Comparison of amplification methods for transcriptomic analyses of low abundance prokaryotic RNA sources

Microarrays have established as instrumental for bacterial detection, identification, and genotyping as well as for transcriptomic studies. For gene expression analyses using limited numbers of bacteria (derived from in vivo or ex vivo origin, for example), RNA amplification is often required prior to labeling and hybridization onto microarrays. Evaluation of the fidelity of the amplification methods is crucial for the robustness and reproducibility of microarray results. We report here the first utilization of random primers and the highly processive Phi29 phage polymerase to amplify material for transcription profiling analyses. We compared two commercial amplification methods (GenomiPhi and MessageAmp kits) with direct reverse-transcription as the reference method, focusing on the robustness of mRNA quantification using either microarrays or quantitative RT-PCR. Both amplification methods using either poly-A tailing followed by in vitro transcription, or direct strand displacement polymerase, showed appreciable linearity. Strand displacement technique was particularly affordable compared to in vitro transcription-based (IVT) amplification methods and consisted in a single tube reaction leading to high amplification yields. Real-time measurements using low-, medium-, and highly expressed genes revealed that this simple method provided linear amplification with equivalent results in terms of relative messenger abundance as those obtained by conventional direct reverse-transcription.

Gilbert syndrome and the development of antiretroviral therapy-associated hyperbilirubinemia

BACKGROUND: Unconjugated hyperbilirubinemia results from Gilbert syndrome and from antiretroviral therapy (ART) containing protease inhibitors. An understanding of the interaction between genetic predisposition and ART may help to identify individuals at highest risk for developing jaundice. METHODS: We quantified the contribution of UGT1A1*28 and ART to hyperbilirubinemia by longitudinally modeling 1386 total bilirubin levels in 96 human immunodeficiency virus (HIV)-infected individuals during a median of 6 years. RESULTS: The estimated average bilirubin level was 8.8 micromol/L (0.51 mg/dL). Atazanavir increased bilirubin levels by 15 mu mol/L (0.87 mg/dL), and indinavir increased bilirubin levels by 8 micromol/L (0.46 mg/dL). Ritonavir, lopinavir, saquinavir, and nelfinavir had no or minimal effect on bilirubin levels. Homozygous UGT1A1*28 increased bilirubin levels by 5.2 micromol/L (0.3 mg/dL). As a consequence, 67% of individuals homozygous for UGT1A1*28 and receiving atazanavir or indinavir had > or =2 episodes of hyperbilirubinemia in the jaundice range (>43 micromol/L [>2.5 mg/dL]), versus 7% of those with the common allele and not receiving either of those protease inhibitors (P<.001). Efavirenz resulted in decreased bilirubin levels, which is consistent with the induction of UDP-glucuronosyltransferase 1A1. CONCLUSIONS: Genotyping for UGT1A1*28 before initiation of ART would identify HIV-infected individuals at risk for hyperbilirubinemia and decrease episodes of jaundice.

Influence of CYP2B6 polymorphism on plasma and intracellular concentrations and toxicity of efavirenz and nevirapine in HIV-infected patients

BACKGROUND: Efavirenz (EFV) and nevirapine (NVP) are metabolized by cytochrome P450 2B6 (CYP2B6). Allele 516 G>T (Gln172His) is associated with diminished activity of this isoenzyme, and may lead to differences in drug exposure. METHODS: We evaluated this allele as a pharmacogenetic marker of EFV and NVP pharmacokinetics and EFV toxicity in 167 participants receiving EFV and 59 receiving NVP recruited within the genetics project of the Swiss HIV Cohort Study. Drug concentrations were measured in plasma and in peripheral blood mononuclear cells (PBMCs) from the same sample. Neuropsychological toxicity of EFV (sleep disorders, mood disorders, fatigue) was assessed using a standardized questionnaire. RESULTS AND CONCLUSIONS: CYP2B6 516TT was associated with greater plasma and intracellular exposure to EFV, and greater plasma exposure to NVP. Intracellular drug concentration, and CYP2B6 genotype were predictors of EFV neuropsychological toxicity. CYP2B6 genotyping may be useful to complement an individualization strategy based on plasma drug determinations to increase the safety and tolerability of EFV.

rs1004819 is the main disease-associated IL23R variant in German Crohn's disease patients: combined analysis of IL23R, CARD15, and OCTN1/2 variants

BACKGROUND: The IL23R gene has been identified as a susceptibility gene for inflammatory bowel disease (IBD) in the North American population. The aim of our study was to test this association in a large German IBD cohort and to elucidate potential interactions with other IBD genes as well as phenotypic consequences of IL23R variants. METHODS: Genomic DNA from 2670 Caucasian individuals including 833 patients with Crohn's disease (CD), 456 patients with ulcerative colitis (UC), and 1381 healthy unrelated controls was analyzed for 10 IL23R SNPs. Genotyping included the NOD2 variants p.Arg702Trp, p.Gly908Arg, and p.Leu1007fsX1008 and polymorphisms in SLC22A4/OCTN1 (1672 C-->T) and SLC22A5/OCTN2 (-207 G-->C). RESULTS: All IL23R gene variants analyzed displayed highly significant associations with CD. The strongest association was found for the SNP rs1004819 [P = 1.92x10(-11); OR 1.56; 95 % CI (1.37-1.78)]. 93.2% of the rs1004819 TT homozygous carriers as compared to 78% of CC wildtype carriers had ileal involvement [P = 0.004; OR 4.24; CI (1.46-12.34)]. The coding SNP rs11209026 (p.Arg381Gln) was protective for CD [P = 8.04x10(-8); OR 0.43; CI (0.31-0.59)]. Similar, but weaker associations were found in UC. There was no evidence for epistasis between the IL23R gene and the CD susceptibility genes CARD15 and SLC22A4/5. CONCLUSION: IL23R is an IBD susceptibility gene, but has no epistatic interaction with CARD15 and SLC22A4/5. rs1004819 is the major IL23R variant associated with CD in the German population, while the p.Arg381Gln IL23R variant is a protective marker for CD and UC.

Emergence and spread of Chlamydia trachomatis variant, Sweden

A variant of Chlamydia trachomatis that had escaped detection by commonly used systems was discovered in Sweden in 2006. In a nationwide study, we found that it is now prevalent across Sweden, irrespective of the detection system used. Genetic analysis by multilocus sequence typing identified a predominant variant, suggesting recent emergence.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.

Statistical methods for multi-marker testing in genetic association studies

As the development of genotyping and next-generation sequencing technologies, multi-marker testing in genome-wide association study and rare variant association study became active research areas in statistical genetics. This dissertation contains three methodologies for association study by exploring different genetic data features and demonstrates how to use those methods to test genetic association hypothesis. The methods can be categorized into in three scenarios: 1) multi-marker testing for strong Linkage Disequilibrium regions, 2) multi-marker testing for family-based association studies, 3) multi-marker testing for rare variant association study. I also discussed the advantage of using these methods and demonstrated its power by simulation studies and applications to real genetic data.

Alcohol and colorectal cancer: the role of alcohol dehydrogenase 1C polymorphism

BACKGROUND: Chronic alcohol consumption is a risk factor for colorectal cancer. Animal experiments as well as genetic linkage studies in Japanese individuals with inactive acetaldehyde dehydrogenase leading to elevated acetaldehyde concentrations following ethanol ingestion support the hypothesis that acetaldehyde may be responsible for this carcinogenic effect of alcohol. In Caucasians, a polymorphism of alcohol dehydrogenase 1C (ADH1C) exists resulting in different acetaldehyde concentrations following ethanol oxidation. METHODS: To evaluate whether the association between alcohol consumption and colorectal tumor development is modified by ADH1C polymorphism, we recruited 173 individuals with colorectal tumors diagnosed by colonoscopy and 788 control individuals without colorectal tumors. Genotyping was performed using genomic DNA extracted from whole blood followed by polymerase chain reaction. RESULTS: Genotype ADH1C*1/1 was more frequent in patients with alcohol-associated colorectal neoplasia compared to patients without cancers in the multivariate model controlling for age, gender, and alcohol intake (odds ratio = 1.674, 95% confidence interval = 1.110-2.524, 2-sided p from Wald test = 0.0139). In addition, the joint test of the genetic effect and interaction between ADH1C genotype and alcohol intake (2-sided p = 0.0007) indicated that the difference in ADH1C*1 polymorphisms between controls and colorectal neoplasia is strongly influenced by the alcohol consumption and that only individuals drinking more than 30 g ethanol per day with the genotype ADH1C*1/1 had an increased risk for colorectal tumors. CONCLUSIONS: These data identify ADH1C homozygosity as a genetic risk marker for colorectal tumors in individuals consuming more than 30 g alcohol per day and emphasize the role of acetaldehyde as a carcinogenic agent in alcohol-related colorectal carcinogenesis.

The HCP5 single-nucleotide polymorphism: a simple screening tool for prediction of hypersensitivity reaction to abacavir

The HLA-B 5701 allele is predictive of hypersensitivity reaction to abacavir, a response herein termed "ABC-HSR." This study of 1,103 individuals infected with human immunodeficiency virus assessed the usefulness of genotyping a HCP5 single-nucleotide polymorphism (SNP), rs2395029, in relation to ABC-HSR. In populations with European ancestry, rs2395029 is in linkage disequilibrium with HLA-B 5701. The HCP5 SNP was present in all 98 HLA-B 5701-positive individuals and was absent in 999 of 1005 HLA-B 5701-negative individuals. rs2395029 was overrepresented in 25 individuals with clinically likely ABC-HSR, compared with its frequency in 175 ABC-tolerant individuals (80% vs. 2%, respectively; P < .0001). Therefore, HCP5 genotyping could serve as a simple screening tool for ABC-HSR, particularly in settings where sequence-based HLA typing is not available.

STrengthening the REporting of Genetic Association studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

STrengthening the REporting of Genetic Association Studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the strengthening the reporting of observational studies in epidemiology (STROBE) statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence, the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association (STREGA) studies initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

STrengthening the REporting of Genetic Association studies (STREGA): an extension of the STROBE Statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.