Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17

Authors

  • Nathan Tintle,

    Corresponding author
    1. Department of Mathematics, Statistics, and Computer Science, Dordt College, Sioux Center, IA, USA
    • Department of Mathematics, Statistics, and Computer Science, Dordt College, 498 4th Ave. NE, Sioux Center, IA 51250
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  • Hugues Aschard,

    1. Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
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  • Inchi Hu,

    1. Department of Information Systems, Business Statistics, and Operations Management (ISOM), Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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  • Nora Nock,

    1. Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University, Cleveland, OH, USA
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  • Haitian Wang,

    1. Department of Information Systems, Business Statistics, and Operations Management (ISOM), Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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  • Elizabeth Pugh

    1. Center for Inherited Disease Research, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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Abstract

As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype-phenotype association in next-generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next-generation sequencing data: type I error and false-positive report probability rates higher than those expected based on empirical type I error levels (as high as 90%). Two main causes emerged: population stratification and long-range correlation (gametic phase disequilibrium) between rare variants. Population stratification was expected because of the diverse sample. Correlation between rare variants was attributable to both random causes (e.g., nearly 10,000 of 25,000 markers were private variants, and the sample size was small [n = 697]) and nonrandom causes (more correlation was observed than was expected by random chance). Principal components analysis was used to control for population structure and helped to minimize type I errors, but this was at the expense of identifying fewer causal variants. A novel multiple regression approach showed promise to handle correlation between markers. Further work is needed, first, to identify best practices for the control of type I errors in the analysis of sequencing data and then to explore and compare the many promising new aggregating approaches for identifying markers associated with disease phenotypes. Genet. Epidemiol. 35:S56–S60, 2011. © 2011 Wiley Periodicals, Inc.

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