Ulf Gyllensten

Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain

Transcriptome sequencing allows for analysis of mature RNAs at base pair resolution. Here we show that RNA-seq can also be used for studying nascent RNAs undergoing transcription. We sequenced total RNA from human brain and liver and found a large fraction of reads (up to 40%) within introns. Intronic RNAs were abundant in brain tissue, particularly for genes involved in axonal growth and synaptic transmission. Moreover, we detected significant differences in intronic RNA levels between fetal and adult brains. We show that the pattern of intronic sequence read coverage is explained by nascent transcription in combination with co-transcriptional splicing. Further analysis of co-transcriptional splicing indicates a correlation between slowly removed introns and alternative splicing. Our data show that sequencing of total RNA provides unique insight into the transcriptional processes in the cell, with particular importance for normal brain development.

MICA polymorphism: biology and importance in cancer

The major histocompatibility complex class I polypeptide-related sequence A gene (MICA) encodes a membrane-bound protein acting as a ligand to stimulate an activating receptor, NKG2D, expressed on the surface of essentially all human natural killer (NK), γδ T and CD8(+) αβ T cells. MICA protein is absent from most cells but can be induced by infections and oncogenic transformation and is frequently expressed in epithelial tumors. Upon binding to MICA, NKG2D activates cytolytic responses of NK and γδ T cells against infected and tumor cells expressing MICA. Therefore, membrane-bound MICA acts as a signal during the early immune response against infection or spontaneously arising tumors. On the other hand, human tumor cells spontaneously release a soluble form of MICA, causing the downregulation of NKG2D and in turn severe impairment of the antitumor immune response of NK and CD8(+) T cells. This is considered to promote tumor immune evasion and also to compromise host resistance to infections. MICA is the most polymorphic non-classical class I gene. A possible association of MICA polymorphism with genetic predisposition to different cancer types has been investigated in candidate gene-based studies. Two genome-wide association studies have identified loci in MICA that influence susceptibility to cervical neoplasia and hepatitis C virus-induced hepatocellular carcinoma, respectively. Given the current level of interest in the field of MICA gene, we discuss the genetics and biology of the MICA gene and the role of its polymorphism in cancer. Gaps in our understanding and future research needs are also discussed.

Lessons and implications from association studies and post-GWAS analyses of cervical cancer

Cervical cancer has a heritable genetic component. A large number of genetic associations with cervical cancer have been reported in hypothesis-driven candidate gene studies, but many of these results are either inconsistent or have failed to be independently replicated. Genome-wide association studies (GWAS) have identified additional susceptibility loci previously not implicated in cervical cancer development, highlighting the power of genome-wide unbiased association analyses. Post-GWAS analyses including pathway-based analysis and functional characterization of associated variants have provided new insights into the pathogenesis of cervical cancer. In this review we summarize findings from candidate gene association studies, GWAS, and post-GWAS analyses of cervical cancer. We also discuss gaps in our understanding, possible clinical implications of the findings, and lessons for studies of other complex diseases.

CanvasDB: a local database infrastructure for analysis of targeted- and whole genome re-sequencing projects

CanvasDB is an infrastructure for management and analysis of genetic variants from massively parallel sequencing (MPS) projects. The system stores SNP and indel calls in a local database, designed to handle very large datasets, to allow for rapid analysis using simple commands in R. Functional annotations are included in the system, making it suitable for direct identification of disease-causing mutations in human exome- (WES) or whole-genome sequencing (WGS) projects. The system has a built-in filtering function implemented to simultaneously take into account variant calls from all individual samples. This enables advanced comparative analysis of variant distribution between groups of samples, including detection of candidate causative mutations within family structures and genome-wide association by sequencing. In most cases, these analyses are executed within just a matter of seconds, even when there are several hundreds of samples and millions of variants in the database. We demonstrate the scalability of canvasDB by importing the individual variant calls from all 1092 individuals present in the 1000 Genomes Project into the system, over 4.4 billion SNPs and indels in total. Our results show that canvasDB makes it possible to perform advanced analyses of large-scale WGS projects on a local server. Database URL: https://github.com/UppsalaGenomeCenter/CanvasDB

Genome-wide DNA methylation study identifies genes associated with the cardiovascular biomarker GDF-15

Growth-differentiation factor 15 (GDF-15) is expressed in low to moderate levels in most healthy tissues and increases in response to inflammation. GDF-15 is associated with cardiovascular dysfunction and over-expressed in the myocardium of patients with myocardial infarction (MI). However, little is known about the function of GDF-15 in cardiovascular disease, and the underlying regulatory network of GDF-15 is not known. To investigate a possible association between GDF-15 levels and DNA methylation, we performed a genome-wide DNA methylation study of white blood cells in a population-based study (N = 717). Significant loci where replicated in an independent cohort (N = 963). We also performed a gene ontology (GO) enrichment analysis. We identified and replicated 16 CpG-sites (false discovery rate [FDR] < 0.05), at 11 independent loci including MIR21. MIR21 encodes a microRNA (miR-21) that has previously been shown to be associated with the development of heart disease. Interestingly, GDF15 mRNA contains a binding site for miR-21. Four sites were also differentially methylated in blood from participants previously diagnosed with MI and 14 enriched GO terms (FDR < 0.05, enrichment > 2) were identified, including cardiac muscle cell differentiation. This study shows that GDF-15 levels are associated with differences in DNA methylation in blood cells, and a subset of the loci are also differentially methylated in participants with MI. However, there might be interactions between GDF-15 levels and methylation in other tissues not addressed in this study. These results provide novel links between GDF-15 and cardiovascular disease.

PATZ1 down-regulates FADS1 by binding to rs174557 and is opposed by SP1/SREBP1c

Abstract The FADS1 and FADS2 genes in the FADS cluster encode the rate-limiting enzymes in the synthesis of long-chain polyunsaturated fatty acids (LC-PUFAs). Genetic variation in this region has been associated with a large number of diseases and traits many of them correlated to differences in metabolism of PUFAs. However, the causative variants leading to these associations have not been identified. Here we find that the multiallelic rs174557 located in an AluYe5 element in intron 1 of FADS1 is functional and lies within a PATZ1 binding site. The derived allele of rs174557, which is the common variant in most populations, diminishes binding of PATZ1, a transcription factor conferring allele-specific downregulation of FADS1. The PATZ1 binding site overlaps with a SP1 site. The competitive binding between the suppressive PATZ1 and the activating complex of SP1 and SREBP1c determines the enhancer activity of this region, which regulates expression of FADS1.

A variant upstream of HLA‐DRB1 and multiple variants in MICA influence susceptibility to cervical cancer in a Swedish population

In a genome‐wide association study, we have previously identified and performed the initial replication of three novel susceptibility loci for cervical cancer: rs9272143 upstream of HLA‐DRB1, rs2516448 adjacent to MHC class I polypeptide‐related sequence A gene (MICA), and rs3117027 at HLA‐DPB2. The risk allele T of rs2516448 is in perfect linkage disequilibrium with a frameshift mutation (A5.1) in MICA exon 5, which results in a truncated protein. To validate these associations in an independent study and extend our prior work to MICA exon 5, we genotyped the single‐nucleotide polymorphisms at rs9272143, rs2516448, rs3117027 and the MICA exon 5 microsatellite in a nested case–control study of 961 cervical cancer patients (827 carcinoma in situ and 134 invasive carcinoma) and 1725 controls from northern Sweden. The C allele of rs9272143 conferred protection against cervical cancer (odds ratio [OR] = 0.73, 95% confidence interval [CI] = 0.65–0.82; P = 1.6 × 10−7), which is associated with higher expression level of HLA‐DRB1, whereas the T allele of rs2516448 increased the susceptibility to cervical cancer (OR = 1.33, 95% CI = 1.19–1.49; P = 5.8 × 10−7), with the same association shown with MICA‐A5.1. The direction and the magnitude of these associations were consistent with our previous findings. We also identified protective effects of the MICA‐A4 (OR = 0.80, 95% CI = 0.68–0.94; P = 6.7 × 10−3) and MICA‐A5 (OR = 0.60, 95% CI = 0.50–0.72; P = 3.0 × 10−8) alleles. The associations with these variants are unlikely to be driven by the nearby human leukocyte antigen (HLA) alleles. No association was observed between rs3117027 and risk of cervical cancer. Our results support the role of HLA‐DRB1 and MICA in the pathogenesis of cervical cancer.

Analysis of the genetic architecture of susceptibility to cervical cancer indicates that common SNPs explain a large proportion of the heritability

The genetic architecture of susceptibility to cervical cancer is not well-understood. By using a genome-wide association study (GWAS) of 1034 cervical cancer patients and 3948 controls with 632668 single-nucleotide polymorphisms (SNPs), we estimated that 24.0% [standard error (SE) = 5.9%, P = 3.19×10(-6)] of variation in liability to cervical cancer is captured by autosomal SNPs, a bit lower than the heritability estimated from family study (27.0%), suggesting that a substantial proportion of the heritability is tagged by common SNPs. The remaining missing heritability most probably reflects incomplete linkage disequilibrium between causal variants and the genotyped SNPs. The variance explained by each chromosome is not related to its length (R (2) = 0.020, P = 0.516). Published genome-wide significant variants only explain 2.1% (SE = 1.5%, P = 0) of phenotypic variance, which reveals that most of the heritability has not been detected, presumably due to small individual effects. Another 2.1% (SE = 1.1%, P = 0.013) of variation is attributable to biological pathways associated with risk of cervical cancer, supporting that pathway analysis can identify part of the hidden heritability. Except for human leukocyte antigen genes and MHC class I polypeptide-related sequence A (MICA), none of the 82 candidate genes/regions reported in other association studies contributes to the heritability of cervical cancer in our dataset. This study shows that risk of cervical cancer is influenced by many common germline genetic variants of small effects. The findings are important for further study design to identify the hiding heritability that has not yet been revealed. More susceptibility loci are yet to be found in GWASs with higher power.

A cis-eQTL of HLA-DRB1 and a frameshift mutation of MICA contribute to the pattern of association of HLA alleles with cervical cancer

The association of classic human leukocyte antigen (HLA) alleles with risk of cervical cancer has been extensively studied, and a protective effect has consistently been found for DRB1*1301, DQA1*0103, and/or DQB1*0603 (these three alleles are in perfect linkage disequilibrium [LD] and often occur on the same haplotype in Europeans), while reports have differed widely with respect to the effect of HLA‐B*07, DRB1*1501, and/or DQB1*0602 (the last two alleles are also in perfect LD in Europeans). It is not clear whether the reported HLA alleles are responsible for the differences in cervical cancer susceptibility, or if functional variants at other locations within the major histocompatibility complex (MHC) region may explain the effect. In order to assess the relative contribution of both classic HLA alleles and single‐nucleotide polymorphisms (SNPs) within the MHC region to cervical cancer susceptibility, we have imputed classic HLA alleles in 1034 cervical cancer patients and 3948 controls in a Swedish population for an integrated analysis. We found that the protective haplotype DRB1*1301‐DQA1*0103‐DQB1*0603 has a direct effect on cervical cancer and always occurs together with the C allele of a HLA‐DRB1 cis‐eQTL (rs9272143), which increases the expression of HLA‐DRB1. The haplotype rs9272143C‐DRB1*1301‐DQA1*0103‐DQB1*0603 conferred the strongest protection against cervical cancer (odds ratio [OR] = 0.41, 95% confidence interval [CI] = 0.32–0.52, P = 6.2 × 10−13). On the other hand, the associations with HLA‐B*0702 and DRB1*1501‐DQB1*0602 are attributable to the joint effects of both the HLA‐DRB1 cis‐eQTL (rs9272143) and a frameshift mutation (G inserion of rs67841474, also known as A5.1) of the MHC class I polypeptide‐related sequence A gene (MICA). Variation in LD between the classic HLA loci, rs9272143 and rs67841474 between populations may explain the different associations of HLA‐B*07 and DRB1*1501‐DQB1*0602 with cervical cancer between studies. The mechanism suggested may also explain similar inconsistent results for other HLA‐associated diseases.

SweGen: A whole-genome map of genetic variability in a cross-section of the Swedish population

Here we describe the SweGen dataset, a high-quality map of genetic variation in the Swedish population. This data represents a basic resource for clinical genetics laboratories as well as for sequencing-based association studies, by providing information on the frequencies of genetic variants in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide population based cohort of over 10,000 individuals. From this sample collection, 1,000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole genome sequencing (WGS). Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a whole-genome map of aggregated variant frequencies in the Swedish population that we hereby release to the scientific community.