Yes / The majority of Mycobacterium tuberculosis isolates resistant to isoniazid harbour a mutation in katG. Since these mutations cause a wide range of minimum inhibitory concentrations (MICs), largely below the serum level reached with higher dosing (15 mg/L upon 15–20 mg/kg), the drug might still remain partly active in presence of a katG mutation. We therefore investigated which genetic mutations predict the level of phenotypic isoniazid resistance in clinical M. tuberculosis isolates. To this end, the association between known and unknown isoniazid resistance-conferring mutations in whole genome sequences, and the isoniazid MICs of 176 isolates was examined. We found mostly moderate-level resistance characterized by a mode of 6.4 mg/L for the very common katG Ser315Thr mutation, and always very high MICs (≥19.2 mg/L) for the combination of katG Ser315Thr and inhA c-15t. Contrary to common belief, isolates harbouring inhA c-15t alone, partly also showed moderate-level resistance, particularly when combined with inhA Ser94Ala. No overt association between low-confidence or unknown mutations, except in katG, and isoniazid resistance (level) was found. Except for the rare katG deletion, line probe assay is thus not sufficiently accurate to predict the level of isoniazid resistance for a single mutation in katG or inhA. / European Research Council (Starting Grant INTERRUPTB 311725 to CM, LR and BdJ), The Damien Foundation

Yes / Appeals have been made for eye care professionals to start prescribing anti-myopia therapies as part of their routine management of myopic children. 1–3 These calls are fuelled by two key considerations. Firstly, that interventions to slow myopia progression have shown success in randomized controlled trials (RCTs) 4–7, and secondly, appreciation that the risk of sight-threatening complications rises dose-dependently with the level of myopia. 8,9 Notwithstanding existing gaps in knowledge regarding the efficacy of current treatments (see below), these considerations argue that myopia control interventions should be widely adopted, and that they should be instigated at an early age – especially in children most at risk – in order to reduce the final level of myopia. Therefore in managing a child with myopia, an eye care professional would have to decide not only which therapy to recommend, but at what age to start treatment. In this review we discuss the future role of genetic prediction in helping clinicians treat myopia. / NIHR Senior Research Fellowship. Grant Number: SRF‐2015‐08‐005

Holistic and growth stage-specific screening is needed for identifying tolerant genotypes and for formulating strategies to mitigate the negative effects of abiotic stresses on crops. The objectives of this study were to characterize the genetic variability of 100 rice lines for early-season vigor, growth and physiological plasticity, and drought and temperature tolerance. Five studies were conducted to accomplish these objectives. In study 1 and 2, 100 rice genotypes consisting of several cultivars and experimental breeding lines were characterized for early-season vigor using several shoot and root morphological, physiological, and yield related traits. In study 3, low- and high-temperature tolerance assessed on select rice cultivars/hybrids during early-season. In study 4, genotypic variability in response to drought stress tolerance using morpo-physiological traits including roots was assessed under pot-culture conditions in a mini-greenhouse conditions. In study 5, the 100 rice genotypes were used to identify and validate SNP markers, and genome-wide association study (GWAS) to generate genotypic and phenotypic data with the objective of identifying new genetic loci controlling drought stress traits. Significant variability was recorded among rice genotypes and treatments for many traits measured. Early-season cumulative vigor response indices (CVRI) developed by summing individual responses indices for each trait varied among the rice genotypes, 21.36 (RU1404196) to 36.17 (N-22). Based on means and standard deviation of the CVRI, rice genotypes were classified as low- (43) and moderately low- (33), high- (16), and very high-vigor (5) groups. Total low-temperature response index values ranged from 18.48 to 23.15 whereas total high-temperature responses index values ranged from 42.01 to 48.82. Antonio, CLXL 745, and Mermentau were identified as sensitive to cold- and heat, and XL 753 was highly cold and heat tolerant genotypes tested. A cumulative drought stress response index (CDSRI) values varied between 14.7 (CHENIERE) and 27.9 (RU1402174) among the genotypes tested. This preliminary analysis of GWA indicated that substantial phenotypic and genotypic diversity exists in the 100 rice genotypes, despite their narrow genetic pool. The stress tolerant and high vigor rice genotypes will be valuable for rice breeders for developing new genotypes best suited under growing environments prone to early-season drought and temperature.

No description available.

No description available.

Background: South Asians are at an increased risk of developing dysglycemia during and after pregnancy. In pregnant women, dysglycemia often develops in the form of gestational diabetes mellitus (GDM), which may predispose their newborns to adverse health outcomes through abnormal cord blood insulin levels. However, reasons for the elevated risk of dysglycemia in South Asians have not been extensively studied. Genetic factors may contribute to the heritability of GDM and abnormal cord blood insulin levels in South Asians. Objectives: The objectives of this thesis were to test the association of: 1) A type 2 diabetes polygenic risk score with GDM in South Asian pregnant women from the South Asian Birth Cohort (START); 2) maternal and newborn insulin-based polygenic risk scores with cord blood insulin and glucose/insulin ratio in South Asian newborns from START Methods: Three polygenic risk scores were created to test their association with participant data (N=1012) from START. GDM was defined using cut-offs established by the Born in Bradford cohort of South Asian women. The type 2 diabetes polygenic risk score was created in 832 START mothers and included 35,274 independent variants. The maternal and newborn insulin-based polygenic risk scores were created in 604 START newborns and included 1128017 independent variants. Univariate and multiple logistic and linear regression models were used to test the associations between the polygenic risk scores and dysglycemia outcomes. Results: The type 2 diabetes polygenic risk score was associated with GDM in both univariate (OR: 2.00, 95% CI: 1.46-2.75, P<0.001), and multivariable models (OR: 1.81, 95% CI: 1.30-2.53, P<0.001). The maternal insulin-based polygenic risk score was not associated with cord blood insulin or cord glucose/insulin ratio. However, the newborn insulin-based polygenic risk score was associated with cord blood insulin in a multivariable model adjusted for maternal insulin-based polygenic risk score (β = 0.036, 95% CI: 0.002 – 0.069; P=0.038 among other factors. Conclusion: A type 2 diabetes polygenic risk score and a newborn insulin-based polygenic risk score may be associated with maternal and newborn dysglycemia. / Thesis / Master of Science (MSc) / Background: South Asians are approximately two times more at risk for developing gestational diabetes mellitus (GDM) compared to white Caucasians. Genetic factors may contribute to this elevated risk. Polygenic risk scores (PRSs), which combine the effects of multiple disease loci and variants associated with the disease into one variable could be useful in further understanding how GDM develops in South Asians. Methods: Data from the South Asian Birth Cohort (START) was used to test the association of three PRSs with the outcomes of interest. Results: The type 2 diabetes PRS was independently associated with GDM. The insulin-based maternal PRS was not associated with cord blood insulin but the insulin-based newborn PRS was independently associated with cord blood insulin. However, neither the insulin-based maternal nor newborn PRS was associated with cord blood glucose/insulin ratio. Conclusion: The PRSs suggests a possible genetic component, which contributes to abnormal glycemic status development in South Asian mothers and their newborns.

The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single marker association methods. As an alternative to Single Marker Analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of Penalized Regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by False Discovery Rate (FDR) control, and assess their performance (including penalties incorporating linkage disequilibrium) in comparison with SMA. PR methods were compared with SMA on realistically simulated GWAS data consisting of genotype data from single and multiple chromosomes and a continuous phenotype and on real data. Based on our comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for GWAS. We found that PR with FDR control provides substantially more power than SMA with genome-wide type-I error control but somewhat less power than SMA with Benjamini-Hochberg FDR control. PR controlled the FDR conservatively while SMA-BH may not achieve FDR control in all situations. Differences among PR methods seem quite small when the focus is on variable selection with FDR control. Incorporating LD into PR by adapting penalties developed for covariates measured on graphs can improve power but also generate morel false positives or wider regions for follow-up. We recommend using the Elastic Net with a mixing weight for the Lasso penalty near 0.5 as the best method. / Ph. D.

The missing heritability in genome-wide association studies (GWAS) is an intriguing open scientific problem which has attracted great recent interest. The interaction effects among risk factors, both genetic and environmental, are hypothesized to be one of the main missing heritability sources. Moreover, detection of multilocus interaction effect may also have great implications for revealing disease/biological mechanisms, for accurate risk prediction, personalized clinical management, and targeted drug design. However, current analysis of GWAS largely ignores interaction effects, partly due to the lack of tools that meet the statistical and computational challenges posed by taking into account interaction effects. Here, we propose a novel statistically-based framework (Significant Conditional Association) for systematically exploring, assessing significance, and detecting interaction effect. Further, our SCA work has also revealed new theoretical results and insights on interaction detection, as well as theoretical performance bounds. Using in silico data, we show that the new approach has detection power significantly better than that of peer methods, while controlling the running time within a permissible range. More importantly, we applied our methods on several real data sets, confirming well-validated interactions with more convincing evidence (generating smaller p-values and requiring fewer samples) than those obtained through conventional methods, eliminating inconsistent results in the original reports, and observing novel discoveries that are otherwise undetectable. The proposed methods provide a useful tool to mine new knowledge from existing GWAS and generate new hypotheses for further research. Microarray gene expression studies provide new opportunities for the molecular characterization of heterogeneous diseases. Multiclass gene selection is an imperative task for identifying phenotype-associated mechanistic genes and achieving accurate diagnostic classification. Most existing multiclass gene selection methods heavily rely on the direct extension of two-class gene selection methods. However, simple extensions of binary discriminant analysis to multiclass gene selection are suboptimal and not well-matched to the unique characteristics of the multi-category classification problem. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone phenotypic up-regulated genes (OVEPUGs) and matches this gene selection with a one-versus-rest support vector machine. Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes, and intends to assure the statistical reproducibility and biological plausibility of the selected genes. We evaluated the fold changes of OVEPUGs and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed OVEPUG method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods. / Ph. D.

<p>Cerebrovascular disease (stroke), especially ischemic stroke, is a major cause of death and neurological disability in adults. Because of its clinical heterogeneity, stroke is considered as a multi-factorial and polygenic disorder. Most current genetic studies of ischemic stroke focus on genetic susceptibility rather than factors determining stroke outcome. The genetic components of ischemic stroke outcome are difficult to study in humans due to environmental factors and medical intervention. Thus, we proposed to use a surgically induced, permanent, focal cerebral ischemic stroke mouse model to investigate genetic factors of ischemic stroke outcome measured by infarct volume. This model is the middle cerebral artery occlusion (MCAO) model. First, we screened infarct volumes across 32 inbred mouse strains. The infarct volume varies between strains, and this strongly suggests that infarct volume is genetically determined. To identify these genetic factors, we used genome-wide association study [Efficient Mixed-Model Association (EMMA) analysis] on infarct volume from 32 inbred mouse strains. Using the EMMA analysis, we identified 11 infarct volume-associated loci; however, most loci were mapped with missing alleles. This suggests that these loci might be false positives. Thus, we used specifically designed scripts of EMMA analysis with updated mouse SNP database to correct for potential false positives. The loci identified by the updated EMMA analyses will led us to the identification of genes involved in ischemic stroke outcome. </p><p> There are two major mechanisms were proposed to be determinants of infarct volume, the extent of native collateral circulation and neuroprotection. Using the infarct volume screening panel from 32 inbred strains, we observed that infarct volume is inversely correlated with the native collateral vessel number. However, among these inbred strains, we also observed several strains differ significantly in infarct volumes but harbor similar collateral numbers. In order to identify genetic factors determining infarct volume in a collateral-independent manner (neuroprotection), we used quantitative trait locus (QTL) mapping on mouse strains that exhibit the most difference in infarct volumes but the least difference in collateral numbers (C57BL/6J and C3H/HeJ). From the F2 B6 x C3H cross, we mapped 4 loci determining infarct volume (cerebral infarct volume QTL 4 to 7, Civq4 to Civq7). The Civq4 locus is the strongest locus (LOD 9.8) that contributes 21% of phenotypic variance in infarct volume. We also used a parallel F2 B6 x C3H cross to perform a QTL mapping on collateral vessel traits to further verify these collateral-independent loci. Among these 4 loci, the Civq4 and Civq7 loci appear to be truly collateral-independent. Based on strain-specific sequence variants and mRNA expression differences, we proposed Msr1 and Mtmr7 are the potential candidate genes of the Civq4 locus. Identification of the collateral-independent genetic factors will help to understand the genetic architecture, disease pathophysiology and potential therapeutic targets for of ischemic stroke</p> / Dissertation

La lignine représente 25% de la biomasse des végétaux terrestre. Sa quantité et sa qualité sont variables au sein des populations naturelles et sont devenues des cibles de l’amélioration génétique des eucalyptus. L’identification des polymorphismes génétiques impliqués dans la variation de ces caractères permettrait de disposer d’outils de diagnostic moléculaire pour une sélection précoce des meilleurs géniteurs et ainsi contribuer à l’augmentation des gains génétiques par unité de temps. Dans ce travail de thèse nous avons décrit la variabilité nucléotidique de gènes impliqués dans la biosynthèse des lignines, ainsi que la part de la variation génétique de ces deux caractères chez trois espèces d’Eucalyptus. En intégrant ces deux niveaux de variabilité au sein de plans de croisement factoriels, nous avons identifié des polymorphismes associés à la variation des caractères. Ces travaux posent les bases de la sélection assistée par marqueurs chez l’eucalyptus. / Lignins represent 25% of plant biomass on earth. Lignins quantity and quality vary within natural populations and have become major targets for genetic improvement of eucalyptus. Identifying genetic polymorphisms involved in the variation of these traits could provide molecular tools for early selection of plus trees and contribute to increase genetic gains expected by time units. In this thesis work, we described the nucleotide diversity of genes involved in lignin biosynthesis and the genetic part of the variation of lignins quantity and quality in three eucalyptus species. Integrating these two levels of variation in a factorial matting design, we identified Single Nucleotide Polymorphisms statistically associated to the variation of lignin quality. This work paves the way to marker assisted selection in eucalyptus.