Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits

Bakshi, Andrew, Zhu, Zhihong, Vinkhuyzen, Anna A. E., Hill, W. David, Mcrae, Allan F., Visscher, Peter M. and Yang, Jian (2016) Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Scientific Reports, 6 32894-32894. doi:10.1038/srep32894


Author Bakshi, Andrew
Zhu, Zhihong
Vinkhuyzen, Anna A. E.
Hill, W. David
Mcrae, Allan F.
Visscher, Peter M.
Yang, Jian
Title Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits
Journal name Scientific Reports   Check publisher's open access policy
ISSN 2045-2322
Publication date 2016-09-08
Sub-type Article (original research)
DOI 10.1038/srep32894
Open Access Status DOI
Volume 6
Start page 32894
End page 32894
Total pages 9
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Abstract We propose a method (fastBAT) that performs a fast set-based association analysis for human complex traits using summary-level data from genome-wide association studies (GWAS) and linkage disequilibrium (LD) data from a reference sample with individual-level genotypes. We demonstrate using simulations and analyses of real datasets that fastBAT is more accurate and orders of magnitude faster than the prevailing methods. Using fastBAT, we analyze summary data from the latest meta-analyses of GWAS on 150,064-339,224 individuals for height, body mass index (BMI), and schizophrenia. We identify 6 novel gene loci for height, 2 for BMI, and 3 for schizophrenia at PfastBAT < 5 × 10(-8). The gain of power is due to multiple small independent association signals at these loci (e.g. the THRB and FOXP1 loci for schizophrenia). The method is general and can be applied to GWAS data for all complex traits and diseases in humans and to such data in other species.
Formatted abstract
We propose a method (fastBAT) that performs a fast set-based association analysis for human complex traits using summary-level data from genome-wide association studies (GWAS) and linkage disequilibrium (LD) data from a reference sample with individual-level genotypes. We demonstrate using simulations and analyses of real datasets that fastBAT is more accurate and orders of magnitude faster than the prevailing methods. Using fastBAT, we analyze summary data from the latest meta-analyses of GWAS on 150,064-339,224 individuals for height, body mass index (BMI), and schizophrenia. We identify 6 novel gene loci for height, 2 for BMI, and 3 for schizophrenia at PfastBAT < 5 × 10-8. The gain of power is due to multiple small independent association signals at these loci (e.g. the THRB and FOXP1 loci for schizophrenia). The method is general and can be applied to GWAS data for all complex traits and diseases in humans and to such data in other species.
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID MC_PC_15018
MR/K026992/1
Institutional Status UQ

 
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