Single nucleotide polymorphisms (SNPs) associated with TGF-beta pathway and their significance in systemic sclerosis - A multilevel analysis

Systemic sclerosis (SSc) or Scleroderma is a complex disease and its etiopathogenesis remains unelucidated. Fibrosis in multiple organs is a key feature of SSc and studies have shown that transforming growth factor-β (TGF-β) pathway has a crucial role in fibrotic responses. For a complex disease such as SSc, expression quantitative trait loci (eQTL) analysis is a powerful tool for identifying genetic variations that affect expression of genes involved in this disease. In this study, a multilevel model is described to perform a multivariate eQTL for identifying genetic variation (SNPs) specifically associated with the expression of three members of TGF-β pathway, CTGF, SPARC and COL3A1. The uniqueness of this model is that all three genes were included in one model, rather than one gene being examined at a time. A protein might contribute to multiple pathways and this approach allows the identification of important genetic variations linked to multiple genes belonging to the same pathway. In this study, 29 SNPs were identified and 16 of them located in known genes. Exploring the roles of these genes in TGF-β regulation will help elucidate the etiology of SSc, which will in turn help to better manage this complex disease. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Ligamiento genético entre variables asociadas a calidad del fruto de tomate y polipéptidos expresados en dos estados de madurez

Los perfiles de polipéptidos proveen información sobre la constitución genética de un individuo y su expresión, y son útiles como marcadores moleculares. El objetivo del trabajo fue detectar ligamiento entre los perfiles de polipéptidos del pericarpio en dos estados de madurez y caracteres cuantitativos y de calidad de los frutos, analizando 21 genotipos de tomate. Se obtuvieron los perfiles polipéptidos en los estados verde y rojo maduro de frutos de 18 líneas endocriadas recombinantes (RILs, recombinant inbred lines), derivadas de un cruzamiento interespecífico entre el cultivar Caimanta de S. lycopersicum y la entrada LA722 de S. pimpinellifolium, que se incluyeron como testigos experimentales junto a su F1. En estos 21 genotipos se evaluaron también vida poscosecha, peso, firmeza, porcentaje de reflectancia, índice cromático, forma, pH, acidez titulable, contenido de sólidos solubles, espesor de pericarpio y número de lóculos de los frutos. Los perfiles mostraron polimorfismo entre los estados de madurez dentro de un mismo genotipo y entre genotipos para un mismo estado de madurez. Algunos polipéptidos segregaron de forma mendeliana (1:1) y, por análisis de un único punto, mostraron ligamiento con caracteres de calidad del fruto. Se detectaron loci de caracteres cuantitativos (QTLs, quantitative trait loci) asociados a número de lóculos, peso, pH, firmeza y vida poscosecha de los frutos.

Heat stress effects on crop performance and tools for tolerance breeding

Abiotic stress is one of the most common causes of crop deficit and loss and hence an important area of study. Moreover, concerns regarding global climate change over past decades mean the study of different abiotic stresses appears to be essential if its effects are to be mitigated. The current review covers the effects of heat stress on crop performance, the response crops make when subjected to this stress and the development of tools designed to breed for stress tolerant crops. Distinct levels of the problem are considered, from the morphological/anatomical, through the physiological and to the biochemical/molecular. The study of heat shock proteins (HSPs), quantitative trait loci (QTLs) identification and the relationship between metabolomics (OMICS) and heat stress are given special consideration.

The nitrogen costs of photosynthesis in a diatom under current and future pCO2

With each cellular generation, oxygenic photoautotrophs must accumulate abundant protein complexes that mediate light capture, photosynthetic electron transport and carbon fixation. In addition to this net synthesis, oxygenic photoautotrophs must counter the light-dependent photoinactivation of Photosystem II (PSII), using metabolically expensive proteolysis, disassembly, resynthesis and re-assembly of protein subunits. We used growth rates, elemental analyses and protein quantitations to estimate the nitrogen (N) metabolism costs to both accumulate the photosynthetic system and to maintain PSII function in the diatom Thalassiosira pseudonana, growing at two pCO2 levels across a range of light levels. The photosynthetic system contains c. 15-25% of total cellular N. Under low growth light, N (re)cycling through PSII repair is only c. 1% of the cellular N assimilation rate. As growth light increases to inhibitory levels, N metabolite cycling through PSII repair increases to c. 14% of the cellular N assimilation rate. Cells growing under the assumed future 750 ppmv pCO2 show higher growth rates under optimal light, coinciding with a lowered N metabolic cost to maintain photosynthesis, but then suffer greater photoinhibition of growth under excess light, coincident with rising costs to maintain photosynthesis. We predict this quantitative trait response to light will vary across taxa.

Polyploid formation created unique avenues for response to selection in Gossypium (cotton)

A detailed restriction fragment length polymorphism map was used to determine the chromosomal locations and subgenomic distributions of quantitative trait loci (QTLs) segregating in a cross between cultivars of allotetraploid (AADD) Gossypium hirsutum (“Upland” cotton) and Gossypium barbadense (“Sea Island,” “Pima,” or “Egyptian” cotton) that differ markedly in the quality and quantity of seed epidermal fibers. Most QTLs influencing fiber quality and yield are located on the “D” subgenome, derived from an ancestor that does not produce spinnable fibers. D subgenome QTLs may partly account for the fact that domestication and breeding of tetraploid cottons has resulted in fiber yield and quality levels superior to those achieved by parallel improvement of “A” genome diploid cottons. The merger of two genomes with different evolutionary histories in a common nucleus appears to offer unique avenues for phenotypic response to selection. This may partly compensate for reduction in quantitative variation associated with polyploid formation and be one basis for the prominence of polyploids among extant angiosperms. These findings impel molecular dissection of the roles of divergent subgenomes in quantitative inheritance in many other polyploids and further exploration of both “synthetic” polyploids and exotic diploid genotypes for agriculturally useful variation.

Evolution of genome–phenome diversity under environmental stress

The genomic era revolutionized evolutionary biology. The enigma of genotypic-phenotypic diversity and biodiversity evolution of genes, genomes, phenomes, and biomes, reviewed here, was central in the research program of the Institute of Evolution, University of Haifa, since 1975. We explored the following questions. (i) How much of the genomic and phenomic diversity in nature is adaptive and processed by natural selection? (ii) What is the origin and evolution of adaptation and speciation processes under spatiotemporal variables and stressful macrogeographic and microgeographic environments? We advanced ecological genetics into ecological genomics and analyzed globally ecological, demographic, and life history variables in 1,200 diverse species across life, thousands of populations, and tens of thousands of individuals tested mostly for allozyme and partly for DNA diversity. Likewise, we tested thermal, chemical, climatic, and biotic stresses in several model organisms. Recently, we introduced genetic maps and quantitative trait loci to elucidate the genetic basis of adaptation and speciation. The genome–phenome holistic model was deciphered by the global regressive, progressive, and convergent evolution of subterranean mammals. Our results indicate abundant genotypic and phenotypic diversity in nature. The organization and evolution of molecular and organismal diversity in nature at global, regional, and local scales are nonrandom and structured; display regularities across life; and are positively correlated with, and partly predictable by, abiotic and biotic environmental heterogeneity and stress. Biodiversity evolution, even in small isolated populations, is primarily driven by natural selection, including diversifying, balancing, cyclical, and purifying selective regimes, interacting with, but ultimately overriding, the effects of mutation, migration, and stochasticity.

Rapid, local adaptation of zooplankton behavior to changes in predation pressure in the absence of neutral genetic changes

Organisms producing resting stages provide unique opportunities for reconstructing the genetic history of natural populations. Diapausing seeds and eggs often are preserved in large numbers, representing entire populations captured in an evolutionary inert state for decades and even centuries. Starting from a natural resting egg bank of the waterflea Daphnia, we compare the evolutionary rates of change in an adaptive quantitative trait with those in selectively neutral DNA markers, thus effectively testing whether the observed genetic changes in the quantitative trait are driven by natural selection. The population studied experienced variable and well documented levels of fish predation over the past 30 years and shows correlated genetic changes in phototactic behavior, a predator-avoidance trait that is related to diel vertical migration. The changes mainly involve an increased plasticity response upon exposure to predator kairomone, the direction of the changes being in agreement with the hypothesis of adaptive evolution. Genetic differentiation through time was an order of magnitude higher for the studied behavioral trait than for neutral markers (DNA microsatellites), providing strong evidence that natural selection was the driving force behind the observed, rapid, evolutionary changes.

Multivariate QTL linkage analysis suggests a QTL for platelet count on chromosome 19q

Platelet count is a highly heritable trait with genetic factors responsible for around 80% of the phenotypic variance. We measured platelet count longitudinally in 327 monozygotic and 418 dizygotic twin pairs at 12, 14 and 16 years of age. We also performed a genome-wide linkage scan of these twins and their families in an attempt to localize QTLs that influenced variation in platelet concentrations. Suggestive linkage was observed on chromosome 19q13.13-19q13.31 at 12 (LOD=2.12, P=0.0009), 14 (LOD=2.23, P=0.0007) and 16 (LOD=1.01, P=0.016) years of age and multivariate analysis of counts at all three ages increased the LOD to 2.59 (P=0.0003). A possible candidate in this region is the gene for glycoprotein VI, a receptor involved in platelet aggregation. Smaller linkage peaks were also seen at 2p, 5p, 5q, 10p and 15q. There was little evidence for linkage to the chromosomal regions containing the genes for thrombopoietin (3q27) and the thrombopoietin receptor (1q34), suggesting that polymorphisms in these genes do not contribute substantially to variation in platelet count between healthy individuals.

Genetic effects of kangaroo harvesting

There is concern that the commercial harvest of kangaroos (Macropus spp.) is affecting species fitness and evolutionary potential because the harvest selects for larger individuals, particularly males. This paper reviews the likely effect of selective harvesting on specific traits associated with fitness, including size, and on adaptive genotypes through generalised loss of gene diversity. Heritability for traits associated with fitness is low generally. The intensity of selection imposed by harvesting is low for several reasons: the geographic size of genetic populations is much larger than the harvest localities, which are therefore not closed but open with immigration acting to correct any change in allele frequencies through harvesting; the harvest targets kangaroos above a threshold weight that includes all adult males, not the largest males specifically; larger, older males may not confer significant fitness benefits on offspring; fitness traits are inherited through both sexes while males are targeted predominantly; populations are not at a selective equilibrium because food availability fluctuates, and the fittest is unlikely to be the largest. Comparisons of harvested and unharvested populations do not show any loss of gene diversity as a result of harvesting. The likelihood of a long-term genetic impact of kangaroo harvesting as currently practiced is negligible.

Comparison of identity by descent and identity by state for detecting genetic regions under selection in a sorghum pedigree breeding program

Seventy sorghum inbred lines which formed part of the Queensland Department of Primary Industries (QDPI) sorghum breeding program were screened with 104 previously mapped RFLP markers. The lines were related by pedigree and consisted of ancestral source lines, intermediate lines and recent releases from the program. We compared the effect of defining marker alleles using either identity by state (IBS) or identity by descent (IBD) on our capacity to trace markers through the pedigree and detect evidence of selection for particular alleles. Allelic identities defined using IBD were much more sensitive for detecting non-Mendelian segregation in this pedigree. Only one marker allele showed significant evidence of selection when IBS was used compared with ten regions with particular allelic identities when IBD was used. Regions under selection were compared with the location of QTLs for agronomic traits known to be under selection in the breeding program. Only two of the ten regions were associated with known QTLs that matched with knowledge of the agronomic characteristics of the ancestral lines. Some of the other regions were hypothesised to be associated with genes for particular traits based on the properties of the ancestral source lines.

On systems thinking, systems biology and the in Silico Plant

The recent summary report of a Department of Energy Workshop on Plant Systems Biology (P.V. Minorsky [2003] Plant Physiol 132: 404-409) offered a welcomed advocacy for systems analysis as essential in understanding plant development, growth, and production. The goal of the Workshop was to consider methods for relating the results of molecular research to real-world challenges in plant production for increased food supplies, alternative energy sources, and environmental improvement. The rather surprising feature of this report, however, was that the Workshop largely overlooked the rich history of plant systems analysis extending over nearly 40 years (Sinclair and Seligman, 1996) that has considered exactly those challenges targeted by the Workshop. Past systems research has explored and incorporated biochemical and physiological knowledge into plant simulation models from a number of perspectives. The research has resulted in considerable understanding and insight about how to simulate plant systems and the relative contribution of various factors in influencing plant production. These past activities have contributed directly to research focused on solving the problems of increasing biomass production and crop yields. These modeling approaches are also now providing an avenue to enhance integration of molecular genetic technologies in plant improvement (Hammer et al., 2002).

A genome scan for epidermal skin pattern in adolescent twins reveals suggestive linkage on 12p13.31

Deterioration in stratum corneum reticular patterning (skin pattern or skin wrinkling) has been associated with increased rates of solar keratoses and skin cancer. A previous analysis of data from the twin sample used in this investigation has shown that 86% of the variation in skin pattern is genetic at age 12 and 62% in an adult sample (mean age 47.5). Variation due to genetic influences is likely to be influenced by more than one locus. Here, we present results of a genome-wide linkage scan of skin pattern in adolescent twins and siblings from 428 nuclear twin families. Sib-pair linkage analysis was performed on skin pattern data collected from twins at age 12 (378 informative families) and 14 (316 families). Suggestive linkage was found at marker D12S397 (12p13.31, logarithm of the odds (lod) 1.94), when the effect of the trait locus was modelled to influence the skin pattern equally at both ages 12 and 14. In the same analysis, a peak was seen at 4q23 with a lod score of 1.55. A possible candidate for the peak at 12p13.31 is the protease inhibitor, alpha-2-macroglobulin.

Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings

The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within- family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.

A linkage study of academic skills defined by the Queensland Core Skills Test

This study used genome-wide linkage analysis to detect Quantitative Trait Loci (QTLs) implicated in variation in general academic achievement as measured by the Queensland Core Skills Test (QCST) (Queensland Studies Authority, 2004). Data from 210 families were analysed. While no empirically derived significant or suggestive peaks for general academic achievement were indicated a peak on chromosome 2 was observed in a region where Posthuma et al. (2005) reported significant linkage for Performance IQ (PIQ) and suggestive linkage for Full Scale IQ (FSIQ), and Luciano et al. (this issue) observed significant linkage for PIQ and word reading. A peak on chromosome 18 was also observed approximately 20 cM removed from a region recently implicated in reading achievement. In addition, on chromosomes 2 and 18 peaks for a number of specific academic skills, two of which were suggestive, coincided with the general academic achievement peaks. The findings suggest that variation in general academic achievement is influenced by genes on chromosome 2 which have broad influence on a variety of cognitive abilities.