Omics-informed CNV calls reduce false-positive rates and improve power for CNV-trait associations

Maarja Lepamets; Chiara Auwerx; Margit Nõukas; Annique Claringbould; Eleonora Porcu; Mart Kals; Tuuli Jürgenson; Estonian Biobank Research Team; Andrew Paul Morris; Urmo Võsa; Murielle Bochud; Silvia Stringhini; Cisca Wijmenga; Lude Franke; Hedi Peterson; Jaak Vilo; Kaido Lepik; Reedik Mägi; Zoltán Kutalik

doi:10.1016/j.xhgg.2022.100133

Omics-informed CNV calls reduce false-positive rates and improve power for CNV-trait associations

HGG Adv. 2022 Aug 1;3(4):100133. doi: 10.1016/j.xhgg.2022.100133. eCollection 2022 Oct 13.

Authors

Maarja Lepamets^{1

2}, Chiara Auwerx^{3

4

5

6}, Margit Nõukas^{1

2}, Annique Claringbould⁷, Eleonora Porcu^{3

5

6}, Mart Kals^{1

8}, Tuuli Jürgenson^{1

9}; Estonian Biobank Research Team; Andrew Paul Morris^{1

10}, Urmo Võsa¹, Murielle Bochud⁶, Silvia Stringhini¹¹, Cisca Wijmenga¹², Lude Franke^{12

13}, Hedi Peterson¹⁴, Jaak Vilo¹⁴, Kaido Lepik^{4

5

6

14}, Reedik Mägi¹, Zoltán Kutalik^{4

5

6}

Affiliations

¹ Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia.
² Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia.
³ Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland.
⁴ Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.
⁵ Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.
⁶ Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne 1010, Switzerland.
⁷ Structural and Computational Biology Unit, EMBL, Heidelberg 69117, Germany.
⁸ Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00014, Finland.
⁹ Institute of Mathematics and Statistics, University of Tartu, Tartu 51009, Estonia.
¹⁰ Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester M13 9PL, UK.
¹¹ Unit of Population Epidemiology, Division of Primary Care, Geneva 1205, Switzerland.
¹² University of Groningen, University Medical Center Groningen, Department of Genetics, 9713 AV Groningen, the Netherlands.
¹³ Oncode Institute, 3521 AL Utrecht, the Netherlands.
¹⁴ Institute of Computer Science, University of Tartu, Tartu 51009, Estonia.

Abstract

Copy-number variations (CNV) are believed to play an important role in a wide range of complex traits, but discovering such associations remains challenging. While whole-genome sequencing (WGS) is the gold-standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV); however, these calls suffer from elevated false-positive and -negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNVs) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics, and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (p < 3.0 × 10^-90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 56% compared with published quality filtering methods or scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls, and demonstrated its utility by improving the statistical power for downstream association analyses.

Keywords: PennCNV; anthropometric traits; copy-number variation; gene expression; methylation; multi-omics; structural variation; whole genome sequencing.