Tests for candidate-gene interaction for longitudinal quantitative traits measured in a large cohort

For the Framingham Heart Study (FHS) and simulated FHS (FHSsim) data, we tested for gene-gene interaction in quantitative traits employing a longitudinal nonparametric association test (LNPT) and, for comparison, a survival analysis. We report results for the Offspring Cohort by LNPT analysis and on all longitudinal cohorts by survival analysis with cohort effect adjustment. We verified that type I errors were not inflated. We compared the power of both methods to detect in FHSsim data two sets of gene pairs that interact for the trait coronary artery calcification. In FHS, we tested eight gene pairs from a list of candidate genes for interaction effects on body mass index. Both methods found evidence for pairwise non-additive effects of mutations in the genes FTO, PON1, and PFKP on body mass index

Filtering genetic variants and placing informative priors based on putative biological function

High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure

Statistical Mechanics of Learning: A Variational Approach for Real Data

Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone.Comment: 4 pages, 2 figure

Surgery for brain metastases: radiooncology scores predict survival-score index for radiosurgery, graded prognostic assessment, recursive partitioning analysis

BACKGROUND: Radiooncological scores are used to stratify patients for radiation therapy. We assessed their ability to predict overall survival (OS) in patients undergoing surgery for metastatic brain disease. METHODS: We performed a post-hoc single-center analysis of 175 patients, prospectively enrolled in the MetastaSys study data. Score index of radiosurgery (SIR), graded prognostic assessment (GPA), and recursive partitioning analysis (RPA) were assessed. All scores consider age, systemic disease, and performance status prior to surgery. Furthermore, GPA and SIR include the number of intracranial lesions while SIR additionally requires metastatic lesion volume. Predictive values for case fatality at 1 year after surgery were compared among scoring systems. RESULTS: All scores produced accurate reflections on OS after surgery (p ≤ 0.003). Median survival was 21–24 weeks in patients scored in the unfavorable cohorts, respectively. In cohorts with favorable scores, median survival ranged from 42 to 60 weeks. Favorable SIR was associated with a hazard ratio (HR) of 0.44 [0.29, 0.66] for death within 1 year. For GPA, the HR amounted to 0.44 [0.25, 0.75], while RPA had a HR of 0.30 [0.14, 0.63]. Overall test performance was highest for the SIR. CONCLUSIONS: All scores proved useful in predicting OS. Considering our data, we recommend using the SIR for preoperative prognostic evaluation and counseling

Functional consequences of genetic polymorphisms in the NKG2D receptor signaling pathway and putative gene interactions

NKG2D (NK group 2, member D) is an activating natural killer (NK) receptor, which is expressed on NK and CD8+ T cells. On NK cells, NKG2D elicits cytotoxicity and release of cytokines. On CD8+ T cells, it functions as a co-stimulatory molecule. The receptor recognizes several ligands including the major histocompatibility complex (MHC) class I chain-related molecules A (MICA) and B (MICB) as well as the UL16-binding proteins (ULBP). The diversity of NKG2D ligands is further increased by a high degree of genetic variability of the ligands. Recently, an amino acid exchange from valine to methionine at position 129 in MICA has been found to be associated with the outcome of allogeneic hematopoietic stem cell transplantation (HSCT), and the functional consequences of this specific genetic variation have been elucidated. The clinical associations found after HSCT were explainable by the functional differences of the MICA-129 variants. Herein, we discuss how the genetic polymorphisms of NKG2D ligands and NKG2D itself interact and may affect the outcome of HSCT and the susceptibility to other diseases, which have been associated with polymorphisms in the NKG2D signaling pathway

Prophylactic Palmitoylethanolamide Prolongs Survival and Decreases Detrimental Inflammation in Aged Mice With Bacterial Meningitis

Easy-to-achieve interventions to promote healthy longevity are desired to diminish the incidence and severity of infections, as well as associated disability upon recovery. The dietary supplement palmitoylethanolamide (PEA) exerts anti-inflammatory and neuroprotective properties. Here, we investigated the effect of prophylactic PEA on the early immune response, clinical course, and survival of old mice after intracerebral E. coli K1 infection. Nineteen-month-old wild type mice were treated intraperitoneally with two doses of either 0.1 mg PEA/kg in 250 μl vehicle solution (n = 19) or with 250 μl vehicle solution only as controls (n = 19), 12 h and 30 min prior to intracerebral E. coli K1 infection. The intraperitoneal route was chosen to reduce distress in mice and to ensure exact dosing. Survival time, bacterial loads in cerebellum, blood, spleen, liver, and microglia counts and activation scores in the brain were evaluated. We measured the levels of IL-1β, IL-6, MIP-1α, and CXCL1 in cerebellum and spleen, as well as of bioactive lipids in serum in PEA- and vehicle-treated animals 24 h after infection. In the absence of antibiotic therapy, the median survival time of PEA-pre-treated infected mice was prolonged by 18 h compared to mice of the vehicle-pre-treated infected group (P = 0.031). PEA prophylaxis delayed the onset of clinical symptoms (P = 0.037). This protective effect was associated with lower bacterial loads in the spleen, liver, and blood compared to those of vehicle-injected animals (P ≤ 0.037). PEA-pre-treated animals showed diminished levels of pro-inflammatory cytokines and chemokines in spleen 24 h after infection, as well as reduced serum concentrations of arachidonic acid and of one of its metabolites, 20-hydroxyeicosatetraenoic acid. In the brain, prophylactic PEA tended to reduce bacterial titers and attenuated microglial activation in aged infected animals (P = 0.042). Our findings suggest that prophylactic PEA can counteract infection associated detrimental responses in old animals. Accordingly, PEA treatment slowed the onset of infection symptoms and prolonged the survival of old infected mice. In a clinical setting, prophylactic administration of PEA might extend the potential therapeutic window where antibiotic therapy can be initiated to rescue elderly patients

Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function

BACKGROUND: Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function. METHODS: A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function. RESULTS: The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue. CONCLUSION: The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies. FUNDING: For detailed information per study, see Acknowledgments.This work was supported by a grant from the US National Heart, Lung, and Blood Institute (N01-HL-25195; R01HL 093328 to RSV), a MAIFOR grant from the University Medical Center Mainz, Germany (to PSW), the Center for Translational Vascular Biology (CTVB) of the Johannes Gutenberg-University of Mainz, and the Federal Ministry of Research and Education, Germany (BMBF 01EO1003 to PSW). This work was also supported by the research project Greifswald Approach to Individualized Medicine (GANI_MED). GANI_MED was funded by the Federal Ministry of Education and Research and the Ministry of Cultural Affairs of the Federal State of Mecklenburg, West Pomerania (contract 03IS2061A). We thank all study participants, and the colleagues and coworkers from all cohorts and sites who were involved in the generation of data or in the analysis. We especially thank Andrew Johnson (FHS) for generation of the gene annotation database used for analysis. We thank the German Center for Cardiovascular Research (DZHK e.V.) for supporting the analysis and publication of this project. RSV is a member of the Scientific Advisory Board of the DZHK. Data on CAD and MI were contributed by CARDIoGRAMplusC4D investigators. See Supplemental Acknowledgments for consortium details. PSW, JFF, AS, AT, TZ, RSV, and MD had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis

Approximate analytical bootstrap averages for support vector classifiers

We compute approximate analytical bootstrap averages for support vector classification using a combination of the replica method of statistical physics and the TAP approach for approximate inference. We test our method on a few datasets and compare it with exact averages obtained by extensive Monte-Carlo sampling.

An approximate analytical approach to resampling averages

Using a novel reformulation, we develop a framework to compute approximate resampling data averages analytically. The method avoids multiple retraining of statistical models on the samples. Our approach uses a combination of the replica "trick" of Statistical Physics and the TAP approach for approximate Bayesian inference. We demonstrate our approach on regression with Gaussian processes. A comparison with averages obtained by Monte-Carlo sampling shows that our method achieves good accuracy