  A large number of recent genome-wide association studies (GWASs) for complex
phenotypes confirm the early conjecture for polygenicity, suggesting the
presence of large number of variants with only tiny or moderate effects.
However, due to the limited sample size of a single GWAS, many associated
genetic variants are too weak to achieve the genome-wide significance. These
undiscovered variants further limit the prediction capability of GWAS.
Restricted access to the individual-level data and the increasing availability
of the published GWAS results motivate the development of methods integrating
both the individual-level and summary-level data. How to build the connection
between the individual-level and summary-level data determines the efficiency
of using the existing abundant summary-level resources with limited
individual-level data, and this issue inspires more efforts in the existing
area.
  In this study, we propose a novel statistical approach, LEP, which provides a
novel way of modeling the connection between the individual-level data and
summary-level data. LEP integrates both types of data by \underline{LE}veraing
\underline{P}leiotropy to increase the statistical power of risk variants
identification and the accuracy of risk prediction. The algorithm for parameter
estimation is developed to handle genome-wide-scale data. Through comprehensive
simulation studies, we demonstrated the advantages of LEP over the existing
methods. We further applied LEP to perform integrative analysis of Crohn's
disease from WTCCC and summary statistics from GWAS of some other diseases,
such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP
was able to significantly increase the statistical power of identifying risk
variants and improve the risk prediction accuracy from 63.39\% ($\pm$ 0.58\%)
to 68.33\% ($\pm$ 0.32\%) using about 195,000 variants.
