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  1. Article ; Online: Leveraging pleiotropic association using sparse group variable selection in genomics data

    Matthew Sutton / Pierre-Emmanuel Sugier / Therese Truong / Benoit Liquet

    BMC Medical Research Methodology, Vol 22, Iss 1, Pp 1-

    2022  Volume 12

    Abstract: Abstract Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often ... ...

    Abstract Abstract Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.
    Keywords Genetic epidemiology ; High dimensional data ; Lasso penalization ; Oncology ; Pathway analysis ; Pleiotropy ; Medicine (General) ; R5-920
    Subject code 310
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Is health research undertaken where the burden of disease is greatest? Observational study of geographical inequalities in recruitment to research in England 2013–2018

    Peter Bower / Christos Grigoroglou / Laura Anselmi / Evangelos Kontopantelis / Matthew Sutton / Mark Ashworth / Philip Evans / Stephen Lock / Stephen Smye / Kathryn Abel

    BMC Medicine, Vol 18, Iss 1, Pp 1-

    2020  Volume 11

    Abstract: Abstract Background Research is fundamental to high-quality care, but concerns have been raised about whether health research is conducted in the populations most affected by high disease prevalence. Geographical distribution of research activity is ... ...

    Abstract Abstract Background Research is fundamental to high-quality care, but concerns have been raised about whether health research is conducted in the populations most affected by high disease prevalence. Geographical distribution of research activity is important for many reasons. Recruitment is a major barrier to research delivery, and undertaking recruitment in areas of high prevalence could be more efficient. Regional variability exists in risk factors and outcomes, so research done in healthier populations may not generalise. Much applied health research evaluates interventions, and their impact may vary by context (including geography). Finally, fairness dictates that publically funded research should be accessible to all, so that benefits of participating can be fairly distributed. We explored whether recruitment of patients to health research is aligned with disease prevalence in England. Methods We measured disease prevalence using the Quality and Outcomes Framework in England (total long-term conditions, mental health and diabetes). We measured research activity using data from the NIHR Clinical Research Network. We presented descriptive data on geographical variation in recruitment rates. We explored associations between the recruitment rate and disease prevalence rate. We calculated the share of patient recruitment that would need to be redistributed to align recruitment with prevalence. We assessed whether associations between recruitment rate and disease prevalence varied between conditions, and over time. Results There was significant geographical variation in recruitment rates. When areas were ranked by disease prevalence, recruitment was not aligned with prevalence, with disproportionately low recruitment in areas with higher prevalence of total long-term and mental health conditions. At the level of 15 local networks, analyses suggested that around 12% of current recruitment activity would need to be redistributed to align with disease prevalence. Overall, alignment showed little change over time, ...
    Keywords Research activity ; Recruitment ; Equity ; Medicine ; R
    Subject code 333
    Language English
    Publishing date 2020-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  3. Article ; Online: Collaborative case management to aid return to work after long-term sickness absence

    Cassandra Kenning / Karina Lovell / Mark Hann / Raymond Agius / Penny E Bee / Carolyn Chew-Graham / Peter A Coventry / Christina M van der Feltz-Cornelis / Simon Gilbody / Gillian Hardy / Stephen Kellett / David Kessler / Dean McMillan / David Reeves / Joanne Rick / Matthew Sutton / Peter Bower

    Public Health Research, Vol 6, Iss

    a pilot randomised controlled trial

    2018  Volume 2

    Abstract: Background: Despite high levels of employment among working-age adults in the UK, there is still a significant minority who are off work with ill health at any one time (so-called ‘sickness absence’). Long-term sickness absence results in significant ... ...

    Abstract Background: Despite high levels of employment among working-age adults in the UK, there is still a significant minority who are off work with ill health at any one time (so-called ‘sickness absence’). Long-term sickness absence results in significant costs to the individual, to the employer and to wider society. Objective: The overall objective of the intervention was to improve employee well-being with a view to aiding return to work. To meet this aim, a collaborative case management intervention was adapted to the needs of UK employees who were entering or experiencing long-term sickness absence. Design: A pilot randomised controlled trial, using permuted block randomisation. Recruitment of patients with long-term conditions in settings such as primary care was achieved by screening of routine records, followed by mass mailing of invitations to participants. However, the proportion of patients responding to such invitations can be low, raising concerns about external validity. Recruitment in the Case Management to Enhance Occupational Support (CAMEOS) study used this method to test whether or not it would transfer to a population with long-term sickness absence in the context of occupational health (OH). Participants: Employed people on long-term sickness absence (between 4 weeks and 12 months). The pilot was run with two different collaborators: a large organisation that provided OH services for a number of clients and a non-profit community-based organisation. Intervention: Collaborative case management was delivered by specially trained case managers from the host organisations. Sessions were delivered by telephone and supported use of a self-help handbook. The comparator was usual care as provided by participants’ general practitioner (GP) or OH provider. This varied for participants according to the services available to them. Neither participants nor the research team were blind to randomisation. Main outcome measures: Recruitment rates, intervention delivery and acceptability to participants were the ...
    Keywords sickness absence ; long-term ; occupational health ; case management ; pilot ; randomised controlled trial ; failure to recruit ; Public aspects of medicine ; RA1-1270
    Subject code 150
    Language English
    Publishing date 2018-01-01T00:00:00Z
    Publisher NIHR Journals Library
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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