Insights into the molecular genetics of celiac disease : Applying family- and population-based strategies

Celiac disease, or gluten intolerance, is triggered by dietary glutens in genetically susceptible individuals and it affects approximately 1% of the Caucasian population. The best known genetic risk factors for celiac disease are HLA DQ2 and DQ8 heterodimers, which are necessary for the development of the disease. However, they alone are not sufficient for disease induction, other risk factors are required. This thesis investigated genetic factors for celiac disease, concentrating on susceptibility loci on chromosomes 5q31-q33, 19p13 and 2q12 previously reported in genome-wide linkage and association studies. In addition, a novel genotyping method for the detection of HLA DQ2 and DQ8 coding haplotypes was validated. This study was conducted using Finnish and Hungarian family materials, and Finnish, Hungarian and Italian case-control materials. Genetic linkage and association were analysed in these materials using candidate gene and fine-mapping approaches. The results confirmed linkage to celiac disease on the chromosomal regions 5q31-q33 and 19p13. Fine-mapping on chromosome 5q31-q33 revealed several modest associations in the region, and highlighted the need for further investigations to locate the causal risk variants. The MYO9B gene on chromosome 19p13 showed evidence for linkage and association particularly with dermatitis herpetiformis, the skin manifestation of celiac disease. This implies a potential difference in the genetic background of the intestinal and skin forms of the disease, although studies on larger samplesets are required. The IL18RAP locus on chromosome 2q12, shown to be associated with celiac disease in a previous genome-wide association study and a subsequent follow-up, showed association in the Hungarian population in this study. The expression of IL18RAP was further investigated in small intestinal tissue and in peripheral blood mononuclear cells. The results showed that IL18RAP is expressed in the relevant tissues. Two putative isoforms of IL18RAP were detected by Western blot analysis, and the results suggested that the ratios and total levels of these isoforms may contribute to the aetiology of celiac disease. A novel genotyping method for celiac disease-associated HLA haplotypes was also validated in this thesis. The method utilises single-nucleotide polymorphisms tagging these HLA haplotypes with high sensitivity and specificity. Our results suggest that this method is transferable between populations, and it is suitable for large-scale analysis. In conclusion, this doctorate study provides an insight into the roles of the 5q31-q33, MYO9B, IL18RAP and HLA loci in the susceptibility to celiac disease in the Finnish, Hungarian and Italian populations, highlighting the need for further studies at these genetic loci and examination of the function of the candidate genes.

Common SNPs explain a large proportion of the heritability for human height

SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.

Tobacco Streak Virus in cotton-scoping study

In 2006, Tobacco streak virus (TSV) was identified as the causal agent of the devastating sunflower necrosis disease in central Queensland (CQ), and subsequently in 2007 as the cause of major losses in mungbeans in the same area. It has been a major factor in the recent downturn in the sunflower industry in CQ. Surveys in 2007/2008 as part of a one year scoping study (project 03DAQ005) found TSV in cotton in CQ. The symptoms were mostly confined to the feeding sites of the thrips and appeared as reddish spots and rings, but only occasionally the plants were systemically infected and showed a chlorotic mosaic and leaf deformation. The major objectives of this project (DAQ0002) were to determine: the incidence and distribution of TSV in cotton and its likely effect on yield; the thrips vector species associated with TSV infections in cotton; and the factors that may lead to systemic infections. In contrast to the extensive damage observed in sunflower and mungbean crops from the same region, TSV has caused no measurable damage in commercial cotton crops surveyed in CQ over the seasons 2008/9 to 2010/11. No TSV infected cotton was found in regions outside of CQ and the geographical distribution of TSV disease in cotton (and other susceptible hosts) appears to be closely related to the distribution of the major alternative host, parthenium weed. The most likely thrips species responsible for transmission of TSV into cotton is the tomato thrips (Frankliniella schultzei) and onion thrips (Thrips tabaci). Systemically infected plants are rarely seen in commercial crops and have also been rarely produced in controlled tests. It appears that systemic infection may be transient with only mild symptoms being produced intermittently. With current cultivars and conditions, it appears likely that TSV will continue to cause only minor levels of mild local lesions with no impact on yield in cotton crops. It appears that no specific control strategies are required to limit the impact of TSV in cotton. However, general farm hygiene to minimise the presence of the major alternative host of TSV, parthenium weed, is advised and may be of vital importance if TSV susceptible rotational crops such as mung beans are grown.

The shared genetics of migraine and anxious depression

OBJECTIVES To investigate: - (1) whether shared genetic factors influence migraine and anxious depression; - (2) whether the genetic architecture of migraine depends on anxious depression; - (3) whether the association between migraine and anxious depression is causal. BACKGROUND Migraine and anxious depression frequently occur together, but little is known about the mechanisms causing this association. METHODS A twin study was conducted to model the genetic architecture of migraine and anxious depression and the covariance between them. Anxious depression was also added to the model as a moderator variable to examine whether anxious depression affects the genetic architecture of migraine. Causal models were explored with the co-twin control method. RESULTS Modest but significant phenotypic (rP=0.28), genetic (rG=0.30), and nonshared environmental (rE=0.26) correlations were found between the 2 traits. Interestingly, the heritability of migraine depended on the level of anxious depression: the higher the anxious depression score, the lower the relative contribution of genetic factors to the individual differences in migraine susceptibility. The observed risk patterns in discordant twins are most consistent with a bidirectional causal relationship. CONCLUSIONS These findings confirm the genetic association between migraine and anxious depression and are consistent with a syndromic association between the 2 traits. This highlights the importance of taking comorbidity into account in genetic studies of migraine, especially in the context of selection for large-scale genotyping efforts. Genetic studies may be most effective when migraine with and without comorbid anxious depression are treated as separate phenotypes.

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons limultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

Genome-wide linkage analysis of multiple measures of neuroticism of 2 large cohorts from Australia and the Netherlands

CONTEXT People meeting diagnostic criteria for anxiety or depressive disorders tend to score high on the personality scale of neuroticism. Studying this personality dimension can give insights into the etiology of these important psychiatric disorders. OBJECTIVES To undertake a comprehensive genome-wide linkage study of neuroticism using large study samples that have been measured multiple times and to compare the results between countries for replication and across time within countries for consistency. DESIGN Genome-wide linkage scan. SETTING Twin individuals and their family members from Australia and the Netherlands. PARTICIPANTS Nineteen thousand six hundred thirty-five sibling pairs completed self-report questionnaires for neuroticism up to 5 times over a period of up to 22 years. Five thousand sixty-nine sibling pairs were genotyped with microsatellite markers. METHODS Nonparametric linkage analyses were conducted in MERLIN-REGRESS for the mean neuroticism scores averaged across time. Additional analyses were conducted for the time-specific measures of neuroticism from each country to investigate consistency of linkage results. RESULTS Three chromosomal regions exceeded empirically derived thresholds for suggestive linkage using mean neuroticism scores: 10p 5 Kosambi cM (cM) (Dutch study sample), 14q 103 cM (Dutch study sample), and 18q 117 cM (combined Australian and Dutch study sample), but only 14q retained significance after correction for multiple testing. These regions all showed evidence for linkage in individual time-specific measures of neuroticism and 1 (18q) showed some evidence for replication between countries. Linkage intervals for these regions all overlap with regions identified in other studies of neuroticism or related traits and/or in studies of anxiety in mice. CONCLUSIONS Our results demonstrate the value of the availability of multiple measures over time and add to the optimism reported in recent reviews for replication of linkage regions for neuroticism. These regions are likely to harbor causal variants for neuroticism and its related psychiatric disorders and can inform prioritization of results from genome-wide association studies.

Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time

Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714-718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.

Identification of a new Polerovirus (family Luteoviridae) associated with cotton bunchy top disease in Australia

Cotton bunchy top (CBT) disease has caused significant yield losses in Australia and is now managed by control of its vector, the cotton aphid (Aphis gossypii). Its mode of transmission and similarities in symptoms to cotton Blue Disease suggested it may also be caused by a luteovirus or related virus. Degenerate primers to conserved regions of the genomes of the family Luteoviridae were used to amplify viral cDNAs from CBT-affected cotton leaf tissue that were not present in healthy plants. Partial genome sequence of a new virus (Cotton bunchy top virus, CBTV) was obtained spanning part of the RNA-dependent-RNA-polymerase (RdRP), all of the coat protein and part of the aphid-transmission protein. CBTV sequences could be detected in viruliferous aphids able to transmit CBT, but not aphids from non-symptomatic plants, indicating that it is associated with the disease and may be the causal agent. All CBTV open-reading frames had their closest similarity to viruses of the genus Polerovirus. The partial RdRP had 90 % amino acid identity to the RdRP of Cotton leafroll dwarf virus (CLRDV) that causes cotton blue disease, while other parts of the genome were more similar to other poleroviruses. The sequence similarity and genome organization of CBTV suggest that it should be considered a new member of the genus Polerovirus. This partial genome sequence of CBTV opens up the possibility for developing diagnostic tests for detection of the virus in cotton plants, aphids and weeds as well as alternative strategies for engineering CBT resistance in cotton plants through biotechnology. © 2012 Australasian Plant Pathology Society Inc.

Genetic variability in high temperature effects on seed-set in sorghum

Sorghum (Sorghum bicolor (L.) Moench) is grown as a dryland crop in semiarid subtropical and tropical environments where it is often exposed to high temperatures around flowering. Projected climate change is likely to increase the incidence of exposure to high temperature, with potential adverse effects on growth, development and grain yield. The objectives of this study were to explore genetic variability for the effects of high temperature on crop growth and development, in vitro pollen germination and seed-set. Eighteen diverse sorghum genotypes were grown at day : night temperatures of 32 : 21 degrees C (optimum temperature, OT) and 38 : 21 degrees C (high temperature, HT during the middle of the day) in controlled environment chambers. HT significantly accelerated development, and reduced plant height and individual leaf size. However, there was no consistent effect on leaf area per plant. HT significantly reduced pollen germination and seed-set percentage of all genotypes; under HT, genotypes differed significantly in pollen viability percentage (17-63%) and seed-set percentage (7-65%). The two traits were strongly and positively associated (R-2 = 0.93, n = 36, P < 0.001), suggesting a causal association. The observed genetic variation in pollen and seed-set traits should be able to be exploited through breeding to develop heat-tolerant varieties for future climates.

Sequence variation in the putative effector gene SIX8 facilitates molecular differentiation of Fusarium oxysporum f. sp. cubense

Fusarium oxysporum f. sp. cubense (Foc), causal agent of fusarium wilt of banana, is among the most destructive pathogens of banana and plantain. The development of a molecular diagnostic capable of reliably distinguishing between the various races of the pathogen is of key importance to disease management. However, attempts to distinguish isolates using the standard molecular loci typically used for fungal phylogenetics have been complicated by a poor correlation between phylogeny and pathogenicity. Among the available alternative loci are several putative effector genes, known as SIX genes, which have been successfully used to differentiate the three races of F. oxysporum f. sp. lycopersici. In this study, an international collection of Foc isolates was screened for the presence of the putative effector SIX8. Using a PCR and sequencing approach, variation in Foc-SIX8 was identified which allowed race 4 to be differentiated from race 1 and 2 isolates, and tropical and subtropical race 4 isolates to be distinguished from one another.

Influence of dingoes on sheep distribution in Australia

Objective To describe the influence of the dingo (Canis lupus dingo) on the past, present and future distributions of sheep in Australia. Design The role of the dingo in the rise and fall of sheep numbers is reviewed, revised data are provided on the present distribution and density of sheep and dingoes, and historical patterns of sheep distribution are used to explore the future of rangeland sheep grazing. Results Dingoes are a critical causal factor in the distribution of sheep at the national, regional and local levels. Dingo predation contributed substantially to the historical contraction of the sheep industry to its present-day distribution, which is almost exclusively confined to areas within fenced dingo exclusion zones. Dingo populations and/or their influence are now present and increasing in all sheep production zones of Australia, inclusive of areas that were once dingo free'. Conclusions Rangeland production of wool and sheep meat is predicted to disappear within 30-40 years if the present rate of contraction of the industry continues unabated. Understanding the influence of dingoes on sheep production may help refine disease response strategies and help predict the future distribution of sheep and their diseases.

Environmental factors affecting the occurrence of periglacial landforms in Finnish Lapland: a numerical approach

Determination of the environmental factors controlling earth surface processes and landform patterns is one of the central themes in physical geography. However, the identification of the main drivers of the geomorphological phenomena is often challenging. Novel spatial analysis and modelling methods could provide new insights into the process-environment relationships. The objective of this research was to map and quantitatively analyse the occurrence of cryogenic phenomena in subarctic Finland. More precisely, utilising a grid-based approach the distribution and abundance of periglacial landforms were modelled to identify important landscape scale environmental factors. The study was performed using a comprehensive empirical data set of periglacial landforms from an area of 600 km2 at a 25-ha resolution. The utilised statistical methods were generalized linear modelling (GLM) and hierarchical partitioning (HP). GLMs were used to produce distribution and abundance models and HP to reveal independently the most likely causal variables. The GLM models were assessed utilising statistical evaluation measures, prediction maps, field observations and the results of HP analyses. A total of 40 different landform types and subtypes were identified. Topographical, soil property and vegetation variables were the primary correlates for the occurrence and cover of active periglacial landforms on the landscape scale. In the model evaluation, most of the GLMs were shown to be robust although the explanation power, prediction ability as well as the selected explanatory variables varied between the models. The great potential of the combination of a spatial grid system, terrain data and novel statistical techniques to map the occurrence of periglacial landforms was demonstrated in this study. GLM proved to be a useful modelling framework for testing the shapes of the response functions and significances of the environmental variables and the HP method helped to make better deductions of the important factors of earth surface processes. Hence, the numerical approach presented in this study can be a useful addition to the current range of techniques available to researchers to map and monitor different geographical phenomena.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.

Using old concepts to gain new insights: Addressing the issue of consistency

Purpose – This paper aims to go beyond a bookkeeping approach to evolutionary analysis whereby surviving firms are better adapted and extinct firms were less adapted. From discussion of the preliminary findings of research into the Hobart pizza industry, evidence is presented of the need to adopt a more traditional approach to applying evolutionary theories with organizational research. Design/methodology/approach – After a brief review of the relevant literature, the preliminary findings of research into the Hobart pizza industry are presented. Then, several evolutionary concepts that are commonplace in ecological research are introduced to help explain the emergent findings. The paper concludes with consideration given to advancing a more consistent approach to employing evolutionary theories within organizational research. Findings – The paper finds that the process of selection cannot be assumed to occur evenly across time and/or space. Within geographically small markets different forms of selection operate in different ways and degrees requiring the use of more traditional evolutionary theories to highlight the causal process associated with population change. Research limitations/implications – The paper concludes by highlighting Geoffrey Hodgson’s Principle of Consistency. It is demonstrated that a failure to truly understand how and why theory is used in one domain will likely result in its misuse in another domain. That, at present, too few evolutionary concepts are employed in organisational research to ensure an appreciation of any underlying causal processes through which social change occurs. Originality/value – The concepts introduced throughout this paper, whilst not new, provide new entry points for organizational researchers intent on employing an evolutionary approach to understand the process of social change.