Extreme sampling for genetic rare variant association analysis of dichotomous traits with focus on infectious disease susceptibility

Mapping Intimacies ◽

10.1101/2021.12.02.470949 ◽

2021 ◽

Author(s):

Mary J. Emond ◽

T.Eoin West

Keyword(s):

Infectious Disease ◽

Rare Variant ◽

Statistical Power ◽

Rare Variants ◽

Control Design ◽

Case Control ◽

Type I ◽

Rare Variant Association ◽

Extreme Phenotypes ◽

Ordinary Case

As genomic sequencing becomes more accurate and less costly, large cohorts and consortiums of cohorts are providing high power for rare variant association studies for many conditions. When large sample sizes are not attainable and the phenotype under study is continuous, an extreme phenotypes design can provide high statistical power with a small to moderate sample size. We extend the extreme phenotypes design to the dichotomous infectious disease outcome by sampling on extremes of the pathogenic exposure instead of sampling on extremes of phenotype. We use a likelihood ratio test (LRT) to test the significance of association between infection status and presence of susceptibility rare variants. More than 10 billion simulations are studied to assess the method. The method results in high sample enrichment for rare variants affecting susceptibility. Greater than 90% power to detect rare variant associations is attained in reasonable scenarios. The ordinary case-control design requires orders of magnitude more samples to achieve the same power. The Type I error rate of the LRT is accurate even for p-values < 10 -7 . We find that erroroneous exposure assessment can lead to power loss more severe than excluding the observations with errors. Nevertheless, careful sampling on exposure extremes can make a study feasible by providing adequate statistical power. Limitations of this method are not unique to this design, and the power is never less than that of the ordinary case-control design. The method applies without modification to other dichotomous outcomes that have strong association with a continuous covariate.

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

10.1101/552950 ◽

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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A permutation method for detecting trend correlations in rare variant association studies

Genetics Research ◽

10.1017/s0016672319000120 ◽

2019 ◽

Vol 101 ◽

Author(s):

Lifeng Liu ◽

Pengfei Wang ◽

Jingbo Meng ◽

Lili Chen ◽

Wensheng Zhu ◽

...

Keyword(s):

Rare Variant ◽

Type I Error ◽

Rare Variants ◽

Association Studies ◽

Complex Diseases ◽

Type I ◽

Phenotypic Variance ◽

Rare Variant Association ◽

Significance Level ◽

Association Analyses

Abstract In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

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

10.1101/015917 ◽

2015 ◽

Cited By ~ 2

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.

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Bootstrat: Population Informed Bootstrapping for Rare Variant Tests

10.1101/068999 ◽

2016 ◽

Cited By ~ 1

Author(s):

Hailiang Huang ◽

Gina M. Peloso ◽

Daniel Howrigan ◽

Barbara Rakitsch ◽

Carl Johann Simon-Gabriel ◽

...

Keyword(s):

Rare Variant ◽

Error Rate ◽

Population Stratification ◽

Statistical Power ◽

Type I Error ◽

Rare Variants ◽

Null Distribution ◽

Population Heterogeneity ◽

Type I ◽

Testing Methods

AbstractRecent advances in genotyping and sequencing technologies have made detecting rare variants in large cohorts possible. Various analytic methods for associating disease to rare variants have been proposed, including burden tests, C-alpha and SKAT. Most of these methods, however, assume that samples come from a homogeneous population, which is not realistic for analyses of large samples. Not correcting for population stratification causes inflated p-values and false-positive associations. Here we propose a population-informed bootstrap resampling method that controls for population stratification (Bootstrat) in rare variant tests. In essence, the Bootstrat procedure uses genetic distance to create a phenotype probability for each sample. We show that this empirical approach can effectively correct for population stratification while maintaining statistical power comparable to established methods of controlling for population stratification. The Bootstrat scheme can be easily applied to existing rare variant testing methods with reasonable computational complexity.Author SummaryRecent technology advances have enabled large-scale analysis of rare variants, but properly testing rare variants remains a significant challenge as most rare variant testing methods assume a sample of homogenous ethnicity, an assumption often not true for large cohorts. Failure to account for this heterogeneity increases the type I error rate. Here we propose a bootstrap scheme applicable to most existing rare variant testing methods to control for population heterogeneity. This scheme uses a randomization layer to establish a null distribution of the test statistics while preserving the sample genetic relationships. The null distribution is then used to calculate an empirical p-value that accounts for population heterogeneity. We demonstrate how this scheme successfully controls the type I error rate without loss of statistical power.

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Efficient unified rare variant association test by modeling the population genetic distribution in case-control studies

Genetic Epidemiology ◽

10.1002/gepi.21995 ◽

2016 ◽

Vol 40 (7) ◽

pp. 579-590 ◽

Cited By ~ 2

Author(s):

Huilin Li ◽

Jinbo Chen

Keyword(s):

Rare Variant ◽

Population Genetic ◽

Case Control ◽

Association Test ◽

Case Control Studies ◽

Rare Variant Association ◽

Genetic Distribution ◽

Rare Variant Association Test

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A Robust and Powerful Set-Valued Approach to Rare Variant Association Analyses of Secondary Traits in Case-Control Sequencing Studies

Genetics ◽

10.1534/genetics.116.192377 ◽

2016 ◽

Vol 205 (3) ◽

pp. 1049-1062 ◽

Cited By ~ 3

Author(s):

Guolian Kang ◽

Wenjian Bi ◽

Hang Zhang ◽

Stanley Pounds ◽

Cheng Cheng ◽

...

Keyword(s):

Rare Variant ◽

Case Control ◽

Rare Variant Association ◽

Association Analyses ◽

Sequencing Studies ◽

Secondary Traits

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Efficient and Flexible Integration of Variant Characteristics in Rare Variant Association Studies Using Integrated Nested Laplace Approximation

10.1101/2020.03.12.988584 ◽

2020 ◽

Author(s):

Hana Susak ◽

Laura Serra-Saurina ◽

Raquel Rabionet Janssen ◽

Laura Domènech ◽

Mattia Bosio ◽

...

Keyword(s):

Statistical Methods ◽

Genome Sequencing ◽

Rare Variant ◽

Rare Variants ◽

Association Studies ◽

Complex Diseases ◽

Phenotypic Variance ◽

Rare Variant Association ◽

Genome Wide ◽

Whole Exome

AbstractRare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 75% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the ‘Rare Variant Genome Wide Association Study’ (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.Data availability and implementationAll relevant data are within the manuscript and pipeline implementation on https://github.com/hanasusak/rvGWASAuthor summaryComplex diseases are characterized by being related to genetic factors and environmental factors such as air pollution, diet etc. that together define the susceptibility of each individual to develop a given disease. Much effort has been applied to advance the knowledge of the genetic bases of such diseases, specially in the discovery of frequent genetic variants in the population increasing disease risk. However, these variants usually explain a little part of the etiology of such diseases. Previous studies have shown that rare variants, i.e. variants present in less than 1% of the population, may explain the rest of the variability related to genetic aspects of the disease.Genome sequencing offers the opportunity to discover rare variants, but powerful statistical methods are needed to discriminate those variants that induce susceptibility to the disease. Here we have developed a powerful and flexible statistical approach for the detection of rare variants associated with a disease and we have integrated it into a computer tool that is easy and intuitive for the researchers and clinicians to use. We have shown that our approach outperformed other common statistical methods specially in a situation where these variants explain just a small part of the disease. The discovery of these rare variants will contribute to the knowledge of the molecular mechanism of complex diseases.

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Taking population stratification into account by local permutations in rare-variant association studies on small samples

10.1101/2020.01.29.924977 ◽

2020 ◽

Cited By ~ 1

Author(s):

J. Mullaert ◽

M. Bouaziz ◽

Y. Seeleuthner ◽

B. Bigio ◽

J-L. Casanova ◽

...

Keyword(s):

Sample Size ◽

Rare Variant ◽

Population Stratification ◽

Type I Error ◽

Small Sample Size ◽

Association Studies ◽

Small Sample ◽

Small Samples ◽

Type I ◽

Rare Variant Association

AbstractMany methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individuals are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.

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Leveraging Family History in Case-Control Analyses of Rare Variation

10.1101/665075 ◽

2019 ◽

Author(s):

Claudia R. Solis-Lemus ◽

S. Taylor Fischer ◽

Andrei Todor ◽

Cuining Liu ◽

Elizabeth J. Leslie ◽

...

Keyword(s):

Family History ◽

Rare Variant ◽

Cleft Lip And Palate ◽

Association Studies ◽

Case Control ◽

Outcome Variable ◽

Family History Information ◽

Rare Variant Association ◽

Rare Variation ◽

History Information

AbstractStandard 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 (Broadaway et al., 2016) 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.

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