SEQSpark: A Complete Analysis Tool for Large-Scale Rare Variant Association Studies Using Whole-Genome and Exome Sequence Data

Di Zhang; Linhai Zhao; Biao Li; Zongxiao He; Gao T Wang; Dajiang J Liu; Suzanne M Leal

doi:10.1016/j.ajhg.2017.05.017

SEQSpark: A Complete Analysis Tool for Large-Scale Rare Variant Association Studies Using Whole-Genome and Exome Sequence Data

Am J Hum Genet. 2017 Jul 6;101(1):115-122. doi: 10.1016/j.ajhg.2017.05.017. Epub 2017 Jun 29.

Authors

Di Zhang¹, Linhai Zhao¹, Biao Li¹, Zongxiao He¹, Gao T Wang², Dajiang J Liu³, Suzanne M Leal⁴

Affiliations

¹ Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
² Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
³ Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA.
⁴ Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address: sleal@bcm.edu.

Abstract

Massively parallel sequencing technologies provide great opportunities for discovering rare susceptibility variants involved in complex disease etiology via large-scale imputation and exome and whole-genome sequence-based association studies. Due to modest effect sizes, large sample sizes of tens to hundreds of thousands of individuals are required for adequately powered studies. Current analytical tools are obsolete when it comes to handling these large datasets. To facilitate the analysis of large-scale sequence-based studies, we developed SEQSpark which implements parallel processing based on Spark to increase the speed and efficiency of performing data quality control, annotation, and association analysis. To demonstrate the versatility and speed of SEQSpark, we analyzed whole-genome sequence data from the UK10K, testing for associations with waist-to-hip ratios. The analysis, which was completed in 1.5 hr, included loading data, annotation, principal component analysis, and single variant and rare variant aggregate association analysis of >9 million variants. For rare variant aggregate analysis, an exome-wide significant association (p < 2.5 × 10^-6) was observed with CCDC62 (SKAT-O [p = 6.89 × 10^-7], combined multivariate collapsing [p = 1.48 × 10^-6], and burden of rare variants [p = 1.48 × 10^-6]). SEQSpark was also used to analyze 50,000 simulated exomes and it required 1.75 hr for the analysis of a quantitative trait using several rare variant aggregate association methods. Additionally, the performance of SEQSpark was compared to Variant Association Tools and PLINK/SEQ. SEQSpark was always faster and in some situations computation was reduced to a hundredth of the time. SEQSpark will empower large sequence-based epidemiological studies to quickly elucidate genetic variation involved in the etiology of complex traits.

Keywords: CCDC62; Spark; UK10K; complex traits; computational tools; imputed data; quality control; rare variant aggregate association analysis; waist-to-hip ratio; whole-genome sequence data.

MeSH terms

Databases, Nucleic Acid*
Exome / genetics*
Genetic Variation*
Genome-Wide Association Study / methods*
Humans
Principal Component Analysis
Sequence Analysis, DNA / methods*
Software*
Waist-Hip Ratio

Abstract

MeSH terms

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