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      Five-year absolute risk estimates of colorectal cancer based on CCRAT model and polygenic risk scores: A validation study using the Quebec population-based cohort CARTaGENE

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          Abstract

          The objective was to evaluate the predictive performance of the Colorectal Cancer Risk Assessment Tool (CCRAT) and three polygenic risk scores (Hsu et al., 2015; Law et al., 2019, Archambault et al., 2020) to predict the occurrence of colorectal cancer at five years in a Quebec population-based cohort. By using the CARTaGENE cohort, we computed the absolute risk of colorectal cancer with the CCRAT model, the polygenic risk scores (PRS) and combined clinico-genetic models (CCRAT + PRS). We also tailored the CCRAT model by using the marginal age-specific colorectal incidence rates in Canada and the risk score distribution. We reported the calibration and the discrimination. Performances of the PRSs, combined and tailored CCRAT models were compared to the original CCRAT model. The expected-to-observed ratio of the original CCRAT model was 0.54 [0.43–0.68]. The c-index was 74.79 [68.3–80.5]. The tailored CCRAT model improved the expected-to-observed ratio (0.74 [0.59–0.94]) and c-index (76.39 [69.7–82.1]). All PRS improved the expected-to-observed ratios (around 0.83, confidence intervals including one). PRSs’ c-indexes were not significantly different from CCRAT models. Results from the combined models were close to those from the PRS models, Archambault combined model’s c-index being significantly higher than the original and tailored CCRAT models (78.67 [70.8–86.5]; p < 0.001 and p = 0.028, respectively). In this Quebec cohort, CCRAT model has a good discrimination with a poor calibration. While the tailored CCRAT provides some gain in calibration, clinico-genetic models improved both calibration and discrimination. However, better calibrations must be obtained before a practical use among the inhabitants of Quebec province.

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          Discovery of common and rare genetic risk variants for colorectal cancer

          To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P<5×10−8, bringing the number of known independent signals for CRC to approximately 100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs, somatic drivers, and support a role of immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of underlying biology, and impact personalized screening strategies and drug development.
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            Methods for identifying 30 chronic conditions: application to administrative data

            Background Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity. Methods We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as “high validity”; those with positive predictive value ≥70% and sensitivity <70% were graded as “moderate validity”. To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year. Results Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities. Conclusions We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions. Electronic supplementary material The online version of this article (doi:10.1186/s12911-015-0155-5) contains supplementary material, which is available to authorized users.
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              Association analyses identify 31 new risk loci for colorectal cancer susceptibility

              Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, and has a strong heritable basis. We report a genome-wide association analysis of 34,627 CRC cases and 71,379 controls of European ancestry that identifies SNPs at 31 new CRC risk loci. We also identify eight independent risk SNPs at the new and previously reported European CRC loci, and a further nine CRC SNPs at loci previously only identified in Asian populations. We use in situ promoter capture Hi-C (CHi-C), gene expression, and in silico annotation methods to identify likely target genes of CRC SNPs. Whilst these new SNP associations implicate target genes that are enriched for known CRC pathways such as Wnt and BMP, they also highlight novel pathways with no prior links to colorectal tumourigenesis. These findings provide further insight into CRC susceptibility and enhance the prospects of applying genetic risk scores to personalised screening and prevention.
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                Author and article information

                Contributors
                Journal
                Prev Med Rep
                Preventive Medicine Reports
                2211-3355
                27 December 2021
                February 2022
                27 December 2021
                : 25
                : 101678
                Affiliations
                [a ]CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
                [b ]Université de Montréal, Montréal, Québec, Canada
                [c ]University Paris-Saclay, CESP, INSERM, Villejuif, France
                [d ]Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, 12 Avenue Paul Vaillant Couturier, 94807 Villejuif, France
                Author notes
                [* ]Corresponding author at: 3175 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1C5, Canada. rodolphe.jantzen@ 123456gmail.com
                Article
                S2211-3355(21)00369-7 101678
                10.1016/j.pmedr.2021.101678
                8800052
                35127357
                8ac99ed4-ce67-4de5-a0ba-162e2cb2bdaa
                © 2022 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 22 August 2021
                : 21 October 2021
                : 24 December 2021
                Categories
                Regular Article

                polygenic risk score (prs),ccrat,model calibration,model discrimination,accuracy,colorectal cancer occurrence

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