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I’am hiQ – A Novel Accuracy Index for Imputed Genotypes by Albert Rosenberger; Viola Tozzi; Rayjean J Hung; David C Christiani; Neil E Caporaso; Geoffrey Liu; Stig E Bojesen; Loic Le Marchand; Demetrios Albanes; Melinda C Aldrich; Adonina Tardon; Guillermo Fernández-Tardón; Gad Rennert; John K Field; Mike Davies; Triantafillos Liloglou; Lambertus A Kiemeney; Philip Lazarus; Aage Haugen; Shanbeh Zienolddiny; Stephen Lam; Matthew B Schabath; Angeline S Andrew; Eric J Duell; Susanne M Arnold; Hans Brunnsstöm; Olle Melander; Gary E Goodman; Chu Chen; Jennifer A Doherty; Marion Dawn Teare; Angela Cox; Penella J Woll; Angela Risch; Thomas R Muley; Mikael Johansson; Paul Brennan; Maria Teresa Landi; Sanjay S Shete; Christopher I Amos; Heike Bickeböller is a scholarly article available to read on EtoBox.

What is I’am hiQ – A Novel Accuracy Index for Imputed Genotypes about?

## Abstract Background: Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper we introduce I’am hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. I’am (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. Results: Applying both measures to a large case-control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for I’am and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of I’am and hiQ can be useful to identify regi

Author

Albert Rosenberger; Viola Tozzi; Rayjean J Hung; David C Christiani; Neil E Caporaso; Geoffrey Liu; Stig E Bojesen; Loic Le Marchand; Demetrios Albanes; Melinda C Aldrich; Adonina Tardon; Guillermo Fernández-Tardón; Gad Rennert; John K Field; Mike Davies; Triantafillos Liloglou; Lambertus A Kiemeney; Philip Lazarus; Aage Haugen; Shanbeh Zienolddiny; Stephen Lam; Matthew B Schabath; Angeline S Andrew; Eric J Duell; Susanne M Arnold; Hans Brunnsstöm; Olle Melander; Gary E Goodman; Chu Chen; Jennifer A Doherty; Marion Dawn Teare; Angela Cox; Penella J Woll; Angela Risch; Thomas R Muley; Mikael Johansson; Paul Brennan; Maria Teresa Landi; Sanjay S Shete; Christopher I Amos; Heike Bickeböller

Publisher

Research Square Platform LLC

Published

2021

Language

EN