| 1. |
- Law, Philip J., et al.
(author)
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Association analyses identify 31 new risk loci for colorectal cancer susceptibility
- 2019
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10
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Journal article (peer-reviewed)abstract
- Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, and has a strong heritable basis. We report a genome-wide association analysis of 34,627 CRC cases and 71,379 controls of European ancestry that identifies SNPs at 31 new CRC risk loci. We also identify eight independent risk SNPs at the new and previously reported European CRC loci, and a further nine CRC SNPs at loci previously only identified in Asian populations. We use in situ promoter capture Hi-C (CHi-C), gene expression, and in silico annotation methods to identify likely target genes of CRC SNPs. Whilst these new SNP associations implicate target genes that are enriched for known CRC pathways such as Wnt and BMP, they also highlight novel pathways with no prior links to colorectal tumourigenesis. These findings provide further insight into CRC susceptibility and enhance the prospects of applying genetic risk scores to personalised screening and prevention.
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| 2. |
- Conti, David, V, et al.
(author)
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Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction
- 2021
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In: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 53:1, s. 65-75
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Journal article (peer-reviewed)abstract
- Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction. A meta-analysis of genome-wide association studies across different populations highlights new risk loci and provides a genetic risk score that can stratify prostate cancer risk across ancestries.
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| 3. |
- Wang, Anqi, et al.
(author)
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Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants
- 2023
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In: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 55:12, s. 2065-2074
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Journal article (peer-reviewed)abstract
- The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.
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| 4. |
- Bonner, Stephen, et al.
(author)
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A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
- 2022
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In: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; In Press
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Research review (peer-reviewed)abstract
- Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.
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| 5. |
- Van Deerlin, Vivian M, et al.
(author)
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Common variants at 7p21 are associated with frontotemporal lobar degeneration with TDP-43 inclusions
- 2010
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:3, s. 234-239
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Journal article (peer-reviewed)abstract
- Frontotemporal lobar degeneration (FTLD) is the second most common cause of presenile dementia. The predominant neuropathology is FTLD with TAR DNA-binding protein (TDP-43) inclusions (FTLD-TDP). FTLD-TDP is frequently familial, resulting from mutations in GRN (which encodes progranulin). We assembled an international collaboration to identify susceptibility loci for FTLD-TDP through a genome-wide association study of 515 individuals with FTLD-TDP. We found that FTLD-TDP associates with multiple SNPs mapping to a single linkage disequilibrium block on 7p21 that contains TMEM106B. Three SNPs retained genome-wide significance following Bonferroni correction (top SNP rs1990622, P = 1.08 x 10(-11); odds ratio, minor allele (C) 0.61, 95% CI 0.53-0.71). The association replicated in 89 FTLD-TDP cases (rs1990622; P = 2 x 10(-4)). TMEM106B variants may confer risk of FTLD-TDP by increasing TMEM106B expression. TMEM106B variants also contribute to genetic risk for FTLD-TDP in individuals with mutations in GRN. Our data implicate variants in TMEM106B as a strong risk factor for FTLD-TDP, suggesting an underlying pathogenic mechanism.
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| 6. |
- Hamilton, Paul J., et al.
(author)
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Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder
- 2016
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In: Psychiatry Research. - : Elsevier. - 0925-4927 .- 1872-7506. ; 249, s. 91-96
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Journal article (peer-reviewed)abstract
- Neural models of major depressive disorder (MDD) posit that over-response of components of the brains salience network (SN) to negative stimuli plays a crucial role in the pathophysiology of MDD. In the present proof-of-concept study, we tested this formulation directly by examining the affective consequences of training depressed persons to down-regulate response of SN nodes to negative material. Ten participants in the real neurofeedback group saw, and attempted to learn to down-regulate, activity from an empirically identified node of the SN. Ten other participants engaged in an equivalent procedure with the exception that they saw SN-node neurofeedback indices from participants in the real neurofeedback group. Before and after scanning, all participants completed tasks assessing emotional responses to negative scenes and to negative and positive self-descriptive adjectives. Compared to participants in the sham-neurofeedback group, from pre- to post-training, participants in the realneurofeedback group showed a greater decrease in SN-node response to negative stimuli, a greater decrease in self-reported emotional response to negative scenes, and a greater decrease in self-reported emotional response to negative self-descriptive adjectives. Our findings provide support for a neural formulation in which the SN plays a primary role in contributing to negative cognitive biases in MDD. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
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| 7. |
- Belov, Vladimir, et al.
(author)
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
- 2024
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In: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
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Journal article (peer-reviewed)abstract
- Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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| 8. |
- Chávez de Paz, Luis Eduardo, et al.
(author)
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Oral bacteria in biofilms exhibit slow reactivation from nutrient deprivation
- 2008
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In: Microbiology. - : Microbiology Society. - 1350-0872 .- 1465-2080. ; 154, s. 1927-1938
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Journal article (peer-reviewed)abstract
- The ability of oral bacteria to enter a non-growing state is believed to be an important mechanism for survival in the starved micro-environments of the oral cavity. In this study, we examined the reactivation of nutrient-deprived cells of two oral bacteria in biofilms, Streptococcus anginosus and Lactobacillus salivarius. Non-growing cells were generated by incubation in 10 mM potassium phosphate buffer for 24 h and the results were compared to those of planktonic cultures. When both types of cells were shifted from a rich, peptone-yeast extract-glucose (PYG) medium to buffer for 24 h, dehydrogenase and esterase activity measured by the fluorescent dyes 5-cyano-2,3-ditolyl-tetrazolium chloride (CTC) and fluorescein diacetate (FDA), respectively, was absent in both species. However, the membranes of the vast majority of nutrient-deprived cells remained intact as assessed by LIVE/DEAD staining. Metabolic reactivation of the nutrient-deprived biofilm cells was not observed for at least 48 h following addition of fresh PYG medium, whereas the non-growing planktonic cultures of the same two strains were in rapid growth in less than 2 h. At 72 h, the S. anginosus biofilm cells had recovered 78 % of the dehydrogenase activity and 61 % of the esterase activity and the biomass mm(-2) had increased by 30-35 %. With L. salivarius at 72 h, the biofilms had recovered 56 % and 75 % of dehydrogenase and esterase activity, respectively. Reactivation of both species in biofilms was enhanced by removal of glucose from PYG, and S. anginosus cells were particularly responsive to yeast extract (YE) medium. The data suggest that the low reactivity of non-growing biofilm cells to the introduction of fresh nutrients may be a survival strategy employed by micro-organisms in the oral cavity.
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| 9. |
- Frost Bellgowan, Julie, et al.
(author)
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A Neural Substrate for Behavioral Inhibition in the Risk for Major Depressive Disorder
- 2015
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In: Journal of the American Academy of Child and Adolescent Psychiatry. - : ELSEVIER SCIENCE BV. - 0890-8567 .- 1527-5418. ; 54:10, s. 841-848
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Journal article (peer-reviewed)abstract
- Objective: Behavioral inhibition (BI) is an early developing trait associated with cautiousness and development of clinical depression and anxiety. Little is known about the neural basis of BI and its predictive importance concerning risk for internalizing disorders. We looked at functional connectivity of the default-mode network (DMN) and salience network (SN), given their respective roles in self-relational and threat processing, in the risk for internalizing disorders, with an emphasis on determining the functional significance of these networks for BI. Method: We used functional magnetic resonance imaging to scan, during the resting state, children and adolescents 8 to 17 years of age who were either at high familial risk (HR; n = 16) or low familial risk (LR; n = 18) for developing clinical depression and/or anxiety. Whole-brain DMN and SN functional connectivity were estimated for each participant and compared across groups. We also compared the LR and HR groups on levels of BI and anxiety, and incorporated these data into follow-up neurobehavioral correlation analyses. Results: The HR group, relative to the LR group, showed significantly decreased DMN connectivity with the ventral striatum and bilateral sensorimotor cortices. Within the HR group, trait BI increased as DMN connectivity with the ventral striatum and sensorimotor cortex decreased. The HR and LR groups did not differ with respect to SN connectivity. Conclusion: Our findings show, in the risk for internalizing disorders, a negative functional relation between brain regions supporting self-relational processes and reward prediction. These findings represent a potential neural substrate for behavioral inhibition in the risk for clinical depression and anxiety.
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| 10. |
- Gallo, Selene, et al.
(author)
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Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies
- 2023
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In: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 28:7, s. 3013-3022
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Journal article (peer-reviewed)abstract
- The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.
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