Original ArticlesGenetics

Pilot Study of Return of Genetic Results to Patients in Adult Nephrology

Jordan G. Nestor, Maddalena Marasa, Hila Milo-Rasouly, Emily E. Groopman, S. Ali Husain, Sumit Mohan, Hilda Fernandez, Vimla S. Aggarwal, Dina F. Ahram, Natalie Vena, Kelsie Bogyo, Andrew S. Bomback, Jai Radhakrishnan, Gerald B. Appel, Wooin Ahn, David J. Cohen, Pietro A. Canetta, Geoffrey K. Dube, Maya K. Rao, Heather K. Morris, Russell J. Crew, Simone Sanna-Cherchi, Krzysztof Kiryluk and Ali G. Gharavi
CJASN May 2020, 15 (5) 651-664; DOI: https://doi.org/10.2215/CJN.12481019

Abstract

Background and objectives Actionable genetic findings have implications for care of patients with kidney disease, and genetic testing is an emerging tool in nephrology practice. However, there are scarce data regarding best practices for return of results and clinical application of actionable genetic findings for kidney patients.

Design, setting, participants, & measurements We developed a return of results workflow in collaborations with clinicians for the retrospective recontact of adult nephrology patients who had been recruited into a biobank research study for exome sequencing and were identified to have medically actionable genetic findings.

Results Using this workflow, we attempted to recontact a diverse pilot cohort of 104 nephrology research participants with actionable genetic findings, encompassing 34 different monogenic etiologies of nephropathy and five single-gene disorders recommended by the American College of Medical Genetics and Genomics for return as medically actionable secondary findings. We successfully recontacted 64 (62%) participants and returned results to 41 (39%) individuals. In each case, the genetic diagnosis had meaningful implications for the patients’ nephrology care. Through implementation efforts and qualitative interviews with providers, we identified over 20 key challenges associated with returning results to study participants, and found that physician knowledge gaps in genomics was a recurrent theme. We iteratively addressed these challenges to yield an optimized workflow, which included standardized consultation notes with tailored management recommendations, monthly educational conferences on core topics in genomics, and a curated list of expert clinicians for patients requiring extranephrologic referrals.

Conclusions Developing the infrastructure to support return of genetic results in nephrology was resource-intensive, but presented potential opportunities for improving patient care.

Introduction

Massively parallel sequencing approaches, including exome sequencing, are increasingly utilized in many clinical disciplines, including in nephrology (1,2). Recent studies have shown that exome sequencing can pinpoint causal variants in 10%–35% of patients with nephropathy (3–8). Importantly, a genetic diagnosis can support personalized care, including informing targeted workup, disease prognosis, choice of therapy, and/or family counseling (6,8). In addition, it may help prioritize donor selection for transplantation among at-risk family members. However, broader utilization of genetic testing in routine clinical care raises a number of technical, logistical, and ethical questions regarding return of results.

To start, genetic testing may yield various types of results. Beyond identification of a diagnostic finding explicative of the patient’s condition, it may identify variants of uncertain significance, which could prompt additional clinical testing (9). Genome-wide sequencing approaches may also uncover incidental or secondary findings that, although unrelated to the primary test indication, may nonetheless be medically actionable (e.g., detection of predisposition to hereditary cancers or cardiovascular disorders) (10) and also have implications for nephrology care (5,6). Furthermore, genetic testing results can effect insurability and confidentiality, which many patients and providers may not fully realize. Ordering clinicians can often be expected to understand the types of results that may emerge from ordering a genetic test, provide patients pretest counseling to ensure informed consent, and translate the genetic findings into personalized care. However, because genetic testing is an emerging tool in nephrology, physicians may lack the requisite knowledge and infrastructure to effectively use clinical genetic testing and apply the resultant findings into clinical practice (11,12).

Return of results is further complicated when the initial sequencing occurs in the research setting. The promise of receiving medically relevant findings has encouraged more patients to participate in genomic research (13,14), and sparked calls to return research findings to study participants (15). Investigators with existing biobanks and archived data sets (16) who wish to return research findings to study participants have had to update their protocols to include an option for return of results, along with incorporating the requisite clinical standards into their sequencing pathway to return research results. Importantly, in the United States, only test results obtained through laboratories that meet federal quality standards set by the Clinical Laboratory Improvement Amendments (CLIA) of 1988 (17) can be applied to patient care. Thus, research findings identified by research-grade sequencing cannot be returned to patients unless they are confirmed with clinical-grade testing.

Currently, the available data for optimal practices for return of results in a research context, and for nephrology patients, are highly limited and necessitate further study. Here, we describe our experience returning medically actionable genetic results to a diverse cohort of nephrology patients, followed in a large urban tertiary medical care center, who underwent research-grade genetic sequencing through their participation in a biobank study.

Materials and Methods

Return of Results Workflow

We developed a return of results workflow for adult research participants (aged ≥18 years) enrolled in Columbia University’s Genetic Studies of CKD biobanking protocol with medically actionable genetic findings detected by exome sequencing (5,6). The protocol was first updated in January 2015 to include return of results. Actionable findings included: primary diagnostic, defined as variants classified as “pathogenic” or “likely pathogenic” per the American College of Medical Genetics and Genomics (ACMG) criteria (18) potentially explicative for patients’ nephropathy; and secondary, defined as known and expected pathogenic variants in the 59 genes recommended by the ACMG for return as medically actionable secondary findings (10). We next identified participants with actionable findings who opted for recontact. Primary diagnostic findings underwent a rigorous two-part review process. Each variant initially identified underwent a second review by a team of nephrologists with expertise in hereditary nephropathies and a molecular pathologist, to further confirm their pathogenicity. We then examined these participants’ electronic health records (EHRs) and consulted with their treating nephrologist to verify that primary diagnostic findings were indeed explicative of the patient’s kidney disease.

In consultation with clinical nephrologists at our center, the research team developed a standardized workflow (Figure 1 and section S1 of the Supplemental Material), which involved sending participants a letter on behalf of their treating nephrologist informing them that a research-level finding was detected and inviting them to come in to discuss clinical genetic testing with the Precision Nephrology fellow, an American Board of Internal Medicine-certified nephrologist, and a member of the study team who is bilingual (fluent in English and Spanish). Participants who did not respond after 30 days received up to two telephone calls. Those who agreed to confirmatory testing after pretest counseling provided written consent. A fresh blood sample was then sent to the New York Genome Center or Columbia University’s Personalized Genomics Laboratory, clinically (CLIA) certified laboratories, for targeted dideoxy terminator (Sanger) sequencing of the variant(s) identified by exome sequencing. The referring nephrologist was notified of patients where recontact was not established.

Figure 1.
Figure 1.

Developing a standardized workflow for return of medically actionable genetic findings to nephrology research participants. Optimization of a workflow for return of results in nephrology: the workflow was iteratively developed on the basis of feasibility and challenges encountered with return of results, alongside provider feedback. The strategies implemented to address various challenges faced with return of results informed the final optimized workflow, which included five key steps: (1) genetic sequence analysis; (2) notifying the referring nephrologist; (3) participant recontact; (4) return of clinically confirmed results with post-test counseling; and (5) clinical application of findings.

A subset of biobank participants, enrolled in 2016, were dually consented for research- and clinical-grade sequencing. Clinical sequencing was offered through the Electronic Medical Records and Genomics (eMERGE) Network’s (19) phase 3 study, where sequencing was performed on the eMERGE-Seq platform, a next-generation sequencing panel of 74 actionable genes (described in Section S1 of the Supplemental Material). Because sequencing for these participants was performed in a clinical-grade environment, participants with diagnostic findings identified on exome sequencing, also identified on this panel, did not require clinical retesting.

Clinically confirmed findings were returned by clinicians specialized in the treatment of hereditary nephropathies. The visit also included a comprehensive clinical evaluation, with post-test counseling. Each patient received a standardized clinical consultation note that detailed the findings and management recommendations to share with outside providers, along with a simplified note to share with family members (Section S1 in the Supplemental Material). These data were entered into the EHR after the genetic findings were discussed with the referring nephrologist.

The Cost of the Return of Results Workflow

To evaluate the fixed start-up cost for this pilot study, we estimated direct labor costs of the research team, made up of eight individuals (four faculty members, two research scientists, a research staff member, and a research trainee), along with other direct (e.g., clinical retesting, etc.) and indirect costs (further detailed in Section S1 in the Supplemental Material).

Clinical Implications of Return of Results

To explore the clinical effect of return of results, we first differentiated between patients where the genetic findings confirmed the suspected hereditary cause, identified a molecular cause for an undiagnosed condition, reclassified the disease, or detected a variant diagnostic for an otherwise medically actionable condition. For each case, we also examined the implications of the genetically informed management recommendations (e.g., specialty referrals, cascade screening, etc.). We also met with referring nephrologists one-on-one and asked them open-ended questions about their level of satisfaction with the workflow. Their responses were documented in field notes.

Data Management

Study data were collected and managed using the Research Electronic Data Capture (20) tool hosted by Columbia University. Additional mechanisms for ensuring data security and patient privacy were detailed in an earlier publication (6).

Statistical Analyses

Baseline characteristics were described using counts and percentages for categorical variables, and medians and interquartile ranges (IQRs) for continuous variables. We compared baseline sociodemographic and clinical data of participants by their recontact status using a chi-squared or Wilcoxon rank-sum test, as appropriate. All analyses were performed using STATA version 15. We considered P values <0.05 as statistically significant.

Results

Characteristics of the Pilot Cohort

We initially identified 213 study participants with medically relevant findings, the majority (205/213) of whom were included in earlier publications (5,6). Of these participants, 113 were adults who opted for return of actionable findings as part of their informed consent and were eligible for return of results (Figure 2 and section S2 in the Supplemental Material). After EHR review, an additional nine participants were excluded because they had attained a genetic diagnosis via clinical genetic testing and had their results returned outside of this workflow [referring providers were notified that the same variant(s) was identified on research-grade exome sequencing]. The remaining 104 eligible adults were selected for this pilot study.

Figure 2.
Figure 2.

Results of piloting return of results workflow among genetic study research participants. The return of results study flow: we identified 213 study participants with medically relevant findings. Of these participants, 113 were adults who opted for return of actionable findings as part of their informed consent and were eligible for thorough review of their electronic health record, an additional nine participants were excluded as they had attained a genetic diagnosis via clinical genetic testing outside of this workflow. The remaining 104 participants were all included in the pilot cohort. Of these 104 participants, seven individuals (7%) were dual enrolled in the eMERGE Network’s phase 3 study and consented for clinical-grade sequencing on the eMERGE-seq platform. In total, we successfully recontacted 64 of the 104 (62%) participants, including all seven individuals crossenrolled in the eMERGE study. Among the 48 individuals who consented for clinical-grade sequencing (including the seven participants enrolled in eMERGE), 41 had their results returned by our nephrogenetics team. In one case, the research-level findings were not confirmed due to a technical limitation of the confirmatory test modality used (detailed in Section S2 of the Supplemental Material).

The 104 pilot study participants (Table 1) had a median age of 38 (IQR, 28.0–51) years and >50% (58%) self-identified as white. Five participants (5%) were exclusively Spanish-speaking and the remainder were proficient English speakers. Over one-third (37%) of participants reported no family history of kidney disease at the time of enrollment. On the basis of their EHRs, 26 (25%) individuals had public insurance (i.e., Medicare, Medicaid, or both). One-half of the patients (52%) had a clinical diagnosis of a glomerulopathy; for 26 participants (25%), the primary etiology of their kidney disease was unknown. In addition, 42 (40%) individuals had reached kidney failure at the time of study enrollment. The median interval between time of enrollment and attempted recontact was 2.9 (IQR, 1.9–3.8) years.

View this table:
Table 1.

Clinical characteristics of the pilot cohort (n=104)

Of the 104 participants, eight (8%) individuals had findings in one of the 59 ACMG medically actionable secondary genes (Table 2, Supplemental Table 1 in the Supplemental Material). The remaining 96 participants had primary diagnostic findings encompassing 34 distinct single-gene etiologies. Of the 34 distinct monogenic nephropathies in our cohort, the most recurrent primary genetic findings were in COL4A3/4/5 genes associated with type IV collagen-associated nephropathy, also known as Alport syndrome.

View this table:
Table 2.

Diagnostic utility and clinical implications of genetic diagnosis in patients who completed return of results (n=41)

Recontact and Return of Results

Of these 104 participants, seven (7%) were dual enrolled in the eMERGE Network’s phase 3 study and underwent clinical-grade sequencing. Fifty-seven participants were recontacted for clinical retesting, whereas seven participants were recontacted for return of results (Figure 2). In total, we successfully recontacted 64 (62%) participants, including all seven individuals crossenrolled in the eMERGE study. Inability to recontact was due to no response to communication (n=32) and out-of-date contact information, such as invalid or disconnected telephone number (n=5). In addition, three participants were deceased at the time of recontact. Participants successfully recontacted (Table 1) were more likely to be female (50% versus 20%, P=0.002), white (70% versus 38%, P<0.001) versus nonwhite, have private insurance (83% versus 63%, P=0.02) versus other, and experienced a shorter interval between enrollment and recontact attempt (2.4 years; IQR, 1.7–3.7 versus 3.3 years; IQR 2.8–4.1, P=0.001).

Of the 57 participants who were recontacted for clinical retesting, 16 refused. Reasons for declining confirmatory testing included lack of interest (n=8), insufficient time (n=2), prior knowledge of the clinical diagnosis (here, Alport syndrome, Gitelman syndrome, and Fabry disease; n=3), or relocation to another state (n=3). Individuals who moved away were referred to a local genetic counselor for clinical genetic testing.

Among the 48 individuals who underwent clinical-grade sequencing (including the seven participants enrolled in eMERGE), 41 had their results returned by our nephrogenetics team, including 21 males and 20 females, most of whom self-identified as white (n=29; 71%). Six individuals failed to return for their results (Figure 2 and Section S2 of the Supplemental Material). In one case, results were not confirmed due to a technical limitation of the confirmatory test modality used (described in Section S2 of the Supplemental Material). The referring nephrologists were notified of the findings and their confirmatory genetic report entered in the EHR.

Clinical Implications of the Genetic Findings in Nephrology Care

Disclosure of the genetic findings had direct implications to the care of all 41 participants who received their results: the results either confirmed the suspected hereditary cause (n=18), identified a molecular cause for an undiagnosed condition (n=13), reclassified the disease (n=8), or identified a genetic variant diagnostic for an otherwise medically actionable condition (n=2), (Table 2). Importantly, for over one-half of the participants, the genetic diagnosis had implications for therapy [e.g., use of thiazides for hypercalciuria in Dent disease (21), etc.] (n=22; 54%), informed clinical prognosis (e.g., risk for disease progression and/or transplantation) (n=29; 71%), and initiated subspecialty care referrals for workup of associated extrakidney manifestations (n=27; 66%). The referrals encompassed subspecialists spanning a wide range of clinical domains, including otolaryngology, ophthalmology, cardiology, endocrinology, hematology, breast oncology, and maternal–fetal medicine. The genetic diagnoses guided familial testing of at-risk family members of 13 (32%) individuals, and facilitated allograft donor selection for eight (20%) participants.

With respect to otherwise medically actionable secondary findings, the participant with a pathogenic variant in the SCN5A gene, associated with Brugada syndrome 1/long QT syndrome type 3, was referred to a cardiologist specialized in cardiac electrophysiology for specialized diagnostic testing and assessment for an automatic implantable cardioverter-defibrillator (22). In addition, although not diagnostic of the patient’s underlying glomerulopathy, the genetic finding had implications for his nephrology care, including avoidance of medications that prolong the QT interval, or deplete serum magnesium and potassium levels (23) (e.g., verapamil, loop diuretics, etc.), and increase the risk for sudden death. The individual with a pathogenic BRCA2 variant, associated with hereditary breast and ovarian cancer, was diagnosed with breast cancer after an abnormal diagnostic mammogram 1 month before return of results. The genetic finding ultimately led to cascade screening and prophylactic mastectomies (24) in two of her daughters, who were found to also harbor the mutation.

Lessons Learned from Return of Results

Over 20 major challenges were identified in implementing the return of results workflow (Table 3). We iteratively addressed these challenges to yield an optimized workflow (Figure 1), which includes standardized consultation notes with tailored management recommendations, monthly educational conferences on core topics in genomics, and a curated list of expert clinicians for patients requiring extranephrologic referrals.

View this table:
Table 3.

Defining and refining a nephrology return of results workflow: key challenges encountered and solutions developed to address them

Cost of the Return of Results Workflow

The eight-member study team dedicated an estimated 1452 hours to Return of Results efforts over 31 months. The fixed start-up cost for this pilot study was estimated to be $92,249.31 (Supplemental Tables 2–5).

Discussion

We developed a return of results workflow for medically actionable genetic findings emerging from research-grade exome sequencing of nephrology biobank participants. This nephrology-specific workflow was iteratively developed to address the challenges encountered integrating genetic sequencing into nephrology practice at a tertiary care referral center. Using this workflow, we successfully returned results to 41 nephrology patients across 23 single-gene disorders, and observed how medically actionable genetic findings can shape management in nephrology care by informing choice of therapy and prognosis [e.g., cautious use of diabetogenic drugs such as tacrolimus and corticosteroids in patients with Renal cysts and diabetes syndrome (HNF1B) who are at increased risk for developing diabetes (25), greater risk for antiglomerular basement membrane disease in allograft recipients with Alport syndrome due to truncating variants in COL4A5 (26), etc.], family counseling, and transplant donor selection (Tables 2 and 4). In addition, we developed standardized communication materials to help surmount physician knowledge gaps, yielding a valuable resource for return of results programs (See Section S1 of the Supplemental Material).

Prior return of results protocols have focused on the return of actionable secondary findings to research participants enrolled in population biobank studies. Sapp et al. (27) utilized an a priori list of the then 56 genes deemed medically actionable by the ACMG, whereas Schwartz et al. (28) expanded on this gene set for a total of 76 genes for return. Similarly, in addition to returning primary findings, potentially explicative for individuals’ nephropathy, our study returned medically actionable secondary findings in the updated (59 genes) ACMG medically actionable genes, making it, to our knowledge, the first study to return such medically actionable secondary findings in the context of clinical nephrology. Our experience returning ACMG 59 gene variants also reveals the global importance of secondary findings for patient care (Table 4, Supplemental Table 6 in the Supplemental Material). For example, hereditary cancer predisposition could favor modification of the duration, intensity, or choice of immunosuppression regimen, such as in the context of GN or transplantation. Similarly, findings for hereditary cardiac arrhythmias may support more vigilant electrolytes and volume status management, and influence diuretic therapy and dialysis prescriptions. Because approximately 1%–5% of unselected adults harbor such secondary findings (29,30), further study is needed to assess their potential implications on nephrology care and determine optimal approaches for management. Moreover, our results support the importance of considering return of findings for non-nephrologic disorders as otherwise medically actionable findings (i.e., secondary genetic findings of kidney significance).

Finally, our return of results program was resource-intensive and the yield was modest, which is consistent with prior studies. The success of return of results efforts likely depends on the primary purpose of the study and the interval since enrollment. Because our biobank protocol was initially designed solely for genetic discovery, we elected to revise our study and establish a workflow to enable return of clinically confirmed, medically actionable results, including in the ACMG 59 genes. Furthermore, research funds covered the costs of these efforts, although typically, the cost of confirmatory testing and follow-up falls within the clinical domain. Overall, our study reflects the evolution of translational research since the early 2000s, and the known challenges incorporating requisite clinical standards when merging research and clinical sequencing in the genomic era (14). It is also in line with current standards for genetic research, wherein investigators who detect medically actionable findings in the course of analyses, are expected to ensure that valid, clinically confirmed results are communicated to study participants, along with a plan for follow-up (15,31,32). Data suggests that disclosure of genetic findings does not cause grave psychologic distress in research participants (33–37) and our findings emphasize that the detection of a monogenic disorder, whether as a primary or a medically actionable secondary finding, can meaningfully inform care. This highlights opportunities for future research in precision nephrology, and the importance of including return of results mechanisms in the planning stages of investigations that involve genetic sequence analyses and the possibility of detecting medically actionable findings. Wider implementation of genetic testing in nephrology will also require maintaining an up-to-date list of nephropathy-associated genes, establishing best practice guidelines for periodic sequence reanalysis, and for the return of variants of uncertain significance, developing efficient pipelines for rapid and iterative variant evaluation as new genes and variants are identified, and prior genetic findings are reclassified (38), and obtaining third-party payer coverage for the requisite follow-up care associated with detecting medically actionable genetic findings. Addressing physician knowledge gaps is also critical, and potentially met through strategies that include the introduction of algorithms alerting clinicians about a possible monogenic disease (39), development of decision support tools for the EHR, and remote consultation options for centers lacking genetic expertise (40) and/or the resources required for return of results. Future studies will need to comprehensively evaluate the relative diagnostic yields between different genetic sequencing modalities and the long-term effect of both primary and secondary genetic findings on nephrology care, including on treatment decisions, preimplantation genetic diagnostics, transplantation eligibility, and third-party payer coverage. Further systematic study is also needed to examine ethical and legal questions that may arise from return of results (41), and to assess the long-term effect of the genetic findings on clinical outcomes and healthcare utilization.

View this table:
Table 4.

Examples of how genetic sequence data, along with clinical data, can be a valuable resource to guide personalized management

Disclosures

Dr. Gharavi reports receiving other payment from the AstraZeneca Center for Genomics Research and Goldfinch Bio, outside the submitted work. Dr. Kiryluk reports receiving other payment from AstraZeneca and Goldfinch Bio, outside the submitted work. Dr. Mohan holds scientific advisory board membership with Angion Pharmaceuticals and has received personal fees from Kidney International Reports. All remaining authors have nothing to disclose.

Funding

The project was supported by National Institutes of Health grants T32DK108741-01 and TL1TR001875 (to Dr. Nestor), 1F30DK116473 (to Dr. Groopman), KL2TR001874 (to Dr. Fernandez), and 5U01HG008680-04 (eMERGE); the American Society of Nephrology Foundation for Kidney Research Donald E. Wesson Research Fellowship (to Dr. Milo-Rasouly); the Renal Research Institute University Grants award (to Dr. Gharavi); and the Columbia Precision Medicine Initiative (to Dr. Gharavi). Dr. Husain reports grants from the National Kidney Foundation and the National Center for Advancing Translational Sciences, from outside the submitted work.

Acknowledgments

We thank all study participants for contributing to this effort, along with the clinical research coordinators and our colleagues in the Division of Nephrology at Columbia University Medical Center.

Dr. Husain reports grants from the National Kidney Foundation and the National Center for Advancing Translational Sciences, from outside the submitted work.

Supplemental Material

This article contains the following supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.12481019/-/DCSupplemental.

Supplemental Table 1. Clinical phenotype and genetic spectrum of the 104 pilot study participants.

Supplemental Table 2. Fixed start-up costs for the development and implementation of the return of results workflow.

Supplemental Table 3. Faculty hours, FTE and direct costs with fringe+indirect costs.

Supplemental Table 4. Research scientist/research staff hours, FTE, and direct costs with fringe+indirect costs.

Supplemental Table 5. Research trainee (precision nephrology fellow): hours, FTE, and direct trainee costs+indirect costs.

Supplemental Table 6. Examples of the clinical utility of ACMG 59 gene findings in participants who underwent clinical genetic testing and had their genetic results returned outside of the return of results workflow.

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

  • Received October 14, 2019.
  • Accepted March 12, 2020.

References

  1. ↵
    1. Santín S,
    2. Bullich G,
    3. Tazón-Vega B,
    4. García-Maset R,
    5. Giménez I,
    6. Silva I,
    7. Ruíz P,
    8. Ballarín J,
    9. Torra R,
    10. Ars E
    : Clinical utility of genetic testing in children and adults with steroid-resistant nephrotic syndrome. Clin J Am Soc Nephrol 6: 1139–1148, 2011pmid:21415313
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Mallett AJ,
    2. McCarthy HJ,
    3. Ho G,
    4. Holman K,
    5. Farnsworth E,
    6. Patel C,
    7. Fletcher JT,
    8. Mallawaarachchi A,
    9. Quinlan C,
    10. Bennetts B,
    11. Alexander SI
    : Massively parallel sequencing and targeted exomes in familial kidney disease can diagnose underlying genetic disorders. Kidney Int 92: 1493–1506, 2017pmid:28844315
    OpenUrlCrossRefPubMed
  3. ↵
    1. Warejko JK,
    2. Tan W,
    3. Daga A,
    4. Schapiro D,
    5. Lawson JA,
    6. Shril S,
    7. Lovric S,
    8. Ashraf S,
    9. Rao J,
    10. Hermle T,
    11. Jobst-Schwan T,
    12. Widmeier E,
    13. Majmundar AJ,
    14. Schneider R,
    15. Gee HY,
    16. Schmidt JM,
    17. Vivante A,
    18. van der Ven AT,
    19. Ityel H,
    20. Chen J,
    21. Sadowski CE,
    22. Kohl S,
    23. Pabst WL,
    24. Nakayama M,
    25. Somers MJG,
    26. Rodig NM,
    27. Daouk G,
    28. Baum M,
    29. Stein DR,
    30. Ferguson MA,
    31. Traum AZ,
    32. Soliman NA,
    33. Kari JA,
    34. El Desoky S,
    35. Fathy H,
    36. Zenker M,
    37. Bakkaloglu SA,
    38. Müller D,
    39. Noyan A,
    40. Ozaltin F,
    41. Cadnapaphornchai MA,
    42. Hashmi S,
    43. Hopcian J,
    44. Kopp JB,
    45. Benador N,
    46. Bockenhauer D,
    47. Bogdanovic R,
    48. Stajić N,
    49. Chernin G,
    50. Ettenger R,
    51. Fehrenbach H,
    52. Kemper M,
    53. Munarriz RL,
    54. Podracka L,
    55. Büscher R,
    56. Serdaroglu E,
    57. Tasic V,
    58. Mane S,
    59. Lifton RP,
    60. Braun DA,
    61. Hildebrandt F
    : Whole exome sequencing of patients with steroid-resistant nephrotic syndrome. Clin J Am Soc Nephrol 13: 53–62, 2018pmid:29127259
    OpenUrlAbstract/FREE Full Text
    1. van der Ven AT,
    2. Connaughton DM,
    3. Ityel H,
    4. Mann N,
    5. Nakayama M,
    6. Chen J,
    7. Vivante A,
    8. Hwang DY,
    9. Schulz J,
    10. Braun DA,
    11. Schmidt JM,
    12. Schapiro D,
    13. Schneider R,
    14. Warejko JK,
    15. Daga A,
    16. Majmundar AJ,
    17. Tan W,
    18. Jobst-Schwan T,
    19. Hermle T,
    20. Widmeier E,
    21. Ashraf S,
    22. Amar A,
    23. Hoogstraaten CA,
    24. Hugo H,
    25. Kitzler TM,
    26. Kause F,
    27. Kolvenbach CM,
    28. Dai R,
    29. Spaneas L,
    30. Amann K,
    31. Stein DR,
    32. Baum MA,
    33. Somers MJG,
    34. Rodig NM,
    35. Ferguson MA,
    36. Traum AZ,
    37. Daouk GH,
    38. Bogdanović R,
    39. Stajić N,
    40. Soliman NA,
    41. Kari JA,
    42. El Desoky S,
    43. Fathy HM,
    44. Milosevic D,
    45. Al-Saffar M,
    46. Awad HS,
    47. Eid LA,
    48. Selvin A,
    49. Senguttuvan P,
    50. Sanna-Cherchi S,
    51. Rehm HL,
    52. MacArthur DG,
    53. Lek M,
    54. Laricchia KM,
    55. Wilson MW,
    56. Mane SM,
    57. Lifton RP,
    58. Lee RS,
    59. Bauer SB,
    60. Lu W,
    61. Reutter HM,
    62. Tasic V,
    63. Shril S,
    64. Hildebrandt F
    : Whole-exome sequencing identifies causative mutations in families with congenital anomalies of the kidney and urinary tract. J Am Soc Nephrol 29: 2348–2361, 2018pmid:30143558
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Lata S,
    2. Marasa M,
    3. Li Y,
    4. Fasel DA,
    5. Groopman E,
    6. Jobanputra V,
    7. Rasouly H,
    8. Mitrotti A,
    9. Westland R,
    10. Verbitsky M,
    11. Nestor J,
    12. Slater LM,
    13. D’Agati V,
    14. Zaniew M,
    15. Materna-Kiryluk A,
    16. Lugani F,
    17. Caridi G,
    18. Rampoldi L,
    19. Mattoo A,
    20. Newton CA,
    21. Rao MK,
    22. Radhakrishnan J,
    23. Ahn W,
    24. Canetta PA,
    25. Bomback AS,
    26. Appel GB,
    27. Antignac C,
    28. Markowitz GS,
    29. Garcia CK,
    30. Kiryluk K,
    31. Sanna-Cherchi S,
    32. Gharavi AG
    : Whole-exome sequencing in adults with chronic kidney disease: A pilot study. Ann Intern Med 168: 100–109, 2018pmid:29204651
    OpenUrlCrossRefPubMed
  6. ↵
    1. Groopman EE,
    2. Marasa M,
    3. Cameron-Christie S,
    4. Petrovski S,
    5. Aggarwal VS,
    6. Milo-Rasouly H,
    7. Li Y,
    8. Zhang J,
    9. Nestor J,
    10. Krithivasan P,
    11. Lam WY,
    12. Mitrotti A,
    13. Piva S,
    14. Kil BH,
    15. Chatterjee D,
    16. Reingold R,
    17. Bradbury D,
    18. DiVecchia M,
    19. Snyder H,
    20. Mu X,
    21. Mehl K,
    22. Balderes O,
    23. Fasel DA,
    24. Weng C,
    25. Radhakrishnan J,
    26. Canetta P,
    27. Appel GB,
    28. Bomback AS,
    29. Ahn W,
    30. Uy NS,
    31. Alam S,
    32. Cohen DJ,
    33. Crew RJ,
    34. Dube GK,
    35. Rao MK,
    36. Kamalakaran S,
    37. Copeland B,
    38. Ren Z,
    39. Bridgers J,
    40. Malone CD,
    41. Mebane CM,
    42. Dagaonkar N,
    43. Fellström BC,
    44. Haefliger C,
    45. Mohan S,
    46. Sanna-Cherchi S,
    47. Kiryluk K,
    48. Fleckner J,
    49. March R,
    50. Platt A,
    51. Goldstein DB,
    52. Gharavi AG
    : Diagnostic utility of exome sequencing for kidney disease. N Engl J Med 380: 142–151, 2019pmid:30586318
    OpenUrlCrossRefPubMed
    1. Connaughton DM,
    2. Kennedy C,
    3. Shril S,
    4. Mann N,
    5. Murray SL,
    6. Williams PA,
    7. Conlon E,
    8. Nakayama M,
    9. van der Ven AT,
    10. Ityel H,
    11. Kause F,
    12. Kolvenbach CM,
    13. Dai R,
    14. Vivante A,
    15. Braun DA,
    16. Schneider R,
    17. Kitzler TM,
    18. Moloney B,
    19. Moran CP,
    20. Smyth JS,
    21. Kennedy A,
    22. Benson K,
    23. Stapleton C,
    24. Denton M,
    25. Magee C,
    26. O’Seaghdha CM,
    27. Plant WD,
    28. Griffin MD,
    29. Awan A,
    30. Sweeney C,
    31. Mane SM,
    32. Lifton RP,
    33. Griffin B,
    34. Leavey S,
    35. Casserly L,
    36. de Freitas DG,
    37. Holian J,
    38. Dorman A,
    39. Doyle B,
    40. Lavin PJ,
    41. Little MA,
    42. Conlon PJ,
    43. Hildebrandt F
    : Monogenic causes of chronic kidney disease in adults. Kidney Int 95: 914–928, 2019pmid:30773290
    OpenUrlCrossRefPubMed
  8. ↵
    1. Mann N,
    2. Braun DA,
    3. Amann K,
    4. Tan W,
    5. Shril S,
    6. Connaughton DM,
    7. Nakayama M,
    8. Schneider R,
    9. Kitzler TM,
    10. van der Ven AT,
    11. Chen J,
    12. Ityel H,
    13. Vivante A,
    14. Majmundar AJ,
    15. Daga A,
    16. Warejko JK,
    17. Lovric S,
    18. Ashraf S,
    19. Jobst-Schwan T,
    20. Widmeier E,
    21. Hugo H,
    22. Mane SM,
    23. Spaneas L,
    24. Somers MJG,
    25. Ferguson MA,
    26. Traum AZ,
    27. Stein DR,
    28. Baum MA,
    29. Daouk GH,
    30. Lifton RP,
    31. Manzi S,
    32. Vakili K,
    33. Kim HB,
    34. Rodig NM,
    35. Hildebrandt F
    : Whole-exome sequencing enables a precision medicine approach for kidney transplant recipients. J Am Soc Nephrol 30: 201–215, 2019pmid:30655312
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. Rasouly HM,
    2. Groopman EE,
    3. Heyman-Kantor R,
    4. Fasel DA,
    5. Mitrotti A,
    6. Westland R,
    7. Bier L,
    8. Weng C,
    9. Ren Z,
    10. Copeland B,
    11. Krithivasan P,
    12. Chung WK,
    13. Sanna-Cherchi S,
    14. Goldstein DB,
    15. Gharavi AG
    : The burden of candidate pathogenic variants for kidney and genitourinary disorders emerging from exome sequencing. Ann Intern Med 170: 11–21, 2019pmid:30476936
    OpenUrlCrossRefPubMed
  10. ↵
    1. Kalia SS,
    2. Adelman K,
    3. Bale SJ,
    4. Chung WK,
    5. Eng C,
    6. Evans JP,
    7. Herman GE,
    8. Hufnagel SB,
    9. Klein TE,
    10. Korf BR,
    11. McKelvey KD,
    12. Ormond KE,
    13. Richards CS,
    14. Vlangos CN,
    15. Watson M,
    16. Martin CL,
    17. Miller DT
    : Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): A policy statement of the American College of Medical Genetics and Genomics. Genet Med 19: 249–255, 2017
    OpenUrlCrossRefPubMed
  11. ↵
    1. Hauser D,
    2. Obeng AO,
    3. Fei K,
    4. Ramos MA,
    5. Horowitz CR
    : Views of primary care providers on testing patients for genetic risks for common chronic diseases. Health Aff (Millwood) 37: 793–800, 2018pmid:29733703
    OpenUrlPubMed
  12. ↵
    1. Berns JS
    : A survey-based evaluation of self-perceived competency after nephrology fellowship training. Clin J Am Soc Nephrol 5: 490–496, 2010pmid:20089491
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Facio FM,
    2. Eidem H,
    3. Fisher T,
    4. Brooks S,
    5. Linn A,
    6. Kaphingst KA,
    7. Biesecker LG,
    8. Biesecker BB
    : Intentions to receive individual results from whole-genome sequencing among participants in the ClinSeq study. Eur J Hum Genet 21: 261–265, 2013pmid:22892536
    OpenUrlCrossRefPubMed
  14. ↵
    1. Rehm HL
    : Evolving health care through personal genomics. Nat Rev Genet 18: 259–267, 2017pmid:28138143
    OpenUrlCrossRefPubMed
  15. ↵
    1. Downey AS,
    2. Busta ER,
    3. Mancher M,
    4. Botkin JR
    National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, Committee on the Return of Individual-Specific Research Results Generated in Research Laboratories: Returning Individual Research Results to Participants: Guidance for a New Research Paradigm, edited by Downey AS, Busta ER, Mancher M, Botkin JR, Washington (DC), National Academies Press, 2018
  16. ↵
    1. Wolf SM,
    2. Crock BN,
    3. Van Ness B,
    4. Lawrenz F,
    5. Kahn JP,
    6. Beskow LM,
    7. Cho MK,
    8. Christman MF,
    9. Green RC,
    10. Hall R,
    11. Illes J,
    12. Keane M,
    13. Knoppers BM,
    14. Koenig BA,
    15. Kohane IS,
    16. Leroy B,
    17. Maschke KJ,
    18. McGeveran W,
    19. Ossorio P,
    20. Parker LS,
    21. Petersen GM,
    22. Richardson HS,
    23. Scott JA,
    24. Terry SF,
    25. Wilfond BS,
    26. Wolf WA
    : Managing incidental findings and research results in genomic research involving biobanks and archived data sets. Genet Med 14: 361–384, 2012
    OpenUrlCrossRefPubMed
  17. ↵
    1. Jarvik GP,
    2. Amendola LM,
    3. Berg JS,
    4. Brothers K,
    5. Clayton EW,
    6. Chung W,
    7. Evans BJ,
    8. Evans JP,
    9. Fullerton SM,
    10. Gallego CJ,
    11. Garrison NA,
    12. Gray SW,
    13. Holm IA,
    14. Kullo IJ,
    15. Lehmann LS,
    16. McCarty C,
    17. Prows CA,
    18. Rehm HL,
    19. Sharp RR,
    20. Salama J,
    21. Sanderson S,
    22. Van Driest SL,
    23. Williams MS,
    24. Wolf SM,
    25. Wolf WA,
    26. Burke W
    ; eMERGE Act-ROR Committee and CERC Committee; CSER Act-ROR Working Group: Return of genomic results to research participants: The floor, the ceiling, and the choices in between. Am J Hum Genet 94: 818–826, 2014pmid:24814192
    OpenUrlCrossRefPubMed
  18. ↵
    1. Richards S,
    2. Aziz N,
    3. Bale S,
    4. Bick D,
    5. Das S,
    6. Gastier-Foster J,
    7. Grody WW,
    8. Hegde M,
    9. Lyon E,
    10. Spector E,
    11. Voelkerding K,
    12. Rehm HL
    ; ACMG Laboratory Quality Assurance Committee: 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. Genet Med 17: 405–424, 2015
    OpenUrlCrossRefPubMed
  19. ↵
    1. eMERGE Consortium
    : Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network. Am. J. Hum. Genet 105: 588–605, 2019
    OpenUrl
  20. ↵
    1. Harris PA,
    2. Taylor R,
    3. Thielke R,
    4. Payne J,
    5. Gonzalez N,
    6. Conde JG
    : Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42: 377–381, 2009pmid:18929686
    OpenUrlCrossRefPubMed
  21. ↵
    1. Blanchard A,
    2. Vargas-Poussou R,
    3. Peyrard S,
    4. Mogenet A,
    5. Baudouin V,
    6. Boudailliez B,
    7. Charbit M,
    8. Deschesnes G,
    9. Ezzhair N,
    10. Loirat C,
    11. Macher MA,
    12. Niaudet P,
    13. Azizi M
    : Effect of hydrochlorothiazide on urinary calcium excretion in dent disease: An uncontrolled trial. Am J Kidney Dis 52: 1084–1095, 2008pmid:18976849
    OpenUrlCrossRefPubMed
  22. ↵
    1. Epstein AE,
    2. DiMarco JP,
    3. Ellenbogen KA,
    4. Estes NA 3rd.,
    5. Freedman RA,
    6. Gettes LS,
    7. Gillinov AM,
    8. Gregoratos G,
    9. Hammill SC,
    10. Hayes DL,
    11. Hlatky MA,
    12. Newby LK,
    13. Page RL,
    14. Schoenfeld MH,
    15. Silka MJ,
    16. Stevenson LW,
    17. Sweeney MO,
    18. Tracy CM,
    19. Epstein AE,
    20. Darbar D,
    21. DiMarco JP,
    22. Dunbar SB,
    23. Estes NA 3rd.,
    24. Ferguson TB Jr..,
    25. Hammill SC,
    26. Karasik PE,
    27. Link MS,
    28. Marine JE,
    29. Schoenfeld MH,
    30. Shanker AJ,
    31. Silka MJ,
    32. Stevenson LW,
    33. Stevenson WG,
    34. Varosy PD
    ; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines; Heart Rhythm Society: 2012 ACCF/AHA/HRS focused update incorporated into the ACCF/AHA/HRS 2008 guidelines for device-based therapy of cardiac rhythm abnormalities: A report of the American College of cardiology foundation/American heart association task force on practice guidelines and the heart rhythm society. J Am Coll Cardiol 61: e6–e75, 2013pmid:23265327
    OpenUrlFREE Full Text
  23. ↵
    1. Postema PG,
    2. Neville J,
    3. de Jong JS,
    4. Romero K,
    5. Wilde AA,
    6. Woosley RL
    : Safe drug use in long QT syndrome and Brugada syndrome: Comparison of website statistics. Europace 15: 1042–1049, 2013
    OpenUrlCrossRefPubMed
  24. ↵
    1. National Collaborating Centre for Cancer (UK)
    :Familial Breast Cancer: Classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer. NICE Clinical Guidelines, 2013. Available at https://www.nice.org.uk/guidance/cg164/evidence. Accessed June 26, 2019
  25. ↵
    1. Zuber J,
    2. Bellanné-Chantelot C,
    3. Carette C,
    4. Canaud G,
    5. Gobrecht S,
    6. Gaha K,
    7. Mallet V,
    8. Martinez F,
    9. Thervet E,
    10. Timsit J,
    11. Legendre C,
    12. Dubois-Laforgue D
    : HNF1B-related diabetes triggered by renal transplantation. Nat Rev Nephrol 5: 480–484, 2009pmid:19639018
    OpenUrlCrossRefPubMed
  26. ↵
    1. Savige J,
    2. Ariani F,
    3. Mari F,
    4. Bruttini M,
    5. Renieri A,
    6. Gross O,
    7. Deltas C,
    8. Flinter F,
    9. Ding J,
    10. Gale DP,
    11. Nagel M,
    12. Yau M,
    13. Shagam L,
    14. Torra R,
    15. Ars E,
    16. Hoefele J,
    17. Garosi G,
    18. Storey H
    : Expert consensus guidelines for the genetic diagnosis of Alport syndrome. Pediatr Nephrol 34: 1175–1189, 2019pmid:29987460
    OpenUrlPubMed
  27. ↵
    1. Sapp JC,
    2. Johnston JJ,
    3. Driscoll K,
    4. Heidlebaugh AR,
    5. Miren Sagardia A,
    6. Dogbe DN,
    7. Umstead KL,
    8. Turbitt E,
    9. Alevizos I,
    10. Baron J,
    11. Bönnemann C,
    12. Brooks B,
    13. Donkervoort S,
    14. Jee YH,
    15. Linehan WM,
    16. McMahon FJ,
    17. Moss J,
    18. Mullikin JC,
    19. Nielsen D,
    20. Pelayo E,
    21. Remaley AT,
    22. Siegel R,
    23. Su H,
    24. Zarate C,
    25. Manolio TA,
    26. Biesecker BB,
    27. Biesecker LG
    ; NISC Comparative Sequencing Program: Evaluation of recipients of positive and negative secondary findings evaluations in a hybrid CLIA-research sequencing pilot. Am J Hum Genet 103: 358–366, 2018pmid:30122538
    OpenUrlCrossRefPubMed
  28. ↵
    1. Schwartz MLB,
    2. McCormick CZ,
    3. Lazzeri AL,
    4. Lindbuchler DM,
    5. Hallquist MLG,
    6. Manickam K,
    7. Buchanan AH,
    8. Rahm AK,
    9. Giovanni MA,
    10. Frisbie L,
    11. Flansburg CN,
    12. Davis FD,
    13. Sturm AC,
    14. Nicastro C,
    15. Lebo MS,
    16. Mason-Suares H,
    17. Mahanta LM,
    18. Carey DJ,
    19. Williams JL,
    20. Williams MS,
    21. Ledbetter DH,
    22. Faucett WA,
    23. Murray MF
    : A model for genome-first care: Returning secondary genomic findings to participants and their healthcare providers in a large research cohort. Am J Hum Genet 103: 328–337, 2018pmid:30100086
    OpenUrlCrossRefPubMed
  29. ↵
    1. Amendola LM,
    2. Dorschner MO,
    3. Robertson PD,
    4. Salama JS,
    5. Hart R,
    6. Shirts BH,
    7. Murray ML,
    8. Tokita MJ,
    9. Gallego CJ,
    10. Kim DS,
    11. Bennett JT,
    12. Crosslin DR,
    13. Ranchalis J,
    14. Jones KL,
    15. Rosenthal EA,
    16. Jarvik ER,
    17. Itsara A,
    18. Turner EH,
    19. Herman DS,
    20. Schleit J,
    21. Burt A,
    22. Jamal SM,
    23. Abrudan JL,
    24. Johnson AD,
    25. Conlin LK,
    26. Dulik MC,
    27. Santani A,
    28. Metterville DR,
    29. Kelly M,
    30. Foreman AK,
    31. Lee K,
    32. Taylor KD,
    33. Guo X,
    34. Crooks K,
    35. Kiedrowski LA,
    36. Raffel LJ,
    37. Gordon O,
    38. Machini K,
    39. Desnick RJ,
    40. Biesecker LG,
    41. Lubitz SA,
    42. Mulchandani S,
    43. Cooper GM,
    44. Joffe S,
    45. Richards CS,
    46. Yang Y,
    47. Rotter JI,
    48. Rich SS,
    49. O’Donnell CJ,
    50. Berg JS,
    51. Spinner NB,
    52. Evans JP,
    53. Fullerton SM,
    54. Leppig KA,
    55. Bennett RL,
    56. Bird T,
    57. Sybert VP,
    58. Grady WM,
    59. Tabor HK,
    60. Kim JH,
    61. Bamshad MJ,
    62. Wilfond B,
    63. Motulsky AG,
    64. Scott CR,
    65. Pritchard CC,
    66. Walsh TD,
    67. Burke W,
    68. Raskind WH,
    69. Byers P,
    70. Hisama FM,
    71. Rehm H,
    72. Nickerson DA,
    73. Jarvik GP
    : Actionable exomic incidental findings in 6503 participants: Challenges of variant classification. Genome Res 25: 305–315, 2015pmid:25637381
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Dorschner MO,
    2. Amendola LM,
    3. Turner EH,
    4. Robertson PD,
    5. Shirts BH,
    6. Gallego CJ,
    7. Bennett RL,
    8. Jones KL,
    9. Tokita MJ,
    10. Bennett JT,
    11. Kim JH,
    12. Rosenthal EA,
    13. Kim DS,
    14. Tabor HK,
    15. Bamshad MJ,
    16. Motulsky AG,
    17. Scott CR,
    18. Pritchard CC,
    19. Walsh T,
    20. Burke W,
    21. Raskind WH,
    22. Byers P,
    23. Hisama FM,
    24. Nickerson DA,
    25. Jarvik GP
    ; National Heart, Lung, and Blood Institute Grand Opportunity Exome Sequencing Project: Actionable, pathogenic incidental findings in 1,000 participants’ exomes. Am J Hum Genet 93: 631–640, 2013pmid:24055113
    OpenUrlCrossRefPubMed
  31. ↵
    1. Aronson SJ,
    2. Rehm HL
    : Building the foundation for genomics in precision medicine. Nature 526: 336–342, 2015pmid:26469044
    OpenUrlCrossRefPubMed
  32. ↵
    1. Bombard Y,
    2. Brothers KB,
    3. Fitzgerald-Butt S,
    4. Garrison NA,
    5. Jamal L,
    6. James CA,
    7. Jarvik GP,
    8. McCormick JB,
    9. Nelson TN,
    10. Ormond KE,
    11. Rehm HL,
    12. Richer J,
    13. Souzeau E,
    14. Vassy JL,
    15. Wagner JK,
    16. Levy HP
    : The responsibility to recontact research participants after reinterpretation of genetic and genomic research results. Am J Hum Genet 104: 578–595, 2019pmid:30951675
    OpenUrlCrossRefPubMed
  33. ↵
    1. Green RC,
    2. Roberts JS,
    3. Cupples LA,
    4. Relkin NR,
    5. Whitehouse PJ,
    6. Brown T,
    7. Eckert SL,
    8. Butson M,
    9. Sadovnick AD,
    10. Quaid KA,
    11. Chen C,
    12. Cook-Deegan R,
    13. Farrer LA
    ; REVEAL Study Group: Disclosure of APOE genotype for risk of Alzheimer’s disease. N Engl J Med 361: 245–254, 2009pmid:19605829
    OpenUrlCrossRefPubMed
    1. Lineweaver TT,
    2. Bondi MW,
    3. Galasko D,
    4. Salmon DP
    : Effect of knowledge of APOE genotype on subjective and objective memory performance in healthy older adults. Am J Psychiatry 171: 201–208, 2014pmid:24170170
    OpenUrlCrossRefPubMed
    1. Hamilton JG,
    2. Robson ME
    : Psychosocial effects of multigene panel testing in the context of cancer genomics. Hastings Cent Rep 49[Suppl 1]: S44–S52, 2019pmid:31268573
    OpenUrlPubMed
    1. Wynn J,
    2. Martinez J,
    3. Bulafka J,
    4. Duong J,
    5. Zhang Y,
    6. Chiuzan C,
    7. Preti J,
    8. Cremona ML,
    9. Jobanputra V,
    10. Fyer AJ,
    11. Klitzman RL,
    12. Appelbaum PS,
    13. Chung WK
    : Impact of receiving secondary results from genomic research: A 12-month longitudinal study. J Genet Couns 27: 709–722, 2018pmid:29168042
    OpenUrlPubMed
  37. ↵
    1. Parens E,
    2. Appelbaum PS
    : On what we have learned and still need to learn about the psychosocial impacts of genetic testing. Hastings Cent Rep 49[Suppl 1]: S2–S9, 2019pmid:31268574
    OpenUrlPubMed
  38. ↵
    1. Liu P,
    2. Meng L,
    3. Normand EA,
    4. Xia F,
    5. Song X,
    6. Ghazi A,
    7. Rosenfeld J,
    8. Magoulas PL,
    9. Braxton A,
    10. Ward P,
    11. Dai H,
    12. Yuan B,
    13. Bi W,
    14. Xiao R,
    15. Wang X,
    16. Chiang T,
    17. Vetrini F,
    18. He W,
    19. Cheng H,
    20. Dong J,
    21. Gijavanekar C,
    22. Benke PJ,
    23. Bernstein JA,
    24. Eble T,
    25. Eroglu Y,
    26. Erwin D,
    27. Escobar L,
    28. Gibson JB,
    29. Gripp K,
    30. Kleppe S,
    31. Koenig MK,
    32. Lewis AM,
    33. Natowicz M,
    34. Mancias P,
    35. Minor L,
    36. Scaglia F,
    37. Schaaf CP,
    38. Streff H,
    39. Vernon H,
    40. Uhles CL,
    41. Zackai EH,
    42. Wu N,
    43. Sutton VR,
    44. Beaudet AL,
    45. Muzny D,
    46. Gibbs RA,
    47. Posey JE,
    48. Lalani S,
    49. Shaw C,
    50. Eng CM,
    51. Lupski JR,
    52. Yang Y
    : Reanalysis of clinical exome sequencing data. N Engl J Med 380: 2478–2480, 2019pmid:31216405
    OpenUrlCrossRefPubMed
  39. ↵
    1. Son JH,
    2. Xie G,
    3. Yuan C,
    4. Ena L,
    5. Li Z,
    6. Goldstein A,
    7. Huang L,
    8. Wang L,
    9. Shen F,
    10. Liu H,
    11. Mehl K,
    12. Groopman EE,
    13. Marasa M,
    14. Kiryluk K,
    15. Gharavi AG,
    16. Chung WK,
    17. Hripcsak G,
    18. Friedman C,
    19. Weng C,
    20. Wang K
    : Deep phenotyping on electronic health records facilitates genetic diagnosis by clinical exomes. Am J Hum Genet 103: 58–73, 2018pmid:29961570
    OpenUrlCrossRefPubMed
  40. ↵
    1. Aymé S,
    2. Bockenhauer D,
    3. Day S,
    4. Devuyst O,
    5. Guay-Woodford LM,
    6. Ingelfinger JR,
    7. Klein JB,
    8. Knoers NVAM,
    9. Perrone RD,
    10. Roberts J,
    11. Schaefer F,
    12. Torres VE,
    13. Cheung M,
    14. Wheeler DC,
    15. Winkelmayer WC
    ; Conference Participants: Common elements in rare kidney diseases: Conclusions from a kidney disease: Improving global outcomes (KDIGO) controversies conference. Kidney Int 92: 796–808, 2017pmid:28938953
    OpenUrlPubMed
  41. ↵
    1. Wolf SM,
    2. Branum R,
    3. Koenig BA,
    4. Petersen GM,
    5. Berry SA,
    6. Beskow LM,
    7. Daly MB,
    8. Fernandez CV,
    9. Green RC,
    10. LeRoy BS,
    11. Lindor NM,
    12. O'Rourke PP,
    13. Breitkopf CR,
    14. Rothstein MA,
    15. Van Ness B,
    16. Wilfond BS
    : Returning a research participant’s genomic results to relatives: Analysis and recommendations. J Law Med Ethics 43: 440–463, 2015
    OpenUrlCrossRefPubMed
    1. Stefanidis CJ,
    2. Querfeld U
    : The podocyte as a target: Cyclosporin A in the management of the nephrotic syndrome caused by WT1 mutations. Eur J Pediatr 170: 1377–1383, 2011pmid:21298518
    OpenUrlCrossRefPubMed
    1. Manuelli M,
    2. De Luca L,
    3. Iaria G,
    4. Tatangelo P,
    5. Sforza D,
    6. Perrone L,
    7. Bellini MI,
    8. Angelico R,
    9. Anselmo A,
    10. Tisone G
    : Conversion to rapamycin immunosuppression for malignancy after kidney transplantation. Transplant Proc 42: 1314–1316, 2010pmid:20534289
    OpenUrlPubMed
    1. Savige J,
    2. Gregory M,
    3. Gross O,
    4. Kashtan C,
    5. Ding J,
    6. Flinter F
    : Expert guidelines for the management of Alport syndrome and thin basement membrane nephropathy. J Am Soc Nephrol 24: 364–375, 2013pmid:23349312
    OpenUrlAbstract/FREE Full Text
    1. Schrier RW
    : Blood pressure in early autosomal dominant polycystic kidney disease. N Engl J Med 372: 976–977, 2015pmid:25738676
    OpenUrlPubMed
    1. Hu Z,
    2. Huang S,
    3. Wu Y,
    4. Liu Y,
    5. Liu X,
    6. Su D,
    7. Tao Y,
    8. Fu P,
    9. Zhang X,
    10. Peng Z,
    11. Zhang S,
    12. Yang Y
    : Hereditary features, treatment, and prognosis of the lipoprotein glomerulopathy in patients with the APOE Kyoto mutation. Kidney Int 85: 416–424, 2014pmid:24025644
    OpenUrlCrossRefPubMed
    1. Gevers TJ,
    2. Inthout J,
    3. Caroli A,
    4. Ruggenenti P,
    5. Hogan MC,
    6. Torres VE,
    7. Nevens F,
    8. Drenth JP
    : Young women with polycystic liver disease respond best to somatostatin analogues: A pooled analysis of individual patient data. Gastroenterology 145: 357–365.e1-2, 2013
    OpenUrlCrossRefPubMed
    1. Tan R,
    2. Colville D,
    3. Wang YY,
    4. Rigby L,
    5. Savige J
    : Alport retinopathy results from “severe” COL4A5 mutations and predicts early renal failure. Clin J Am Soc Nephrol 5: 34–38, 2010pmid:19965530
    OpenUrlAbstract/FREE Full Text
    1. Jais JP,
    2. Knebelmann B,
    3. Giatras I,
    4. De Marchi M,
    5. Rizzoni G,
    6. Renieri A,
    7. Weber M,
    8. Gross O,
    9. Netzer KO,
    10. Flinter F,
    11. Pirson Y,
    12. Verellen C,
    13. Wieslander J,
    14. Persson U,
    15. Tryggvason K,
    16. Martin P,
    17. Hertz JM,
    18. Schröder C,
    19. Sanak M,
    20. Krejcova S,
    21. Carvalho MF,
    22. Saus J,
    23. Antignac C,
    24. Smeets H,
    25. Gubler MC
    : X-linked Alport syndrome: Natural history in 195 families and genotype- phenotype correlations in males. J Am Soc Nephrol 11: 649–657, 2000pmid:10752524
    OpenUrlAbstract/FREE Full Text
    1. Izzedine H,
    2. Tankere F,
    3. Launay-Vacher V,
    4. Deray G
    : Ear and kidney syndromes: Molecular versus clinical approach. Kidney Int 65: 369–385, 2004pmid:14717907
    OpenUrlCrossRefPubMed
    1. Clissold RL,
    2. Hamilton AJ,
    3. Hattersley AT,
    4. Ellard S,
    5. Bingham C
    : HNF1B-associated renal and extra-renal disease-an expanding clinical spectrum. Nat Rev Nephrol 11: 102–112, 2015pmid:25536396
    OpenUrlCrossRefPubMed
    1. Chapman AB,
    2. Devuyst O,
    3. Eckardt KU,
    4. Gansevoort RT,
    5. Harris T,
    6. Horie S,
    7. Kasiske BL,
    8. Odland D,
    9. Pei Y,
    10. Perrone RD,
    11. Pirson Y,
    12. Schrier RW,
    13. Torra R,
    14. Torres VE,
    15. Watnick T,
    16. Wheeler DC
    ; Conference Participants: Autosomal-dominant polycystic kidney disease (ADPKD): Executive summary from a kidney disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int 88: 17–27, 2015pmid:25786098
    OpenUrlCrossRefPubMed
    1. Poitou C,
    2. Francois H,
    3. Bellanne-Chantelot C,
    4. Noel C,
    5. Jacquet A,
    6. Clauin S,
    7. Beaudreuil S,
    8. Damieri H,
    9. Hebibi H,
    10. Hammoudi Y,
    11. Benoit G,
    12. Charpentier B,
    13. Durrbach A
    : Maturity onset diabetes of the young: Clinical characteristics and outcome after kidney and pancreas transplantation in MODY3 and RCAD patients: A single center experience. Transpl Int 25: 564–572, 2012pmid:22432796
    OpenUrlPubMed
Back to top

Podcast

Subscribe to podcast
Download MP3

In this issue

View Selected Citations (0)
Print
Download PDF
Email Article
Citation Tools
Request Permissions
Pilot Study of Return of Genetic Results to Patients in Adult Nephrology
Jordan G. Nestor, Maddalena Marasa, Hila Milo-Rasouly, Emily E. Groopman, S. Ali Husain, Sumit Mohan, Hilda Fernandez, Vimla S. Aggarwal, Dina F. Ahram, Natalie Vena, Kelsie Bogyo, Andrew S. Bomback, Jai Radhakrishnan, Gerald B. Appel, Wooin Ahn, David J. Cohen, Pietro A. Canetta, Geoffrey K. Dube, Maya K. Rao, Heather K. Morris, Russell J. Crew, Simone Sanna-Cherchi, Krzysztof Kiryluk, Ali G. Gharavi
CJASN May 2020, 15 (5) 651-664; DOI: 10.2215/CJN.12481019
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo

Jump to section

Keywords