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The strength and durability of systemic anti-tumor immune responses induced by cancer vaccines depends on adjuvants to support an immunogenic vaccine site microenvironment (VSME). Adjuvants include water-in-oil emulsions with incomplete Freund's adjuvant (IFA) and combinations of toll-like receptor (TLR) agonists, including a preparation containing TLR4 and TLR9 agonists with QS-21 (AS15). IFA-containing vaccines can promote immune cell accumulation at the VSME, whereas effects of AS15 are largely unexplored. Therefore, we assessed innate and adaptive immune cell accumulation and gene expression at the VSME after vaccination with AS15 and compared to effects with IFA. We hypothesized that AS15 would promote less accumulation of innate and adaptive immune cells at the VSME than IFA vaccines. In two clinical trials, patients with resected high-risk melanoma received either a multipeptide vaccine with IFA or a recombinant MAGE-A3 protein vaccine with AS15. Vaccine site biopsies were obtained after one or multiple vaccines. T cells accumulated early after vaccines with AS15, but this was not durable or of the same magnitude as vaccination in IFA. Vaccines with AS15 increased durable expression of DC- and T cell-related genes, as well as PD-L1 and IDO1, suggesting complex activation and regulation of innate and adaptive immune function with AS15. These changes were generally greater with vaccines containing IFA, but IFA induced reduction in myeloid suppressor cells markers. Evidence of tertiary lymphoid structure (TLS) formation was observed with both adjuvants. Our findings highlight adjuvant-dependent changes in immune features at the VSME that may impact systemic immune responses.

Infectious disease forecasting has been a useful tool for public health planning and messaging during the COVID-19 pandemic. In partnership with the CDC, the organizers of the COVID-19 Forecast Hub have created a mechanism for forecasters from academia, industry, and government organizations to submit weekly near-term predictions of COVID-19 targets in the United States. Here we describe our efforts to participate in the COVID-19 Forecast Hub through theForecastingCOVID-19 in theUnitedStates (FOCUS) project. The effort led to more than three months of weekly submissions and development of an automated pipeline to generate forecasts. The models used in FOCUS yielded forecasts that ranked relatively well in terms of precision and accuracy.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Objective To identify differences in the transcriptomic profiles during placentation from pregnancies conceived spontaneously vs. those with infertility using non-in vitro fertilization (IVF) fertility treatment (NIFT) or IVF. Design Cohort study. Setting Academic medical center. Patient(s) Women undergoing chorionic villus sampling at gestational age 11–13 weeks (n = 141), with pregnancies that were conceived spontaneously (n = 74), with NIFT (n = 33), or with IVF (n = 34), resulting in the delivery of viable offspring. Intervention(s) Collection of chorionic villus samples from women who conceived spontaneously, with NIFT, or with IVF for gene expression analysis using RNA sequencing. Main Outcome Measure(s) Baseline maternal, paternal, and fetal demographics, maternal medical conditions, pregnancy complications, and outcomes. Differential gene expression of first-trimester placenta. Result(s) There were few differences in the transcriptome of first-trimester placenta from NIFT, IVF, and spontaneous pregnancies. There was one protein-coding differentially expressed gene (DEG) between the spontaneous and infertility groups, CACNA1I, one protein-coding DEG between the spontaneous and IVF groups, CACNA1I, and five protein-coding DEGs between the NIFT and IVF groups, SLC18A2, CCL21, FXYD2, PAEP, and DNER. Conclusion(s) This is the first and largest study looking at transcriptomic profiles of first-trimester placenta demonstrating similar transcriptomic profiles in pregnancies conceived using NIFT or IVF and spontaneous conceptions. Gene expression differences found to be highest in the NIFT group suggest that the underlying infertility, in addition to treatment-related factors, may contribute to the observed gene expression profiles.

The placenta serves as a regulator of fetal growth throughout pregnancy. Signaling at the maternal-fetal interface is critical during placentation and lays the groundwork for placenta function, affecting pregnancy outcomes. Fetal growth is impacted by fetal sex, with males larger than females, and maternal gestational diabetes and obesity independently increase the risk of macrosomia in male fetuses only. We previously demonstrated differentially expressed genes (DEGs) among sexes involves ancient canonical pathways and metabolic functions in placenta tissue. As these are likely impacted by signaling at the maternal-fetal interface, our aim here was to identify sex differences in signaling at the maternal-fetal interface and among individual cell types within the placenta to explain these differences. RNA-sequencing of first trimester placenta and maternal decidua as well as single cell RNA-sequencing in first trimester placenta was performed in ongoing pregnancies. We identified 91 sexually dimorphic receptor-ligand pairs across the maternal-fetal interface. From these, 35 of 115 receptors and/or ligand genes were also found to be upstream regulators of pathways critical in sexually dimorphic placentation which may define regulation. Single cell analysis identified five major cell types (trophoblasts, stromal cells, hofbauer cells, antigen presenting cells, and endothelial cells), and all had sexually dimorphic genes. Among individual cell types, ligands from the CC-family of cytokines were most highly representative in females, with their corresponding receptors present on the maternal surface. Furthermore, upstream regulator analysis of sexually dimorphic genes demonstrated TGFβ1 and estradiol to significantly affect all cell types. Dihydrotestosterone, which is produced by the male fetus, was an upstream regulator that was most significant for the trophoblast population. In addition, gene ontology enrichment analysis identified distinctive enriched functions between male and female trophoblasts, with cytokine mediated signaling pathways most representative. MUC15 and NOTUM were the most highly expressed sexually dimorphic autosomal genes found in distinct cell types of the trophoblast population, cell types critical for placentation and nutrient exchange. Thus, differences in hormone and immune signaling pathways may account for differential gene expression and differences in trophoblast function during placentation, which may in turn explain developmental differences, including fetal size, well-being, and overall outcomes.

Clostridium difficile (C. difficile) incidence has tripled over the past 15 years and is attributed to the emergence of hypervirulent strains. While it is clear that C. difficile toxins cause damaging colonic inflammation, the immune mechanisms protecting from tissue damage require further investigation. Through a transcriptome analysis, we identify IL-33 as an immune target upregulated in response to hypervirulent C. difficile. We demonstrate that IL-33 prevents C. difficile-associated mortality and epithelial disruption independently of bacterial burden or toxin expression. IL-33 drives colonic group 2 innate lymphoid cell (ILC2) activation during infection and IL-33 activated ILC2s are sufficient to prevent disease. Furthermore, intestinal IL-33 expression is regulated by the microbiota as fecal microbiota transplantation (FMT) rescues antibiotic-associated depletion of IL-33. Lastly, dysregulated IL-33 signaling via the decoy receptor, sST2, predicts C. difficile-associated mortality in human patients. Thus, IL-33 signaling to ILC2s is an important mechanism of defense from C. difficile colitis., Here, Frisbee et al. show that hypervirulent Clostridium difficile induces IL-33 expression in the gut and IL-33 reduces mortality and morbidity via group 2 innate lymphoid cells. Furthermore, serum levels of the soluble IL-33 decoy receptor, sST2, are associated with enhanced disease severity in human C. difficile patients.

Pregnancies conceived with infertility are at increased risk of adverse outcomes. It is unclear if these outcomes are due to the underlying infertility or the fertility treatments, which includes in vitro fertilization (IVF) and non-IVF (NIFT) treatments. Adverse outcomes, such as low birth weight and preeclampsia, are likely due to implantation and placentation defects early in pregnancy. Previously, we identified hormonal differences in maternal plasma early in gestation that may influence placentation. The underlying treatment including the hormonal influence may impact the epigenome during placentation. Therefore, we investigated DNA methylation in the late first trimester of ongoing pregnancies conceived with and without infertility with respect to the fertility treatments utilized. Tissue was obtained via chorionic villus sampling. DNA methylation for 112 subjects was performed using the Illumina Infinium MethylationEPIC BeadChip array for 866,836 human sites. Two analyses were performed to assess the effect of infertility on the human epigenome: spontaneous pregnancies versus all pregnancies with infertility (including IVF and NIFT pregnancies), and spontaneous pregnancies versus IVF pregnancies only. After normalization, regression analysis for each comparison was performed to identify differentially methylated positions. Three sites were significantly differentially methylated in both patient comparisons, overlapping with gene regions for melanotranferrin (MFI2), apoptotic chromatin condensation inducer 1 (ACIN1), and ankyrin repeat domain 17 (ANKRD17). These genes are involved in iron-binding, alternative splicing, apoptotic chromatin condensation, DNA replication, and innate immune pathways. Since these three differentially methylated genes are seen in the infertility as well as the IVF cohort, the methylation differences may arise from the infertility itself rather than the specific fertility treatments. In contrast, additional DNA methylated differences only found in the spontaneous versus IVF comparison may be due to the underlying treatment, including in vitro embryo development as well as the high hormonal state found in IVF pregnancies. Additional studies investigating the cause as well as the impact of these differentially methylated genes in placentation and pregnancy outcomes are needed.

Genetic variation at insulin-like growth factor 1 (IGF1) has been linked to prostate cancer risk. However, the specific predisposing variants have not been identified. In this study, we fine-mapped the IGF1 locus for prostate cancer risk in African Americans. We conducted targeted Roche GS-Junior 454 resequencing of a 156-kb region of IGF1 in 80 African American aggressive prostate cancer cases. Three hundred and thirty-four IGF1 SNPs were examined for their association with prostate cancer risk in 1,000 African American prostate cancer cases and 991 controls. The top associated SNP in African Americans, rs148371593, was examined in an additional 3,465 prostate cancer cases and 3,425 controls of non-African American ancestry—European Americans, Japanese Americans, Latinos, and Native Hawaiians. The overall association of 334 IGF1 SNPs and prostate cancer risk was assessed using logistic kernel-machine methods. The association between each SNP and prostate cancer risk was evaluated through unconditional logistic regression. A false discovery rate threshold of q < 0.1 was used to determine statistical significance of associations. We identified 8 novel IGF1 SNPs. The cumulative effect of the 334 IGF1 SNPs was not associated with prostate cancer risk (P = 0.13) in African Americans. Twenty SNPs were nominally associated with prostate cancer at P < 0.05. The top associated SNP among African Americans, rs148371593 [minor allele frequency (MAF) = 0.03; P = 0.0014; q > 0.1], did not reach our criterion of statistical significance. This polymorphism was rare in non-African Americans (MAF < 0.003) and was not associated with prostate cancer risk (P = 0.98). Our findings do not support the role of IGF1 variants and prostate cancer risk among African Americans. Cancer Epidemiol Biomarkers Prev; 23(9); 1928–32. ©2014 AACR.

// Su-Fern Tan 1 , Xin Liu 2 , Todd E. Fox 3 , Brian M. Barth 4 , Arati Sharma 2 , Stephen D. Turner 5 , Andy Awwad 2 , Alden Dewey 2 , Kenichiro Doi 6 , Barbara Spitzer 7 , Mithun Vinod Shah 8 , Samy A.F. Morad 9, 10 , Dhimant Desai 11 , Shantu Amin 11 , Junjia Zhu 2 , Jason Liao 2 , Jong Yun 2, 11 , Mark Kester 3 , David F. Claxton 2 , Hong-Gang Wang 2, 12 , Myles C. Cabot 9 , Edward H. Schuchman 13 , Ross L. Levine 7 , David J. Feith 1, 14 , Thomas P. Loughran, Jr 1, 14 1 Department of Medicine, University of Virginia, Charlottesville, VA, USA 2 Penn State Hershey Cancer Institute, Hershey, PA, USA 3 Department of Pharmacology, University of Virginia, Charlottesville, VA, USA 4 Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, NH, USA 5 Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA 6 Department of Pathology, Osaka City University Medical School, Osaka, Japan 7 Human Oncology and Pathogenesis Program and Leukemia Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA 8 Department of Stem Cell Transplantation and Cellular Therapy, University of Texas MD Anderson Cancer Center, Houston, TX, USA 9 Department of Biochemistry and Molecular Biology, East Carolina University, Brody School of Medicine, Greenville, NC, USA 10 Department of Pharmacology, Faculty of Veterinary Medicine, South Valley University, Qena, Egypt 11 Department of Pharmacology, Pennsylvania State University College of Medicine, Hershey, PA, USA 12 Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, PA, USA 13 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, USA 14 University of Virginia Cancer Center, Charlottesville, VA, USA Correspondence to: Thomas P. Loughran, Jr, email: tploughran@virginia.edu Keywords: acid ceramidase, myeloid cell leukemia sequence 1 protein, ceramide, sphingosine 1-phosphate, leukemia Received: May 20, 2016 Accepted: October 13, 2016 Published: November 4, 2016 ABSTRACT There is an urgent unmet need for new therapeutics in acute myeloid leukemia (AML) as standard therapy has not changed in the past three decades and outcome remains poor for most patients. Sphingolipid dysregulation through decreased ceramide levels and elevated sphingosine 1-phosphate (S1P) promotes cancer cell growth and survival. Acid ceramidase (AC) catalyzes ceramide breakdown to sphingosine, the precursor for S1P. We report for the first time that AC is required for AML blast survival. Transcriptome analysis and enzymatic assay show that primary AML cells have high levels of AC expression and activity. Treatment of patient samples and cell lines with AC inhibitor LCL204 reduced viability and induced apoptosis. AC overexpression increased the expression of anti-apoptotic Mcl-1, significantly increased S1P and decreased ceramide. Conversely, LCL204 induced ceramide accumulation and decreased Mcl-1 through post-translational mechanisms. LCL204 treatment significantly increased overall survival of C57BL/6 mice engrafted with leukemic C1498 cells and significantly decreased leukemic burden in NSG mice engrafted with primary human AML cells. Collectively, these studies demonstrate that AC plays a critical role in AML survival through regulation of both sphingolipid levels and Mcl-1. We propose that AC warrants further exploration as a novel therapeutic target in AML.

BACKGROUND Meta-analysis of genome-wide association studies have implicated multiple single nucleotide polymorphisms (SNPs) and associated genes with Alzheimer disease. The role of these SNPs in cognitive impairment in Parkinson disease (PD) remains incompletely evaluated. OBJECTIVE The objective of this study was to test alleles associated with risk of Alzheimer disease for association with cognitive impairment in Parkinson disease (PD). METHODS Two datasets with PD subjects accessed through the NIH database of Genotypes and Phenotypes contained both single nucleotide polymorphism (SNP) arrays and mini-mental state exam (MMSE) scores. Genetic data underwent rigorous quality control and we selected SNPs for genes associated with AD other than APOE. We constructed logistic regression and ordinal regression models, adjusted for sex, age at MMSE, and duration of PD, to assess the association between selected SNPs and MMSE score. RESULTS In one dataset, PICALM rs3851179 was associated with cognitive impairment (MMSE 70 years old (OR = 2.3; adjusted p-value = 0.017; n = 250) but not in PD subjects ≤ 70 years old. CONCLUSIONS Our finding suggests that PICALM rs3851179 could contribute to cognitive impairment in older patients with PD. It is important that future studies consider the interaction of age and genetic risk factors in the development of cognitive impairment in PD.

SUMMARY In addition to the ever-present concern of medical professionals about epidemics of infectious diseases, the relative ease of access and low cost of obtaining, producing, and disseminating pathogenic organisms or biological toxins mean that bioterrorism activity should also be considered when facing a disease outbreak. Utilization of whole-genome sequencing (WGS) in outbreak analysis facilitates the rapid and accurate identification of virulence factors of the pathogen and can be used to identify the path of disease transmission within a population and provide information on the probable source. Molecular tools such as WGS are being refined and advanced at a rapid pace to provide robust and higher-resolution methods for identifying, comparing, and classifying pathogenic organisms. If these methods of pathogen characterization are properly applied, they will enable an improved public health response whether a disease outbreak was initiated by natural events or by accidental or deliberate human activity. The current application of next-generation sequencing (NGS) technology to microbial WGS and microbial forensics is reviewed.

Glioblastoma (GBM) is the most common and most lethal primary malignant brain tumor. The receptor tyrosine kinase MET is frequently upregulated or over activated in GBM. Clinically applicable MET inhibitors have been developed and tested in the lab and in clinical trials. However, resistance to single modality anti-MET drugs frequently occurs, rendering these agents ineffective. This study aimed to determine the mechanisms of MET inhibitor resistance in GBM and to use the acquired information to develop novel therapeutic approaches to overcome resistance. We investigated two clinically applicable MET inhibitors: PF-02341066, an ATP-competitive small molecule inhibitor of MET, and MetMab, a monovalent monoclonal antibody that binds to the extracellular domain of the MET receptor. We generated PF-02341066 and MetMab resistant GBM cell lines and primary cells by subjecting them to increasing concentrations of drug over a period of time. We utilized RNA sequencing (RNA-seq) and reverse phase protein arrays (RPPA) in addition to death and proliferation assays to identify the pathways altered in the resistant GBM cells compared to wild type GBM cells. We discovered many critical proteins that were altered in the resistant cells lines compared to wild type cells. These included FAK, COX-2, p-FGFR1, Vimentin, mTOR and p-STAT3. There was substantial but not complete overlap between the molecules that were altered in cells resistant to the small molecule as compared to molecules that were changed in cells resistant to the antibody. The protein changes in resistant GBM cells were verified by western blotting. Notably, we discovered that both COX-2 and p-FGFR1 were upregulated in GBM resistant cells and thus investigated whether inhibition of these targets could restore MET inhibitor sensitivity. Celecoxib, an FDA-approved drug, acts to inhibit COX-2 and Debio-1347 acts to inhibit the FGFR family, with a higher affinity for FGFR1. Combining Celecoxib or Debio-1347 with PF-02341066 or MetMab led to increased cell death in resistant cells compared to either agent alone. In addition, the drug combination decreased resistance to cell proliferation inhibition compared to either agent alone, indicating restored sensitivity to both MET inhibitors. We are currently undertaking in vivo experiments to establish whether the combination of celecoxib and MET inhibitors can restore sensitivity to MET inhibition in resistant cells as effectively in vivo as seen with in vitro experiments. These data indicate that MET inhibitor resistance can be overcome by targeting the resulting upregulated pathways using FDA-approved drugs and that multi-drug combinations may revert resistance during treatment. Citation Format: Nichola A. Cruickshanks, Ying Zhang, Sarah Hatef, Julia Wulfkuhle, Isela Gallagher, Alexander Koeppel, David Schiff, See Phan, Stephen Turner, Emanuel Petricoin, Roger Abounader. Overcoming MET inhibitor resistance in GBM therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 102. doi:10.1158/1538-7445.AM2017-102

High throughput sequencing (HTS) generates large amounts of high quality sequence data for microbial genomics. The value of HTS for microbial forensics is the speed at which evidence can be collected and the power to characterize microbial-related evidence to solve biocrimes and bioterrorist events. As HTS technologies continue to improve, they provide increasingly powerful sets of tools to support the entire field of microbial forensics. Accurate, credible results allow analysis and interpretation, significantly influencing the course and/or focus of an investigation, and can impact the response of the government to an attack having individual, political, economic or military consequences. Interpretation of the results of microbial forensic analyses relies on understanding the performance and limitations of HTS methods, including analytical processes, assays and data interpretation. The utility of HTS must be defined carefully within established operating conditions and tolerances. Validation is essential in the development and implementation of microbial forensics methods used for formulating investigative leads attribution. HTS strategies vary, requiring guiding principles for HTS system validation. Three initial aspects of HTS, irrespective of chemistry, instrumentation or software are: 1) sample preparation, 2) sequencing, and 3) data analysis. Criteria that should be considered for HTS validation for microbial forensics are presented here. Validation should be defined in terms of specific application and the criteria described here comprise a foundation for investigators to establish, validate and implement HTS as a tool in microbial forensics, enhancing public safety and national security. Published version

Part 1: Synthesis The Fundamentals of Community-based Natural Resource Management * Community-based Natural Resource Management and Rural Livelihoods * Political Economy, Governance and Community-based Natural Resource Management * Putting Out Fires: Does the 'C' in CBNRM Stand for Community or Centrifuge? * Reconciling Biodiversity Conservation with Rural Development: The Holy Grail of CBNRM? * Part 2: Case Studies - Community-based Natural Resource Management, Traditional Governance and Spiritual Ecology in Southern Africa: The Case of Chiefs, Deviners and Spirit Mediums * The Contribution of Bees to Livelihoods in Southern Africa * Everyday Resources are Valuable Enough for Community-based Natural Resource Management Programme Support: Evidence from South Africa * Community-based Resource Management in the Okavango Delta * Local Ecological Knowledge and the Basarwa in the Okavango Delta: The Case of Xaxaba, Ngamiland District * a Land Without Fences: Range Management in Lesotho * Beach Village Committees as a Vehicle for Community Participation: Lake Malombe/Upper Shire River Participatory Programme * Key Issues in Namibia's Communal Conservancy Movement * The Torra Conservancy in Namibia * The Tchumo Tchato Project in Mozambique: Community-based Natural Resource Management in Transition * The Richtersveld and Makuleke Contractual Parks in South Africa: Win-win for Communities and Conservation? *The Lungwa Integrated Rural Development Project, Zambia * Community Wildlife Management in Zimbabwe: The Case of CAMPFIRE in the Zambezi Valley * New Configurations of Power Around Mafaungautsi State Forest in Zimbabwe * Conclusions and Recommendation: What We Have Learned from a Decade of Experimentation * Index

Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient in extracting all of the information about the genetic components of common, complex human diseases. A contributing factor to the problem of analysis is that amongst the small main effects of each single gene on disease susceptibility, there are non-linear, gene-gene interactions that can be difficult for traditional, parametric analyses to detect. In addition, exhaustively searching all multi-locus combinations has proved computationally impractical. Novel strategies for analysis have been developed to address these issues. The Analysis Tool for Heritable and Environmental Network Associations (ATHENA) is an analytical tool that incorporates grammatical evolution neural networks (GENN) to detect interactions among genetic factors. Initial parameters define how the evolutionary process will be implemented. This research addresses how different parameter settings affect detection of disease models involving interactions. In the current study, we iterate over multiple parameter values to determine which combinations appear optimal for detecting interactions in simulated data for multiple genetic models. Our results indicate that the factors that have the greatest influence on detection are: input variable encoding, population size, and parallel computation.

Pharmacogenomics is a rapidly developing sector of human genetics research with arguably the highest potential for immediate benefit. There is a considerable body of evidence demonstrating that variability in drug-treatment response can be explained in part by genetic variation. Subsequently, much research has ensued and is ongoing to identify genetic variants associated with drug-response phenotypes. To reap the full benefits of the data we collect we must give careful consideration to the study population under investigation, the phenotype being examined and the statistical methodology used in data analysis. Here, we discuss principles of study design and optimizing statistical methods for pharmacogenomic studies when the outcome of interest is a continuous measure. We review traditional hypothesis testing procedures, as well as novel approaches that may be capable of accounting for more variance in a quantitative pharmacogenomic trait. We give examples of studies that have employed the analytical methodologies discussed here, as well as resources for acquiring software to run the analyses.

The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for models, and then performs an uncorrected test for interaction. We also show with simulation that when strong main effects are present and the null hypothesis of no interaction is true, that MDR and MDR-PDT reject at far greater than the nominal rate. We also provide a valid regression-based permutation test procedure that specifically tests the null hypothesis of no interaction, and does not reject the null when only main effects are present. The regression-based permutation test implemented here conducts a valid test of interaction after a search for multilocus models, and can be applied to any method that conducts a search to find a multilocus model representing an interaction.

Understanding the genetic underpinnings of common heritable human traits has enormous public health benefits with implications for risk prediction, development of novel drugs, and personalized medicine. Many complex human traits are highly heritable, yet little of the variability in such traits can be accounted for by examining single DNA variants at a time. Seldom explored non-additive gene-gene interactions are thought to be one source of this "missing" heritability. Approaches that can account for this complexity are more aptly suited to find combinations of genetic and environmental exposures that can lead to disease. Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene interactions that influence human traits, yet the search space is nearly infinite because of the vast number of variables collected in contemporary human genetics studies. In this work we assess the performance and feasibility of sensible initialization of an evolutionary algorithm using domain knowledge.

Evolutionary algorithms such as genetic programming and grammatical evolution have been used for simultaneously optimizing network architecture, variable selection, and weights for artificial neural networks. Using an evolutionary algorithm to perform variable selection while searching for non-linear interactions is akin to searching for a needle in a haystack. There is, however, a considerable amount of correlation among variables in biological datasets, such as in microarray or genetic studies. Using the XOR problem, we show that correlation between non-functional and functional variables alters the variable selection fitness landscape by broadening the fitness peak over a wider range of potential input variables. Furthermore, when sub-optimal weights are used, local optima in the variable selection fitness landscape appear centered on each of the two functional variables. These attributes of the fitness landscape may supply building blocks for evolutionary search procedures, and may provide a rationale for conducting a local search for variable selection.

BACKGROUND:Characterization of abdominal and intra-abdominal fat requires imaging, and thus is not feasible in large epidemiologic studies. OBJECTIVE:We investigated whether biomarkers may complement anthropometry (body mass index [BMI], waist circumference [WC], and waist-hip ratio [WHR]) in predicting the size of the body fat compartments by analyzing blood biomarkers, including adipocytokines, insulin resistance markers, sex steroid hormones, lipids, liver enzymes and gastro-neuropeptides. METHODS:Fasting levels of 58 blood markers were analyzed in 60 healthy, Caucasian or Japanese American postmenopausal women who underwent anthropometric measurements, dual energy X-ray absorptiometry (DXA), and abdominal magnetic resonance imaging. Total, abdominal, visceral and hepatic adiposity were predicted based on anthropometry and the biomarkers using Random Forest models. RESULTS:Total body fat was well predicted by anthropometry alone (R(2) = 0.85), by the 5 best predictors from the biomarker model alone (leptin, leptin-adiponectin ratio [LAR], free estradiol, plasminogen activator inhibitor-1 [PAI1], alanine transaminase [ALT]; R(2) = 0.69), or by combining these 5 biomarkers with anthropometry (R(2) = 0.91). Abdominal adiposity (DXA trunk-to-periphery fat ratio) was better predicted by combining the two types of predictors (R(2) = 0.58) than by anthropometry alone (R(2) = 0.53) or the 5 best biomarkers alone (25(OH)-vitamin D(3), insulin-like growth factor binding protein-1 [IGFBP1], uric acid, soluble leptin receptor [sLEPR], Coenzyme Q10; R(2) = 0.35). Similarly, visceral fat was slightly better predicted by combining the predictors (R(2) = 0.68) than by anthropometry alone (R(2) = 0.65) or the 5 best biomarker predictors alone (leptin, C-reactive protein [CRP], LAR, lycopene, vitamin D(3); R(2) = 0.58). Percent liver fat was predicted better by the 5 best biomarker predictors (insulin, sex hormone binding globulin [SHBG], LAR, alpha-tocopherol, PAI1; R(2) = 0.42) or by combining the predictors (R(2) = 0.44) than by anthropometry alone (R(2) = 0.29). CONCLUSION:The predictive ability of anthropometry for body fat distribution may be enhanced by measuring a small number of biomarkers. Studies to replicate these data in men and other ethnic groups are warranted.

The severe problem of soil erosion in Lesotho in southern Africa is discussed and the 45 years' conservation work undertaken to combat it is outlined. It has become apparent that rural villagers understand the processes of erosion and conservation, but that they lack the motivation to make the heavy, long-term sacrifices which conservation demands. Moreover, the disruption caused by conservation is such that this work should be organised and administered at village level, through existing or adapted village institutions. This calls for intensive and costly extension work and, if conservation is to have lasting value, further costs may have to be incurred by linking it with schemes to improve the productivity of the land protected.