Many factors, including environmental and genetic variables, contribute to Colorectal Cancer (CRC) risk. Some of these risk factors may share underlying genetics with CRC. We investigated potential shared genetics by performing a Phenome-wide association study (PheWAS) with a multi-ancestry CRC polygenic risk score (PRS). The discovery cohort (N=426,464) consisted of ancestrally diverse participants from the United Kingdom Biobank. The replication cohort (N=87,271) consisted of ancestrally diverse participants from the electronic Medical Records and Genomics Network. We used a mixed-effects model to adjust for the presence of related individuals in both datasets. To preserve power, we limited testing to ancestor phecodes derived from the electronic health record (EHR), which were not likely to be a result of CRC or its treatment. We discovered and replicated associations between the CRC PRS and breast cancer, prostate cancer, obesity, smoking and alcohol use (discovery p< 1.1e-4; replication p<0.0019). As these results corroborate findings from other studies using orthogonal methods, we demonstrate that a CRC PRS can be used as a proxy for genetic risk for CRC when investigating shared genetics between CRC and other phenotypes. Further study of the relationship between PRS from multiple traits with EHR data may reveal additional shared genetic factors.
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http://dx.doi.org/10.1101/2025.02.26.25322864 | DOI Listing |
JAMA Netw Open
March 2025
Department of Epidemiology, University of North Carolina at Chapel Hill.
Importance: Numerous efforts have been made to include diverse populations in genetic studies, but American Indian populations are still severely underrepresented. Polygenic scores derived from genetic data have been proposed in clinical care, but how polygenic scores perform in American Indian individuals and whether they can predict disease risk in this population remains unknown.
Objective: To study the performance of polygenic scores for cardiometabolic risk factors of lipid traits and C-reactive protein in American Indian adults and to determine whether such scores are helpful in clinical prediction for cardiometabolic diseases.
J Am Heart Assoc
March 2025
Background: Type 2 diabetes (T2D) is a major risk factor for atherosclerotic cardiovascular disease (ASCVD). This study examined the interplay between watching television and T2D genetic risk for risk of ASCVD.
Methods: We included 346 916 White British individuals from UK Biobank.
Am J Epidemiol
March 2025
Department of Public Health, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland.
Socioeconomic disadvantage at individual level is associated with poor cognitive outcomes but the link of neighbourhood disadvantage with cognitive function is unclear. We used data from Young Finns Study, a population-based cohort, to examine the associations of neighbourhood and individual-level disadvantage in childhood (age 3-21 years) and adulthood (age 22 up to the time of cognitive assessment) with cognitive function in mid-adulthood (age 35-49 years). Neighbourhood disadvantage was ascertained based on register data, including geo-coded address history.
View Article and Find Full Text PDFPharmacol Rep
March 2025
Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm, 11364, Sweden.
Background: OSU6162, a monoamine stabilizer, has demonstrated efficacy in reducing alcohol and anxiety-related behaviors in preclinical settings. In a previous randomized, double-blind, placebo-controlled trial involving patients with alcohol dependence (AD), OSU6162 significantly reduced craving for alcohol but did not alter drinking behaviors. This retrospective secondary analysis explores whether genetic predispositions related to AD and associated traits might influence the response to OSU6162 treatment in original trial participants.
View Article and Find Full Text PDFNat Med
March 2025
Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
The genetic background of many female reproductive health diagnoses remains uncharacterized, compromising our understanding of the underlying biology. Here, we map the genetic architecture across 42 female-specific health conditions using data from up to 293,618 women from two large population-based cohorts, the Estonian Biobank and the FinnGen study. Our study illustrates the utility of genetic analyses in understanding women's health better.
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