Genotype and SNP calling from next-generation sequencing data

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.     

SNP calling, genotype calling, and sample allele frequency estimation from new-generation sequencing data

We present a statistical framework for estimation and application of sample allele frequency spectra from New-Generation Sequencing (NGS) data. In this method, we first estimate the allele frequency spectrum using maximum likelihood. In contrast to previous methods, the likelihood function is calculated using a dynamic programming algorithm and numerically optimized using analytical derivatives. We then use a Bayesian method for estimating the sample allele frequency in a single site, and show how the method can be used for genotype calling and SNP calling. We also show how the method can be extended to various other cases including cases with deviations from Hardy-Weinberg equilibrium. We evaluate the statistical properties of the methods using simulations and by application to a real data set.     

Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

SNP HiTLink: a high-throughput linkage analysis system employing dense SNP data

Background  

During this recent decade, microarray-based single nucleotide polymorphism (SNP) data are becoming more widely used as markers for linkage analysis in the identification of loci for disease-associated genes. Although microarray-based SNP analyses have markedly reduced genotyping time and cost compared with microsatellite-based analyses, applying these enormous data to linkage analysis programs is a time-consuming step, thus, necessitating a high-throughput platform.     

Ultra-fast local-haplotype variant calling using paired-end DNA-sequencing data reveals somatic mosaicism in tumor and normal blood samples

Somatic mosaicism refers to the existence of somatic mutations in a fraction of somatic cells in a single biological sample. Its importance has mainly been discussed in theory although experimental work has started to emerge linking somatic mosaicism to disease diagnosis. Through novel statistical modeling of paired-end DNA-sequencing data using blood-derived DNA from healthy donors as well as DNA from tumor samples, we present an ultra-fast computational pipeline, LocHap that searches for multiple single nucleotide variants (SNVs) that are scaffolded by the same reads. We refer to scaffolded SNVs as local haplotypes (LH). When an LH exhibits more than two genotypes, we call it a local haplotype variant (LHV). The presence of LHVs is considered evidence of somatic mosaicism because a genetically homogeneous cell population will not harbor LHVs. Applying LocHap to whole-genome and whole-exome sequence data in DNA from normal blood and tumor samples, we find wide-spread LHVs across the genome. Importantly, we find more LHVs in tumor samples than in normal samples, and more in older adults than in younger ones. We confirm the existence of LHVs and somatic mosaicism by validation studies in normal blood samples. LocHap is publicly available at http://www.compgenome.org/lochap.     

Bis-SNP: Combined DNA methylation and SNP calling for Bisulfite-seq data

ABSTRACT: Bisulfite treatment of DNA followed by high-throughput sequencing (Bisulfite-seq) is an important method for studying DNA methylation and epigenetic gene regulation, yet current software tools do not adequately address single nucleotide polymorphisms (SNPs). Identifying SNPs is important for accurate quantification of methylation levels and for identification of allele-specific epigenetic events such as imprinting. We have developed a model-based bisulfite SNP caller, Bis-SNP, that results in substantially better SNP calls than existing methods, thereby improving methylation estimates. At an average 30× genomic coverage, Bis-SNP correctly identified 96% of SNPs using the default high-stringency settings. The open-source package is available at http://epigenome.usc.edu/publicationdata/bissnp2011.     

A genotype calling algorithm for affymetrix SNP arrays

MOTIVATION: A classification algorithm, based on a multi-chip, multi-SNP approach is proposed for Affymetrix SNP arrays. Current procedures for calling genotypes on SNP arrays process all the features associated with one chip and one SNP at a time. Using a large training sample where the genotype labels are known, we develop a supervised learning algorithm to obtain more accurate classification results on new data. The method we propose, RLMM, is based on a robustly fitted, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variance is reduced through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as across thousands of SNPs for accurate classification. In this paper, we apply RLMM to Affymetrix 100 K SNP array data, present classification results and compare them with genotype calls obtained from the Affymetrix procedure DM, as well as to the publicly available genotype calls from the HapMap project.     

A new genotype calling method for affymetrix SNP arrays

Current genotype-calling methods such as Robust Linear Model with Mahalanobis Distance Classifier (RLMM) and Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) provide accurate calling results for Affymetrix Single Nucleotide Polymorphisms (SNP) chips. However, these methods are computationally expensive as they employ preprocess procedures, including chip data normalization and other sophisticated statistical techniques. In the small sample case the accuracy rate may drop significantly. We develop a new genotype calling method for Affymetrix 100 k and 500 k SNP chips. A two-stage classification scheme is proposed to obtain a fast genotype calling algorithm. The first stage uses unsupervised classification to quickly discriminate genotypes with high accuracy for the majority of the SNPs. And the second stage employs a supervised classification method to incorporate allele frequency information either from the HapMap data or from a self-training scheme. Confidence score is provided for every genotype call. The overall performance is shown to be comparable to that of CRLMM as verified by the known gold standard HapMap data and is superior in small sample cases. The new algorithm is computationally simple and standalone in the sense that a self-training scheme can be used without employing any other training data. A package implementing the calling algorithm is freely available at http://www.sfs.ecnu.edu.cn/teachers/xuj_en.html.     

A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side

Background

Single Nucleotide Polymorphism (SNP) in the genome has become crucial information for clinical use. For example, the targeted cancer therapy is primarily based on the information which clinically important SNPs are detectable from the tumor. Many hospitals have developed their own panels that include clinically important SNPs. The genome information exchange between the patient and the hospital has become more popular. However, the genome sequence information is innate and irreversible and thus its leakage has serious consequences. Therefore, protecting one’s genome information is critical. On the other side, hospitals may need to protect their own panels. There is no known secure SNP panel scheme to protect both.

Results

In this paper, we propose a secure SNP panel scheme using homomorphically encrypted K-mers without requiring SNP calling on the user side and without revealing the panel information to the user. Use of the powerful homomorphic encryption technique is desirable, but there is no known algorithm to efficiently align two homomorphically encrypted sequences. Thus, we designed and implemented a novel secure SNP panel scheme utilizing the computationally feasible equality test on two homomorphically encrypted K-mers. To make the scheme work correctly, in addition to SNPs in the panel, sequence variations at the population level should be addressed. We designed a concept of Point Deviation Tolerance (PDT) level to address the false positives and false negatives. Using the TCGA BRCA dataset, we demonstrated that our scheme works at the level of over a hundred thousand somatic mutations. In addition, we provide a computational guideline for the panel design, including the size of K-mer and the number of SNPs.

Conclusions

The proposed method is the first of its kind to protect both the user’s sequence and the hospital’s panel information using the powerful homomorphic encryption scheme. We demonstrated that the scheme works with a simulated dataset and the TCGA BRCA dataset. In this study, we have shown only the feasibility of the proposed scheme and much more efforts should be done to make the scheme usable for clinical use.

     

Effects of ascertainment bias and marker number on estimations of barley diversity from high-throughput SNP genotype data

The capability of molecular markers to provide information of genetic structure is influenced by their number and the way they are chosen. This study evaluates the effects of single nucleotide polymorphism (SNP) number and selection strategy on estimates of germplasm diversity and population structure for different types of barley germplasm, namely cultivar and landrace. One hundred and sixty-nine barley landraces from Syria and Jordan and 171 European barley cultivars were genotyped with 1536 SNPs. Different subsets of 384 and 96 SNPs were selected from the 1536 set, based on their ability to detect diversity in landraces or cultivated barley in addition to corresponding randomly chosen subsets. All SNP sets except the landrace-optimised subsets underestimated the diversity present in the landrace germplasm, and all subsets of SNP gave similar estimates for cultivar germplasm. All marker subsets gave qualitatively similar estimates of the population structure in both germplasm sets, but the 96 SNP sets showed much lower data resolution values than the larger SNP sets. From these data we deduce that pre-selecting markers for their diversity in a germplasm set is very worthwhile in terms of the quality of data obtained. Second, we suggest that a properly chosen 384 SNP subset gives a good combination of power and economy for germplasm characterization, whereas the rather modest gain from using 1536 SNPs does not justify the increased cost and 96 markers give unacceptably low performance. Lastly, we propose a specific 384 SNP subset as a standard genotyping tool for middle-eastern landrace barley.     

Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions

ABSTRACT: BACKGROUND: Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome. RESULTS: Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents. CONCLUSIONS: The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.     

High-resolution, high-throughput SNP mapping in Drosophila melanogaster

Single nucleotide polymorphisms (SNPs) are useful markers for genetic mapping experiments in model organisms. Here we report the establishment of a high-density SNP map and high-throughput genotyping assays for Drosophila melanogaster. Our map comprises 27,367 SNPs in common laboratory Drosophila stocks. These SNPs were clustered within 2,238 amplifiable markers at an average density of 1 marker every 50.3 kb, or 6.3 genes. We have also constructed a set of 62 Drosophila stocks, each of which facilitates the generation of recombinants within a defined genetic interval of 1-2 Mb. For flexible, high-throughput SNP genotyping, we used fluorescent tag-array mini-sequencing (TAMS) assays. We designed and validated TAMS assays for 293 SNPs at an average resolution of 391.3 kb, and demonstrated the utility of these tools by rapidly mapping 14 mutations that disrupt embryonic muscle patterning. These resources enable high-resolution high-throughput genetic mapping in Drosophila.     

A statistical method for predicting classical HLA alleles from SNP data

Genetic variation at classical HLA alleles is a crucial determinant of transplant success and susceptibility to a large number of infectious and autoimmune diseases. However, large-scale studies involving classical type I and type II HLA alleles might be limited by the cost of allele-typing technologies. Although recent studies have shown that some common HLA alleles can be tagged with small numbers of markers, SNP-based tagging does not offer a complete solution to predicting HLA alleles. We have developed a new statistical methodology to use SNP variation within the region to predict alleles at key class I (HLA-A, HLA-B, and HLA-C) and class II (HLA-DRB1, HLA-DQA1, and HLA-DQB1) loci. Our results indicate that a single panel of approximately 100 SNPs typed across the region is sufficient for predicting both rare and common HLA alleles with up to 95% accuracy in both African and non-African populations. Furthermore, we show that HLA alleles can be successfully predicted by using previously genotyped SNPs that are within the MHC and that had not been chosen for their ability to predict HLA alleles, such as those included on genome-wide products. These results indicate that our methodology, combined with an extended database of reference haplotypes, will facilitate large-scale experiments, including disease-association studies and vaccine trials, in which detailed information about HLA type is valuable.     

A microarray platform and novel SNP calling algorithm to evaluate Plasmodium falciparum field samples of low DNA quantity

Background

Analysis of single nucleotide polymorphisms (SNPs) derived from whole-genome studies allows for rapid evaluation of genome-wide diversity, and genomic epidemiology studies of Plasmodium falciparum provide insights into parasite population structure, gene flow, drug resistance and vaccine development. In areas with adequate cold chain facilities, large volumes of leukocyte-depleted patient blood can be frozen for use in parasite genomic analyses. In more remote endemic areas smaller volumes of infected blood are taken by finger prick, and dried and stored on filter paper. These dried blood spots do not generally yield enough concentrated parasite DNA for whole-genome sequencing.

Results

A DNA microarray was designed for use on field samples to type a genome-wide set of SNPs which prior sequencing had shown to be variable in Africa, Southeast Asia, and Papua New Guinea. An algorithm was designed to call SNPs in samples with low parasite DNA. With this new algorithm SNP-calling accuracy of 98% was measured by hybridizing purified DNA from malaria lab strains and comparing calls with SNPs called from full genome sequences. An average accuracy of >98% was likewise obtained for DNA extracted from malaria field samples collected in studies in Southeast Asia, with an average call rate of > 82%.

Conclusion

This new high-density microarray provided high quality SNP calls from a wide range of parasite DNA quantities, and represents a robust tool for genome-wide analysis of malaria parasites in diverse settings.