Biology subjects

Crane, P.

Publications and source records attributed to Crane, P..

11 recordsLinked to original sources

bioRxiv2026

Continuous amyloid staging complements global amyloid burden in early Alzheimer's disease

Introduction: Early Alzheimer's disease (AD) staging remains challenging because clinical categories and global amyloid PET measures provide only coarse characterization of disease progression. We developed a continuous amyloid staging score from regional amyloid PET to complement global amyloid burden. Methods: Using longitudinal florbetapir PET from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we trained an end-to-end deep learning model to directly estimate a continuous amyloid staging score. We evaluated longitudinal consistency, its relationship with global amyloid burden, regional contributors to early-stage variation, and whether the staging score stratified subsequent memory decline among participants matched for baseline global amyloid burden. Cognitive analyses were replicated in A4+LEARN. Results: The staging score better preserved longitudinal visit order than existing amyloid staging approaches. During early disease stages, it captured regional amyloid variation despite relatively stable global amyloid burden. Among participants with similar baseline global amyloid burden, higher staging scores were associated with faster subsequent memory decline in both ADNI and A4+LEARN. Discussion: A continuous amyloid staging score complements global amyloid burden for early AD staging and cognitive risk stratification. Keywords: Alzheimer's disease; amyloid PET; disease staging; cognitive decline

neuroscience↗

bioRxiv2026

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning

Accurate in vivo prediction of neuropathology is critical for advancing diagnosis and treatment of Alzheimers disease and related dementias (ADRDs). As many individuals with ADRDs have mixed pathologies ({beta}-amyloid, pathologic tau, cerebrovascular disease, vascular brain injury, pathologic TDP-43, hippocampal sclerosis, Lewy bodies), there is interest in determining how accurately we can infer these pathologic changes from clinical data, biofluid assays (e.g., CSF), and neuroimaging. Here we evaluated automated machine learning models trained on data curated by the AD Sequencing Project Phenotype Harmonization Consortium (N=7,894 individuals), to predict 26 autopsy-confirmed neuropathological outcomes. Predictors included in vivo clinical and cognitive composite scores, brain measures from 3D structural MRI and diffusion tensor imaging, image-derived measures of white matter hyperintensities (WMH), and CSF biomarkers. Predictive models were trained using ensemble learning with stratified cross-validation. We assessed performance using Spearmans rank correlation and Matthews correlation coefficient, to accommodate co-occurring pathologic changes. The added value of neuroimaging and CSF versus clinical features alone was quantified. Braak stage was among the most consistently predicted outcomes. CSF biomarkers best predicted {beta}-amyloid and tau pathology, but diffusion MRI metrics best captured vascular brain injury and white matter injury, and outperformed clinical and cognitive measures and anatomical MRI in predicting Lewy body disease. Anatomical measures from structural MRI outperformed standard clinical assessments in assessing neurodegeneration and hippocampal sclerosis, and WMH complemented cognitive measures in predicting TDP-43 pathology. These results establish a baseline for comparing modalities for inferring neuropathology.

neuroscience↗

bioRxiv2025

Increased grain weight conferred by GW2 mutations in wheat does not translate into yield gains in multi-year field trials of near-isogenic lines

Multiple studies have identified genes affecting grain morphology, yet their capacity to deliver yield gains under field conditions remains unclear. We performed a multiyear, multilocation factorial evaluation of GRAIN WIDTH2 (TaGW2) mutants in hexaploid wheat using BC4 near-isogenic lines, sowing-density treatments and semi-dwarfing RHT1 backgrounds. Loss-of-function mutations in TaGW2 increased grain size and thousand grain weight (TGW) additively; with the aaBBDD single mutant showing the most stable singlelocus effect, while the aabbdd triple mutant achieved ~20% higher TGW across twelve field trials. However, overall grain yield remained unchanged or slightly reduced, reflecting a compensatory trade-off with grain number. Spike phenotyping of both main and secondary tillers showed comparable increases in TGW and spike yield despite fewer grains per spike, indicating that limited yield gain primarily reflects reduced spike number per unit area rather than decreased spike-level productivity. Effects were stable across sowing densities, whereas interactions with semi-dwarfing alleles were allele-specific: RHT-B1b partially suppressed TGW gains and accentuated yield penalties, whereas RHT-D1b maintained the large-grain phenotype and productivity. Across experiments, the TaGW2-A1D1 double mutant increased TGW (~14%) while maintaining yield stability, identifying it as a promising genotype for breeding. We conclude that TaGW2 is a reliable modifier of grain size but not yield in isolation.

plant biology↗

bioRxiv2024

Hierarchical Bayesian inference to model continuous phenotypical progression in Alzheimer's Disease

Throughout an organisms life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimers Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurode-generative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimers Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.

neuroscience↗

bioRxiv2021

Long distance migration is a major factor driving local adaptation at continental scale in Coho Salmon

Inferring the genomic basis of local adaptation is a long-standing goal of evolutionary biology. Beyond its fundamental evolutionary implications, such knowledge can guide conservation decisions for populations of conservation and management concern. Here, we investigated the genomic basis of local adaptation in the Coho salmon (Oncorhynchus kisutch) across its entire North American range. We hypothesized that extensive spatial variation in environmental conditions and the species homing behavior may promote the establishment of local adaptation. We genotyped 7,829 individuals representing 217 sampling locations at more than 100,000 high-quality RADseq loci to investigate how recombination might affect the detection of loci putatively under selection and took advantage of the precise description of the demographic history of the species from our previous work to draw accurate population genomic inferences about local adaptation. Results indicated that genetic differentiation scans and genetic-environment association analyses were both significantly affected by variation in recombination rate as low recombination regions displayed an increased number of outliers. By taking these confounding factors into consideration, we revealed that migration distance was the primary selective factor driving local adaptation and partial parallel divergence among distant populations. Moreover, we identified several candidates SNP associated with long distance migration and altitude including a gene known to be involved in adaptation to altitude in other species. The evolutionary implications of our findings are discussed along with conservation applications.

evolutionary biology↗

bioRxiv2020

Increased and ectopic expression of Triticum polonicum VRT-A2 underlies elongated glumes and grains in hexaploid wheat in a dosage-dependent manner

Flower development is a major determinant of yield in crops. In wheat, natural variation for the size of spikelet and floral organs is particularly evident in Triticum polonicum, a tetraploid subspecies of wheat with long glumes, lemmas, and grains. Using map-based cloning, we identified VRT2, a MADS-box transcription factor belonging to the SVP family, as the gene underlying the T. polonicum long-glume (P1) locus. The causal P1 mutation is a sequence re-arrangement in intron-1 that results in both increased and ectopic expression of the T. polonicum VRT-A2 allele. Based on allelic variation studies, we propose that the intron-1 mutation in VRT-A2 is the unique T. polonicum species defining polymorphism, which was later introduced into hexaploid wheat via natural hybridizations. Near-isogenic lines differing for the P1 locus revealed a gradient effect of P1 across florets. Transgenic lines of hexaploid wheat carrying the T. polonicum VRT-A2 allele show that expression levels of VRT-A2 are highly correlated with spike, glume, grain, and floral organ length. These results highlight how changes in expression profiles, through variation in cis-regulation, can impact on agronomic traits in a dosage-dependent manner in polyploid crops. One-sentence summaryAn intron-1 rearrangement in the MADS-box transcription factor VRT-A2 leads to its misexpression and defines the long-glume phenotype of Polish wheat (T. polonicum).

plant biology↗

bioRxiv2019

17q21.31 sub-haplotypes underlying H1-associated risk for Parkinson's disease and progressive supranuclear palsy converge on altered glial regulation

Parkinsons disease (PD) is genetically associated with the H1 haplotype of the MAPT 17q.21.31 locus, although the causal gene and variants underlying this association have not been identified. To better understand the genetic contribution of this region to PD, we fine-mapped the 17q21.31 locus in order to identify novel mechanisms conferring risk for the disease. We identified three novel H1 sub-haplotype blocks across the 17q21.31 locus associated with PD risk. Protective sub-haplotypes were associated with increased LRRC37A/2 copy number and expression in human brain tissue. We found that LRRC37A/2 is a membrane-associated protein that plays a role in cellular migration, chemotaxis and astroglial inflammation. In human substantia nigra, LRRC37A/2 was primarily expressed in astrocytes, interacted directly with soluble -synuclein, and co-localized with Lewy bodies in PD brain tissue. These data indicate that a novel candidate gene, LRRC37A/2, contributes to the association between the 17q21.31 locus and PD via its interaction with -synuclein and its effects on astrocytic function and inflammatory response. These data are the first to associate the genetic association at the 17q21.31 locus with PD pathology, and highlight the importance of variation at the 17q21.31 locus in the regulation of multiple genes other than MAPT and KANSL1, as well as its relevance to non-neuronal cell types.

neuroscience↗

bioRxiv2019

Demographic history, linked selection, and recombination shape the genomic landscape of a broadly distributed Pacific salmon.

A thorough reconstruction of historical processes is essential for a comprehensive understanding the mechanisms shaping patterns of genetic diversity. Indeed, past and current conditions influencing effective population size have important evolutionary implications for the efficacy of selection, increased accumulation of deleterious mutations, and loss of adaptive potential. Here, we gather extensive genome-wide data that represent the extant diversity of the Coho salmon (Oncorhynchus kisutch) to address two objectives. We demonstrate that a single glacial refugium is the source of most of the present-day genetic diversity, with detectable inputs from a putative secondary micro-refugium. We found statistical support for a scenario whereby ancestral populations located south of the ice sheets expanded in postglacial time, swamping out most of the diversity from other putative micro-refugia. Demographic inferences revealed that genetic diversity was also affected by linked selection in large parts of the genome. Moreover, we demonstrate that the recent demographic history of this species generated regional differences in the load of deleterious mutations among populations, a finding that mirrors recent results from human populations and provides increased support for models of expansion load. We propose that insights from these historical inferences should be better integrated in conservation planning of wild organisms, which currently focuses largely on neutral genetic diversity and local adaptation, with the role of potentially maladaptive variation being generally ignored.

evolutionary biology↗

bioRxiv2019

Failure to detect synergy between variants in transferrin and hemochromatosis and Alzheimer’s disease in large cohort

Alzheimers disease (AD) is the most common cause of dementia and, despite decades of effort, there is no effective treatment. In the last decade, many association studies have identified genetic markers that are associated with AD status. Two of these studies suggest that an epistatic interaction between variants rs1049296 in the Transferrin (TF) gene and rs1800562 in the Homeostatic Iron Regulator (HFE) gene, commonly known as \"the hemochromatosis gene\", is in genetic association with AD. TF and HFE are involved in the transport and regulation of iron in the brain, and disrupting these processes exacerbates AD pathology through increased neurodegeneration and oxidative stress. However, by using a significantly larger dataset from the Alzheimers Disease Genetics Consortium (ADGC), we fail to detect an association between TF rs1049296 or HFE rs1800562 with AD risk (TF rs1049296 p=0.38 and HFE rs1800562 p=0.40). In addition, logistic regression with an interaction term and a Synergy Factor Analysis (SFA) both failed to detect epistasis between TF rs1049296 and HFE rs1800562 (SF=0.94; p=0.48) in AD cases. Each of these analyses had sufficient statistical power (Power>0.99), suggesting that previously-reported associations may be the result of more complex epistatic interactions, genetic heterogeneity, or were false-positive associations due to limited sample sizes.

genetics↗

bioRxiv2019

Systematic analysis of dark and camouflaged genes: disease-relevant genes hiding in plain sight

BackgroundThe human genome contains dark gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions that are dark by depth (few mappable reads) and others that are camouflaged (ambiguous alignment), and we assess how well long-read technologies resolve these regions. We further present an algorithm to resolve most camouflaged regions (including in short-read data) and apply it to the Alzheimers Disease Sequencing Project (ADSP; 13142 samples), as a proof of principle.\n\nResultsBased on standard whole-genome lllumina sequencing data, we identified 37873 dark regions in 5857 gene bodies (3635 protein-coding) from pathways important to human health, development, and reproduction. Of the 5857 gene bodies, 494 (8.4%) were 100% dark (142 protein-coding) and 2046 (34.9%) were [≥]5% dark (628 protein-coding). Exactly 2757 dark regions were in protein-coding exons (CDS) across 744 genes. Long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduced dark CDS regions to approximately 45.1%, 33.3%, and 18.2% respectively. Applying our algorithm to the ADSP, we rescued 4622 exonic variants from 501 camouflaged genes, including a rare, ten-nucleotide frameshift deletion in CR1, a top Alzheimers disease gene, found in only five ADSP cases and zero controls.\n\nConclusionsWhile we could not formally assess the CR1 frameshift mutation in Alzheimers disease (insufficient sample-size), we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.

genomics↗

bioRxiv2018

Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimers disease (AD; N=61,212). Among 1,738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Further, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

genetics↗