Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 x 10(-8)) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30-80%, depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures: word reading, nonword reading, spelling, phoneme awareness, and nonword repetition, in samples of 13,633 to 33,959 participants aged 5-26 years. We identified genome-wide significant association with word reading (rs11208009, p=1.098 x 10-8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP-heritability, accounting for 13-26% of trait variability. Genomic structural equation modelling revealed a shared genetic factor explaining most variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis of multivariate GWAS results with neuroimaging traits identified association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain, and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide new avenues for deciphering the biological underpinnings of uniquely human traits

Exploratory analysis applied in study of pharmaceutical formulations with piroxicam

A identificação de diferentes formulações de medicamentos manipulados contendo piroxicam foi estudada, empregando espectros de reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS), em associação com a técnica de análise por agrupamentos hierárquicos (AAH). Os espectros de amostras, de 5 diferentes farmácias de manipulação, contendo piroxicam (10 mg e 20 mg) e seus respectivos excipientes, foram adquiridos em um espectrofotômetro NICOLET Magna 550, obtendo-se duas réplicas de cada amostra. Para a análise multivariada, as informações espectrais foram tratadas no programa Pirouette® 2.7 da Infometrix, utilizando-se as regiões espectrais 1340 a 1470 cm-1, 1535 a 1680 cm-1, 2800 a 3004 cm-1 e 3290 a 3400 cm-1. Os dendogramas foram construídos com os dados auto-escalados, e correção do espalhamento da luz (MSC), utilizando três tipos de construção: simples, flexível e incremental. Com a aplicação da análise hierárquica de agrupamentos constatou-se a formação de dois grupos distintos, um contendo os princípios ativos, e outro contendo os excipientes. Os resultados demonstram que a técnica DRIFTS em conjunto com análise por agrupamentos hierárquicos constitui uma alternativa para o controle de qualidade dos processos de produção de medicamentos.The identification of different pharmaceutical formulations with piroxicam was studied, using spectra of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), in association with hierarchical cluster analysis (HCA) technique. The spectra of samples of the 5 different compounding pharmacies, containing 10 or 20 mg of piroxicam and its respective inactive ingredients, had been collected in Nicolet Magna 550 spectrophotometer. For the multivariate analysis, the spectral information had been processed in software package Pirouette® 2.7 of the Infometrix. The dendograms had been constructed with the autoscaled data, and multiplicative scatter correction (MSC), using three types of linkage methods: single, flexible and incremental. By applying the hierarchical cluster analysis the formation of two distinct groups: one with the active principles and another group with the inactive ingredients was proved. These results demonstrate that the DRIFTS associated with chemometrics tools, constitute a quality control alternative of drugs production processes

miRNA-197 and miRNA-223 Predict Cardiovascular Death in a Cohort of Patients with Symptomatic Coronary Artery Disease.

Circulating microRNAs (miRNAs) have been described as potential diagnostic biomarkers in cardiovascular disease and in particular, coronary artery disease (CAD). Few studies were undertaken to perform analyses with regard to risk stratification of future cardiovascular events. miR-126, miR-197 and miR-223 are involved in endovascular inflammation and platelet activation and have been described as biomarkers in the diagnosis of CAD. They were identified in a prospective study in relation to future myocardial infarction.The aim of our study was to further evaluate the prognostic value of these miRNAs in a large prospective cohort of patients with documented CAD.Levels of miR-126, miR-197 and miR-223 were evaluated in serum samples of 873 CAD patients with respect to the endpoint cardiovascular death. miRNA quantification was performed using real time polymerase chain reaction (RT-qPCR).The median follow-up period was 4 years (IQR 2.78-5.04). The median age of all patients was 64 years (IQR 57-69) with 80.2% males. 38.9% of the patients presented with acute coronary syndrome (ACS), 61.1% were diagnosed with stable angina pectoris (SAP). Elevated levels of miRNA-197 and miRNA-223 reliably predicted future cardiovascular death in the overall group (miRNA-197: hazard ratio (HR) 1.77 per one standard deviation (SD) increase (95% confidence interval (CI) 1.20; 2.60), p = 0.004, C-index 0.78; miRNA-223: HR 2.23 per one SD increase (1.20; 4.14), p = 0.011, C-index 0.80). In ACS patients the prognostic power of both miRNAs was even higher (miRNA-197: HR 2.24 per one SD increase (1.25; 4.01), p = 0.006, C-index 0.89); miRA-223: HR 4.94 per one SD increase (1.42; 17.20), p = 0.012, C-index 0.89).Serum-derived circulating miRNA-197 and miRNA-223 were identified as predictors for cardiovascular death in a large patient cohort with CAD. These results reinforce the assumption that circulating miRNAs are promising biomarkers with prognostic value with respect to future cardiovascular events

Kaplan Meier Plots for each analyzed miRNA.

<p>miRNA levels are dichotomized using their second tertile. miRNA levels are presented in minus ΔCT values so that larger values correspond to high miRNA concentrations. The p-values given describe the log-rank test.</p

Correlations of the miRNAs.

<p>Correlations of the miRNAs.</p

Characteristics of subjects of the AtheroGene study.

<p>Characteristics of subjects of the AtheroGene study.</p