Challenges in providing residual risks in carrier testing.

The probability an individual is a carrier for a recessive disorder despite a negative carrier test, referred to as residual risk, has been part of carrier screening for over 2 decades. Residual risks are calculated by subtracting the frequency of carriers of pathogenic variants detected by the test from the carrier frequency in a population, estimated from the incidence of the disease. Estimates of the incidence (and therefore carrier frequency) of many recessive disorders differ among different population groups and are inaccurate or unavailable for many genes on large carrier screening panels for most of the world's populations. The pathogenic variants detected by the test and their frequencies also vary across groups and over time as variants are newly discovered or reclassified, which requires today's residual carrier risks to be continually updated. Even when a residual carrier risk is derived using accurate data obtained in a particular group, it may not apply to many individuals in that group because of misattributed ancestry or unsuspected admixture. Missing or inaccurate data, the challenge of determining meaningful ancestry-specific risks and applying them appropriately, and a lack of evidence they impact management, suggest that patients be counseled that although carrier screening may miss a small fraction of carriers, residual risks with contemporary carrier screening are well below the risk posed by invasive prenatal diagnosis, even if one member of the couple is a carrier, and that efforts to provide precise residual carrier risks are unnecessary.

Evaluation and classification of severity for 176 genes on an expanded carrier screening panel.

BACKGROUND: Disease severity is important when considering genes for inclusion on reproductive expanded carrier screening (ECS) panels. We applied a validated and previously published algorithm that classifies diseases into four severity categories (mild, moderate, severe, and profound) to 176 genes screened by ECS. Disease traits defining severity categories in the algorithm were then mapped to four severity-related ECS panel design criteria cited by the American College of Obstetricians and Gynecologists (ACOG). METHODS: Eight genetic counselors (GCs) and four medical geneticists (MDs) applied the severity algorithm to subsets of 176 genes. MDs and GCs then determined by group consensus how each of these disease traits mapped to ACOG severity criteria, enabling determination of the number of ACOG severity criteria met by each gene. RESULTS: Upon consensus GC and MD application of the severity algorithm, 68 (39%) genes were classified as profound, 71 (40%) as severe, 36 (20%) as moderate, and one (1%) as mild. After mapping of disease traits to ACOG severity criteria, 170 out of 176 genes (96.6%) were found to meet at least one of the four criteria, 129 genes (73.3%) met at least two, 73 genes (41.5%) met at least three, and 17 genes (9.7%) met all four. CONCLUSION: This study classified the severity of a large set of Mendelian genes by collaborative clinical expert application of a trait-based algorithm. Further, it operationalized difficult to interpret ACOG severity criteria via mapping of disease traits, thereby promoting consistency of ACOG criteria interpretation.

Hereditary Cancer Risk Using a Genetic Chatbot Before Routine Care Visits.

OBJECTIVE: To examine user uptake and experience with a clinical chatbot that automates hereditary cancer risk triage by collecting personal and family cancer history in routine women's health care settings. METHODS: We conducted a multicenter, retrospective observational study of patients who used a web-based chatbot before routine care appointments to assess their risk for hereditary breast and ovarian cancer, Lynch syndrome, and adenomatous polyposis syndromes. Outcome measures included uptake and completion of the risk-assessment and educational section of the chatbot interaction and identification of hereditary cancer risk as evaluated against National Comprehensive Cancer Network criteria. RESULTS: Of the 95,166 patients invited, 61,070 (64.2%) engaged with the clinical chatbot. The vast majority completed the cancer risk assessment (89.4%), and most completed the genetic testing education section (71.4%), indicating high acceptability among those who opted to engage. The mean duration of use was 15.4 minutes (SD 2 hours, 56.2 minutes) when gaps of inactivity longer than 5 minutes were excluded. A personal history of cancer was reported by 19.1% (10,849/56,656) and a family history of cancer was reported by 66.7% (36,469/54,652) of patients who provided the relevant information. One in four patients (14,850/54,547) screened with the chatbot before routine care appointments met National Comprehensive Cancer Network criteria for genetic testing. Among those who were tested, 5.6% (73/1,313) had a disease-causing pathogenic variant. CONCLUSION: A chatbot digital health tool can help identify patients at high risk for hereditary cancer syndromes before routine care appointments. This scalable intervention can effectively provide cancer risk assessment, engage patients with educational information, and facilitate a path toward preventive genetic testing. FUNDING SOURCE: Implementation of the chatbot in clinics was funded by industry support from commercial genetic testing laboratories Ambry, Invitae, and Progenity.

Case-control study of a gastroschisis cluster in Nevada.

OBJECTIVE: To identify potential risk factors associated with a sudden increase in gastroschisis cases in northern Nevada. DESIGN: Case-control study. SETTING: Medical centers and a pregnancy care center in Reno, Nevada. PARTICIPANTS: Participants (n = 14) were women who gave birth to infants with gastroschisis at either of the 2 medical centers in Reno, Nevada, from April 5, 2007, through April 4, 2008. Controls (n = 57) were selected from the same pregnancy center providing perinatal care to the cases and were matched 4:1 to the case mothers by maternal date of birth within 1 year. MAIN EXPOSURES: Environmental exposures and illnesses during pregnancy. OUTCOME MEASURES: Association of gastroschisis with illnesses, medications, or environmental exposures. RESULTS: Gastroschisis was associated with the use of methamphetamine (odds ratio [OR], 7.15; 95% confidence interval [CI], 1.35-37.99) or any vasoconstrictive recreational drug (methamphetamine, amphetamine, cocaine, ecstasy) (OR, 4.46; 95% CI, 1.21-16.44) before pregnancy. When we limited self-reported illnesses to those occurring during the first trimester of pregnancy, chest colds (OR, 16.77; 95% CI, 1.88-150.27) and sore throats (OR, 12.72; 95% CI, 1.32-122.52) were associated with gastroschisis. CONCLUSIONS: These findings add strength to the hypothesis that use of methamphetamine and related drugs is a risk factor for gastroschisis and raise questions about the risks associated with infections.