Detecting tandem repeat variants in coding regions using code-adVNTR

Jonghun Park; Mehrdad Bakhtiari; Bernt Popp; Michael Wiesener; Vineet Bafna

doi:10.1016/j.isci.2022.104785

Detecting tandem repeat variants in coding regions using code-adVNTR

iScience. 2022 Jul 19;25(8):104785. doi: 10.1016/j.isci.2022.104785. eCollection 2022 Aug 19.

Authors

Jonghun Park¹, Mehrdad Bakhtiari¹, Bernt Popp^{2

3}, Michael Wiesener⁴, Vineet Bafna¹

Affiliations

¹ Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
² Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
³ Institute of Human Genetics, University of Leipzig Hospitals and Clinics, Leipzig, Germany.
⁴ Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.

Abstract

The human genome contains more than one million tandem repeats (TRs), DNA sequences containing multiple approximate copies of a motif repeated contiguously. TRs account for significant genetic variation, with 50 + diseases attributed to changes in motif number. A few diseases have been to be caused by small indels in variable number tandem repeats (VNTRs) including poly-cystic kidney disease type 1 (MCKD1) and monogenic type 1 diabetes. However, small indels in VNTRs are largely unexplored mainly due to the long and complex structure of VNTRs with multiple motifs. We developed a method, code-adVNTR, that utilizes multi-motif hidden Markov models to detect both, motif count variation and small indels, within VNTRs. In simulated data, code-adVNTR outperformed GATK-HaplotypeCaller in calling small indels within large VNTRs. We used code-adVNTR to characterize coding VNTRs in the 1000 genomes data identifying many population-specific variants, and to reliably call MUC1 mutations for MCKD1.

Keywords: Genetics; Genomics; Human genetics; Molecular genetics.

Abstract

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