SHEsisPCA: A GPU-Based Software to Correct for Population Stratification that Efficiently Accelerates the Process for Handling Genome-Wide Datasets

Jiawei Shen; Zhiqiang Li; Yongyong Shi

doi:10.1016/j.jgg.2015.06.007

Volume 42 Issue 8

Aug. 2015

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Article Navigation > Journal of Genetics and Genomics > 2015 > 42(8): 445-453

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SHEsisPCA: A GPU-Based Software to Correct for Population Stratification that Efficiently Accelerates the Process for Handling Genome-Wide Datasets

doi: 10.1016/j.jgg.2015.06.007

Jiawei Shen^{a, b, c},
Zhiqiang Li^{a, b},
Yongyong Shi^{a, b, d}

a
Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200230, China
b
Institute of Social Cognitive and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
c
School of Bio-medical Engineering, Shanghai Jiao Tong University, Shanghai 200230, China
d
Shanghai Changning Mental Health Center, Shanghai 200042, China

More Information

Corresponding author: E-mail address: shiyongyong@gmail.com (Yongyong Shi)
Received Date: 2015-03-24
Accepted Date: 2015-06-10
Rev Recd Date: 2015-05-30

Available Online: 2015-07-09

Publish Date: 2015-08-20

Abstract

Abstract

Population stratification is a problem in genetic association studies because it is likely to highlight loci that underlie the population structure rather than disease-related loci. At present, principal component analysis (PCA) has been proven to be an effective way to correct for population stratification. However, the conventional PCA algorithm is time-consuming when dealing with large datasets. We developed a Graphic processing unit (GPU)-based PCA software named SHEsisPCA (http://analysis.bio-x.cn/SHEsisMain.htm) that is highly parallel with a highest speedup greater than 100 compared with its CPU version. A cluster algorithm based on X-means was also implemented as a way to detect population subgroups and to obtain matched cases and controls in order to reduce the genomic inflation and increase the power. A study of both simulated and real datasets showed that SHEsisPCA ran at an extremely high speed while the accuracy was hardly reduced. Therefore, SHEsisPCA can help correct for population stratification much more efficiently than the conventional CPU-based algorithms.
- Population stratification,
- Principal component analysis,
- Graphic processing unit,
- Cluster,
- Matched cases and controls,
- Genetic studies

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References(16)

References

[1]	Asimit, J., Zeggini, E. Rare variant association analysis methods for complex traits Annu. Rev. Genet., 44 (2010),pp. 293-308
[2]	Duda, R.O., Hart, P.E.
[3]	Epstein, M.P., Duncan, R., Broadaway, K.A. et al. Stratification-score matching improves correction for confounding by population stratification in case-control association studies Genet. Epidemiol., 36 (2012),pp. 195-205
[4]	Ewens, W.J.
[5]	Helgason, A., Yngvadóttir, B., Hrafnkelsson, B. et al. An Icelandic example of the impact of population structure on association studies Nat. Genet., 37 (2004),pp. 90-95
[6]	Madsen, B.E., Browning, S.R. A groupwise association test for rare mutations using a weighted sum statistic PLoS Genet., 5 (2009),p. e1000384
[7]	Marchini, J., Cardon, L.R., Phillips, M.S. et al. The effects of human population structure on large genetic association studies Nat. Genet., 36 (2004),pp. 512-517
[8]	NVIDIA Corporation
[9]	Patterson, N., Price, A., Reich, D. Population structure and Eigenanalysis PLoS Genet., 2 (2006),p. e190
[10]	Pelleg, D., Moore, A.
[11]	Purcell, S., Neale, B., Todd-Brown, K. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses Am. J. Hum. Genet., 81 (2007),pp. 559-575
[12]	Schwarz, G. Estimating the dimension of a model Ann. Stat., 6 (1978),pp. 461-464
[13]	Shi, Y., Li, L., Hu, Z. et al. A genome-wide association study identifies two new cervical cancer susceptibility loci at 4q12 and 17q12 Nat. Genet., 45 (2013),pp. 918-922
[14]	Solovieff, N., Hartley, S.W., Baldwin, C.T. et al. Clustering by genetic ancestry using genome-wide SNP data BMC Genet., 11 (2010),p. 108
[15]	Tomov, S., Dongarra, J., Volkov, V. et al.
[16]	Wright, S. Evolution in Mendelian populations Genetics, 16 (1931),p. 97