Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Pathogenic Mutations in Cancer-Predisposing Genes: A Survey of 300 Patients with Whole-Genome Sequencing and Lifetime Electronic Health Records

  • Karen Y. He,

    Affiliation Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America

  • Yiqing Zhao,

    Affiliation Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America

  • Elizabeth W. McPherson,

    Affiliation Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America

  • Quan Li,

    Affiliation Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California, United States of America

  • Fan Xia,

    Affiliation Deparment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America

  • Chunhua Weng,

    Affiliation Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America

  • Kai Wang , (KW); (MMH)

    Affiliations Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California, United States of America, Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America, Institute for Genomic Medicine, Columbia University Medical Center, New York, New York, United States of America

  • Max M. He (KW); (MMH)

    Affiliations Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America, Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America, Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America



It is unclear whether and how whole-genome sequencing (WGS) data can be used to implement genomic medicine. Our objective is to retrospectively evaluate whether WGS can facilitate improving prevention and care for patients with susceptibility to cancer syndromes.

Methods and Findings

We analyzed genetic mutations in 60 autosomal dominant cancer-predisposition genes in 300 deceased patients with WGS data and nearly complete long-term (over 30 years) medical records. To infer biological insights from massive amounts of WGS data and comprehensive clinical data in a short period of time, we developed an in-house analysis pipeline within the SeqHBase software framework to quickly identify pathogenic or likely pathogenic variants. The clinical data of the patients who carried pathogenic and/or likely pathogenic variants were further reviewed to assess their clinical conditions using their lifetime EHRs. Among the 300 participants, 5 (1.7%) carried pathogenic or likely pathogenic variants in 5 cancer-predisposing genes: one in APC, BRCA1, BRCA2, NF1, and TP53 each. When assessing the clinical data, each of the 5 patients had one or more different types of cancers, fully consistent with their genetic profiles. Among these 5 patients, 2 died due to cancer while the others had multiple disorders later in their lifetimes; however, they may have benefited from early diagnosis and treatment for healthier lives, had the patients had genetic testing in their earlier lifetimes.


We demonstrated a case study where the discovery of pathogenic or likely pathogenic germline mutations from population-wide WGS correlates with clinical outcome. The use of WGS may have clinical impacts to improve healthcare delivery.


Next-generation sequencing (NGS) technologies are increasingly used in biomedical research and clinical practice to identify disease-associated genetic variants for advancing precision medicine [1]. Precision medicine allows researchers and physicians to predict more accurately which treatment and prevention strategies for a particular disease will work in which groups of people based on their genetic differences [2]. More than 4,000 Mendelian disorders have been studied at the genetic level [3]. Assessment of genetic pathogenicity leveraging whole-genome sequencing (WGS), whole-exome sequencing (WES), or other sequencing data and electronic health records (EHRs) has recently become feasible as EHRs have been implemented widely in healthcare systems [4, 5].

Even though tens of millions of genetic variants are uncovered in the human genomes, we do not have a clear understanding of the majority of their roles in health and disease [6]. The American College of Medical Genetics and Genomics (ACMG) has recommended identification and return of incidental findings (IFs) in a set of 56 actionable genes [7, 8]. A study on the NHLBI Exome Sequencing Project (ESP) cohorts has reported actionable exomic IFs from 112 genes in 6,503 participants [9]. The spectrum of pathogenic genetic variations across a diverse set of genes spanning dominant and recessively inherited disorders in the Exome Aggregation Consortium (ExAC) population has been assessed [10]. A recent study that focuses on two genes suggests that up to 3% of individuals may be at risk for heart arrhythmias [5]. Germline mutations in 565 cancer-predisposition genes with an emphasis on the analysis of 60 autosomal dominant cancer-predisposing genes have been studied by Zhang et al. [11]. Other studies suggest that 1%-3% of the population may carry clinically actionable variants linked to Mendelian diseases [1214]. Although these studies have provided significant insights into the human genome and have been able to identify individuals who carry clinically actionable genetic variants, their clinical impact remains unknown.

Our aims were to assess how often cancer gene screening identifies actionable cancer risk genes and to retrospectively evaluate whether the combination of WGS and EHR can facilitate improving prevention and care for patients with susceptibility to cancer syndromes. In this study, we firstly classified genetic pathogenicity of germline mutations in 60 autosomal dominant cancer-predisposition genes (S1 Table) among 300 deceased patients at Marshfield Clinic with WGS data using an in-house analysis pipeline called SeqHBase [15] based on the latest ACMG guidelines issued by the ACMG and the Association of for Molecular Pathology (AMP) [16]. Then we assessed clinical conditions for the patients who carried pathogenic or likely pathogenic variants using clinical data derived from their lifetime EHRs, followed by manual review of medical charts on selected patients by a MD clinical geneticist.

Materials and Methods

Sample Selection

In this study, 300 deceased patients were recruited from a Personalized Medicine Research Project (PMRP) [1719] launched at Marshfield Clinic in 2002. The PMRP is a unique biorepository resource that includes serum, plasma, and DNA from over 20,000 patients with links to their EHRs, including diagnosis, treatment, procedure codes, laboratory values, prescriptions, pharmacy, and physician notes. This cohort represents a very stable population that are primarily non-Hispanic whites with over 70% claiming German ancestry [20]. Each of them has an average of over 30 years of dynamic, continuous, virtually comprehensive, and extractable EHR data as well as diet and activity data linked to participant biospecimens. Since their enrollment, about 2,000 PMRP participants have passed away, and over 1,000 of them have more than 30 years of longitudinal and nearly comprehensive EHR data. 300 of the 1,000 deceased patients were randomly selected, resulting in 161 females and 139 males. In addition, EHR is not being used to identify those patients. It could be biased towards including more patients with late-onset diseases (e.g. cancer) for collecting patients with having about 30-year EHRs. All participants in the PMRP had previously consented for research in written and this project was approved by Marshfield Clinic’s Institutional Review Board.

Generation of 300 WGS Data

The blood samples of the 300 participants were sequenced by Complete Genomics (Mountain View, CA) according to manufacturer’s guidelines. The sequencing data were aligned to human reference (hg19). A minimum read-depth of 10 was used for variant calling. The VCF files, including single nucleotide variants (SNVs) and small insertions and/or deletions (INDELs), of the 300 genomes were provided by Complete Genomics. After quality control, over 27 million unique SNV and/or INDEL variants were identified across the 300 genomes.

Computational Methods

NGS technology is an essential component supporting genomic medicine, but the volume and complexity of the data pose challenges for its use in biomedical research [21]. Sequencing a single human genome generates about 200 gigabytes of data. Therefore, enormous challenges for analyzing large-scale NGS and clinical data still exist including data storage, processing, scaling, quality control management, and interpretation [22]. It is critical to develop an efficient computational framework and tools to analyze large-scale sequencing and clinical data. To infer biological insights from massive amounts of NGS data and comprehensive clinical data in a short period of time, we developed an in-house analysis pipeline within a software framework called SeqHBase to quickly catalogue, retrieve and query genetic variants, and to help classify genetic pathogenicity based on the latest ACMG guidelines [16]. We used ANNOVAR [23] to annotate the 300 WGS data, then the variation and annotation information were managed and analyzed by the in-house system SeqHBase.

Variant Classification

Variants in the 60 cancer-predisposition genes (S1 Table) were classified in the 300 genomes. In SeqHBase pipeline, there are a number of data quality filters, including minimum read-depth (e.g. reads > = 30X), maximum variant minor allele frequency (MAF; e.g., MAF < = 0.05%) in the 1000 Genomes Project [13], the ESP [24], and the ExAC [25], variant classification by ClinVar [26], and biological functions interested (e.g., splicing, nonsynonymous, stop-gain, stop-loss, and frameshift). We collected genetic variants of reads > = 30X, including (i) any variant present in the ClinVar database and annotated in one of the biological functions interested and (ii) new variants absent in the ClinVar database and annotated in the biological functions interested with MAF < = 0.5%, in the 60 cancer-predisposing genes. Variants departing from Hardy-Weinberg equilibrium (exact test P ≤ 1E-6) [27] were further filtered. All variants collected can then be classified as “pathogenic,” “likely pathogenic,” “uncertain significance,” “likely benign,” and “benign” using a combination of automated assessment and manual review, by following the latest ACMG guidelines [16].


Germline Mutations in the 60 Cancer-Predisposing Genes

In the 300 whole genomes, the data presented in those biological functions interested across the 60 cancer-predisposing genes spanning diverse autosomal dominant cancers encompass 207 classified variants (S2 Table). Of the 207 variants in the 60 genes, 5 variants were classified as “pathogenic” or “likely pathogenic” (Fig 1). The 5 variants are shown in Table 1, and all of them are absent in the 1000 Genomes Project, the ESP, and the ExAC cohorts. To further confirm that our automated analysis is reliable, a certified medical geneticist manually reviewed the variant data and provided clinical interpretation on these variants (Table 2).

Fig 1. Pie chart of variant categories (pathogenicity) in 60 autosomal dominant cancer-predisposing genes.

Provided are the percent of total variants for each category shown in S2 Table.

Table 1. Pathogenic/likely pathogenic variants in the 60 cancer-predisposing genes identified by WGS of 300 deceased patients*.

Table 2. Clinically Significant Variants Associated with Cancers.

Clinical Impact

5 of the 300 participants carried presumed “pathogenic” or “likely pathogenic” variants in the 60 autosomal dominant cancer-predisposing genes. The 5 “pathogenic” or “likely pathogenic” variants identified in this study are located in 5 genes including APC, BRCA1, BRCA2, NF1, and TP53. The APC gene encodes a multi-domain protein that plays an essential role in tumor suppression by antagonizing the WNT signaling pathway [28]. Inappropriate activation of this pathway through loss of APC function contributes to cancer progression, as in familial adenomatous polyposis [29]. Mutations in APC may result in colorectal cancer [30], prostate cancer [31], and other cancers [32]. The BRCA1 gene is a protein product and is responsible for DNA repair [33]. It forms several distinct complexes through association with different adaptor proteins, and each complex forms in a mutually exclusive manner [34]. Mutations in BRCA1 may result in breast and/or ovarian cancer [35] and pancreatic cancer [36]. The BRCA2 gene is also a protein product responsible for DNA repair [37]. It is a key mediator of homologous recombination [38]. Mutations in BRCA2 may result in breast and/or ovarian cancer [39], pancreatic cancer [40, 41], and prostate cancer [42] as well. The NF1 gene encodes neurofibromin, a cytoplasmic protein that is predominantly expressed in neurons, Schwann cells, oligodendrocytes, and leukocytes. Mutations in NF1 may result in juvenile myelomonocytic leukemia [43], neurofibromatosis [44], Neurofibromatosis-Noonan syndrome [45], and Watson syndrome [46]. Interestingly, a multidisciplinary team at Yale University, led by Yale Cancer Center members, has confirmed that NF1 is a “major player” in the development of skin cancer [47], which is also observed in this study. The TP53 gene responds to diverse cellular stress to regulate target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, and/or changes in metabolism [48]. Mutations in TP53 may result in adrenal cortical carcinoma [49], breast cancer [50], choroid plexus papilloma [51], colorectal cancer [52], hepatocellular carcinoma [53], Li-Fraumeni syndrome [54], nasopharyngeal carcinoma [55], osteosarcoma [56], pancreatic cancer [57], basal cell carcinoma [58], and glioma susceptibility [59].

The 5 patients, who carried “pathogenic” or “likely pathogenic” variants, were expected to express autosomal dominant cancer-predisposing syndromes based on their genetic profiles. We reviewed the 5 patients’ lifetime EHRs and found the following results: (i) One male patient who carried a stop-gain mutation (rs72953290) in APC had prostate cancer, many colon polyps, and colorectal cancer. In addition, he had family history of colon cancer. (ii) One female carried a frameshift deletion (rs80357735) in BRCA1 that is predicted to result in a significantly increased risk for breast and ovarian cancer. In fact, this patient had no known family history of breast cancer and did not receive yearly breast exams or mammograms. She was diagnosed with breast and ovarian cancer in her early-50s and died 6 years later. (iii) One male patient, who carried a frameshift insertion (rs80359499) in BRCA2, had prostate cancer in his early-70’s and died a few years later. (iv) One male patient, who carried a stop-gain mutation (rs866445127) in NF1, had multiple skin (both basal cell and squamous) cancers. (v) One female patient, who carried a missense mutation (rs587782596) in TP53, had pancreatic cancer. Additionally her child died from melanoma in his 30’s. The summary information is presented in Table 2. Namely, each of the 5 patients had one or more different types of cancers, demonstrating consistency with their genetic profiles.

Even though there are no family members in the 300 deceased patients, it is conceivable that family history recorded in EHRs can help disease prevention. As mentioned in the 2th patient who carried the cancer-predisposing mutation in BRCA1, she died from breast and ovarian cancer in her later 50’s. This case implied that it could be important to have aggressive screening and prophylactic surgery for patients with BRCA1 mutations. In addition, it is very likely that more aggressive surveillance or preventative measures could have extended the lives of those patients if genetic testing had been done in their earlier ages. Therefore, combining WGS and EHRs could potentially improve personalized healthcare.


Combining the functional characterization of identified genetic variants with comprehensive clinical data available in EHRs has the potential to provide compelling evidence to implicate novel disease-associated variants in phenotypically well-characterized patients. In this study, we analyzed germline mutations in the 60 autosomal dominant cancer-predisposition genes in 300 deceased patients with WGS data and nearly complete long-term medical records. To infer biological insights from massive amounts of WGS data and comprehensive clinical data in a short period of time, we developed an in-house analysis pipeline within a software framework called SeqHBase to quickly classify genetic pathogenicity based on the latest ACMG guidelines [16]. The pathogenic and/or likely pathogenic variants identified in this study were further reviewed using the carriers’ lifetime EHRs. Of the 300 participants, each of the 5 (1.7%) carried a presumed “pathogenic” or “likely pathogenic” variant in one of the 60 cancer-predisposing genes. When assessing extensive clinical data, each of the 5 patients had one or more different cancers, exhibiting fully consistency with their genetic profiles. The results generated in this study demonstrated that genetic mutations in autosomal dominant cancer-predisposing genes could be potentially used in clinical diagnosis, prevention, and personalized treatments, showcasing the power of combining WGS and EHR to accelerate biomedical discoveries. It also showed potential impacts of clinically actionable genetic variants over a lifetime and demonstrated that genomic sequencing could be helpful in precise disease diagnoses and risk prediction. Meanwhile, we have realized that the 300 patients selected in this study should not be regarded as a representative population at Marshfield Clinic. That is, younger people may need to be recruited with WGS study in the future.

We acknowledge that WES or targeted sequencing may provide similar results at a reduce cost. However, WES or targeted sequencing technologies may not capture the whole genome coding regions comprehensively while WGS generates more complete coverage for the whole genome regions [60]. Although sequencing costs have dropped substantially in the past a few years, the cost for data analysis and interpretation remain very high. Further comprehensive studies are needed as true impacts on clinical outcomes may be much more complex. It may not be feasible to use WGS for screening general population in clinical practice now.

In addition, more efforts are needed to distinguish genetic variants that are truly clinically actionable, that is, the variants are useful for guiding clinical decisions regarding interventions to improve health outcomes. As multiple independent evaluations might have classified variant pathogenicity differently [5], more stringent criteria and the latest ACMG guidelines should be compiled prior to reporting pathogenic variants [61].

In summary, clinical research studies of the implementation of genomic data in healthcare can provide valuable lessons on how genomic data should be managed, and patient privacy should be protected, when incorporating genomic data into clinical practice on a larger scale. These lessons can alert healthcare institutions of the scientific and technical challenges of using genomic data in precision medicine. NGS technological advances in clinical genome sequencing and adoption of EHRs will pave the way to create patient-centered precision medicine in clinical practice. The rise of Big Data in NGS and clinical data will contribute to better treatment paradigms, leading to improvements in diagnosis and personalized treatments that may ultimately lead to an overall reduction in healthcare cost. This study portrayed a promising method for assessing genetic pathogenicity by using WGS data.

Supporting Information

S1 Table. 60 Autosomal Dominant Cancer-Predisposition Genes.


S2 Table. Genetic Variants of the 300 Patients Across the 60 Cancer-Predisposition Genes.



Y.Z. would like to thank the Summer Student Research Internship Program (SSRIP) at Marshfield Clinic Research Foundation (MCRF) for financial support. The authors would like to thank the staff in the Biomedical Informatics Research Center at MCRF for collecting genomic and clinical data, and providing computing infrastructure support as well.

Author Contributions

  1. Conceptualization: MMH.
  2. Data curation: MMH YZ.
  3. Formal analysis: MMH QL.
  4. Funding acquisition: KYH KW MMH.
  5. Investigation: MMH QL KW EWM FX.
  6. Methodology: MMH KW QL FX.
  7. Project administration: MMH.
  8. Resources: MMH.
  9. Software: MMH QL.
  10. Supervision: MMH KW.
  11. Validation: EWM FX.
  12. Visualization: MMH QL.
  13. Writing – original draft: KYH KW MMH.
  14. Writing – review & editing: KYH CW KW MMH.


  1. 1. Collins FS, Varmus H. A new initiative on precision medicine. The New England journal of medicine. 2015;372(9):793–5. Epub 2015/01/31. pmid:25635347
  2. 2. Vassy JL, Korf BR, Green RC. How to know when physicians are ready for genomic medicine. Science translational medicine. 2015;7(287):287fs19. Epub 2015/05/15. pmid:25971999
  3. 3. Brunham LR, Hayden MR. Hunting human disease genes: lessons from the past, challenges for the future. Human genetics. 2013;132(6):603–17. Epub 2013/03/19. pmid:23504071
  4. 4. Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA, et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genetics in medicine: official journal of the American College of Medical Genetics. 2013;15(10):761–71. Epub 2013/06/08.
  5. 5. Van Driest SL, Wells QS, Stallings S, Bush WS, Gordon A, Nickerson DA, et al. Association of Arrhythmia-Related Genetic Variants With Phenotypes Documented in Electronic Medical Records. Jama. 2016;315(1):47–57. Epub 2016/01/10. pmid:26746457
  6. 6. Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, et al. ClinGen—the Clinical Genome Resource. The New England journal of medicine. 2015;372(23):2235–42. Epub 2015/05/28. pmid:26014595
  7. 7. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genetics in medicine: official journal of the American College of Medical Genetics. 2013;15(7):565–74. Epub 2013/06/22.
  8. 8. Hampel H, Bennett RL, Buchanan A, Pearlman R, Wiesner GL. A practice guideline from the American College of Medical Genetics and Genomics and the National Society of Genetic Counselors: referral indications for cancer predisposition assessment. Genetics in medicine: official journal of the American College of Medical Genetics. 2015;17(1):70–87. Epub 2014/11/14.
  9. 9. Amendola LM, Dorschner MO, Robertson PD, Salama JS, Hart R, Shirts BH, et al. Actionable exomic incidental findings in 6503 participants: challenges of variant classification. Genome research. 2015;25(3):305–15. Epub 2015/02/01. pmid:25637381
  10. 10. Song W, Gardner SA, Hovhannisyan H, Natalizio A, Weymouth KS, Chen W, et al. Exploring the landscape of pathogenic genetic variation in the ExAC population database: insights of relevance to variant classification. Genetics in medicine: official journal of the American College of Medical Genetics. 2015. Epub 2015/12/19.
  11. 11. Zhang J, Walsh MF, Wu G, Edmonson MN, Gruber TA, Easton J, et al. Germline Mutations in Predisposition Genes in Pediatric Cancer. The New England journal of medicine. 2015;373(24):2336–46. Epub 2015/11/19. pmid:26580448
  12. 12. Yang Y, Muzny DM, Xia F, Niu Z, Person R, Ding Y, et al. Molecular findings among patients referred for clinical whole-exome sequencing. Jama. 2014;312(18):1870–9. Epub 2014/10/19. pmid:25326635
  13. 13. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. Epub 2015/10/04. pmid:26432245
  14. 14. Roche MI, Berg JS. Incidental Findings with Genomic Testing: Implications for Genetic Counseling Practice. Current genetic medicine reports. 2015;3(4):166–76. Epub 2015/11/14. pmid:26566463
  15. 15. He M, Person TN, Hebbring SJ, Heinzen E, Ye Z, Schrodi SJ, et al. SeqHBase: a big data toolset for family based sequencing data analysis. Journal of medical genetics. 2015;52(4):282–8. Epub 2015/01/15. pmid:25587064
  16. 16. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in medicine: official journal of the American College of Medical Genetics. 2015;17(5):405–24. Epub 2015/03/06.
  17. 17. McCarty CA, Nair A, Austin DM, Giampietro PF. Informed consent and subject motivation to participate in a large, population-based genomics study: the Marshfield Clinic Personalized Medicine Research Project. Community genetics. 2007;10(1):2–9. Epub 2006/12/15. pmid:17167244
  18. 18. McCarty CA, Chapman-Stone D, Derfus T, Giampietro PF, Fost N. Community consultation and communication for a population-based DNA biobank: the Marshfield clinic personalized medicine research project. American journal of medical genetics Part A. 2008;146A(23):3026–33. Epub 2008/11/14. pmid:19006210
  19. 19. McCarty CA, Mukesh BN, Kitchner TE, Hubbard WC, Wilke RA, Burmester JK, et al. Intraocular pressure response to medication in a clinical setting: the Marshfield Clinic Personalized Medicine Research Project. Journal of glaucoma. 2008;17(5):372–7. Epub 2008/08/16. pmid:18703947
  20. 20. McCarty CA, Wilke RA, Giampietro PF, Wesbrook SD, Caldwell MD. Marshfield Clinic Personalized Medicine Research Project (PMRP): design, methods and recruitment for a large population-based biobank. Personalized medicine. 2005;2(1):49–71.
  21. 21. Gullapalli RR, Lyons-Weiler M, Petrosko P, Dhir R, Becich MJ, LaFramboise WA. Clinical integration of next-generation sequencing technology. Clinics in laboratory medicine. 2012;32(4):585–99. Epub 2012/10/20. pmid:23078661
  22. 22. Carter TC, He MM. Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine. Journal of healthcare engineering. 2016;2016. Epub 2016/05/20.
  23. 23. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research. 2010;38(16):e164. Epub 2010/07/06. pmid:20601685
  24. 24. Fu W, O'Connor TD, Jun G, Kang HM, Abecasis G, Leal SM, et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature. 2013;493(7431):216–20. Epub 2012/12/04. pmid:23201682
  25. 25. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–91. Epub 2016/08/19. pmid:27535533
  26. 26. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic acids research. 2016;44(D1):D862–8. Epub 2015/11/20. pmid:26582918
  27. 27. Wigginton JE, Cutler DJ, Abecasis GR. A note on exact tests of Hardy-Weinberg equilibrium. American journal of human genetics. 2005;76(5):887–93. Epub 2005/03/25. pmid:15789306
  28. 28. Komiya Y, Habas R. Wnt signal transduction pathways. Organogenesis. 2008;4(2):68–75. Epub 2009/03/13. pmid:19279717
  29. 29. Giardiello FM, Yang VW, Hylind LM, Krush AJ, Petersen GM, Trimbath JD, et al. Primary chemoprevention of familial adenomatous polyposis with sulindac. The New England journal of medicine. 2002;346(14):1054–9. Epub 2002/04/05. pmid:11932472
  30. 30. Markowitz SD, Bertagnolli MM. Molecular origins of cancer: Molecular basis of colorectal cancer. The New England journal of medicine. 2009;361(25):2449–60. Epub 2009/12/19. pmid:20018966
  31. 31. Chen Y, Li J, Yu X, Li S, Zhang X, Mo Z, et al. APC gene hypermethylation and prostate cancer: a systematic review and meta-analysis. European journal of human genetics: EJHG. 2013;21(9):929–35. Epub 2013/01/10. pmid:23299921
  32. 32. Aoki K, Taketo MM. Adenomatous polyposis coli (APC): a multi-functional tumor suppressor gene. Journal of cell science. 2007;120(Pt 19):3327–35. Epub 2007/09/21. pmid:17881494
  33. 33. James CR, Quinn JE, Mullan PB, Johnston PG, Harkin DP. BRCA1, a potential predictive biomarker in the treatment of breast cancer. The oncologist. 2007;12(2):142–50. Epub 2007/02/14. pmid:17296808
  34. 34. Wang B, Hurov K, Hofmann K, Elledge SJ. NBA1, a new player in the Brca1 A complex, is required for DNA damage resistance and checkpoint control. Genes & development. 2009;23(6):729–39. Epub 2009/03/06.
  35. 35. Andersen TI, Borresen AL, Moller P. A common BRCA1 mutation in Norwegian breast and ovarian cancer families? American journal of human genetics. 1996;59(2):486–7. Epub 1996/08/01. pmid:8755943
  36. 36. Al-Sukhni W, Rothenmund H, Borgida AE, Zogopoulos G, O'Shea AM, Pollett A, et al. Germline BRCA1 mutations predispose to pancreatic adenocarcinoma. Human genetics. 2008;124(3):271–8. Epub 2008/09/03. pmid:18762988
  37. 37. Friedenson B. The BRCA1/2 pathway prevents hematologic cancers in addition to breast and ovarian cancers. BMC cancer. 2007;7:152. Epub 2007/08/09. pmid:17683622
  38. 38. Jensen RB, Carreira A, Kowalczykowski SC. Purified human BRCA2 stimulates RAD51-mediated recombination. Nature. 2010;467(7316):678–83. Epub 2010/08/24. pmid:20729832
  39. 39. Gayther SA, Mangion J, Russell P, Seal S, Barfoot R, Ponder BA, et al. Variation of risks of breast and ovarian cancer associated with different germline mutations of the BRCA2 gene. Nature genetics. 1997;15(1):103–5. Epub 1997/01/01. pmid:8988179
  40. 40. Ozcelik H, Schmocker B, Di Nicola N, Shi XH, Langer B, Moore M, et al. Germline BRCA2 6174delT mutations in Ashkenazi Jewish pancreatic cancer patients. Nature genetics. 1997;16(1):17–8. Epub 1997/05/01. pmid:9140390
  41. 41. Hu C, Hart SN, Bamlet WR, Moore RM, Nandakumar K, Eckloff BW, et al. Prevalence of Pathogenic Mutations in Cancer Predisposition Genes among Pancreatic Cancer Patients. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2016;25(1):207–11. Epub 2015/10/21.
  42. 42. Edwards SM, Kote-Jarai Z, Meitz J, Hamoudi R, Hope Q, Osin P, et al. Two percent of men with early-onset prostate cancer harbor germline mutations in the BRCA2 gene. American journal of human genetics. 2003;72(1):1–12. Epub 2002/12/11. pmid:12474142
  43. 43. Side L, Taylor B, Cayouette M, Conner E, Thompson P, Luce M, et al. Homozygous inactivation of the NF1 gene in bone marrow cells from children with neurofibromatosis type 1 and malignant myeloid disorders. The New England journal of medicine. 1997;336(24):1713–20. Epub 1997/06/12. pmid:9180088
  44. 44. Johnson MR, Look AT, DeClue JE, Valentine MB, Lowy DR. Inactivation of the NF1 gene in human melanoma and neuroblastoma cell lines without impaired regulation of GTP.Ras. Proceedings of the National Academy of Sciences of the United States of America. 1993;90(12):5539–43. Epub 1993/06/15. pmid:8516298
  45. 45. Baralle D, Mattocks C, Kalidas K, Elmslie F, Whittaker J, Lees M, et al. Different mutations in the NF1 gene are associated with Neurofibromatosis-Noonan syndrome (NFNS). American journal of medical genetics Part A. 2003;119A(1):1–8. Epub 2003/04/23. pmid:12707950
  46. 46. Tassabehji M, Strachan T, Sharland M, Colley A, Donnai D, Harris R, et al. Tandem duplication within a neurofibromatosis type 1 (NF1) gene exon in a family with features of Watson syndrome and Noonan syndrome. American journal of human genetics. 1993;53(1):90–5. Epub 1993/07/01. pmid:8317503
  47. 47. Krauthammer M, Kong Y, Bacchiocchi A, Evans P, Pornputtapong N, Wu C, et al. Exome sequencing identifies recurrent mutations in NF1 and RASopathy genes in sun-exposed melanomas. Nature genetics. 2015;47(9):996–1002. Epub 2015/07/28. pmid:26214590
  48. 48. Surget S, Khoury MP, Bourdon JC. Uncovering the role of p53 splice variants in human malignancy: a clinical perspective. OncoTargets and therapy. 2013;7:57–68. Epub 2014/01/01. pmid:24379683
  49. 49. Ribeiro RC, Sandrini F, Figueiredo B, Zambetti GP, Michalkiewicz E, Lafferty AR, et al. An inherited p53 mutation that contributes in a tissue-specific manner to pediatric adrenal cortical carcinoma. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(16):9330–5. Epub 2001/08/02. pmid:11481490
  50. 50. Colaluca IN, Tosoni D, Nuciforo P, Senic-Matuglia F, Galimberti V, Viale G, et al. NUMB controls p53 tumour suppressor activity. Nature. 2008;451(7174):76–80. Epub 2008/01/04. pmid:18172499
  51. 51. Rutherford J, Chu CE, Duddy PM, Charlton RS, Chumas P, Taylor GR, et al. Investigations on a clinically and functionally unusual and novel germline p53 mutation. British journal of cancer. 2002;86(10):1592–6. Epub 2002/06/27. pmid:12085209
  52. 52. Baker SJ, Fearon ER, Nigro JM, Hamilton SR, Preisinger AC, Jessup JM, et al. Chromosome 17 deletions and p53 gene mutations in colorectal carcinomas. Science. 1989;244(4901):217–21. Epub 1989/04/14. pmid:2649981
  53. 53. Hsu IC, Metcalf RA, Sun T, Welsh JA, Wang NJ, Harris CC. Mutational hotspot in the p53 gene in human hepatocellular carcinomas. Nature. 1991;350(6317):427–8. Epub 1991/04/04. pmid:1849234
  54. 54. Stein T, Crighton D, Boyle JM, Varley JM, White RJ. RNA polymerase III transcription can be derepressed by oncogenes or mutations that compromise p53 function in tumours and Li-Fraumeni syndrome. Oncogene. 2002;21(19):2961–70. Epub 2002/06/26. pmid:12082526
  55. 55. Sun Y, Hegamyer G, Cheng YJ, Hildesheim A, Chen JY, Chen IH, et al. An infrequent point mutation of the p53 gene in human nasopharyngeal carcinoma. Proceedings of the National Academy of Sciences of the United States of America. 1992;89(14):6516–20. Epub 1992/07/15. pmid:1631151
  56. 56. Chen PL, Chen YM, Bookstein R, Lee WH. Genetic mechanisms of tumor suppression by the human p53 gene. Science. 1990;250(4987):1576–80. Epub 1990/12/14. pmid:2274789
  57. 57. Sonoyama T, Sakai A, Mita Y, Yasuda Y, Kawamoto H, Yagi T, et al. TP53 codon 72 polymorphism is associated with pancreatic cancer risk in males, smokers and drinkers. Molecular medicine reports. 2011;4(3):489–95. Epub 2011/04/07. pmid:21468597
  58. 58. Stacey SN, Sulem P, Jonasdottir A, Masson G, Gudmundsson J, Gudbjartsson DF, et al. A germline variant in the TP53 polyadenylation signal confers cancer susceptibility. Nature genetics. 2011;43(11):1098–103. Epub 2011/09/29. pmid:21946351
  59. 59. Kyritsis AP, Bondy ML, Xiao M, Berman EL, Cunningham JE, Lee PS, et al. Germline p53 gene mutations in subsets of glioma patients. Journal of the National Cancer Institute. 1994;86(5):344–9. Epub 1994/03/02. pmid:8308926
  60. 60. Belkadi A, Bolze A, Itan Y, Cobat A, Vincent QB, Antipenko A, et al. Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proceedings of the National Academy of Sciences of the United States of America. 2015;112(17):5473–8. Epub 2015/04/02. pmid:25827230
  61. 61. Biesecker LG. Long QT Syndrome and Potentially Pathogenic Genetic Variants. Jama. 2016;315(22):2467. Epub 2016/06/15.