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 Bayesian model comparison for rare variant association studies of multiple phenotypes

2018 ◽  
Author(s):  
Christopher DeBoever ◽  
Matthew Aguirre ◽  
Yosuke Tanigawa ◽  
Chris C. A. Spencer ◽  
Timothy Poterba ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Rare Variant ◽  
Genetic Variants ◽  
Model Comparison ◽  
Rare Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Rare Variant Association ◽  
Physical Measurements ◽  
Comparison Approach

AbstractWhole genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery and inference that are not addressed by the traditional one variant-one phenotype association study. Here we introduce a model comparison approach we refer to as MRP for rare variant association studies that considers correlation, scale, and location of genetic effects across a group of genetic variants, phenotypes, and studies. We consider the use of summary statistic data to apply univariate and multivariate gene-based meta-analysis models for identifying rare variant associations with an emphasis on protective protein-truncating variants that can expedite drug discovery. Through simulation studies, we demonstrate that the proposed model comparison approach can improve ability to detect rare variant association signals. We also apply the model to two groups of phenotypes from the UK Biobank: 1) asthma diagnosis, eosinophil counts, forced expiratory volume, and forced vital capacity; and 2) glaucoma diagnosis, intra-ocular pressure, and corneal resistance factor. We are able to recover known associations such as the protective association between rs146597587 in IL33 and asthma. We also find evidence for novel protective associations between rare variants in ANGPTL7 and glaucoma. Overall, we show that the MRP model comparison approach is able to retain and improve upon useful features from widely-used meta-analysis approaches for rare variant association analyses and prioritize protective modifiers of disease risk.Author summaryDue to the continually decreasing cost of acquiring genetic data, we are now beginning to see large collections of individuals for which we have both genetic information and trait data such as disease status, physical measurements, biomarker levels, and more. These datasets offer new opportunities to find relationships between inherited genetic variation and disease. While it is known that there are relationships between different traits, typical genetic analyses only focus on analyzing one genetic variant and one phenotype at a time. Additionally, it is difficult to identify rare genetic variants that are associated with disease due to their scarcity, even among large sample sizes. In this work, we present a method for identifying associations between genetic variation and disease that considers multiple rare variants and phenotypes at the same time. By sharing information across rare variant and phenotypes, we improve our ability to identify rare variants associated with disease compared to considering a single rare variant and a single phenotype. The method can be used to identify candidate disease genes as well as genes that might represent attractive drug targets.

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 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 Genome-Wide Meta-Analysis of Late-Onset Alzheimer's Disease Using Rare Variant Imputation in 324,809 Subjects Identifies Novel Rare Variant Locus NCK2: The International Genomics of Alzheimer's Project (IGAP)

2021 ◽  
Author(s):  
Adam C. Naj ◽  
Ganna Leonenko ◽  
Xueqiu Jian ◽  
Benjamin Grenier-Boley ◽  
Maria Carolina Dalmasso ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rare Variant ◽  
Late Onset ◽  
Sequence Data ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
The Uk

Risk for late-onset Alzheimer's disease (LOAD) is driven by multiple loci primarily identified by genome-wide association studies, many of which are common variants with minor allele frequencies (MAF)>0.01. To identify additional common and rare LOAD risk variants, we performed a GWAS on 25,170 LOAD subjects and 41,052 cognitively normal controls in 44 datasets from the International Genomics of Alzheimer's Project (IGAP). Existing genotype data were imputed using the dense, high-resolution Haplotype Reference Consortium (HRC) r1.1 reference panel. Stage 1 associations of P<10-5 were meta-analyzed with the European Alzheimer's Disease Biobank (EADB) (n=20,301 cases; 21,839 controls) (stage 2 combined IGAP and EADB). An expanded meta-analysis was performed using a GWAS of parental AD/dementia history in the UK Biobank (UKBB) (n=35,214 cases; 180,791 controls) (stage 3 combined IGAP, EADB, and UKBB). Common variant (MAF≥0.01) associations were identified for 29 loci in stage 2, including novel genome-wide significant associations at TSPAN14 (P=2.33×10-12), SHARPIN (P=1.56×10-9), and ATF5/SIGLEC11 (P=1.03[mult]10-8), and newly significant associations without using AD proxy cases in MTSS1L/IL34 (P=1.80×10-8), APH1B (P=2.10×10-13), and CLNK (P=2.24×10-10). Rare variant (MAF<0.01) associations with genome-wide significance in stage 2 included multiple variants in APOE and TREM2, and a novel association of a rare variant (rs143080277; MAF=0.0054; P=2.69×10-9) in NCK2, further strengthened with the inclusion of UKBB data in stage 3 (P=7.17×10-13). Single-nucleus sequence data shows that NCK2 is highly expressed in amyloid-responsive microglial cells, suggesting a role in LOAD pathology.

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
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