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A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data
Dajiang J. Liu
, Suzanne M. Leal
Department of Public Health Sciences
Penn State Cancer Institute
Cancer Institute, Cancer Control
Institute for Personalized Medicine
Division of Biostatistics and Bioinformatics
One Health Microbiome Center
Department of Molecular and Precision Medicine
Research output
:
Contribution to journal
›
Article
›
peer-review
12
Scopus citations
Overview
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Dive into the research topics of 'A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data'. Together they form a unique fingerprint.
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Keyphrases
Quantitative Traits
100%
Sequence Data
100%
Pedigree
100%
Rare Variants
100%
Unified Framework
100%
Unrelated Individuals
100%
Trait Association
100%
Genetic Association
75%
Rare Variant Association Study
50%
Trait-based Model
25%
Sequence Analysis
25%
Family Data
25%
Fixed Threshold
25%
Sequence-based
25%
Genetic Traits
25%
Complex Traits
25%
Genetic Association Studies
25%
Population Genetics
25%
Biochemistry, Genetics and Molecular Biology
Quantitative Trait
100%
Pedigree
100%
Rare Variant
100%
Genetic Association
50%
Genetic Association Study
16%
Population Genetics
16%