TroX: a new method to learn about the genesis of aneuploidy from trisomic products of conception

Kermany, Amir R.; Ségurel, Laure; Oliver, Tiffany; Przeworski, Molly

doi:10.1093/bioinformatics/btu159

Cited by 2 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An alternative approach is to model a chromosome as a hidden Markov model (HMM) of sequential loci and determine the most likely chromosomal copy number status at each locus and consequently the overall chromosomal ploidy. Kermany and colleagues used HMM to detect fetal trisomy using high-density SNP markers from a trisomic individual and one parent [20], and similar HMM-based approaches have been previously used to detect both full and sub-chromosomal aneuploidies using binned read counts [21,22].…”

Section: Introductionmentioning

confidence: 99%

Computational framework for targeted high-coverage sequencing based NIPT

et al. 2019

View full text Add to dashboard Cite

Non-invasive prenatal testing (NIPT) enables accurate detection of fetal chromosomal trisomies. The majority of publicly available computational methods for sequencing-based NIPT analyses rely on low-coverage whole-genome sequencing (WGS) data and are not applicable for targeted high-coverage sequencing data from cell-free DNA samples. Here, we present a novel computational framework for a targeted high-coverage sequencing-based NIPT analysis. The developed framework uses a hidden Markov model (HMM) in conjunction with a supplemental machine learning model, such as decision tree (DT) or support vector machine (SVM), to detect fetal trisomy and parental origin of additional fetal chromosomes. These models were developed using simulated datasets covering a wide range of biologically relevant scenarios with various chromosomal quantities, parental origins of extra chromosomes, fetal DNA fractions, and sequencing read depths. Developed models were tested on simulated and experimental targeted sequencing datasets. Consequently, we determined the functional feasibility and limitations of each proposed approach and demonstrated that read count-based HMM achieved the best overall classification accuracy of 0.89 for detecting fetal euploidies and trisomies on simulated dataset. Furthermore, we show that by using the DT and SVM on the HMM classification results, it was possible to increase the final trisomy classification accuracy to 0.98 and 0.99, respectively. We demonstrate that read count and allelic ratio-based models can achieve a high accuracy (up to 0.98) for detecting fetal trisomy even if the fetal fraction is as low as 2%. Currently, existing commercial NIPT analysis requires at least 4% of fetal fraction, which can be possibly a challenge in case of early gestational age (<10 weeks) or high maternal body mass index (>35 kg/m 2 ). More accurate detection can be achieved at higher sequencing depth using HMM in conjunction with supplemental models, which significantly improve the trisomy detection especially in borderline scenarios (e.g., very low fetal fraction) and enables to perform NIPT even earlier than 10 weeks of pregnancy.

show abstract

Section: Introductionmentioning

confidence: 99%

Computational framework for targeted high-coverage sequencing based NIPT

et al. 2019

View full text Add to dashboard Cite

show abstract

“…An alternative approach is to model a chromosome as hidden Markov model (HMM) of sequential loci and determine the most likely chromosomal copy number status at each locus and consequently the overall chromosomal ploidy. Kermany and colleagues used HMM to detect fetal trisomy using high-density SNP markers from a trisomic individual and one parent (19), and similar HMM-based approaches have been previously used to detect both full and sub-chromosomal aneuploidies using binned read counts (20,21).…”

Section: Introductionmentioning

confidence: 99%

Computational framework for targeted high-coverage sequencing based NIPT

Teder

Paluoja

Salumets

et al. 2018

Preprint

View full text Add to dashboard Cite

23 Non-invasive prenatal testing (NIPT) enables accurate detection of fetal chromosomal 24 trisomies. The majority of existing computational methods for sequencing-based NIPT 25 analyses rely on low-coverage whole-genome sequencing (WGS) data and are not applicable 26 for targeted high-coverage sequencing data from cell-free DNA samples. 2728 Here, we present a novel computational framework for a targeted high-coverage sequencing-29 based NIPT analysis. The developed methods use a hidden Markov model (HMM)-based 30 approach in conjunction with supplemental machine learning methods, such as decision tree 31 (DT) and support vector machine (SVM), to detect fetal trisomy and parental origin of 32 additional fetal chromosomes. These methods were tested with simulated datasets covering a 33 wide range of biologically relevant scenarios with various chromosomal quantities, parental 34 origins of extra chromosomes, fetal DNA fractions and sequencing read depths.35 Consequently, we determined the functional feasibility and limitations of each proposed 36 approach and demonstrated that read count-based HMM achieved the best overall 37 classification accuracy of 0.89 for detecting fetal euploidies and trisomies. Furthermore, we 38 show that by using the DT and SVM methods on the HMM state classification results, it was 39 possible to increase the final trisomy classification accuracy to 0.98 and 0.99, respectively. 40 41 We demonstrated that read count and allelic ratio-based models can achieve a high accuracy 42 (up to 0.98) for detecting fetal trisomy even if the fetal fraction is as low as 2%. Currently 43 existing methods require at least 4% fetal fraction, which can be an issue in the case of early 44 gestational age (<10 weeks) or elevated maternal body mass index (>35 kg/m 2 ). More 45 accurate detection can be achieved at higher sequencing depth using HMM in conjunction 46 with supplemental methods, which significantly improve the trisomy detection especially in 3 47 borderline scenarios (e.g., very low fetal fraction) and can enable to perform NIPT even 48 earlier than 10 weeks of pregnancy. 49 4 50

show abstract

TroX: a new method to learn about the genesis of aneuploidy from trisomic products of conception

Abstract: An R package implementing TroX is available for download at http://przeworski.uchicago.edu/.

Cited by 2 publications

References 30 publications

Computational framework for targeted high-coverage sequencing based NIPT

Computational framework for targeted high-coverage sequencing based NIPT

Computational framework for targeted high-coverage sequencing based NIPT

Contact Info

Product

Resources

About