The best-documented example for transmission distortion (TD) to normal offspring are the t haplotypes on mouse chromosome 17. In healthy humans, TD has been described for whole chromosomes and for particular loci, but multiple comparisons have presented a statistical obstacle in wide-ranging analyses. Here we provide six high-resolution TD maps of the short arm of human chromosome 6 (Hsa6p), based on single-nucleotide polymorphism (SNP) data from 60 trio families belonging to two ethnicities that are available through the International HapMap Project. We tested all approximately 70 000 previously genotyped SNPs within Hsa6p by the transmission disequilibrium test. TagSNP selection followed by permutation testing was performed to adjust for multiple testing. A statistically significant evidence for TD was observed among male parents of European ancestry, due to strong and wide-ranging skewed segregation in a 730 kb long region containing the transcription factor-encoding genes SUPT3H and RUNX2, as well as the microRNA locus MIRN586. We also observed that this chromosomal segment coincides with pronounced linkage disequilibrium (LD), suggesting a relationship between TD and LD. The fact that TD may be taking place in samples not selected for a genetic disease implies that linkage studies must be assessed with particular caution in chromosomal segments with evidence of TD.
transmission distortion; linkage disequilibrium; human chromosome 6p; SUPT3H; MIRN586; RUNX2
Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.
Nowadays, the availability of cheaper and accurate assays to quantify multiple (endo)phenotypes in large population cohorts allows multi-trait studies. However, these studies are limited by the lack of flexible models integrated with efficient computational tools for genome-wide multi SNPs-traits analyses. To overcome this problem, we propose a novel Bayesian analysis strategy and a new algorithmic implementation which exploits parallel processing architecture for fully multivariate modeling of groups of correlated phenotypes at the genome-wide scale. In addition to increased power of our algorithm over alternative Bayesian and well-established non-Bayesian multi-phenotype methods, we provide an application to a real case study of several blood lipid traits, and show how our method recovered most of the major associations and is better at refining multi-trait polygenic associations than alternative methods. We reveal and replicate in independent cohorts new associations with two phenotypic groups that were not detected by competing multivariate approaches and not noticed by a large meta-GWAS. We also discuss the applicability of the proposed method to large meta-analyses involving hundreds of thousands of individuals and to diverse genomic datasets where complex dependencies in the predictor space are present.
This systematic review determines the best known form of biofeedback (BF) and/or electrical stimulation (ES) for the treatment of fecal incontinence in adults and rates the quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation. Attention is given to type, strength, and application mode of the current for ES and to safety.
Methods followed the Cochrane Handbook. Randomized controlled trials were included. Studies were searched in The Cochrane Library, MEDLINE, and EMBASE (registration number (PROSPERO): CRD42011001334).
BF and/or ES were studied in 13 randomized parallel-group trials. In 12 trials, at least one therapy group received BF alone and/or in combination with ES, while ES alone was evaluated in seven trials. Three (four) trials were rated as of high (moderate) quality. Average current strength was reported in three of seven studies investigating ES; only two studies reached the therapeutic window. No trial showed superiority of control, or of BF alone or of ES alone when compared with BF + ES. Superiority of BF + ES over any monotherapy was demonstrated in several trials. Amplitude-modulated medium-frequency (AM-MF) stimulation, also termed pre-modulated interferential stimulation, combined with BF was superior to both low-frequency ES and BF alone, and 50 % of the patients were continent after 6 months of treatment. Effects increased with treatment duration. Safety reporting was bad, and there are safety issues with some forms of low-frequency ES.
There is sufficient evidence for the efficacy of BF plus ES combined in treating fecal incontinence. AM-MF plus BF seems to be the most effective and safe treatment.
• The higher the quality of the randomized trial the more likely was a significant difference between treatment groups.
• Two times more patients became continent when biofeedback was used instead of a control, such as pelvic floor exercises.
• Two times more patients became continent when biofeedback plus electrical stimulation was used instead of biofeedback only.
• Low-frequency electrical stimulation can have adverse device effects, and this is in contrast to amplitude-modulated medium-frequency electrical stimulation.
• There is high quality evidence that amplitude-modulated medium-frequency electrical stimulation plus electromyography biofeedback is the best second-line treatment for fecal incontinence.
Electronic supplementary material
The online version of this article (doi:10.1007/s00384-013-1739-0) contains supplementary material, which is available to authorized users.
Conservative treatment; Biofeedback; Cleveland Clinic score; Electrical stimulation; Fecal incontinence; Meta-analysis
Combined analyses of gene networks and DNA sequence variation can provide new insights into the aetiology of common diseases. Here, we used integrated genome-wide approaches across seven rat tissues to identify gene networks and the loci underlying their regulation. We defined an interferon regulatory factor 7 (IRF7)1-driven inflammatory network (iDIN) enriched for viral response genes, which represents a molecular biomarker for macrophages and was regulated in multiple tissues by a locus on rat chromosome 15q25. At this locus, Epstein-Barr virus induced gene 2 (Ebi2 or Gpr183), which we localised to macrophages and is known to control B lymphocyte migration2,3, regulated the iDIN. The human chromosome 13q32 locus, orthologous to rat 15q25, controlled the human equivalent of iDIN, which was conserved in monocytes. For the macrophage-associated autoimmune disease type 1 diabetes (T1D) iDIN genes were more likely to associate with T1D susceptibility than randomly selected immune response genes (P = 8.85 × 10−6). The human locus controlling the iDIN, was associated with the risk of T1D at SNP rs9585056 (P = 7.0 × 10−10, odds ratio = 1.15), which was one of five SNPs in this region associated with EBI2 expression. These data implicate IRF7 network genes and their regulatory locus in the pathogenesis of T1D.
There is evidence across several species for genetic control of phenotypic variation of complex traits1–4, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using 170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype)5–7, is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of 0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI8, possibly mediated by DNA methylation9,10. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.
In order to assess whether gene expression variability could be influenced by several SNPs acting in cis, either through additive or more complex haplotype effects, a systematic genome-wide search for cis haplotype expression quantitative trait loci (eQTL) was conducted in a sample of 758 individuals, part of the Cardiogenics Transcriptomic Study, for which genome-wide monocyte expression and GWAS data were available. 19,805 RNA probes were assessed for cis haplotypic regulation through investigation of ∼2,1×109 haplotypic combinations. 2,650 probes demonstrated haplotypic p-values >104-fold smaller than the best single SNP p-value. Replication of significant haplotype effects were tested for 412 probes for which SNPs (or proxies) that defined the detected haplotypes were available in the Gutenberg Health Study composed of 1,374 individuals. At the Bonferroni correction level of 1.2×10−4 (∼0.05/412), 193 haplotypic signals replicated. 1000G imputation was then conducted, and 105 haplotypic signals still remained more informative than imputed SNPs. In-depth analysis of these 105 cis eQTL revealed that at 76 loci genetic associations were compatible with additive effects of several SNPs, while for the 29 remaining regions data could be compatible with a more complex haplotypic pattern. As 24 of the 105 cis eQTL have previously been reported to be disease-associated loci, this work highlights the need for conducting haplotype-based and 1000G imputed cis eQTL analysis before commencing functional studies at disease-associated loci.
In order to assess whether gene expression variability could be influenced by the presence of more than one cis-acting SNP, we have conducted a systematic genome-wide search for haplotypic cis eQTL effects in a sample of 758 individuals and replicated the findings in an independent sample of 1,374 subjects. In both studies, genome-wide monocytes expression and genotype data were available. We identified 105 genes whose monocyte expression was under the influence of multiple cis-acting SNPs. About 75% of the detected genetic effects were related to independent additive SNP effects and the last quarter due to more complex haplotype effects. Of note, 24 of the genes identified to be affected by multiple cis eSNPs have been previously reported to reside at disease-associated loci. This could suggest that such multiple locus-specific genetic effects could contribute to the susceptibility to human diseases.
Like human infants, songbirds learn their species-specific vocalizations through imitation learning. The birdsong system has emerged as a widely used experimental animal model for understanding the underlying neural mechanisms responsible for vocal production learning. However, how neural impulses are translated into the precise motor behavior of the complex vocal organ (syrinx) to create song is poorly understood. First and foremost, we lack a detailed understanding of syringeal morphology.
To fill this gap we combined non-invasive (high-field magnetic resonance imaging and micro-computed tomography) and invasive techniques (histology and micro-dissection) to construct the annotated high-resolution three-dimensional dataset, or morphome, of the zebra finch (Taeniopygia guttata) syrinx. We identified and annotated syringeal cartilage, bone and musculature in situ in unprecedented detail. We provide interactive three-dimensional models that greatly improve the communication of complex morphological data and our understanding of syringeal function in general.
Our results show that the syringeal skeleton is optimized for low weight driven by physiological constraints on song production. The present refinement of muscle organization and identity elucidates how apposed muscles actuate different syringeal elements. Our dataset allows for more precise predictions about muscle co-activation and synergies and has important implications for muscle activity and stimulation experiments. We also demonstrate how the syrinx can be stabilized during song to reduce mechanical noise and, as such, enhance repetitive execution of stereotypic motor patterns. In addition, we identify a cartilaginous structure suited to play a crucial role in the uncoupling of sound frequency and amplitude control, which permits a novel explanation of the evolutionary success of songbirds.
Microarray profiling of gene expression is widely applied in molecular biology and functional genomics. Experimental and technical variations make meta-analysis of different studies challenging. In a total of 3358 samples, all from German population-based cohorts, we investigated the effect of data preprocessing and the variability due to sample processing in whole blood cell and blood monocyte gene expression data, measured on the Illumina HumanHT-12 v3 BeadChip array.
Gene expression signal intensities were similar after applying the log2 or the variance-stabilizing transformation. In all cohorts, the first principal component (PC) explained more than 95% of the total variation. Technical factors substantially influenced signal intensity values, especially the Illumina chip assignment (33–48% of the variance), the RNA amplification batch (12–24%), the RNA isolation batch (16%), and the sample storage time, in particular the time between blood donation and RNA isolation for the whole blood cell samples (2–3%), and the time between RNA isolation and amplification for the monocyte samples (2%). White blood cell composition parameters were the strongest biological factors influencing the expression signal intensities in the whole blood cell samples (3%), followed by sex (1–2%) in both sample types. Known single nucleotide polymorphisms (SNPs) were located in 38% of the analyzed probe sequences and 4% of them included common SNPs (minor allele frequency >5%). Out of the tested SNPs, 1.4% significantly modified the probe-specific expression signals (Bonferroni corrected p-value<0.05), but in almost half of these events the signal intensities were even increased despite the occurrence of the mismatch. Thus, the vast majority of SNPs within probes had no significant effect on hybridization efficiency.
In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses.
Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.
To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics.
On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, that reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.
The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.
Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.
Biomedical informatics; data mining; data analysis; data-driven methods; translational bioinformatics
We aimed to assess whether pri-miRNA SNPs (miSNPs) could influence monocyte gene expression, either through marginal association or by interacting with polymorphisms located in 3'UTR regions (3utrSNPs). We then conducted a genome-wide search for marginal miSNPs effects and pairwise miSNPs × 3utrSNPs interactions in a sample of 1,467 individuals for which genome-wide monocyte expression and genotype data were available. Statistical associations that survived multiple testing correction were tested for replication in an independent sample of 758 individuals with both monocyte gene expression and genotype data. In both studies, the hsa-mir-1279 rs1463335 was found to modulate in cis the expression of LYZ and in trans the expression of CNTN6, CTRC, COPZ2, KRT9, LRRFIP1, NOD1, PCDHA6, ST5 and TRAF3IP2 genes, supporting the role of hsa-mir-1279 as a regulator of several genes in monocytes. In addition, we identified two robust miSNPs × 3utrSNPs interactions, one involving HLA-DPB1 rs1042448 and hsa-mir-219-1 rs107822, the second the H1F0 rs1894644 and hsa-mir-659 rs5750504, modulating the expression of the associated genes.
As some of the aforementioned genes have previously been reported to reside at disease-associated loci, our findings provide novel arguments supporting the hypothesis that the genetic variability of miRNAs could also contribute to the susceptibility to human diseases.
After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.
Electronic supplementary material
The online version of this article (doi:10.1007/s00439-012-1194-y) contains supplementary material, which is available to authorized users.
Biomarkers are of increasing importance for personalized medicine, with applications including diagnosis, prognosis, and selection of targeted therapies. Their use is extremely diverse, ranging from pharmacodynamics to treatment monitoring. Following a concise review of terminology, we provide examples and current applications of three broad categories of biomarkers—DNA biomarkers, DNA tumor biomarkers, and other general biomarkers. We outline clinical trial phases for identifying and validating diagnostic and prognostic biomarkers. Predictive biomarkers, more generally termed companion diagnostic tests predict treatment response in terms of efficacy and/or safety. We consider suitability of clinical trial designs for predictive biomarkers, including a detailed discussion of validation study designs, with emphasis on interpretation of study results. We specifically discuss the interpretability of treatment effects if a large set of DNA biomarker profiles is available and the number of therapies is identical to the number of different profiles.
Evidence exist that motor observation activates the same cortical motor areas that are involved in the performance of the observed actions. The so called “mirror neuron system” has been proposed to be responsible for this phenomenon. We employ this neural system and its capability to re-enact stored motor representations as a tool for rehabilitating motor control. In our new neurorehabilitative schema (videotherapy) we combine observation of daily actions with concomitant physical training of the observed actions focusing on the upper limbs. Following a pilot study in chronic patients in an ambulatory setting, we currently designed a new multicenter clinical study dedicated to patients in the sub-acute state after stroke using a home-based self-induced training. Within our protocol we assess 1) the capability of action observation to elicit rehabilitational effects in the motor system, and 2) the capacity of this schema to be performed by patients without assistance from a physiotherapist. The results of this study would be of high health and economical relevance.
A controlled, randomized, multicenter, paralleled, 6 month follow-up study will be conducted on three groups of patients: one group will be given the experimental treatment whereas the other two will participate in control treatments. All patients will undergo their usual rehabilitative treatment beside participation in the study. The experimental condition consists in the observation and immediate imitation of common daily hand and arm actions. The two parallel control groups are a placebo group and a group receiving usual rehabilitation without any trial-related treatment. Trial randomization is provided via external data management. The primary efficacy endpoint is the improvement of the experimental group in a standardized motor function test (Wolf Motor Function Test) relative to control groups. Further assessments refer to subjective and qualitative rehabilitational scores. This study has been reviewed and approved by the ethics committee of Aachen University.
This therapy provides an extension of therapeutic procedures for recovery after stroke and emphasizes the importance of action perception in neurorehabilitation The results of the study could become implemented into the wide physiotherapeutic practice, for example as an ad on and individualized therapy.
To examine the association of polymorphisms in ATM (codon 158), GSTP1 (codon 105), SOD2 (codon 16), TGFB1 (position −509), XPD (codon 751), and XRCC1 (codon 399) with the risk of severe erythema after breast conserving radiotherapy.
Methods and materials
Retrospective analysis of 83 breast cancer patients treated with breast conserving radiotherapy. A total dose of 50.4 Gy was administered, applying 1.8 Gy/fraction within 42 days. Erythema was evaluated according to the Radiation Therapy Oncology Group (RTOG) score. DNA was extracted from blood samples and polymorphisms were determined using either the Polymerase Chain Reaction based Restriction-Fragment-Length-Polymorphism (PCR-RFL) technique or Matrix-Assisted-Laser-Desorption/Ionization –Time-Of-Flight-Mass-Spectrometry (MALDI-TOF). Relative excess heterozygosity (REH) was investigated to check compatibility of genotype frequencies with Hardy-Weinberg equilibrium (HWE). In addition, p-values from the standard exact HWE lack of fit test were calculated using 100,000 permutations. HWE analyses were performed using R.
Fifty-six percent (46/83) of all patients developed erythema of grade 2 or 3, with this risk being higher for patients with large breast volume (odds ratio, OR = 2.55, 95% confidence interval, CI: 1.03–6.31, p = 0.041). No significant association between SNPs and risk of erythema was found when all patients were considered. However, in patients with small breast volume the TGFB1 SNP was associated with erythema (p = 0.028), whereas the SNP in XPD showed an association in patients with large breast volume (p = 0.046). A risk score based on all risk alleles was neither significant in all patients nor in patients with small or large breast volume. Risk alleles of most SNPs were different compared to a previously identified risk profile for fibrosis.
The genetic risk profile for erythema appears to be different for patients with small and larger breast volume. This risk profile seems to be specific for erythema as compared to a risk profile for fibrosis.
Single nucleotide polymorphisms (SNPs); Erythema; Breast cancer; Radiotherapy
Genome-wide association studies have successfully elucidated the genetic background of complex diseases, but X chromosomal data have usually not been analyzed. A reason for this is that there is no consensus approach for the analysis taking into account the specific features of X chromosomal data. This contribution evaluates test statistics proposed for X chromosomal markers regarding type I error frequencies and power.
We performed extensive simulation studies covering a wide range of different settings. Besides characteristics of the general population, we investigated sex-balanced or unbalanced sampling procedures as well as sex-specific effect sizes, allele frequencies and prevalence. Finally, we applied the test statistics to an association data set on Crohn's disease.
Simulation results imply that in addition to standard quality control, sex-specific allele frequencies should be checked to control for type I errors. Furthermore, we observed distinct differences in power between test statistics which are determined by sampling design and sex specificity of effect sizes. Analysis of the Crohn's disease data detects two previously unknown genetic regions on the X chromosome.
Although no test is uniformly most powerful under all settings, recommendations are offered as to which test performs best under certain conditions.
Crohn's disease; Genetic association; Genome-wide association; Sex specific; X chromosome
Detection of epistatic interaction between loci has been postulated to provide a more in-depth understanding of the complex biological and biochemical pathways underlying human diseases. Studying the interaction between two loci is the natural progression following traditional and well-established single locus analysis. However, the added costs and time duration required for the computation involved have thus far deterred researchers from pursuing a genome-wide analysis of epistasis. In this paper, we propose a method allowing such analysis to be conducted very rapidly. The method, dubbed EPIBLASTER, is applicable to case–control studies and consists of a two-step process in which the difference in Pearson's correlation coefficients is computed between controls and cases across all possible SNP pairs as an indication of significant interaction warranting further analysis. For the subset of interactions deemed potentially significant, a second-stage analysis is performed using the likelihood ratio test from the logistic regression to obtain the P-value for the estimated coefficients of the individual effects and the interaction term. The algorithm is implemented using the parallel computational capability of commercially available graphical processing units to greatly reduce the computation time involved. In the current setup and example data sets (211 cases, 222 controls, 299468 SNPs; and 601 cases, 825 controls, 291095 SNPs), this coefficient evaluation stage can be completed in roughly 1 day. Our method allows for exhaustive and rapid detection of significant SNP pair interactions without imposing significant marginal effects of the single loci involved in the pair.
Epistasis; genome-wide interaction analysis; graphical processing unit
Lymphadenectomy is performed to assess patient prognosis and to prevent metastasizing. Recently, it was questioned whether lymph node metastases were capable of metastasizing and therefore, if lymphadenectomy was still adequate. We evaluated whether the nodal status impacts on the occurrence of distant metastases by analyzing a highly selected cohort of colon cancer patients.
1,395 patients underwent surgery exclusively for colon cancer at the University of Lübeck between 01/1993 and 12/2008. The following exclusion criteria were applied: synchronous metastasis, R1-resection, prior/synchronous second carcinoma, age < 50 years, positive family history, inflammatory bowel disease, FAP, HNPCC, and follow-up < 5 years. The remaining 421 patients were divided into groups with (TM+, n = 75) or without (TM-, n = 346) the occurrence of metastasis throughout a 5-year follow-up.
Five-year survival rates for TM + and TM- were 21% and 73%, respectively (p < 0.0001). Survival rates differed significantly for N0 vs. N2, grading 2 vs. 3, UICC-I vs. -II and UICC-I vs. -III (p < 0.05). Regression analysis revealed higher age upon diagnosis, increasing N- and increasing T-category to significantly impact on recurrence free survival while increasing N-and T-category were significant parameters for the risk to develop metastases within 5-years after surgery (HR 1.97 and 1.78; p < 0.0001).
Besides a higher T-category, a positive N-stage independently implies a higher probability to develop distant metastases and correlates with poor survival. Our data thus show a prognostic relevance of lymphadenectomy which should therefore be retained until conclusive studies suggest the unimportance of lmyphadenectomy.
Colon cancer; Lymph nodes; Metastasis; Prognosis; Survival; Recurrence free survival; Regression analysis
The crustacean cuticle consists of a complex organic matrix and a mineral phase. The physical and chemical properties of the cuticle are corellated to the specific functions of cuticular elements, leading to a large variety in its structure and composition. Investigation of the structure-function relationship in crustacean cuticle requires sophisticated methodological tools for the analysis of different aspects of the cuticular architecture. In the present paper we report improved preparation methods that, in combination with various electron microscopic techniques, have led to new insights of cuticle structure and composition in the tergite cuticle of Porcellio scaber. We used thin sections of non-decalcified tergites and decalcified resin embedded material for transmission electron microscopy and scanning transmission electron microscopy. Etched sagittal planes of bulk tergite samples were analysed with field emission scanning electron microscopy. We have found a distinct distal region within the exocuticle that differs from the subjacent proximal exocuticle in the arrangement of fibres. Within this distal exocuticle chitin-protein fibrils assemble to fibres with diameters between 15 and 50 nm that are embedded in a mineral matrix. In the proximal exocuticle and the endocuticle fibrils do not assemble to fibres and are surrounded by mineral individually. Furthermore, we show that the pore canals are filled with mineral, and demonstrate that mild etching of polished sagittal cuticle surfaces reveals regions containing mineral of diverse solubility.
Isopoda; cuticle; ultrastructure; Porcellio scaber
Background: The incidence of therapy-related acute leukaemia (TRAL) in mitoxantrone treatment in multiple sclerosis (MS) is controversially discussed.
Methods and results: In a retrospective meta-analysis from six centres, we observed six cases of acute myeloid leukaemia (AML) (incidence 0.41% for patients with mean follow up after end of treatment of 3.6 years, n = 1.156; incidence 0.25% for all patients, n = 2.261). Potential influencing factors such as myelotoxic or glucocorticosteroid pretreatment/cotreatment were present in all but one case of TRAL. Between 1990 and 2010, 11 cases of TRAL were reported to the Drug Commission of the German Medical Association (estimated risk of 0.09–0.13%).
Conclusions: Regional differences in reported TRAL incidence may point to confounding cofactors such as administration protocols and cotreatments.
escalation therapy; leukaemia; mitoxantrone; multiple sclerosis; risk profile
Next-generation sequencing technology allows investigation of both common and rare variants in humans. Exomes are sequenced on the population level or in families to further study the genetics of human diseases. Genetic Analysis Workshop 17 (GAW17) provided exomic data from the 1000 Genomes Project and simulated phenotypes. These data enabled evaluations of existing and newly developed statistical methods for rare variant sequence analysis for which standard statistical methods fail because of the rareness of the alleles. Various alternative approaches have been proposed that overcome the rareness problem by combining multiple rare variants within a gene. These approaches are termed collapsing methods, and our GAW17 group focused on studying the performance of existing and novel collapsing methods using rare variants. All tested methods performed similarly, as measured by type I error and power. Inflated type I error fractions were consistently observed and might be caused by gametic phase disequilibrium between causal and noncausal rare variants in this relatively small sample as well as by population stratification. Incorporating prior knowledge, such as appropriate covariates and information on functionality of SNPs, increased the power of detecting associated genes. Overall, collapsing rare variants can increase the power of identifying disease-associated genes. However, studying genetic associations of rare variants remains a challenging task that requires further development and improvement in data collection, management, analysis, and computation.
1000 Genomes Project; association; collapsing methods; next-generation sequencing
With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane-Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data-adaptive methods.
association; collapsing methods; collection of rare variants; common disease/rare variant hypothesis; contingency table; generalized linear model; next-generation sequencing; pooling methods
Genetic Analysis Workshop 17 (GAW17) focused on the transition from genome-wide association study designs and methods to the study designs and statistical genetic methods that will be required for the analysis of next-generation sequence data including both common and rare sequence variants. In the 166 contributions to GAW17, a wide variety of statistical methods were applied to simulated traits in population- and family-based samples, and results from these analyses were compared to the known generating model. In general, many of the statistical genetic methods used in the population-based sample identified causal sequence variants (SVs) when the estimated locus-specific heritability, as measured in the population-based sample, was greater than about 0.08. However, SVs with locus-specific heritabilities less than 0.03 were rarely identified consistently. In the family-based samples, many of the methods detected SVs that were rarer than those detected in the population-based sample, but the estimated locus-specific heritabilities for these rare SVs, as measured in the family-based samples, were substantially higher (>0.2) than their corresponding heritabilities in the population-based samples. Substantial inflation of the type I error rate was observed across a wide variety of statistical methods. Although many of the contributions found little inflation in type I error for Q4, a trait with no causal SVs, type I error rates for Q1 and Q2 were well above their nominal levels with the inflation for Q1 being higher than that for Q2. It seems likely that this inflation in type I error is due to correlations among SVs.
linkage; association; next-generation sequencing; computer simulation