Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data

DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies.     

Utilizing Genotype Imputation for the Augmentation of Sequence Data

Background

In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits. However, the SNPs identified from these GWAS are not necessarily the functional variants. Therefore, the next phase in GWAS will involve the refining of these putative loci.

Methodology

A next step for GWAS would be to catalog all variants, especially rarer variants, within the detected loci, followed by the association analysis of the detected variants with the disease trait. However, sequencing a locus in a large number of subjects is still relatively expensive. A more cost effective approach would be to sequence a portion of the individuals, followed by the application of genotype imputation methods for imputing markers in the remaining individuals. A potentially attractive alternative option would be to impute based on the 1000 Genomes Project; however, this has the drawbacks of using a reference population that does not necessarily match the disease status and LD pattern of the study population. We explored a variety of approaches for carrying out the imputation using a reference panel consisting of sequence data for a fraction of the study participants using data from both a candidate gene sequencing study and the 1000 Genomes Project.

Conclusions

Imputation of genetic variation based on a proportion of sequenced samples is feasible. Our results indicate the following sequencing study design guidelines which take advantage of the recent advances in genotype imputation methodology: Select the largest and most diverse reference panel for sequencing and genotype as many “anchor” markers as possible.     

GINDEL: Accurate Genotype Calling of Insertions and Deletions from Low Coverage Population Sequence Reads

Insertions and deletions (indels) are important types of structural variations. Obtaining accurate genotypes of indels may facilitate further genetic study. There are a few existing methods for calling indel genotypes from sequence reads. However, none of these tools can accurately call indel genotypes for indels of all lengths, especially for low coverage sequence data. In this paper, we present GINDEL, an approach for calling genotypes of both insertions and deletions from sequence reads. GINDEL uses a machine learning approach which combines multiple features extracted from next generation sequencing data. We test our approach on both simulated and real data and compare with existing tools, including Genome STRiP, Pindel and Clever-sv. Results show that GINDEL works well for deletions larger than 50 bp on both high and low coverage data. Also, GINDEL performs well for insertion genotyping on both simulated and real data. For comparison, Genome STRiP performs less well for shorter deletions (50–200 bp) on both simulated and real sequence data from the 1000 Genomes Project. Clever-sv performs well for intermediate deletions (200–1500 bp) but is less accurate when coverage is low. Pindel only works well for high coverage data, but does not perform well at low coverage. To summarize, we show that GINDEL not only can call genotypes of insertions and deletions (both short and long) for high and low coverage population sequence data, but also is more accurate and efficient than other approaches. The program GINDEL can be downloaded at: http://sourceforge.net/p/gindel     

Detecting Identity by Descent and Estimating Genotype Error Rates in Sequence Data

Existing methods for identity by descent (IBD) segment detection were designed for SNP array data, not sequence data. Sequence data have a much higher density of genetic variants and a different allele frequency distribution, and can have higher genotype error rates. Consequently, best practices for IBD detection in SNP array data do not necessarily carry over to sequence data. We present a method, IBDseq, for detecting IBD segments in sequence data and a method, SEQERR, for estimating genotype error rates at low-frequency variants by using detected IBD. The IBDseq method estimates probabilities of genotypes observed with error for each pair of individuals under IBD and non-IBD models. The ratio of estimated probabilities under the two models gives a LOD score for IBD. We evaluate several IBD detection methods that are fast enough for application to sequence data (IBDseq, Beagle Refined IBD, PLINK, and GERMLINE) under multiple parameter settings, and we show that IBDseq achieves high power and accuracy for IBD detection in sequence data. The SEQERR method estimates genotype error rates by comparing observed and expected rates of pairs of homozygote and heterozygote genotypes at low-frequency variants in IBD segments. We demonstrate the accuracy of SEQERR in simulated data, and we apply the method to estimate genotype error rates in sequence data from the UK10K and 1000 Genomes projects.     

Simultaneous Genotype Calling and Haplotype Phasing Improves Genotype Accuracy and Reduces False-Positive Associations for Genome-wide Association Studies

We present a novel method for simultaneous genotype calling and haplotype-phase inference. Our method employs the computationally efficient BEAGLE haplotype-frequency model, which can be applied to large-scale studies with millions of markers and thousands of samples. We compare genotype calls made with our method to genotype calls made with the BIRDSEED, CHIAMO, GenCall, and ILLUMINUS genotype-calling methods, using genotype data from the Illumina 550K and Affymetrix 500K arrays. We show that our method has higher genotype-call accuracy and yields fewer uncalled genotypes than competing methods. We perform single-marker analysis of data from the Wellcome Trust Case Control Consortium bipolar disorder and type 2 diabetes studies. For bipolar disorder, the genotype calls in the original study yield 25 markers with apparent false-positive association with bipolar disorder at a p < 10⁻⁷ significance level, whereas genotype calls made with our method yield no associated markers at this significance threshold. Conversely, for markers with replicated association with type 2 diabetes, there is good concordance between genotype calls used in the original study and calls made by our method. Results from single-marker and haplotypic analysis of our method''s genotype calls for the bipolar disorder study indicate that our method is highly effective at eliminating genotyping artifacts that cause false-positive associations in genome-wide association studies. Our new genotype-calling methods are implemented in the BEAGLE and BEAGLECALL software packages.     

Inferring Coalescence Times from DNA Sequence Data

The paper is concerned with methods for the estimation of the coalescence time (time since the most recent common ancestor) of a sample of intraspecies DNA sequences. The methods take advantage of prior knowledge of population demography, in addition to the molecular data. While some theoretical results are presented, a central focus is on computational methods. These methods are easy to implement, and, since explicit formulae tend to be either unavailable or unilluminating, they are also more useful and more informative in most applications. Extensions are presented that allow for the effects of uncertainty in our knowledge of population size and mutation rates, for variability in population sizes, for regions of different mutation rate, and for inference concerning the coalescence time of the entire population. The methods are illustrated using recent data from the human Y chromosome.     

Homology and the Optimization of DNA Sequence Data

Three methods of nucleotide character analysis are discussed. Their implications for molecular sequence homology and phylogenetic analysis are compared. The criterion of inter-data set congruence, both character based and topological, are applied to two data sets to elucidate and potentially discriminate among these parsimony-based ideas.     

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

Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.     

Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data

High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.     

Estimation of Levels of Gene Flow from DNA Sequence Data

We compare the utility of two methods for estimating the average levels of gene flow from DNA sequence data. One method is based on estimating FST from frequencies at polymorphic sites, treating each site as a separate locus. The other method is based on computing the minimum number of migration events consistent with the gene tree inferred from their sequences. We compared the performance of these two methods on data that were generated by a computer simulation program that assumed the infinite sites model of mutation and that assumed an island model of migration. We found that in general when there is no recombination, the cladistic method performed better than FST while the reverse was true for rates of recombination similar to those found in eukaryotic nuclear genes, although FST performed better for all recombination rates for very low levels of migration (Nm = 0.1).     

Present-Day DNA Contamination in Ancient DNA Datasets

Present-day contamination can lead to false conclusions in ancient DNA studies. A number of methods are available to estimate contamination, which use a variety of signals and are appropriate for different types of data. Here an overview of currently available methods highlighting their strengths and weaknesses is provided, and a classification based on the signals used to estimate contamination is proposed. This overview aims at enabling researchers to choose the most appropriate methods for their dataset. Based on this classification, potential avenues for the further development of methods are discussed.     

An Experimentally Derived Data Set Constructed for Testing Large-Scale DNA Sequence Assembly Algorithms

A data set consisting of DNA sequences from a large-scale shotgun DNA cloning and sequencing project has been collected and posted for public release. The purpose is to propose a standard genomic DNA sequencing data set by which various algorithms and implementations can be tested. This set of data is divided into two subsets, one containing raw DNA sequence data (1023 clones) and the other consisting of the corresponding partially refined or edited DNA sequence data (820 clones). Suggested criteria or guidelines for this data refinement are presented so that algorithms for preprocessing and screening raw sequences may be developed. Development of such preprocessing, screening, aligning, and assembling algorithms will expedite large-scale DNA sequencing projects so that the complete unambiguous consensus DNA sequences will be made available to the general research community in a quicker manner. Smaller scale routine DNA sequencing projects will also be greatly aided by such computational efforts.     

Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data

Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing.     

DNA Data Visualization (DDV): Software for Generating Web-Based Interfaces Supporting Navigation and Analysis of DNA Sequence Data of Entire Genomes

Data visualization methods are necessary during the exploration and analysis activities of an increasingly data-intensive scientific process. There are few existing visualization methods for raw nucleotide sequences of a whole genome or chromosome. Software for data visualization should allow the researchers to create accessible data visualization interfaces that can be exported and shared with others on the web. Herein, novel software developed for generating DNA data visualization interfaces is described. The software converts DNA data sets into images that are further processed as multi-scale images to be accessed through a web-based interface that supports zooming, panning and sequence fragment selection. Nucleotide composition frequencies and GC skew of a selected sequence segment can be obtained through the interface. The software was used to generate DNA data visualization of human and bacterial chromosomes. Examples of visually detectable features such as short and long direct repeats, long terminal repeats, mobile genetic elements, heterochromatic segments in microbial and human chromosomes, are presented. The software and its source code are available for download and further development. The visualization interfaces generated with the software allow for the immediate identification and observation of several types of sequence patterns in genomes of various sizes and origins. The visualization interfaces generated with the software are readily accessible through a web browser. This software is a useful research and teaching tool for genetics and structural genomics.     

Correcting errors in synthetic DNA through consensus shuffling

Although efficient methods exist to assemble synthetic oligonucleotides into genes and genomes, these suffer from the presence of 1–3 random errors/kb of DNA. Here, we introduce a new method termed consensus shuffling and demonstrate its use to significantly reduce random errors in synthetic DNA. In this method, errors are revealed as mismatches by re-hybridization of the population. The DNA is fragmented, and mismatched fragments are removed upon binding to an immobilized mismatch binding protein (MutS). PCR assembly of the remaining fragments yields a new population of full-length sequences enriched for the consensus sequence of the input population. We show that two iterations of consensus shuffling improved a population of synthetic green fluorescent protein (GFPuv) clones from ~60 to >90% fluorescent, and decreased errors 3.5- to 4.3-fold to final values of ~1 error per 3500 bp. In addition, two iterations of consensus shuffling corrected a population of GFPuv clones where all members were non-functional, to a population where 82% of clones were fluorescent. Consensus shuffling should facilitate the rapid and accurate synthesis of long DNA sequences.     

Small Sample Asymptotics for Type I Censored Data

For Location and Scale models with Type I Censored Data the estimation of the parameters based on likelihood is analyzed. When the sample size is very small the usual procedures for inference based on the asymptotic distribution of the statistics do not function properly. We develop higher‐order asymptotic methods and their performance is investigated by Monte Carlo experiments.     

Subgenotyping of Genotype C Hepatitis B Virus: Correcting Misclassifications and Identifying a Novel Subgenotype

Background

More than ten subgenotypes of genotype C Hepatitis B virus (HBV) have been reported, including C1 to C16 and two C/D recombinant subgenotypes (CD1 and CD2), however, inconsistent designations of these subgenotypes still exist.

Methodology/Principal Findings

We performed a phylogenetic analysis of all full-length genotype C HBV genome sequences to correct the misclassifications of HBV subgenotypes and to study the influence of recombination on HBV subgenotyping. Our results showed that although inclusion of the recombinant sequences changed the topology of the phylogenetic tree, it did not affect the subgenotyping of the non-recombinant sequences, except subgenotype C2. In addition, most of the subgenotypes have been properly designated. However, several misclassifications of HBV subgenotypes have been identified and corrected. For example, C11 proposed by Utsumi and colleagues in 2011 was found to be grouped with C12 proposed by Mulyanto and colleagues. Two sequences, {"type":"entrez-nucleotide","attrs":{"text":"GQ358157","term_id":"282929781","term_text":"GQ358157"}}GQ358157 and {"type":"entrez-nucleotide","attrs":{"text":"GU721029","term_id":"289721059","term_text":"GU721029"}}GU721029, previously designated as C6 have been re-designated as C12 and C7, respectively. Moreover, a quasi-subgenotype C2 was proposed, which included the old C2, several previously unclassified sequences and previously designated C14. In particular, we identified a novel subgenotype, tentative C14, which was well supported by phylogenetic analysis and sequence divergence of >4%.

Conclusions/Significance

A number of misclassifications in the subgenotyping of genotype C HBV have been identified in this study. After correcting the misclassifications, we proposed a better classification for the subgenotyping of genotype C HBV, in which a novel quasi-subgenotype C2 and a novel subgenotype, tentative C14, were described. Based on this large-scale analysis, we propose that a novel subgenotype should only be reported after a complete comparison of all relevant sequences rather than a few representative sequences only.     

A Simple Strategy for Reducing False Negatives in Calling Variants from Single-Cell Sequencing Data

Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in single-cell sequencing data analysis. Simulation results show that this method is highly reliable, with an error rate of 4.94×10^-5, which is orders of magnitude lower than the expected false negative rate (~34%) estimated from a single-cell exome dataset, though the method is limited by the low SNP density in the human genome. We applied this method to analyze the exome data of a few dozen single tumor cells generated in previous studies, and extracted cell specific mutation information for a small set of sites. Interestingly, we found that there are difficulties in using the classical clonal model of tumor cell growth to explain the mutation patterns observed in some tumor cells.