Leveraging nonlinear dynamic models to predict progression of neuroimaging biomarkers

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed‐effects model framework, we introduce subject‐ and biomarker‐specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.     

Estimating marginal proportions and intraclass correlations with clustered binary data

A logistic regression with random effects model is commonly applied to analyze clustered binary data, and every cluster is assumed to have a different proportion of success. However, it could be of interest to obtain the proportion of success over clusters (i.e. the marginal proportion of success). Furthermore, the degree of correlation among data of the same cluster (intraclass correlation) is also a relevant concept to assess, but when using logistic regression with random effects it is not possible to get an analytical expression of the estimators for marginal proportion and intraclass correlation. In our paper, we assess and compare approaches using different kinds of approximations: based on the logistic‐normal mixed effects model (LN), linear mixed model (LMM), and generalized estimating equations (GEE). The comparisons are completed by using two real data examples and a simulation study. The results show the performance of the approaches strongly depends on the magnitude of the marginal proportion, the intraclass correlation, and the sample size. In general, the reliability of the approaches get worsen with low marginal proportion and large intraclass correlation. LMM and GEE approaches arises as reliable approaches when the sample size is large.     

Design and Inference for Cancer Biomarker Study with an Outcome and Auxiliary‐Dependent Subsampling

Summary In cancer research, it is important to evaluate the performance of a biomarker (e.g., molecular, genetic, or imaging) that correlates patients' prognosis or predicts patients' response to treatment in a large prospective study. Due to overall budget constraint and high cost associated with bioassays, investigators often have to select a subset from all registered patients for biomarker assessment. To detect a potentially moderate association between the biomarker and the outcome, investigators need to decide how to select the subset of a fixed size such that the study efficiency can be enhanced. We show that, instead of drawing a simple random sample from the study cohort, greater efficiency can be achieved by allowing the selection probability to depend on the outcome and an auxiliary variable; we refer to such a sampling scheme as outcome and auxiliary‐dependent subsampling (OADS). This article is motivated by the need to analyze data from a lung cancer biomarker study that adopts the OADS design to assess epidermal growth factor receptor (EGFR) mutations as a predictive biomarker for whether a subject responds to a greater extent to EGFR inhibitor drugs. We propose an estimated maximum‐likelihood method that accommodates the OADS design and utilizes all observed information, especially those contained in the likelihood score of EGFR mutations (an auxiliary variable of EGFR mutations) that is available to all patients. We derive the asymptotic properties of the proposed estimator and evaluate its finite sample properties via simulation. We illustrate the proposed method with a data example.     

芯片数据标准化方法比较研究

在基因芯片实验中,基因表达水平之间的相关性在推断基因间相互关系时起到非常重要的作用.未经标准化处理的芯片数据基因之间往往都呈现出很强的相关性,这些高相关性一部分是由基因表达水平变化引起的,而另外一部分是由系统偏差引起的.对芯片数据进行标准化处理的目的之一是消除系统偏差引起的高相关性,同时保留由真正生物学原因引起的基因表达水平高相关性.虽然目前对标准化方法已经有了不少比较研究,但还较少有人研究标准化方法对基因之间相关系数的影响,以及哪种方法最有利于恢复基因之间的相关性结构.通过对基因表达水平数据的模拟,具体比较了几种常用标准化方法的效果,从而给出最有利于恢复基因之间相关性结构的那种标准化方法.     

Genetics of epidermal ridge minutiae

Epidermal ridge minutiae have been utilized widely in Forensic Sciences for personal identification for a long time; but genetical significance of minutiae is yet to be understood thoroughly. In the present communication an attempt has been made to study the genetical aspects of finger ridge minutiae on the basis of family material. Data for the present study were collected from 76 families belonging to the Namasudras, a Scheduled Caste residing in Nadia district, West Bengal, India. An eight-fold classification of minutiae was followed in the present study. Out of eight minutiae types ridge end and connecting ridge have shown the maximum and minimum occurrences, respectively. Bilateral difference in minutia types was not evident but the sex difference was found to be significant. The genetics of minutia types was studied by familial correlation (r) and heritability (h ²) estimates. Statistically significant familial correlation values and high heritability estimates for some of the minutia types such as fork, ridge end and total minutiae are suggestive of hereditary control of minutiae. The results of the present study suggest that minutiae showing significant correlation values and high heritability estimates (more than 60%) are useful in genetical studies and perhaps diagnosis of some genetical disorders in addition to their regular application in personal identification.     

Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time‐course omics data

Omics experiments endowed with a time‐course design may enable us to uncover the dynamic interplay among genes of cellular processes. Multivariate techniques (like VAR(1) models describing the temporal and contemporaneous relations among variates) that may facilitate this goal are hampered by the high‐dimensionality of the resulting data. This is resolved by the presented ridge regularized maximum likelihood estimation procedure for the VAR(1) model. Information on the absence of temporal and contemporaneous relations may be incorporated in this procedure. Its computational efficient implemention is discussed. The estimation procedure is accompanied with an LOOCV scheme to determine the associated penalty parameters. Downstream exploitation of the estimated VAR(1) model is outlined: an empirical Bayes procedure to identify the interesting temporal and contemporaneous relationships, impulse response analysis, mutual information analysis, and covariance decomposition into the (graphical) relations among variates. In a simulation study the presented ridge estimation procedure outperformed a sparse competitor in terms of Frobenius loss of the estimates, while their selection properties are on par. The proposed machinery is illustrated in the reconstruction of the p53 signaling pathway during HPV‐induced cellular transformation. The methodology is implemented in the ragt2ridges R‐package available from CRAN.     

Exploratory Bayesian model selection for serial genetics data

Characterizing the process by which molecular and cellular level changes occur over time will have broad implications for clinical decision making and help further our knowledge of disease etiology across many complex diseases. However, this presents an analytic challenge due to the large number of potentially relevant biomarkers and the complex, uncharacterized relationships among them. We propose an exploratory Bayesian model selection procedure that searches for model simplicity through independence testing of multiple discrete biomarkers measured over time. Bayes factor calculations are used to identify and compare models that are best supported by the data. For large model spaces, i.e., a large number of multi-leveled biomarkers, we propose a Markov chain Monte Carlo (MCMC) stochastic search algorithm for finding promising models. We apply our procedure to explore the extent to which HIV-1 genetic changes occur independently over time.     

PENN biomarker core of the Alzheimer's disease Neuroimaging Initiative

There is a pressing need to develop effective prevention and disease-modifying treatments for Alzheimer's disease (AD), a dreaded affliction whose incidence increases almost logarithmically with age starting at about 65 years. A key need in the field of AD research is the validation of imaging and biochemical biomarkers. Biomarker tests that are shown to reliably predict the disease before it is clinically expressed would permit testing of new therapeutics at the earliest time point possible in order to give the best chance for delaying the onset of dementia in these patients. In this review the current state of AD biochemical biomarker research is discussed. A new set of guidelines for the diagnosis of AD in the research setting places emphasis on the inclusion of selected imaging and biochemical biomarkers, in addition to neuropsychological behavioral testing. Importantly, the revised guidelines were developed to identify patients at the earliest stages prior to full-blown dementia as well as patients with the full spectrum of the disease. The Alzheimer's Disease Neuroimaging Initiative is a multicenter consortium study that includes as one of its primary goals the development of standardized neuroimaging and biochemical biomarker methods for AD clinical trials, as well as using these to measure changes over time in mildly cognitively impaired patients who convert to AD as compared to the natural variability of these in control subjects and their further change over time in AD patients. Validation of the biomarker results by correlation analyses with neuropsychological and neurobehavioral test data is one of the primary outcomes of this study. This validation data will hopefully provide biomarker test performance needed for effective measurement of the efficacy of new treatment and prevention therapeutic agents.     

Monitored Natural Attenuation of Explosives

Explosives are subject to several attenuation processes that potentially reduce concentrations in groundwater over time. Some of these processes are well defined, while others are poorly understood. The objective of the project was to optimize data collection and processing procedures for evaluation and implementation of monitored natural attenuation of explosives. After conducting experiments to optimize data quality, a protocol was established for quarterly monitoring of thirty wells over a 2-year period at a former waste disposal site. Microbial biomarkers and stable isotopes of nitrogen and carbon were explored as additional approaches to tracking attenuation processes. The project included a cone penetrometry sampling event to characterize site lithology and to obtain sample material for biomarker studies. A three-dimensional groundwater model was applied to conceptualize and predict future behavior of the contaminant plume. The groundwater monitoring data demonstrated declining concentrations of explosives over the 2 years. Biomarker data showed the potential for microbial degradation and provided an estimate of the degradation rate. Measuring stable isotopic fractions of nitrogen in TNT was a promising method of monitoring TNT attenuation. Overall, results of the demonstration suggest that monitored natural attenuation is a viable option that should be among the options considered for remediation of explosives-contaminated sites.     

High Connectivity of Animal Populations in Deep-Sea Hydrothermal Vent Fields in the Central Indian Ridge Relevant to Its Geological Setting

Dispersal ability plays a key role in the maintenance of species in spatially and temporally discrete niches of deep-sea hydrothermal vent environments. On the basis of population genetic analyses in the eastern Pacific vent fields, dispersal of animals in the mid-oceanic ridge systems generally appears to be constrained by geographical barriers such as trenches, transform faults, and microplates. Four hydrothermal vent fields (the Kairei and Edmond fields near the Rodriguez Triple Junction, and the Dodo and Solitaire fields in the Central Indian Ridge) have been discovered in the mid-oceanic ridge system of the Indian Ocean. In the present study, we monitored the dispersal of four representative animals, Austinograea rodriguezensis, Rimicaris kairei, Alviniconcha and the scaly-foot gastropods, among these vent fields by using indirect methods, i.e., phylogenetic and population genetic analyses. For all four investigated species, we estimated potentially high connectivity, i.e., no genetic difference among the populations present in vent fields located several thousands of kilometers apart; however, the direction of migration appeared to differ among the species, probably because of different dispersal strategies. Comparison of the intermediate-spreading Central Indian Ridge with the fast-spreading East Pacific Rise and slow-spreading Mid-Atlantic Ridge revealed the presence of relatively high connectivity in the intermediate- and slow-spreading ridge systems. We propose that geological background, such as spreading rate which determines distance among vent fields, is related to the larval dispersal and population establishment of vent-endemic animal species, and may play an important role in controlling connectivity among populations within a biogeographical province.     

A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods

Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3–40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31–0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated.     

广义岭回归在家禽育种值估计中的应用

讨论了岭回归方法应用于混合线性模型方程组中估计家禽育种值的方法,其实质是将传统的混合线性模型方程组理解为一种广义岭回归估计,为确定遗传参数的估计提供了一种途径;同时,以番鸭为例,考虑了一个性状和两个固定效应,采用广义岭回归法对公番鸭育种值进行了估计,并与最佳线性无偏预测法(BLUP 法)进行了比较,结果表明,广义岭回归方法和BLUP 法估计的育种值及其排序非常接近,其相关系数和秩相关系数分别达到了0.998~(**)和0.986~(**),且采用广义岭回归法预测的误差率低(在±10%以内);表明在混合线性模型方程组中使用广义岭回归估计动物育种值的方法具有可行性,并可省去估计遗传参数的过程,使BLUP 法在动物选育中的应用更具实用性.     

A legion of potential regulatory sRNAs exists beyond the typical microRNAs microcosm

Post ENCODE, regulatory sRNAs (rsRNAs) like miRNAs have established their status as one of the core regulatory elements of cell systems. However, large number of rsRNAs are compromised due to traditional approaches to identify miRNAs, limiting the otherwise vast world of rsRNAs mainly to hair-pin loop bred typical miRNAs. The present study has analyzed for the first time a huge volume of sequencing data from 4997 individuals and 25 cancer types to report 11 234 potentially regulatory small RNAs which appear to have deep reaching impact. The rsRNA-target interactions have been studied and validated extensively using experimental data from AGO-crosslinking, DGCR8 knockdown, CLASH, proteome and expression data. A subset of such interactions was also validated independently in the present study using multiple cell lines, by qPCR. Several of the potential rsRNAs have emerged as a critical cancer biomarker controlling some important spots of cell system. The entire study has been presented into an interactive info-analysis portal handling more than 260 GB of processed data. The possible degree of cell system regulation by sRNAs appears to be much higher than previously assumed.     

Interactions Among Wildland Fires in a Long-Established Sierra Nevada Natural Fire Area

We investigate interactions between successive naturally occurring fires, and assess to what extent the environments in which fires burn influence these interactions. Using mapped fire perimeters and satellite-based estimates of post-fire effects (referred to hereafter as fire severity) for 19 fires burning relatively freely over a 31-year period, we demonstrate that fire as a landscape process can exhibit self-limiting characteristics in an upper elevation Sierra Nevada mixed conifer forest. We use the term ‘self-limiting’ to refer to recurring fire as a process over time (that is, fire regime) consuming fuel and ultimately constraining the spatial extent and lessening fire-induced effects of subsequent fires. When the amount of time between successive adjacent fires is under 9 years, and when fire weather is not extreme (burning index <34.9), the probability of the latter fire burning into the previous fire area is extremely low. Analysis of fire severity data by 10-year periods revealed a fair degree of stability in the proportion of area burned among fire severity classes (unchanged, low, moderate, high). This is in contrast to a recent study demonstrating increasing high-severity burning throughout the Sierra Nevada from 1984 to 2006, which suggests freely burning fires over time in upper elevation Sierra Nevada mixed conifer forests can regulate fire-induced effects across the landscape. This information can help managers better anticipate short- and long-term effects of allowing naturally ignited fires to burn, and ultimately, improve their ability to implement Wildland Fire Use programs in similar forest types. BC wrote paper, performed analysis; JM gathered/processed data, performed analysis, contributed to writing; AT gathered/processed data, conducted field research; MK contributed new methods for analysis; JvW performed analysis, conceived the study; SS designed study, contributed to writing.     

Within-person variability of the ratios of urinary 2-hydroxyestrone to 16alpha-hydroxyestrone in Caucasian women

The ratio of urinary 2-hydroxyestrone (2-OHE1) to 16alpha-hydroxyestrone (16alpha-OHE1) has been suggested as a potential biomarker for breast cancer risk. We evaluated within-person variability of this biomarker in ten healthy Caucasian women aged 23-58 years. Each study participant was asked to provide an overnight fasting morning urine sample once a week for an average of 8 weeks. These urine samples were assayed for 2-OHE1 and 16alpha-OHE1 by using competitive enzyme immunoassay kits purchased from the ImmunaCare Corporation. The coefficients of variation for urinary 2-OHE1/16alpha-OHE1 over the study period ranged from 13.7 to 59.6% (mean, 33.3%) in our study participants. There was a good correlation between the level of the urinary 2-OHE1/16alpha-OHE1 ratio in any single urine sample and the average ratio over the 8-week study period from the same woman, with the mean correlation coefficient of 0.85. These results indicated that the within-person variation of the 2-OHE1 to 16alpha-OHE1 ratio for most women was moderate and the level of this ratio in a single urine sample, in general, reflects reasonably well the level of this biomarker over a 2-month period.     

PRISM: a data management system for high-throughput proteomics

Advanced proteomic research efforts involving areas such as systems biology or biomarker discovery are enabled by the use of high level informatics tools that allow the effective analysis of large quantities of differing types of data originating from various studies. Performing such analyses on a large scale is not feasible without a computational platform that performs data processing and management tasks. Such a platform must be able to provide high-throughput operation while having sufficient flexibility to accommodate evolving data analysis tools and methodologies. The Proteomics Research Information Storage and Management system (PRISM) provides a platform that serves the needs of the accurate mass and time tag approach developed at Pacific Northwest National Laboratory. PRISM incorporates a diverse set of analysis tools and allows a wide range of operations to be incorporated by using a state machine that is accessible to independent, distributed computational nodes. The system has scaled well as data volume has increased over several years, while allowing adaptability for incorporating new and improved data analysis tools for more effective proteomics research.     

Within-person variability of urinary 6beta-hydroxycortisol to urinaryl ratios in Caucasian women

Cortisol is metabolized to 6beta-hydroxycortisol by human cytochrome p450-3A4 (CYP3A4), an important enzyme involved in the metabolism of a variety of exogenous and endogenous compounds. Both cortisol and 6beta-hydroxycortisol are excreted in urine, and the ratio of these steroids has been proposed as an indicator of CYP3A4 activity. We evaluated within-person variability of this biomarker in 10 healthy Caucasian women, aged 23-58 years. Each study participant was asked to provide a fasting morning urine sample once a week consecutively for 8 weeks. Urinary cortisol and 6beta-hydroxycortisol were determined by immunoassay kits purchased from the DiaSorin (Stillwater, MN) and the Stabiligen (Nancy, France), respectively. The coefficients of variation (CV) of urinary 6beta-hydroxycortisol to cortisol ratios from study participants ranged from 16.7 to 51.4% (mean, 31.1%) over the study period. The level of the ratio measured in any single urine sample was correlated reasonably well with the average of the ratios over the 8-week study period from the same woman, with the mean correlation coefficient of 0.79. These results indicated that urinary 6beta-hydroxycortisol to cortisol ratios measured in a spot urine sample may reflect the level of this biomarker over a relatively longer time period in Caucasian women, and thus, it can be used in epidemiologic studies as a biomarker to evaluate the association between CYP3A4 activity and disease risk.     

Was Rodney Ledward a statistical outlier? Retrospective analysis using routine hospital data to identify gynaecologists' performance

Objectives To investigate whether routinely collected data from hospital episode statistics could be used to identify the gynaecologist Rodney Ledward, who was suspended in 1966 and was the subject of the Ritchie inquiry into quality and practice within the NHS.Design A mixed scanning approach was used to identify seven variables from hospital episode statistics that were likely to be associated with potentially poor performance. A blinded multivariate analysis was undertaken to determine the distance (known as the Mahalanobis distance) in the seven indicator multidimensional space that each consultant was from the average consultant in each year. The change in Mahalanobis distance over time was also investigated by using a mixed effects model.Setting NHS hospital trusts in two English regions, in the five years from 1991-2 to 1995-6.Population Gynaecology consultants (n = 143) and their hospital episode statistics data.Main outcome measure Whether Ledward was a statistical outlier at the 95% level.Results The proportion of consultants who were outliers in any one year (at the 95% significance level) ranged from 9% to 20%. Ledward appeared as an outlier in three of the five years. Our mixed effects (multi-year) model identified nine high outlier consultants, including Ledward.Conclusion It was possible to identify Ledward as an outlier by using hospital episode statistics data. Although our method found other outlier consultants, we strongly caution that these outliers should not be overinterpreted as indicative of “poor” performance. Instead, a scientific search for a credible explanation should be undertaken, but this was outside the remit of our study. The set of indicators used means that cancer specialists, for example, are likely to have high values for several indicators, and the approach needs to be refined to deal with case mix variation. Even after allowing for that, the interpretation of outlier status is still as yet unclear. Further prospective evaluation of our method is warranted, but our overall approach may be potentially useful in other settings, especially where performance entails several indicator variables.     

On Estimating the Relationship between Longitudinal Measurements and Time‐to‐Event Data Using a Simple Two‐Stage Procedure

Summary Ye, Lin, and Taylor (2008, Biometrics 64 , 1238–1246) proposed a joint model for longitudinal measurements and time‐to‐event data in which the longitudinal measurements are modeled with a semiparametric mixed model to allow for the complex patterns in longitudinal biomarker data. They proposed a two‐stage regression calibration approach that is simpler to implement than a joint modeling approach. In the first stage of their approach, the mixed model is fit without regard to the time‐to‐event data. In the second stage, the posterior expectation of an individual's random effects from the mixed‐model are included as covariates in a Cox model. Although Ye et al. (2008) acknowledged that their regression calibration approach may cause a bias due to the problem of informative dropout and measurement error, they argued that the bias is small relative to alternative methods. In this article, we show that this bias may be substantial. We show how to alleviate much of this bias with an alternative regression calibration approach that can be applied for both discrete and continuous time‐to‐event data. Through simulations, the proposed approach is shown to have substantially less bias than the regression calibration approach proposed by Ye et al. (2008) . In agreement with the methodology proposed by Ye et al. (2008) , an advantage of our proposed approach over joint modeling is that it can be implemented with standard statistical software and does not require complex estimation techniques.     

Relative clonal density of malaria parasites in mixed-genotype infections: validation of a technique using microsatellite markers for Plasmodium falciparum and P. mexicanum

Quantifying the relative proportion of coexisting genotypes (clones) of a malaria parasite within its vertebrate host's blood would provide insights into critical features of the biology of the parasite, including competition among clones, gametocyte sex ratio, and virulence. However, no technique has been available to extract such data for natural parasite-host systems when the number of clones cycling in the overall parasite population is likely to be large. Recent studies find that data from genetic analyzer instruments for microsatellite markers allow measuring clonal proportions. We conducted a validation study for Plasmodium mexicanum and Plasmodium falciparum by mixing DNA from single-clone infections to simulate mixed infections of each species with known proportions of clones. Results for any mixture of DNA gave highly reproducible results. The relationship between known and measured relative proportions of clones was linear, with high regression r2 values. Known and measured clone proportions for simulated infections followed over time (mixtures) were compared with 3 methods: using uncorrected data, with uncorrected data and confidence intervals constructed from observed experimental error, and using a baseline mixture of equal proportions to calibrate all other results. All 3 methods demonstrated value in studies of mixed-genotype infections sampled a single time or followed over time. Thus, the method should open new windows into the biology of malaria parasites.