Quantitative 1H-magnetic resonance spectroscopy of human brain: Influence of composition and parameterization of the basis set in linear combination model-fitting

Localized short-echo-time (1)H-MR spectra of human brain contain contributions of many low-molecular-weight metabolites and baseline contributions of macromolecules. Two approaches to model such spectra are compared and the data acquisition sequence, optimized for reproducibility, is presented. Modeling relies on prior knowledge constraints and linear combination of metabolite spectra. Investigated was what can be gained by basis parameterization, i.e., description of basis spectra as sums of parametric lineshapes. Effects of basis composition and addition of experimentally measured macromolecular baselines were investigated also. Both fitting methods yielded quantitatively similar values, model deviations, error estimates, and reproducibility in the evaluation of 64 spectra of human gray and white matter from 40 subjects. Major advantages of parameterized basis functions are the possibilities to evaluate fitting parameters separately, to treat subgroup spectra as independent moieties, and to incorporate deviations from straightforward metabolite models. It was found that most of the 22 basis metabolites used may provide meaningful data when comparing patient cohorts. In individual spectra, sums of closely related metabolites are often more meaningful. Inclusion of a macromolecular basis component leads to relatively small, but significantly different tissue content for most metabolites. It provides a means to quantitate baseline contributions that may contain crucial clinical information.

Pollen-based quantitative reconstructions of Holocene regional vegetation cover (plant-functional types and land-cover types) in Europe suitable for climate modelling

We present quantitative reconstructions of regional vegetation cover in north-western Europe, western Europe north of the Alps, and eastern Europe for five time windows in the Holocene around 6k, 3k, 0.5k, 0.2k, and 0.05k calendar years before present (bp)] at a 1 degrees x1 degrees spatial scale with the objective of producing vegetation descriptions suitable for climate modelling. The REVEALS model was applied on 636 pollen records from lakes and bogs to reconstruct the past cover of 25 plant taxa grouped into 10 plant-functional types and three land-cover types evergreen trees, summer-green (deciduous) trees, and open land]. The model corrects for some of the biases in pollen percentages by using pollen productivity estimates and fall speeds of pollen, and by applying simple but robust models of pollen dispersal and deposition. The emerging patterns of tree migration and deforestation between 6k bp and modern time in the REVEALS estimates agree with our general understanding of the vegetation history of Europe based on pollen percentages. However, the degree of anthropogenic deforestation (i.e. cover of cultivated and grazing land) at 3k, 0.5k, and 0.2k bp is significantly higher than deduced from pollen percentages. This is also the case at 6k in some parts of Europe, in particular Britain and Ireland. Furthermore, the relationship between summer-green and evergreen trees, and between individual tree taxa, differs significantly when expressed as pollen percentages or as REVEALS estimates of tree cover. For instance, when Pinus is dominant over Picea as pollen percentages, Picea is dominant over Pinus as REVEALS estimates. These differences play a major role in the reconstruction of European landscapes and for the study of land cover-climate interactions, biodiversity and human resources.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

The paradox of alloreactivity and self MHC restriction: Quantitative analysis and statistics

Although 1–24% of T cells are alloreactive, i.e., respond to MHC molecules encoded by a foreign haplotype, it is generally believed that T cells cannot recognize foreign peptides binding foreign MHC molecules. We show using a quantitative model that, if T cell selection and activation are affinity-driven, then an alloreactivity of 1–24% is incompatible with the textbook notion that self MHC restriction is absolute. If an average of 1% of clones are alloreactive, then according to our model, at most 20-fold more clones should, on average, be activated by antigens presented on self MHC than by antigens presented on foreign MHC. This ratio is at best 5 if alloreactivity is 5%. These results describe average properties of the murine immune system, but not the outcome of individual experiments. Using supercomputer technology, we simulated 100,000 MHC restriction experiments. Although the average restriction ratio was 7.1, restriction was absolute in 10% of the simulated experiments, greater than 100, although not absolute, in 29%, and below 6 in 24%. This extreme variability agrees with experimental estimates. Our analysis suggests that alloreactivity and average self MHC restriction both cannot be high, but that a low average restriction level is compatible with high levels in a significant number of experiments.

Identification of multiple quantitative trait loci linked to prion disease incubation period in mice

Polymorphisms in the prion protein gene are known to affect prion disease incubation times and susceptibility in humans and mice. However, studies with inbred lines of mice show that large differences in incubation times occur even with the same amino acid sequence of the prion protein, suggesting that other genes may contribute to the observed variation. To identify these loci we analyzed 1,009 animals from an F2 intercross between two strains of mice, CAST/Ei and NZW/OlaHSd, with significantly different incubation periods when challenged with RML scrapie prions. Interval mapping identified three highly significantly linked regions on chromosomes 2, 11, and 12; composite interval mapping suggests that each of these regions includes multiple linked quantitative trait loci. Suggestive evidence for linkage was obtained on chromosomes 6 and 7. The sequence conservation between the mouse and human genome suggests that identification of mouse prion susceptibility alleles may have direct relevance to understanding human susceptibility to bovine spongiform encephalopathy (BSE) infection, as well as identifying key factors in the molecular pathways of prion pathogenesis. However, the demonstration of other major genetic effects on incubation period suggests the need for extreme caution in interpreting estimates of variant Creutzfeldt–Jakob disease epidemic size utilizing existing epidemiological models.

Quantitative justification for target concentration intervention - parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides

Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

Contrasting quantitative traits and neutral genetic markers for genetic resource assessment of Mesoamerican Cedrela odorata

We compared within-population variability and degree of population differentiation for neutral genetic markers (RAPDS) and eight quantitative traits in Central American populations of the endangered tree, Cedrela odorata. Whilst population genetic diversity for neutral markers (Shannon index) and quantitative traits (heritability, coefficient of additive genetic variation) were uncorrelated, both marker types revealed strong differentiation between populations from the Atlantic coast of Costa Rica and the rest of the species' distribution. The degree of interpopulation differentiation was higher for RAPD markers (F-ST 0.67 for the sampled Mesoamerican range) than for quantitative traits (Q(ST) = 0.30). Hence, the divergence in quantitative traits was lower than could have been achieved by genetic drift alone, suggesting that balancing selection for similar phenotypes in different populations of this species. Nevertheless, a comparison of pair-wise estimates of population differentiation in neutral genetic markers and quantitative traits revealed a strong positive correlation (r = 0.66) suggesting that, for C. odorata, neutral marker divergence could be used as a surrogate for adaptive gene divergence for conservation planning. The utility of this finding and suggested further work are discussed.

Heritability estimates for growth in the tropical abalone Haliotis asinina using microsatellites to assign parentage

The tropical abalone Haliotis asinina is a wild-caught and cultured species throughout the Indo-Pacific as well as being an emerging model species for the study of haliotids. H. asinina has the fastest recorded natural growth rate of any abalone and reaches sexual maturity within one year. As such, it is a suitable abalone species for selective breeding for commercially important traits such as rapid growth. Estimating the amount of variation in size that is attributable to heritable genetic differences can assist the development of such a selective breeding program. Here we estimated heritability for growth-related traits at 12 months of age by creating a single cohort of 84 families in a full-factorial mating design consisting of 14 sires and 6 dams. Of 500 progeny sampled, 465 were successfully assigned to their parents based on shared alleles at 5 polymorphic microsatellite loci. Using an animal model, heritability estimates were 0.48 +/- 0.15 for shell length, 0.38 +/- 0.13 for shell width and 0.36 +/- 0.13 for weight. Genetic correlations were > 0.98 between shell parameters and weight, indicating that breeding for weight gains could be successfully achieved by selecting for shell length. (c) 2006 Elsevier B.V. All rights reserved.

Quantifying the pathology of neurodegenerative disorders:Quantitative measurements, sampling strategies and data analysis

The use of quantitative methods has become increasingly important in the study of neurodegenerative disease. Disorders such as Alzheimer's disease (AD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This article reviews the advantages and limitations of the different methods of quantifying the abundance of pathological lesions in histological sections, including estimates of density, frequency, coverage, and the use of semiquantitative scores. The major sampling methods by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are also described. In addition, the data analysis methods commonly used to analyse quantitative data in neuropathology, including analyses of variance (ANOVA) and principal components analysis (PCA), are discussed. These methods are illustrated with reference to particular problems in the pathological diagnosis of AD and dementia with Lewy bodies (DLB).

Quantitative assessment of the pharmacotherapy of biopolar disorder and schizophrenia

The work present in this thesis was aimed at assessing the efficacy of lithium in the acute treatment of mania and for the prophylaxis of bipolar disorder, and investigating the value of plasma haloperidol concentration for predicting response to treatment in schizophrenia. The pharmacogenetics of psychotropic drugs is critically appraised to provide insights into interindividual variability in response to pharmacotherapy, In clinical trials of acute mania, a number of measures have been used to assess the severity of illness and its response to treatment. Rating instruments need to be validated in order for a clinical study to provide reliable and meaningful estimates of treatment effects, Eight symptom-rating scales were identified and critically assessed, The Mania Rating Scale (MRS) was the most commonly used for assessing treatment response, The advantage of the MRS is that there is a relatively extensive database of studies based on it and this will no doubt ensure that it remains a gold standard for the foreseeable future. Other useful rating scales are available for measuring mania but further cross-validation and validation against clinically meaningful global changes are required. A total of 658 patients from 12 trials were included in an evaluation of the efficacy of lithium in the treatment of acute mania. Treatment periods ranged from 3 to 4 weeks. Efficacy was estimated using (i) the differences in the reduction in mania severity scores, and (ii) the ratio and difference in improvement response rates. The response rate ratio for lithium against placebo was 1.95 (95% CI 1.17 to 3.23). The mean number needed to treat was 5 (95% CI 3 to 20). Patients were twice as likely to obtain remission with lithium than with chlorpromazine (rate ratio = 1.96, 95% CI 1.02 to 3.77). The mean number needed to treat (NNT) was 4 (95% CI 3 to 9). Neither carbamazepine nor valproate was more effective than lithium. The response rate ratios were 1.01 (95% CI 0.54 to 1.88) for lithium compared to carbarnazepine and 1.22 (95% CI 0.91 to 1.64) for lithium against valproate. Haloperidol was no better than lithium on the basis of improvement based on assessment of global severity. The differences in effects between lithium and risperidone were -2.79 (95% CI -4.22 to -1.36) in favour of risperidone with respect to symptom severity improvement and -0.76 (95% CI -1.11 to -0,41) on the basis of reduction in global severity of disease. Symptom and global severity was at least as well controlIed with lithium as with verapamil. Lithium caused more side-effects than placebo and verapamil, but no more than carbamazepine or valproate. A total of 554 patients from 13 trials were included in the statistical analysis of lithium's efficacy in the prophylaxis of bipolar disorder. The mean follow-up period was 5-34 months. The relapse risk ratio for lithium versus placebo was 0.47 (95% CI 0.26 to 0.86) and the NNT was 3 (95% CI 2 to 7). The relapse risk ratio for lithium versus imipramine was 0.62 (95% CI 0.46 to 0.84) and the NNT was 4 (951% Cl 3 to 7), The combination of lithium and imipramine was no more effective than lithium alone. The risk of relapse was greater with lithium alone than with the lithium-divalproate combination. A risk difference of 0.60 (95% CI 0.21 to 0.99) and an NNT of 2 (95% CI 1 to 5) were obtained. Lithium was as effective as carbamazepine. Based on individual data concerning plasma haloperidol concentration and percent improvement in psychotic symptoms, our results suggest an acceptable concentration range of 11.20-30.30 ng/mL A minimum of 2 weeks should be allowed before evaluating therapeutic response. Monitoring of drug plasma levels seems not to be necessary unless behavioural toxicity or noncompliance is suspected. Pharmacokinetics and pharmacodynamics, which are mainly determined by genetic factors, contribute to interindividual and interethnic variations in clinical response to drugs. These variations are primarily due to differences in drug metabolism. Variability in pharmacokinetics of a number of drugs is associated with oxidation polymorphism. Debrisoquine/sparteine hydroxylase (CYP2D6) and the S-mephenytoin hydroxylase (CYP2C19) are polymorphic P450 enzymes with particular importance in psychopharmacotherapy. The enzymes are responsible for the metabolism of many commonly used antipsychotic and antidepressant drugs. The incidence of poor metabolisers of debrisoquine and S-mephenytoin varies widely among populations. Ethnic variations in polymorphic isoenzymes may, at least in part, explain ethnic differences in response to pharmacotherapy of antipsychotics and antidepressant drugs.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Global variations and time trends in the prevalence of childhood myopia, a systematic review and quantitative meta-analysis:implications for aetiology and early prevention

The aim of this review was to quantify the global variation in childhood myopia prevalence over time taking account of demographic and study design factors. A systematic review identified population-based surveys with estimates of childhood myopia prevalence published by February 2015. Multilevel binomial logistic regression of log odds of myopia was used to examine the association with age, gender, urban versus rural setting and survey year, among populations of different ethnic origins, adjusting for study design factors. 143 published articles (42 countries, 374 349 subjects aged 1- 18 years, 74 847 myopia cases) were included. Increase in myopia prevalence with age varied by ethnicity. East Asians showed the highest prevalence, reaching 69% (95% credible intervals (CrI) 61% to 77%) at 15 years of age (86% among Singaporean-Chinese). Blacks in Africa had the lowest prevalence; 5.5% at 15 years (95% CrI 3% to 9%). Time trends in myopia prevalence over the last decade were small in whites, increased by 23% in East Asians, with a weaker increase among South Asians. Children from urban environments have 2.6 times the odds of myopia compared with those from rural environments. In whites and East Asians sex differences emerge at about 9 years of age; by late adolescence girls are twice as likely as boys to be myopic. Marked ethnic differences in age-specific prevalence of myopia exist. Rapid increases in myopia prevalence over time, particularly in East Asians, combined with a universally higher risk of myopia in urban settings, suggest that environmental factors play an important role in myopia development, which may offer scope for prevention.

Quantitative Comparison of Plant Community Hydrology Using Large-Extent, Long-Term Data

Large-extent vegetation datasets that co-occur with long-term hydrology data provide new ways to develop biologically meaningful hydrologic variables and to determine plant community responses to hydrology. We analyzed the suitability of different hydrological variables to predict vegetation in two water conservation areas (WCAs) in the Florida Everglades, USA, and developed metrics to define realized hydrologic optima and tolerances. Using vegetation data spatially co-located with long-term hydrological records, we evaluated seven variables describing water depth, hydroperiod length, and number of wet/dry events; each variable was tested for 2-, 4- and 10-year intervals for Julian annual averages and environmentally-defined hydrologic intervals. Maximum length and maximum water depth during the wet period calculated for environmentally-defined hydrologic intervals over a 4-year period were the best predictors of vegetation type. Proportional abundance of vegetation types along hydrological gradients indicated that communities had different realized optima and tolerances across WCAs. Although in both WCAs, the trees/shrubs class was on the drier/shallower end of hydrological gradients, while slough communities occupied the wetter/deeper end, the distribution ofCladium, Typha, wet prairie and Salix communities, which were intermediate for most hydrological variables, varied in proportional abundance along hydrologic gradients between WCAs, indicating that realized optima and tolerances are context-dependent.