Methods for testing association between uncertain genotypes and quantitative traits.

Interpretability and power of genome-wide association studies can be increased by imputing unobserved genotypes, using a reference panel of individuals genotyped at higher marker density. For many markers, genotypes cannot be imputed with complete certainty, and the uncertainty needs to be taken into account when testing for association with a given phenotype. In this paper, we compare currently available methods for testing association between uncertain genotypes and quantitative traits. We show that some previously described methods offer poor control of the false-positive rate (FPR), and that satisfactory performance of these methods is obtained only by using ad hoc filtering rules or by using a harsh transformation of the trait under study. We propose new methods that are based on exact maximum likelihood estimation and use a mixture model to accommodate nonnormal trait distributions when necessary. The new methods adequately control the FPR and also have equal or better power compared to all previously described methods. We provide a fast software implementation of all the methods studied here; our new method requires computation time of less than one computer-day for a typical genome-wide scan, with 2.5 M single nucleotide polymorphisms and 5000 individuals.

Statistical discrimination of steroid profiles in doping control with support vector machines.

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society. To screen for misuse with these compounds, drug testing laboratories monitor the urinary concentrations of endogenous steroid metabolites and their ratios, which constitute the steroid profile and compare them with reference ranges to detect unnaturally high values. However, the interpretation of the steroid profile is difficult due to large inter-individual variances, various confounding factors and different endogenous steroids marketed that influence the steroid profile in various ways. A support vector machine (SVM) algorithm was developed to statistically evaluate urinary steroid profiles composed of an extended range of steroid profile metabolites. This model makes the interpretation of the analytical data in the quest for deviating steroid profiles feasible and shows its versatility towards different kinds of misused endogenous steroids. The SVM model outperforms the current biomarkers with respect to detection sensitivity and accuracy, particularly when it is coupled to individual data as stored in the Athlete Biological Passport.

Adaptive strategy for the statistical analysis of connectomes.

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.

Testing the predictive adaptive response in a host-parasite system

1. Harsh environmental conditions experienced during development can reduce the performance of the same individuals in adulthood. However, the 'predictive adaptive response' hypothesis postulates that if individuals adapt their phenotype during development to the environments where they are likely to live in the future, individuals exposed to harsh conditions in early life perform better when encountering the same harsh conditions in adulthood compared to those never exposed to these conditions before. 2. Using the common vole (Microtus arvalis) as study organism, we tested how exposure to flea parasitism during the juvenile stage affects the physiology (haematocrit, resistance to oxidative stress, resting metabolism, spleen mass, and testosterone), morphology (body mass, testis mass) and motor performance (open field activity and swimming speed) of the same individuals when infested with fleas in adulthood. According to the 'predictive adaptive response' hypothesis, we predicted that voles parasitized at the adult stage would perform better if they had already been parasitized with fleas at the juvenile stage. 3. We found that voles exposed to fleas in adulthood had a higher metabolic rate if already exposed to fleas when juvenile, compared to voles free of fleas when juvenile and voles free of fleas in adulthood. Independently of juvenile parasitism, adult parasitism impaired adult haematocrit and motor performances. Independently of adult parasitism, juvenile parasitism slowed down crawling speed in adult female voles. 4. Our results suggest that juvenile parasitism has long-term effects that do not protect from the detrimental effects of adult parasitism. On the contrary, experiencing parasitism in early-life incurs additional costs upon adult parasitism measured in terms of higher energy expenditure, rather than inducing an adaptive shift in the developmental trajectory. 5. Hence, our study provides experimental evidence for long term costs of parasitism. We found no support for a predictive adaptive response in this host-parasite system.

Iowa GED Statistical Report for Calendar Year2001, 2001

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar year 2001

Iowa GED Statistical Report, 2004

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar year 2004.

Iowa GED Statistical Report, 2002

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar year 2002

Iowa GED Statistical Report, 2006

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar Year 2005.

Stepwise multiple testing as formalized data snooping

It is common in econometric applications that several hypothesis tests arecarried out at the same time. The problem then becomes how to decide whichhypotheses to reject, accounting for the multitude of tests. In this paper,we suggest a stepwise multiple testing procedure which asymptoticallycontrols the familywise error rate at a desired level. Compared to relatedsingle-step methods, our procedure is more powerful in the sense that itoften will reject more false hypotheses. In addition, we advocate the useof studentization when it is feasible. Unlike some stepwise methods, ourmethod implicitly captures the joint dependence structure of the teststatistics, which results in increased ability to detect alternativehypotheses. We prove our method asymptotically controls the familywise errorrate under minimal assumptions. We present our methodology in the context ofcomparing several strategies to a common benchmark and deciding whichstrategies actually beat the benchmark. However, our ideas can easily beextended and/or modied to other contexts, such as making inference for theindividual regression coecients in a multiple regression framework. Somesimulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.

Further evidence on the statistical properties of real GNP

The well-known lack of power of unit root tests has often been attributed to the shortlength of macroeconomic variables and also to DGP s that depart from the I(1)-I(0)alternatives. This paper shows that by using long spans of annual real GNP and GNPper capita (133 years) high power can be achieved, leading to the rejection of both theunit root and the trend-stationary hypothesis. This suggests that possibly neither modelprovides a good characterization of these data. Next, more flexible representations areconsidered, namely, processes containing structural breaks (SB) and fractional ordersof integration (FI). Economic justification for the presence of these features in GNP isprovided. It is shown that the latter models (FI and SB) are in general preferred to theARIMA (I(1) or I(0)) ones. As a novelty in this literature, new techniques are appliedto discriminate between FI and SB models. It turns out that the FI specification ispreferred, implying that GNP and GNP per capita are non-stationary, highly persistentbut mean-reverting series. Finally, it is shown that the results are robust when breaksin the deterministic component are allowed for in the FI model. Some macroeconomicimplications of these findings are also discussed.

Is the observed persistence spurious? A test for fractional integration versus short memory and structural breaks

Although it is commonly accepted that most macroeconomic variables are nonstationary, it is often difficult to identify the source of the non-stationarity. In particular, it is well-known that integrated and short memory models containing trending components that may display sudden changes in their parameters share some statistical properties that make their identification a hard task. The goal of this paper is to extend the classical testing framework for I(1) versus I(0)+ breaks by considering a a more general class of models under the null hypothesis: non-stationary fractionally integrated (FI) processes. A similar identification problem holds in this broader setting which is shown to be a relevant issue from both a statistical and an economic perspective. The proposed test is developed in the time domain and is very simple to compute. The asymptotic properties of the new technique are derived and it is shown by simulation that it is very well-behaved in finite samples. To illustrate the usefulness of the proposed technique, an application using inflation data is also provided.

In-vitro testing of biofilm formation on infected bone grafts and bone substitutes

Background: Bacteria form biofilms on the surface of orthopaedic devices, causing persistent infections. Monitoring biofilm formation on bone grafts and bone substitutes is challenging due to heterogeneous surface characteristics. We analyzed various bone grafts and bone substitutes regarding their propensity for in-vitro biofilm formation caused by S. aureus and S. epidermidis. Methods: Beta-tricalciumphosphate (b-TCP, ChronOsTM), processed human spongiosa (TutoplastTM) and PMMA (PalacosTM) were investigated. PE was added as a growth control. As test strains S. aureus (ATCC 29213) and S. epidermidis RP62A (ATCC 35984) were used. Test materials were incubated with 105 cfu/ml. After 24 h, test materials were removed and washed, followed by a standardised sonication protocol. The resulting sonication fluid was plated and bacterial counts were enumerated and expressed as cfu/sample. Sonicated samples were transferred to a microcalorimeter (TA Instrument) and heat flow monitored over a 24 h period with a precision of 0.0001°C and a sensitiviy of 200 μW. Experiments were performed in triplicates to calculate the mean ± SD. One-way ANOVA analysis was used for statistical analysis. Results: Bacterial counts (log10 cfu/sample) were highest on b-TCP (S. aureus 7.67 ± 0.17; S. epidermidis 8.14 ± 0.05) while bacterial density (log10 cfu/surface) was highest on PMMA (S. aureus 6.12 ± 0.2, S. epidermidis 7.65 ± 0.13). Detection time for S. aureus biofilms was shorter for the porous materials (b-TCP and Tutoplast, p <0.001) compared to the smooth materials (PMMA and PE) with no differences between b-TCP and TutoplastTM (p >0.05) or PMMA and PE (p >0.05). In contrast, for S. epidermidis biofilms the detection time was different (p <0.001) between all materials except between Tutoplast and PE (p >0.05). Conclusion: Our results demonstrate biofilm formation with both strains on all tested materials. Microcalorimetry was able to detect quantitatively the amount of biofilm. Further studies are needed to see whether calorimetry is a suitable tool also to monitor approaches to prevent and treat infections associated with bone grafts and bone substitutes.

Paper 4: EUROCAT statistical monitoring: identification and investigation of ten year trends of congenital anomalies in Europe.

BACKGROUND: As part of EUROCAT's surveillance of congenital anomalies in Europe, a statistical monitoring system has been developed to detect recent clusters or long-term (10 year) time trends. The purpose of this article is to describe the system for the identification and investigation of 10-year time trends, conceived as a "screening" tool ultimately leading to the identification of trends which may be due to changing teratogenic factors.METHODS: The EUROCAT database consists of all cases of congenital anomalies including livebirths, fetal deaths from 20 weeks gestational age, and terminations of pregnancy for fetal anomaly. Monitoring of 10-year trends is performed for each registry for each of 96 non-independent EUROCAT congenital anomaly subgroups, while Pan-Europe analysis combines data from all registries. The monitoring results are reviewed, prioritized according to a prioritization strategy, and communicated to registries for investigation. Twenty-one registries covering over 4 million births, from 1999 to 2008, were included in monitoring in 2010.CONCLUSIONS: Significant increasing trends were detected for abdominal wall anomalies, gastroschisis, hypospadias, Trisomy 18 and renal dysplasia in the Pan-Europe analysis while 68 increasing trends were identified in individual registries. A decreasing trend was detected in over one-third of anomaly subgroups in the Pan-Europe analysis, and 16.9% of individual registry tests. Registry preliminary investigations indicated that many trends are due to changes in data quality, ascertainment, screening, or diagnostic methods. Some trends are inevitably chance phenomena related to multiple testing, while others seem to represent real and continuing change needing further investigation and response by regional/national public health authorities.

More on Testing for Validity Instead of Looking for It

Using Monte Carlo simulations and reanalyzing the data of a validation study of the AEIM emotional intelligence test, we demonstrated that an atheoretical approach and the use of weak statistical procedures can result in biased validity estimates. These procedures included stepwise regression-and the general case of failing to include important theoretical controls-extreme scores analysis, and ignoring heteroscedasticity as well as measurement error. The authors of the AEIM test responded by offering more complete information about their analyses, allowing us to further examine the perils of ignoring theory and correct statistical procedures. In this paper we show with extended analyses that the AEIM test is invalid.