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The genetic component of Alzheimer’s disease (AD) has been mainly assessed using Genome Wide Association Studies (GWAS), which do not capture the risk contributed by rare variants. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals —16,036 AD cases and 16,522 controls— in a two-stage analysis. Next to known genes TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Next to these genes, the rare variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential driver genes in AD-GWAS loci. Rare damaging variants in these genes, and in particular loss-of-function variants, have a large effect on AD-risk, and they are enriched in early onset AD cases. The newly identified AD-associated genes provide additional evidence for a major role for APP-processing, Aβ-aggregation, lipid metabolism and microglial function in AD.

Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method—Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)—that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.

The harms of youth homelessness are well described in the academic literature, but less is known about transitions into homelessness among at risk youth. Given the importance of preventing youth homelessness, and in particular, the first incidence of homelessness, quantitative and qualitative data from street involved youth in Vancouver were analyzed in order to determine significant factors associated with this transition and generate policy options for addressing this issue. Ultimately, this study recommends placing youth workers in secondary schools to support the academic and social development of at risk youth, as well as provide connections to appropriate community supports such as housing. This is the first known study to directly ask youth for their thoughts on how to prevent the first incidence of homelessness, and the results from this Capstone provides policy makers with opportunities for targeted interventions to address youth homelessness in Vancouver.

The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (P<5 × 10^−8). Gene-based analysis identified an additional three Bonferroni-corrected significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e.=0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

Osteoarthritis (OA) of the knee affects between 5.4% and 38% of older adults and this prevalence is increasing as the population ages and becomes more obese. As health costs rise, it is important to have accurate and cost-effective methods to assess knee OA and the risk for OA. One risk factor for progression of knee OA is lower extremity (LE) frontal-plane malalignment. The first goal of this thesis was to assess the suitability of knee radiographs and LE photographs for the estimation of frontal-plane LE alignment. In the first study, several versions of the femoral shaft-tibial shaft (FS-TS) angle, assessed from knee radiographs, were compared to the hip-knee-ankle (HKA) angle, assessed from full-length radiographs. We concluded that the FS-TS angle is not a recommended substitute for the HKA angle, because the association between the two measures differs depending on alignment, OA severity and the method of determining the FS-TS angle. In the second study, the hip-knee-ankle angle determined from a pelvis-to-ankle photograph (HKA-P) was assessed for its ability to estimate the HKA angle. The HKA-P angle was reliable and highly correlated to the HKA. It therefore shows promise as an accurate and cost-effective assessment tool for the estimation of LE alignment. Commonly-used grading scales for the severity of knee OA seen on a radiograph emphasize just one feature of OA; therefore the second goal of this thesis was to assess the psychometric properties of the unicompartmental osteoarthritis grade (UCOAG), a composite scale which grades several features of OA in the tibiofemoral (TF) compartment. In the third and fourth studies, the reliability, validity and sensitivity to change of the UCOAG scale was assessed and compared to two commonly-used scales (Kellgren-Lawrence and Osteoarthritis Research Society International joint space narrowing). The UCOAG scale showed moderate to excellent reliability. All three scales demonstrated comparable validity and sensitivity to change. The UCOAG is therefore recommended for the assessment of OA severity and change over time. This research provides evidence for the use of accurate and cost-effective measures to assess LE alignment using photographs, and TF OA severity using radiographs, for clinical assessment and research purposes.

The Epidemiology and Development of Alzheimer’s Disease in Urban and Rural Costa Rica research project is a longitudinal study of memory and aging harmonized with the University of Kansas’ Alzheimer Disease Center (ADC). This is the first paperless ADC based entirely in the REDCap database. The investigators developed a parallel assessment toolkit/battery in Spanish and deployed it in San Jose and Guanacaste Costa Rica. To meet the needs of Latin American colleagues, the assessment battery was migrated to Colectica and then redeveloped using QueXF, so that all data forms could be optically character recognized. The investigators will review the hurdles and plans for the next steps of development.

Using Southern blot analysis of RT-PCR products, mRNA for three different somatostatin (SS) precursors (PSS-I, -II, and -III), which encode for SS14, goldfish brain (gb)SS28, and [Pro2]SS14, respectively, were detected in goldfish hypothalamus. PSS-I and -II mRNA, but not PSS-III mRNA, were also detected in cultured pituitary cells. We subsequently examined the effects of the mature peptides, SS14, gbSS28, and [Pro2]SS14, on somatotrope signaling and GH secretion. The gbSS28 was more potent than either SS14 or [Pro2]SS14 in reducing basal GH release but was the least effective in reducing basal cellular cAMP. The ability of SS14, [Pro2]SS14, and gbSS28 to attenuate GH responses to GnRH were comparable. However, gbSS28 was less effective than SS14 and [Pro2]SS14 in diminishing dopamine- and pituitary adenylate cyclase-activating polypeptide-stimulated GH release, as well as GH release resulting from the activation of their underlying signaling cascades. In contrast, the actions of a different 28-aminoacid SS, mammalian SS28, were more similar to those of SS14 and [Pro2]SS14. We conclude that, in goldfish, SSs differentially couple to the intracellular cascades regulating GH secretion from pituitary somatotropes. This raises the possibility that such differences may allow for the selective regulation of various aspects of somatotrope function by different SS peptides.