Understanding Alzheimer Disease at the interface between genetics and transcriptomics

Over 25 genes are known to affect the risk of developing Alzheimer disease (AD), the most common neurodegenerative dementia. However, mechanistic insights and improved disease management remains limited, due to difficulties in determining the functional consequences of genetic associations. Transcriptomics is increasingly being used to corroborate or enhance interpretation of genetic discoveries. These approaches, which include second and third generation sequencing, single-cell sequencing, and bioinformatics, reveal allele-specific events connecting AD risk genes to expression profiles, and provide converging evidence of pathophysiological pathways underlying AD. Simultaneously, they highlight brain region- and cell-type-specific expression patterns, and alternative splicing events that affect the straightforward relation between a genetic variant and AD, re-emphasizing the need for an integrated approach of genetics and transcriptomics in understanding AD. © 2018 The Author

Testing perceivers' accuracy and accuracy awareness when forming personality impressions from faces

People spontaneously judge others' personality based on their facial appearance and these impressions guide many important decisions. Although the consequences of personality impressions are well documented, studies on the accuracy of personality impressions have yielded mixed results. Moreover, relatively little is known about people's accuracy awareness (i.e., whether they are aware of their judgment accuracy). Even if accuracy is generally low, awareness of accuracy would allow people to rely on their impressions in the right situations. In two studies (one preregistered), we estimated perceivers' accuracy and accuracy awareness when forming personality impressions based on facial photographs. Our studies have three crucial advantages as compared to previous studies (a) by incentivizing accuracy and accuracy awareness, (b) by relying on substantially larger samples of raters (n(Study 1)= 223, n(Study 2) = 423) and targets (k(Study 1)= 140, k(Study 2) = 1,260 unique pairs with 280 unique targets), and (c) by conducting Bayesian analyses to also quantify evidence for the null hypothesis. Our findings suggest that face-based personality impressions are not accurate, that perceivers lack insight into their (in)accuracy, and that most people overestimate their accuracy

Where the <i>really</i> hard problems Aren’t

Not all problem instances in combinatorial optimization are equally hard. One famous study "Where the Really Hard Problems Are" shows that for three decision problems and one optimization problem, computational costs can vary dramatically for equally sized instances. Moreover, runtimes could be predicted from an "order parameter", which is a property of the problem instance itself. For the only optimization problem in the study, the asymmetric traveling salesman problem (ATSP), the proposed order parameter was the standard deviation in the probability distribution used for generating distance matrices. For greater standard deviations, most randomly generated instances turned out to be easily solved to optimality, whereas smaller standard deviations produced harder instances. In this replication study, we show these findings can be contested. Most likely, the difference in instance hardness stems from a roundoff error that was possibly overlooked. This gives rise to a sudden emergence of minimum-cost tours, a feature that is readily exploited by most branch and bound algorithms. This new contradiction renders the earlier proposed order parameter unsuitable and changes the perspective on the fundamentals of ATSP instance hardness for this kind of algorithm

The development of a questionnaire on metacognition for students in higher education

Background Interest in the role of metacognition has been steadily rising in most forms of education. This study focuses on the construction of a questionnaire for measuring metacognitive knowledge, metacognitive regulation and metacognitive responsiveness among students in higher education and the subsequent process of testing to determine its validity. Purpose The aim of the study was to construct an original instrument for measuring features of metacognition, henceforth referred to as the Awareness of Independent Learning Inventory (AILI), and further to establish the similarities and differences between this model and existing instruments for measuring metacognition. Sample The AILI questionnaire was distributed to 1058 students in various types of Teacher Training Institutes in the Netherlands and Belgium. The abridged English version of the questionnaire was administered to another sample of 729 students reading Economics and Business Administration at the University of Maastricht in the south of the Netherlands. Design and methods The AILI instrument was constructed on the basis of a facet design along two dimensions: components of metacognition and topics of concern to students in higher education. The data gathered with the instrument was analyzed by means of a generalisability study and a decision study, respectively. The validity of the instrument was investigated by using confirmatory factor analysis. Results The generalisability study showed that the reliability of the instrument was satisfactory. The decision study revealed that the number of items included in the questionnaire could be reduced substantially by leaving out two components of one of the dimensions in the facet design, without losing too much generalisability. The validity study showed that there was a considerable level of congruity between parts of the AILI questionnaire and the relevant parts of the Motivated Strategies for Learning Questionnaire (MSLQ). Conclusions The AILI questionnaire is a reliable and valid instrument for measuring metacognitive knowledge, regulation and responsiveness. It is suitable for use in the evaluation of the effects of interventions that purport to increase metacognitive knowledge, regulation and responsiveness of students in higher education

The Secure Anonymised Information Linkage databank Dementia e-cohort (SAIL-DeC)

Introduction: The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care. Objectives:We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers. Methods:SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors. Results:From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years. Conclusion:We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research

TMEM106B a Novel Risk Factor for Frontotemporal Lobar Degeneration

Recently, the first genome-wide association (GWA) study in frontotemporal lobar degeneration (FTLD) identified common genetic variability at the TMEM106B gene on chromosome 7p21.3 as a potential important risk-modifying factor for FTLD with pathologic inclusions of TAR DNA-binding protein (FTLD-TDP), the most common pathological subtype in FTLD. To gather additional evidence for the implication of TMEM106B in FTLD risk, multiple replication studies in geographically distinct populations were set up. In this review, we revise all recent replication and follow-up studies of the FTLD-TDP GWA study and summarize the growing body of evidence that establish TMEM106B as a bona fide risk factor for FTLD. With the TMEM106B gene, a new player has been identified in the pathogenic cascade of FTLD which could hold important implications for the future development of disease-modifying therapies

Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size. Keywords: Alzheimer’s disease; amyloid β; artificial neural networks; machine learning; neurodegeneration; plasma proteomics; ta

Linkage and association studies identify a novel locus for Alzheimer disease at 7q36 in a Dutch population-based sample

We obtained conclusive linkage of Alzheimer disease (AD) with a candidate region of 19.7 cM at 7q36 in an extended multiplex family, family 1270, ascertained in a population-based study of early-onset AD in the northern Netherlands. Single-nucleotide polymorphism and haplotype association analyses of a Dutch patient-control sample further supported the linkage at 7q36. In addition, we identified a shared haplotype at 7q36 between family 1270 and three of six multiplex AD-affected families from the same geographical region, which is indicative of a founder effect and defines a priority region of 9.3 cM. Mutation analysis of coding exons of 29 candidate genes identified one linked synonymous mutation, g.38030G-->C in exon 10, that affected codon 626 of the PAX transactivation domain interacting protein gene (PAXIP1). It remains to be determined whether PAXIP1 has a functional role in the expression of AD in family 1270 or whether another mutation at this locus explains the observed linkage and sharing. Together, our linkage data from the informative family 1270 and the association data in the population-based early-onset AD patient-control sample strongly support the identification of a novel AD locus at 7q36 and re-emphasize the genetic heterogeneity of AD