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      Genetic Risk, Health-Associated Lifestyle, and Risk of Early-onset Total Cancer and Breast Cancer

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      , MD, MPH 1 , # , , PhD 2 , 3 , , PhD 4 , 5 , 6 , , PhD, MS 5 , 7 , #
      medRxiv
      Cold Spring Harbor Laboratory

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          Abstract

          Importance

          Early-onset cancer (diagnosed under 50 years of age) is associated with aggressive disease characteristics and its rising incidence is a global concern. The association between healthy lifestyle and early-onset cancer and whether it varies by common genetic variants is unknown.

          Objective

          To examine the associations between genetic risk, lifestyle, and risk of early-onset cancers.

          Design, Setting, and Participants

          We analyzed a prospective cohort of 66,308 white British participants who were under age 50 and free of cancer at baseline in the UK Biobank.

          Exposures

          Sex-specific composite total cancer polygenic risk scores (PRSs), a breast cancer-specific PRS, and sex-specific health-associated lifestyle scores (HLSs, which summarize smoking status, body mass index [males only], physical activity, alcohol consumption, and diet).

          Main Outcomes and Measures

          Hazard ratios (HRs) and 95% confidence intervals (CIs) for early-onset total and breast cancer.

          Results

          A total of 1,247 incident invasive early-onset cancer cases (female: 820, male: 427, breast: 386) were documented. In multivariable-adjusted analyses with 2-year latency, higher genetic risk (highest vs. lowest tertile of PRS) was associated with significantly increased risks of early-onset total cancer in females (HR, 95% CI: 1.85, 1.50–2.29) and males (1.94, 1.45–2.59) as well as early-onset breast cancer in females (3.06, 2.20–4.25). An unfavorable lifestyle (highest vs. lowest category of HLS) was associated with higher risk of total cancer and breast cancer in females across genetic risk categories; the association with total cancer was stronger in the highest genetic risk category than the lowest: HRs in females and men were 1.85 (1.02, 3.36), 3.27 (0.78, 13.72) in the highest genetic risk category and 1.15 (0.44, 2.98), 1.16 (0.39, 3.40) in the lowest.

          Conclusions and Relevance

          Both genetic and lifestyle factors were independently associated with early-onset total and breast cancer risk. Compared to those with low genetic risk, individuals with a high genetic risk may benefit more from adopting a healthy lifestyle in preventing early-onset cancer.

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          Most cited references39

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

            Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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              The UK Biobank resource with deep phenotyping and genomic data

              The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                06 April 2024
                : 2024.04.04.24305361
                Affiliations
                [1. ]Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
                [2. ]Department of Epidemiology, University of Washington, Seattle, WA, USA
                [3. ]Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
                [4. ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
                [5. ]Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
                [6. ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [7. ]Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
                Author notes

                Author Contributions

                YZ, YL and PK have full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: YZ, YL, and PK. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: YZ and YL. Critical revision of the manuscript for important intellectual content: SL and PK. Statistical analysis: YZ and YL. Obtained funding: YZ, SL, and PK. Administrative, technical, or material support: SL and PK. Supervision: PK and YL.

                [# ]Corresponding Author: Yuxi Liu, PhD, MS, yuxiliu@ 123456mail.harvard.edu ; Yin Zhang, MD, MPH, yin.zhang@ 123456channing.harvard.edu
                Author information
                http://orcid.org/0000-0001-6367-6583
                http://orcid.org/0000-0003-2484-151X
                Article
                10.1101/2024.04.04.24305361
                11023660
                38633776
                d3a3efa6-d964-45d1-9046-d79f8c4e4a4b

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

                History
                Funding
                YZ is supported by Irene M. & Fredrick J. Stare Nutrition Education Fund Doctoral Scholarship and Mayer Fund Doctoral Scholarship. SL is supported by NIH grant R01CA194393. PK is supported by NIH grants R01 CA260352 and U01 CA249866. The funding sources played no role in the study design, data collection, data analysis, and interpretation of results, or the decisions made in preparation and submission of the article. UK Biobank has received core funding from the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency, the Welsh Government, British Heart Foundation, Cancer Research UK and Diabetes UK, National Institute for Health and Care Research. The details of UK Biobank core funding and additional funding are reported at https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/our-funding.
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