SNP detection for massively parallel whole-genome resequencing

TLDR

Next‑generation massively parallel sequencing delivers ultrahigh throughput at a fraction of the cost of Sanger sequencing and is widely used to study genetic variation through whole‑genome or targeted resequencing. The authors developed a consensus‑calling and SNP‑detection method tailored to Illumina Genome Analyzer sequencing‑by‑synthesis data. The method integrates data quality, alignment, and experimental error considerations into a Bayesian base‑level quality score to assess consensus accuracy. Applied to a 36× human resequencing dataset, the approach produced high‑quality consensus sequences covering 92.25 % of autosomes and 88.07 % of the X chromosome, achieving >98 % consistency with Illumina 1M BeadChip genotypes and a very low false‑call rate even at low depth.

Abstract

Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-genome or target region resequencing. Here, we have developed a consensus-calling and SNP-detection method for sequencing-by-synthesis Illumina Genome Analyzer technology. We designed this method by carefully considering the data quality, alignment, and experimental errors common to this technology. All of this information was integrated into a single quality score for each base under Bayesian theory to measure the accuracy of consensus calling. We tested this methodology using a large-scale human resequencing data set of 36× coverage and assembled a high-quality nonrepetitive consensus sequence for 92.25% of the diploid autosomes and 88.07% of the haploid X chromosome. Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed that 98.6% of the 37,933 genotyped alleles on the X chromosome and 98% of 999,981 genotyped alleles on autosomes were covered at 99.97% and 99.84% consistency, respectively. At a low sequencing depth, we used prior probability of dbSNP alleles and were able to improve coverage of the dbSNP sites significantly as compared to that obtained using a nonimputation model. Our analyses demonstrate that our method has a very low false call rate at any sequencing depth and excellent genome coverage at a high sequencing depth.