scholarly journals A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects

2016 ◽  
Vol 24 (9) ◽  
pp. 1344-1351 ◽  
Author(s):  
Jianping Sun ◽  
◽  
Karim Oualkacha ◽  
Vincenzo Forgetta ◽  
Hou-Feng Zheng ◽  
...  
Keyword(s):  
Rare Variant ◽  
Association Studies ◽  
Rare Variant Association

scholarly journals A novel statistical method for rare-variant association studies in general pedigrees

2016 ◽  
Vol 10 (S7) ◽  
Author(s):  
Huanhuan Zhu ◽  
Zhenchuan Wang ◽  
Xuexia Wang ◽  
Qiuying Sha
Keyword(s):  
Statistical Method ◽  
Rare Variant ◽  
Association Studies ◽  
Rare Variant Association

scholarly journals Strategies to improve the performance of rare variant association studies by optimizing the selection of controls

2015 ◽  
pp. btv457
Author(s):  
Na Zhu ◽  
Verena Heinrich ◽  
Thorsten Dickhaus ◽  
Jochen Hecht ◽  
Peter N. Robinson ◽  
...  
Keyword(s):  
Rare Variant ◽  
Association Studies ◽  
Rare Variant Association ◽  
Selection Of

scholarly journals An adaptive test for meta‐analysis of rare variant association studies

2019 ◽  
Vol 44 (1) ◽  
pp. 104-116
Author(s):  
Tianzhong Yang ◽  
Junghi Kim ◽  
Chong Wu ◽  
Yiding Ma ◽  
Peng Wei ◽  
...  
Keyword(s):  
Rare Variant ◽  
Association Studies ◽  
Meta Analysis ◽  
Rare Variant Association ◽  
Adaptive Test

scholarly journals The exhaustive genomic scan approach, with an application to rare-variant association analysis

2019 ◽  
Author(s):  
George Kanoungi ◽  
Michael Nothnagel ◽  
Tim Becker ◽  
Dmitriy Drichel
Keyword(s):  
Rare Variant ◽  
Association Studies ◽  
A Priori ◽  
Error Rates ◽  
Age Related Macular Degeneration ◽  
Data Sets ◽  
Rare Variant Association ◽  
Age Related ◽  
Genome Wide ◽  
Definition Of

AbstractRegion-based genome-wide scans are usually performed by use of a priori chosen analysis regions. Such an approach will likely miss the region comprising the strongest signal and, thus, may result in increased type II error rates and decreased power. Here, we propose a genomic exhaustive scan approach that analyzes all possible subsequences and does not rely on a prior definition of the analysis regions. As a prime instance, we present a computationally ultra-efficient implementation using the rare-variant collapsing test for phenotypic association, the genomic exhaustive collapsing scan (GECS). Our implementation allows for the identification of regions comprising the strongest signals in large, genome-wide rare-variant association studies while controlling the family-wise error rate via permutation. Application of GECS to two genomic data sets revealed several novel significantly associated regions for age-related macular degeneration and for schizophrenia. Our approach also offers a high potential for genome-wide scans for selection, methylation and other analyses.

scholarly journals Incorporating Prior Biologic Information for High-Dimensional Rare Variant Association Studies

2012 ◽  
Vol 74 (3-4) ◽  
pp. 184-195 ◽  
Author(s):  
Melanie A. Quintana ◽  
Fredrick R. Schumacher ◽  
Graham Casey ◽  
Jonine L. Bernstein ◽  
Li Li ◽  
...  
Keyword(s):  
Rare Variant ◽  
Association Studies ◽  
High Dimensional ◽  
Rare Variant Association

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

2021 ◽  
Author(s):  
Jimmy Mullaert ◽  
Matthieu Bouaziz ◽  
Yoann Seeleuthner ◽  
Benedetta Bigio ◽  
Jean‐Laurent Casanova ◽  
...  
Keyword(s):  
Rare Variant ◽  
Population Stratification ◽  
Association Studies ◽  
Small Samples ◽  
Rare Variant Association

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

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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