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The germline mutation rate determines the pace of genome evolution and is an evolving parameter itself1. However, little is known about what determines its evolution, as most studies of mutation rates have focused on single species with different methodologies2. Here we quantify germline mutation rates across vertebrates by sequencing and comparing the high-coverage genomes of 151 parent–offspring trios from 68 species of mammals, fishes, birds and reptiles. We show that the per-generation mutation rate varies among species by a factor of 40, with mutation rates being higher for males than for females in mammals and birds, but not in reptiles and fishes. The generation time, age at maturity and species-level fecundity are the key life-history traits affecting this variation among species. Furthermore, species with higher long-term effective population sizes tend to have lower mutation rates per generation, providing support for the drift barrier hypothesis3. The exceptionally high yearly mutation rates of domesticated animals, which have been continually selected on fecundity traits including shorter generation times, further support the importance of generation time in the evolution of mutation rates. Overall, our comparative analysis of pedigree-based mutation rates provides ecological insights on the mutation rate evolution in vertebrates.

Mantle cell lymphoma (MCL) is characterized by marked differences in outcome, emphasizing the need for strong prognostic biomarkers. Here, we explore expression patterns and prognostic relevance of circular RNAs (circRNAs), a group of endogenous non-coding RNA molecules, in MCL. We profiled the circRNA expression landscape using RNA-sequencing and explored the prognostic potential of 40 abundant circRNAs in samples from the Nordic MCL2 and MCL3 clinical trials, using NanoString nCounter Technology. We report a circRNA-based signature (circSCORE) developed in the training cohort MCL2 that is highly predictive of time to progression (TTP) and lymphoma-specific survival (LSS). The dismal outcome observed in the large proportion of patients assigned to the circSCORE high-risk group was confirmed in the independent validation cohort MCL3, both in terms of TTP (HR 3.0; P = 0.0004) and LSS (HR 3.6; P = 0.001). In Cox multiple regression analysis incorporating MIPI, Ki67 index, blastoid morphology and presence of TP53 mutations, circSCORE retained prognostic significance for TTP (HR 3.2; P = 0.01) and LSS (HR 4.6; P = 0.01). In conclusion, circRNAs are promising prognostic biomarkers in MCL and circSCORE improves identification of high-risk disease among younger patients treated with cytarabine-containing chemoimmunotherapy and autologous stem cell transplant.

The mutation rate of a specific position in the human genome depends on the sequence context surrounding it. Modeling the mutation rate by estimating a rate for each possible k-mer, however, only works for small values of k since the data becomes too sparse for larger values of k. Here we propose a new method that solves this problem by grouping similar k-mers using IUPAC patterns. We refer to the method as k-mer pattern partition and have implemented it in a software package called kmerPaPa. We use a large set of human de novo mutations to show that this new method leads to improved prediction of mutation rates and makes it possible to create models using wider sequence contexts than previous studies. Revealing that for some mutation types, the mutation rate of a position is significantly affected by nucleotides that are up to four base pairs away. As the first method of its kind, it does not only predict rates for point mutations but also indels. We have additionally created a software package called Genovo that, given a k-mer pattern partition model, predicts the expected number of synonymous, missense, and other functional mutation types for each gene. Using this software, we show that the created mutation rate models increase the statistical power to detect genes containing disease-causing variants and to identify genes under strong constraint, e.g. haploinsufficient genes.

The lack of consensus methods to estimate germline mutation rates from pedigrees has led to substantial differences in computational pipelines in the published literature. Here, we answer Susanne Pfeifer's opinion piece discussing the pipeline choices of our recent article estimating the germline mutation rate of rhesus macaques (Macaca mulatta). We acknowledge the differences between the method that we applied and the one preferred by Pfeifer. Yet, we advocate for full transparency and justification of choices as long as rigorous comparison of pipelines remains absent because it is the only way to conclude on best practices for the field.

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a “Mutationathon”, a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

Sequencing of cell-free DNA (cfDNA) is currently being used to detect cancer by searching both for mutational and non-mutational alterations. Recent work has shown that the length distribution of cfDNA fragments from a cancer patient can inform tumor load and type. Here, we propose non-negative matrix factorization (NMF) of fragment length distributions as a novel method to study fragment lengths in cfDNA samples.Using shallow whole-genome sequencing (sWGS) of cfDNA from a cohort of patients with metastatic castration-resistant prostate cancer (mCRPC), we demonstrate how NMF accurately infers the true tumor fragment length distribution as an NMF component - and that the sample weights of this component correlate with ctDNA levels (r=0.75). We further demonstrate how using several NMF components enables accurate cancer detection on data from various early stage cancers (AUC=0.96). Finally, we show that NMF, when applied across genomic regions, can be used to discover fragment length signatures associated with open chromatin.

Background Understanding the rate and pattern of germline mutations is of fundamental importance for understanding evolutionary processes. Results Here we analyzed 19 parent-offspring trios of rhesus macaques (Macaca mulatta) at high sequencing coverage of ∼76× per individual and estimated a mean rate of 0.77 × 10−8de novo mutations per site per generation (95% CI: 0.69 × 10−8 to 0.85 × 10−8). By phasing 50% of the mutations to parental origins, we found that the mutation rate is positively correlated with the paternal age. The paternal lineage contributed a mean of 81% of the de novo mutations, with a trend of an increasing male contribution for older fathers. Approximately 3.5% of de novo mutations were shared between siblings, with no parental bias, suggesting that they arose from early development (postzygotic) stages. Finally, the divergence times between closely related primates calculated on the basis of the yearly mutation rate of rhesus macaque generally reconcile with divergence estimated with molecular clock methods, except for the Cercopithecoidea/Hominoidea molecular divergence dated at 58 Mya using our new estimate of the yearly mutation rate. Conclusions When compared to the traditional molecular clock methods, new estimated rates from pedigree samples can provide insights into the evolution of well-studied groups such as primates.

Mantle cell lymphoma (MCL) is characterized by marked differences in outcome, emphasizing the need for strong prognostic biomarkers. Here, we explore expression patterns and prognostic relevance of circular RNAs (circRNAs), a group of endogenous non-coding RNA molecules, in MCL. We profiled the circRNA expression landscape using RNA-sequencing and explored the prognostic potential of 40 abundant circRNAs in samples from the Nordic MCL2 and MCL3 clinical trials, using NanoString nCounter Technology. We report a circRNA-based signature (circSCORE) developed in the training cohort MCL2 that is highly predictive of time to progression (TTP) and lymphoma-specific survival (LSS). The dismal outcome observed in the large proportion of patients assigned to the circSCORE high-risk group was confirmed in the independent validation cohort MCL3, both in terms of TTP (HR 3.0; P = 0.0004) and LSS (HR 3.6; P = 0.001). In Cox multiple regression analysis incorporating MIPI, Ki67 index, blastoid morphology and presence of TP53 mutations, circSCORE retained prognostic significance for TTP (HR 3.2; P = 0.01) and LSS (HR 4.6; P = 0.01). In conclusion, circRNAs are promising prognostic biomarkers in MCL and circSCORE improves identification of high-risk disease among younger patients treated with cytarabine-containing chemoimmunotherapy and autologous stem cell transplant.

Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) constitutes a heterogeneous category of lymphomas, which do not fit into any of the specifically defined T-cell lymphoma entities. Both the pathogenesis and tumor biology in PTCL-NOS are poorly understood. Protein expression in pretherapeutic PTCL-NOS tumors was analyzed by proteomics. Differentially expressed proteins were compared in 3 distinct scenarios: (A) PTCL-NOS tumor tissue (n = 18) vs benign lymphoid tissue (n = 8), (B) clusters defined by principal component analysis (PCA), and (C) tumors from patients with chemosensitive vs refractory PTCL-NOS. Selected differentially expressed proteins identified by proteomics were correlated with clinico-pathological features and outcome in a larger cohort of patients with PTCL-NOS (n = 87) by immunohistochemistry (IHC). Most proteins with altered expression were identified comparing PTCL-NOS vs benign lymphoid tissue. PCA of the protein profile defined 3 distinct clusters. All benign samples clustered together, whereas PTCL-NOS tumors separated into 2 clusters with different patient overall survival rates (P = .001). Differentially expressed proteins reflected large biological diversity among PTCL-NOS, particularly associated with alterations of “immunological” pathways. The 2 PTCL-NOS subclusters defined by PCA showed disturbance of “stress-related” and “protein metabolic” pathways. α-Enolase 1 (ENO1) was found differentially expressed in all 3 analyses, and high intratumoral ENO1 expression evaluated by IHC correlated with poor outcome (hazard ratio, 2.09; 95% confidence interval, 1.17-3.73; P = .013). High expression of triosephosphate isomerase (TPI1) also showed a tendency to correlate with poor survival (P = .057). In conclusion, proteomic profiling of PTCL-NOS provided evidence of markedly altered protein expression and identified ENO1 as a novel potential prognostic marker.

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3–5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right treatment choice and thus improve the clinical outcome. In this paper, we present a new method for predicting the location of the primary tumor using gene expression data: locating cancers of unknown primary (LoCUP). The method models the data as a mixture of normal and tumor cells and thus allows correct classification even in impure samples, where the tumor biopsy is contaminated by a large fraction of normal cells. We find that our method provides a significant increase in classification accuracy (95.8% over 90.8%) on simulated low-purity metastatic samples and shows potential on a small dataset of real metastasis samples with known origin.

In this chapter, we give a short introduction to the genetics of complex diseases emphasizing evolutionary models for disease genes and the effect of different models on the genetic architecture, and we give a survey of the state-of-the-art of genome-wide association studies (GWASs).

Improved prognostic biomarkers are needed to guide personalized prostate cancer (PC) treatment decisions. Due to the prominent molecular heterogeneity of PC, multimarker panels may be more robust. Here, 25 selected top-candidate miRNA and methylation markers for PC were profiled by qPCR in malignant radical prostatectomy (RP) tissue specimens from 198 PC patients (Cohort 1, training). Using GLMnet, we trained a novel multimarker model (miMe) comprising nine miRNAs and three methylation markers that predicted postoperative biochemical recurrence (BCR) independently of the established clinicopathological CAPRA-S nomogram in Cox multivariate regression analysis in Cohort 1 (HR [95% CI]: 1.53 [1.26-1.84], p < 0.001). This result was successfully validated in two independent RP cohorts (Cohort 2, n = 159: HR [95% CI]: 1.35 [1.06-1.73], p = 0.015. TCGA, n = 350: HR [95% CI]: 1.34 [1.01-1.77], p = 0.04). Notably, in CAPRA-S low-risk patients, a high miMe score was associated with >6 times higher risk of BCR, suggesting that miMe may help identify PC patients at high risk of progression despite favorable clinicopathological factors postsurgery. Finally, miMe was a significant predictor of cancer-specific survival (p = 0.019, log-rank test) in a merged analysis of 357 RP patients. In conclusion, we trained, tested and validated a novel 12-marker panel (miMe) that showed significant independent prognostic value in three RP cohorts. In the future, combining miMe score with existing clinical nomograms may improve PC risk stratification and thus help guide treatment decisions.

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to locate the primary cancer. As standard treatments are based on the cancer type, such cases are hard to treat and have very poor prognosis. Using molecular data from the metastatic cancer to predict the primary site can make treatment choice easier and enable targeted therapy. In this article, we first examine the ability to predict cancer type using different types of omics data. Methylation data lead to slightly better prediction than gene expression and both these are superior to classification using somatic mutations. After using 3 data types independently, we notice some differences between the classes that tend to be misclassified, suggesting that integrating the data might improve accuracy. In light of the different levels of information provided by different omics types and to be able to handle missing data, we perform multi-omics classification by hierarchically combining the classifiers. The proposed hierarchical method first classifies based on the most informative type of omics data and then uses the other types of omics data to classify samples that did not get a high confidence classification in the first step. The resulting hierarchical classifier has higher accuracy than any of the single omics classifiers and thus proves that the combination of different data types is beneficial. Our results show that using multi-omics data can improve the classification of cancer types. We confirm this by testing our method on metastatic cancers from the MET500 dataset.

In this chapter, we give a short introduction to the genetics of complex diseases emphasizing evolutionary models for disease genes and the effect of different models on the genetic architecture, and we give a survey of the state-of-the-art of genome-wide association studies (GWASs).

The human mutation rate per generation estimated from trio sequencing has revealed an almost linear relationship with the age of the father and the age of the mother, with fathers contributing about three times as many mutations per year as mothers. The yearly trio-based mutation rate estimate of around 0.43 × 10 is markedly lower than previous indirect estimates of about 1 × 10 per year from phylogenetic comparisons of the great apes calibrated by fossil evidence. This suggests either a slowdown in the accumulation of mutations per year in the human lineage over the past 10 million years or an inaccurate interpretation of the fossil record. Here we inferred de novo mutations in chimpanzee, gorilla, and orangutan parent-offspring trios. Extrapolating the relationship between the mutation rate and the age of parents from humans to these other great apes, we estimated that each species has higher mutation rates per year by factors of 1.50 ± 0.10, 1.51 ± 0.23, and 1.42 ± 0.22 for chimpanzee, gorilla, and orangutan, respectively, and by a factor of 1.48 ± 0.08 for the three species combined. These estimates suggest an appreciable slowdown in the yearly mutation rate in the human lineage that is likely to be recent as genome comparisons almost adhere to a molecular clock. If the nonhuman rates rather than the human rate are extrapolated over the phylogeny of the great apes, we estimate divergence and speciation times that are much more in line with the fossil record and the biogeography., The study was supported by grant number 6108-00385A from the Danish Council for Independent Research | Natural Sciences (to M.H.S.).

The human mutation rate per generation estimated from trio sequencing has revealed an almost linear relationship with the age of the father and the age of the mother. The yearly trio-based mutation rate estimate of ~0.43×10−9 is markedly lower than prior indirect estimates of ~1×10−9 per year from phylogenetic comparisons of the great apes. This suggests either a slowdown over the past 10 million years or an inaccurate interpretation of the fossil record. Here we use sequencing of chimpanzee, gorilla and orangutan trios and find that each species has higher estimated mutation rates per year by factors of 1.67+/− 0.22, 1.54+/− 0.2 and 1.84+/− 0.19, respectively. These estimates suggest a very recent and appreciable slowdown in human mutation rate, and, if extrapolated over the great apes phylogeny, yields divergence estimates much more in line with the fossil record and the biogeography.

Understanding of sequence diversity is the cornerstone of analysis of genetic disorders, population genetics, and evolutionary biology. Here, we present an update of our sequencing set to 15,220 Icelanders who we sequenced to an average genome-wide coverage of 34X. We identified 39,020,168 autosomal variants passing GATK filters: 31,079,378 SNPs and 7,940,790 indels. Calling de novo mutations (DNMs) is a formidable challenge given the high false positive rate in sequencing datasets relative to the mutation rate. Here we addressed this issue by using segregation of alleles in three-generation families. Using this transmission assay, we controlled the false positive rate and identified 108,778 high quality DNMs. Furthermore, we used our extended family structure and read pair tracing of DNMs to a panel of phased SNPs, to determine the parent of origin of 42,961 DNMs.

BackgroundDetailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.ResultsTo take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.ConclusionWe find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

In this paper, we mine full mtDNA sequences from an exome capture data set of 2000 Danes, showing that it is possible to get high-quality full-genome sequences of the mitochondrion from this resource. The sample includes 1000 individuals with type 2 diabetes and 1000 controls. We characterise the variation found in the mtDNA sequence in Danes and relate the variation to diabetes risk as well as to several blood phenotypes of the controls but find no significant associations. We report 2025 polymorphisms, of which 393 have not been reported previously. These 393 mutations are both very rare and estimated to be caused by very recent mutations but individuals with type 2 diabetes do not possess more of these variants. Population genetics analysis using Bayesian skyline plot shows a recent history of rapid population growth in the Danish population in accordance with the fact that >40% of variable sites are observed as singletons.European Journal of Human Genetics advance online publication, 22 January 2014; doi:10.1038/ejhg.2013.282.

Mutation of the DNA molecule is one of the most fundamental processes in biology. In this study, we use 283 parent-offspring trios to estimate the rate of mutation for both single nucleotide variants (SNVs) and short length variants (indels) in humans and examine the mutation process. We found 17812 SNVs, corresponding to a mutation rate of 1.29 × 10−8 per position per generation (PPPG) and 1282 indels corresponding to a rate of 9.29 × 10−10 PPPG. We estimate that around 3% of human de novo SNVs are part of a multi-nucleotide mutation (MNM), with 558 (3.1%) of mutations positioned less than 20kb from another mutation in the same individual (median distance of 525bp). The rate of de novo mutations is greater in late replicating regions (p = 8.29 × 10−19) and nearer recombination events (p = 0.0038) than elsewhere in the genome., Author Summary In each generation new genetic variants are introduced by mutations. In this study we use whole genome sequence data from Icelandic families to directly observe such new mutations. Our estimate of the mutation rate implies that a newborn with 30-year-old parents will on average carry 75 new SNV mutations and 6 new short indel mutations. We observe that some of the found mutations occur much closer together than would be expected by chance. Our analysis shows that mutational hotspots cannot explain this clustering, instead the clustering mutations are likely created by a single mutational event. We observe a different composition for mutations that cluster very close together compared to more distant clustering mutations. This suggests that there is likely more than one type of underlying mutational mechanism creating the multi nucleotide mutation events. We furthermore observe a higher mutation rate near recombination events but find that this effect cannot explain the large number of clustering mutations that we observe.

In the version of this article initially published, Tomas Marques-Bonet was missing the following affiliations: Catalan Institution of Research and Advanced Studies (ICREA), Barcelona, Spain; CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain; and Institut Catala de Paleontologia Miquel Crusafont, Universitat Autonoma de Barcelona, Barcelona, Spain. The affiliations have been added in the PDF and HTML versions of the article.

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecological malignancy in the developed world, accounting for 4% of the deaths from cancer in women. We performed a three-phase genome-wide association study of EOC survival in 8,951 individuals with EOC (cases) with available survival time data and a parallel association analysis of EOC susceptibility. Two SNPs at 19p13.11, rs8170 and rs2363956, showed evidence of association with survival (overall P = 5 × 10⁻⁴ and P = 6 × 10⁻⁴, respectively), but they did not replicate in phase 3. However, the same two SNPs demonstrated genome-wide significance for risk of serous EOC (P = 3 × 10⁻⁹ and P = 4 × 10⁻¹¹, respectively). Expression analysis of candidate genes at this locus in ovarian tumors supported a role for the BRCA1-interacting gene C19orf62, also known as MERIT40, which contains rs8170, in EOC development.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field. Mutations generate sequence diversity and provide a substrate for selection. The rate of de novo mutations is therefore of major importance to evolution. Here we conduct a study of genome-wide mutation rates by sequencing the entire genomes of 78 Icelandic parent-offspring trios at high coverage. We show that in our samples, with an average father's age of 29.7, the average de novo mutation rate is 1.20 × 10(-8) per nucleotide per generation. Most notably, the diversity in mutation rate of single nucleotide polymorphisms is dominated by the age of the father at conception of the child. The effect is an increase of about two mutations per year. An exponential model estimates paternal mutations doubling every 16.5 years. After accounting for random Poisson variation, father's age is estimated to explain nearly all of the remaining variation in the de novo mutation counts. These observations shed light on the importance of the father's age on the risk of diseases such as schizophrenia and autism. National Institutes of Health MH071425 info:eu-repo/grantAgreement/EC/FP7/223423 IAPP-MC-251592 European Community IMI grant EU-AIMS 115300

Contains fulltext : 97981.pdf (Publisher’s version ) (Closed access) We tested 16 million SNPs, identified through whole-genome sequencing of 457 Icelanders, for association with gout and serum uric acid levels. Genotypes were imputed into 41,675 chip-genotyped Icelanders and their relatives, for effective sample sizes of 968 individuals with gout and 15,506 individuals for whom serum uric acid measurements were available. We identified a low-frequency missense variant (c.1580C>G) in ALDH16A1 associated with gout (OR = 3.12, P = 1.5 x 10(-16), at-risk allele frequency = 0.019) and serum uric acid levels (effect = 0.36 s.d., P = 4.5 x 10(-21)). We confirmed the association with gout by performing Sanger sequencing on 6,017 Icelanders. The association with gout was stronger in males relative to females. We also found a second variant on chromosome 1 associated with gout (OR = 1.92, P = 0.046, at-risk allele frequency = 0.986) and serum uric acid levels (effect = 0.48 s.d., P = 4.5 x 10(-16)). This variant is close to a common variant previously associated with serum uric acid levels. This work illustrates how whole-genome sequencing data allow the detection of associations between low-frequency variants and complex traits.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field. Three genome-wide association studies in Europe and the USA have reported eight urinary bladder cancer (UBC) susceptibility loci. Using extended case and control series and 1000 Genomes imputations of 5 340 737 single-nucleotide polymorphisms (SNPs), we searched for additional loci in the European GWAS. The discovery sample set consisted of 1631 cases and 3822 controls from the Netherlands and 603 cases and 37 781 controls from Iceland. For follow-up, we used 3790 cases and 7507 controls from 13 sample sets of European and Iranian ancestry. Based on the discovery analysis, we followed up signals in the urea transporter (UT) gene SLC14A. The strongest signal at this locus was represented by a SNP in intron 3, rs17674580, that reached genome-wide significance in the overall analysis of the discovery and follow-up groups: odds ratio = 1.17, P = 7.6 × 10(-11). SLC14A1 codes for UTs that define the Kidd blood group and are crucial for the maintenance of a constant urea concentration gradient in the renal medulla and, through this, the kidney's ability to concentrate urine. It is speculated that rs17674580, or other sequence variants in LD with it, indirectly modifies UBC risk by affecting urine production. If confirmed, this would support the 'urogenous contact hypothesis' that urine production and voiding frequency modify the risk of UBC. info:eu-repo/grantAgreement/EC/FP7/018827 info:eu-repo/grantAgreement/EC/FP7/218071 Radboud University Nijmegen Medical Centre (RUNMC) Prinses Beatrix Fonds VSB Fonds National Institute of Mental Health (NIH/NIMH) MH078075 Cancer Research UK Yorkshire Cancer Research European Union 513943 Compagnia di San Paolo-Human Genetics Foundation (HuGeF) Italian Association for Cancer Research, Italy Piedmont Region Progetti di Ricerca Sanitaria Finalizzata Flemish government Belgian province of Limburg Swedish Cancer Society Swedish Research Council Shiraz Institute for Cancer Research Shiraz, Iran ICR-87-502 Red Tematica de Investigacion Cooperativa en Cancer RD06/0020/1054

Mycosis fungoides (MF) is the most frequent form of cutaneous T-cell lymphoma. The disease often takes an indolent course, but in approximately one-third of the patients, the disease progresses to an aggressive malignancy with a poor prognosis. At the time of diagnosis, it is impossible to predict which patients develop severe disease and are in need of aggressive treatment. Accordingly, we investigated the prognostic potential of microRNAs (miRNAs) at the time of diagnosis in MF. Using a quantitative reverse transcription polymerase chain reaction platform, we analyzed miRNA expression in diagnostic skin biopsies from 154 Danish patients with early-stage MF. The patients were subdivided into a discovery cohort (n = 82) and an independent validation cohort (n = 72). The miRNA classifier was built using a LASSO (least absolute shrinkage and selection operator) Cox regression to predict progression-free survival (PFS). We developed a 3-miRNA classifier, based on miR-106b-5p, miR-148a-3p, and miR-338-3p, which successfully separated patients into high-risk and low-risk groups of disease progression. PFS was significantly different between these groups in both the discovery cohort and the validation cohort. The classifier was stronger than existing clinical prognostic factors and remained a strong independent prognostic tool after stratification and adjustment for these factors. Importantly, patients in the high-risk group had a significantly reduced overall survival. The 3-miRNA classifier is an effective tool to predict disease progression of early-stage MF at the time of diagnosis. The classifier adds significant prognostic value to existing clinical prognostic factors and may facilitate more individualized treatment of these patients.

Previously, we reported germline DNA variants associated with risk of urinary bladder cancer (UBC) in Dutch and Icelandic subjects. Here we expanded the Icelandic sample set and tested the top 20 markers from the combined analysis in several European case-control sample sets, with a total of 4,739 cases and 45,549 controls. The T allele of rs798766 on 4p16.3 was found to associate with UBC (odds ratio = 1.24, P = 9.9 × 10 12). rs798766 is located in an intron of TACC3, 70 kb from FGFR3, which often harbors activating somatic mutations in low-grade, noninvasive UBC. Notably, rs798766[T] shows stronger association with low-grade and low-stage UBC than with more aggressive forms of the disease and is associated with higher risk of recurrence in low-grade stage Ta tumors. The frequency of rs798766[T] is higher in Ta tumors that carry an activating mutation in FGFR3 than in Ta tumors with wild-type FGFR3. Our results show a link between germline variants, somatic mutations of FGFR3 and risk of UBC. © 2010 Nature America, Inc. All rights reserved., Vlaamse regering 513943 Compagnia di San Paolo Yorkshire Cancer Research Adessium Foundation European Commission: LSHC-CT-2005 218071 Associazione Italiana per la Ricerca sul Cancro Prinses Beatrix Spierfonds, We thank the individuals who participated in the study and whose contribution made this work possible. We also thank the nurses at deCODE’s recruitment center and the personnel at the deCODE core facilities. We acknowledge the Icelandic Cancer Registry for assistance in the ascertainment of the Icelandic UBC cases. C.Z. and S.B. are funded by a FP7-MC-IAPP Grant agreement no. 218071 (CancerGene). Collection of samples and data in Iceland and The Netherlands was funded in part by the European Commission (POLYGENE: LSHC-CT-2005) and a research investment grant of the Radboud University Nijmegen Medical Centre. Control samples for the Dutch follow-up group were genotyped with generous support from the ‘Prinses Beatrix Fonds’, VSB Fonds, H. Kersten and M. Kersten (Kersten Foundation), The Netherlands ALS Foundation, J.R. van Dijk and the Adessium foundation. The controls from the Dutch Schizophrenia GWA study were genotyped with the support of the US National Institute of Mental Health (R.A.O.). The Leeds Bladder Cancer Study was funded by Cancer Research UK and Yorkshire Cancer Research. The Torino Bladder Cancer Case Control Study was supported by a grant to ECNIS (Environmental Cancer Risk, Nutrition and Individual Susceptibility), a network of excellence operating within the European Union Sixth Framework Program, Priority 5:‘Food Quality and Safety’ (Contract No. 513943), and by grants of the Compagnia di San Paolo, of the Italian Association for Cancer Research and of the Piedmont Region Progetti de Ricerca Sanitaria Finalizzata, Italy. The Belgian case-control study on bladder cancer risk was supported by a grant of the Flemish government, the government of the Belgian province of Limburg and the Limburg Cancer Fund., 1Department of Epidemiology, Biostatistics and Health Technology Assessment and 2Department of Urology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. 3Comprehensive Cancer Center East, Nijmegen, The Netherlands. 4deCODE Genetics, Reykjavik, Iceland. 5Section of Epidemiology and Biostatistics and 6Section of Experimental Oncology, Leeds Institute of Molecular Medicine, St. James’s University Hospital, Leeds, UK. 7Christie Hospital National Health Service Foundation Trust, Manchester, UK. 8Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden. 9National Institute of Environmental Health, Budapest, Hungary. 10Environmental Health Centre, Cluj-Napoca, Romania. 11State Health Institute, Banska Bystrica, Slovakia. 12Institute for Scientific Interchange (ISI) Foundation, Torino, Italy. 13Department of Epidemiology and Public Health, Imperial College, London, UK. 14Department of Genetics, Biology and Biochemistry, University of Torino, Torino, Italy. 15Unit of Cancer Epidemiology, University of Torino, Torino, Italy. 16Centre for Cancer Epidemiology and Prevention (CPO Piemonte), Torino, Italy. 17Department of Experimental and Applied Medicine, Section of Occupational Medicine and Industrial Hygiene, University of Brescia, Brescia, Italy. 18Unit of Genetic Epidemiology, Department of Public Health and Epidemiology, University of Birmingham, Birmingham, UK. 19Department of Complex Genetics, Cluster of Genetics and Cell Biology, Nutrition and Toxicology Research Institute, Maastricht University, Maastricht, The Netherlands. 20Leuven University Centre for Cancer Prevention (LUCK), Leuven, Belgium. 21Department of Medicine, University of Zaragoza, Zaragoza, Spain. 22Division of Urology, San Jorge Hospital, Huesca, Spain. 23Division of Urology, Hospital Clinico, Zaragoza, Spain. 24Department of Urology, University of Zaragoza School of Medicine, Zaragoza, Spain. 25Division of Surgical Pathology, San Jorge Hospital, Huesca, Spain. 26Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany. 27Department of Urology, Paul Gerhardt Foundation, Lutherstadt Wittenberg, Germany. 28Department of Medical Genetics, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. 29University of California Los Angeles Center for Neurobehavioral Genetics, Los Angeles, USA. 30Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. 31Department of Medical Oncology, 32Department of Urology and 33Department of Pathology, Landspitali–University Hospital, Reykjavik, Iceland. 34Department of Urology, Zonguldak Karaelmas University, Zonguldak, Turkey. 35Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden. 36Department of General Practice, Catholic University of Leuven, Leuven, Belgium. 37Department of General Practice, Maastricht University, Maastricht, The Netherlands. 38Division of Medical Oncology, University of Zaragoza, Zaragoza, Spain. 39Division of Molecular Genetic Epidemiology, German Cancer Research Centre, Heidelberg, Germany. 40Section of Clinical Cancer Epidemiology, Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden. 41Division of Clinical Cancer Epidemiology, Department of Oncology, Institute of Clinical Sciences, The Sahlgrenska Academy, Gothenburg, Sweden. 42Gray Institute for Radiation Oncology and Biology, University of Oxford, Oxford, UK. 43Faculty of Medicine, University of Iceland, Reykjavik, Iceland. 44These authors contributed equally to this work. Correspondence should be addressed to L.A.K. (b.kiemeney@ebh.umcn.nl) or K.S. (kstefans@decode.is).

We present a new method, termed QBlossoc, for linkage disequilibrium (LD) mapping of genetic variants underlying a quantitative trait. The method uses principles similar to a previously published method, Blossoc, for LD mapping of case/control studies. The method builds local genealogies along the genome and looks for a significant clustering of quantitative trait values in these trees. We analyze its efficiency in terms of localization and ranking of true positives among a large number of negatives and compare the results with single-marker approaches. Simulation results of markers at densities comparable to contemporary genotype chips show that QBlossoc is more accurate in localization of true positives as expected since it uses the additional information of LD between markers simultaneously. More importantly, however, for genomewide surveys, QBlossoc places regions with true positives higher on a ranked list than single-marker approaches, again suggesting that a true signal displays itself more strongly in a set of adjacent markers than a spurious (false) signal. The method is both memory and central processing unit (CPU) efficient. It has been tested on a real data set of height data for 5000 individuals measured at ∼317,000 markers and completed analysis within 5 CPU days.

We have characterized the development of seeds in the model legume Lotus japonicus. Like soybean (Glycine max) and pea (Pisum sativum), Lotus develops straight seed pods and each pod contains approximately 20 seeds that reach maturity within 40 days. Histological sections show the characteristic three developmental phases of legume seeds and the presence of embryo, endosperm, and seed coat in desiccated seeds. Furthermore, protein, oil, starch, phytic acid, and ash contents were determined, and this indicates that the composition of mature Lotus seed is more similar to soybean than to pea. In a first attempt to determine the seed proteome, both a two-dimensional polyacrylamide gel electrophoresis approach and a gel-based liquid chromatography-mass spectrometry approach were used. Globulins were analyzed by two-dimensional polyacrylamide gel electrophoresis, and five legumins, LLP1 to LLP5, and two convicilins, LCP1 and LCP2, were identified by matrix-assisted laser desorption ionization quadrupole/time-of-flight mass spectrometry. For two distinct developmental phases, seed filling and desiccation, a gel-based liquid chromatography-mass spectrometry approach was used, and 665 and 181 unique proteins corresponding to gene accession numbers were identified for the two phases, respectively. All of the proteome data, including the experimental data and mass spectrometry spectra peaks, were collected in a database that is available to the scientific community via a Web interface (http://www.cbs.dtu.dk/cgi-bin/lotus/db.cgi). This database establishes the basis for relating physiology, biochemistry, and regulation of seed development in Lotus. Together with a new Web interface (http://bioinfoserver.rsbs.anu.edu.au/utils/PathExpress4legumes/) collecting all protein identifications for Lotus, Medicago, and soybean seed proteomes, this database is a valuable resource for comparative seed proteomics and pathway analysis within and beyond the legume family.

Here we describe the insights gained from sequencing the whole genomes of 2,636 Icelanders to a median depth of 20×. We found 20 million SNPs and 1.5 million insertions-deletions (indels). We describe the density and frequency spectra of sequence variants in relation to their functional annotation, gene position, pathway and conservation score. We demonstrate an excess of homozygosity and rare protein-coding variants in Iceland. We imputed these variants into 104,220 individuals down to a minor allele frequency of 0.1% and found a recessive frameshift mutation in MYL4 that causes early-onset atrial fibrillation, several mutations in ABCB4 that increase risk of liver diseases and an intronic variant in GNAS associating with increased thyroid-stimulating hormone levels when maternally inherited. These data provide a study design that can be used to determine how variation in the sequence of the human genome gives rise to human diversity.

Psoriasis is a chronic cutaneous inflammatory disease. The immunopathogenesis is a complex interplay between T cells, dendritic cells and the epidermis in which T cells and dendritic cells maintain skin inflammation. Anti-tumour necrosis factor (anti-TNF)-α agents have been approved for therapeutic use across a range of inflammatory disorders including psoriasis, but the anti-inflammatory mechanisms of anti-TNF-α in lesional psoriatic skin are not fully understood. We investigated early events in skin from psoriasis patients after treatment with anti-TNF-α antibodies by use of bioinformatics tools. We used the Human Gene 1.0 ST Array to analyse gene expression in punch biopsies taken from psoriatic patients before and also 4 and 14 days after initiation of treatment with the anti-TNF-α agent adalimumab. The gene expression was analysed by gene set enrichment analysis using the Functional Annotation Tool from DAVID Bioinformatics Resources. The most enriched pathway was visualised by the Pathview Package on Kyoto Encyclopedia of Genes and Genomes (KEGG) graphs. The analysis revealed new very early events in psoriasis after adalimumab treatment. Some of these events have been described after longer periods of anti-TNF-α treatment when clinical and histological changes appear, suggesting that effects of anti-TNF-α treatment on gene expression appear very early before clinical and histological changes. Combining microarray data on biopsies from psoriasis patients with pathway analysis allowed us to integrate in vitro findings into the identification of mechanisms that may be important in vivo. Furthermore, these results may reflect primary effect of anti-TNF-α treatment in contrast to studies of gene expression changes following clinical and histological changes, which may reflect secondary changes correlated to the healing of the skin.

Contains fulltext : 128630.pdf (Publisher’s version ) (Open Access) Smoking influences body weight such that smokers weigh less than non-smokers and smoking cessation often leads to weight increase. The relationship between body weight and smoking is partly explained by the effect of nicotine on appetite and metabolism. However, the brain reward system is involved in the control of the intake of both food and tobacco. We evaluated the effect of single-nucleotide polymorphisms (SNPs) affecting body mass index (BMI) on smoking behavior, and tested the 32 SNPs identified in a meta-analysis for association with two smoking phenotypes, smoking initiation (SI) and the number of cigarettes smoked per day (CPD) in an Icelandic sample (N=34,216 smokers). Combined according to their effect on BMI, the SNPs correlate with both SI (r=0.019, P=0.00054) and CPD (r=0.032, P=8.0 x 10(-7)). These findings replicate in a second large data set (N=127,274, thereof 76,242 smokers) for both SI (P=1.2 x 10(-5)) and CPD (P=9.3 x 10(-5)). Notably, the variant most strongly associated with BMI (rs1558902-A in FTO) did not associate with smoking behavior. The association with smoking behavior is not due to the effect of the SNPs on BMI. Our results strongly point to a common biological basis of the regulation of our appetite for tobacco and food, and thus the vulnerability to nicotine addiction and obesity.