| 1. |
- Craddock, Nick, et al.
(author)
-
Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls
- 2010
-
In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 464:7289, s. 713-720
-
Journal article (peer-reviewed)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.
|
|
| 2. |
- Cronin, M. F., et al.
(author)
-
Developing an Observing Air-Sea Interactions Strategy (OASIS) for the global ocean
- 2022
-
In: Ices Journal of Marine Science. - : Oxford University Press (OUP). - 1054-3139 .- 1095-9289. ; 80:2, s. 367-73
-
Journal article (peer-reviewed)abstract
- The Observing Air-Sea Interactions Strategy (OASIS) is a new United Nations Decade of Ocean Science for Sustainable Development programme working to develop a practical, integrated approach for observing air-sea interactions globally for improved Earth system (including ecosystem) forecasts, CO2 uptake assessments called for by the Paris Agreement, and invaluable surface ocean information for decision makers. Our "Theory of Change" relies upon leveraged multi-disciplinary activities, partnerships, and capacity strengthening. Recommendations from >40 OceanObs'19 community papers and a series of workshops have been consolidated into three interlinked Grand Ideas for creating #1: a globally distributed network of mobile air-sea observing platforms built around an expanded array of long-term time-series stations; #2: a satellite network, with high spatial and temporal resolution, optimized for measuring air-sea fluxes; and #3: improved representation of air-sea coupling in a hierarchy of Earth system models. OASIS activities are organized across five Theme Teams: (1) Observing Network Design & Model Improvement; (2) Partnership & Capacity Strengthening; (3) UN Decade OASIS Actions; (4) Best Practices & Interoperability Experiments; and (5) Findable-Accessible-Interoperable-Reusable (FAIR) models, data, and OASIS products. Stakeholders, including researchers, are actively recruited to participate in Theme Teams to help promote a predicted, safe, clean, healthy, resilient, and productive ocean.
|
|
| 3. |
|
|
| 4. |
- Martin, J., et al.
(author)
-
Sex-specific manifestation of genetic risk for attention deficit hyperactivity disorder in the general population
- 2018
-
In: Journal of Child Psychology and Psychiatry. - : Wiley. - 0021-9630 .- 1469-7610. ; 59:8, s. 908-916
-
Journal article (peer-reviewed)abstract
- Background: Attention deficit hyperactivity disorder (ADHD) is more commonly diagnosed in males than in females. A growing body of research suggests that females with ADHD might be underdiagnosed or receive alternative diagnoses, such as anxiety or depression. Other lines of reasoning suggest that females might be protected from developing ADHD, requiring a higher burden of genetic risk to manifest the disorder. Methods: We tested these two hypotheses, using common variant genetic data from two population-based cohorts. First, we tested whether females and males diagnosed with anxiety or depression differ in terms of their genetic risk for ADHD, assessed as polygenic risk scores (PRS). Second, we tested whether females and males with ADHD differed in ADHD genetic risk burden. We used three different diagnostic definitions: registry-based clinical diagnoses, screening-based research diagnoses and algorithm-based research diagnoses, to investigate possible referral biases. Results: In individuals with a registry-based clinical diagnosis of anxiety or depression, females had higher ADHD PRS than males [OR(CI) = 1.39 (1.12-1.73)] but there was no sex difference for screening-based [OR(CI) = 1.15 (0.94-1.42)] or algorithm-based [OR(CI) = 1.04 (0.89-1.21)] diagnoses. There was also no sex difference in ADHD PRS in individuals with ADHD diagnoses that were registry-based [OR(CI) = 1.04 (0.84-1.30)], screening-based [OR(CI) = 0.96 (0.85-1.08)] or algorithm-based [OR(CI) = 1.15 (0.78-1.68)]. Conclusions: This study provides genetic evidence that ADHD risk may be more likely to manifest or be diagnosed as anxiety or depression in females than in males. Contrary to some earlier studies, the results do not support increased ADHD genetic risk in females with ADHD as compared to affected males.
|
|
| 5. |
|
|
| 6. |
- Riglin, L, et al.
(author)
-
Investigating the genetic underpinnings of early-life irritability
- 2017
-
In: Translational psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 7:9, s. e1241-
-
Journal article (peer-reviewed)abstract
- Severe irritability is one of the commonest reasons prompting referral to mental health services. It is frequently seen in neurodevelopmental disorders that manifest early in development, especially attention-deficit/hyperactivity disorder (ADHD). However, irritability can also be conceptualized as a mood problem because of its links with anxiety/depressive disorders; notably DSM-5 currently classifies severe, childhood-onset irritability as a mood disorder. Investigations into the genetic nature of irritability are lacking although twin studies suggest it shares genetic risks with both ADHD and depression. We investigated the genetic underpinnings of irritability using a molecular genetic approach, testing the hypothesis that early irritability (in childhood/adolescence) is associated with genetic risk for ADHD, as indexed by polygenic risk scores (PRS). As a secondary aim we investigated associations between irritability and PRS for major depressive disorder (MDD). Three UK samples were utilized: two longitudinal population-based cohorts with irritability data from childhood (7 years) to adolescence (15–16 years), and one ADHD patient sample (6–18 years). Irritability was defined using parent reports. PRS were derived from large genome-wide association meta-analyses. We observed associations between ADHD PRS and early irritability in our clinical ADHD sample and one of the population samples. This suggests that early irritability traits share genetic risk with ADHD in the general population and are a marker of higher genetic loading in individuals with an ADHD diagnosis. Associations with MDD PRS were not observed. This suggests that early-onset irritability could be conceptualized as a neurodevelopmental difficulty, behaving more like disorders such as ADHD than mood disorders.
|
|
| 7. |
- Romagnoni, A, et al.
(author)
-
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
-
In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
-
Journal article (peer-reviewed)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.
|
|
