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Medium-coverage DNA sequencing in the design of the genetic association study

European Journal of Human Genetics volume 28, pages 1459–1466 (2020)Cite this article

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Abstract

DNA sequencing is a widely used tool in genetic association study. Sequencing cost remains a major concern in sequencing-based study, although the application of next generation sequencing has dramatically decreased the sequencing cost and increased the efficiency. The choice of sequencing depth and the sequencing sample size will largely determine the final study investment and performance. Many studies have been conducted to find a cost-effective design of sequencing depth that can achieve certain sequencing accuracy using minimal sequencing cost. The strategies previously studied can be classified into two groups: (1) single-stage to sequence all the samples using either high (>~30×) or low (<~10×) sequencing depth; and (2) two-stage to sequence an affordable number of individuals at a high-coverage followed by a large sample of low-coverage sequencing. However, limited studies examined the performance of the medium-coverage (10–30×) sequencing depth for a genetic association study, where the optimum sequencing depth may exist. In this study, using a published simulation framework, we comprehensively compared the medium-coverage sequencing (MCS) to the single- and two-stage high/low-coverage sequencing in terms of the power and type I error of the variant discovery and association testing. We found, given certain sequencing effort, MCS yielded a comparable discovery power and better type I error control compared with the best (highest power) scenarios using other high- and low-coverage single-stage or two-stage designs. However, MCS was not as competent as other designs with respect to the association power, especially for the rare variants and when the sequencing investment was limited.

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Fig. 1: Sequencing investment and discovery power of variants.
Fig. 2: Common variant association power versus sequencing depth and sample size.
Fig. 3: Common and low-frequency variant association power and type I error.
Fig. 4: Rare variant association power and type I error.

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Acknowledgements

The work was partially supported by grants from the National Institutes of Health (R01 AR059781, R01 MH104680, R01 AR069055, U19 AG055373, and P20GM109036), Edward G. Schlieder Endowment, and startup funds from Tulane University. This research was supported in part using high performance computing (HPC) resources and services provided by Technology Services at Tulane University, New Orleans, LA.

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  1. Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA

    Chao Xu, Ruiyuan Zhang, Hui Shen & Hong-Wen Deng

  2. Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA

    Chao Xu

  3. School of Basic Medical Science, Central South University, 410013, Changsha, China

    Hong-Wen Deng

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  1. Chao Xu
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Correspondence to Hong-Wen Deng.

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Xu, C., Zhang, R., Shen, H. et al. Medium-coverage DNA sequencing in the design of the genetic association study. Eur J Hum Genet 28, 1459–1466 (2020). https://doi.org/10.1038/s41431-020-0656-2

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