Genotyping microsatellites in next-generation sequencing data
KeywordsRepeat Unit Polymorphic Single Nucleotide Exome Sequencing High Depth Amplicon Sequencing
Microsatellites are short (2-6bp) DNA sequences repeated in tandem, which make up approximately 3% of the human genome . These loci are prone to frequent mutations and high polymorphism with the estimated mutation rates of 10−2 - 10−6 events per locus per generation, orders of magnitude higher than other parts of the genome . Dozens of neurological and developmental disorders have been attributed to microsatellite expansions . Microsatellites have also been implicated in a range of functions such as DNA replication and repair, chromatin organisation and regulation of gene expression .
Traditionally, microsatellite variation has been measured using capillary gel electrophoresis . In addition to being time-consuming, and expensive, this method fails to reveal the full complexity at these loci because it does not directly sequence the fragment but only measure the number of bases in the repeat.
Next-generation sequencing has the potential to address these problems. However, determining microsatellite lengths using next-generation sequencing data is difficult. In particular, polymerase slippage during PCR amplification introduces stutter noise. A small number of software tools have been written to genotype simple microsatellites in next-generation sequencing data [6, 7, 8], however they fail to address the issues of SNPs and compound repeats, and in some cases provide only approximate genotypes.
We have begun to develop a microsatellite genotyping algorithm that addresses these issues, providing high accuracy as well as more detailed analysis of microsatellite loci. We have validated it using high depth amplicon sequencing data of microsatellites near the AVPR1A gene.
Concordance of microsatellite variance calls three validation methods: electrophoresis, manual inspection and Mendelian inheritance.
The algorithm was approximately 95% correct at calling the exact same genotype on high depth sequencing data. When it did call a genotype incorrectly, the genotype was only one repeat unit different. The algorithm can perform at approximately 90% accuracy to within one repeat unit with as few as 20 informative reads and reaches almost 100% accuracy to within one repeat unit with 100 or more informative reads.
Future work will include expanding the algorithm to genotype compound microsatellites and further validation and comparison with other algorithms will be performed on whole genome data sets.
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