GtTR: Bayesian estimation of absolute tandem repeat copy number using sequence capture and high throughput sequencing

Abstract

BackgroundTandem repeats comprise significant proportion of the human genome including coding and regulatory regions. They are highly prone to repeat number variation and nucleotide mutation due to their repetitive and unstable nature, making them a major source of genomic variation between individuals. Despite recent advances in high throughput sequencing, analysis of tandem repeats in the context of complex diseases is still hindered by technical limitations.MethodsWe report a novel targeted sequencing approach, which allows simultaneous analysis of hundreds of repeats. We developed a Bayesian algorithm, namely – GtTR - which combines information from a reference long-read dataset with a short read counting approach to genotype tandem repeats at population scale. PCR sizing analysis was used for validation.ResultsWe used a PacBio long-read sequenced sample to generate a reference tandem repeat genotype dataset with on average 13% absolute deviation from PCR sizing results. Using this reference dataset GtTR generated estimates of VNTR copy number with accuracy within 95% high posterior density (HPD) intervals of 68% and 83% for capture sequence data and 200X WGS data respectively, improving to 87% and 94% with use of a PCR reference. We show that the genotype resolution increases as a function of depth, such that the median 95% HPD interval lies within 25%, 14%, 12% and 8% of the its midpoint copy number value for 30X, 200X WGS, 395X and 800X capture sequence data respectively. We validated nine targets by PCR sizing analysis and genotype estimates from sequencing results correlated well with PCR results.ConclusionsThe novel genotyping approach described here presents a new cost-effective method to explore previously unrecognized class of repeat variation in GWAS studies of complex diseases at the population level. Further improvements in accuracy can be obtained by improving accuracy of the reference dataset.

High-throughput multiplexed tandem repeat genotyping using targeted long-read sequencing

“…Genotype estimates from targeted long-read sequencing datasets were estimated using our tool VNTRTyper [12] with the improvements described in Methods. We also applied Tandem-genotypes [24] to determine the genotypes of TRs from long-read sequencing data.…”

“…We have previously demonstrated that targeted sequence capture of repetitive TR sequences are feasible using short-read sequencing technologies [12]. In this study, we demonstrate the targeted sequence capture of repetitive TRs using Oxford Nanopore long-read sequencing technologies.…”

“…In the recent decade, significant progress has been made in utilising high throughput short-read sequencing data for genotyping STRs [10]. Our group and others have also demonstrated targeted sequencing approaches using short-read sequencing for TR analysis [11, 12]. Several computational tools have been developed to improve the accuracy of TR genotyping from short-read sequencing data with varying performance [13–19].…”

“…al. (2018) [12], which utilizes short-read sequencing data to genotype longer TRs. GtTR reports absolute copy number of the TRs, but it does not report the exact genotype of two alleles due to the use of short-read sequencing data.…”

“…al. (2018) [12] to genotype TRs from long-read whole genome sequencing data. Despite the availability of genotyping tools, long-read sequencing is not widely used for TR analysis, due to the high costs associated with whole genome long-read sequencing.…”