Assessing the function of genetic variants in candidate gene association studies

Genetic factors linked to aberrant neural activity of individuals with substance use disorder phenotypes: A systematic review and meta-analysis of EEG studies

Background: Alterations in EEG activity have been considered valid endophenotypes of substance use disorders (SUDs). Empirical evidence has supported the association between genetic factors (e.g., genes, single nucleotide polymorphisms [SNPs]) and SUDs, considering both clinical samples and individuals with a positive family history of SUDs [F⁺SUD]). Nevertheless, the relationship between genetic factors and intermediate phenotypes (i.e., altered EEG activity) among individuals with SUD phenotypes remains unclear.Objective(s): The current study aims at summarizing genetic factors linked to aberrant EEG activity among individuals with SUDs and those with F⁺SUD.Methods: Sixteen studies (5 [N = 986] + 11 from the Collaborative Studies On Genetics of Alcoholism [COGA] sample [432 ≤ N ≤ 8810]) were included for a qualitative systematic review. Thirteen studies (5 + 8 studies from the COGA sample) were used for multi-level meta-analytic procedures.Results: Qualitative analyses highlighted a multivariate genetic architecture linked to alterations in EEG waves among individuals with SUD phenotypes (i.e., augmented resting-state beta waves; reduced resting-state alpha waves; reduced resting-state and task-dependent theta waves). The most recurrent genetic factors were involved in cellular energy homeostasis, modulation of inhibitory and excitatory neural activity together with neural cell growth. Meta-analytic results showed a moderate association between genetic factors and altered resting-state and task-dependent EEG activity. Meta-analytic results also suggested non-additive genetic effects on altered EEG activity.Conclusions: Complex genetic interactions mediating neural activity and brain development might constitute a causal pathway toward intermediate phenotypes associated with phenotypic features, which in turn are linked to SUDs.

Evolutionary analyses reveal immune cell receptor GPR84 as a conserved receptor for bacteria-derived molecules

The G protein-coupled receptor 84 (GPR84) is found in immune cells and its expression is increased under inflammatory conditions. Activation of GPR84 by medium-chain fatty acids results in pro-inflammatory responses. Here, we screened available vertebrate genome data and found that GPR84 is present in vertebrates for more than 500 million years but absent in birds and a pseudogene in bats. Cloning and functional characterization of several mammalian GPR84 orthologs in combination with evolutionary and model-based structural analyses revealed evidence for positive selection of bear GPR84 orthologs. Naturally occurring human GPR84 variants are most frequent in Asian populations causing a loss of function. Further, we identified cis- and trans-2-decenoic acid, both known to mediate bacterial communication, as evolutionary highly conserved ligands. Our integrated set of approaches contributes to a comprehensive understanding of GPR84 in terms of evolutionary and structural aspects, highlighting GPR84 as a conserved immune cell receptor for bacteria-derived molecules.

Cytochromes P450: Role in Carcinogenesis and Relevance to Cancers

Abstracts:

Cancer is a leading factor of mortality globally. Cytochrome P450 (CYP) enzymes play a pivotal role in the biotransformation of both endogenous and exogenous compounds. Evidence from numerous epidemiological, animal, and clinical studies points to instrumental role of CYPs in cancer initiation, metastasis, and prevention. Substantial research has found that CYPs are involved in activating different carcinogenic chemicals in the environment, such as polycyclic aromatic hydrocarbons and tobacco-related nitrosamines. Electrophilic intermediates produced from these chemicals can covalently bind to DNA, inducing mutation and cellular transformation that collectively result in cancer development. While bioactivation of procarcinogens and promutagens by CYPs has long been established, the role of CYP-derived endobiotics in carcinogenesis has emerged in recent years. Eicosanoids derived from arachidonic acid via CYP oxidative pathways have been implicated in tumorigenesis, cancer progression and metastasis. The purpose of this review is to update on the current state of knowledge about the cancer molecular mechanism involving CYPs with focus on the biochemical and biotransformation mechanisms in the various CYP-mediated carcinogenesis, and the role of CYP-derived reactive metabolites, from both external and endogenous sources, on cancer growth and tumour formation.

Computational algorithmic and molecular dynamics study of functional and structural impacts of non-synonymous single nucleotide polymorphisms in human DHFR gene

Human dihydrofolate reductase (DHFR) is a conserved enzyme that is central to folate metabolism and is widely targeted in pathogenic diseases as well as cancers. Although studies have reported the fact that genetic mutations in DHFR leads to a rare autosomal recessive inborn error of folate metabolism and drug resistance, there is a lack of an extensive study on how the deleterious non-synonymous SNPs (nsSNPs) disrupt its phenotypic effects. In this study, we aim at discovering the structural and functional consequences of nsSNPs in DHFR by employing a combined computational approach consisting of ten recently developed in silico tools for identification of damaging nsSNPs and molecular dynamics (MD) simulation for getting deeper insights into the magnitudes of damaging effects. Our study revealed the presence of 12 most deleterious nsSNPs affecting the native phenotypic effects, with three (R71T, G118D, Y122D) identified in the co-factor and ligand binding active sites. MD simulations also suggested that these three SNPs particularly Y122D, alter the overall structural flexibility and dynamics of the native DHFR protein which can provide more understandings into the crucial roles of these mutants in influencing the loss of DHFR function.

Determine genetic variations in heat shock factor gene family (HSFs) and study their effect on the functional and structural characterization of protein in Tali goat

In this study, the effect of genetic variations of four heat shock transcription factor genes (HSF1, HSF2, HSF4, and HSF5) on the 3 D protein structure and function were studied. We defined the breed-specific genetic variations of pooled DNA of Tali goat that differed from the goat reference sequence (CHI2.0). Disordered regions of HSF proteins were predicted using PONDR. Post-translation changes were studied by several predicted online servers. Then, the structure of the order region of proteins was anticipated by using the Swiss model. Tali goat HSF genes contain a total number of 181, 679, 91, and 301 SNPs for HSF1, 2, 4, and 5, respectively. Also, 5 and 3 variants were identified as nsSNPs in the coding region of HSF4 and HSF5, respectively. (r.145A/S), (r.322P/Y), (r.379T/C) in HSF4 and (r.300Q/P), (r.573E/Q) in HSF5 obtained the tolerant and high confidence (SIFT score) for nsSNPs. More than half of these proteins are predicted to be disordered (56, 50, 52, and 50%, respectively for HSF1, 2, 4, and 5). Phosphorylation, acetylation, glycosylation, and Sumoylation sites of HSFs were compared between Tali goat and reference goat. Three residues S145, S263, and S322 of HSF4 in Tali goat were phosphorylation sites, and in HSF5, the reference goat has a phosphorylation site in S593.

Intricate Regulation of Phosphoenolpyruvate Carboxykinase (PEPCK) Isoforms in Normal Physiology and Disease

BACKGROUND

The phosphoenolpyruvate carboxykinase (PEPCK) isoforms are considered as rate-limiting enzymes for gluconeogenesis and glyceroneogenesis pathways. PEPCK exhibits several interesting features such as a) organelle-specific isoforms (cytosolic and a mitochondrial) in vertebrate clade, b) tissue-specific expression of isoforms and c) organism-specific requirement of ATP or GTP as a cofactor. In higher organisms, PEPCK isoforms are intricately regulated and activated through several physiological and pathological stimuli such as corticoids, hormones, nutrient starvation and hypoxia. Isoform-specific transcriptional/translational regulation and their interplay in maintaining glucose homeostasis remain to be fully understood. Mounting evidence indicates the significant involvement of PEPCK isoforms in physiological processes (development and longevity) and in the progression of a variety of diseases (metabolic disorders, cancer, Smith-Magenis syndrome).

OBJECTIVE

The present systematic review aimed to assimilate existing knowledge of transcriptional and translational regulation of PEPCK isoforms derived from cell, animal and clinical models.

CONCLUSION

Based on current knowledge and extensive bioinformatics analysis, in this review we have provided a comparative (epi)genetic understanding of PCK1 and PCK2 genes encompassing regulatory elements, disease-associated polymorphisms, copy number variations, regulatory miRNAs and CpG densities. We have also discussed various exogenous and endogenous modulators of PEPCK isoforms and their signaling mechanisms. A comprehensive review of existing knowledge of PEPCK regulation and function may enable identification of the underlying gaps to design new pharmacological strategies and interventions for the diseases associated with gluconeogenesis.

Genetic Variation and Response to Neurocritical Illness: a Powerful Approach to Identify Novel Pathophysiological Mechanisms and Therapeutic Targets

Disease-specific therapeutic options for critically ill neurological patients are limited. The identification of new preventive, therapeutic, and rehabilitation strategies is of the utmost importance in the field of neurocritical care research. Population genetics offers powerful tools to identify and prioritize biological pathways to be targeted by novel interventions. New treatments with supportive genetic evidence have twice the chances of obtaining final FDA approval compared to those without this support. Large collaborations, public access to data, reproducible science, and innovative analytical methods have exponentially increased the pace of discoveries related to neurocritical care genetics.

Single-Nucleotide Polymorphisms (SNP) Mining and Their Effect on the Tridimensional Protein Structure Prediction in a Set of Immunity-Related Expressed Sequence Tags (EST) in Atlantic Salmon (Salmo salar)

Single-nucleotide polymorphisms (SNPs) are single genetic code variations considered one of the most common forms of nucleotide modifications. Such SNPs can be located in genes associated to immune response and, therefore, they may have direct implications over the phenotype of susceptibility to infections affecting the productive sector. In this study, a set of immune-related genes (cc motif chemokine 19 precursor [ccl19], integrin β2 (itβ2, also named cd18), glutathione transferase omega-1 [gsto-1], heat shock 70 KDa protein [hsp70], major histocompatibility complex class I [mhc-I]) were analyzed to identify SNPs by data mining. These genes were chosen based on their previously reported expression on infectious pancreatic necrosis virus (IPNV)-infected Atlantic salmon phenotype. The available EST sequences for these genes were obtained from the Unigene database. Twenty-eight SNPs were found in the genes evaluated and identified most of them as transition base changes. The effect of the SNPs located on the 5’-untranslated region (UTR) or 3’-UTR upon transcription factor binding sites and alternative splicing regulatory motifs was assessed and ranked with a low-medium predicted FASTSNP score risk. Synonymous SNPs were found on itβ2 (c.2275G > A), gsto-1 (c.558G > A), and hsp70 (c.1950C > T) with low FASTSNP predicted score risk. The difference in the relative synonymous codon usage (RSCU) value between the variant codons and the wild-type codon (ΔRSCU) showed one negative (hsp70 c.1950C > T) and two positive ΔRSCU values (itβ2 c.2275G > A; gsto-1 c.558G > A), suggesting that these synonymous SNPs (sSNPs) may be associated to differences in the local rate of elongation. Nonsynonymous SNPs (nsSNPs) in the gsto-1 translatable gene region were ranked, using SIFT and POLYPHEN web-tools, with the second highest (c.205A > G; c484T > C) and the highest (c.499T > C; c.769A > C) predicted score risk possible. Using homology modeling to predict the effect of these nonsynonymous SNPs, the most relevant nucleotide changes for gsto-1 were observed for the nsSNPs c.205A > G, c484T > C, and c.769A > C. Molecular dynamics was assessed to analyze if these GSTO-1 variants have significant differences in their conformational dynamics, suggesting these SNPs could have allosteric effects modulating its catalysis. Altogether, these results suggest that candidate SNPs identified may play a crucial potential role in the immune response of Atlantic salmon.

Screening and insilico analysis of deleterious nsSNPs (missense) in human CSF3 for their effects on protein structure, stability and function

Human granulocyte colony stimulating factor (hG-CSF), known as CSF3, plays an important role in the growth, differentiation, proliferation, survival, and activation of the granulocyte cell lineage such as neutrophils and their precursors. Functional reduction in native CSF3 protein results in reduced proliferation and activation of neutrophils and leads to neutropenia. Single nucleotide polymorphisms (SNPs) in the CSF3 gene may have deleterious effects on the CSF3 protein function. This study was undertaken to find the functional SNPs in the human CSF3 gene. Results suggest that 18.9% of all the SNPs in the dbSNP database for CSF3 gene were present in the coding region. Out of 59 non-synonymous SNPs (nsSNPs), 26 nsSNPs were predicted to be non-tolerable by SIFT whereas 18 and 7 nsSNPs were predicted as probably damaging and possibly damaging, respectively by PolyPhen. Among this 31 nsSNPs, 16 nsSNPs were identified to be potentially deleterious by PANTHER server, and 4 nsSNPs were found to be neutral by PROVEAN. SNPAnalyzer predicted 7 nsSNPs to be neutral phenotype and the remaining 24 nsSNPs to be associated with diseases. Among the predicted nsSNPs, rs144408123, rs144408123, rs145136406, rs145311241, rs373191696, rs762945096, rs763688260, rs767572172, rs775326370, rs777777864, rs777983866, rs781596455, rs139072004, rs757612684, rs772911210, rs139072004, rs746634544, rs749993200, rs763426127, rs772466210 were identified as deleterious and potentially damaging. I-Mutant analysis revealed that th 20 nsSNPs were important for protein stability of CSF3. Therefore, th 20 nsSNPs may be used for the wider population-based genetic studies and also should be taken into account while engineering the recombinant CSF3 protein for clinical use.

A new panel of SNPs to assess thyroid carcinoma risk: a pilot study in a Brazilian admixture population

BACKGROUND

Thyroid cancer is a common malignant disease of the endocrine system with increasing incidence rates over the last few decades. In this study, we sought to analyze the possible association of 45 single nucleotide polymorphisms (SNPs) with thyroid cancer in a population from Rio Grande do Norte, Brazil.

METHODS

Based on histological analysis by a pathologist, 80 normal thyroid specimens of tissue adjacent to thyroid tumors were obtained from the biobank at the Laboratory of Pathology of Liga Norte Riograndense Contra o Câncer, Natal, RN. Patient samples were then genotyped using the MassARRAY platform (Sequenon, Inc) followed by statistical analysis employing the SNPassoc package in R program. The genotypic frequencies of all 45 SNPs obtained from the International HapMap Project database and based on data from the ancestral populations of European and African origin were used to compose the control study group.

RESULTS

In our study, the following 9 SNPs showed significant differences in their frequency when comparing the study and control groups: rs3744962, rs258107, rs1461855, rs4075022, rs9943744, rs4075570, rs2356508, rs17485896, and rs2651339. Furthermore, the SNPs rs374492 C/T and rs258107 C/T were associated with a relative risk for thyroid carcinoma of 3.78 (p = 6.27 × 10e^-5) and 2.91 (p = 8.27 × 10e^-5), respectively, after Bonferroni's correction for multiple comparisons.

CONCLUSIONS

These nine polymorphisms could be potential biomarkers of predisposition to thyroid carcinoma in the population from Rio Grande do Norte. However, complementary studies including a control group with samples obtained from healthy subjects in Rio Grande do Norte state, should be conducted to confirm these results.

Single Nucleotide Polymorphism Genotyping in Single-Molecule Electronic Circuits

Establishing low-cost, high-throughput, simple, and accurate single nucleotide polymorphism (SNP) genotyping techniques is beneficial for understanding the intrinsic relationship between individual genetic variations and their biological functions on a genomic scale. Here, a straightforward and reliable single-molecule approach is demonstrated for precise SNP authentication by directly measuring the fluctuations in electrical signals in an electronic circuit, which is fabricated from a high-gain field-effect silicon nanowire decorated with a single hairpin DNA, in the presence of different target DNAs. By simply comparing the proportion difference of a probe-target duplex structure throughout the process, this study implements allele-specific and accurate SNP detection. These results are supported by the statistical analyses of different dynamic parameters such as the mean lifetime and the unwinding probability of the duplex conformation. In comparison with conventional polymerase chain reaction and optical methods, this convenient and label-free method is complementary to existing optical methods and also shows several advantages, such as simple operation and no requirement for fluorescent labeling, thus promising a futuristic route toward the next-generation genotyping technique.

The genetic epidemiology of substance use disorder: A review

BACKGROUND

Substance use disorder (SUD) remains a significant public health issue. A greater understanding of how genes and environment interact to regulate phenotypes comprising SUD will facilitate directed treatments and prevention.

METHODS

The literature studying the neurobiological correlates of SUD with a focus on the genetic and environmental influences underlying these mechanisms was reviewed. Results from twin/family, human genetic association, gene-environment interaction, epigenetic literature, phenome-wide association studies are summarized for alcohol, nicotine, cannabinoids, cocaine, and opioids.

RESULTS

There are substantial genetic influences on SUD that are expected to influence multiple neurotransmission pathways, and these influences are particularly important within the dopaminergic system. Genetic influences involved in other aspects of SUD etiology including drug processing and metabolism are also identified. Studies of gene-environment interaction emphasize the importance of environmental context in SUD. Epigenetic studies indicate drug-specific changes in gene expression as well as differences in gene expression related to the use of multiple substances. Further, gene expression is expected to differ by stage of SUD such as substance initiation versus chronic substance use. While a substantial literature has developed for alcohol and nicotine use disorders, there is comparatively less information for other commonly abused substances.

CONCLUSIONS

A better understanding of genetically-mediated mechanisms involved in the neurobiology of SUD provides increased opportunity to develop behavioral and biologically based treatment and prevention of SUD.

SNP variants associated with non-Hodgkin lymphoma (NHL) correlate with human leukocyte antigen (HLA) class II expression

Large consortia efforts and genome-wide association studies (GWASs) have linked a number of genetic variants within the 6p21 chromosomal region to non-Hodgkin lymphoma (NHL). Complementing these efforts, we genotyped previously reported SNPs in the human leukocyte antigen (HLA) class I (rs6457327) and class II (rs9271100, rs2647012 and rs10484561) regions in a total of 1,145 subjects (567 NHL cases and 578 healthy controls) from two major ethnic groups in Malaysia, the Malays and the Chinese. We identified a NHL-associated (P_{NHL_add} = 0.0008; OR_{NHL_add} = 0.54; 95% CI = 0.37–0.77) and B-cell associated (P_{Bcell_add} = 0.0007; OR_{Bcell_add} = 0.51; 95% CI = 0.35–0.76) SNP rs2647012 in the Malaysian Malays. In silico cis-eQTL analysis of rs2647012 suggests potential regulatory function of nearby HLA class II molecules. Minor allele rs2647012-T is linked to higher expression of HLA-DQB1, rendering a protective effect to NHL risk. Our findings suggest that the HLA class II region plays an important role in NHL etiology.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Polymorphisms in cancer-related pathway genes and lung cancer

We evaluated the associations between potentially functional variants in a comprehensive list of cancer-related genes and lung cancer in a Korean population.

A total of 1969 potentially functional single nucleotide polymorphisms (SNPs) of 1151 genes involved in carcinogenesis were evaluated using an Affymetrix custom-made GeneChip in 610 nonsmall cell lung cancer patients and 610 healthy controls. A replication study was conducted in an independent set of 490 cases and 486 controls. 68 SNPs were significantly associated with lung cancer in the discovery set and tested for replication.

Among the 68 SNPs, three SNPs (corepressor interacting with RBPJ 1 (CIR1) rs13009079T>C, ribonucleotide reductase M1 (RRM1) rs1465952T>C and solute carrier family 38, member 4 (SLC38A4) rs2429467C>T) consistantly showed significant associations with lung cancer in the replication study. In combined analysis, adjusted odds ratio for CIR1 rs13009079T>C, RRM1 rs1465952T>C and SLC38A4 rs2429467C>T were 0.69, 0.71 and 0.73, respectively (p=4×10−5, 0.01 and 0.001, respectively) under the dominant model. The relative mRNA expression level of CIR1 was significantly associated with rs13009079T>C genotypes in normal lung tissues (ptrend=0.03).

These results suggest that the three SNPs, particularly CIR1 rs13009079T>C, may play a role in the pathogenesis of lung cancer.

Identification of hub glycogenes and their nsSNP analysis from mouse RNA-Seq data

Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.

RACK1 is a candidate gene associated with the prognosis of patients with early stage non-small cell lung cancer

Background

This study was conducted to identify genetic polymorphisms associated with the prognosis of patients with early stage NSCLC.

Materials and Methods

We genotyped 1,969 potentially functional single nucleotide polymorphisms (SNPs) of 1,151 genes involved in carcinogenesis in 166 NSCLC patients who underwent curative surgery, using the Affymetrix custom-made GeneChip. A replication study was performed in an independent cohort of 626 patients.

Results

Fifty six SNPs which were associated with both overall survival (OS) and disease-free survival (DFS) with log-rank P values < 0.05 in discovery set were selected for validation. Among those, five SNPs (RACK1 rs1279736C>A and rs3756585T>G, C3 rs2287845T>C, PCAF rs17006625A>G, and PCM1 rs17691523C>G) were found to be significantly associated with survival in the same direction as the discovery set. In combined analysis, the rs1279736C>A and rs3756585T>G were most significantly associated with OS and DFS in multivariate analysis (P for OS = 4 × 10⁻⁵ and 7 × 10⁻⁵, respectively; and P for DFS = 0.003, both; under codominant model). In vitro promoter assay and electrophoretic mobility shift assay revealed that the rs3756585 T-to-G change increased promoter activity and transcription factor binding of RACK1.

Conclusions

We identified five SNPs, especially RACK1 rs3756585T>G, as markers for prognosis of patients with surgically resected NSCLC.

Functional characterization of two single nucleotide polymorphisms of acyl-coenzyme A:cholesterol acyltransferase 2

BACKGROUND

Acyl-coenzyme A:cholesterol acyltransferase 2 (ACAT2) plays a critical role in the formation of cholesteryl esters from cholesterol and fatty acids, and is a potential target for treating hypercholesterolemia. We recently reported the significant effects of two human ACAT2 gene polymorphisms, 41A>G (Glu(14)Gly, rs9658625) and 734C>T (Thr(254)Ile, rs2272296), on plasma lipid levels and coronary artery disease susceptibility in a case-control association study. In the present study, we evaluated the possible biological influence of the two polymorphism using two approaches.

METHODS

In the first approach, the functional impact of the two polymorphisms was predicted in-silico using available web-based software, and in the second approach, the varying functions of the two polymorphisms were characterized in in vitro experiments, using ACAT2-deficient AC-29 cells.

RESULTS

Our results show that the enzymatic activity of mutant Glu(14)Gly is approximately two times higher than wildtype, and that this increase is primarily due to the increased expression and/or stability of the mutant ACAT2 protein.

CONCLUSIONS

These results suggest that the genetic variation at Glu(14)Gly is functionally important and may contribute to ACAT2 protein expression and stability.

Evaluation of DNA variants associated with androgenetic alopecia and their potential to predict male pattern baldness

Androgenetic alopecia, known in men as male pattern baldness (MPB), is a very conspicuous condition that is particularly frequent among European men and thus contributes markedly to variation in physical appearance traits amongst Europeans. Recent studies have revealed multiple genes and polymorphisms to be associated with susceptibility to MPB. In this study, 50 candidate SNPs for androgenetic alopecia were analyzed in order to verify their potential to predict MPB. Significant associations were confirmed for 29 SNPs from chromosomes X, 1, 5, 7, 18 and 20. A simple 5-SNP prediction model and an extended 20-SNP model were developed based on a discovery panel of 305 males from various European populations fitting one of two distinct phenotype categories. The first category consisted of men below 50 years of age with significant baldness and the second; men aged 50 years or older lacking baldness. The simple model comprised the five best predictors: rs5919324 near AR, rs1998076 in the 20p11 region, rs929626 in EBF1, rs12565727 in TARDBP and rs756853 in HDAC9. The extended prediction model added 15 SNPs from five genomic regions that improved overall prevalence-adjusted predictive accuracy measured by area under the receiver characteristic operating curve (AUC). Both models were evaluated for predictive accuracy using a test set of 300 males reflecting the general European population. Applying a 65% probability threshold, high prediction sensitivity of 87.1% but low specificity of 42.4% was obtained in men aged <50 years. In men aged ≥50, prediction sensitivity was slightly lower at 67.7% while specificity reached 90%. Overall, the AUC=0.761 calculated for men at or above 50 years of age indicates these SNPs offer considerable potential for the application of genetic tests to predict MPB patterns, adding a highly informative predictive system to the emerging field of forensic analysis of externally visible characteristics.

DFLAT: functional annotation for human development

Background

Recent increases in genomic studies of the developing human fetus and neonate have led to a need for widespread characterization of the functional roles of genes at different developmental stages. The Gene Ontology (GO), a valuable and widely-used resource for characterizing gene function, offers perhaps the most suitable functional annotation system for this purpose. However, due in part to the difficulty of studying molecular genetic effects in humans, even the current collection of comprehensive GO annotations for human genes and gene products often lacks adequate developmental context for scientists wishing to study gene function in the human fetus.

Description

The Developmental FunctionaL Annotation at Tufts (DFLAT) project aims to improve the quality of analyses of fetal gene expression and regulation by curating human fetal gene functions using both manual and semi-automated GO procedures. Eligible annotations are then contributed to the GO database and included in GO releases of human data. DFLAT has produced a considerable body of functional annotation that we demonstrate provides valuable information about developmental genomics. A collection of gene sets (genes implicated in the same function or biological process), made by combining existing GO annotations with the 13,344 new DFLAT annotations, is available for use in novel analyses. Gene set analyses of expression in several data sets, including amniotic fluid RNA from fetuses with trisomies 21 and 18, umbilical cord blood, and blood from newborns with bronchopulmonary dysplasia, were conducted both with and without the DFLAT annotation.

Conclusions

Functional analysis of expression data using the DFLAT annotation increases the number of implicated gene sets, reflecting the DFLAT’s improved representation of current knowledge. Blinded literature review supports the validity of newly significant findings obtained with the DFLAT annotations. Newly implicated significant gene sets also suggest specific hypotheses for future research. Overall, the DFLAT project contributes new functional annotation and gene sets likely to enhance our ability to interpret genomic studies of human fetal and neonatal development.

Two-phase and family-based designs for next-generation sequencing studies

The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.

A functional variant at 19q13.3, rs967591G>A, is associated with shorter survival of early-stage lung cancer

PURPOSE

This study was conducted to investigate the associations between single-nucleotide polymorphisms (SNP) in 19q13.3 and survival of patients with early-stage non-small cell lung cancer (NSCLC), and to define the causative functional SNP of the association.

EXPERIMENTAL DESIGN

A two-stage study design was used to evaluate five SNPs in relation to survival outcomes in 328 patients and then to validate the results in an independent patient population (n = 483). Luciferase assay and real-time PCR were conducted to examine functional relevance of a potentially functional SNP.

RESULTS

Of the five SNPs, three SNPs (rs105165C>T, rs967591G>A, and rs735482A>C) were significantly associated with survival outcomes in a stage I study. The rs967591A allele had significantly higher activity of the CD3EAP promoter compared with the rs967591G allele (P = 0.002), but the SNP did not have an effect on the activity of PPP1R13L promoter. The rs967591G>A was associated with the level of CD3EAP mRNA expression in lung tissues (P = 0.01). The rs967591G>A exhibited consistent associations in a stage II study. In combined analysis, the rs967591 AA genotype exhibited a worse overall survival (adjusted HR = 1.69; 95% confidence interval = 1.29-2.20; P = 0.0001).

CONCLUSION

The rs967591G>A affects CD3EAP expression and thus influences survival in early-stage NSCLC. The analysis of the rs967591G>A polymorphism can help identify patients at high risk of a poor disease outcome.

Biomarkers for smoking cessation

One way to enhance therapeutic development is through the identification and development of evaluative tools such as biomarkers. This review focuses on putative diagnostic, pharmacodynamic, and predictive biomarkers for smoking cessation. These types of biomarkers may be used to more accurately diagnose a disease, personalize treatment, identify novel targets for drug discovery, and enhance the efficiency of drug development. Promising biomarkers are presented across a range of approaches including metabolism, genetics, and neuroimaging. A preclinical viewpoint is also offered, as are analytical considerations and a regulatory perspective summarizing a pathway toward biomarker qualification.

Are research papers reporting results from nutrigenetics clinical research a potential source of biohype?

Nutrigenetics is a promising field, but the achievability of expected benefits is challenged by the methodological limitations that are associated with clinical research in that field. The mere existence of these limitations suggests that promises about potential outcomes may be premature. Thus, benefits claimed in scientific journal articles in which these limitations are not acknowledged might stimulate biohype. This article aims to examine whether nutrigenetics clinical research articles are a potential source of biohype. Of the 173 articles identified, 16 contained claims in which clinical applications were extrapolated from study results. The methodological limitations being incompletely acknowledged, these articles could potentially be a source of biohype.

Significance of MDM2 and P14 ARF polymorphisms in susceptibility to differentiated thyroid carcinoma

BACKGROUND

Murine double minute 2 (MDM2) oncoprotein and p14(ARF) tumor suppressor play pivotal roles in regulating p53 and function in the MAPK pathway, which is mutated frequently in differentiated thyroid carcinoma (DTC). We hypothesized that functional polymorphisms in the promoters of MDM2 and p14(ARF) contribute to the interindividual difference in predisposition to DTC.

METHODS

MDM2-rs2279744, MDM2-rs937283, p14(ARF)-rs3731217, and p14(ARF)-rs3088440 were genotyped in 303 patients with DTC and 511 cancer-free healthy controls. Multivariate logistic regression analysis was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

MDM2-rs2279744 and p14(ARF)-rs3731217 were associated with a significantly increased risk of DTC (MDM2-rs2279744: TT versus TG/GG; OR, 1.5; 95% CI, 1.1-2.0; p14(ARF)-rs3731217: TG/GG versus TT; OR, 1.7; 95% CI, 1.2-2.3). No association was found for MDM2-rs937283 or p14(ARF)-rs3088440. Individuals carrying 3-4 risk genotypes of MDM2 and p14(ARF) had 2.2 times (95% CI, 1.4-3.5) the risk for DTC of individuals carrying 0-1 risk genotypes (P trend = .021). The combined effect of MDM2 and p14(ARF) on risk of DTC was confined to young subjects (≤ 45 years), nonsmokers, nondrinkers, and subjects with a first-degree family history of cancer. These associations were quite similar in strength when cases were restricted to those with papillary thyroid cancer.

CONCLUSION

Our results suggest that polymorphisms of MDM2 and p14(ARF) contribute to the interindividual difference in susceptibility to DTC, either alone or more likely jointly. The observed associations warrant further confirmation in independent studies.

Distinct and competitive regulatory patterns of tumor suppressor genes and oncogenes in ovarian cancer

Background

So far, investigators have found numerous tumor suppressor genes (TSGs) and oncogenes (OCGs) that control cell proliferation and apoptosis during cancer development. Furthermore, TSGs and OCGs may act as modulators of transcription factors (TFs) to influence gene regulation. A comprehensive investigation of TSGs, OCGs, TFs, and their joint target genes at the network level may provide a deeper understanding of the post-translational modulation of TSGs and OCGs to TF gene regulation.

Methodology/Principal Findings

In this study, we developed a novel computational framework for identifying target genes of TSGs and OCGs using TFs as bridges through the integration of protein-protein interactions and gene expression data. We applied this pipeline to ovarian cancer and constructed a three-layer regulatory network. In the network, the top layer was comprised of modulators (TSGs and OCGs), the middle layer included TFs, and the bottom layer contained target genes. Based on regulatory relationships in the network, we compiled TSG and OCG profiles and performed clustering analyses. Interestingly, we found TSGs and OCGs formed two distinct branches. The genes in the TSG branch were significantly enriched in DNA damage and repair, regulating macromolecule metabolism, cell cycle and apoptosis, while the genes in the OCG branch were significantly enriched in the ErbB signaling pathway. Remarkably, their specific targets showed a reversed functional enrichment in terms of apoptosis and the ErbB signaling pathway: the target genes regulated by OCGs only were enriched in anti-apoptosis and the target genes regulated by TSGs only were enriched in the ErbB signaling pathway.

Conclusions/Significance

This study provides the first comprehensive investigation of the interplay of TSGs and OCGs in a regulatory network modulated by TFs. Our application in ovarian cancer revealed distinct regulatory patterns of TSGs and OCGs, suggesting a competitive regulatory mechanism acting upon apoptosis and the ErbB signaling pathway through their specific target genes.

Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans

We propose guidelines to evaluate the cumulative evidence of gene-environment (G × E) interactions in the causation of human cancer. Our approach has its roots in the HuGENet and IARC Monographs evaluation processes for genetic and environmental risk factors, respectively, and can be applied to common chronic diseases other than cancer. We first review issues of definitions of G × E interactions, discovery and modelling methods for G × E interactions, and issues in systematic reviews of evidence for G × E interactions, since these form the foundation for appraising the credibility of evidence in this contentious field. We then propose guidelines that include four steps: (i) score the strength of the evidence for main effects of the (a) environmental exposure and (b) genetic variant; (ii) establish a prior score category and decide on the pattern of interaction to be expected; (iii) score the strength of the evidence for interaction between the environmental exposure and the genetic variant; and (iv) examine the overall plausibility of interaction by combining the prior score and the strength of the evidence and interpret results. We finally apply the scheme to the interaction between NAT2 polymorphism and tobacco smoking in determining bladder cancer risk.

Analysis of exome sequences with and without incorporating prior biological knowledge

Next-generation sequencing technology provides new opportunities and challenges in the search for genetic variants that underlie complex traits. It will also presumably uncover many new rare variants, but exactly how these variants should be incorporated into the data analysis remains a question. Several papers in our group from Genetic Analysis Workshop 17 evaluated different methods of rare variant analysis, including single-variant, gene-based, and pathway-based analyses and analyses that incorporated biological information. Although the performance of some of these methods strongly depends on the underlying disease model, integration of known biological information is helpful in detecting causal genes. Two work groups demonstrated that use of a Bayesian network and a collapsing receiver operating characteristic curve approach improves risk prediction when a disease is caused by many rare variants. Another work group suggested that modeling local rather than global ancestry may be beneficial when controlling the effect of population structure in rare variant association analysis.

Disease mutations in disordered regions--exception to the rule?

Intrinsically disordered proteins (IDPs) have been implicated in a number of human diseases, including cancer, diabetes, neurodegenerative and cardiovascular disorders. Although for some of these conditions molecular mechanisms are now better understood, the big picture connecting distinct structural properties and functional repertoire of IDPs to pathogenesis and disease progression is still incomplete. Recent studies suggest that signaling and regulatory roles carried out by IDPs require them to be tightly regulated, and that altered IDP abundance may lead to disease. Here, we propose another link between IDPs and disease that takes into account disease-associated missense mutations located in the intrinsically disordered regions. We argue that such mutations are more prevalent and have larger functional impact than previously thought. In addition, we demonstrate that deleterious amino acid substitutions that cause disorder-to-order transitions are particularly enriched among disease mutations compared to neutral polymorphisms. Finally, we discuss potential differences in functional outcomes between disease mutations in ordered and disordered regions, and challenge the conventional structure-centric view of missense mutations.

Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects

Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).

Characterization of Sucrose transporter alleles and their association with seed yield-related traits in Brassica napus L

BACKGROUND

Sucrose is the primary photosynthesis product and the principal translocating form within higher plants. Sucrose transporters (SUC/SUT) play a critical role in phloem loading and unloading. Photoassimilate transport is a major limiting factor for seed yield. Our previous research demonstrated that SUT co-localizes with yield-related quantitative trait loci. This paper reports the isolation of BnA7.SUT1 alleles and their promoters and their association with yield-related traits.

RESULTS

Two novel BnA7.SUT1 genes were isolated from B. napus lines 'Eagle' and 'S-1300' and designated as BnA7.SUT1.a and BnA7.SUT1.b, respectively. The BnA7.SUT1 protein exhibited typical SUT features and showed high amino acid homology with related species. Promoters of BnA7.SUT1.a and BnA7.SUT1.b were also isolated and classified as pBnA7.SUT1.a and pBnA7.SUT1.b, respectively. Four dominant sequence-characterized amplified region markers were developed to distinguish BnA7.SUT1.a and BnA7.SUT1.b. The two genes were estimated as alleles with two segregating populations (F2 and BC1) obtained by crossing '3715'×'3769'. BnA7.SUT1 was mapped to the A7 linkage group of the TN doubled haploid population. In silico analysis of 55 segmental BnA7.SUT1 alleles resulted three BnA7.SUT1 clusters: pBnA7.SUT1.a- BnA7.SUT1.a (type I), pBnA7.SUT1.b- BnA7.SUT1.a (type II), and pBnA7.SUT1.b- BnA7.SUT1.b (type III). Association analysis with a diverse panel of 55 rapeseed lines identified single nucleotide polymorphisms (SNPs) in promoter and coding domain sequences of BnA7.SUT1 that were significantly associated with one of three yield-related traits: number of effective first branches (EFB), siliques per plant (SP), and seed weight (n = 1000) (TSW) across all four environments examined. SNPs at other BnA7.SUT1 sites were also significantly associated with at least one of six yield-related traits: EFB, SP, number of seeds per silique, seed yield per plant, block yield, and TSW. Expression levels varied over various tissue/organs at the seed-filling stage, and BnA7.SUT1 expression positively correlated with EFB and TSW.

CONCLUSIONS

Sequence, mapping, association, and expression analyses collectively showed significant diversity between the two BnA7.SUT1 alleles, which control some of the phenotypic variation for branch number and seed weight in B. napus consistent with expression levels. The associations between allelic variation and yield-related traits may facilitate selection of better genotypes in breeding.

Statistical analysis of genetic interactions

Many common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene-gene and gene-environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.

Avoiding mouse traps in schizophrenia genetics: lessons and promises from current and emerging mouse models

Schizophrenia is one of the most common psychiatric disorders, but despite progress in identifying the genetic factors implicated in its development, the mechanisms underlying its etiology and pathogenesis remain poorly understood. Development of mouse models is critical for expanding our understanding of the causes of schizophrenia. However, translation of disease pathology into mouse models has proven to be challenging, primarily due to the complex genetic architecture of schizophrenia and the difficulties in the re-creation of susceptibility alleles in the mouse genome. In this review we highlight current research on models of major susceptibility loci and the information accrued from their analysis. We describe and compare the different approaches that are necessitated by diverse susceptibility alleles, and discuss their advantages and drawbacks. Finally, we discuss emerging mouse models, such as second-generation pathophysiology models based on innovative approaches that are facilitated by the information gathered from the current genetic mouse models.

Identifying functional single nucleotide polymorphisms in the human CArGome

Regulatory SNPs (rSNPs) reside primarily within the nonprotein coding genome and are thought to disturb normal patterns of gene expression by altering DNA binding of transcription factors. Nevertheless, despite the explosive rise in SNP association studies, there is little information as to the function of rSNPs in human disease. Serum response factor (SRF) is a widely expressed DNA-binding transcription factor that has variable affinity to at least 1,216 permutations of a 10 bp transcription factor binding site (TFBS) known as the CArG box. We developed a robust in silico bioinformatics screening method to evaluate sequences around RefSeq genes for conserved CArG boxes. Utilizing a predetermined phastCons threshold score, we identified 8,252 strand-specific CArGs within an 8 kb window around the transcription start site of 5,213 genes, including all previously defined SRF target genes. We then interrogated this CArG dataset for the presence of previously annotated common polymorphisms. We found a total of 118 unique CArG boxes harboring a SNP within the 10 bp CArG sequence and 1,130 CArG boxes with SNPs located just outside the CArG element. Gel shift and luciferase reporter assays validated SRF binding and functional activity of several new CArG boxes. Importantly, SNPs within or just outside the CArG box often resulted in altered SRF binding and activity. Collectively, these findings demonstrate a powerful approach to computationally define rSNPs in the human CArGome and provide a foundation for similar analyses of other TFBS. Such information may find utility in genetic association studies of human disease where little insight is known regarding the functionality of rSNPs.

Single-nucleotide polymorphisms in DNA repair genes and association with breast cancer risk in the web study

Base excision repair (BER) and nucleotide excision repair (NER) pathways repair damaged DNA, and polymorphisms in these genes might affect breast cancer susceptibility. We evaluated associations between seven single-nucleotide polymorphisms in four DNA repair genes (ERCC4 rs1799801, XPC rs2227998, rs2228001, rs2228000, OGG1 rs1052133 and XRCC1 rs25487 and rs25486) and breast cancer risk, examining modification by smoking and alcohol consumption, using data from the Western New York Exposures and Breast Cancer Study. Women aged 35-79 years with incident breast cancer (n = 1170) and age- and race-matched controls (n = 2115) were enrolled. Genotyping was performed using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs). No significant associations were observed in premenopausal women. Among postmenopausal women, rs25487 and rs25486 (OR = 1.24; 95% CI 1.01-1.51 and OR = 1.23; 95% CI 1.01-1.49, respectively, for combined heterozygous and homozygous variant compared with reference) were associated with increased risk of breast cancer. Postmenopausal women carrying the variant allele of the synonymous XPC polymorphism (rs2227998) were also at borderline significantly increased risk (OR = 1.24; 95% CI 1.01-1.52, heterozygous variant compared with reference; OR = 1.22; 95% CI 1.01-1.48, for combined heterozygous and homozygous variant compared with reference). There was no evidence of genotype-smoking and genotype-alcohol consumption interactions for pre- and postmenopausal women. These results indicate that some of the variants in BER and NER genes may influence risk of postmenopausal breast cancer.

Functionally informative tag SNP selection using a Pareto-optimal approach

Selecting a representative set of single nucleotide polymorphism (SNP) markers for facilitating association studies is an important step to uncover the genetic basis of human disease. Tag SNP selection and functional SNP selection are the two main approaches for addressing the SNP selection problem. However, little was done so far to effectively combine these distinct and possibly competing approaches. Here, we present a new multiobjective optimization framework for identifying SNPs that are both informative tagging and have functional significance (FS). Our selection algorithm is based on the notion of Pareto optimality, which has been extensively used for addressing multiobjective optimization problems in game theory, economics, and engineering. We applied our method to 34 disease-susceptibility genes for lung cancer and compared the performance with that of other systems which support both tag SNP selection and functional SNP selection methods. The comparison shows that our algorithm always finds a subset of SNPs that improves upon the subset selected by other state-of-the-art systems with respect to both selection objectives.

Genética, performance física humana e doping genético: o senso comum versus a realidade científica

Atletas de elite são reconhecidos como fenômenos esportivos e o potencial para atingir níveis superiores de performance no esporte está parcialmente sob o controle de genes. A excelência atlética é essencialmente multifatorial e determinada por complexas interações entre fatores ambientais e genéticos. Existem aproximadamente 10 milhões de variantes genéticas dispersas por todo o genoma humano e uma parcela destas variantes têm demonstrado influenciar a responsividade ao treinamento físico. Os fenótipos de performance física humana parecem ser altamente poligênicos e alguns estudos têm comprovado a existência de raras combinações genotípicas em atletas. No entanto, os mecanismos pelos quais genes se interagem para amplificar a performance física são desconhecidos. O conhecimento sobre os genes que influenciam a treinabilidade somado ao potencial uso indevido dos avanços da terapia gênica, como a possível introdução de genes em células de atletas, fez surgir o termo doping genético, um novo e censurado método de amplificação da performance física, além dos limites fisiológicos. Aumentos na hipertrofia muscular esquelética e nos níveis de hematócrito estão sendo conseguidos através da manipulação da expressão de genes específicos, mas a grande parte das impressionáveis alterações foi obtida em experimentação com animais de laboratório. A compreensão dos resultados científicos envolvendo genética, performance física humana e doping genético é uma difícil tarefa. Com o propósito de evitar a contínua má interpretação e propagação de conceitos errôneos, esta revisão, intencionalmente, vem discutir as evidências científicas produzidas até o momento sobre o tema, permitindo a compreensão do atual "estado da arte"

Digital gene expression analysis of gastrointestinal helminth resistance in Scottish blackface lambs

Digital gene expression (DGE) analysis offers a route to gene discovery which by-passes the need to develop bespoke arrays for nonmodel species, and is therefore a potentially valuable tool for molecular ecologists. Scottish blackface sheep, which vary in resistance to the common abomasal parasitic nematode Teladorsagia circumcincta, were trickle-infected with L3 larvae over 3 months to mimic the natural progression of infection. DGE was performed on abomasal lymph node tissue after the resolution of infection in resistant animals. Susceptible (low resistance) animals showed a large number of differentially expressed genes associated with inflammation and cell activation, but generally few differentially regulated genes in either the susceptible or the resistant group were directly involved in the adaptive immune function. Our results are consistent with the hypothesis that both resistance and susceptibility are active responses to infection and that susceptibility is associated with dysfunction in T cell differentiation and regulation.

Use of pathway information in molecular epidemiology

Candidate gene studies are generally motivated by some form of pathway reasoning in the selection of genes to be studied, but seldom has the logic of the approach been carried through to the analysis. Marginal effects of polymorphisms in the selected genes, and occasionally pairwise gene-gene or gene-environment interactions, are often presented, but a unified approach to modelling the entire pathway has been lacking. In this review, a variety of approaches to this problem is considered, focusing on hypothesis-driven rather than purely exploratory methods. Empirical modelling strategies are based on hierarchical models that allow prior knowledge about the structure of the pathway and the various reactions to be included as 'prior covariates'. By contrast, mechanistic models aim to describe the reactions through a system of differential equations with rate parameters that can vary between individuals, based on their genotypes. Some ways of combining the two approaches are suggested and Bayesian model averaging methods for dealing with uncertainty about the true model form in either framework is discussed. Biomarker measurements can be incorporated into such analyses, and two-phase sampling designs stratified on some combination of disease, genes and exposures can be an efficient way of obtaining data that would be too expensive or difficult to obtain on a full candidate gene sample. The review concludes with some thoughts about potential uses of pathways in genome-wide association studies.

Genomic similarity and kernel methods II: methods for genomic information

Measures of genomic similarity are often the basis of flexible statistical analyses, and when based on kernel methods, they provide a powerful platform to take advantage of a broad and deep statistical theory, and a wide range of existing software; see the companion paper for a review of this material [1]. The kernel method converts information - perhaps complex or high-dimensional information - for a pair of subjects to a quantitative value representing either similarity or dissimilarity, with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This approach provides enormous opportunities to enhance genetic analyses by including a wide range of publically-available data as structured kernel 'prior' information. Kernel methods are appealing for their generality, yet this generality can make it challenging to formulate measures of similarity that directly address a specific scientific aim, or that are most powerful to detect a specific genetic mechanism. Although it is difficult to create a cook book of kernels for genetic studies, useful guidelines can be gleaned from a variety of novel published approaches. We review some novel developments of kernels for specific analyses and speculate on how to build kernels for complex genomic attributes based on publically available data. The creativity of analysts, with rigorous evaluations by applications to real and simulated data, will ultimately provide a much stronger array of kernel 'tools' for genetic analyses.

Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies

Despite the considerable enthusiasm about the yield of novel and replicated discoveries of genetic associations from the new generation of genome-wide association studies (GWAS), the proportion of the heritability of most complex diseases that have been studied to date remains small. Some of this "dark matter" could be due to gene-environment (G x E) interactions or more complex pathways involving multiple genes and exposures. We review the basic epidemiologic study design and statistical analysis approaches to studying G x E interactions individually and then consider more comprehensive approaches to studying entire pathways or GWAS data. In addition to the usual issues in genetic association studies, particular care is needed in exposure assessment, and very large sample sizes are required. Although hypothesis-driven, pathway-based and agnostic GWA study approaches are generally viewed as opposite poles, we suggest that the two can be usefully married using hierarchical modeling strategies that exploit external pathway knowledge in mining genome-wide data.

Functional polymorphisms and methotrexate treatment outcome in recent-onset rheumatoid arthritis

AIMS

Clinical response to methotrexate (MTX) treatment differs among rheumatoid arthritis patients. Genetic variation can partly account for this phenomenon. In this study, functional polymorphisms in genes related to the mechanism of action of MTX or immunopathogenesis of rheumatoid arthritis were studied for association with treatment outcome in a Dutch cohort of patients with early rheumatoid arthritis. Furthermore, tests for replication of previous research on these genetic variants were performed according to reported end points.

MATERIALS & METHODS

Seven polymorphisms in seven genes were analyzed in 205 genotyped patients with active rheumatoid arthritis. All patients received standardized MTX treatment (< or =25 mg per week orally) combined with folic acid. MTX treatment outcome was evaluated by disease activity score criteria and adverse drug events. The following genetic variants were analyzed and correlated: ABCB1 3435C>T, ITPA IVS2 +21A>C, HLA-G (-14 bp >+14 bp), TGFB1 +869T>C and TLR4 +896A>G. In case of significant differences, regression analyses were applied. Since carriers of the minor alleles of the SNPs DHFR 829C>T and IMPDH2 +787C>T were not observed, no statistical analyses could be performed.

RESULTS

No significant associations or replications of these genetic variants with MTX efficacy were demonstrated. Regarding toxicity, patients carrying the ABCB1 3435T-allele and TLR4 +896G-allele were 2.5-times more likely to develop adverse drug events at 6 months (odds ratio: 2.6; 95% CI: 1.1-6.2, and odds ratio: 2.5; 95% CI: 1.1-6.1, respectively). Additionally, this chance increased almost fourfold in patients with the two unfavorable genotypes (odds ratio: 3.9; 95% CI: 1.5-10.3). However, none of these associations remained significant after correction for multiple testing (p < 0.004).

CONCLUSION

Our data indicate that MTX toxicity was potentially associated with ABCB1 3435C>T and TLR4 +896A>G. However, after correction, none of these associations remained significant. Furthermore, no significant associations or replications of these functional variants with efficacy were found.

Gene-environment interaction and children's health and development

PURPOSE OF REVIEW

A systematic approach to studying gene-environment interaction can have immediate impact on our understanding of how environmental factors induce developmental disease and toxicity and will provide biological insight for potential treatment and prevention measures.

RECENT FINDINGS

Because DNA sequence is static, genetic studies typically are not conducted prospectively. This limits the ability to incorporate environmental data into an analysis, as such data is usually collected cross-sectionally. Prospective environmental data collection could account for the role of critical windows of susceptibility that likely correspond to the expression of specific genes and gene pathways. The use of large-scale genomic platforms to discover genetic variants that modify environmental exposure in conjunction with a-priori planned replication studies would reduce the number of false positive results.

SUMMARY

Using a genome-wide approach, combined with prospective longitudinal measures of environmental exposure at critical developmental windows, is the optimal design for gene-environment interaction research. This approach would discover susceptibility variants, and then validate the findings in an independent sample of children. Designs that combine the strengths and methodologies of each field will yield data that can account for both genetic variability and the role of critical developmental windows in the etiology of childhood disease and development.

Gene--environment-wide association studies: emerging approaches

Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene--environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This Review provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed for studying entire pathways and available techniques for mining interactions in GWA data. I also explore methods for marrying hypothesis-driven pathway-based approaches with 'agnostic' GWA studies.