Candidate gene analysis for quantitative traits using the transmission disequilibrium test : the example of the melanocortin 4-receptor in pigs

Population-wide associations between loci due to linkage disequilibrium can be used to map quantitative trait loci (QTL) with high resolution. However, spurious associations between markers and QTL can also arise as a consequence of population stratification. Statistical methods that cannot differentiate between loci associations due to linkage disequilibria from those caused in other ways can render false-positive results. The transmission-disequilibrium test (TDT) is a robust test for detecting QTL. The TDT exploits within-family associations that are not affected by population stratification. However, some TDTs are formulated in a rigid-form, with reduced potential applications. In this study we generalize TDT using mixed linear models to allow greater statistical flexibility. Allelic effects are estimated with two independent parameters: one exploiting the robust within-family information and the other the potentially biased between-family information. A significant difference between these two parameters can be used as evidence for spurious association. This methodology was then used to test the effects of the fourth melanocortin receptor (MC4R) on production traits in the pig. The new analyses supported the previously reported results; i.e., the studied polymorphism is either causal of in very strong linkage disequilibrium with the causal mutation, and provided no evidence for spurious association.

Quantitative traits correlative analysis and growth comparison among different populations of bay scallop, Argopecten irradians

The shell traits and weight traits are measured in cultured populations of bay scallop, Argopecten irradians. The results of regression analysis show that the regression relationships for all the traits are significant (P < 0.01). The correlative coefficients between body weight, as well as tissue weight with shell length, shell height and shell width are significant (P < 0.05). But the correlative coefficients between the anterior and posterior auricle length with body weight as well as tissue weight are not significant (P > 0.05). The multiple regression equation is obtained to estimate live body weight and tissue weight. The above traits except anterior and posterior auricle length are used for the growth and production comparison among three cultured populations, Duncan's new multiple range procedure analysis shows that all the traits in the Lingshuiqiao (LSQ) population are much more significant than those of the other two populations (P < 0.01), and there is no significant difference between the Qipanmo (QPM) and Dalijia (DLJ) populations in all traits (P > 0.05). The results indicate that the LSQ population has a higher growth rate and is expected to be more productive than the other two populations.

A unifying approach for haplotype analysis of quantitative traits in family-based association studies: Testing and estimating gene-environment interactions with complex exposure variables

We propose robust and e±cient tests and estimators for gene-environment/gene-drug interactions in family-based association studies. The methodology is designed for studies in which haplotypes, quantitative pheno- types and complex exposure/treatment variables are analyzed. Using causal inference methodology, we derive family-based association tests and estimators for the genetic main effects and the interactions. The tests and estimators are robust against population admixture and strati¯cation without requiring adjustment for confounding variables. We illustrate the practical relevance of our approach by an application to a COPD study. The data analysis suggests a gene-environment interaction between a SNP in the Serpine gene and smok- ing status/pack years of smoking that reduces the FEV1 volume by about 0.02 liter per pack year of smoking. Simulation studies show that the pro- posed methodology is su±ciently powered for realistic sample sizes and that it provides valid tests and effect size estimators in the presence of admixture and stratification.

Cost and power analysis of linkage disequilibrium methods for locating genes influencing quantitative traits in humans

Linkage disequilibrium methods can be used to find genes influencing quantitative trait variation in humans. Linkage disequilibrium methods can require smaller sample sizes than linkage equilibrium methods, such as the variance component approach to find loci with a specific effect size. The increase in power is at the expense of requiring more markers to be typed to scan the entire genome. This thesis compares different linkage disequilibrium methods to determine which factors influence the power to detect disequilibrium. The costs of disequilibrium and equilibrium tests were compared to determine whether the savings in phenotyping costs when using disequilibrium methods outweigh the additional genotyping costs.^ Nine linkage disequilibrium tests were examined by simulation. Five tests involve selecting isolated unrelated individuals while four involved the selection of parent child trios (TDT). All nine tests were found to be able to identify disequilibrium with the correct significance level in Hardy-Weinberg populations. Increasing linked genetic variance and trait allele frequency were found to increase the power to detect disequilibrium, while increasing the number of generations and distance between marker and trait loci decreased the power to detect disequilibrium. Discordant sampling was used for several of the tests. It was found that the more stringent the sampling, the greater the power to detect disequilibrium in a sample of given size. The power to detect disequilibrium was not affected by the presence of polygenic effects.^ When the trait locus had more than two trait alleles, the power of the tests maximized to less than one. For the simulation methods used here, when there were more than two-trait alleles there was a probability equal to 1-heterozygosity of the marker locus that both trait alleles were in disequilibrium with the same marker allele, resulting in the marker being uninformative for disequilibrium.^ The five tests using isolated unrelated individuals were found to have excess error rates when there was disequilibrium due to population admixture. Increased error rates also resulted from increased unlinked major gene effects, discordant trait allele frequency, and increased disequilibrium. Polygenic effects did not affect the error rates. The TDT, Transmission Disequilibrium Test, based tests were not liable to any increase in error rates.^ For all sample ascertainment costs, for recent mutations ($<$100 generations) linkage disequilibrium tests were less expensive than the variance component test to carry out. Candidate gene scans saved even more money. The use of recently admixed populations also decreased the cost of performing a linkage disequilibrium test. ^

Genome-wide association studies of quantitative traits with related individuals: Little (power) lost but much to be gained

For complex disease genetics research in human populations, remarkable progress has been made in recent times with the publication of a number of genome-wide association scans (GWAS) and subsequent statistical replications. These studies have identified new genes and pathways implicated in disease, many of which were not known before. Given these early successes, more GWAS are being conducted and planned, both for disease and quantitative phenotypes. Many researchers and clinicians have DNA samples available on collections of families, including both cases and controls. Twin registries around the world have facilitated the collection of large numbers of families, with DNA and multiple quantitative phenotypes collected on twin pairs and their relatives. In the design of a new GWAS with a fixed budget for the number of chips, the question arises whether to include or exclude related individuals. It is commonly believed to be preferable to use unrelated individuals in the first stage of a GWAS because relatives are 'over-matched' for genotypes. In this study, we quantify that for GWAS of a quantitative phenotype, relative to a sample of unrelated individuals surprisingly little power is lost when using relatives. The advantages of using relatives are manifold, including the ability to perform more quality control, the choice to perform within-family tests of association that are robust to population stratification, and the ability to perform joint linkage and association analysis. Therefore, the advantages of using relatives in GWAS for quantitative traits may well outweigh the small disadvantage in terms of statistical power.

The use of genetic correlations to evaluate associations between SNP markers and quantitative traits

Open-pollinated progeny of Corymbia citriodora established in replicated field trials were assessed for stem diameter, wood density, and pulp yield prior to genotyping single nucleotide polymorphisms (SNP) and testing the significance of associations between markers and assessment traits. Multiple individuals within each family were genotyped and phenotyped, which facilitated a comparison of standard association testing methods and an alternative method developed to relate markers to additive genetic effects. Narrow-sense heritability estimates indicated there was significant additive genetic variance within this population for assessment traits ( h ˆ 2 =0.28to0.44 ) and genetic correlations between the three traits were negligible to moderate (r G = 0.08 to 0.50). The significance of association tests (p values) were compared for four different analyses based on two different approaches: (1) two software packages were used to fit standard univariate mixed models that include SNP-fixed effects, (2) bivariate and multivariate mixed models including each SNP as an additional selection trait were used. Within either the univariate or multivariate approach, correlations between the tests of significance approached +1; however, correspondence between the two approaches was less strong, although between-approach correlations remained significantly positive. Similar SNP markers would be selected using multivariate analyses and standard marker-trait association methods, where the former facilitates integration into the existing genetic analysis systems of applied breeding programs and may be used with either single markers or indices of markers created with genomic selection processes.

Power and Robustness of Linkage Tests for Quantitative Traits in General Pedigrees

There are numerous statistical methods for quantitative trait linkage analysis in human studies. An ideal such method would have high power to detect genetic loci contributing to the trait, would be robust to non-normality in the phenotype distribution, would be appropriate for general pedigrees, would allow the incorporation of environmental covariates, and would be appropriate in the presence of selective sampling. We recently described a general framework for quantitative trait linkage analysis, based on generalized estimating equations, for which many current methods are special cases. This procedure is appropriate for general pedigrees and easily accommodates environmental covariates. In this paper, we use computer simulations to investigate the power robustness of a variety of linkage test statistics built upon our general framework. We also propose two novel test statistics that take account of higher moments of the phenotype distribution, in order to accommodate non-normality. These new linkage tests are shown to have high power and to be robust to non-normality. While we have not yet examined the performance of our procedures in the context of selective sampling via computer simulations, the proposed tests satisfy all of the other qualities of an ideal quantitative trait linkage analysis method.

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.

Novel keratins identified by quantitative proteomic analysis as the major cytoskeletal proteins of equine (Equus caballus) hoof lamellar tissue

The dermo-epidermal interface that connects the equine distal phalanx to the cornified hoof wall withstands great biomechanical demands, but is also a region where structural failure often ensues as a result of laminitis. The cytoskeleton in this region maintains cell structure and facilitates intercellular adhesion, making it likely to be involved in laminitis pathogenesis, although it is poorly characterized in the equine hoof lamellae. The objective of the present study was to identify and quantify the cytoskeletal proteins present in the epidermal and dermal lamellae of the equine hoof by proteomic techniques. Protein was extracted from the mid-dorsal epidermal and dermal lamellae from the front feet of 5 Standardbred geldings and 1 Thoroughbred stallion. Mass spectrometry-based spectral counting techniques, PAGE, and immunoblotting were used to identify and quantify cytoskeletal proteins, and indirect immunofluorescence was used for cellular localization of K14 and K124 (where K refers to keratin). Proteins identified by spectral counting analysis included 3 actin microfilament proteins; 30 keratin proteins along with vimentin, desmin, peripherin, internexin, and 2 lamin intermediate filament proteins; and 6 tubulin microtubule proteins. Two novel keratins, K42 and K124, were identified as the most abundant cytoskeletal proteins (22.0 ± 3.2% and 23.3 ± 4.2% of cytoskeletal proteins, respectively) in equine hoof lamellae. Immunoreactivity to K14 was localized to the basal cell layer, and that to K124 was localized to basal and suprabasal cells in the secondary epidermal lamellae. Abundant proteins K124, K42, K14, K5, and α1-actin were identified on 1- and 2-dimensional polyacrylamide gels and aligned with the results of previous studies. Results of the present study provide the first comprehensive analysis of cytoskeletal proteins present in the equine lamellae by using mass spectrometry-based techniques for protein quantification and identification.

The measurement of adenosine and estrogen receptor expression in rat brains following ovariectomy using quantitative PCR analysis

In our laboratory we have developed a quantitative-polymerase chain reaction (Q-PCR) strategy to examine the differential expression of adenosine receptor (ADOR), A(1), A(2A), A(2B) and A(3), and estrogen receptors (ER) alpha and beta. Brain and uterine mRNA were first used to optimise specific amplification conditions prior to SYBR Green I real time analysis of receptor subtype expression. SYBR Green I provided a convenient and sensitive means of examining specific PCR amplification product in real time, and allowed the generation of standard curves from which relative receptor abundance could be determined. Real time Q-PCR analysis was then performed, to examine changes in receptor expression levels in brains of adult female Wistar rats 3-month post ovariectomy. Comparison with sham-operated age-matched control rats demonstrated both comparative and absolute-copy number changes in receptor levels. Evaluation of both analytical methods investigated 18S rRNA as an internal reference for comparative gene expression analysis in the brain. The results of this study revealed preferential repression of ADORA(2A) (>4-fold down) and consistent (>2-fold) down-regulation of ADORA(1), ADORA(3), and ER-beta, following ovariectomy. No change was found in ADORA(2B) or ER-alpha. Analysis of absolute copy number in this study revealed a correlation between receptor expression in response to ovariectomy, and relative receptor subtype abundance in the brain.

Australian domestic violence protection order legislation : a comparative quantitative content analysis of victim safety provisions

In Australia, protection orders are a key legal response to domestic violence, and are often viewed as a way of providing for victim safety. For instance, recently the joint Australian and New South Wales Law Reform Commissions recommended that a common core purpose of all state and territory domestic violence legislation should be ‘to ensure or maximise the safety and protection of persons who fear or experience family violence’ (2010:Recommendation 7-4). Drawing and building upon prior research in Australia and the United States (‘US’), this paper uses comparative quantitative content analysis to assess the victim safety focus of domestic violence protection order legislation in each Australian state and territory. The findings of this analysis show that the Northern Territory, South Australia and Victoria ‘stand out’ from the other jurisdictions, having the highest victim safety focus in their legislation. However, there remains sizeable scope for improvement in all Australian jurisdictions, in terms of the victim safety focus of their legislative provisions and the considerations of legislative inconsistency between jurisdictions.

Quantitative fit analysis of orthopedic bone plates : methods, criteria and approach

Studies on quantitative fit analysis of precontoured fracture fixation plates emerged within the last few years and therefore, there is a wide research gap in this area. Quantitative fit assessment facilitates the measure of the gap between a fracture fixation plate and the underlying bone, and specifies the required plate fit criteria. For clinically meaningful fit assessment outcome, it is necessary to establish the appropriate criteria and parameter. The present paper studies this subject and recommends using multiple fit criteria and the maximum distance between the plate and underlying bone as fit parameter for clinically relevant outcome. We also propose the development of a software tool for automatic plate positioning and fit assessment for the purpose of implant design validation and optimization in an effort to provide better fitting implant that can assist proper fracture healing. The fundamental specifications of the software are discussed.

An investigation of goethite-seeded Al(OH)3 precipitation using in situ X-ray diffraction and Rietveld-based quantitative phase analysis

An in situ X-ray diffraction investigation of goethite-seeded Al(OH)3 precipitation from synthetic Bayer liquor at 343 K has been performed. The presence of iron oxides and oxyhydroxides in the Bayer process has implications for alumina reversion, which causes significant process losses through unwanted gibbsite precipitation, and is also relevant for the nucleation and growth of scale on mild steel process equipment. The gibbsite, bayerite and nordstrandite polymorphs of Al(OH)3 precipitated from the liquor; gibbsite appeared to precipitate first, with subsequent formation of bayerite and nordstrandite. A Rietveld-based approach to quantitative phase analysis was implemented for the determination of absolute phase abundances as a function of time, from which kinetic information for the formation of the Al(OH)3 phases was determined.

Using quantitative wastewater analysis to measure daily usage of conventional and emerging illicit drugs at an annual music festival

Introduction and Aims Wastewater analysis provides a non-intrusive way of measuring drug use within a population. We used this approach to determine daily use of conventional illicit drugs [cannabis, cocaine, methamphetamine and 3,4-methylenedioxymethamphetamine (MDMA)] and emerging illicit psychostimulants (benzylpiperazine, mephedrone and methylone) in two consecutive years (2010 and 2011) at an annual music festival. Design and Methods Daily composite wastewater samples, representative of the festival, were collected from the on-site wastewater treatment plant and analysed for drug metabolites. Data over 2 years were compared using Wilcoxon matched-pair test. Data from 2010 festival were compared with data collected at the same time from a nearby urban community using equivalent methods. Results Conventional illicit drugs were detected in all samples whereas emerging illicit psychostimulants were found only on specific days. The estimated per capita consumption of MDMA, cocaine and cannabis was similar between the two festival years. Statistically significant (P < 0.05; Z = −2.0–2.2) decreases were observed in use of methamphetamine and one emerging illicit psychostimulant (benzyl piperazine). Only consumption of MDMA was elevated at the festival compared with the nearby urban community. Discussion and Conclusions Rates of substance use at this festival remained relatively consistent over two monitoring years. Compared with the urban community, drug use among festival goers was only elevated for MDMA, confirming its popularity in music settings. Our study demonstrated that wastewater analysis can objectively capture changes in substance use at a music setting without raising major ethical issues. It would potentially allow effective assessments of drug prevention strategies in such settings in the future.