Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study

Jiang, Hu, Wang, Zhang, Zhu, Bai, Tong, Li, Zhao (2019) Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study Front Genet (IF: 3.7) 10 670

Abstract

Ventricular septal defect (VSD) is a fatal congenital heart disease showing severe consequence in affected infants. Early diagnosis plays an important role, particularly through genetic variants. Existing panel-based approaches of variants mining suffer from shortage of large panels, costly sequencing, and missing rare variants. Although a trio-based method alleviates these limitations to some extent, it is agnostic to novel mutations and computational intensive. Considering these limitations, we are studying a novel variants mining algorithm from trio-based sequencing data and apply it on a VSD trio to identify associated mutations. Our approach starts with irrelevant k-mer filtering from sequences of a trio via a newly conceived coupled Bloom Filter, then corrects sequencing errors by using a statistical approach and extends kept k-mers into long sequences. These extended sequences are used as input for variants needed. Later, the obtained variants are comprehensively analyzed against existing databases to mine VSD-related mutations. Experiments show that our trio-based algorithm narrows down candidate coding genes and lncRNAs by about 10- and 5-folds comparing with single sequence-based approaches, respectively. Meanwhile, our algorithm is 10 times faster and 2 magnitudes memory-frugal compared with existing state-of-the-art approach. By applying our approach to a VSD trio, we fish out an unreported gene-CD80, a combination of two genes-MYBPC3 and TRDN and a lncRNA-NONHSAT096266.2, which are highly likely to be VSD-related.

Links

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694746
http://www.ncbi.nlm.nih.gov/pubmed/31440271
http://dx.doi.org/10.3389/fgene.2019.00670

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