scholarly journals More powerful genetic association testing via a new statistical framework for integrative genomics

Biometrics ◽  
2014 ◽  
Vol 70 (4) ◽  
pp. 881-890 ◽  
Author(s):  
Sihai D. Zhao ◽  
T. Tony Cai ◽  
Hongzhe Li
Keyword(s):  
Genetic Association ◽  
Integrative Genomics ◽  
Association Testing ◽  
Statistical Framework ◽  
Genetic Association Testing

scholarly journals Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Aaron M. Holleman ◽  
K. Alaine Broadaway ◽  
Richard Duncan ◽  
Andrei Todor ◽  
Lynn M. Almli ◽  
...  
Keyword(s):  
Genetic Association ◽  
Questionnaire Data ◽  
Genetic Studies ◽  
Association Testing ◽  
Psychiatric Genetic ◽  
Genetic Association Testing

NOVEL QUANTITATIVE METHOD FOR GENETIC ASSOCIATION TESTING

2019 ◽  
Vol 29 ◽  
pp. S963-S964
Author(s):  
Gido Schoenmacker ◽  
Tom Claassen ◽  
Tom Heskes ◽  
Barbara Franke ◽  
Jan Buitelaar ◽  
...  
Keyword(s):  
Genetic Association ◽  
Quantitative Method ◽  
Association Testing ◽  
Genetic Association Testing

Statistical aspects of genetic association testing in small samples, based on selective DNA pooling data in the arctic fox

2008 ◽  
Vol 49 (1) ◽  
pp. 81-92
Author(s):  
Joanna Szyda ◽  
Zengting Liu ◽  
Magdalena Zatoń-Dobrowolska ◽  
Heliodor Wierzbicki ◽  
Anna Rząsa
Keyword(s):  
Genetic Association ◽  
The Arctic ◽  
Small Samples ◽  
Arctic Fox ◽  
Dna Pooling ◽  
Association Testing ◽  
Genetic Association Testing ◽  
Selective Dna Pooling

scholarly journals Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies

2018 ◽  
Author(s):  
Aaron M. Holleman ◽  
K. Alaine Broadaway ◽  
Richard Duncan ◽  
Lynn M. Almli ◽  
Bekh Bradley ◽  
...  
Keyword(s):  
Genetic Association ◽  
Arbitrary Dimension ◽  
Simulated Data ◽  
Causal Variant ◽  
Questionnaire Data ◽  
Symptom Scale ◽  
Genetic Studies ◽  
Association Testing ◽  
Common Genetic Variants ◽  
Genetic Association Testing

ABSTRACTGenetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom data from questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate questionnaire data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom data collectively using a statistical framework that compares similarity in multivariate symptom-scale data from questionnaires to similarity in common genetic variants across a gene. We use simulated data to demonstrate this strategy provides substantially increased power over standard approaches that collapse questionnaire data into a single surrogate outcome. We also illustrate our approach using GWAS data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom data of arbitrary dimension (thereby aligning with National Institute of Mental Health’s Research Domain Criteria).

scholarly journals Genetic association testing using the GENESIS R/Bioconductor package

2019 ◽  
Author(s):  
Stephanie M Gogarten ◽  
Tamar Sofer ◽  
Han Chen ◽  
Chaoyu Yu ◽  
Jennifer A Brody ◽  
...  
Keyword(s):  
Data Storage ◽  
Genomic Analysis ◽  
Supplementary Information ◽  
Storage And Retrieval ◽  
Association Testing ◽  
Link Functions ◽  
Efficient Storage ◽  
Genetic Association Testing ◽  
Analysis Workflow ◽  
Complete Genomic

Abstract Summary The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment. Availability and implementation https://bioconductor.org/packages/GENESIS; vignettes included. Supplementary information Supplementary data are available at Bioinformatics online.

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