Dietary fatty acid intake in childhood and the risk of islet autoimmunity and type 1 diabetes: the DIPP birth cohort study

Purpose The aim was to study the associations between dietary intake of fatty acids in childhood and the risk of islet autoimmunity and type 1 diabetes (T1D).Methods The prospective Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study included children with genetic susceptibility to T1D born between 1996 and 2004. Participants were followed up every 3 to 12 months up to 6 years for diet, islet autoantibodies, and T1D. Dietary intake of several fatty acids at the age of 3 months to 6 years was assessed 1-8 times per participant with a 3-day food record. Joint models adjusted for energy intake, sex, HLA genotype and familial diabetes were used to investigate the associations of longitudinal intake of fatty acids and the development of islet autoimmunity and T1D.Results During the 6-year follow-up, 247 (4.4%) children of 5626 developed islet autoimmunity and 94 (1.7%) children of 5674 developed T1D. Higher intake of monounsaturated fatty acids (HR 0.63; 95% CI 0.47, 0.82), arachidonic acid (0.69; 0.50, 0.94), total n-3 fatty acids (0.64; 0.48, 0.84), and long-chain n-3 fatty acids (0.14; 0.04, 0.43), was associated with a decreased risk of islet autoimmunity with and without energy adjustment. Higher intake of total fat (0.73; 0.53, 0.98), and saturated fatty acids (0.55; 0.33, 0.90) was associated with a decreased risk of T1D only when energy adjusted.Conclusion Intake of several fatty acids was associated with a decreased risk of islet autoimmunity or T1D among high-risk children. Our findings support the idea that dietary factors, including n-3 fatty acids, may play a role in the disease process of T1D.</p

The correlation between reading and mathematics ability at age twelve has a substantial genetic component

Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve

Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis.

Sepsis is a deleterious inflammatory response to infection with high mortality. Reliable sepsis biomarkers could improve diagnosis, prognosis, and treatment. Integration of human genetics, patient metabolite and cytokine measurements, and testing in a mouse model demonstrate that the methionine salvage pathway is a regulator of sepsis that can accurately predict prognosis in patients. Pathway-based genome-wide association analysis of nontyphoidal Salmonella bacteremia showed a strong enrichment for single-nucleotide polymorphisms near the components of the methionine salvage pathway. Measurement of the pathway's substrate, methylthioadenosine (MTA), in two cohorts of sepsis patients demonstrated increased plasma MTA in nonsurvivors. Plasma MTA was correlated with levels of inflammatory cytokines, indicating that elevated MTA marks a subset of patients with excessive inflammation. A machine-learning model combining MTA and other variables yielded approximately 80% accuracy (area under the curve) in predicting death. Furthermore, mice infected with Salmonella had prolonged survival when MTA was administered before infection, suggesting that manipulating MTA levels could regulate the severity of the inflammatory response. Our results demonstrate how combining genetic data, biomolecule measurements, and animal models can shape our understanding of disease and lead to new biomarkers for patient stratification and potential therapeutic targeting

Risk of nontyphoidal Salmonella bacteraemia in African children is modified by STAT4

Nontyphoidal Salmonella (NTS) is a major cause of bacteraemia in Africa. The disease typically affects HIV-infected individuals and young children, causing substantial morbidity and mortality. Here we present a genome-wide association study (180 cases, 2677 controls) and replication analysis of NTS bacteraemia in Kenyan and Malawian children. We identify a locus in STAT4, rs13390936, associated with NTS bacteraemia. rs13390936 is a context-specific expression quantitative trait locus for STAT4 RNA expression, and individuals carrying the NTS-risk genotype demonstrate decreased interferon-gamma (IFN gamma) production in stimulated natural killer cells, and decreased circulating IFN gamma concentrations during acute NTS bacteraemia. The NTS-risk allele at rs13390936 is associated with protection against a range of autoimmune diseases. These data implicate interleukin-12-dependent IFN gamma-mediated immunity as a determinant of invasive NTS disease in African children, and highlight the shared genetic architecture of infectious and autoimmune disease.Peer reviewe

Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke

Genetic factors have been implicated in stroke risk but few replicated associations have been reported. We conducted a genome-wide association study (GWAS) in ischemic stroke and its subtypes in 3,548 cases and 5,972 controls, all of European ancestry. Replication of potential signals was performed in 5,859 cases and 6,281 controls. We replicated reported associations between variants close to PITX2 and ZFHX3 with cardioembolic stroke, and a 9p21 locus with large vessel stroke. We identified a novel association for a SNP within the histone deacetylase 9(HDAC9) gene on chromosome 7p21.1 which was associated with large vessel stroke including additional replication in a further 735 cases and 28583 controls (rs11984041, combined P = 1.87×10−11, OR=1.42 (95% CI) 1.28-1.57). All four loci exhibit evidence for heterogeneity of effect across the stroke subtypes, with some, and possibly all, affecting risk for only one subtype. This suggests differing genetic architectures for different stroke subtypes

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis.

Streptococcus pneumoniae is a common nasopharyngeal colonizer, but can also cause life-threatening invasive diseases such as empyema, bacteremia and meningitis. Genetic variation of host and pathogen is known to play a role in invasive pneumococcal disease, though to what extent is unknown. In a genome-wide association study of human and pathogen we show that human variation explains almost half of variation in susceptibility to pneumococcal meningitis and one-third of variation in severity, identifying variants in CCDC33 associated with susceptibility. Pneumococcal genetic variation explains a large amount of invasive potential (70%), but has no effect on severity. Serotype alone is insufficient to explain invasiveness, suggesting other pneumococcal factors are involved in progression to invasive disease. We identify pneumococcal genes involved in invasiveness including pspC and zmpD, and perform a human-bacteria interaction analysis. These genes are potential candidates for the development of more broadly-acting pneumococcal vaccines

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers