SNP detection for massively parallel whole-genome resequencing

• Published in: Genome Research Vol. 19; no. 6; pp. 1124 - 1132
• Main Authors: Li, Ruiqiang, Li, Yingrui, Fang, Xiaodong, Yang, Huanming, Wang, Jian, Kristiansen, Karsten, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 01.06.2009
• Subjects: Algorithms; Asian Continental Ancestry Group - genetics; Chromosomes, Human, X - genetics; Computational Biology - methods; Genetics, Population - methods; Genome, Human - genetics; Genotype; Humans; Likelihood Functions; Models, Genetic; Models, Statistical; Polymorphism, Single Nucleotide; Probability; Reproducibility of Results; Resource; Sequence Analysis, DNA - instrumentation; Sequence Analysis, DNA - methods; Software
• ISSN: 1088-9051; 1549-5477
• DOI: 10.1101/gr.088013.108

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.