The challenges for molecular nutrition research 1: linking genotype to healthy nutrition

Nutrition science finds itself at a major crossroad. On the one hand we can continue the current path, which has resulted in some substantial advances, but also many conflicting messages which impair the trust of the general population, especially those who are motivated to improve their health through diet. The other road is uncharted and is being built over the many exciting new developments in life sciences. This new era of nutrition recognizes the complex relation between the health of the individual, its genome, and the life-long dietary exposure, and has lead to the realisation that nutrition is essentially a gene - environment interaction science. This review on the relation between genotype, diet and health is the first of a series dealing with the major challenges in molecular nutrition, analyzing the foundations of nutrition research. With the unravelling of the human genome and the linking of its variability to a multitude of phenotypes from " healthy'' to an enormously complex range of predispositions, the dietary modulation of these propensities has become an area of active research. Classical genetic approaches applied so far in medical genetics have steered away from incorporating dietary effects in their models and paradoxically, most genetic studies analyzing diet-associated phenotypes and diseases simply ignore diet. Yet, a modest but increasing number of studies are accounting for diet as a modulator of genetic associations. These range from observational cohorts to intervention studies with prospectively selected genotypes. New statistical and bioinformatics approaches are becoming available to aid in design and evaluation of these studies. This review discusses the various approaches used and provides concrete recommendations for future research.

Polymorphisms of DNA repair genes XRCC1 and XRCC3, interaction with environmental exposure and risk of chronic gastritis and gastric cancer

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Polymorphisms of DNA repair genes XRCC1 and XRCC3, interaction with environmental exposure and risk of chronic gastritis and grastic cancer

Aim: To evaluate the association between polymorphisms XRCC1 Arg194Trp and Arg399Gln and XRCC3 Thr241Met and the risk for chronic gastritis and gastric cancer, in a Southeastern Brazilian population. Methods: Genotyping by PCR-RFLP was carried out on 202 patients with chronic gastritis (CG) and 160 patients with gastric cancer (GC), matched to 202 (C1) and 150 (C2) controls, respectively. Results: No differences were observed among the studied groups with regard to the genotype distribution of XRCC1 codons 194 and 399 and of XRCC3 codon 241. However, the combined analyses of the three variant alleles (194Trp, 399Gln and 241Met) showed an increased risk for chronic gastritis when compared to the GC group. Moreover, an interaction between the polymorphic alleles and demographic and environmental factors was observed in the CG and GC groups. XRCC1 194Trp was associated with smoking in the CG group, while the variant alleles XRCC1 399Gln and XRCC3 241Met were related with gender, smoking, drinking and H pylori infection in the CG and GC groups. Conclusion: Our results showed no evidence of a rela-tionship between the polymorphisms XRCC1 Arg194Trp and Arg399Gln and XRCC3 Thr241Met and the risk of chronic gastritis and gastric cancer in the Brazilian population, but the combined effect of these variants may interact to increase the risk for chronic gastritis, considered a premalignant lesion. Our data also indicate a gene-environment interaction in the susceptibility to chronic gastritis and gastric cancer. © 2005 The WJG Press and Elsevier Inc. All rights reserved.

Physiological and production response of dairy goats bred in a tropical climate

The aim of this work was to determine the adaptability of Saanen and A1/2Saanen x A1/2Anglo-Nubian (A1/2SA1/2AN) goats bred in tropical climates. The study included 30 goats, 15 Saanen and 15 A1/2SA1/2AN. The data was collected during the rainy and dry seasons. During the whole experimental period, the environment variables were recorded, as well as rectal temperature (RT), superficial temperature (ST), respiratory rate (RR) and heart rate (HR) and milk production (MP). The adaptability coefficient (AC) was calculated for both genotypes. The averages were evaluated by ANOVA at 5 % probability. There was a genotype and period of year effect, as well as the interaction genotype x period of year. Pearson's simple correlation analysis was then carried out between milk production and physiological and environment variables. There was a statistical difference (p < 0.05) between the seasons for RT, ST and RR. RT, RR and HR were lower for A1/2SA1/2AN than Saanen goats, regardless of the season. MP was greater in the dry season (p < 0.05) (2.52 A +/- 0.50 kg/day for A1/2SA1/2AN and 2.41 A +/- 0.38 kg/day for Saanen) than the rainy season (2.17 A +/- 0.27 kg/day for A1/2SA1/2AN and 2.28 A +/- 0.53 kg/day for Saanen). The MP correlations were very significant (p < 0.05), however low and negative, where it was higher when correlated with RR in Saanen goats. Under the conditions of the present study, it is concluded that the goats were influenced by climatic factors, where the rainy period was more likely to cause thermal stress in the animals.

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.

Alternativas de análise para experimentos G × E multiatributo

Geralmente, nos experimentos genótipo por ambiente (G × E) é comum observar o comportamento dos genótipos em relação a distintos atributos nos ambientes considerados. A análise deste tipo de experimentos tem sido abordada amplamente para o caso de um único atributo. Nesta tese são apresentadas algumas alternativas de análise considerando genótipos, ambientes e atributos simultaneamente. A primeira, é baseada no método de mistura de máxima verossimilhança de agrupamento - Mixclus e a análise de componentes principais de 3 modos - 3MPCA, que permitem a análise de tabelas de tripla entrada, estes dois métodos têm sido muito usados na área da psicologia e da química, mas pouco na agricultura. A segunda, é uma metodologia que combina, o modelo de efeitos aditivos com interação multiplicativa - AMMI, modelo eficiente para a análise de experimentos (G × E) com um atributo e a análise de procrustes generalizada, que permite comparar configurações de pontos e proporcionar uma medida numérica de quanto elas diferem. Finalmente, é apresentada uma alternativa para realizar imputação de dados nos experimentos (G × E), pois, uma situação muito frequente nestes experimentos, é a presença de dados faltantes. Conclui-se que as metodologias propostas constituem ferramentas úteis para a análise de experimentos (G × E) multiatributo.

Molecular Genetic Basis of Opposite Gene by Environment Interactions in Reading Disability and Attention Deficit/Hyperactivity Disorder

The goal of this study is to better understand the genetic basis of Reading Disability (RD) and Attention Deficit Hyperactivity Disorder (ADHD) by examining molecular G x E interactions with parental education for each disorder. Research indicates that despite sharing genetic risk factors, RD and ADHD are influenced by different types of G x E interactions with parental education - a diathesis stress interaction in the case of ADHD and a bioecological interaction in RD. In order to resolve this apparent paradox, we conducted a preliminary study using behavioral genetic methods to test for G x E interactions in RD and the inattentive subtype of ADHD (ADHD-I) in the same sample of monozygotic and dizygotic Colorado Learning Disabilities Research Center same-sex twin pairs (DeFries et al., 1997), and our findings were consistent with the literature. We posited a genetic hypothesis for this opposite pattern of interactions, which suggests that only genes specific to each disorder enter into these opposite interactions, not the shared genes underlying their comorbidity. This study sought to further investigate this paradox using molecular genetics methods. We examined multiple candidate genes identified for RD or related language phenotypes and those identified for ADHD for G x E interactions with parental education. The specific aims of this study were as follows: 1) partition known risk alleles for RD and/or related language phenotypes and ADHD-I into those which are pleiotropic and non-pleiotropic by testing each risk allele for association with both RD and ADHD-I, 2) explore the main effects of parental education on both RD and ADHD-I, 3) address G-E correlations, and 4) conduct exploratory G x E interaction analyses in order to test the genetic hypothesis. Analyses suggested a number of pleiotropic genes that influence both RD and ADHD; however, results did not remain after correcting for multiple comparisons. Although exploratory G x E interaction findings were not significant after multiple comparison correction, results suggested a G x E interaction in the bioecological direction with KIAA0319, parental education, and ADHD-I. Given the limited power in the current study, replication of these findings with larger samples is necessary.

Neurobiological alterations in the human alcoholic brain: effect of genotype

Long-term alcohol abuse by human subjects leads to selective brain damage that is restricted in extent and variable in severity. Within the cerebral cortex, neuronal loss is most marked in the superior frontal cortex and relatively mild in motor cortex. Cirrhotic alcoholics and subjects with alcohol-related Wernicke-Korsakoff syndrome show more severe and more extensive damage than do uncomplicated cases. Accumulating evidence suggests that the likelihood of developing alcohol dependency is associated with one or more genetic markers. In previous work we showed that GABAA receptor functionality, and the subunit isoform expression that underlies this, differed in region- and disease-specific ways between alcoholics and controls. By contrast, glutamate receptor (NMDA, KA, AMPA) differences were muted or absent. Here we asked if genotype differentiated the form, pharmacology, or expression of glutamate and GABA receptors in pathologically vulnerable and spared cortical regions, with a view to determining whether such subject factors might influence the severity of alcohol-induced brain damage. Cerebrocortical tissue was obtained at autopsy under informed, written consent from uncomplicated and alcoholic-cirrhotic Caucasian (predominantly Anglo-Celtic) cases, together with matched controls and cases with cirrhosis of non-alcoholic origin. All subjects had pathological confirmation of liver and brain diagnosis; none had been polydrug abusers. Samples were processed for synaptic membrane receptor binding, mRNA analysis by quantitative RT-PCR, and protein analysis by Western blot. Genotyping was performed by PCR methods, in the main using published primers. Several genetic markers differentiated between our alcoholic and control subjects, including the GABAA receptor 2 subunit (GABB2) gene ( 2 (3) 10.329, P 0.01), the dopamine D2 receptor B1 (DRD2B) allele ( 2 (3) 10.109, P 0.01) and a subset of the alcohol dehydrogenase-3 (ADH3) alleles ( 2 (2) 4.730, P 0.05). Although neither the type-2 glutamate transporter (EAAT2) nor the serotonin transporter (5HTT) genes were significantly associated with alcoholism, only EAAT2 heterozygotes showed a significant association between ADH3 genotype and alcoholism ( 2 (3) 7.475, P 0.05). Other interactions between genotypes were also observed. DRD2A, DRD2B, GABB2, EAAT2 and 5HTT genotypes did not divide alcoholic cases and controls on NMDA receptor parameters, although in combined subjects there was a significant DRD2B X Area Interaction with glutamateNMDA receptor efficacy (F(1,57) 4.67; P 0.05), measured as the extent of glutamate-enhanced MK801 binding. In contrast, there was a significant Case-group X ADH3 X Area Interaction with glutamateNMDA receptor efficacy (F(3,57) 2.97; P 0.05). When GABAA receptor subunit isoform expression was examined, significant Case-group X Genotype X Area X Isoform interactions were found for EAAT2 with subunit mRNA (F(1,37) 4.22; P0.05), for GABB2 with isoform protein (F(1,37) 5.69; P 0.05), and for DRD2B with isoform protein (F(2,34)5.69; P0.05). The results suggest that subjects’ genetic makeup may modulate the effectiveness of amino acid-mediated transmission in different cortical regions, and thereby influence neuronal vulnerability to excitotoxicity.

Interactions between serotonergic genotype and amino acid transmitter receptor expression in human alcoholics

Serotonin can modulate the activity of neural reward pathways that are strongly implicated in mediating the effects of chronic alcohol misuse, and its treatment, in human subjects. In previous work and as discussed elsewhere at this meeting, we and others have found consistent differences in the parameters of GABA and glutamate receptors, and the expression of their component subunit transcripts and proteins, in areas of the alcoholic brain that are altered by alcoholism. We did not fi nd clear changes in GABA and glutamate transport function in such samples, but a series of microarray analyses showed consistent upregulation of the presynaptic GABA/betaine transporter SLC6A12. Microarray studies showed no signifi cant differences in the expression of transcripts associated with 5HT transmission; however, only a small number of such elements were present on the arrays. Here we partitioned GABAA and NMDA pharmacology, and subunit mRNA and protein expression, measured in samples of frontal and motor cortex obtained at autopsy from alcoholics without comorbid disease, alcoholics with liver cirrhosis, and controls, according to 5HTTLPR (SLC6A4) and 5HT1B (HTR1B) polymorphisms. We found no effect of these genotypes on the expression of GABAA receptor gene products, but there was a signifi cant mRNA Transcript X Area X Group X 5HTTLPR Interaction with NMDA subunit isoform expression measured by Real Time PCR with GAPDH normalization. Further analysis showed the effect to be selective for alcoholics with cirrhosis, to be most marked in the pathologically vulnerable frontal cortex, and to vary with subunit transcript (F2,76 = 6.545, P = 0.002). NR1 expression was most affected, followed by NR2A, with NR2B expression least altered. Pilot data suggest 5HT1B genotype may also modulate NMDA subunit expression. Interactions between amino acid and serotonin transmission may infl uence susceptibility to alcohol dependence or pathogenesis

GGE biplot analysis of multi-environment yield trials of rice produced in a temperate climate

Low temperature is one of the main environmental constraints for rice ( Oryza sativa L.) grain production yield. It is known that multi-environment studies play a critical role in the sustainability of rice production across diverse environments. However, there are few studies based on multi-environment studies of rice in temperate climates. The aim was to study the performance of rice plants in cold environments. Four experimental lines and six cultivars were evaluated at three locations during three seasons. The grain yield data were analyzed with ANOVA, mixed models based on the best linear unbiased predictors (BLUPs), and genotype plus Genotype × Environment interaction (GGE) biplot. High genotype contribution (> 25%) was observed in grain yield and the interaction between genotype and locations was not very important. Results also showed that ‘Quila 241319’ was the best experimental line with the highest grain yield (11.3 t ha-1) and grain yield stability across the environments; commercial cultivars were classified as medium grain yield genotypes.

The benefits of polyandry in the free-spawning polychaete Galeolaria caespitosa

In many species, females are thought to benefit from polyandry due to the reduced risks of fertilization by genetically incompatible sperm. However, few studies that have reported such benefits have directly attributed variation in female reproductive success to the interacting effects of males and females at fertilization. In this paper, we determine whether male x female interactions influence fertilization in vitro in the free-spawning, sessile polychaete Galeolaria caespitosa. Furthermore, we determined whether polyandry results in direct fertilization benefits for females by experimentally manipulating the number of males contributing towards staged spawning events. To test for male x female interaction effects we performed an initial experiment that crossed seven males with six females (in all 42 combinations), enabling us to assess fertilization rates for each specific male-female pairing and attribute variation in fertilization success to males, females and their interaction. This initial experiment revealed a strong interaction between males and females at fertilization, confirming that certain male-female combinations were more compatible than others. A second experiment tested the hypothesis that polyandry enhances female reproductive success by exposing each female's eggs to either a single male's sperm (monandry) or the sperm from three males simultaneously (polyandry). We performed this second experiment at two ecologically relevant sperm concentrations. This latter experiment revealed a strong fertilization benefit of polyandry, independent of the effects of sperm concentration (which were also significant). We suggest that these direct fertilization gains arising from polyandry will constitute an important source of selection on females to mate multiply in nature.

Reciprocal interactions of obstructive sleep apnea and hypertension associated with ACE I/D polymorphism in males

Background: The angiotensin-converting enzyme (ACE) insertion/deletion (I/D) polymorphism gene contributes to the genesis of hypertension (HTN) and may help explain the relationship between obstructive sleep apnea (OSA) and HTN. However, ACE is a pleiotropic gene that has several influences, including skeletal muscle and control of ventilation. We therefore tested the hypothesis that ACE polymorphism influences OSA severity. Methods: Male OSA patients (apnea-hypopnea index [AHI] > 5 events/h) from 2 university sleep centers were evaluated by polysomnography and ACE I/D polymorphism genotyping. Results: We studied 266 males with OSA (age = 48 +/- 13y, body mass index = 29 5kg/m(2), AHI = 34 +/- 25events/h). HTN was present in 114 patients (43%) who were older (p < 0.01), heavier (p < 0.05) and had more severe OSA (p < 0.01). The I allele was associated with HTN in patients with mild to moderate OSA (p < 0.01), but not in those with severe OSA. ACE I/D polymorphism was not associated with apnea severity among normotensive patients. In contrast. the only variables independently associated with OSA severity among patients with hypertension in multivariate analysis were BMI (OR = 1.12) and 11 genotype (OR = 0.27). Conclusions: Our results indicate reciprocal interactions between OSA and HTN with ACE I/D polymorphism, suggesting that among hypertensive OSA males, the homozygous ACE I allele protects from severe OSA. (C) 2009 Elsevier B.V. All rights reserved.

Genotype effects on body temperature in dairy cows under grazing conditions in a hot climate including evidence for heterosis

We compared diurnal patterns of vaginal temperature in lactating cows under grazing conditions to evaluate genotype effects on body temperature regulation. Genotypes evaluated were Holstein, Jersey, Jersey x Holstein and Swedish Red x Holstein. The comparison of Holstein and Jersey versus Jersey x Holstein provided a test of whether heterosis effects body temperature regulation. Cows were fitted with intravaginal temperature recording devices that measured vaginal temperature every 15 min for 7 days. Vaginal temperature was affected by time of day (P < 0.0001) and genotype x time (P < 0.0001) regardless of whether days in milk and milk yield were used as covariates. Additional analyses indicated that the Swedish Red x Holstein had a different pattern of vaginal temperatures than the other three genotypes (Swedish Red x Holstein vs others x time; P < 0.0001) and that Holstein and Jersey had a different pattern than Jersey x Holstein [(Holstein + Jersey vs Jersey x Holstein) x time, P < 0.0001]. However, Holstein had a similar pattern to Jersey [(Holstein vs Jersey) x time, P > 0.10]. These genotype x time interactions reflect two effects. First, Swedish Red x Holstein had higher vaginal temperatures than the other genotypes in the late morning and afternoon but not after the evening milking. Secondly, Jersey x Holstein had lower vaginal temperatures than other genotypes in the late morning and afternoon and again in the late night and early morning. Results point out that there are effects of specific genotypes and evidence for heterosis on regulation of body temperature of lactating cows maintained under grazing conditions and suggest that genetic improvement for thermotolerance through breed choice or genetic selection is possible.

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.