Assessing the reproducibility of exome copy number variations predictions

Celine S Hong; Larry N Singh; James C Mullikin; Leslie G Biesecker

doi:10.1186/s13073-016-0336-6

Assessing the reproducibility of exome copy number variations predictions

Genome Med. 2016 Aug 8;8(1):82. doi: 10.1186/s13073-016-0336-6.

Authors

Celine S Hong¹, Larry N Singh¹, James C Mullikin^{2

3}, Leslie G Biesecker^{4

5}

Affiliations

¹ Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
² NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20852, USA.
³ Comparative Genomics Analysis Unit, Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20852, USA.
⁴ Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA. lesb@mail.nih.gov.
⁵ NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20852, USA. lesb@mail.nih.gov.

Abstract

Background: Reproducibility is receiving increased attention across many domains of science and genomics is no exception. Efforts to identify copy number variations (CNVs) from exome sequence (ES) data have been increasing. Many algorithms have been published to discover CNVs from exomes and a major challenge is the reproducibility in other datasets. Here we test exome CNV calling reproducibility under three conditions: data generated by different sequencing centers; varying sample sizes; and varying capture methodology.

Methods: Four CNV tools were tested: eXome Hidden Markov Model (XHMM), Copy Number Inference From Exome Reads (CoNIFER), EXCAVATOR, and Copy Number Analysis for Targeted Resequencing (CONTRA). To examine the reproducibility, we ran the callers on four datasets, varying sample sizes of N = 10, 30, 75, 100, 300, and data with different capture methodology. We examined the false negative (FN) calls and false positive (FP) calls for potential limitations of the CNV callers. The positive predictive value (PPV) was measured by checking the CNV call concordance against single nucleotide polymorphism array.

Results: Using independently generated datasets, we examined the PPV for each dataset and observed wide range of PPVs. The PPV values were highly data dependent (p <0.001). For the sample sizes and capture method analyses, we tested the callers in triplicates. Both analyses resulted in wide ranges of PPVs, even for the same test. Interestingly, negative correlations between the PPV and the sample sizes were observed for CoNIFER (ρ = -0.80). Further examination of FN calls showed that 44 % of these were missed by all callers and were attributed to the CNV size (46 % spanned ≤3 exons). Overlap of the FP calls showed that FPs were unique to each caller, indicative of algorithm dependency.

Conclusions: Our results demonstrate that further improvements in CNV callers are necessary to improve reproducibility and to include wider spectrum of CNVs (including the small CNVs). These CNV callers should be evaluated on multiple independent, heterogeneously generated datasets of varying size to increase robustness and utility. These approaches to the evaluation of exome CNV are essential to support wide utility and applicability of CNV discovery in exome studies.

Keywords: CNV predictions; Copy number variations (CNV); Exomes; Reproducibility.

MeSH terms

Algorithms*
DNA Copy Number Variations*
Datasets as Topic
Exome*
High-Throughput Nucleotide Sequencing
Humans
Markov Chains
Polymorphism, Single Nucleotide
Reproducibility of Results
Sample Size
Sequence Analysis, DNA / statistics & numerical data*

Associated data

dbGaP/phs000971.v1.p1