Determining the quality and complexity of next-generation sequencing data without a reference genome

Genomics, epigenetics, population genetics and bioinformatic

Low Mutational Burden of Extranodal Marginal Zone Lymphoma of Mucosa-Associated Lymphoid Tissue in Patients with Primary Sjogren's Syndrome

SIMPLE SUMMARY: Patients with primary Sjogren’s syndrome (pSS) are at risk of developing extranodal marginal zone lymphoma (ENMZL) of the mucosa-associated lymphoid tissue (MALT) in the parotid glands. The genetic mechanism underlying development of MALT lymphoma in the context of pSS is unknown. The aim of our study was to define the genomic landscape of pSS-associated MALT lymphoma. For 17 localized pSS-associated MALT lymphomas, we analyzed the presence of nonsynonymous mutations, copy number alterations (CNAs) and MALT1 translocations. pSS-associated MALT lymphomas were characterized by a low mutational load (median number of nonsynonymous somatic variants per case was 7, range 2–78) and a limited number of CNAs. Unlike the recurrent genomic aberrations observed in MALT lymphoma, which were not associated with pSS, pSS-associated MALT lacked a clear lymphoma-related profile. The data suggest that localized pSS-associated MALT lymphomas are a distinct type of ENMZL, which are genomically stable and most likely depend on a stimulatory micro-environment. ABSTRACT: Patients with primary Sjogren’s syndrome (pSS) are at risk of developing extranodal marginal zone lymphoma (ENMZL) of the mucosa-associated lymphoid tissue (MALT) in the parotid glands. Unlike recurrent genomic aberrations observed in MALT lymphoma, which were not associated with pSS (non-pSS), it is unknown which somatic aberrations underlie the development of pSS-associated MALT lymphomas. Whole-exome sequencing was performed on 17 pSS-associated MALT lymphomas. In total, 222 nonsynonymous somatic variants affecting 182 genes were identified across the 17 cases. The median number of variants was seven (range 2–78), including three cases with a relatively high mutational load (≥24/case). Out of 16 recurrently mutated genes, ID3, TBL1XR1, PAX5, IGLL5 and APC are known to be associated with lymphomagenesis. A total of 18 copy number alterations were detected in eight cases. MALT1 translocations were not detected. With respect to outcome, only two cases relapsed outside of the salivary glands. Both had a high mutational load, suggesting a more advanced stage of lymphoma. The low mutational load and lack of a clear lymphoma-related mutation profile suggests that localized pSS-associated MALT lymphomas are genomically more stable than non-pSS MALT lymphomas and most likely depend on a stimulatory micro-environment

Genomic profiling of post-transplant lymphoproliferative disorders using cell-free DNA

Diagnosing post-transplant lymphoproliferative disorder (PTLD) is challenging and often requires invasive procedures. Analyses of cell-free DNA (cfDNA) isolated from plasma is minimally invasive and highly effective for genomic profiling of tumors. We studied the feasibility of using cfDNA to profile PTLD and explore its potential to serve as a screening tool. We included seventeen patients with monomorphic PTLD after solid organ transplantation in this multi-center observational cohort study. We used low-coverage whole genome sequencing (lcWGS) to detect copy number variations (CNVs) and targeted next-generation sequencing (NGS) to identify Epstein-Barr virus (EBV) DNA load and somatic single nucleotide variants (SNVs) in cfDNA from plasma. Seven out of seventeen (41%) patients had EBV-positive tumors, and 13/17 (76%) had stage IV disease. Nine out of seventeen (56%) patients showed CNVs in cfDNA, with more CNVs in EBV-negative cases. Recurrent gains were detected for 3q, 11q, and 18q. Recurrent losses were observed at 6q. The fraction of EBV reads in cfDNA from EBV-positive patients was 3-log higher compared to controls and EBV-negative patients. 289 SNVs were identified, with a median of 19 per sample. SNV burden correlated significantly with lactate dehydrogenase levels. Similar SNV burdens were observed in EBV-negative and EBV-positive PTLD. The most commonly mutated genes were TP53 and KMT2D (41%), followed by SPEN, TET2 (35%), and ARID1A, IGLL5, and PIM1 (29%), indicating DNA damage response, epigenetic regulation, and B-cell signaling/NFkB pathways as drivers of PTLD. Overall, CNVs were more prevalent in EBV-negative lymphoma, while no difference was observed in the number of SNVs. Our data indicated the potential of analyzing cfDNA as a tool for PTLD screening and response monitoring.</p

Full-length mRNA sequencing uncovers a widespread coupling between transcription initiation and mRNA processing

Background: The multifaceted control of gene expression requires tight coordination of regulatory mechanisms at transcriptional and post-transcriptional level. Here, we studied the interdependence of transcription initiation, splicing and polyadenylation events on single mRNA molecules by full-length mRNA sequencing. Results: In MCF-7 breast cancer cells, we find 2700 genes with interdependent alternative transcription initiation, splicing and polyadenylation events, both in proximal and distant parts of mRNA molecules, including examples of coupling between transcription start sites and polyadenylation sites. The analysis of three human primary tissues (brain, heart and liver) reveals similar patterns of interdependency between transcription initiation and mRNA processing events. We predict thousands of novel open reading frames from full-length mRNA sequences and obtained evidence for their translation by shotgun proteomics. The mapping database rescues 358 previously unassigned peptides and improves the assignment of others. By recognizing sample-specific amino-acid changes and novel splicing patterns, full-length mRNA sequencing improves proteogenomics analysis of MCF-7 cells. Conclusions: Our findings demonstrate that our understanding of transcriptome complexity is far from complete and provides a basis to reveal largely unresolved mechanisms that coordinate transcription initiation and mRNA processing. Electronic supplementary material The online version of this article (10.1186/s13059-018-1418-0) contains supplementary material, which is available to authorized users

Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted

The Genome of the Netherlands:design, and project goals

Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean = 53 years; SD = 16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.</p

Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands'

Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with ‘true' genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed significant improvement in the imputation quality for rare variants (MAF 0.05–0.5%) compared with 1000G. In Dutch samples, the mean observed Pearson correlation, r2, increased from 0.61 to 0.71. We also saw improved imputation accuracy for other European populations (in the British samples, r2 improved from 0.58 to 0.65, and in the Italians from 0.43 to 0.47). A combined reference set comprising 1000G and GoNL improved the imputation of rare variants even further. The Italian samples benefitted the most from this combined reference (the mean r2 increased from 0.47 to 0.50). We conclude that the creation of a large population-specific reference is advantageous for imputing rare variants and that a combined reference panel across multiple populations yields the best imputation results

Next-generation sequencing-based genome diagnostics across clinical genetics centers: Implementation choices and their effects

Implementation of next-generation DNA sequencing (NGS) technology into routine diagnostic genome care requires strategic choices. Instead of theoretical discussions on the consequences of such choices, we compared NGS-based diagnostic practices in eight clinical genetic centers in the Netherlands, based on genetic testing of nine pre-selected patients with cardiomyopathy. We highlight critical implementation choices, including the specific contributions of laboratory and medical specialists, bioinformaticians and researchers to diagnostic genome care, and how these affect interpretation and reporting of variants. Reported pathogenic mutations were consistent for all but one patient. Of the two centers that were inconsistent in their diagnosis, one reported to have found 'no causal variant', thereby underdiagnosing this patient. The other provided an alternative diagnosis, identifying another variant as causal than the other centers. Ethical and legal analysis showed that informed consent procedures in all centers were generally adequate for diagnostic NGS applications that target a limited set of genes, but not for exome- and genome-based diagnosis. We propose changes to further improve and align these procedures, taking into account the blurring boundary between diagnostics and research, and specific counseling options for exome- and genome-based diagnostics. We conclude that alternative diagnoses may infer a certain level of 'greediness' to come to a positive diagnosis in interpreting sequencing results. Moreover, there is an increasing interdependence of clinic, diagnostics and research departments for comprehensive diagnostic genome care. Therefore, we invite clinical geneticists, physicians, researchers, bioinformatics experts and patients to reconsider their role and position in future diagnostic genome care

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking