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Dou J, Wu D, Ding L, Wang K, Jiang M, Chai X, Reilly DF, Tai ES, Liu J, Sim X, Cheng S, Wang C. Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis and polygenic risk prediction. Brief Bioinform 2020; 22:5857014. [PMID: 32591784 DOI: 10.1093/bib/bbaa084] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/09/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022] Open
Abstract
Whole-exome sequencing (WES) has been widely used to study the role of protein-coding variants in genetic diseases. Non-coding regions, typically covered by sparse off-target data, are often discarded by conventional WES analyses. Here, we develop a genotype calling pipeline named WEScall to analyse both target and off-target data. We leverage linkage disequilibrium shared within study samples and from an external reference panel to improve genotyping accuracy. In an application to WES of 2527 Chinese and Malays, WEScall can reduce the genotype discordance rate from 0.26% (SE= 6.4 × 10-6) to 0.08% (SE = 3.6 × 10-6) across 1.1 million single nucleotide polymorphisms (SNPs) in the deeply sequenced target regions. Furthermore, we obtain genotypes at 0.70% (SE = 3.0 × 10-6) discordance rate across 5.2 million off-target SNPs, which had ~1.2× mean sequencing depth. Using this dataset, we perform genome-wide association studies of 10 metabolic traits. Despite of our small sample size, we identify 10 loci at genome-wide significance (P < 5 × 10-8), including eight well-established loci. The two novel loci, both associated with glycated haemoglobin levels, are GPATCH8-SLC4A1 (rs369762319, P = 2.56 × 10-12) and ROR2 (rs1201042, P = 3.24 × 10-8). Finally, using summary statistics from UK Biobank and Biobank Japan, we show that polygenic risk prediction can be significantly improved for six out of nine traits by incorporating off-target data (P < 0.01). These results demonstrate WEScall as a useful tool to facilitate WES studies with decent amounts of off-target data.
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Affiliation(s)
- Jinzhuang Dou
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Degang Wu
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lin Ding
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Wang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Minghui Jiang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | | | - E Shyong Tai
- Saw Swee Hock School of Public Health, Duke-NUS Medical School, and Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jianjun Liu
- Genome Institute of Singapore and a professor at Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Shanshan Cheng
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chaolong Wang
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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