TLR9 polymorphisms in African populations: no association with severe malaria, but evidence of cis-variants acting on gene expression

BACKGROUND: During malaria infection the Toll-like receptor 9 (TLR9) is activated through induction with plasmodium DNA or another malaria motif not yet identified. Although TLR9 activation by malaria parasites is well reported, the implication to the susceptibility to severe malaria is not clear. The aim of this study was to assess the contribution of genetic variation at TLR9 to severe malaria. METHODS: This study explores the contribution of TLR9 genetic variants to severe malaria using two approaches. First, an association study of four common single nucleotide polymorphisms was performed on both family- and population-based studies from Malawian and Gambian populations (n>6000 individual). Subsequently, it was assessed whether TLR9 expression is affected by cis-acting variants and if these variants could be mapped. For this work, an allele specific expression (ASE) assay on a panel of HapMap cell lines was carried out. RESULTS: No convincing association was found with polymorphisms in TLR9 for malaria severity, in either Gambian or Malawian populations, using both case-control and family based study designs. Using an allele specific expression assay it was observed that TLR9 expression is affected by cis-acting variants, these results were replicated in a second experiment using biological replicates. CONCLUSION: By using the largest cohorts analysed to date, as well as a standardized phenotype definition and study design, no association of TLR9 genetic variants with severe malaria was found. This analysis considered all common variants in the region, but it is remains possible that there are rare variants with association signals. This report also shows that TLR9 expression is potentially modulated through cis-regulatory variants, which may lead to differential inflammatory responses to infection between individuals

Is Replication the Gold Standard for Validating Genome-Wide Association Findings?

With the advent of genome-wide association (GWA) studies, researchers are hoping that reliable genetic association of common human complex diseases/traits can be detected. Currently, there is an increasing enthusiasm about GWA and a number of GWA studies have been published. In the field a common practice is that replication should be used as the gold standard to validate an association finding. In this article, based on empirical and theoretical data, we emphasize that replication of GWA findings can be quite difficult, and should not always be expected, even when true variants are identified. The probability of replication becomes smaller with the increasing number of independent GWA studies if the power of individual replication studies is less than 100% (which is usually the case), and even a finding that is replicated may not necessarily be true. We argue that the field may have unreasonably high expectations on success of replication. We also wish to raise the question whether it is sufficient or necessary to treat replication as the ultimate and gold standard for defining true variants. We finally discuss the usefulness of integrating evidence from multiple levels/sources such as genetic epidemiological studies (at the DNA level), gene expression studies (at the RNA level), proteomics (at the protein level), and follow-up molecular and cellular studies for eventual validation and illumination of the functional relevance of the genes uncovered

Gene Expression Studies in Major Depression

The dramatic technical advances in methods to measure gene expression on a genome-wide level thus far have not been paralleled by breakthrough discoveries in psychiatric disorders—including major depression (MD)—using these hypothesis-free approaches. In this review, we first describe the methodologic advances made in gene expression analysis, from quantitative polymerase chain reaction to next-generation sequencing. We then discuss issues in gene expression experiments specific to MD, ranging from the choice of target tissues to the characterization of the case group. We provide a synopsis of the gene expression studies published thus far for MD, with a focus on studies using mRNA microarray methods. Finally, we discuss possible new strategies for the gene expression studies in MD that circumvent some of the addressed issues

Design considerations in a sib-pair study of linkage for susceptibility loci in cancer

<p>Abstract</p> <p>Background</p> <p>Modern approaches to identifying new genes associated with disease allow very fine analysis of associaton and can be performed in population based case-control studies. However, the sibpair design is still valuable because it requires few assumptions other than acceptably high penetrance to identify genetic loci.</p> <p>Methods</p> <p>We conducted simulation studies to assess the impact of design factors on relative efficiency for a linkage study of colorectal cancer. We considered two test statistics, one comparing the mean IBD probability in affected pairs to its null value of 0.5, and one comparing the mean IBD probabilities between affected and discordant pairs. We varied numbers of parents available, numbers of affected and unaffected siblings, reconstructing the genotype of an unavailable affected sibling by a spouse and offspring, and elimination of sibships where the proband carries a mutation at another locus.</p> <p>Results</p> <p>Power and efficiency were most affected by the number of affected sibs, the number of sib pairs genotyped, and the risk attributable to linked and unlinked loci. Genotyping unaffected siblings added little power for low penetrance models, but improved validity of tests when there was genetic heterogeneity and for multipoint testing. The efficiency of the concordant-only test was nearly always better than the concordant-discordant test. Replacement of an unavailable affected sibling by a spouse and offspring recovered some linkage information, particularly if several offspring were available. In multipoint analysis, the concordant-only test was showed a small anticonservative bias at 5 cM, while the multipoint concordant-discordant test was generally the most powerful test, and was not biased away from the null at 5 cM.</p> <p>Conclusion</p> <p>Genotyping parents and unaffected siblings is useful for detecting genotyping errors and if allele frequencies are uncertain. If adequate allele frequency data are available, we suggest a single-point affecteds-only analysis for an initial scan, followed by a multipoint analysis of affected and unaffected members of all available sibships with additional markers around initial hits.</p

Global Analysis of the Impact of Environmental Perturbation on cis-Regulation of Gene Expression

Genetic variants altering cis-regulation of normal gene expression (cis-eQTLs) have been extensively mapped in human cells and tissues, but the extent by which controlled, environmental perturbation influences cis-eQTLs is unclear. We carried out large-scale induction experiments using primary human bone cells derived from unrelated donors of Swedish origin treated with 18 different stimuli (7 treatments and 2 controls, each assessed at 2 time points). The treatments with the largest impact on the transcriptome, verified on two independent expression arrays, included BMP-2 (t = 2h), dexamethasone (DEX) (t = 24h), and PGE2 (t = 24h). Using these treatments and control, we performed expression profiling for 18,144 RefSeq transcripts on biological replicates of the complete study cohort of 113 individuals (ntotal = 782) and combined it with genome-wide SNP-genotyping data in order to map treatment-specific cis-eQTLs (defined as SNPs located within the gene ±250 kb). We found that 93% of cis-eQTLs at 1% FDR were observed in at least one additional treatment, and in fact, on average, only 1.4% of the cis-eQTLs were considered as treatment-specific at high confidence. The relative invariability of cis-regulation following perturbation was reiterated independently by genome-wide allelic expression tests where only a small proportion of variance could be attributed to treatment. Treatment-specific cis-regulatory effects were, however, 2- to 6-fold more abundant among differently expressed genes upon treatment. We further followed-up and validated the DEX–specific cis-regulation of the MYO6 and TNC loci and found top cis-regulatory variants located 180 kb and 250 kb upstream of the transcription start sites, respectively. Our results suggest that, as opposed to tissue-specificity of cis-eQTLs, the interactions between cellular environment and cis-variants are relatively rare (∼1.5%), but that detection of such specific interactions can be achieved by a combination of functional genomic approaches as described here

Common Versus Unique Variance Across Measures of Worry and Rumination: Predictive Utility and Mediational Models for Anxiety and Depression

Repetitive negative thinking (RNT) has been identified as a transdiagnostic construct. However, diagnosis- specific questionnaires have traditionally been used to measure RNT across emotional disorders, and thus the degree to which they assess shared versus unique aspects of RNT is unclear. Furthermore, the degree to which shared versus unique variance across these measures contributes to the prediction of anxiety and depression symptoms is yet to be fully understood. This study had three aims. First, confirmatory factor analysis was used to test the degree to which two common, diagnosis-specific questionnaires assess common versus unique variance in RNT. One questionnaire measured worry whereas the other measured two aspects of rumination (brooding, reflection). Second, the contribution of the shared and unique variance in predicting symptoms of anxiety and depression was determined. Third, the role of shared and unique variance in mediating the relationships between the vulnerability factor of negative affectivity and symptoms of anxiety and depression was assessed

RNA-Seq Identifies SNP Markers for Growth Traits in Rainbow Trout

Fast growth is an important and highly desired trait, which affects the profitability of food animal production, with feed costs accounting for the largest proportion of production costs. Traditional phenotype-based selection is typically used to select for growth traits; however, genetic improvement is slow over generations. Single nucleotide polymorphisms (SNPs) explain 90% of the genetic differences between individuals; therefore, they are most suitable for genetic evaluation and strategies that employ molecular genetics for selective breeding. SNPs found within or near a coding sequence are of particular interest because they are more likely to alter the biological function of a protein. We aimed to use SNPs to identify markers and genes associated with genetic variation in growth. RNA-Seq whole-transcriptome analysis of pooled cDNA samples from a population of rainbow trout selected for improved growth versus unselected genetic cohorts (10 fish from 1 full-sib family each) identified SNP markers associated with growth-rate. The allelic imbalances (the ratio between the allele frequencies of the fast growing sample and that of the slow growing sample) were considered at scores >5.0 as an amplification and <0.2 as loss of heterozygosity. A subset of SNPs (n = 54) were validated and evaluated for association with growth traits in 778 individuals of a three-generation parent/offspring panel representing 40 families. Twenty-two SNP markers and one mitochondrial haplotype were significantly associated with growth traits. Polymorphism of 48 of the markers was confirmed in other commercially important aquaculture stocks. Many markers were clustered into genes of metabolic energy production pathways and are suitable candidates for genetic selection. The study demonstrates that RNA-Seq at low sequence coverage of divergent populations is a fast and effective means of identifying SNPs, with allelic imbalances between phenotypes. This technique is suitable for marker development in non-model species lacking complete and well-annotated genome reference sequences

Benefits and Harms of Sodium-Glucose Co-Transporter 2 Inhibitors in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis

Sodium-glucose co-transporter 2 inhibitors (SGLT2-i) are a novel drug class for the treatment of diabetes. We aimed at describing the maximal benefits and risks associated with SGLT2-i for patients with type 2 diabetes.Systematic review and meta-analysis.We included double-blinded, randomised controlled trials (RCTs) evaluating SGLT2-i administered in the highest approved therapeutic doses (canagliflozin 300 mg/day, dapagliflozin 10 mg/day, and empagliflozin 25 mg/day) for ≥12 weeks. Comparison groups could receive placebo or oral antidiabetic drugs (OAD) including metformin, sulphonylureas (SU), or dipeptidyl peptidase 4 inhibitors (DPP-4-i). Trials were identified through electronic databases and extensive manual searches. Primary outcomes were glycated haemoglobin A1c (HbA1c) levels, serious adverse events, death, severe hypoglycaemia, ketoacidosis and CVD. Secondary outcomes were fasting plasma glucose, body weight, blood pressure, heart rate, lipids, liver function tests, creatinine and adverse events including infections. The quality of the evidence was assessed using GRADE.Meta-analysis of 34 RCTs with 9,154 patients showed that SGLT2-i reduced HbA1c compared with placebo (mean difference -0.69%, 95% confidence interval -0.75 to -0.62%). We downgraded the evidence to 'low quality' due to variability and evidence of publication bias (P = 0.015). Canagliflozin was associated with the largest reduction in HbA1c (-0.85%, -0.99% to -0.71%). There were no differences between SGLT2-i and placebo for serious adverse events. SGLT2-i increased the risk of urinary and genital tract infections and increased serum creatinine, and exerted beneficial effects on bodyweight, blood pressure, lipids and alanine aminotransferase (moderate to low quality evidence). Analysis of 12 RCTs found a beneficial effect of SGLT2-i on HbA1c compared with OAD (-0.20%, -0.28 to -0.13%; moderate quality evidence).This review includes a large number of patients with type 2 diabetes and found that SGLT2-i reduces HbA1c with a notable increased risk in non-serious adverse events. The analyses may overestimate the intervention benefit due bias

Genetic Analysis of Genome-Scale Recombination Rate Evolution in House Mice

The rate of meiotic recombination varies markedly between species and among individuals. Classical genetic experiments demonstrated a heritable component to population variation in recombination rate, and specific sequence variants that contribute to recombination rate differences between individuals have recently been identified. Despite these advances, the genetic basis of species divergence in recombination rate remains unexplored. Using a cytological assay that allows direct in situ imaging of recombination events in spermatocytes, we report a large (∼30%) difference in global recombination rate between males of two closely related house mouse subspecies (Mus musculus musculus and M. m. castaneus). To characterize the genetic basis of this recombination rate divergence, we generated an F2 panel of inter-subspecific hybrid males (n = 276) from an intercross between wild-derived inbred strains CAST/EiJ (M. m. castaneus) and PWD/PhJ (M. m. musculus). We uncover considerable heritable variation for recombination rate among males from this mapping population. Much of the F2 variance for recombination rate and a substantial portion of the difference in recombination rate between the parental strains is explained by eight moderate- to large-effect quantitative trait loci, including two transgressive loci on the X chromosome. In contrast to the rapid evolution observed in males, female CAST/EiJ and PWD/PhJ animals show minimal divergence in recombination rate (∼5%). The existence of loci on the X chromosome suggests a genetic mechanism to explain this male-biased evolution. Our results provide an initial map of the genetic changes underlying subspecies differences in genome-scale recombination rate and underscore the power of the house mouse system for understanding the evolution of this trait

Genetic Determinants of Lipid Traits in Diverse Populations from the Population Architecture using Genomics and Epidemiology (PAGE) Study

For the past five years, genome-wide association studies (GWAS) have identified hundreds of common variants associated with human diseases and traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. Approximately 95 loci associated with lipid levels have been identified primarily among populations of European ancestry. The Population Architecture using Genomics and Epidemiology (PAGE) study was established in 2008 to characterize GWAS–identified variants in diverse population-based studies. We genotyped 49 GWAS–identified SNPs associated with one or more lipid traits in at least two PAGE studies and across six racial/ethnic groups. We performed a meta-analysis testing for SNP associations with fasting HDL-C, LDL-C, and ln(TG) levels in self-identified European American (∼20,000), African American (∼9,000), American Indian (∼6,000), Mexican American/Hispanic (∼2,500), Japanese/East Asian (∼690), and Pacific Islander/Native Hawaiian (∼175) adults, regardless of lipid-lowering medication use. We replicated 55 of 60 (92%) SNP associations tested in European Americans at p<0.05. Despite sufficient power, we were unable to replicate ABCA1 rs4149268 and rs1883025, CETP rs1864163, and TTC39B rs471364 previously associated with HDL-C and MAFB rs6102059 previously associated with LDL-C. Based on significance (p<0.05) and consistent direction of effect, a majority of replicated genotype-phentoype associations for HDL-C, LDL-C, and ln(TG) in European Americans generalized to African Americans (48%, 61%, and 57%), American Indians (45%, 64%, and 77%), and Mexican Americans/Hispanics (57%, 56%, and 86%). Overall, 16 associations generalized across all three populations. For the associations that did not generalize, differences in effect sizes, allele frequencies, and linkage disequilibrium offer clues to the next generation of association studies for these traits