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Gong B, Li D, Zhang Y, Kusko R, Lababidi S, Cao Z, Chen M, Chen N, Chen Q, Chen Q, Dai J, Gan Q, Gao Y, Guo M, Hariani G, He Y, Hou W, Jiang H, Kushwaha G, Li JL, Li J, Li Y, Liu LC, Liu R, Liu S, Meriaux E, Mo M, Moore M, Moss TJ, Niu Q, Patel A, Ren L, Saremi NF, Shang E, Shang J, Song P, Sun S, Urban BJ, Wang D, Wang S, Wen Z, Xiong X, Yang J, Yin L, Zhang C, Zhang R, Bhandari A, Cai W, Eterovic AK, Megherbi DB, Shi T, Suo C, Yu Y, Zheng Y, Novoradovskaya N, Sears RL, Shi L, Jones W, Tong W, Xu J. Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project. Sci Rep 2024; 14:7028. [PMID: 38528062 DOI: 10.1038/s41598-024-57439-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 03/18/2024] [Indexed: 03/27/2024] Open
Abstract
Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.
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Affiliation(s)
- Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Yifan Zhang
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Rebecca Kusko
- Cellino Bio, 750 Main Street, Cambridge, MA, 02143, USA
| | - Samir Lababidi
- Office of Data Analytics and Research, Office of Digital Transformation, Office of the Commissioner, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Mingyang Chen
- Human Phenome Institute, Fudan University, Shanghai, 201203, China
| | - Ning Chen
- iGeneTech Bioscience Co., Ltd., 8 Shengmingyuan Rd., Changping, Beijing, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Jiacheng Dai
- Human Phenome Institute, Fudan University, Shanghai, 201203, China
| | - Qiang Gan
- Clinical Diagnostics Division, Thermo Fisher Scientific, 46500 Kato Rd., Fremont, CA, 94538, USA
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Mingkun Guo
- College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, China
| | - Gunjan Hariani
- Q squared Solutions Genomics, 2400 Ellis Road, Durham, NC, 27703, USA
| | - Yujie He
- College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Garima Kushwaha
- Guardant Health, Inc., 505 Penobscot Drive, Redwood City, CA, 94063, USA
| | - Jian-Liang Li
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA
| | - Jianying Li
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA
| | - Yulan Li
- College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Liang-Chun Liu
- Clinical Diagnostics Division, Thermo Fisher Scientific, 46500 Kato Rd., Fremont, CA, 94538, USA
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Shiming Liu
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Edwin Meriaux
- CMINDS Research Center, University of Massachusetts, Lowell, MA, 01854, USA
| | - Mengqing Mo
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
| | | | - Tyler J Moss
- Eurofins Viracor, LLC, 18000 W 99th St., Lenexa, KS, 66219, USA
| | - Quanne Niu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Ananddeep Patel
- Eurofins Viracor Biopharma Services, Inc., 18000 W 99th St., Lenexa, KS, 66219, USA
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Nedda F Saremi
- Agilent Technologies, Inc., 11011 N Torrey Pines Rd., La Jolla, CA, 92037, USA
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Ping Song
- Cancer Genomics Laboratory, Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Siqi Sun
- ResearchDx, Irvine, CA, 92618, USA
| | - Brent J Urban
- Eurofins Viracor Biopharma Services, Inc., 18000 W 99th St., Lenexa, KS, 66219, USA
| | - Danke Wang
- Human Phenome Institute, Fudan University, Shanghai, 201203, China
| | - Shangzi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Zhining Wen
- College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, China
| | - Xiangyi Xiong
- College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Lihui Yin
- PathGroup, Nashville, TN, 37217, USA
| | - Chao Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | | | - Wanshi Cai
- iGeneTech Bioscience Co., Ltd., 8 Shengmingyuan Rd., Changping, Beijing, China
| | - Agda Karina Eterovic
- Eurofins Viracor Biopharma Services, Inc., 18000 W 99th St., Lenexa, KS, 66219, USA
| | - Dalila B Megherbi
- CMINDS Research Center, University of Massachusetts, Lowell, MA, 01854, USA
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Chen Suo
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | | | - Renee L Sears
- Velsera, 6 Cityplace Dr Suite 550, Creve Coeur, MO, 63141, USA
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Wendell Jones
- Q squared Solutions Genomics, 2400 Ellis Road, Durham, NC, 27703, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.
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Boatwright JL, Sapkota S, Jin H, Schnable JC, Brenton Z, Boyles R, Kresovich S. Sorghum Association Panel whole-genome sequencing establishes cornerstone resource for dissecting genomic diversity. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:888-904. [PMID: 35653240 PMCID: PMC9544330 DOI: 10.1111/tpj.15853] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 05/26/2023]
Abstract
Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole-genome sequencing (WGS) of 400 sorghum (Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25-72×), enabling the development of a high-density genomic marker set of 43 983 694 variants including single-nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5-kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six Fst peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.
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Affiliation(s)
- J. Lucas Boatwright
- Department of Plant and Environmental SciencesClemson UniversityClemsonSouth Carolina29634USA
- Advanced Plant TechnologyClemson UniversityClemsonSouth Carolina29634USA
| | - Sirjan Sapkota
- Advanced Plant TechnologyClemson UniversityClemsonSouth Carolina29634USA
| | - Hongyu Jin
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska68588USA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska68588USA
| | | | - Richard Boyles
- Department of Plant and Environmental SciencesClemson UniversityClemsonSouth Carolina29634USA
- Pee Dee Research and Education CenterClemson UniversityFlorenceSouth Carolina29506USA
| | - Stephen Kresovich
- Department of Plant and Environmental SciencesClemson UniversityClemsonSouth Carolina29634USA
- Advanced Plant TechnologyClemson UniversityClemsonSouth Carolina29634USA
- Feed the Future Innovation Lab for Crop ImprovementCornell UniversityIthacaNew York14850USA
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3
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Han F, Qian L, Zhang Y, Liu P, Li R, Chen M. C2CD4A/B variants in the predisposition of lung cancer in the Chinese Han population. Funct Integr Genomics 2022; 22:331-340. [PMID: 35212842 DOI: 10.1007/s10142-022-00827-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 01/20/2022] [Accepted: 01/20/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND The mRNA levels of C2CD4A and C2CD4B were dysregulation in lung cancer (LC). We aimed to evaluate the role of C2CD4A/B variants in LC susceptibility. METHODS There were 710 cases with LC and 710 healthy controls enrolled in the study. The genotyping of twelve variants in C2CD4A/B was carried out by Agena MassARRAY system. Odds ratios (ORs) were calculated by logistic regression analysis to assess the relationship between these variants and LC predisposition. RESULTS Rs8037894 (OR = 0.81, p = 0.005), rs7172432 (OR = 0.83, p = 0.013), rs11856307 (OR = 0.86, p = 0.043), and rs1436953 (OR = 0.79, p = 0.002) were related to the reduced risk of LC. Besides, the relation of rs7172432 with LC risk in subjects aged > 60 years was observed. Rs4502156 conferred to the increased LC risk, while rs1436953 was associated with the lower susceptibility to LC among males. Rs731820, rs4502156, rs11071657, rs7172432, and rs11856307 contributed to the predisposition of LC among subjects with BMI > 24 kg/m2, while rs7495253 was associated with an increased risk of LC in subjects with BMI ≤ 24 kg/m2. The increased LC risk was found in rs4502156, while the protective risk effect of rs8037894, rs7172432, rs11856307, and rs1436953 on the occurrence of LC was observed in smokers and non-drinkers. Moreover, rs7495253 and rs7495931 had a higher risk of lymphatic metastasis. Rs1436953 was related to the reduced risk of lung adenocarcinoma, while rs4502156, rs8037894, rs7172432, rs11856307, and rs1436953 were related to the risk of small cell carcinoma. CONCLUSIONS Our results first display that C2CD4A/B polymorphisms served as protective factors for LC predisposition in a Chinese Han population. These findings could provide new biological insight into the understanding of C2CD4A/B genes on LC pathogenesis.
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Affiliation(s)
- Feifei Han
- Department of Respiratory Medicine, The First Afliated Hospital of School of Medicine of Xi'an Jiaotong University, #277 Yanta West Road, Xi'an, Shaanxi, 710061, China.,Department of Endocrinology, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, 710018, Shaanxi, China
| | - Lu Qian
- Department of Endocrinology, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, 710018, Shaanxi, China
| | - Yi Zhang
- Department of Endocrinology, Ninth Hospital of Xi'an, Xi'an, Shaanxi, 710054, China
| | - Ping Liu
- Department of Endocrinology, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, 710018, Shaanxi, China
| | - Rui Li
- Department of Rheumatology, Xi'an No.5 Hospital, Xi'an, 710082, Shaanxi, China
| | - Mingwei Chen
- Department of Respiratory Medicine, The First Afliated Hospital of School of Medicine of Xi'an Jiaotong University, #277 Yanta West Road, Xi'an, Shaanxi, 710061, China.
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4
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Cai Y, Zhang S, Zhang J, Liu X, Ma K, Xu W, Deng X, Yang J, Ma T, Jiang C, Hui W, Cui Y. Visual Detection of ACE I/D Polymorphism Using T-ARMS-PCR Combined with a Lateral Flow Assay and Its Clinical Application. Anal Chem 2022; 94:4686-4694. [PMID: 35271257 DOI: 10.1021/acs.analchem.1c04817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Insertions/deletions (indels) variations have been recognized as a promising marker for the development of various diseases. However, methods used for the genotyping of indels in studies were tedious, complicated, and required sophisticated or expensive instruments, as well as complex data analysis, which makes it difficult to meet the demand of point of care testing. Herein, we presented a fast and accurate biosensor (T-ARMS-PCR-LFA) by the combination of tetra-primer amplification refractory mutation system polymerase chain reaction (T-ARMS-PCR) and GoldMag lateral flow assay (LFA) for visual genotyping of ACE I/D polymorphism. ACE I/D can be distinguished by employing four primers in one PCR reaction, and genotyping results were presented by the visual inspection of colors on the nitrocellulose membrane of LFA strips within 5 min. And 50 of the human genomic DNA samples were used for the detection of ACE I/D to further validate the accuracy of the T-ARMS-PCR-LFA system. As a demonstration, we showed that ACE I/D could be genotyped using a low amount of DNA sample (25 ng) with an accuracy of 100%, without complicated operation steps and data analysis, which is better than that of the conventional method (agarose gel electrophoresis analysis after common PCR). In conclusion, the biosensor is highly applicable for genotyping specific large indel variants in clinical practices, which enables rapid clinical decision-making, improves the management of disease diagnosis, and facilitates personalized medicine.
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Affiliation(s)
- Yu Cai
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
| | - Sinong Zhang
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
| | - Jiaxing Zhang
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
| | - Xiaonan Liu
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
| | - Kang Ma
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
| | - Wei Xu
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
| | - Xianghai Deng
- Department of Clinical Laboratory, Ankang Hospital of Traditional Chinese Medicine, Ankang, Shaanxi 725099, China
| | - Jiangcun Yang
- Department of Blood Transfusion, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi 710068, China
| | - Ting Ma
- Department of Blood Transfusion, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi 710068, China
| | - Chao Jiang
- The Second Affiliated Hospital of Xi'an Medical University and Shaanxi Key Laboratory of Brain Disorders, Xi'an, Shaanxi 710038, China
| | - Wenli Hui
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
| | - Yali Cui
- College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.,National Engineering Research Center for Miniaturized Detection System, Xi'an, Shaanxi 710069, China
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Ramachandran A, Lumetta SS, Klee EW, Chen D. HELLO: improved neural network architectures and methodologies for small variant calling. BMC Bioinformatics 2021; 22:404. [PMID: 34391391 PMCID: PMC8364080 DOI: 10.1186/s12859-021-04311-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 07/30/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy. In this paper, we explore an alternative approach to designing Deep Neural Networks for variant calling, where we use meticulously designed Deep Neural Network architectures and customized variant inference functions that account for the underlying nature of sequencing data instead of converting the problem to one of image recognition. RESULTS Results from 27 whole-genome variant calling experiments spanning Illumina, PacBio and hybrid Illumina-PacBio settings suggest that our method allows vastly smaller Deep Neural Networks to outperform the Inception-v3 architecture used in DeepVariant for indel and substitution-type variant calls. For example, our method reduces the number of indel call errors by up to 18%, 55% and 65% for Illumina, PacBio and hybrid Illumina-PacBio variant calling respectively, compared to a similarly trained DeepVariant pipeline. In these cases, our models are between 7 and 14 times smaller. CONCLUSIONS We believe that the improved accuracy and problem-specific customization of our models will enable more accurate pipelines and further method development in the field. HELLO is available at https://github.com/anands-repo/hello.
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Affiliation(s)
- Anand Ramachandran
- Department of Electrical and Computer Engineering, University of Illinois At Urbana-Champaign, Urbana, IL, 61801, USA
| | - Steven S Lumetta
- Department of Electrical and Computer Engineering, University of Illinois At Urbana-Champaign, Urbana, IL, 61801, USA
| | - Eric W Klee
- Biomedical Statistics and Informatics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Deming Chen
- Department of Electrical and Computer Engineering, University of Illinois At Urbana-Champaign, Urbana, IL, 61801, USA.
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6
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Liu Y, Xia J, McKay J, Tsavachidis S, Xiao X, Spitz MR, Cheng C, Byun J, Hong W, Li Y, Zhu D, Song Z, Rosenberg SM, Scheurer ME, Kheradmand F, Pikielny CW, Lusk CM, Schwartz AG, Wistuba II, Cho MH, Silverman EK, Bailey-Wilson J, Pinney SM, Anderson M, Kupert E, Gaba C, Mandal D, You M, de Andrade M, Yang P, Liloglou T, Davies MPA, Lissowska J, Swiatkowska B, Zaridze D, Mukeria A, Janout V, Holcatova I, Mates D, Stojsic J, Scelo G, Brennan P, Liu G, Field JK, Hung RJ, Christiani DC, Amos CI. Rare deleterious germline variants and risk of lung cancer. NPJ Precis Oncol 2021; 5:12. [PMID: 33594163 PMCID: PMC7887261 DOI: 10.1038/s41698-021-00146-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 12/11/2020] [Indexed: 01/19/2023] Open
Abstract
Recent studies suggest that rare variants exhibit stronger effect sizes and might play a crucial role in the etiology of lung cancers (LC). Whole exome plus targeted sequencing of germline DNA was performed on 1045 LC cases and 885 controls in the discovery set. To unveil the inherited causal variants, we focused on rare and predicted deleterious variants and small indels enriched in cases or controls. Promising candidates were further validated in a series of 26,803 LCs and 555,107 controls. During discovery, we identified 25 rare deleterious variants associated with LC susceptibility, including 13 reported in ClinVar. Of the five validated candidates, we discovered two pathogenic variants in known LC susceptibility loci, ATM p.V2716A (Odds Ratio [OR] 19.55, 95%CI 5.04-75.6) and MPZL2 p.I24M frameshift deletion (OR 3.88, 95%CI 1.71-8.8); and three in novel LC susceptibility genes, POMC c.*28delT at 3' UTR (OR 4.33, 95%CI 2.03-9.24), STAU2 p.N364M frameshift deletion (OR 4.48, 95%CI 1.73-11.55), and MLNR p.Q334V frameshift deletion (OR 2.69, 95%CI 1.33-5.43). The potential cancer-promoting role of selected candidate genes and variants was further supported by endogenous DNA damage assays. Our analyses led to the identification of new rare deleterious variants with LC susceptibility. However, in-depth mechanistic studies are still needed to evaluate the pathogenic effects of these specific alleles.
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Grants
- R01 CA060691 NCI NIH HHS
- U19 CA203654 NCI NIH HHS
- R01 CA084354 NCI NIH HHS
- R01 HL110883 NHLBI NIH HHS
- U01 CA076293 NCI NIH HHS
- R01 CA080127 NCI NIH HHS
- R01 CA141769 NCI NIH HHS
- P30 ES006096 NIEHS NIH HHS
- P50 CA090578 NCI NIH HHS
- P30 CA022453 NCI NIH HHS
- S10 RR024574 NCRR NIH HHS
- HHSN261201300011C NCI NIH HHS
- R01 CA134682 NCI NIH HHS
- R01 CA134433 NCI NIH HHS
- R01 HL113264 NHLBI NIH HHS
- R01 HL082487 NHLBI NIH HHS
- R01 CA250905 NCI NIH HHS
- U19 CA148127 NCI NIH HHS
- P20 GM103534 NIGMS NIH HHS
- R01 CA092824 NCI NIH HHS
- R01 CA087895 NCI NIH HHS
- U01 HL089897 NHLBI NIH HHS
- K07 CA181480 NCI NIH HHS
- HHSN268201100011I NHLBI NIH HHS
- HHSN268201100011C NHLBI NIH HHS
- R01 CA127219 NCI NIH HHS
- R01 CA074386 NCI NIH HHS
- P30 CA023108 NCI NIH HHS
- U01 HL089856 NHLBI NIH HHS
- P30 ES030285 NIEHS NIH HHS
- P30 CA125123 NCI NIH HHS
- DP1 AG072751 NIA NIH HHS
- U01 CA243483 NCI NIH HHS
- HHSN268200782096C NHLBI NIH HHS
- HHSN268201200007C NHLBI NIH HHS
- N01HG65404 NHGRI NIH HHS
- R35 GM122598 NIGMS NIH HHS
- U01 CA209414 NCI NIH HHS
- R03 CA077118 NCI NIH HHS
- 001 World Health Organization
- DP1 CA174424 NCI NIH HHS
- This work was supported by grants from the National Institutes of Health (R01CA127219, R01CA141769, R01CA060691, R01CA87895, R01CA80127, R01CA84354, R01CA134682, R01CA134433, R01CA074386, R01CA092824, R01CA250905, R01HL113264, R01HL082487, R01HL110883, R03CA77118, P20GM103534, P30CA125123, P30CA023108, P30CA022453, P30ES006096, P50CA090578, U01CA243483, U01HL089856, U01HL089897, U01CA76293, U19CA148127, U01CA209414, K07CA181480, N01-HG-65404, HHSN268200782096C, HHSN261201300011I, HHSN268201100011, HHSN268201 200007C, DP1-CA174424, DP1-AG072751, CA125123, RR024574, Intramural Research Program of the National Human Genome Research Institute (JEB-W), and Herrick Foundation. Dr. Amos is an Established Research Scholar of the Cancer Prevention Research Institute of Texas (RR170048). We also want to acknowledge the Cytometry and Cell Sorting Core support by the Cancer Prevention and Research Institute of Texas Core Facility (RP180672). At Toronto, the study is supported by The Canadian Cancer Society Research Institute (# 020214) to R. H., Ontario Institute for Cancer Research to R. H, and the Alan Brown Chair to G. L. and Lusi Wong Programs at the Princess Margaret Hospital Foundation. The Liverpool Lung Project is supported by Roy Castle Lung Cancer Foundation.
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Affiliation(s)
- Yanhong Liu
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Jun Xia
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - James McKay
- International Agency for Research on Cancer, Lyon, France
| | - Spiridon Tsavachidis
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Margaret R Spitz
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Chao Cheng
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jinyoung Byun
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Wei Hong
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Yafang Li
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Dakai Zhu
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Zhuoyi Song
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Susan M Rosenberg
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Michael E Scheurer
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Farrah Kheradmand
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Claudio W Pikielny
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Christine M Lusk
- Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA
| | - Ann G Schwartz
- Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Susan M Pinney
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Elena Kupert
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Colette Gaba
- The University of Toledo College of Medicine, Toledo, OH, USA
| | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Ming You
- Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Ping Yang
- Mayo Clinic College of Medicine, Scottsdale, AZ, USA
| | - Triantafillos Liloglou
- Roy Castle Lung Cancer Research Programme, The University of Liverpool, Department of Molecular and Clinical Cancer Medicine, Liverpool, UK
| | - Michael P A Davies
- Roy Castle Lung Cancer Research Programme, The University of Liverpool, Department of Molecular and Clinical Cancer Medicine, Liverpool, UK
| | - Jolanta Lissowska
- M. Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Beata Swiatkowska
- Nofer Institute of Occupational Medicine, Department of Environmental Epidemiology, Lodz, Poland
| | - David Zaridze
- Russian N.N. Blokhin Cancer Research Centre, Moscow, Russian Federation
| | - Anush Mukeria
- Russian N.N. Blokhin Cancer Research Centre, Moscow, Russian Federation
| | - Vladimir Janout
- Faculty of Health Sciences, Palacky University, Olomouc, Czech Republic
| | - Ivana Holcatova
- Institute of Public Health and Preventive Medicine, Charles University, 2nd Faculty of Medicine, Prague, Czech Republic
| | - Dana Mates
- National Institute of Public Health, Bucharest, Romania
| | - Jelena Stojsic
- Department of Thoracopulmonary Pathology, Service of Pathology, Clinical Center of Serbia, Belgrade, Serbia
| | | | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | - Geoffrey Liu
- Princess Margaret Cancer Center, Toronto, ON, Canada
| | - John K Field
- Roy Castle Lung Cancer Research Programme, The University of Liverpool, Department of Molecular and Clinical Cancer Medicine, Liverpool, UK
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | | | - Christopher I Amos
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
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