Psoriasis prediction from genome-wide SNP profiles

<p>Abstract</p> <p>Background</p> <p>With the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs) to predict psoriasis from searching GWAS data.</p> <p>Methods</p> <p>Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB) method was compared with classical linear discriminant analysis(LDA) for classification performance.</p> <p>Results</p> <p>The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698), while only 0.520(95% CI: 0.472-0.524) was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study.</p> <p>Conclusions</p> <p>The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.</p

Multicollinearity and linear predictor link function problems in regression modelling of longitudinal data

DATA AVAILABILITY STATEMENT: The data is publicly available.In the longitudinal data analysis we integrate flexible linear predictor link function and highcorrelated predictor variables. Our approach uses B-splines for non-parametric part in the linear predictor component. A generalized estimation equation is used to estimate the parameters of the proposed model. We assess the performance of our proposed model using simulations and an application to an analysis of acquired immunodeficiency syndrome data set.The National Research Foundation (NRF) of South Africa, SARChI Research Chair UID: 71199, the South African DST-NRF-MRC SARChI Research Chair in Biostatistics and STATOMET at the Department of Statistics at the University of Pretoria, South Africa.https://www.mdpi.com/journal/mathematicsStatisticsNon

Statistical Methods for Bioinformatics: Estimation of Copy N umber and Detection of Gene Interactions

Identification of copy number aberrations in the human genome has been an important area in cancer research. In the first part of my thesis, I propose a new model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference. The second part of the thesis describes a new method for the detection of gene-gene interactions using gene expression data extracted from micro array experiments. The method is based on a two-step Genetic Algorithm, with the first step detecting main effects and the second step looking for interacting gene pairs. The performances of both algorithms are examined on both simulated data and real cancer data and are compared with popular existing algorithms. Conclusions are given and possible extensions are discussed

More than Clearing the Clutter: The Imperative Role of Efferocytosis in Repair and Immune Reprogramming in the Damaged Nervous System

Evolutionarily, the nervous system and immune system have been intertwined for hundreds of millions of years. In healthy conditions, these systems work diligently to maintain homeostasis and proper functioning. In summation, they keep our bodies moving, our organs operating, our minds thinking, and our bodies safe from foreign pathogenic invaders. However, in the event of a challenge to homeostasis, like a traumatic injury, both systems engage complex signaling cascades to degenerate parts of cells that can’t be saved, protect those that can, remove harmful debris, and regenerate and repair to again obtain homeostasis. A common system to study these complex response mechanisms is that of a peripheral nerve injury. My research over the past several years has been focused around fully understanding the complex immune-nerve communication and consequences that occur following peripheral nerve injury. The work herein keenly elaborates on the time course and content of the immune response after peripheral nerve crush injury. We show that granulocytes are the first to respond with infiltrating monocytes entering a few days later and finally dendritic cells about a week after injury. We however show little evidence of significant immune infiltration into dorsal root ganglia of the sciatic nerve and rather DRG-resident immune cell morphological changes. It is also demonstrated that mesenchymal progenitor cells are key in shaping the inflammatory milieu after injury. The requirement of Csf2 for conditioning-lesion-induced dorsal column axon regeneration is evidenced as well as its role in skewing the inflammatory response. The dynamicity of the immune non-immune responses to nerve injury in wild-type an SARM1 knockout animals at multiple timepoints is compared and contrasted. Finally, we are the first group to show the occurrence of efferocytosis (the phagocytosis of apoptotic cells) in the injured nerve, identify a specific transcriptomic identity for macrophages engaged in this action, and investigate the anti-inflammatory signaling this process propagates.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170010/1/lucashu_1.pd