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Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease.

ABSTRACT: Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

SUBMITTER: Munoz-Fuentes V

PROVIDER: S-EPMC8913486 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease.

Muñoz-Fuentes Violeta V Haselimashhadi Hamed H Santos Luis L Westerberg Henrik H Parkinson Helen H Mason Jeremy J

Mammalian genome : official journal of the International Mammalian Genome Society 20210915 1

Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the ...[more]

PMID: 34524473

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Project description:BackgroundIn drug development, it is important to have an understanding of the full spectrum of co-morbidities to be expected in the group of patients with the disease of interest. It is usually a challenge to identify the less common events associated with the target disease, even if these events are severe. The purpose of this study is to identify co-morbidities associated with rheumatoid arthritis (RA) as compared with a control group, using a large health care database.MethodsMarketscan US claims database was used for this retrospective cohort study. Selected were records of persons aged at least 16 Y with at least two claims for RA, and with active insurance status on June 30, 2007. The control group had at least two claims for eczema/dermatitis. Controls were matched by age, gender and insurance status (Medicare or not). All co-morbidities with an ICD9 diagnostic code were identified in the RA and control groups, during a one-year window. Relative risks (RRs) were calculated. Diagnoses were rank-ordered by magnitude of RR. Codes covering RA and arthropathy were excluded. In order to get stable estimates, rank-ordering was performed for diagnoses occurring in at least 20 persons in the control group.ResultsRecords were selected of 62,681 persons with RA (mean age was 59.0 Y, with 73.8% female, Medicare-covered 35%). A total of 6,897 different ICD9 diagnostic codes were recorded, with 2,220 codes in at least 20 persons of the control group [listed with Relative Risk]. Apart from joint/bone related conditions, strong associations with RA (RR > 3) were found for Adverse effect medicinal and biological substance not elsewhere classified, Unspecified adverse effect drug properly administered, Idiopathic fibrosing alveolitis, Osteomyelitis, Immune deficiency, Elevated sedimentation rate, Tuberculin test reaction abnormal or positive, Anemia and Cushing syndrome.ConclusionsData on a large number (> 60,000) of patients with a diagnosis of RA were used to analyze and to list a large number (> 2,000) of co-morbidities. Rank-ordering of RRs of diagnostic codes is a tool to identify quickly many conditions associated with RA.

Dataset Information

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease.

Publications

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease.

Similar Datasets

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