Although the technical and analytic complexity of whole genome sequencing is generally appreciated, best practices for data cleaning and quality control have not been defined. Family based data can be used to guide the standardization of specific quality control metrics in nonfamily based data. Given the low mutation rate, Mendelian inheritance errors are likely as a result of erroneous genotype calls. Thus, our goal was to identify the characteristics that determine Mendelian inheritance errors. To accomplish this, we used chromosome 3 whole genome sequencing family based data from the Genetic Analysis Workshop 18. Mendelian inheritance errors were provided as part of the GAW18 data set. Additionally, for binary variants we calculated Mendelian inheritance errors using PLINK. Based on our analysis, nonbinary single-nucleotide variants have an inherently high number of Mendelian inheritance errors. Furthermore, in binary variants, Mendelian inheritance errors are not randomly distributed. Indeed, we identified 3 Mendelian inheritance error peaks that were enriched with repetitive elements. However, these peaks can be lessened with the inclusion of a single filter from the sequencing file. In summary, we demonstrated that erroneous sequencing calls are nonrandomly distributed across the genome and quality control metrics can dramatically reduce the number of mendelian inheritance errors. Appropriate quality control will allow optimal use of genetic data to realize the full potential of whole genome sequencing.

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Published in BMC Proceedings, v. 8, supplement 1, article S21, p. 1-5.

© Pilipenko et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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The Genetic Analysis Workshops are supported by NIH grant R01 GM031575 from the National Institute of General Medical Sciences. This work was supported in part by NIH grants 8P20GM103436-12 (DWF, KN), K25 AG043546 (DWF), NS36695 (LD, LJM), AI070235 (HH, LJM, TMB), AI066738 (LJM), HL111459 (LJM, VP), T32-ES10957 (ESA), K12 HD001097-16 (BGK), K01HL103165 (TMB).

The GAW18 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The Genetic Analysis Workshop is supported by NIH grant R01 GM031575.

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This proceeding is from Genetic Analysis Workshop 18: Human Sequence Data in Extended Pedigrees, held in Stevenson, Washington from October 13-17, 2012.

More proceedings from this conference are available at: http://bmcproc.biomedcentral.com/articles/supplements/volume-8-supplement-1

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