A Robust and Powerful Set-Valued Approach to Rare Variant Association Analyses of Secondary Traits in Case-Control Sequencing Studies
Autor: | Yanlong Zhao, Ji-Feng Zhang, Stanley Pounds, Hang Zhang, Fei Zou, Cheng Cheng, Weihua Yue, Wenjian Bi, Sanjay Shete, Guolian Kang |
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Rok vydání: | 2017 |
Předmět: |
0301 basic medicine
Genome-wide association study Disease Investigations Biology Logistic regression Polymorphism Single Nucleotide 01 natural sciences DNA sequencing 010104 statistics & probability 03 medical and health sciences Gene Frequency Genetics Animals Humans Genetic Predisposition to Disease 0101 mathematics Set (psychology) Genetic association Models Genetic Phenotype 030104 developmental biology Trait Identification (biology) Algorithms Genome-Wide Association Study |
Zdroj: | Genetics. 205:1049-1062 |
ISSN: | 1943-2631 |
Popis: | In many case-control designs of genome-wide association (GWAS) or next generation sequencing (NGS) studies, extensive data on secondary traits that may correlate and share the common genetic variants with the primary disease are available. Investigating these secondary traits can provide critical insights into the disease etiology or pathology, and enhance the GWAS or NGS results. Methods based on logistic regression (LG) were developed for this purpose. However, for the identification of rare variants (RVs), certain inadequacies in the LG models and algorithmic instability can cause severely inflated type I error, and significant loss of power, when the two traits are correlated and the RV is associated with the disease, especially at stringent significance levels. To address this issue, we propose a novel set-valued (SV) method that models a binary trait by dichotomization of an underlying continuous variable, and incorporate this into the genetic association model as a critical component. Extensive simulations and an analysis of seven secondary traits in a GWAS of benign ethnic neutropenia show that the SV method consistently controls type I error well at stringent significance levels, has larger power than the LG-based methods, and is robust in performance to effect pattern of the genetic variant (risk or protective), rare or common variants, rare or common diseases, and trait distributions. Because of the SV method’s striking and profound advantage, we strongly recommend the SV method be employed instead of the LG-based methods for secondary traits analyses in case-control sequencing studies. |
Databáze: | OpenAIRE |
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