Andrei S. Rodin

Genomic Association Analysis Suggests Chromosome 12 Locus Influencing Antihypertensive Response to Thiazide Diuretic

We conducted a genome-wide association study to identify novel genes influencing diastolic blood pressure (BP) response to hydrochlorothiazide, a commonly prescribed thiazide diuretic preferred for the treatment of high BP. Affymetrix GeneChip Human Mapping 100K Arrays were used to measure single nucleotide polymorphisms across the 22 autosomes in 194 non-Hispanic black subjects and 195 non-Hispanic white subjects with essential hypertension selected from opposite tertiles of the race- and sex-specific distributions of age-adjusted diastolic BP response to hydrochlorothiazide (25 mg daily, PO, for 4 weeks). The black sample consisted of 97 “good” responders (diastolic BP response [mean±SD]=−18.3±4.2 mm Hg; age=47.1±6.1 years; 51.5% women) and 97 “poor” responders (diastolic BP response=−0.18±4.3; age=47.4±6.5 years; 51.5% women). Haplotype trend regression identified a region of chromosome 12q15 in which haplotypes constructed from 3 successive single nucleotide polymorphisms (rs317689, rs315135, and rs7297610) in proximity to lysozyme (LYZ), YEATS domain containing 4 (YEATS4), and fibroblast growth receptor substrate 2 (FRS2) were significantly associated with diastolic BP response (nominal P=2.39×10−7; Bonferroni corrected P=0.024; simulated experiment-wise P=0.040). Genotyping of 35 additional single nucleotide polymorphisms selected to “tag” linkage disequilibrium blocks in these genes provided corroboration that variation in LYZ and YEATS4 was associated with diastolic BP response in a statistically independent data set of 291 black subjects and in the sample of 294 white subjects. These results support the use of genome-wide association analyses to identify novel genes influencing antihypertensive drug responses.

Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels)

MOTIVATION The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this paper, we introduce the method of Belief (Bayesian) networks to the domain of genotype-to-phenotype analyses and provide an example application. RESULTS A Belief network is a graphical model of a probabilistic nature that represents a joint multivariate probability distribution and reflects conditional independences between variables. Given the data, optimal network topology can be estimated with the assistance of heuristic search algorithms and scoring criteria. Statistical significance of edge strengths can be evaluated using Bayesian methods and bootstrapping. As an example application, the method of Belief networks was applied to 20 SNPs in the apolipoprotein (apo) E gene and plasma apoE levels in a sample of 702 individuals from Jackson, MS. Plasma apoE level was the primary target variable. These analyses indicate that the edge between SNP 4075, coding for the well-known epsilon2 allele, and plasma apoE level was strong. Belief networks can effectively describe complex uncertain processes and can both learn from data and incorporate prior knowledge. AVAILABILITY Various alternative and supplemental networks (not given in the text) as well as source code extensions, are available from the authors. SUPPLEMENTARY INFORMATION http://bioinformatics.oxfordjournals.org.

Pharmacogenetic interactions between ABCB1 and SLCO1B1 tagging SNPs and the effectiveness of statins in the prevention of myocardial infarction

AIMS Genetic variability within the SLCO1B1 and ABCB1 transporter genes has been associated with modification of statin effectiveness in cholesterol management. MATERIALS & METHODS We conducted a case-control study using a population-based registry of pharmacy records linked to the hospital discharge records. Within a hypercholesterolemic cohort, we included 668 myocardial infarction cases and 1217 controls. RESULTS We tested 24 tagging SNPs and found two SNPs within ABCB1 (rs3789244, p = 0.01; rs1922242, p = 0.01) to interact with statin treatment. In addition, we found a nonsignificant haplotype-treatment interaction (p = 0.054). The odds ratio for subjects homozygous for SLCO1B1*1A was 0.49 (95% CI: 0.34-0.71) compared with 0.31 (95% CI: 0.24-0.41) for heterozygous or noncarriers of the *1A allele. CONCLUSION This is the first study to demonstrate that common genetic variability within the SLCO1B1 and ABCB1 genes is associated with the modification of the effectiveness of statins in the prevention of the clinical outcome, myocardial infarction.

Methodological and statistical issues in pharmacogenomics

Pharmacogenomics strives to explain the interindividual variability in response to drugs due to genetic variation. Although technological advances have provided us with relatively easy and cheap methods for genotyping, promises about personalised medicine have not yet met our high expectations. Successful results that have been achieved within the field of pharmacogenomics so far are, to name a few, HLA‐B*5701 screening to avoid hypersensitivity to the antiretroviral abacavir, thiopurine S‐methyltransferase (TPMT) genotyping to avoid thiopurine toxicity, and CYP2C9 and VKORC1 genotyping for better dosing of the anticoagulant warfarin. However, few pharmacogenetic examples have made it into clinical practice in the treatment of complex diseases. Unfortunately, lack of reproducibility of results from observational studies involving many genes and diseases seems to be a common pattern in pharmacogenomic studies.

Systems biology in pharmacogenomic research: the way to personalized prescribing?

Response to pharmacotherapy can be highly variable amongst individuals. Pharmacogenomics may explain the interindividual variability in drug response due to genetic variation. However, besides genetics, many other factors can play a role in the response to pharmacotherapy, including disease severity, co-morbidity, environmental factors, therapy adherence and co-medication use. Better understanding of these factors and inter-relationships should bring about a much more effective approach to disease management. Systems biology that studies organisms as integrated and interacting networks of genes, proteins and biochemical reactions can contribute to this. Organisms are no longer studied part by part, but in a more integral manner. Integration of the genetic data with intermediate and end point phenotypic characterization may prove essential to define the inherent nature of drug effects. Therefore, in the future, a multidisciplinary systems-based approach will be necessary to deal with the bulk of the biological data that is available and, ultimately, to reach the goal of personalized prescribing.

On concerted origin of transfer RNAs with complementary anticodons

Pairs of antiparallelly oriented consensus tRNAs with complementary anticodons show surprisingly small numbers of mispairings within the 17-bp-long anticodon stem and loop region. Even smaller such complementary distances are shown by illegitimately complementary anticodons, i.e. those with allowed pairing between G and U bases. Accordingly, we suppose that transfer RNAs have emerged concertedly as complementary strands of primordial double helix-like RNA molecules. Replication of such molecules with illegitimately complementary anticodons might generate new synonymous codons for the same pair of amino acids. Logically, the idea of tRNA concerted origin dictates very ancient establishment of direct links between anticodons and the type of amino acids with which pre-tRNAs were to be charged. More specifically, anticodons (first of all, the 2nd base) could selectively target ‘their’ amino acids, reaction of acylating itself being performed by another non-specific site of pre-tRNA or even by another ribozyme. In all, the above findings and speculations are consistent to the hypercyclic concept (Eigen and Schuster, 1979), and throw new light on the genetic code origin and associated problems. Also favoring this idea are data on complementary codon usage patterns in different genomes.

Enhanced evolution by stochastically variable modification of epigenetic marks in the early embryo

Significance In this paper we investigate by quantitative modeling the effect on evolution of epigenetic variation during a window of opportunity in the early embryo. It is generally accepted that generation of new functions is primarily driven by gene duplication. However, pseudogenization (degradation of a new gene copy) is statistically much more likely than gaining a new function, and thus this remains a serious conceptual problem. We find that epigenetic variation, even in a constant environment, can essentially eliminate the pseudogenization problem and dramatically improve the efficacy of evolution by gene duplication. Evolution by gene duplication is generally accepted as one of the crucial driving forces for the gain of new complexity and functions, but the formation of pseudogenes remains a problem for this mechanism. Here we expand on earlier ideas that epigenetic modifications can drive neo- and subfunctionalization in evolution by gene duplication. We explore the effects of stochastic epigenetic modifications on the evolution (and thus development) of complex organisms in a constant environment. Modeling is done both using a modified genetic drift analytical treatment and computer simulations, which were found to agree. A transposon silencing model is also explored. Some key assumptions made include (i) stochastic, incomplete removal (or addition) of repressive epigenetic marks takes place during a window(s) of opportunity in the zygote and early embryo; (ii) there is no statistical variation of the marks after the window closes; and (iii) the genes affected are sensitive to dosage. Our genetic drift treatment takes into account that after gene duplication the prevailing case upon which selection operates is a duplicate/singlet heterozygote; to the best of our knowledge, this has not been considered in previous treatments. We conclude from our modeling that stochastic epigenetic modifications, with rates consistent with experimental observation, can both increase the rate of gene fixation and decrease pseudogenization, thus dramatically improving the efficacy of evolution by gene duplication. We also find that a transposon silencing model is advantageous for fixation of recessive genes in diploid organisms, especially with large effective population sizes.

Variants of ADAMTS1 modify the effectiveness of statins in reducing the risk of myocardial infarction.

Objective To investigate the influence of tagging single-nucleotide polymorphisms (SNPs) within candidate genes involved in the putative anti-inflammatory effects of statins on the effectiveness of statins in reducing the risk of myocardial infarction (MI). Methods We conducted a case–control study in a population-based registry of pharmacy records linked to hospital discharge records (PHARMO). Cases and controls were selected from within a hypercholesterolemic cohort. Cases were hospitalized for MI, whereas controls were not. Logistic regression analysis was used to investigate pharmacogenetic interactions. Results The study population comprised 668 cases and 1217 controls. We genotyped 84 SNPs in 24 genes. The effectiveness of statins was found to be modified by seven SNPs in three genes. Five out of six SNPs that were selected in the A disintegrin and metallopeptidase with thrombospondin motif type I (ADAMTS1) gene were associated with a modified response to statins, three of which were in strong linkage disequilibrium. The strongest interaction was found for ADAMTS1 rs402007. Homozygous carriers of the variant allele had the most benefit from statins [adjusted odds ratio (OR): 0.10, 95% confidence interval (CI): 0.03–0.35], compared with heterozygous (OR: 0.43, 95% CI: 0.24–0.51) and homozygous wildtype carriers (OR: 0.49, 95% CI: 0.32–0.57). Conclusion Consistent with earlier findings, polymorphisms within the ADAMTS1 gene influenced the effectiveness of statins in reducing the risk of MI. Other pharmacogenetic interactions with SNPs in the TNFRSF1A and ITGB2 genes were established and the confirmation will be pursued in future studies.

Exploring Genetic Epidemiology Data with Bayesian Networks

Abstract Recent advances in DNA sequencing and genotyping technologies led to the initiation of large-scale genetic association studies aimed at unraveling complex genotype–(environment)–phenotype relationships underlying common human diseases. Unfortunately, traditional statistical tools are ill-suited for analyzing high dimensional datasets with many small effects. In addition, a primary emphasis of traditional statistical methods is formal hypothesis testing rather than hypothesis generation. When faced with hundreds of thousands (and soon—millions) of potentially predictive variables (e.g., SNPs), we need methods for automated knowledge discovery. Providentially, such methods have long been in development and are well-established in the data mining research community. One of such methods is Bayesian or Belief Network (BN) modeling, which has its roots in both computer science and statistics. A BN is a graphical model that represents a joint multivariate probability distribution and reflects the conditional independences among variables. Given data, the optimal network topology can be estimated with the assistance of local search (optimization) algorithms and model scoring criteria. Statistical significance of edge strengths can be evaluated using model scoring criteria tests, cross-validation and bootstrapping. BNs are an excellent tool for reverse-engineering biological (e.g., physiologic and genetic) networks from “flat” datasets (e.g., generated by the large-scale, or candidate gene, association studies, or microarray gene expression experiments). In this chapter we review various applications of BN methodology to modern human genomics. An example application is detailed. We also discuss various technical aspects of BN reconstruction, with a special emphasis on scalability in the context of modern “omic” data.

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

Abstract Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology—type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types—single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite levels, epidemiological variables, endpoints, and phenotypes, etc.