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      Accurate genome-wide genotyping from archival tissue to explore the contribution of common genetic variants to pre-cancer outcomes

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          Abstract

          Purpose

          The contribution of common genetic variants to pre-cancer progression is understudied due to long follow-up time, rarity of poor outcomes and lack of available germline DNA collection. Alternatively, DNA from diagnostic archival tissue is available, but its somatic nature, limited quantity and suboptimal quality would require an accurate cost-effective genome-wide germline genotyping methodology.

          Experimental design

          Blood and tissue DNA from 10 individuals were used to benchmark the accuracy of Single Nucleotide Polymorphisms (SNP) genotypes, Polygenic Risk Scores (PRS) or HLA haplotypes using low-coverage whole-genome sequencing (lc-WGS) and genotype imputation. Tissue-derived PRS were further evaluated for 36 breast cancer patients (11.7 years median follow-up time) diagnosed with DCIS and used to model the risk of Breast Cancer Subsequent Events (BCSE).

          Results

          Tissue-derived germline DNA profiling resulted in accurate genotypes at common SNPs (blood correlation r 2 > 0.94) and across 22 disease-related polygenic risk scores (PRS, mean correlation r = 0.93). Imputed Class I and II HLA haplotypes were 96.7% and 82.5% concordant with clinical-grade blood HLA haplotypes, respectively. In DCIS patients, tissue-derived PRS was significantly associated with BCSE (HR = 2, 95% CI 1.2–3.8). The top and bottom decile patients had an estimated 28% and 5% chance of BCSE at 10 years, respectively.

          Conclusions

          Archival tissue DNA germline profiling using lc-WGS and imputation, represents a cost and resource-effective alternative in the retrospective design of long-term disease genetic studies. Initial results in breast cancer suggest that common risk variants contribute to pre-cancer progression.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12967-022-03810-z.

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          Most cited references62

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          Cancer statistics, 2022

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.
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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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              Twelve years of SAMtools and BCFtools

              Abstract Background SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.
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                Author and article information

                Contributors
                oharismendy@ucsd.edu
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                27 December 2022
                27 December 2022
                2022
                : 20
                : 623
                Affiliations
                [1 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Bioinformatics and Systems Biology Graduate Program, , University of California San Diego, ; 9500 Gilman Drive, San Diego, CA 92093 USA
                [2 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Biomedical Science Graduate Program, , University of California San Diego, ; 9500 Gilman Drive, San Diego, CA 92093 USA
                [3 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Moores Cancer Center, , University of California San Diego, ; 3855 Health Science Drive, San Diego, CA 92093 USA
                [4 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Surgery, , University of California San Diego, ; 9500 Gilman Drive, San Diego, CA 92093 USA
                [5 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Pathology, , University of California San Diego, ; 9500 Gilman Drive, San Diego, CA 92093 USA
                [6 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Division of Medical Genetics, Department of Medicine, , University of California San Diego, ; La Jolla, CA 92093 USA
                [7 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Division of Biomedical Informatics, Department of Medicine, , University of California San Diego, ; 9500 Gilman Drive, San Diego, CA 92093 USA
                Author information
                http://orcid.org/0000-0002-8098-9888
                Article
                3810
                10.1186/s12967-022-03810-z
                9793518
                36575447
                eb1215c5-c21c-4652-acc5-830d8fee900b
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 3 September 2022
                : 5 December 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100005440, Center for Scientific Review;
                Award ID: U01CA196406
                Award ID: U01CA196383
                Award ID: T32GM008806
                Award ID: T15LM011271
                Award ID: U54CA209891
                Award ID: P30CA023100
                Funded by: FundRef http://dx.doi.org/10.13039/100014599, Mark Foundation For Cancer Research;
                Award ID: #18-022-ELA
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005188, Tobacco-Related Disease Research Program;
                Award ID: 28DT-0011
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Medicine
                low-coverage whole-genome sequencing,breast cancer,ductal carcinoma in situ,polygenic risk score,pre-cancer,genotyping

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