A probabilistic approach for SNP discovery in high-throughput human resequencing data

Abstract

New high-throughput sequencing technologies are generating large amounts of sequence data, allowing the development of targeted large-scale resequencing studies. For these studies, accurate identification of polymorphic sites is crucial. Heterozygous sites are particularly difficult to identify, especially in regions of low coverage. We present a new strategy for identifying heterozygous sites in a single individual by using a machine learning approach that generates a heterozygosity score for each chromosomal position. Our approach also facilitates the identification of regions with unequal representation of two alleles and other poorly sequenced regions. The availability of confidence scores allows for a principled combination of sequencing results from multiple samples. We evaluate our method on a gold standard data genotype set from HapMap. We are able to classify sites in this data set as heterozygous or homozygous with 98.5% accuracy. In de novo data our probabilistic heterozygote detection (''ProbHD'') is able to identify 93% of heterozygous sites at a <5% false call rate (FCR) as estimated based on independent genotyping results. In direct comparison of ProbHD with high-coverage 1000 Genomes sequencing available for a subset of our data, we observe >99.9% overall agreement for genotype calls and close to 90% agreement for heterozygote calls. Overall, our data indicate that high-throughput resequencing of human genomic regions requires careful attention to systematic biases in sample preparation as well as sequence contexts, and that their impact can be alleviated by machine learning-based sequence analyses allowing more accurate extraction of true DNA variants.[Supplemental material is available online at http://www.genome.org. Alignment and SNP-calling software is available at

Cultivar-Based Introgression Mapping Reveals Wild Species-Derived Pm-0, the Major Powdery Mildew Resistance Locus in Squash

“…moschata ‘Burpee’s Butterbush’ F 2 population in order to anchor markers for downstream analyses. Using a custom python script, genotypes represented by less than seven reads were converted to missing data in order to reduce errors associated with under-calling or the false identification of heterozygous loci, common problems for low-coverage loci [44, 45]. Seven reads is the minimum number required to call a heterozygote using at least two reads of the “less tagged allele” based on the binomial likelihood ratio employed in TASSEL and assuming a sequencing error rate of 1%, a conservative estimate for Illumina sequencing [43, 46].…”

Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data

“…ProbHD proposed by Hoberman and colleagues [64] used a machine learning approach that considers multiple features to generate a heterozygosity score for each base. Their method, designed specifically for Roche 454 data, considers a large number of features including total read depth, strand-specific depths, read cycle (within-read relative position), per-base quality scores, read alignment quality, and homopolymer length.…”

PyroHMMsnp: an SNP caller for Ion Torrent and 454 sequencing data

“…Statistical classification methods also use these aforementioned factors as predictive features to discriminate SNPs from sequencing errors. For example, Atlas-SNP2 (14) built a logistic regression model to predict SNPs, whereas ProbHD (15) built a random forest classifier. However, all these methods would fail to detect SNPs if the alignments were incorrect.…”