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LEP: A Statistical Method Integrating Individual-Level and Summary-Level Data of the Same Trait From Different
Mingwei Dai1, Jin Liu2, Can Yang3
1Center of Statistical Research and School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
Abstract:
Statistical approaches for integrating multiple data sets in genome-wide association studies (GWASs) are increasingly important. Proper utilization of more relevant information is expected to improve statistical efficiency in the analysis. Among these approaches, LEP was proposed for joint analysis of individual-level data and summary-level data in the same population by leveraging pleiotropy. The key idea of LEP is to explore correlation of the association status among different data sets while accounting for the heterogeneity. In this commentary, we show that LEP is applicable to integrate individual-level data and summary-level data of the same trait from different populations, providing new insights into the genetic architecture of different populations.
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