| 1. |
- Craddock, Nick, et al.
(författare)
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Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls
- 2010
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 464:7289, s. 713-720
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Tidskriftsartikel (refereegranskat)abstract
- Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed,19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated similar to 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.
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| 2. |
- Hollyfield, Shakira, et al.
(författare)
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An Observational Study of Outcomes Associated With Virtual Pain Management Programs Based on Acceptance and Commitment Therapy Implemented During the COVID-19 Pandemic
- 2023
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Ingår i: The Clinical Journal of Pain. - : Wolters Kluwer. - 0749-8047 .- 1536-5409. ; 39:10, s. 524-536
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Tidskriftsartikel (refereegranskat)abstract
- Objective: In response to COVID-19, virtual, group-based interdisciplinary pain management programs (PMPs) were rapidly implemented. This included implementing different intensities and formats of virtual PMPs to address a range of patient needs and complexity. This observational study investigated outcomes associated with virtual high and low-intensity and pre-neuromodulation PMPs based on acceptance and commitment therapy as part of routine care during the pandemic.Methods: Depending on patients’ needs, participants completed a virtual high-intensity or low-intensity PMP, or a virtual PMP in preparation for neuromodulation, from June 2020 to June 2022. Participants completed standardized measures of pain intensity and interference, work and social adjustment, depression, and pain acceptance before and after treatment. Data from 2018 to 2019 for in-person residential (n=561), outpatient (n=123), and pre-neuromodulation (n=207) PMPs were also examined to provide a historical benchmark of performance.Results: The virtual high-intensity PMP (n=294) showed significant improvements in all variables, with small effects. There were significant improvements with small effects for pain interference, depression, and acceptance for the virtual pre-neuromodulation PMP (n=129). No statistically significant improvements were observed for the virtual low-intensity PMP (n=90). The improvements associated with prepandemic in-person PMPs were generally larger relative to the virtual PMPs of comparable intensity delivered during the pandemic.Discussion: These data provide preliminary support for the potential benefits of high, but not low, intensity virtual acceptance and commitment therapy-based PMPs, including in the context of neuromodulation. Research is needed to maximize the impact of virtual PMPs and match patients with the most appropriate delivery format.
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| 3. |
- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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| 4. |
- Kanai, M, et al.
(författare)
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- 2023
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