Biology subjects

Morris, J. C.

Publications and source records attributed to Morris, J. C..

4 recordsLinked to original sources

bioRxiv2018

Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure

Alzheimers disease (AD) is characterized by neuronal loss and astrocytosis in the cerebral cortex. However, the effects of brain cellular composition are often ignored in high-throughput molecular studies. We developed and optimized a cell-type specific expression reference panel and employed digital deconvolution methods to determine brain cellular distribution in three independent transcriptomic studies. We found that neuronal and astrocyte proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in APP, PSEN1 or PSEN2 presented lower neurons and higher astrocytes proportions compared to sporadic AD. Similarly, the APOE {varepsilon}4 allele also showed decreased neurons and increased astrocytes compared to AD non-carriers. On the contrary, carriers of variants in TREM2 risk showed a lower degree of neuronal loss than matched AD cases in multiple independent studies. These findings suggest that genetic risk factors associated with AD etiology have a specific imprinting in the cellular composition of AD brains. Our digital deconvolution reference panel provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-seq studies for cell composition, and suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD.

genetics↗

bioRxiv2018

Data-driven models of dominantly-inherited Alzheimer’s disease progression

Dominantly-inherited Alzheimers disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimers disease. We use emerging techniques in generative data-driven disease-progression modelling to characterise dominantly-inherited Alzheimers disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset (EYO). We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers: 163 PSEN1; 17 PSEN2; and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1{middle dot}1 {+/-} 1{middle dot}9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential-equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then sub-cortical regions (approximately 24{+/-}11 years before onset); CSF p-tau (17{+/-}8 years), tau and A{beta}42 changes; neurodegeneration first in the putamen and nucleus accumbens (up to 6 {+/-} 2 years); then cognitive decline (7 {+/-} 6 years), cerebral hypometabolism (4 {+/-} 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than EYO: root-mean-squared error of 1{middle dot}35 years versus 5{middle dot}54 years. The models reveal hidden detail on dominantly-inherited Alzheimers disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.

neuroscience↗

bioRxiv2018

Evaluation of gene-based family-based methods to detect novel genes associated with familial late onset Alzheimer disease

Gene-based tests to study the combined effect of rare variants towards a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially for complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We have examined the performance of several collapsing, variance-component and transmission disequilibrium tests across eight different software and twenty-two models utilizing a cohort of 285 families (N=1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the studied phenotype with high confidence and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, MAS1L) as candidates genes for familial LOAD.

genetics↗

bioRxiv2017

Polygenic Risk Score of Sporadic Late Onset Alzheimer Disease Reveals a Shared Architecture with the Familial and Early Onset Forms

ObjectiveTo determine whether the genetic architecture of sporadic late-onset Alzheimers Disease (sLOAD) has an effect on familial late-onset AD (fLOAD), sporadic early-onset (sEOAD) and autosomal dominant early-onset (eADAD).\n\nMethodsPolygenic risk scores (PRS) were constructed using previously identified 21 genome-wide significant loci for LOAD risk.\n\nResultsWe found that there is an overlap in the genetic architecture among sEOAD, fLOAD, and sLOAD. sEOAD showed the highest odds for the PRS (OR=2.27; p=1.29x10-7), followed by fLOAD (OR=1.75; p=1.12x10-7) and sLOAD (OR=1.40; p=1.21x10-3). PRS is associated with cerebrospinal fluid ptau181-A{beta}42 on eADAD.\n\nConclusionOur analysis confirms that the genetic factors identified for sLOAD also modulate risk in fLOAD and sEOAD cohorts. Furthermore, our results suggest that the burden of these risk variants is associated with familial clustering and earlier-onset of AD. Although these variants are not associated with risk in the eADAD, they may be modulating age at onset.

genetics↗