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.     

A de novo germline mutation of APC for inheritable colon cancer in a Chinese family using multigene next generation sequencing

Inheritable colorectal cancers (CRC) accounted for about 20% of the CRC cases, such as hereditary nonpolyposis colorectal cancer (HNPCC), Gardner syndrome and familial adenomatous polyposis (FAP). A four-generation Han Chinese family was found affected with polyposis in colons. Inferred from the pedigree structure, the disease in this family showed an autosomal dominant inheritance model. To locate the causal mutations in this family, genomic DNAs were extracted and the next generation sequencing for 5 genes relating to colon cancer performed by Ion Torrent Personal Genome Machine with a 314 chip. The reads were aligned with human reference genome hg19 to call variants in the 5 genes. After analysis, 14 variants were detected in the sequenced sample and 13 been collected in dbSNP database and assigned with a rs identification number. In these variants, 9 were synonymous, 4 missense and 1 non-sense. In them, 2 rare variants (c.694C>T in APC and c.1690A>G in MSH2) might be the putative causal mutations for familial adenomatous polyposis (FAP) since the rarity of the mutated allele in normal controls. c.694C>T was detected in only affected members and generated a premature stop codon in APC. It should be a de novo germline mutation making APC containing this stop codon as targets for nonsense-mediated mRNA decay (NMD). c.1690A>G in MSH2 was not only detected in affected members, but also in normal ones in the family. Functional prediction revealed that the amino acid affected by this variant had no effect on the function of MSH2. Here, we report a de novo germline mutation of APC as the causal variant in a Chinese family with inheritable colon cancer by the next generation sequencing.     

The Contribution of Mosaic Variants to Autism Spectrum Disorder

De novo mutation is highly implicated in autism spectrum disorder (ASD). However, the contribution of post-zygotic mutation to ASD is poorly characterized. We performed both exome sequencing of paired samples and analysis of de novo variants from whole-exome sequencing of 2,388 families. While we find little evidence for tissue-specific mosaic mutation, multi-tissue post-zygotic mutation (i.e. mosaicism) is frequent, with detectable mosaic variation comprising 5.4% of all de novo mutations. We identify three mosaic missense and likely-gene disrupting mutations in genes previously implicated in ASD (KMT2C, NCKAP1, and MYH10) in probands but none in siblings. We find a strong ascertainment bias for mosaic mutations in probands relative to their unaffected siblings (p = 0.003). We build a model of de novo variation incorporating mosaic variants and errors in classification of mosaic status and from this model we estimate that 33% of mosaic mutations in probands contribute to 5.1% of simplex ASD diagnoses (95% credible interval 1.3% to 8.9%). Our results indicate a contributory role for multi-tissue mosaic mutation in some individuals with an ASD diagnosis.     

Robust classification of protein variation using structural modelling and large-scale data integration

Existing methods for interpreting protein variation focus on annotating mutation pathogenicity rather than detailed interpretation of variant deleteriousness and frequently use only sequence-based or structure-based information. We present VIPUR, a computational framework that seamlessly integrates sequence analysis and structural modelling (using the Rosetta protein modelling suite) to identify and interpret deleterious protein variants. To train VIPUR, we collected 9477 protein variants with known effects on protein function from multiple organisms and curated structural models for each variant from crystal structures and homology models. VIPUR can be applied to mutations in any organism''s proteome with improved generalized accuracy (AUROC .83) and interpretability (AUPR .87) compared to other methods. We demonstrate that VIPUR''s predictions of deleteriousness match the biological phenotypes in ClinVar and provide a clear ranking of prediction confidence. We use VIPUR to interpret known mutations associated with inflammation and diabetes, demonstrating the structural diversity of disrupted functional sites and improved interpretation of mutations associated with human diseases. Lastly, we demonstrate VIPUR''s ability to highlight candidate variants associated with human diseases by applying VIPUR to de novo variants associated with autism spectrum disorders.     

Low-Frequency IL23R Coding Variant Associated with Crohn’s Disease Susceptibility in Japanese Subjects Identified by Personal Genomics Analysis

Background

The common disease-common variant hypothesis is insufficient to explain the complexities of Crohn’s disease (CD) genetics; therefore, rare variants are expected to be important in the disease. We explored rare variants associated with susceptibility to CD in Japanese individuals by personal genomic analysis.

Methods

Two-step analyses were performed. The first step was a trio analysis with whole-exome sequence (WES) analysis and the second was a follow-up case-control association study. The WES analysis pipeline comprised Burrows-Wheeler Aligner, Picard, Genome Analysis Toolkit, and SAMTOOLS. Single nucleotide variants (SNVs)/indels were annotated and filtered by using programs implemented in ANNOVAR in combination with identity-by-descent (IBD), subsequently were subjected to the linkage based, and de novo based strategies. Finally, we conducted an association study that included 176 unrelated subjects with CD and 358 healthy control subjects.

Results

In family members, 234,067–297,523 SNVs/indels were detected and they were educed to 106–146 by annotation based filtering. Fifty-four CD variants common to both individuals of the affected sib pair were identified. The linkage based strategy detected five candidate variants whereas the de novo based strategy identified no variants. Consequently, five candidates were analyzed in the case-control association study. CD showed a significant association with one variant in exon 4 of IL23R, G149R [rs76418789, P = 3.9E-5, odds ratio (OR) 0.21, 95% confidence interval (CI) 0.09–0.47 for the dominant model (AA + AG versus GG), and P = 7.3E-5, OR 0.21, 95% CI 0.10–0.48 for AG versus GG, and P = 7.2E-5, OR 0.23, 95% CI 0.10–0.50 for the allele model].

Conclusions

The present study, using personal genomics analysis of a small CD pedigree, is the first to show that the low-frequency non-synonymous variant of IL23R, rs76418789, protects against CD development in Japanese subjects.     

Genomic landscapes of Chinese sporadic autism spectrum disorders revealed by whole-genome sequencing

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical and genetic heterogeneity.In this study,we identified all classes of genomic variants from whole-genome sequencing (WGS) dataset of 32 Chinese trios with ASD,including de novo mutations,inherited variants,copy number variants (CNVs) and genomic structural variants.A higher mutation rate (Poisson test,P2.2×10~(-16)) in exonic (1.37×10~(-8)) and 3'-UTR regions (1.42×10~(-8)) was revealed in comparison with that of whole genome (1.05×10~(-8)).Using an integrated model,we identified 87 potentially risk genes (P0.01) from 4832 genes harboring various rare deleterious variants,including CHD8 and NRXN2,implying that the disorders may be in favor to multiple-hit.In particular,frequent rare inherited mutations of several microcephaly-associated genes (ASPM,WDR62,and ZNF335)were found in ASD.In chromosomal structure analyses,we found four de novo CNVs and one de novo chromosomal rearrangement event,including a de novo duplication of UBE3A-containing region at 15q11.2-q13.1,which causes Angelman syndrome and microcephaly,and a disrupted TNR due to de novo chromosomal translocation t (1;5) (q25.1;q33.2).Taken together,our results suggest that abnormalities of centrosomal function and chromatin remodeling of the microcephaly-associated genes may be implicated in pathogenesis of ASD.Adoption of WGS as a new yet efficient technique to illustrate the full genetic spectrum in complex disorders,such as ASD,could provide novel insights into pathogenesis,diagnosis and treatment.     

M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits

Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.     

Efficient and cost effective population resequencing by pooling and in-solution hybridization

High-throughput sequencing of targeted genomic loci in large populations is an effective approach for evaluating the contribution of rare variants to disease risk. We evaluated the feasibility of using in-solution hybridization-based target capture on pooled DNA samples to enable cost-efficient population sequencing studies. For this, we performed pooled sequencing of 100 HapMap samples across ～ 600 kb of DNA sequence using the Illumina GAIIx. Using our accurate variant calling method for pooled sequence data, we were able to not only identify single nucleotide variants with a low false discovery rate (<1%) but also accurately detect short insertion/deletion variants. In addition, with sufficient coverage per individual in each pool (30-fold) we detected 97.2% of the total variants and 93.6% of variants below 5% in frequency. Finally, allele frequencies for single nucleotide variants (SNVs) estimated from the pooled data and the HapMap genotype data were tightly correlated (correlation coefficient > = 0.995).     

Two de novo mutations of MFN2 associated with early-onset Charcot-Marie-Tooth disease type 2A neuropathy

Charcot-Marie-Tooth disease type 2A (CMT2A) is one of the subdivisions of CMT2, an axonal defective form of peripheral neuropathy. Different mutations in the mitochondrial GTPase mitofusin 2 (MFN2) gene produce various degrees of severity of CMT2A phenotype or CMT2A related hereditary motor and sensory neuropathy VI (HMSN VI). The occurrence of de novo mutations in MFN2 is by far the most frequent as compared to other CMT genes. About 26% of the pathogenic MFN2 mutations reported in the Inherited Peripheral Neuropathies Mutations Database are de novo. This study identified two de novo mutations of MFN2, c.1048T>C (S350P) and c.310C>T (R104W), from two Korean CMT2A patients with early onset severe clinical symptoms. The comparative genotype-phenotype correlations of these mutations according to a previously reported case were also viewed. The R104W mutation has been reported recurrently, outspread over different ethnic backgrounds as de novo. The re-occurrence of the same pathogenic de novo variants within and amongst different ethnic groups clearly suggests a susceptible hot spot for mutation in the MFN2 gene. If the deleterious mutations discourage fitness and reproduction, this negative selection factor should ultimately reduce the prevalence of the disease. It appears that spontaneous de novo mutations in turn seem to be maintaining the disease phenotype??s prevalence.     

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

There are several lines of evidence supporting the role of de novo mutations as a mechanism for common disorders, such as autism and schizophrenia. First, the de novo mutation rate in humans is relatively high, so new mutations are generated at a high frequency in the population. However, de novo mutations have not been reported in most common diseases. Mutations in genes leading to severe diseases where there is a strong negative selection against the phenotype, such as lethality in embryonic stages or reduced reproductive fitness, will not be transmitted to multiple family members, and therefore will not be detected by linkage gene mapping or association studies. The observation of very high concordance in monozygotic twins and very low concordance in dizygotic twins also strongly supports the hypothesis that a significant fraction of cases may result from new mutations. Such is the case for diseases such as autism and schizophrenia. Second, despite reduced reproductive fitness¹ and extremely variable environmental factors, the incidence of some diseases is maintained worldwide at a relatively high and constant rate. This is the case for autism and schizophrenia, with an incidence of approximately 1% worldwide. Mutational load can be thought of as a balance between selection for or against a deleterious mutation and its production by de novo mutation. Lower rates of reproduction constitute a negative selection factor that should reduce the number of mutant alleles in the population, ultimately leading to decreased disease prevalence. These selective pressures tend to be of different intensity in different environments. Nonetheless, these severe mental disorders have been maintained at a constant relatively high prevalence in the worldwide population across a wide range of cultures and countries despite a strong negative selection against them². This is not what one would predict in diseases with reduced reproductive fitness, unless there was a high new mutation rate. Finally, the effects of paternal age: there is a significantly increased risk of the disease with increasing paternal age, which could result from the age related increase in paternal de novo mutations. This is the case for autism and schizophrenia³. The male-to-female ratio of mutation rate is estimated at about 4–6:1, presumably due to a higher number of germ-cell divisions with age in males. Therefore, one would predict that de novo mutations would more frequently come from males, particularly older males⁴. A high rate of new mutations may in part explain why genetic studies have so far failed to identify many genes predisposing to complexes diseases genes, such as autism and schizophrenia, and why diseases have been identified for a mere 3% of genes in the human genome. Identification for de novo mutations as a cause of a disease requires a targeted molecular approach, which includes studying parents and affected subjects. The process for determining if the genetic basis of a disease may result in part from de novo mutations and the molecular approach to establish this link will be illustrated, using autism and schizophrenia as examples.     

A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals

We present methods for imputing data for ungenotyped markers and for inferring haplotype phase in large data sets of unrelated individuals and parent-offspring trios. Our methods make use of known haplotype phase when it is available, and our methods are computationally efficient so that the full information in large reference panels with thousands of individuals is utilized. We demonstrate that substantial gains in imputation accuracy accrue with increasingly large reference panel sizes, particularly when imputing low-frequency variants, and that unphased reference panels can provide highly accurate genotype imputation. We place our methodology in a unified framework that enables the simultaneous use of unphased and phased data from trios and unrelated individuals in a single analysis. For unrelated individuals, our imputation methods produce well-calibrated posterior genotype probabilities and highly accurate allele-frequency estimates. For trios, our haplotype-inference method is four orders of magnitude faster than the gold-standard PHASE program and has excellent accuracy. Our methods enable genotype imputation to be performed with unphased trio or unrelated reference panels, thus accounting for haplotype-phase uncertainty in the reference panel. We present a useful measure of imputation accuracy, allelic R², and show that this measure can be estimated accurately from posterior genotype probabilities. Our methods are implemented in version 3.0 of the BEAGLE software package.     

Sequence and Functional Analysis of the Envelope Glycoproteins of Hepatitis C Virus Variants Selectively Transmitted to a New Host

Hepatitis C virus (HCV) remains a challenging public health problem worldwide. The identification of viral variants establishing de novo infections and definition of the phenotypic requirements for transmission would facilitate the design of preventive strategies. We explored the transmission of HCV variants in three cases of acute hepatitis following needlestick accidents. We used single-genome amplification of glycoprotein E1E2 gene sequences to map the genetic bottleneck upon transmission accurately. We found that infection was likely established by a single variant in two cases and six variants in the third case. Studies of donor samples showed that the transmitted variant E1E2 amino acid sequences were identical or closely related to those of variants from the donor virus populations. The transmitted variants harbored a common signature site at position 394, within hypervariable region 1 of E2, together with additional signature amino acids specific to each transmission pair. Surprisingly, these E1E2 variants conferred no greater capacity for entry than the E1E2 derived from nontransmitted variants in lentiviral pseudoparticle assays. Mutants escaping the antibodies of donor sera did not predominate among the transmitted variants either. The fitness parameters affecting the selective outgrowth of HCV variants after transmission in an immunocompetent host may thus be more complex than those suggested by mouse models. Human antibodies directed against HCV envelope effectively cross-neutralized the lentiviral particles bearing E1E2 derived from transmitted variants. These findings provide insight into the molecular mechanisms underlying HCV transmission and suggest that viral entry is a potential target for the prevention of HCV infection.     

Whole Exome Sequencing Identifies a Troponin T Mutation Hot Spot in Familial Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) commonly causes heart failure and shows extensive genetic heterogeneity that may be amenable to newly developed next-generation DNA sequencing of the exome. In this study we report the successful use of exome sequencing to identify a pathogenic variant in the TNNT2 gene using segregation analysis in a large DCM family. Exome sequencing was performed on three distant relatives from a large family with a clear DCM phenotype. Missense, nonsense, and splice variants were analyzed for segregation among the three affected family members and confirmed in other relatives by direct sequencing. A c.517T C>T, Arg173Trp TNNT2 variant segregated with all affected family members and was also detected in one additional DCM family in our registry. The inclusion of segregation analysis using distant family members markedly improved the bioinformatics filtering process by removing from consideration variants that were not shared by all affected subjects. Haplotype analysis confirmed that the variant found in both DCM families was located on two distinct haplotypes, supporting the notion of independent mutational events in each family. In conclusion, an exome sequencing strategy that includes segregation analysis using distant affected relatives within a family represents a viable diagnostic strategy in a genetically heterogeneous disease like DCM.     

Evaluation and optimisation of indel detection workflows for ion torrent sequencing of the BRCA1 and BRCA2 genes

Background

The Ion Torrent PGM is a popular benchtop sequencer that shows promise in replacing conventional Sanger sequencing as the gold standard for mutation detection. Despite the PGM’s reported high accuracy in calling single nucleotide variations, it tends to generate many false positive calls in detecting insertions and deletions (indels), which may hinder its utility for clinical genetic testing.

Results

Recently, the proprietary analytical workflow for the Ion Torrent sequencer, Torrent Suite (TS), underwent a series of upgrades. We evaluated three major upgrades of TS by calling indels in the BRCA1 and BRCA2 genes. Our analysis revealed that false negative indels could be generated by TS under both default calling parameters and parameters adjusted for maximum sensitivity. However, indel calling with the same data using the open source variant callers, GATK and SAMtools showed that false negatives could be minimised with the use of appropriate bioinformatics analysis. Furthermore, we identified two variant calling measures, Quality-by-Depth (QD) and VARiation of the Width of gaps and inserts (VARW), which substantially reduced false positive indels, including non-homopolymer associated errors without compromising sensitivity. In our best case scenario that involved the TMAP aligner and SAMtools, we achieved 100% sensitivity, 99.99% specificity and 29% False Discovery Rate (FDR) in indel calling from all 23 samples, which is a good performance for mutation screening using PGM.

Conclusions

New versions of TS, BWA and GATK have shown improvements in indel calling sensitivity and specificity over their older counterpart. However, the variant caller of TS exhibits a lower sensitivity than GATK and SAMtools. Our findings demonstrate that although indel calling from PGM sequences may appear to be noisy at first glance, proper computational indel calling analysis is able to maximize both the sensitivity and specificity at the single base level, paving the way for the usage of this technology for future clinical genetic testing.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-516) contains supplementary material, which is available to authorized users.     

Rare inherited missense variants of POGZ associate with autism risk and disrupt neuronal development

Excess de novo likely gene-disruptive and missense variants within dozens of genes have been identified in autism spectrum disorder(ASD)and other neurodevelopmental disorders.However,many rare inherited missense variants of these high-risk genes have not been thoroughly evaluated.In this study,we analyzed the rare missense variant burden of POGZ in a large cohort of ASD patients from the Autism Clinical and Genetic Resources in China(ACGC)and further dissected the functional effect of diseaseassociated missense variants on neuronal development.Our results showed a significant burden of rare missense variants in ASD patients compared to the control population(P=4.6×10-5,OR=3.96),and missense variants in ASD patients showed more severe predicted functional outcomes than those in controls.Furthermore,by leveraging published large-scale sequencing data of neurodevelopmental disorders(NDDs)and sporadic case reports,we identified 8 de novo missense variants of POGZ in NDD patients.Functional analysis revealed that two inherited,but not de novo,missense variants influenced the cellular localization of POGZ and failed to rescue the defects in neurite and dendritic spine development caused by Pogz knockdown in cultured mouse primary cortical neurons.Significantly,L1CAM,an autism candidate risk gene,is differentially expressed in POGZ deficient cell lines.Reduced expression of L1cam was able to partially rescue the neurite length defects caused by Pogz knockdown.Our study showed the important roles of rare inherited missense variants of POGZ in ASD risk and neuronal development and identified the potential downstream targets of POGZ,which are important for further molecular mechanism studies.     

ROVER variant caller: read-pair overlap considerate variant-calling software applied to PCR-based massively parallel sequencing datasets

Background

We recently described Hi-Plex, a highly multiplexed PCR-based target-enrichment system for massively parallel sequencing (MPS), which allows the uniform definition of library size so that subsequent paired-end sequencing can achieve complete overlap of read pairs. Variant calling from Hi-Plex-derived datasets can thus rely on the identification of variants appearing in both reads of read-pairs, permitting stringent filtering of sequencing chemistry-induced errors. These principles underly ROVER software (derived from Read Overlap PCR-MPS variant caller), which we have recently used to report the screening for genetic mutations in the breast cancer predisposition gene PALB2. Here, we describe the algorithms underlying ROVER and its usage.

Results

ROVER enables users to quickly and accurately identify genetic variants from PCR-targeted, overlapping paired-end MPS datasets. The open-source availability of the software and threshold tailorability enables broad access for a range of PCR-MPS users.

Methods

ROVER is implemented in Python and runs on all popular POSIX-like operating systems (Linux, OS X). The software accepts a tab-delimited text file listing the coordinates of the target-specific primers used for targeted enrichment based on a specified genome-build. It also accepts aligned sequence files resulting from mapping to the same genome-build. ROVER identifies the amplicon a given read-pair represents and removes the primer sequences by using the mapping co-ordinates and primer co-ordinates. It considers overlapping read-pairs with respect to primer-intervening sequence. Only when a variant is observed in both reads of a read-pair does the signal contribute to a tally of read-pairs containing or not containing the variant. A user-defined threshold informs the minimum number of, and proportion of, read-pairs a variant must be observed in for a ‘call’ to be made. ROVER also reports the depth of coverage across amplicons to facilitate the identification of any regions that may require further screening.

Conclusions

ROVER can facilitate rapid and accurate genetic variant calling for a broad range of PCR-MPS users.     

VarSAn: associating pathways with a set of genomic variants using network analysis

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn''s pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.     

Whole-Genome Sequencing of a Single Proband Together with Linkage Analysis Identifies a Mendelian Disease Gene

Although more than 2,400 genes have been shown to contain variants that cause Mendelian disease, there are still several thousand such diseases yet to be molecularly defined. The ability of new whole-genome sequencing technologies to rapidly indentify most of the genetic variants in any given genome opens an exciting opportunity to identify these disease genes. Here we sequenced the whole genome of a single patient with the dominant Mendelian disease, metachondromatosis (OMIM 156250), and used partial linkage data from her small family to focus our search for the responsible variant. In the proband, we identified an 11 bp deletion in exon four of PTPN11, which alters frame, results in premature translation termination, and co-segregates with the phenotype. In a second metachondromatosis family, we confirmed our result by identifying a nonsense mutation in exon 4 of PTPN11 that also co-segregates with the phenotype. Sequencing PTPN11 exon 4 in 469 controls showed no such protein truncating variants, supporting the pathogenicity of these two mutations. This combination of a new technology and a classical genetic approach provides a powerful strategy to discover the genes responsible for unexplained Mendelian disorders.     

Deconvoluting the Composition of Low-Frequency Hepatitis C Viral Quasispecies: Comparison of Genotypes and NS3 Resistance-Associated Variants between HCV/HIV Coinfected Hemophiliacs and HCV Monoinfected Patients in Japan

Pre-existing low-frequency resistance-associated variants (RAVs) may jeopardize successful sustained virological responses (SVR) to HCV treatment with direct-acting antivirals (DAAs). However, the potential impact of low-frequency (∼0.1%) mutations, concatenated mutations (haplotypes), and their association with genotypes (Gts) on the treatment outcome has not yet been elucidated, most probably owing to the difficulty in detecting pre-existing minor haplotypes with sufficient length and accuracy. Herein, we characterize a methodological framework based on Illumina MiSeq next-generation sequencing (NGS) coupled with bioinformatics of quasispecies reconstruction (QSR) to realize highly accurate variant calling and genotype-haplotype detection. The core-to-NS3 protease coding sequences in 10 HCV monoinfected patients, 5 of whom had a history of blood transfusion, and 11 HCV/HIV coinfected patients with hemophilia, were studied. Simulation experiments showed that, for minor variants constituting more than 1%, our framework achieved a positive predictive value (PPV) of 100% and sensitivities of 91.7–100% for genotyping and 80.6% for RAV screening. Genotyping analysis indicated the prevalence of dominant Gt1a infection in coinfected patients (6/11 vs 0/10, p = 0.01). For clinical samples, minor genotype overlapping infection was prevalent in HCV/HIV coinfected hemophiliacs (10/11) and patients who experienced whole-blood transfusion (4/5) but none in patients without exposure to blood (0/5). As for RAV screening, the Q80K/R and S122K/R variants were particularly prevalent among minor RAVs observed, detected in 12/21 and 6/21 cases, respectively. Q80K was detected only in coinfected patients, whereas Q80R was predominantly detected in monoinfected patients (1/11 vs 7/10, p < 0.01). Multivariate interdependence analysis revealed the previously unrecognized prevalence of Gt1b-Q80K, in HCV/HIV coinfected hemophiliacs [Odds ratio = 13.4 (3.48–51.9), p < 0.01]. Our study revealed the distinct characteristics of viral quasispecies between the subgroups specified above and the feasibility of NGS and QSR-based genetic deconvolution of pre-existing minor Gts, RAVs, and their interrelationships.