Airmen and health-care providers' attitudes toward the use of genomic sequencing in the US Air Force: findings from the MilSeq Project.

PURPOSE: The use of genomic sequencing (GS) in military settings poses unique considerations, including the potential for GS to impact service members' careers. The MilSeq Project investigated the use of GS in clinical care of active duty Airmen in the United States Air Force (USAF). METHODS: We assessed perceived risks, benefits, and attitudes toward use of GS in the USAF among patient participants (n = 93) and health-care provider participants (HCPs) (n = 12) prior to receiving or disclosing GS results. RESULTS: Participants agreed that there are health benefits associated with GS (90% patients, 75% HCPs), though more HCPs (75%) than patients (40%) agreed that there are risks (p = 0.048). The majority of both groups (67% HCPs, 77% patients) agreed that they trust the USAF with genetic information, but far fewer agreed that genetic information should be used to make decisions about deployment (5% patients, 17% HCPs) or duty assignments (3% patients, 17% HCPs). Despite their hesitancy, patients were supportive of the USAF testing for nondisease traits that could impact their duty performance. Eighty-seven percent of patients did not think their GS results would influence their career. CONCLUSION: Results suggest favorable attitudes toward the use of GS in the USAF when not used for deployment or assignment decisions.

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine.

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine.

Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study.

PURPOSE: Clinical sequencing emerging in health care may result in secondary findings (SFs). METHODS: Seventy-four of 6240 (1.2%) participants who underwent genome or exome sequencing through the Clinical Sequencing Exploratory Research (CSER) Consortium received one or more SFs from the original American College of Medical Genetics and Genomics (ACMG) recommended 56 gene-condition pair list; we assessed clinical and psychosocial actions. RESULTS: The overall adjusted prevalence of SFs in the ACMG 56 genes across the CSER consortium was 1.7%. Initially 32% of the family histories were positive, and post disclosure, this increased to 48%. The average cost of follow-up medical actions per finding up to a 1-year period was $128 (observed, range: $0-$678) and $421 (recommended, range: $141-$1114). Case reports revealed variability in the frequency of and follow-up on medical recommendations patients received associated with each SF gene-condition pair. Participants did not report adverse psychosocial impact associated with receiving SFs; this was corroborated by 18 participant (or parent) interviews. All interviewed participants shared findings with relatives and reported that relatives did not pursue additional testing or care. CONCLUSION: Our results suggest that disclosure of SFs shows little to no adverse impact on participants and adds only modestly to near-term health-care costs; additional studies are needed to confirm these findings.

Correction: Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study.

The originally published version of this Article contained errors in Fig. 2. The numbers below the black arrowheads were incorrect; please see incorrect Figure in associated Correction. These errors have now been corrected in the PDF and HTML versions of the Article.

A whole genome approach for discovering the genetic basis of blood group antigens: independent confirmation for P1 and Xga.

BACKGROUND: Although P1 and Xga are known to be associated with the A4GALT and XG genes, respectively, the genetic basis of antigen expression has been elusive. Recent reports link both P1 and Xga expression with nucleotide changes in the promotor regions and with antigen-negative phenotypes due to disruption of transcription factor binding. STUDY DESIGN AND METHODS: Whole genome sequencing was performed on 113 individuals as part of the MedSeq Project with serologic RBC antigen typing for P1 (n = 77) and Xga (n = 15). Genomic data were analyzed by two approaches, nucleotide frequency correlation and serologic correlation, to find A4GALT and XG changes associated with P1 and Xga expression. RESULTS: For P1, the frequency approach identified 29 possible associated nucleotide changes, and the serologic approach revealed four among them correlating with the P1+/P1- phenotype: chr22:43,115,523_43,115,520AAAG/delAAAG (rs66781836); chr 22:43,114,551C/T (rs8138197); chr22:43,114,020 T/G (rs2143918); and chr22:43,113,793G/T (rs5751348). For Xga , the frequency approach identified 82 possible associated nucleotide changes, and among these the serologic approach revealed one correlating with the Xg(a+)/Xg(a-) phenotype: chrX:2,666,384G/C (rs311103). CONCLUSION: A bioinformatics analysis pipeline was created to identify genetic changes responsible for RBC antigen expression. This study, in progress before the recently published reports, independently confirms the basis for P1 and Xga . Although this enabled molecular typing of these antigens, the Y chromosome PAR1 region interfered with Xga typing in males. This approach could be used to identify and confirm the genetic basis of antigens, potentially replacing the historical approach using family pedigrees as genomic sequencing becomes commonplace.

Short-term costs of integrating whole-genome sequencing into primary care and cardiology settings: a pilot randomized trial.

PURPOSE: Great uncertainty exists about the costs associated with whole-genome sequencing (WGS). METHODS: One hundred cardiology patients with cardiomyopathy diagnoses and 100 ostensibly healthy primary care patients were randomized to receive a family-history report alone or with a WGS report. Cardiology patients also reviewed prior genetic test results. WGS costs were estimated by tracking resource use and staff time. Downstream costs were estimated by identifying services in administrative data, medical records, and patient surveys for 6 months. RESULTS: The incremental cost per patient of WGS testing was $5,098 in cardiology settings and $5,073 in primary care settings compared with family history alone. Mean 6-month downstream costs did not differ statistically between the control and WGS arms in either setting (cardiology: difference = -$1,560, 95% confidence interval -$7,558 to $3,866, p = 0.36; primary care: difference = $681, 95% confidence interval -$884 to $2,171, p = 0.70). Scenario analyses showed the cost reduction of omitting or limiting the types of secondary findings was less than $69 and $182 per patient in cardiology and primary care, respectively. CONCLUSION: Short-term costs of WGS were driven by the costs of sequencing and interpretation rather than downstream health care. Disclosing additional types of secondary findings has a limited cost impact following disclosure.

The Impact of Whole-Genome Sequencing on the Primary Care and Outcomes of Healthy Adult Patients: A Pilot Randomized Trial.

Background: Whole-genome sequencing (WGS) in asymptomatic adults might prevent disease but increase health care use without clinical value. Objective: To describe the effect on clinical care and outcomes of adding WGS to standardized family history assessment in primary care. Design: Pilot randomized trial. (ClinicalTrials.gov: NCT01736566). Setting: Academic primary care practices. Participants: 9 primary care physicians (PCPs) and 100 generally healthy patients recruited at ages 40 to 65 years. Intervention: Patients were randomly assigned to receive a family history report alone (FH group) or in combination with an interpreted WGS report (FH + WGS group), which included monogenic disease risk (MDR) results (associated with Mendelian disorders), carrier variants, pharmacogenomic associations, and polygenic risk estimates for cardiometabolic traits. Each patient met with his or her PCP to discuss the report. Measurements: Clinical outcomes and health care use through 6 months were obtained from medical records and audio-recorded discussions between PCPs and patients. Patients' health behavior changes were surveyed 6 months after receiving results. A panel of clinician-geneticists rated the appropriateness of how PCPs managed MDR results. Results: Mean age was 55 years; 58% of patients were female. Eleven FH + WGS patients (22% [95% CI, 12% to 36%]) had new MDR results. Only 2 (4% [CI, 0.01% to 15%]) had evidence of the phenotypes predicted by an MDR result (fundus albipunctatus due to RDH5 and variegate porphyria due to PPOX). Primary care physicians recommended new clinical actions for 16% (CI, 8% to 30%) of FH patients and 34% (CI, 22% to 49%) of FH + WGS patients. Thirty percent (CI, 17% to 45%) and 41% (CI, 27% to 56%) of FH and FH + WGS patients, respectively, reported making a health behavior change after 6 months. Geneticists rated PCP management of 8 MDR results (73% [CI, 39% to 99%]) as appropriate and 2 results (18% [CI, 3% to 52%]) as inappropriate. Limitation: Limited sample size and ancestral and socioeconomic diversity. Conclusion: Adding WGS to primary care reveals new molecular findings of uncertain clinical utility. Nongeneticist providers may be able to manage WGS results appropriately, but WGS may prompt additional clinical actions of unclear value. Primary Funding Source: National Institutes of Health.

Returning Results in the Genomic Era: Initial Experiences of the eMERGE Network.

A goal of the 3rd phase of the Electronic Medical Records and Genomics (eMERGE3) Network was to examine the return of results (RoR) of actionable variants in more than 100 genes to consenting participants and their healthcare providers. Each of the 10 eMERGE sites developed plans for three essential elements of the RoR process: Disclosure to the participant, notification of the health care provider, and integration of results into the electronic health record (EHR). Procedures and protocols around these three elements were adapted as appropriate to individual site requirements and limitations. Detailed information about the RoR procedures at each site was obtained through structured telephone interviews and follow-up surveys with the clinical investigator leading or participating in the RoR process at each eMERGE3 institution. Because RoR processes at each of the 10 sites allowed for taking into account differences in population, disease focus and institutional requirements, significant heterogeneity of process was identified, including variability in the order in which patients and clinicians were notified and results were placed in the EHR. This heterogeneity in the process flow for eMERGE3 RoR reflects the "real world" of genomic medicine in which RoR procedures must be shaped by the needs of the patients and institutional environments.