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Published on: April 4, 2018
Imputation of coding variants in African Americans: better performance using data from the exome sequencing project
Qing Duan1, Eric Yi Liu, Paul L Auer
1Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
Using the Exome Sequencing Project reference panel for imputation in African Americans significantly boosts effective sample size for rare coding variants. This approach enhances genetic studies by improving imputation quality compared to the standard 1000 Genomes Project panel.
Area of Science:
- Genomics
- Population Genetics
- Bioinformatics
Background:
- The 1000 Genomes Project reference panel is widely used for genetic imputation.
- Emerging large-scale sequencing projects are creating alternative reference panels.
- Imputation quality is crucial for analyzing genetic variation in diverse populations.
Purpose of the Study:
- To evaluate the effectiveness of the Exome Sequencing Project (ESP) reference panel for imputation in African Americans.
- To compare imputation performance using ESP haplotypes versus the 1000 Genomes Project (1KGP) haplotypes.
- To assess the impact of using panels from phenotypically extreme individuals on imputation quality.
Main Methods:
- Genomic imputation was performed using the ESP and 1KGP reference panels.
- The effective sample size increase was calculated for coding variants with minor allele frequency less than 1% in African Americans.
- Imputation quality was assessed using a panel derived from phenotypically extreme individuals.
Main Results:
- Imputation using 3384 ESP haplotypes increased effective sample size by 8.3-11.4% for rare coding variants in African Americans compared to 2184 1KGP haplotypes.
- No degradation in imputation quality was observed when using a panel constructed from phenotypically extreme individuals.
- The study demonstrated improved imputation performance with the ESP panel.
Conclusions:
- The Exome Sequencing Project reference panel offers superior imputation performance for African Americans compared to the 1000 Genomes Project panel.
- Using ESP haplotypes alone or in combination with 1KGP is recommended over post-imputation quality score selection or IMPUTE2's two-panel approach.
- This improved imputation strategy can enhance the power of genetic association studies in underrepresented populations.
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