ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/

Reduced Expression of IFIH1 Is Protective for Type 1 Diabetes

IFIH1 (interferon induced with helicase C domain 1), also known as MDA5 (melanoma differentiation-associated protein 5), is one of a family of intracellular proteins known to recognise viral RNA and mediate the innate immune response. IFIH1 is causal in type 1 diabetes based on the protective associations of four rare variants, where the derived alleles are predicted to reduce gene expression or function. Originally, however, T1D protection was mapped to the common IFIH1 nsSNP, rs1990760 or Thr946Ala. This common amino acid substitution does not cause a loss of function and evidence suggests the protective allele, Ala946, may mark a haplotype with reduced expression of IFIH1 in line with the protection conferred by the four rare loss of function alleles. We have performed allele specific expression analysis that supports this hypothesis: the T1D protective haplotype correlates with reduced IFIH1 transcription in interferon-β stimulated peripheral blood mononuclear cells (overall p = 0.012). In addition, we have used multiflow cytometry analysis and quantitative PCR assays to prove reduced expression of IFIH1 in individuals heterozygous for three of the T1D-associated rare alleles: a premature stop codon, rs35744605 (Glu627X) and predicted splice variants, rs35337543 (IVS8+1) and rs35732034 (IVS14+1). We also show that the nsSNP, Ile923V, does not alter pre-mRNA levels of IFIH1. These results confirm and extend the new autoimmune disease pathway of reduced IFIH1 expression and protein function protecting from T1D

Decadal-timescale estuarine geomorphic change under future scenarios of climate and sediment supply

© The Authors, 2009. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License. The definitive version was published in Estuaries and Coasts 33 (2010): 15-29, doi:10.1007/s12237-009-9244-y.Future estuarine geomorphic change, in response to climate change, sea-level rise, and watershed sediment supply, may govern ecological function, navigation, and water quality. We estimated geomorphic changes in Suisun Bay, CA, under four scenarios using a tidal-timescale hydrodynamic/sediment transport model. Computational expense and data needs were reduced using the morphological hydrograph concept and the morphological acceleration factor. The four scenarios included (1) present-day conditions; (2) sea-level rise and freshwater flow changes of 2030; (3) sea-level rise and decreased watershed sediment supply of 2030; and (4) sea-level rise, freshwater flow changes, and decreased watershed sediment supply of 2030. Sea-level rise increased water levels thereby reducing wave-induced bottom shear stress and sediment redistribution during the wind-wave season. Decreased watershed sediment supply reduced net deposition within the estuary, while minor changes in freshwater flow timing and magnitude induced the smallest overall effect. In all future scenarios, net deposition in the entire estuary and in the shallowest areas did not keep pace with sea-level rise, suggesting that intertidal and wetland areas may struggle to maintain elevation. Tidal-timescale simulations using future conditions were also used to infer changes in optical depth: though sea-level rise acts to decrease mean light irradiance, decreased suspended-sediment concentrations increase irradiance, yielding small changes in optical depth. The modeling results also assisted with the development of a dimensionless estuarine geomorphic number representing the ratio of potential sediment import forces to sediment export forces; we found the number to be linearly related to relative geomorphic change in Suisun Bay. The methods implemented here are widely applicable to evaluating future scenarios of estuarine change over decadal timescales.This study was supported by the US Geological Survey’s Priority Ecosystems Science program, CALFED Bay/ Delta Program, and the University of California Center for Water Resources

A probit- log- skew-normal mixture model for repeated measures data with excess zeros, with application to a cohort study of paediatric respiratory symptoms

<p>Abstract</p> <p>Background</p> <p>A zero-inflated continuous outcome is characterized by occurrence of "excess" zeros that more than a single distribution can explain, with the positive observations forming a skewed distribution. Mixture models are employed for regression analysis of zero-inflated data. Moreover, for repeated measures zero-inflated data the clustering structure should also be modeled for an adequate analysis.</p> <p>Methods</p> <p>Diary of Asthma and Viral Infections Study (DAVIS) was a one year (2004) cohort study conducted at McMaster University to monitor viral infection and respiratory symptoms in children aged 5-11 years with and without asthma. Respiratory symptoms were recorded daily using either an Internet or paper-based diary. Changes in symptoms were assessed by study staff and led to collection of nasal fluid specimens for virological testing. The study objectives included investigating the response of respiratory symptoms to respiratory viral infection in children with and without asthma over a one year period. Due to sparse data daily respiratory symptom scores were aggregated into weekly average scores. More than 70% of the weekly average scores were zero, with the positive scores forming a skewed distribution. We propose a random effects probit/log-skew-normal mixture model to analyze the DAVIS data. The model parameters were estimated using a maximum marginal likelihood approach. A simulation study was conducted to assess the performance of the proposed mixture model if the underlying distribution of the positive response is different from log-skew normal.</p> <p>Results</p> <p>Viral infection status was highly significant in both probit and log-skew normal model components respectively. The probability of being symptom free was much lower for the week a child was viral positive relative to the week she/he was viral negative. The severity of the symptoms was also greater for the week a child was viral positive. The probability of being symptom free was smaller for asthmatics relative to non-asthmatics throughout the year, whereas there was no difference in the <it>severity </it>of the symptoms between the two groups.</p> <p>Conclusions</p> <p>A positive association was observed between viral infection status and both the probability of experiencing any respiratory symptoms, and their severity during the year. For DAVIS data the random effects probit -log skew normal model fits significantly better than the random effects probit -log normal model, endorsing our parametric choice for the model. The simulation study indicates that our proposed model seems to be robust to misspecification of the distribution of the positive skewed response.</p

siRNA Silencing of Proteasome Maturation Protein (POMP) Activates the Unfolded Protein Response and Constitutes a Model for KLICK Genodermatosis

Keratosis linearis with ichthyosis congenita and keratoderma (KLICK) is an autosomal recessive skin disorder associated with a single-nucleotide deletion in the 5′untranslated region of the proteasome maturation protein (POMP) gene. The deletion causes a relative switch in transcription start sites for POMP, predicted to decrease levels of POMP protein in terminally differentiated keratinocytes. To investigate the pathophysiology behind KLICK we created an in vitro model of the disease using siRNA silencing of POMP in epidermal air-liquid cultures. Immunohistochemical analysis of the tissue constructs revealed aberrant staining of POMP, proteasome subunits and the skin differentiation marker filaggrin when compared to control tissue constructs. The staining patterns of POMP siRNA tissue constructs showed strong resemblance to those observed in skin biopsies from KLICK patients. Western blot analysis of lysates from the organotypic tissue constructs revealed an aberrant processing of profilaggrin to filaggrin in samples transfected with siRNA against POMP. Knock-down of POMP expression in regular cell cultures resulted in decreased amounts of proteasome subunits. Prolonged silencing of POMP in cultured cells induced C/EBP homologous protein (CHOP) expression consistent with an activation of the unfolded protein response and increased endoplasmic reticulum (ER) stress. The combined results indicate that KLICK is caused by reduced levels of POMP, leading to proteasome insufficiency in differentiating keratinocytes. Proteasome insufficiency disturbs terminal epidermal differentiation, presumably by increased ER stress, and leads to perturbed processing of profilaggrin. Our findings underline a critical role for the proteasome in human epidermal differentiation