TY - JOUR
T1 - Effective SNP ranking improves the performance of eQTL mapping
AU - Jeng, X. Jessie
AU - Rhyne, Jacob
AU - Zhang, Teng
AU - Tzeng, Jung Ying
N1 - Funding Information:
Dr. Jeng was partially supported by National Science Foundation Grant DMS‐1811360. Dr. Tzeng was partially supported by National Institutes of Health under grant P01 CA142538.
Publisher Copyright:
© 2020 Wiley Periodicals, Inc.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Genome-wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single-nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans-eQTLs. In this paper, we propose the idea of performing SNP ranking based on the higher criticism statistic, a summary statistic developed in large-scale signal detection. We illustrate how the HC-based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eQTL mapping. Numerical results in simulation studies demonstrate the superior performance of our method compared to existing methods. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs.
AB - Genome-wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single-nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans-eQTLs. In this paper, we propose the idea of performing SNP ranking based on the higher criticism statistic, a summary statistic developed in large-scale signal detection. We illustrate how the HC-based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eQTL mapping. Numerical results in simulation studies demonstrate the superior performance of our method compared to existing methods. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs.
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U2 - 10.1002/gepi.22293
DO - 10.1002/gepi.22293
M3 - Article
C2 - 32216117
AN - SCOPUS:85082320023
VL - 44
SP - 611
EP - 619
JO - Genetic Epidemiology
JF - Genetic Epidemiology
SN - 0741-0395
IS - 6
ER -