Dataset Information

Inheritance model introduces differential bias in CNV calls between parents and offspring.

ABSTRACT: Copy Number Variation (CNV) is increasingly implicated in disease pathogenesis. CNVs are often identified by statistical models applied to data from single nucleotide polymorphism panels. Family information for samples provides additional information for CNV inference. Two modes of PennCNV (the Joint-call and Posterior-call), which are some of the most well-developed family-based CNV calling methods, use a "Joint-model" as a main component. This models all family members' CNV states together with Mendelian inheritance. Methods based on the Joint-model are used to infer CNV calls of cases and controls in a pedigree, which may be compared to each other to test an association. Although benefits from the Joint-model have been shown elsewhere, equality of call rates in parents and offspring has not been evaluated previously. This can affect downstream analyses in studies that compare CNV rates in cases vs. controls in pedigrees. In this paper, we show that the Joint-model can introduce different CNV call rates among family members in the absence of a true difference. We show that the Joint-model may analytically introduce differential CNV calls because of asymmetry of the model. We demonstrate these differential call rates using single-marker simulations. We show that call rates using the two modes of PennCNV also differ between parents and offspring in one multimarker simulated dataset and two real datasets. Our results advise need for caution in use of the Joint-model calls in CNV association studies with family-based datasets.

SUBMITTER: Kim S

PROVIDER: S-EPMC3678551 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Inheritance model introduces differential bias in CNV calls between parents and offspring.

Kim Sulgi S Millard Steven P SP Yu Chang-En CE Leong Lesley L Radant Allen A Dobie Dorcas D Tsuang Debby W DW Wijsman Ellen M EM

Genetic epidemiology 20120524 5

Copy Number Variation (CNV) is increasingly implicated in disease pathogenesis. CNVs are often identified by statistical models applied to data from single nucleotide polymorphism panels. Family information for samples provides additional information for CNV inference. Two modes of PennCNV (the Joint-call and Posterior-call), which are some of the most well-developed family-based CNV calling methods, use a "Joint-model" as a main component. This models all family members' CNV states together wit ...[more]

PMID: 22628073

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Project description:BackgroundWhole genome resequencing projects may implement variant calling using draft reference genomes assembled de novo from short-read libraries. Despite lower quality of such assemblies, they allowed researchers to extend a wide range of population genetic and genome-wide association analyses to non-model species. As the variant calling pipelines are complex and involve many software packages, it is important to understand inherent biases and limitations at each step of the analysis.ResultsIn this article, we report a positional bias present in variant calling performed against draft reference assemblies constructed from de Bruijn or string overlap graphs. We assessed how frequently variants appeared at each position counted from ends of a contig or scaffold sequence, and discovered unexpectedly high number of variants at the positions related to the length of either k-mers or reads used for the assembly. We detected the bias in both publicly available draft assemblies from Assemblathon 2 competition as well as in the assemblies we generated from our simulated short-read data. Simulations confirmed that the bias causing variants are predominantly false positives induced by reads from spatially distant repeated sequences. The bias is particularly strong in contig assemblies. Scaffolding does not eliminate the bias but tends to mitigate it because of the changes in variants' relative positions and alterations in read alignments. The bias can be effectively reduced by filtering out the variants that reside in repetitive elements.ConclusionsDraft genome sequences generated by several popular assemblers appear to be susceptible to the positional bias potentially affecting many resequencing projects in non-model species. The bias is inherent to the assembly algorithms and arises from their particular handling of repeated sequences. It is recommended to reduce the bias by filtering especially if higher-quality genome assembly cannot be achieved. Our findings can help other researchers to improve the quality of their variant data sets and reduce artefactual findings in downstream analyses.

Dataset Information

Inheritance model introduces differential bias in CNV calls between parents and offspring.

Publications

Inheritance model introduces differential bias in CNV calls between parents and offspring.

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