Modifier genes and susceptibility to colorectal cancer in individuals with Lynch syndrome

Lynch syndrome, is caused by inherited germ-line mutations in the DNA mismatch repair genes resulting in cancers at an early age, predominantly colorectal (CRC) and endometrial cancers. Though the median age at onset for CRC is about 45 years, disease penetrance varies suggesting that cancer susceptibility may be modified by environmental or other low-penetrance genes. Genetic variation due to polymorphisms in genes encoding metabolic enzymes can influence carcinogenesis by alterations in the expression and activity level of the enzymes. Variation in MTHFR, an important folate metabolizing enzyme can affect DNA methylation and DNA synthesis and variation in xenobiotic-metabolizing enzymes can affect the metabolism and clearance of carcinogens, thus modifying cancer risk. ^ This study examined a retrospective cohort of 257 individuals with Lynch syndrome, for polymorphisms in genes encoding xenobiotic-metabolizing enzymes-- CYP1A1 (I462V and MspI), EPHX1 (H139R and Y113H), GSTP1 (I105V and A114V), GSTM1 and GSTT1 (deletions) and folate metabolizing enzyme--MTHFR (C677T and A1298C). In addition, a series of 786 cases of sporadic CRC were genotyped for CYP1A1 I462V and EPHX1 Y113H to assess gene-gene interaction and gene-environment interaction with smoking in a case-only analysis. ^ Prominent findings of this study were that the presence of an MTHFR C677T variant allele was associated with a 4 year later age at onset for CRC on average and a reduced age-associated risk for developing CRC (Hazard ratio: 0.55; 95% confidence interval: 0.36–0.85) compared to the absence of any variant allele in individuals with Lynch syndrome. Similarly, Lynch syndrome individuals heterozygous for CYP1A1 I462V A>G polymorphism developed CRC an average of 4 years earlier and were at a 78% increased age-associated risk (Hazard ratio for AG relative to AA: 1.78; 95% confidence interval: 1.16-2.74) than those with the homozygous wild-type genotype. Therefore these two polymorphisms may be additional susceptibility factors for CRC in Lynch syndrome. In the case-only analysis, evidence of gene-gene interaction was seen between CYP1A1 I462V and EPHX1 Y113H and between EPHX1 Y113H and smoking suggesting that genetic and environmental factors may interact to increase sporadic CRC risk. Implications of these findings are the ability to identify subsets of high-risk individuals for targeted prevention and intervention. ^

Challenges to environmental toxicology and epidemiology: where do we stand and which way do we go?

Modern toxicology investigates a wide array of both old and new health hazards. Priority setting is needed to select agents for research from the plethora of exposure circumstances. The changing societies and a growing fraction of the aged have to be taken into consideration. A precise exposure assessment is of importance for risk estimation and regulation. Toxicology contributes to the exploration of pathomechanisms to specify the exposure metrics for risk estimation. Combined effects of co-existing agents are not yet sufficiently understood. Animal experiments allow a separate administration of agents which can not be disentangled by epidemiological means, but their value is limited for low exposure levels in many of today's settings. As an experimental science, toxicology has to keep pace with the rapidly growing knowledge about the language of the genome and the changing paradigms in cancer development. During the pioneer era of assembling a working draft of the human genome, toxicogenomics has been developed. Gene and pathway complexity have to be considered when investigating gene-environment interactions. For a best conduct of studies, modem toxicology needs a close liaison with many other disciplines like epidemiology and bioinformatics. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

The E(NK) model: extending the NK model to incorporate gene-by-environment interactions and epistasis for diploid genomes

The haploid NK model developed by Kauffman can be extended to diploid genomes and to incorporate gene-by-environment interaction effects in combination with epistasis. To provide the flexibility to include a wide range of forms of gene-by-environment interactions, a target population of environment types (TPE) is defined. The TPE consists of a set of E different environment types, each with their own frequency of occurrence. Each environment type conditions a different NK gene network structure or series of gene effects for a given network structure, providing the framework for defining gene-by-environment interactions. Thus, different NK models can be partially or completely nested within the E environment types of a TPE, giving rise to the E(NK) model for a biological system. With this model it is possible to examine how populations of genotypes evolve in context with properties of the environment that influence the contributions of genes to the fitness values of genotypes. We are using the E(NK) model to investigate how both epistasis and gene-by-environment interactions influence the genetic improvement of quantitative traits by plant breeding strategies applied to agricultural systems. © 2002 Wiley Periodicals, Inc.

Gene by smoking interaction in blood pressure

Hypertension (HT) is mediated by the interaction of many genetic and environmental factors. Previous genome-wide linkage analysis studies have found many loci that show linkage to HT or blood pressure (BP) regulation, but the results were generally inconsistent. Gene by environment interaction is among the reasons that potentially explain these inconsistencies between studies. Here we investigate influences of gene by smoking (GxS) interaction on HT and BP in European American (EA), African American (AA) and Mexican American (MA) families from the GENOA study. A variance component-based method was utilized to perform genome-wide linkage analysis of systolic blood pressure (SBP), diastolic blood pressure (DBP), and HT status, as well as bivariate analysis for SBP and DBP for smokers, non-smokers, and combined groups. The most significant results were found for SBP in MA. The strongest signal was for chromosome 17q24 (LOD = 4.2), increased to (LOD = 4.7) in bivariate analysis but there was no evidence of GxS interaction at this locus (p = 0.48). Two signals were identified only in one group: on chromosome 15q26.2 (LOD = 3.37) in non-smokers and chromosome 7q21.11 (LOD = 1.4) in smokers, both of which had strong evidence for GxS interaction (p = 0.00039 and 0.009 respectively). There were also two other signals, one on chromosome 20q12 (LOD = 2.45) in smokers, which became much higher in the combined sample (LOD = 3.53), and one on chromosome 6p22.2 (LOD = 2.06) in non-smokers. Neither peak had very strong evidence for GxS interaction (p = 0.08 and 0.06 respectively). A fine mapping association study was performed using 200 SNPs in 30 genes located under the linkage signals on chromosomes 15 and 17. Under the chromosome 15 peak, the association analysis identified 6 SNPs accounting for a 7 mmHg increase in SBP in MA non-smokers. For the chromosome 17 linkage peak, the association analysis identified 3 SNPs accounting for a 6 mmHg increase in SBP in MA. However, none of these SNPs was significant after correcting for multiple testing, and accounting for them in the linkage analysis produced very small reductions in the linkage signal. ^ The linkage analysis of BP traits considering the smoking status produced very interesting signals for SBP in the MA population. The fine mapping association analysis gave some insight into the contribution of some SNPs to two of the identified signals, but since these SNPs did not remain significant after multiple testing correction and did not explain the linkage peaks, more work is needed to confirm these exploratory results and identify the culprit variations under these linkage peaks. ^

Development and application of in vivo expression technology (IVET) for analysing microbial gene expression in complex environments

Establishing the mechanisms by which microbes interact with their environment, including eukaryotic hosts, is a major challenge that is essential for the economic utilisation of microbes and their products. Techniques for determining global gene expression profiles of microbes, such as microarray analyses, are often hampered by methodological restraints, particularly the recovery of bacterial transcripts (RNA) from complex mixtures and rapid degradation of RNA. A pioneering technology that avoids this problem is In Vivo Expression Technology (IVET). IVET is a 'promoter-trapping' methodology that can be used to capture nearly all bacterial promoters (genes) upregulated during a microbe-environment interaction. IVET is especially useful because there is virtually no limit to the type of environment used (examples to date include soil, oomycete, a host plant or animal) to select for active microbial promoters. Furthermore, IVET provides a powerful method to identify genes that are often overlooked during genomic annotation, and has proven to be a flexible technology that can provide even more information than identification of gene expression profiles. A derivative of IVET, termed resolvase-IVET (RIVET), can be used to provide spatio-temporal information about environment-specific gene expression. More recently, niche-specific genes captured during an IVET screen have been exploited to identify the regulatory mechanisms controlling their expression. Overall, IVET and its various spin-offs have proven to be a valuable and robust set of tools for analysing microbial gene expression in complex environments and providing new targets for biotechnological development.

Interaction between FTO gene variants and lifestyle factors on metabolic traits in an Asian Indian population

Background Lifestyle factors such as diet and physical activity have been shown to modify the association between fat mass and obesity–associated (FTO) gene variants and metabolic traits in several populations; however, there are no gene-lifestyle interaction studies, to date, among Asian Indians living in India. In this study, we examined whether dietary factors and physical activity modified the association between two FTO single nucleotide polymorphisms (rs8050136 and rs11076023) (SNPs) and obesity traits and type 2 diabetes (T2D). Methods The study included 734 unrelated T2D and 884 normal glucose-tolerant (NGT) participants randomly selected from the urban component of the Chennai Urban Rural Epidemiology Study (CURES). Dietary intakes were assessed using a validated interviewer administered semi-quantitative food frequency questionnaire (FFQ). Physical activity was based upon the self-report. Interaction analyses were performed by including the interaction terms in the linear/logistic regression model. Results There was a significant interaction between SNP rs8050136 and carbohydrate intake (% energy) (Pinteraction = 0.04), where the ‘A’ allele carriers had 2.46 times increased risk of obesity than those with ‘CC’ genotype (P = 3.0 × 10−5) among individuals in the highest tertile of carbohydrate intake (% energy, 71 %). A significant interaction was also observed between SNP rs11076023 and dietary fibre intake (Pinteraction = 0.0008), where individuals with AA genotype who are in the 3rd tertile of dietary fibre intake had 1.62 cm lower waist circumference than those with ‘T’ allele carriers (P = 0.02). Furthermore, among those who were physically inactive, the ‘A’ allele carriers of the SNP rs8050136 had 1.89 times increased risk of obesity than those with ‘CC’ genotype (P = 4.0 × 10−5). Conclusions This is the first study to provide evidence for a gene-diet and gene-physical activity interaction on obesity and T2D in an Asian Indian population. Our findings suggest that the association between FTO SNPs and obesity might be influenced by carbohydrate and dietary fibre intake and physical inactivity. Further understanding of how FTO gene influences obesity and T2D through dietary and exercise interventions is warranted to advance the development of behavioral intervention and personalised lifestyle strategies, which could reduce the risk of metabolic diseases in this Asian Indian population.

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.

Transcriptional profiling reveals genes in the human pathogen Trichophyton rubrum that are expressed in response to pH signaling

Trichophyton rubrum is a dermatophyte that infects human skin and nails. Its growth on keratin as its carbon source shifts the ambient pH from acidic to alkaline, which may be an efficient strategy for its successful infection and maintenance in the host. In this study, we used suppression subtractive hybridization to identify genes preferentially expressed in T rubrum incubated at either pH 5.0 or pH 8.0. The functional grouping of the 341 overexpressed unigenes indicated proteins putatively involved in diverse cellular processes, such as membrane remodeling, cellular transport, metabolism, cellular protection, fungal pathogenesis, gene regulation, interaction with the environment, and iron uptake. Although the basic metabolic machinery identified under both growth conditions seems to be functionally similar, distinct genes are upregulated at acidic or alkaline pHs. We also isolated a large number of genes of unknown function, probably unique to T rubrum or dermatophytes. Interestingly, the transcriptional profiling of several genes in a pacC mutant suggests that, in T rubrum, the transcription factor PacC has a diversity of metabolic functions, in response to either acidic or alkaline ambient pH. (C) 2009 Elsevier Ltd. All rights reserved.

An integrative approach for studying the etiology of alcoholism and other addictions

Studies of alcoholism etiology often focus on genetic or psy-chosocial approaches, but not both. Greater understanding of the etiology of alcohol, tobacco and other addictions will come from integration of these research traditions. A research approach is outlined to test three models for the etiology of addictions — behavioral undercontrol, pharmacologic vulnerability, negative affect regulation — addressing key questions including (i) mediators of genetic effects, (ii) genotype-environment correlation effects, (iii) genotype x environment interaction effects, (iv) the developmental unfolding of genetic and environmental effects, (v) subtyping including identification of distinct trajectories of substance involvement, (vi) identification of individual genes that contribute to risk, and (vii) the consequences of excessive use. By using coordinated research designs, including prospective assessment of adolescent twins and their siblings and parents; of adult substance dependent and control twins and their MZ and DZ cotwins, the spouses of these pairs, and their adolescent offspring; and of regular families; by selecting for gene-mapping approaches sibships screened for extreme concordance or discordance on quantitative indices of substance use; and by using experimental (drug challenge) as well as survey approaches, a number of key questions concerning addiction etiology can be addressed. We discuss complementary strengths and weaknesses of different sampling strategies, as well as methods to implement such an integrated approach illustrated for the study of alcoholism etiology. A coordinated program of twin and family studies will allow a comprehensive dissection of the interplay of genetic and environmental risk-factors in the etiology of alcoholism and other addictions.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Biometrical Genetics

Biometrical genetics is the science concerned with the inheritance of quantitative traits. In this review we discuss how the analytical methods of biometrical genetics are based upon simple Mendelian principles. We demonstrate how the phenotypic covariance between related individuals provides information on the relative importance of genetic and environmental factors influencing that trait, and how factors such as assortative mating, gene-environment correlation and genotype-environment interaction complicate such interpretations. Twin and adoption studies are discussed as well as their assumptions and limitations. Structural equation modeling (SEM) is introduced and we illustrate how this approach may be applied to genetic problems. In particular, we show how SEM can be used to address complicated issues such as analyzing the causes of correlation between traits or determining the direction of causation (DOC) between variables. (C) 2002 Elsevier Science B.V. All rights reserved.

The GP problem: quantifying gene-to-phenotype relationships

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco, especialidade em Estatística

No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels.

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

A genome-wide screen for interactions reveals a new locus on 4p15 modifying the effect of waist-to-hip ratio on total cholesterol.

Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ∼25% of the heritability of the phenotypes. To date, no unbiased screen for gene-environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry (maximum N = 32,225). We collected 8 further cohorts (N = 17,102) for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10(-9). There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.