develop a novel evolutionary model of complex traits. We perform numerical calculations and simulate phenotypes under this model using inferred human demographic and selection parameters. We show that rare variants only contribute substantially to complex traits under very strong assumptions about the relationship between effect size and selection strength. We then assess the performance of state-of-the-art rare variant tests using our simulations across a broad range of model parameters. Counterintuitively, we find that statistical power is lowest when rare variants make the greatest contribution to the additive variance, and that power is substantially lower under our model than previously studied models. While many empirical studies have attempted to identify causal loci using rare variant association methods, few have reported novel associations. Some authors have interpreted this to mean that rare variants contribute little to heritability, but our results show that an alternative explanation is that rare variant tests have less power than previously estimated.

scholarly journals RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data: Table 1.

2016 ◽  
Vol 32 (9) ◽  
pp. 1423-1426 ◽  
Author(s):  
Xiaowei Zhan ◽  
Youna Hu ◽  
Bingshan Li ◽  
Goncalo R. Abecasis ◽  
Dajiang J. Liu
Keyword(s):  
Association Analysis ◽  
Rare Variant ◽  
Sequence Data ◽  
Rare Variant Association ◽  
Data Table

Rare, Damaging DNA Variants in CORIN and Risk of Coronary Artery Disease: Insights From Functional Genomics and Large-Scale Sequencing Analyses

2021 ◽  
Author(s):  
Minxian Wang ◽  
Vivian S. Lee-Kim ◽  
Deepak S. Atri ◽  
Nadine H. Elowe ◽  
John Yu ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Association Analysis ◽  
Rare Variant ◽  
Odds Ratio ◽  
Large Scale ◽  
Missense Mutations ◽  
Rare Variant Association ◽  
Missense Variants ◽  
Artery Disease

Background: Corin is a protease expressed in cardiomyocytes that plays a key role in salt handling and intravascular volume homeostasis via activation of natriuretic peptides. It is unknown if Corin loss-of-function (LOF) is causally associated with risk of coronary artery disease (CAD). Methods: We analyzed all coding CORIN variants in an Italian case-control study of CAD. We functionally tested all 64 rare missense mutations in Western Blot and Mass Spectroscopy assays for proatrial natriuretic peptide cleavage. An expanded rare variant association analysis for Corin LOF mutations was conducted in whole exome sequencing data from 37 799 CAD cases and 212 184 controls. Results: We observed LOF variants in CORIN in 8 of 1803 (0.4%) CAD cases versus 0 of 1725 controls ( P , 0.007). Of 64 rare missense variants profiled, 21 (33%) demonstrated <30% of wild-type activity and were deemed damaging in the 2 functional assays for Corin activity. In a rare variant association study that aggregated rare LOF and functionally validated damaging missense variants from the Italian study, we observed no association with CAD—21 of 1803 CAD cases versus 12 of 1725 controls with adjusted odds ratio of 1.61 ([95% CI, 0.79–3.29]; P =0.17). In the expanded sequencing dataset, there was no relationship between rare LOF variants with CAD was also observed (odds ratio, 1.15 [95% CI, 0.89–1.49]; P =0.30). Consistent with the genetic analysis, we observed no relationship between circulating Corin concentrations with incident CAD events among 4744 participants of a prospective cohort study—sex-stratified hazard ratio per SD increment of 0.96 ([95% CI, 0.87–1.07], P =0.48). Conclusions: Functional testing of missense mutations improved the accuracy of rare variant association analysis. Despite compelling pathophysiology and a preliminary observation suggesting association, we observed no relationship between rare damaging variants in CORIN or circulating Corin concentrations with risk of CAD.

scholarly journals A pathway-centric approach to rare variant association analysis

2016 ◽  
Vol 25 (1) ◽  
pp. 123-129 ◽  
Author(s):  
Tom G Richardson ◽  
Nicholas J Timpson ◽  
Colin Campbell ◽  
Tom R Gaunt
Keyword(s):  
Association Analysis ◽  
Rare Variant ◽  
Rare Variant Association

Hierarchical Bayesian Model for Rare Variant Association Analysis Integrating Genotype Uncertainty in Human Sequence Data

2014 ◽  
Vol 39 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Liang He ◽  
Janne Pitkäniemi ◽  
Antti-Pekka Sarin ◽  
Veikko Salomaa ◽  
Mikko J. Sillanpää ◽  
...  
Keyword(s):  
Association Analysis ◽  
Rare Variant ◽  
Bayesian Model ◽  
Sequence Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Rare Variant Association ◽  
Human Sequence

scholarly journals Rare variant association analysis in case-parents studies by allowing for missing parental genotypes

BMC Genetics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Yumei Li ◽  
Yang Xiang ◽  
Chao Xu ◽  
Hui Shen ◽  
Hongwen Deng
Keyword(s):  
Association Analysis ◽  
Rare Variant ◽  
Rare Variant Association

scholarly journals RAREsim: A simulation method for very rare genetic variants

2021 ◽  
Author(s):  
Megan Null ◽  
Josée Dupuis ◽  
Christopher R. Gignoux ◽  
Audrey E. Hendricks
Keyword(s):  
Rare Variant ◽  
Complex Traits ◽  
Rare Variants ◽  
Simulated Data ◽  
Real Data ◽  
Simulation Method ◽  
Sequencing Data ◽  
Variant Annotation ◽  
Causal Variants ◽  
Rare Genetic Variants

AbstractIdentification of rare variant associations is crucial to fully characterize the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirrors the distribution of rare variants and haplotype structure in real data. Additionally, importing real variant annotation enables in silico comparison of methods that focus on putative causal variants, such as rare variant association tests, and polygenic scoring methods. Existing simulation methods are either unable to employ real variant annotation or severely under- or over-estimate the number of singletons and doubletons 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.

scholarly journals Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

2019 ◽  
Author(s):  
Zilin Li ◽  
Xihao Li ◽  
Yaowu Liu ◽  
Jincheng Shen ◽  
Han Chen ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Type I Error ◽  
Rare Variants ◽  
Error Rates ◽  
Type I ◽  
Whole Genome ◽  
Rare Variant Association ◽  
Dynamic Scan

AbstractWhole genome sequencing (WGS) studies are being widely conducted to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set based analyses are commonly used to analyze rare variants. However, existing variant-set based approaches need to pre-specify genetic regions for analysis, and hence are not directly applicable to WGS data due to the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding window method requires pre-specifying fixed window sizes, which are often unknown as a priori, are difficult to specify in practice and are subject to limitations given genetic association region sizes are likely to vary across the genome and phenotypes. We propose a computationally-efficient and dynamic scan statistic method (Scan the Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the locations of rare-variants association regions without the need of specifying a prior fixed window size. The proposed method controls the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected rare variants association region sizes to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative rare-variant association detection methods while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

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