Implementation of Whole Genome Sequencing as Screening in a Diverse Cohort of Healthy Infants (1U01TR003201-01A1)
Phase: NA
Conditions: Genetic Predisposition to Disease, Hereditary Diseases
Sponsor: Brigham and Women's Hospital

Implementation of Whole Genome Sequencing as Screening in a Diverse Cohort of 
Healthy Infants 
 
Protocol 
ClinicalTrials.gov ID NCT05161169 
Last approval date: 11/29/2023 

 
 
 
BabySeq2: Implementation of Genome Sequencing as Screening in a Diverse Cohort of Infants 
 
Approved by the Boston Children’s Hospital Institutional Review Board  
(Protocol # IRB-P00041626, Activation date 4/15/2022, Latest approval date 11/29/2023) 
 
A. Specific Aims/Objectives  
There is growing societal interest in using genomic sequencing (GS) to identify genetic predispositions 
for disease early in life but there is insufficient evidence of its acceptability, psychosocial impact, and clinical 
utility. In order to begin to gather such evidence, over the past 5 years, our multi-disciplinary team launched the 
first randomized controlled trial (RCT) of GS in newborns: the BabySeq Project. We implemented a workflow 
for exome sequencing (ES), and curated 1514 disease-associated genes with favorable validity, age of onset, 
and penetrance. We randomized 325 families to a family history (FH) arm or a FH+GS arm, disclosed results 
and placed reports in each infant’s medical record. Medically, we discovered and disclosed unanticipated 
monogenic disease risks (MDRs) in 11% of the infants randomized to GS, and we discovered previously 
unrecognized signs of underlying disease and relevant family history in over half of these. Behaviorally, we 
found no increased distress among parents or disruption to the parent-child relationship in response to 
receiving GS results. Economically, we found only modest increases in downstream health costs. The majority 
of healthcare providers (HCPs) felt there were health benefits associated with newborn GS.  
The BabySeq Project demonstrated the feasibility of newborn GS, but the generalizability of our results 
was limited, as participant families were recruited as inpatients from one geographic region, predominantly 
White, and socio-economically privileged. We have now received NIH funding from the National Center for 
Advancing Translational Sciences (NCATS) for an entirely new RCT in ethnically/racially/socioeconomically 
diverse communities from Clinical and Translational Science Awards (CTSA) sites at Harvard University in 
Boston (Harvard Catalyst; sites BCH and Brigham and Women’s Hospital [BWH]), Mount Sinai Medical Center 
in New York City, and the University of Alabama Medical Center in Birmingham to study how GS in infants can 
be implemented in diverse, resource-limited, “real-world” outpatient primary care settings. We will also improve 
the technical aspects of the GS offering by moving from ES to GS and adding interpretation of copy number 
variants (CNVs). Through this research we will develop, pilot, implement, and evaluate a practical, sustainable 
approach to GS early in childhood that leverages underserved community engagement to minimize distrust 
and maximize benefit. 
In light of distrust of research among underrepresented minorities (URMs), we have established a 
Stakeholder Board with community representation from all 3 sites (see attached “Stakeholder Board Packet”). 
Under a separate IRB protocol (Protocol ID: IRB-P00038710), we are also soliciting parental input from the 
communities from which we will be enrolling to understand their concerns, especially within URM populations, 
towards GS research and how we can address those concerns as we develop our protocol, recruitment 
strategies, and disclosure methods.  
Aim 1: With input from the stakeholder board, and interviews of parents from the 3 CTSA sites, we will 
develop a recruitment and retention strategy to enroll 500 ethnically and racially diverse infants (0-12 
months) into an RCT of GS. Support will be provided for the HCPs through a structured genomics 
curriculum, and results will be returned to families and their HCP.  
Aim 1a: We will enroll outpatient pediatric HCPs in the clinics from which we will enroll families (in Aim 
1b), into a genomics education program modified from a successful program developed by 
HudsonAlpha SouthSeq that utilizes brief didactic videos and live training sessions. Patients seen in 
clinics where local HCPs have completed this program will be eligible to enroll in Aim 1b. 

 
 
Aim 1b: We will enroll and randomize a cohort of families with infants 0-12 months of age, from clinics 
where HCPs have completed the education program in Aim 1a, from the 3 CTSA sites into a FH arm or 
a GS+FH arm.  Results in both arms will be communicated by genetic counselors to the families and 
the participating HCPs.  
Aim 2: To evaluate the impact of GS on a diverse group of infants, their families and their HCPs. 
Aim 2a: To assess the psychosocial impact of infant GS on parent/guardians from varying ethnic and 
racial backgrounds, we will conduct longitudinal surveys and compare validated scales between arms 
of the RCT.  
Aim 2b: To assess the medical impact of GS on infants and their families, we will review medical 
records and survey parents/guardians to track symptoms, laboratory or diagnostic results, new 
diagnoses, and medical actions attributed to the GS findings. Among infants with an MDR, we will 
determine whether the MDR: (a) reveals an unsuspected phenotype in the infant or family, (b) explains 
a family history of a condition, and/or (c) prompts surveillance in the infant or family.  
Aim 2c: To assess the impact of GS in infants on clinical care, we will collect feedback from HCPs 
throughout the study by monitoring use of the “Genome Resource Center” and conducting interviews 
with HCPs towards the end of the study.  
Aim 3: Exploratory Aim: To evaluate healthcare utilization and associated costs of GS. Using novel 
approaches, we will identify healthcare services motivated by the study in the two study arms and examine 
attributable healthcare costs accrued for the infants and other family members.  
B. Background and Significance  
The era of genomic sequencing (GS) in medicine has arrived,1-3 and numerous laboratories are offering 
CLIA-certified exome (ES) or genome sequencing (GS) as a clinical service for characterization of rare 
disorders4-6 and for individualized cancer treatments.7-10 But many who envision a future of personalized and 
precision medicine have long considered GS to be equally or even more valuable for its potential to reveal 
predispositions to disease that would allow preventive measures,11-13 and to have utility throughout the 
lifespan,14 especially if performed early in life. Indeed, NIH director Francis Collins has said: “…whether you 
like it or not, a complete sequencing of newborns is not far away”15 and former NICHD director, Alan 
Guttmacher, echoed this by stating: “One can imagine the day that 99% of newborns will have their genomes 
sequenced immediately at birth."16 GS in the newborn period could reveal monogenic disease risks (MDR) that 
conventional newborn screening would miss, such as a pathogenic variant in RB1 predisposing an infant to 
develop a retinoblastoma. As infants grow into their reproductive years, they would have access to the variants 
they carry for recessive conditions to use for family planning.17-19 In adult life, sequenced individuals could 
benefit from knowing if they carry MDR variants in cancer predisposition genes like BRCA1,20 Some of the 
arguments against GS early in life arise from our understanding of population-based screening tests, where 
screening large numbers of individuals for rare conditions with poorly understood penetrance could identify 
more individuals at risk than would actually develop the condition. There are also cost considerations, with 
some questioning whether genomic information will lead to increased healthcare expenditures.21; 22 Finally, 
there is concern that the provider workforce may not be prepared to interpret and manage genomic results in 
their patients.23-29  
One of the major impediments to understanding the ultimate value of GS early in life is the absence of 
methodologically rigorous data on psychosocial impact and clinical utility. While there are large-scale attempts 
to match genomic information to phenotype data such as the Electronic Medical Records and Genomics 
(eMERGE) Network, the Geisinger MyCode Program, the Alabama Genomic Health Initiative, and the Mount 
Sinai BioMe Program, these programs are principally oriented toward adults and lack control populations 
against which to measure the clinical utility of GS. Over the past 5 years, our team has taken a rigorous 
approach to start to explore the psychosocial impact and clinical utility of GS in newborns as part of the 
Newborn Sequencing In Genomic medicine and public HealTh (NSIGHT) Consortium by conducting the 

 
 
“BabySeq Project,” the first pilot RCT of newborn GS.30-33 The BabySeq Project demonstrated the feasibility of 
enrolling parents and their newborns in an RCT of GS, analyzing GS data to identify genetic findings that 
indicate risk of a disease, and returning results to parents and health care providers (HCPs). Importantly, there 
was no evidence for harm from returning the results.34 35  
However, there were limitations to the BabySeq Project: parents were recruited in an inpatient setting 
immediately after giving birth, there was only a single site, and those who enrolled were predominantly White 
and of higher socioeconomic status. We now propose a new RCT to explore the implementation of GS in 3 
diverse, resource-limited, “real-world” outpatient primary care settings across the country enriched for 
underrepresented minorities (URMs), focusing upon African-American (AA) and Hispanic (HA) families. AA and 
HA communities have not benefited from genomics research to the degree that White populations have,36-39 in 
part because they tend not to participate in research. In the AA population, issues include mistrust, privacy 
concerns, fear of pain, and confusion around compensation,40-43 which are reinforced by historical abuses, 
including the Tuskegee syphilis experiment and the development of the HeLa cell line from Henrietta Lacks.44 
Among HAs, lack of knowledge about research, lack of dissemination of results, fear of pain or harm, and 
distrust of the healthcare system are primary barriers to research participation.45-48 It is critical to earn the trust 
of communities with robust engagement that involves welcoming patients/participants as essential and equal 
research partners before asking them to enroll in research studies.49-51 The 3 CTSA sites in this project, in 
Boston, MA, New York City, NY, and Birmingham, AL, all have years of extensive experience in community 
engagement and in enrolling and retaining URMs in genomics research (see Preliminary Studies - Prior Work 
and Data). We will leverage this experience and take a systematic approach to community engagement to 
address the concerns of URMs towards research and GS by involving parents, HCPs, and a Stakeholder 
Board in a substantial way as we develop and implement our study. This study is significant in that it asks how 
GS of infants from ethnically and racially diverse populations can be ethically implemented.  
C. Preliminary Studies 
Here we demonstrate that (1) we have the infrastructure and experience to feasibly carry out the 
proposed research project, (2) we have expertise in engaging diverse populations in genetic research, and (3) 
we have experience in safely returning genetic results to HCPs. 
Prior Work and Data: The BabySeq Project  
Study Design: The first phase of the BabySeq Project (2015-2019) was a pilot RCT of two newborn 
cohorts, one healthy and one from neonatal and cardiac intensive care units (NICUs and CICUs). For all 
newborns, we obtained the state mandated newborn screening (NBS) report, a 3- to 4-generation pedigree by 
a genetic counselor (GC), and DNA samples on the newborn and both parents. Within each cohort, half were 
randomized for their parents to receive a report of the assessment of the family history and the NBS report only 
(FH arm) and half to additionally receive their ES report (FH+ES arm). Families in both arms returned for an in-
person disclosure with a study GC and a final report was sent to the parents and to the infant’s HCPs and 
placed in the medical record. Parental surveys were conducted at enrollment (baseline), disclosure, and at 3- 
and 10-months post-disclosure, and HCP surveys were conducted at baseline and after they received a report 
on an enrollee.32; 33; 52  
Recruitment and enrollment: We had a 2-step approach to recruitment. We first approached families to 
assess interest in a genetics research study. For those who were interested in hearing about the study, we 
went over the details of the study and the consent form. Our study staff approached 5,022 inpatient families to 
assess their interest in a genetics research study and about 90% declined prior to hearing the details of the 
study, primarily due to lack of interest in “any research” during the stressful postnatal period. Of the families 

 
 
who agreed to hear about the study, 67% enrolled, for a total of 325 families (257 healthy and 68 from the 
ICUs).53  
Molecular Analysis and Reporting (Figure 1): We curated 1,514 genes associated with monogenic 
disease risk (MDR) based upon the ClinGen clinical validity classification 
framework criteria, age of disease onset, estimated penetrance, and mode 
of inheritance, through evaluation of published evidence.31 Of these, 954 
genes met our criteria for reporting; additional genes were included in the 
analysis as they arose through the pipeline comparisons with known and 
suspected genes and variants.31; 32 Initially, only genes expected to cause 
disease in childhood were included in the reporting criteria, however this 
was later updated to include a select number of highly actionable adult-
onset conditions as defined by the secondary findings list from the 
American College of Medical Genetics and 
Genomics (ACMG)54,90. Variant interpretation 
followed the ACMG/Association of Molecular 
Pathology (ACMG/AMP) guidelines for 
assessment of pathogenicity.55 The Newborn 
Genomic Sequencing Report (NGSR)30; 31 
documented MDR, defined as pathogenic or 
likely pathogenic (P/LP) variants in the genes 
that met reporting criteria, recessive carrier 
variants, and pharmacogenomic (PGx) variants 
associated with medications used in pediatrics. 
The inheritance of the MDR allele/s (but not 
carrier status) were assessed by analysis of 
parental DNA. For infants with a condition 
suspected to have a genetic contribution, an additional Indication Based Analysis (IBA) was generated, which 
included variants of uncertain significance (VUSs) for the gene/s suggested by clinical features. This strategy 
was an attempt to maximize the lifelong value of GS for each infant by routinely searching for MDR in a large 
set of genes and also conducting an IBA for any illness where a genetic etiology was suspected and there 
were candidate genes.  
Medical Outcomes: Among the 159 infants who underwent GS, 18 (11%) had a MDR. As shown in 
Table 1, the findings in 3 cases prompted discovery of a related disease phenotype not previously recognized, 
in 2 cases identified vulnerability to future exposure, and in 1 case explained a previously unrecognized 
genetic etiology.32 The remaining 12 cases offered future disease risk assessment for the infant and the parent 
carrying the variant. While surprisingly high, this is consistent with findings from an earlier GS study that we 
conducted in adults and is probably explained by the fact that few prior studies have comprehensively 
sequenced healthy individuals.56 Carrier status was identified in 140 of the 159 (88%), with an average of 2 
variants per infant (range 0-7).57 Of the 7 genes identified most frequently, only CFTR is captured in 
conventional carrier screening, and most genes are not on 
expanded carrier screening panels.58 Some parents used 
recessive carrier results to inform their reproductive 
planning, including one family that pursued preimplantation 
genetic testing after follow-up clinical carrier testing 
revealed that both parents carried pathogenic variants in 
the same gene.59 
Behavioral Outcomes: We measured the impact of 
receiving GS information on the parent-child relationship 
using a modified version of the Vulnerable Baby Scale 
(VBS),60 Mother-Infant Bonding Scale (MIBS),61 and 

 
 
Parenting Stress Index (PSI-4-SF).62 We assessed parents’ risk for depression and anxiety using validated 
scales. We found no parental distress that could be directly linked to study participation, and for all domains of 
parental distress and family impact we observed no effect between randomization arms, nor between parents 
whose infant did vs. did not have MDR findings in the GS arm (Figure 2). 139 These findings suggest that 
providing the parents with GS information about their newborn, including MDR, does not cause psychosocial 
distress or familial disruption.  
Healthcare Utilization/Cost Outcomes: We explored healthcare utilization and costs using techniques 
developed and piloted in our prior projects.21; 33; 63; 64 Data collected through the parental surveys and medical 
record reviews captured medical costs and parental time lost from work, and we reviewed the notes from the 
disclosure sessions. For those in the GS arm recommendations were frequently made for follow-up and testing 
for the newborn and a family member (Figure 3). Preliminary analyses of total healthcare spending in the 3-
months following disclosure (Figure 4) showed that total costs per newborn were higher in the GS ($567) 
compared to control arm ($352).65 
 
 
 
 
 
 
 
 
 
Prior Work and Data: Engaging Diverse Populations in Genetic Research  
All 3 of the sites bring extensive experience in engaging URMs in genomic research and in addressing 
mistrust, fear, stigma, and distress within URM communities, including through the All of Us Research Program 
(AoURP), the Clinical Sequencing Evidence-Generating Research (CSER) Consortium, and the Implementing 
GeNomics In pracTicE (IGNITE) Network.  
In Boston, Dr. Green has led NIH-funded collaborations that have explored URM issues related to 
genetic testing,66-73 and consistently recruited 15-24% URM participants in some of the earliest trials disclosing 
genetic risk information.74-77 Dr. Green also leads an NIH grant that is the first to systematically return genomic 
results to AA participants in the Jackson Heart Study, as presaged by these early data.78  
In New York, Dr. Horowitz and her team have successfully recruited and retained thousands of AA and 
HA individuals in genomic clinical trials via robust community engagement.79 Her local “genomic stakeholder 
board” of largely Black and Latino members is nationally known for community engagement and working with 
researchers on genomic discovery and translational research,80 and has led engagement with URM groups in 
several ways, including the incorporation of novel tools for digital engagement and navigation.81-83 Dr. Horowitz 
co-chaired the diversity-focused CSER II Consortium,83 and chaired the IGNITE Consortium, forming and 
leading its Diversity Workgroup and engagement strategy.85; 86 She has conducted mixed methods research to 
build, pilot, and revise a trial, similar to what we propose here,87 testing over 2000 hypertensive AA patients at 
16 clinical sites for APOL1 variants that increase risk of kidney failure, retaining 93% at 3- and 88% at 12-
months follow-up,79 and demonstrating positive outcomes.  
In Birmingham, University of Alabama (UAB) investigators are leading the Alabama Genomic Health 
Initiative and SouthSeq (part of the CSER II Consortium), and staff one of the leading recruitment sites for the 
entire national AoURP with an overall recruitment of 88.5% participants designated as URMs in biomedical 

 
 
research, including 67.0% based on race/ethnicity. Each of these studies includes a highly functional 
community advisory board and conducts outreach to community groups to learn about concerns of the 
community regarding the research.88; 89 
Prior Work and Data: Returning Unanticipated Genetic Results to HCPs  
In addition to our experience in the BabySeq Project,30-32; 34; 90 
our group has extensive experience in the return of unanticipated 
genomic results to participants and HCPs and in studying the impact 
on both, which will inform our approach to return of results to HCPs. 
The NIH-funded MedSeq Project led by Dr. Green was the first to 
sequence healthy adults and report results directly to primary care 
HCPs,56; 91; 92 demonstrating that after a minimal amount of education, 
HCPs returned the results to their patients without undue errors or 
distortion of the information.56; 93 Dr. Holm is leading an NHGRI-
funded study of the impact on HCPs of receiving unanticipated GS 
results in the eMERGE III Network. Interviews of HCPs prior to 
receiving GS showed concerns about workflow and lack of guidance 
on handling unanticipated genomic results.94 The UAB group led by 
Dr. Korf is conducting a clinical trial of return of GS results to parents 
of newborns in the special care nursery. In addition, Dr. Korf’s 
colleagues at HudsonAlpha, developed a curriculum of didactic lectures and simulation exercises to train non-
genetics HCPs in the return of GS results, demonstrating clear improvement in provider confidence (see Figure 
5 showing number of providers endorsing each category pre and post curriculum).95  
D. Design and Methods  
1. Study Design 
This is a randomized clinical trial evaluating infant and family outcomes, as well as physician outcomes, 
after participation in one of the two arms. Figure 6 summarizes the proposed details of study participation for 
physicians. Overall design of the RCT is summarized in Figure 7. Figure 8 summarizes the proposed details of 
study participation for infants and parents/guardians. The study has been registered on clinicaltrials.gov. In 
light of distrust of research among underrepresented minorities (URMs), we have established a Stakeholder 
Board with community representation from all three sites. We are also soliciting parental input from the 
communities to understand the concerns, especially within URM populations, towards GS research and how 
we can address those concerns as we develop our protocol, recruitment strategies, and disclosure methods.   
The Stakeholder Board 
Building upon our prior experience,96-99 our Stakeholder Board of parents, community leaders, and 
clinicians, with representatives from each site, consists of 10 members, all of whom are themselves AA or HA 
and a mix of academic experts and patient-participants. The Stakeholder Board has begun meeting quarterly 
via Zoom video conference. Drs. Horowitz and Holm are facilitating the Stakeholder Board meetings. We are 
using community-based participatory research (CBPR) approaches described by Dr. Horowitz to choose 
leaders and rules of engagement.79; 100-102 In year 1, our Stakeholder Board is advising us on: (1) the 
perspectives of URMs and their HCPs towards genetics and research; (2) issues to address in parent 
interviews, who to include, and how best to structure them; and (3) integration of what we learn through the 
parent interviews to develop our protocol and materials. In years 2-4, our Stakeholder Board will review and 
suggest edits to study materials, discuss implementation challenges, and advise on all aspects of the study. 
Stakeholder Board members will receive a yearly stipend as compensation for their time. 
Parent/caregiver interviews 
Under a separate IRB protocol (IRB-P00038710), we are conducting up to 30 semi-structured 
interviews of parents split between each site who have a child 0-5 years of age. The interview guide includes 

 
 
the following domains: (1) how parents prefer to be approached for research; (2) beliefs about genetics, prior 
experiences with genetic testing; (3) barriers to participation; (4) concerns about the study; (5) types of genetic 
information to disclose, e.g. monogenic risk, carrier status, adult-onset conditions, (6) how should genetic 
information be disclosed (in-person, phone, online); (7) what form results should be presented in, e.g., written, 
verbal, pictures, digitally; and (8) what outcomes and study results would be of interest. Interviews of Spanish-
speaking parents will be conducted by a native Spanish-speaking Pediatric Genetics fellow.  
Development of study procedures and materials  
Study procedures and materials are in development and are being shaped by what we are learning 
through the parent interviews and our Stakeholder Board, our prior experiences in genomics research with 
diverse populations, and a literature review. The materials include recruitment strategies, informed consent, 
and results disclosure templates, and will accommodate low-literacy (less than or equal to 8th grade reading 
level) and non-English speaking participants. Stakeholder Board feedback has and will inform revisions.83; 101; 
104; 105 The final material will be submitted to the IRB before being implemented. 
Piloting of study materials and procedures 
Stakeholder board members will pilot test participant surveys for content and length. We will also model 
enrollment and disclosure sessions with Stakeholder Board members. We will collect informal, open-ended 
feedback from initial participants regarding the enrollment approach, materials, and process. Additionally, we 
will collect informal, open-ended feedback from the first several participants at each site regarding their 
experience with the disclosure visit. What we learn will inform changes in our materials and process. 
Aim 1: With input from a diverse stakeholder board and interviews of parents/guardians from 
the 3 CTSA sites, we will develop a recruitment and retention strategy to enroll 500 ethnically 
and racially diverse infants (0-12 months) into an RCT of GS. Support will be provided for the 
HCPs through a structured genomics curriculum, and results will be returned to families and 
their HCP. 
 
Aim 1a: We will modify a successful HudsonAlpha SouthSeq genomics education program utilizing brief 
didactic videos and live online training sessions for the outpatient pediatric HCPs.  
Although the study staff, not the HCPs, will be returning 
the results to the families, the HCPs will receive the results and 
follow the families, as in BabySeq. In order to provide education 
to the HCPs on GS and the results they may receive, an HCP 
education program and assessments will be developed by 
collaborators at the HudsonAlpha Institute for Biotechnology, built 
upon a similar curriculum they developed and used to train 
neonatology providers in the SouthSeq project. Initially, only the 
patients of HCPs who participate in the training will be recruited 
for the RCT. After initial clinic champions have been enrolled and 
completed the full training curriculum, the requirements may be 
modified, condensed, or removed as needed to increase access 
and enrollment.  
1a.1 Recruitment of HCPs 
Prior to initiating recruitment of families into the RCT, we 
will invite all HCPs who care for infants at each site to participate 
in the study. The research study will be introduced to pediatricians through word-of-mouth and presentations 
held during clinic staff meetings, hospital grand rounds and seminars (see attached “HCP Slides”). These 

 
 
presentations will give pediatricians a background on the project and provide them with contact information for 
study staff. HCPs who decide to enroll will participate in the education intervention.  
1a.2 HCP Genomics Education Program  
The genomics education program will focus on helping HCPs understand GS, the contents of the 
Newborn Genomic Sequencing Report (NGSR), what to do next with the results, how to counsel 
parents/guardians about results, and resources available. The HCP curriculum will include brief, didactic 
videos as well as a live training at each site for facilitated small group discussion and simulation (see attached 
“HCP Curriculum Detailed Outline”). Fact sheets and other just-in-time resources will also be provided (see 
attached “Fact Sheets” and “Decision Trees”).  
HCPs may complete brief, anonymous, pre- and post-training online surveys to assess their genetics 
background and perceived confidence reading and using GS results (see attached “HCP Pre- and Post-Ed 
Survey”). Consent to participate in the study will be implied by voluntary completion of the surveys and 
education intervention. HCPs may complete a brief self-reflection survey to engage with the study every 6 
months and earn Maintenance of Certification Part IV credit (see attached “BabySeq2_Provider 
Survey_MOC”). Additionally, HCPs may compete an end-of-study survey at the conclusion of their participation 
(see attached "BabySeq2_Provider Survey_END OF STUDY”). In years 2-4, semi-structured feedback will be 
collected from HCPs through annual phone or video calls with a member of the HudsonAlpha education team. 
Future interview guides will be submitted for IRB review. Topics discussed may include their experiences 
receiving GS results, how often they engage in conversations with families about study results (all types), 
common patient questions, topics they feel more or less confident addressing, and whether gaps in 
education/training exist. Data from the assessments will be used to develop additional clinical decision support 
resources, as well as to provide data about HCP educational needs.  
HCPs may receive either Maintenance of Certificate (MOC) Part IV credit or Continuing Medical 
Education (CME) credits for completion of the genomics education program. Additionally, HCPs may receive 
$50 as compensation for their time spent taking surveys and completing training. The format of compensation 
will depend on site-specific policies and may include gift cards, checks, or payroll additions. 
After the initial clinic champions have been enrolled and trained at each site, the HCP training may be 
modified, condensed, or removed as needed to increase access and enrollment. Additionally, pediatricians in 
other clinics who are not familiar with BabySeq may be invited to compete optional surveys as a comparison 
group to assess the effects of training or the applicability of educational materials and genome reports to 
general pediatricians. Any modifications to training requirements or introductions of new surveys will be done in 
collaboration and with support of local clinics. Clinic champions who have completed the full training will be 
available as a local consultation resource for their colleagues. Additionally, “just in time” resources and 
educational materials including videos, fact sheets, and decision trees will be available on-demand for all 
pediatricians at each recruitment clinic, and live training sessions will be scheduled upon request. The study 
team and Genome Resource Center will be available for any pediatrician questions throughout the study. 
Genetic counselors will return study results to families and provide detailed chart notes for physician reference. 
In the case of a positive result, the study team will reach out directly to the physician with additional information 
and recommended next steps.  
Aim 1b: We will enroll and randomize a cohort of families with infants 0-12 months of age from 3 CTSA sites 
with established expertise in engagement, diversity, and genomic clinical trials. Participants will be randomized 
into FH (Control) or GS+FH (Sequencing) arms, and results will be communicated by genetic counselors to the 
families and the participating HCPs.  

 
 
 
1b.1 Enrollment Sites  
In Boston, the Boston Children's Hospital (BCH) Primary Care Center (CHPCC) is the largest 
pediatric practice in Boston and serves about 16,000 children, most of whom live in low-income neighborhoods 
of Boston. Forty-five percent of patients are AA and 35% are Latino; 15% of families are exclusively non-
English speaking. The Martha Eliot Health Center (MEHC) is owned and operated by BCH and is the second 
oldest community health center in the country. It cares for over 6,000 children, most living in local Boston 
neighborhoods. The patient population is diverse with 20% AA, 70% Latino. 
In Birmingham, the Children’s of Alabama Primary Care Clinic is part of the UAB Department of 
Pediatrics providing primary care for about 4,200 pediatric patients, with 8000-9000 clinical encounters per 
year. The clinic accepts 25 new infants each month, most of whom will have at least 5 visits in the first 6 
months of life. The clinic population is 80% AA and 8% Hispanic. Children’s of Alabama Over the Mountain 
Pediatrics provides comprehensive medical care to approximately 11,000 patients, from birth through 
adolescence, with over 29,500 visits per year, seeing over 80 infants per week who are 21% AA, 3% 
Hispanic/Latino.  
In New York City, the Mount Sinai Pediatric Associates Practice provides comprehensive pediatric 
care to approximately 10,000 patients with over 23,000 visits per year with a patient population that is 40% HA 
and 34% AA.  
1b.2 Recruitment in the postnatal period  
 
Prior to initiating study recruitment, research assistants and other team members from each site will be 
trained on recruitment, enrollment, and consent procedures as well as data entry and follow-up. There will be 
standard operating procedures (SOPs) developed for all study processes. The coordinating site will lead live 
online trainings with mock sessions to promote uniform study conduct, and will be available for questions 
throughout the project.  
Recruitment may be initiated in person or remotely by an RA, or by patient self-referral.  
Prior to clinic visit:  Study RA will pre-screen patients for eligibility prior to scheduled clinic  
appointments. A letter to assess study interest may be sent to eligible families by mail, secure email, or  
patient portal (see “Initial Recruitment Letter”) ahead of a regularly scheduled clinic visit. This letter may be 
sent by the study team directly, or by the recruitment clinic with a “Clinic Cover Letter”. The letter will introduce 
the study and provide contact information as well as include a study flyer (see “Peds Intro Flyer”). If the family 
is interested in learning more or would like to opt-out of additional contact, they will be directed to a study 
phone number and/or email. If a response is not received within 2 weeks of the mailing/email/message, study 
staff may reach out to the parent by phone to introduce the study ahead of their next visit, or meet with the 
Healthy infants (0-12 months), parents, & community pediatricians 

 
 
family at the next visit. Those who opt-out will be noted in the recruitment screening log and not approached at 
future visits. 
In Person: In light of the COVID-19 pandemic, where possible and with permission of clinic 
management, we will embed an RA in each pediatric clinic and will take advantage of the frequent well baby 
visits, at 1-, 2-, and 4-months. Each week the RA will have access to the clinic schedule to pre-screen patients 
for study eligibility by reviewing the patients of HCPs who are enrolled in the study. A member of the patient’s 
clinical staff will ask if they are interested in speaking with someone from the research team. If permission is 
granted, the RA will approach families while they are in the clinic to assess interest and provide 
parents/guardians with study materials, in English or Spanish, to take home. The RA will be Spanish-speaking 
in clinics with a large Spanish-speaking population or will otherwise utilize hospital approved interpreter 
services (in-person or remote interpreters). We will strive to hire RAs from the same communities as our 
participants. They will be trained using techniques proven effective in recruiting URM patients, interviewing 
mock patients, and receiving feedback.106-113   
Remote: Study RAs will have access to the clinic schedule to pre-screen patients for eligibility and 
provide clinical providers with a list of patients. Clinical providers will approach families while they are in the 
clinic to briefly assess interest and provide parents/guardians with study materials. With permission from the 
clinical provider and patient, an RA will contact families who express an interest in the study by their preferred 
method of contact (i.e., phone or email) to continue the consenting and enrollment process.    
Patient Self-referral: Flyers and study handouts will be available in recruiting pediatric clinics, including 
contact information for the study team (see attached "Peds Intro Flyer," "Peds Detailed Flyer," and "Peds FAQ 
Sheet"). Parents/guardians who are interested in the study may contact a study team member about their 
eligibility and any other questions they may have about the study. If the participants HCP is not presently 
enrolled in the study, they will be notified of their patient interest and re-sent information on 
enrolling/completing the HCP education.  
Final enrollment notification: As the infant approaches their first birthday, if parents are still considering 
participation (have not enrolled nor declined) they will be sent a “Final Recruitment Letter”. This will remind 
parents that they are eligible to enroll up until their child turns one year old. 
Participants who are approached for enrollment and then decline to participate will be asked by the RA 
to provide an open-ended reason for decline. The reasons will be tracked along with demographics and 
minimum necessary identifying information so that the family is not re-contacted. 
1b.3 Enrollment session 
An enrollment session will be scheduled with the parents/guardians of newborns who express interest 
in participating in the study. A research assistant will meet with the parent/guardian, verify their interest in 
participating in the study, and initiate the consent process. This session may take place in person, by video, or 
by phone.  
During the enrollment session, parents/guardians will learn about genetics and genomic sequencing, 
the study protocol, the benefits and risks of participation, and will have opportunities to ask any questions. The 
research assistant will verbally review the informed consent form, including discussing all of the study 
procedures. 
After completing the enrollment session, if parents/guardians are unsure and prefer additional time to 
decide about participation, a follow-up conversation will be scheduled if requested.  
It should be noted that if there are multiples (i.e., twins), only one would be enrolled, at the 
parent/guardian discretion.   
1.b.4 Consent 

 
 
For parents/guardians who decide to enroll, informed consent will be obtained through a paper consent 
form or REDCap eConsent (see attached “Primary ICF”). The eConsent platform will be hosted by the 
coordinating institution at BWH and a copy of the consent forms will be stored there. The consent will include 
the following: 
Infant’s participation includes demographic collect, randomization, review medical records from all 
medical providers of the infant, sample collection if in the GS arm, return of results to the parent/guardian and 
their HCP, placement of results in EHR, access of billing information from state and institutional databases, 
and the potential for long-term follow-up, including consent at age 18 years, if the study is still in progress.. 
Parent/guardian participation includes consent to complete surveys and for review of the mother’s 
prenatal records, if applicable. After detailed conversations with the study team and our Stakeholder 
Board, the decision was made to only require the consent of one parent/guardian for participation. This 
is in contrast to the original BabySeq Project I which there was a requirement that both parents enroll. The 
rationale for this decision is as follows: 1) Requiring two parents to consent may exclude a significant 
proportion of families. Our stakeholder board and team members experienced in working with our target 
population felt strongly that this requirement would counteract the goals of equitable access. We saw this in 
BabySeq, where the requirement for both parents to enroll played a significant role in the biased recruitment of 
a non-racially/economically diverse population. Here we want to avoid restricting access to those with only one 
actively involved parent and continuing to propagate the existing disparities in genomic research. 2) One of the 
goals of this study is to study the use of genomic screening in a way that would be as closely applicable to real 
world implementation as possible, with a focus on the infant. In newborn screening, or any other screening 
scenario, only the individual undergoes GS, not family members. 
A corollary of the decision to not require both parents to enroll was the decision to not collect 
samples for DNA on the parent/guardian. If an MDR is found in the infant, we will offer the parent/s and 
sibling/s the option for cascade testing, and consent/assent will be obtained separately for these individuals for 
their own testing (see attached “Cascade ICF). If a relative consents, a saliva collection kit will be mailed for 
them to provide their own sample for targeted testing of only the variant identified in the infant. If only one 
parent chooses to get testing, the study team realizes this may lead to potentially revealing the other parent’s 
status by exclusion - although not with certainty as with each genetic variant there is always a possibility that it 
may be de novo in the child. 
It should be noted that if we become aware that a second parent/guardian opposes their infant’s 
participation in the study, the family will not be enrolled. 
We will collect informal, open-ended feedback from initial participants regarding the enrollment 
approach, materials, and process for quality improvement.  
1b.5. Data collected at enrollment 
Demographics and other study intake information will be recorded in a secure REDCap database 
accessible only to the clinical study team.  
A 3-generation family history will be obtained by the research assistant at this stage of enrollment using 
a standardized template with scripted questions. The family history will be utilized later by the laboratory and 
the consultation team in the interpretation and contextualization of the sequencing report as well as to create a 
family history report (FHR) for the family (see attached “Sample FHR”). In the event that the family history 
suggests that a more targeted form of genetic testing should be pursued (such as a family history suggestive of 
Lynch syndrome in the parents), the family will receive additional genetic counseling and may be referred for 
clinical genetic counseling and/or additional targeted genetic testing for this indication. 
Baseline survey. All surveys (i.e., baseline, post-disclosure, and 6-months post-disclosure) may be 
completed in person, over the phone, or online (via REDCap). Participants will be offered the option to have an 
RA read the survey questions and to respond verbally. 

 
 
1b.6. Randomization 
After completion of the baseline survey, infants will be randomized (1:1) to either standard-of-care 
(family history) or to standard-of-care plus genomic sequencing, divided approximately equally between the 3 
sites. Parents will be notified which group their child is part of and whether a sample needs to be collected. 
1b.7. Sample collection 
Infants randomized to the sequencing group will have a sample collected for GS. Infants randomized to 
the control group will not have a sample taken. Sample collection will take place in the clinic by clinical staff or 
trained research personnel. A blood sample (through a heel stick, finger stick, or venipuncture) of less than 1.0 
ml will be obtained from all infants randomized to the genomic sequencing arm. Alternatively, and when 
possible, leftover blood and/or cord blood samples already obtained may be retrieved, a saliva or buccal swab 
may be collected. In the case that the sample for an infant does not yield enough DNA for sequencing, we 
would offer a second sample collection should they agree to this. These samples will be shipped to the 
Laboratory for Molecular Medicine (LMM) or other CLIA compliant clinical diagnostic facility.  
The decision to only collect a sample from those in the FH+GS arm was based on insight from our 
Stakeholder Board and study team member experienced in recruiting in diverse population, who felt that 
having a blood sample obtained from an infant who ultimately did not undergo GS would be a significant 
deterrent to enrollment in our populations. After a long discussion with the full study team, we decided that 
since we will collect the baseline survey and data PRIOR to randomization, we did not feel that not obtaining a 
sample for DNA in those in the FH only arm will compromise our study outcomes. 
1b.8 Genomic Sequencing and Reporting  
Genomic sequencing of infants randomized to the sequencing arm will be conducted in a CLIA certified 
sequencing laboratory at the Broad Institute Additional genetic testing may be used to help with data 
interpretation and confirmation (for example, chromosomal microarray or Sanger sequencing). Data 
interpretation will be performed at the LMM. Variant classification will be based upon current medical practice 
standards in the laboratory for targeted gene panels and other clinical sequencing.114 The genomic reporting 
framework for this protocol is shown in Appendix 1 below. A CLIA-compliant newborn genomic sequencing 
report (NGSR) will be issued for each case and uploaded to the infant’s medical record after their disclosure 
session.  
1b.8.1 Gene and Variant Reporting Criteria  
 
To help establish which results will be returned, the following reporting criteria will be used to determine 
whether a result should be included on the NGSR. 
1) Monogenic Disease Risk and Carrier Status for Childhood-Onset Disorders 
Gene-level criteria 
Validity of gene-disease association: Gene-disease pairs will be evaluated using the ClinGen 
framework for determining the strength of a gene-disease association.115 In this method, the validity of a gene–
disease association is evaluated by reviewing the evidence reported in the literature, such as the number of 
families with pathogenic variants in the gene and functional studies, and classified into the following categories: 
conflicting evidence, no reported evidence, limited evidence, moderate evidence, strong evidence, and 
definitive evidence. For the NGSR, we will only be returning results in genes that have a definitive or strong 
level of evidence. 
Penetrance: Estimated penetrance will be curated based on the phenotype information for reported 
individuals in the literature. For the NGSR, we will return results associated with genetic conditions with high or 
moderate penetrance.. Results in genes with lower than moderate penetrance will be discussed with the team 
to review available evidence around actionability, severity of the associated condition, burden of available 

 
 
interventions, and other clinical factors. Reporting decisions for these results will be made based on clinical 
judgment.  
Age of Onset: The youngest age at which individuals with pathogenic variants in the gene presented 
with disease will be recorded based on available information in the literature. Results will be returned in genes 
where the earliest reported onset of disease or actionability is in pediatric patients (<18 years old).  
Mode of Inheritance: The most common inheritance pattern for the gene will be determined using the 
GenCC database and relevant literature. This will determine whether the variant indicates that the individual is 
at risk for the disease or is a carrier. 
In addition to the resources mentioned above, online databases that will be used to determine gene 
level criteria include: Medline Plus (formerly Genetics Home Reference; GHR), Orphanet, ClinVar, HGMD, 
gnomAD, OMIM, and Gene Reviews. 
 
Variant-level criteria 
Sequence Variants: Variant calls will be made using the Genomic Analysis Tool Kit (GATK). Variant 
interpretation will follow the American College of Medical Genetics and Genomics/ Association of Molecular 
Pathology (ACMG/AMP) guidelines for assessment of pathogenicity, with additional incorporation of all new 
modifications set forth by ClinGen's Sequence Variant Interpretation working group, including the PVS1 
criterion for predicted loss-of-function (pLOF) variants,116 and rule adaptations specific to certain genes, as 
determined by ClinGen expert panels. Any variant that meets criteria to be likely pathogenic or pathogenic will 
be reported on the NGSR. 
Copy Number Variants: We will employ GATK for structural variants (GATK-SV) to analyze the WGS to 
identify copy number variants (CNVs). CNVs known to confer risk for childhood-onset disorders will be 
reported on the NGSR. We will make use of deeply analyzed benchmark datasets generated by the Genome 
in a Bottle Consortium and the Human Genome Structural Variation Consortium. We will only return 
pathogenic or likely pathogenic variants (not variants of uncertain significance). All variants will be interpreted 
using the ACMG/ClinGen CNV classification criteria.117 Regulatory (noncoding) variants will not be returned 
unless previously classified as disease-causing (e.g., in ClinVar or HGMD), in which case they will be 
evaluated according to the ACMG guidelines for predicting pathogenicity. The criteria for disease validity, age 
of onset, and penetrance will be similar to the reporting criteria for childhood-onset monogenic disease risks. 
2) Monogenic Disease Risk for Highly Actionable Adult-Onset Disorders 
Gene-level criteria 
The NGSR will also include monogenic disease risk for highly actionable adult-onset disorders per the 
current version of the ACMG secondary findings gene list at the time the report is generated (see Appendix 2 
below for version 3.2, updated June 2023140). This list will be updated as new clinical guidelines are published, 
approximately on an annual basis. 140 The most updated version of the list will be used to determine which 
results to return, consistent with clinical practice. 
Variant-level criteria 
The same criteria will used as above (see “Monogenic Disease Risk and Carrier Status for Childhood-
Onset Disorders”). 
1b.8.2 The Genomic Newborn Sequencing Report  
For infants randomized to the GS arm, a “Newborn Genomic Sequencing Report” (NGSR) will be 
generated that will follow a format already being utilized clinically by the LMM, and in the MedSeq Project92 but 
with modifications to include only genes associated with childhood onset conditions and a small subset of 
highly actionable adult-onset conditions (see attached “Sample NGSR”). Separate sections on the NGSR will 
indicate results under the following headings: 

 
 
Monogenic Disease Risk: We will report pathogenic or likely pathogenic variants that meet reporting 
criteria that are in heterozygous state and are associated with autosomal dominant or X-linked disorders; in 
homozygous or compound heterozygous state and are associated with autosomal recessive disorders; or in 
hemizygous state and are associated with X-linked recessive disorders. These variants may be caused by 
alterations in the DNA sequence (i.e., missense, nonsense variants) or alterations in gene dosing and genome 
structure (i.e. microdeletions, copy number variants).  
Highly Actionable Adult-Onset Only Monogenic Disease Risk: We will report pathogenic or likely 
pathogenic variants in highly actionable adult-onset conditions as characterized by the ACMG 
recommendations. Of note, although this was not the initial plan in the initial BabySeq study, this category was 
added in agreement with (and under the advice of) the IRB. Currently this includes hereditary breast and 
ovarian cancer genes (BRCA1, BRCA2) and Lynch syndrome genes (MLH1, MSH2, MSH6, PMS2) that are 
autosomal dominant.118 If the ACMG recommendations are updated and other genes are added that are of 
adult-onset only, we will add these. 
Carrier Risk: In the case of autosomal recessive or X-linked recessive disorders, we will report 
pathogenic or likely pathogenic variants that meet reporting criteria that are in heterozygous state (recessive) 
or in a female (X-linked recessive) and thus the infant is a carrier and not affected.  
1b.8.3 The Indication-Based Analysis 
In the event that one of the babies becomes ill with symptoms where a genomic analysis may be 
helpful for diagnosis or treatment of a particular presentation, an “Indication-Based Analysis” (IBA) will be 
generated in response to requests by the baby’s physicians in consultation with the study team, or by the study 
team physicians. The IBA will include interrogation of genes associated with the syndrome or clinical features 
in question, as currently practiced in medical genetics. In addition, upon request, an IBA may additionally 
include Evidence Class I and II variants from PharmGKB119 for specific indications (e.g., anti-epileptic 
medication in neonates with seizures). For every situation in which an IBA is requested, the study physician will 
be in communication with the clinical team or physician ordering the IBA and will make sure that a re-query of 
the genomic sequence is not ordered in lieu of a targeted genetic test or panel that would be more appropriate 
for a specific presentation. Gene-disease criteria for the IBA will include results in genes that have a moderate 
level of evidence (using the ClinGen framework). Penetrance will not be considered in reporting as these 
individuals will be exhibiting symptoms. The IBA will include variants that are considered Variants of Uncertain 
Significance (VUSs) relevant to the condition, according to the ACMG/AMP criteria in addition to pathogenic 
and likely pathogenic variants. 
1b.9 Consultation and Disclosure of Reports  
A study physician and/or genetic counselor will disclose study 
results during a consultation with each family held either in-person, 
by video, or by phone. The study physician/genetic counselor will 
provide the consultation to families utilizing all available medical 
information. For Spanish-speaking families, the session will be 
completed in Spanish with a professional interpreter and the reports 
will be in Spanish. 
Both arms: The review of the family history report will be 
returned. If the family history obtained during the baseline visit 
suggests that targeted genetic testing for a Mendelian condition 
should be pursued, the family will receive additional genetic 
counseling, and may be referred for clinical genetic counseling 
and/or targeted testing for this indication. For the FH arm, this is the 
only result that will be returned 
(0-12 months) 

 
 
FH+GS arm: In addition to the family history report, the sequencing report(s) will be returned. If an 
MDR is identified in the infant, cascade screening will be offered to all first-degree relatives (biological parents 
and siblings). 
We will collect informal, open-ended feedback from the first several participants at each site regarding 
their experience with the disclosure visit. What we learn will inform changes in our materials and process. 
It should be noted that the raw sequence data will not be returned to participants or placed in the 
medical record at the time of disclosure. This data will be available to parents/guardians and/or physicians 
upon request and consent for release. 
1b.10 Generating a Clinical Note  
The team of study physician and genetic counselor will generate a clinical note following the results 
disclosure visit summarizing the findings of the reports, what was discussed in the consultation, and what 
recommendations were made. This note will be included in the patient’s chart and forwarded to physicians 
involved in the infant’s care. 
1b.11 Return of Results and Follow-up  
Genetics Resource Center (GRC): Oversight of the return of reports to HCPs will be supported through 
the GRC, which will be available to the HCPs and families throughout the study. The GRC will be designed as 
a two-tier e-consult service managed by the GCs and genetics specialists from each site: Dr. Holm (BCH) and 
Dr. Korf (UAB), both pediatricians and medical geneticists; Dr. Green (BWH), a medical geneticist; and Dr. 
Gelb (Mount Sinai), a pediatric cardiologist and Professor of Genetics and Genomic Sciences. Tier 1 involves 
the management of HCP and family inquiries by each individual site, including clinical referrals. Tier 2 involves 
the escalation of study-based inquiries and de-identified clinical questions to a central study resource managed 
by the coordinating site (BWH). The GCs and genetics specialists will be available to discuss the reports and 
other issues with HCPs, and to answer questions for families throughout the study.  
Providing Genomic Reports to HCPs: The FHR +/- the NGSR will be provided to the HCP via EMR 
upload, fax, and/or secure email. If there is a MDR finding on the NGSR, the GRC genetics specialist at the 
site will contact the HCP by phone or in person, discuss the report, and provide advice, if needed, on 
interpretation and clinical management. HCPs can use the results to guide care, make needed referrals, etc., 
informed by the educational activities (Aim 1a). In addition, the GRC geneticist and GC will be available at all 
times for HCP questions about the study, a FHR, or a NGSR. 
1b.12 Follow-up Surveys 
Once the results have been returned, there will be a post-disclosure and 6-months post-disclosure 
survey. These may be completed in person, over the phone, or online (via REDCap). Participants will be 
offered the option to have an RA read the survey questions and to respond verbally.  
1b.13 Retention  
We will leverage methods implemented in the AoURP at UAB and BWH to encourage relationship-
building and retention of participants: 1) RAs will be paired with families to coordinate participation and follow-
up. 2) We will collect multiple contact methods at enrollment, including preferred method. Texting via hospital-
approved methods will be an option. Participants my opt-in to texting and non-secure email during consent and 
these methods of communication may be used for follow-up such as survey and appointment reminders. 3) We 
will keep track of when our participants return for a clinic visit, using that as an opportunity to connect with 
them, answer questions, and encourage continued involvement. 4) We will mail/email birthday cards to the 
child (see attached “Sample Birthday Card”). 5) Newsletters, reviewed by the Stakeholder Board, will be 
shared via email and regular mail, and be available in print at follow-up visits, so families can stay updated on 
study progress and findings (see attached “Sample Newsletter”). 6) Participating families will be contacted 
multiple times and through a variety of contact methods to schedule study visits and complete surveys. Contact 

 
 
methods may include phone, email, text, electronic medical record messaging, physical mail, and/or direct 
contact from pediatricians or other clinic staff.  
Families will be contacted at least annually throughout the funded period of the study by birthday card, 
newsletter, and/or additional survey or interview requests. All communication will: 
1) Provide a link to updated scientific papers publishing results of the study 
2) Remind families that study staff will continue to have access to the child’s medical record for long-
term data collection  
3) Provide study contact information for questions or to opt-out of further participation 
We will monitor retention closely throughout the study and discuss additional strategies and 
troubleshooting with the Stakeholder Board.  
1b.14 Long term follow-up 
We plan to access medical records and may conduct annual surveys or interviews until the infant 
reaches age 18 to best capture the effects of genomic sequencing on the entire pediatric period (if the study is 
still ongoing). Parents will consent to continued involvement throughout the funded study period, potentially up 
to age 18. Given the long-term nature of the project, if the study remains active, we will contact the family after 
the child’s 18th birthday to obtain consent from the participant. No study procedures (i.e. medical record review) 
will take place after the child turns 18 until consent is obtained. If we are unable to reach the family and obtain 
consent from the participant, we will withdraw them from further participation in the study. In some cases, the 
participant may not be cognitively able to provide assent or consent, in which case we will discuss continued 
participation with the parent(s)/legal guardian(s), document the child’s cognitive ability and obtain 
documentation of the parent’s/legal guardian’s legal health care proxy status (in the instance that the child 
turns 18 years).  
1b.15 Study withdrawal 
If parents/guardians ask to withdraw their family’s samples and infant’s genomic data from the study, 
any genomic reports on their infant that have already been placed in the electronic medical record will have 
become part of clinical care and cannot be deleted or withdrawn. Any de-identified sequences or data that 
have been uploaded to shared databases such as the Database of Genotypes and Phenotypes (dbGaP) 
cannot be withdrawn. 
Aim 2: To evaluate the impact of GS on a diverse group of infants, their families and their HCPs. 
Aim 2a: To assess the psychosocial impact of infant GS on parents/guardians from varying ethnic and racial 
backgrounds, we will conduct longitudinal surveys and compare validated scales between arms of the RCT.  
The psychosocial impact of GS on families will be evaluated in three domains: 1) parent-child 
relationship, 2) partner relationship, and 3) personal impact (see Table 2). In order to thoroughly assess the 
impact of GS on families, we will use the Parenting Stress Index Short Form (PSI-4-SF),121 a well-accepted 
measure of parent-child relationships that has been previously used to examine the impact of expanded 
NBS,121-123 as our primary outcome, and the Child Vulnerability Scale124 as a secondary outcome. To study 
relationship conflict and satisfaction, and partner blame, we will measure marital satisfaction with the Kansas 
Marital Satisfaction scale121-123; 125 as our primary outcome, and partner blame using a novel measure 
developed for BabySeq as a secondary outcome. Given that a significant proportion of our parents may be 
single, relationship status will be important to factor into the analyses. Our primary outcome measure of 
personal distress will be anxiety, using the 7-item General Anxiety Index (GAD-7).126 Secondary measures of 
distress will be depression, as assessed with the 9-item Patient Health Questionnaire (PHQ-9),126 and self-
blame using a novel measure created for BabySeq. The study staff will follow-up and offer clinical support 

 
 
services to any parent who scores above a predetermined 
threshold for anxiety or distress on the measures, as we 
did in BabySeq.  
Surveys at baseline (enrollment) and immediately 
and 6-months post-disclosure will be brief (20-30 minutes) 
and completed by the one parent enrolled in the study 
(see attached “Baseline Survey,” “PD Survey,” and “6mth 
PD Survey”). Parents/guardians will complete the surveys 
online (RedCap) via email or text message (if that is an 
option), on paper, a tablet computer, or verbally with an 
RA at the HCP office or over the phone. We will make all 
study materials available in Spanish, which will be 
submitted to the IRB following initial approval of the 
English-language materials. Several of our outcome 
measures have been validated in Spanish-speaking 
populations, including the PSI-4-SF,127 the Kansas Marital Satisfaction scale,128 and the GAD-7.127 Measures 
that are novel or do not have a Spanish language version will be translated by native Spanish speakers using 
forward and back translation procedures.  
Parents/guardians participating in this study will be compensated for each visit and survey completed, 
with remuneration as follows: $50 for the baseline visit and survey; $50 for the disclosure visit and post-
disclosure survey; and $50 for the 6-month post-disclosure survey. Thus, each household will receive $150 if 
they finish the study and complete all of the study surveys from the baseline through the 6-month post-
disclosure survey. Parents/guardians will receive this remuneration in the form of electronic gift cards, checks, 
physical gift cards, or reloadable ClinCards. If a parent withdraws from the study, they will be compensated for 
the surveys completed up until the time of withdrawal.  
If we can obtain long-term funding, we may also contact families for an annual survey or interview until 
the infants turn 18 years to assess how participation in the study affected their family or thought process. At 
this time, we do not have the funding to provide remuneration for the completion of these surveys/interviews; 
however, if funding were to become available, we plan to offer a small payment.  
Aim 2b: To assess the medical impact of GS on infants and their families, we will review medical records and 
survey parents/guardians to track symptoms, laboratory or diagnostic results, new diagnoses, and medical 
actions attributed to the GS findings. Among infants with an MDR, we will determine whether the MDR: (a) 
reveals an unsuspected phenotype in the infant or family, (b) explains a family history of a condition, and/or (c) 
prompts surveillance in the infant or family.  
Data on medical outcomes will provide insight into the impact of the FHR and NGSR on diagnostic 
thinking and intermediate clinical outcomes.129  On a yearly basis, information collected from parents/guardians 
via surveys and/or from the medical records will include: contact information for the child’s HCPs, outpatient 
visits, hospitalizations and surgeries, parent medical visits and/or testing (due to study information), and 
parental records relevant to reproductive decision-making. Using methods developed for BabySeq,33 we will 
create outcome forms specific to each MDR and condition of note on the FHR that list associated diseases, 
diagnostic and screening tests, and treatments. We will query the infants’ medical record to determine if (a) the 
MDR was a new or known diagnosis, (b) family histories for the MDR diseases had been recorded, and (c) 
related diagnostic or screening tests were ordered for an MDR or a condition noted on the FHR. If the 
information collected is incomplete, a study GC or RA may contact the parent for follow-up details. Any future 
interview guide will be reviewed by the study Stakeholder Board and submitted for IRB review. We will keep 
track of emerging signs and symptoms in children with an MDR and will collect the impact on medical 
outcomes. 

 
 
Aim 2c: To assess the impact of GS in infants on clinical care, we will collect feedback from HCPs throughout 
the study by monitoring use of the “Genome Resource Center” and conducting interviews with HCPs towards 
the end of the study. 
HCPs providing care for our participants are well positioned to identify the benefits and challenges of 
implementing GS in their healthy patients and more broadly in the populations enrolled. We will conduct semi-
structured interviews of HCPs who received a NGSR. Interviews will be as efficient as possible (target 30 
minutes) and we will offer an incentive to participate. We will interview HCPs of infants randomized to the 
FH+GS arm until we reach thematic saturation, when additional interviews no longer yield novel information.130 
Our goal is to ensure that a broad range of experiences are represented to capture benefits and challenges 
that may be unique to the different sites, as well as obtain sufficient information power.131 At each site a trained 
RA, with oversight by an investigator, will conduct the interviews by phone, videoconference (e.g., Zoom), or in 
person. HudsonAlpha will lead the development of a future interview guide that will be submitted for IRB 
review. The semi-structured format allows us to guide the HCP to topics we want all interviewees to address, 
while allowing them to share experiences and introduce relevant issues we did not anticipate. Interviews will be 
audio-recorded, transcribed, and the data uploaded to Dedoose for analysis.  
It should be noted that we currently do not have an interview guide for these interviews. Once we have 
developed a guide, we will submit to the IRB for approval before implementing. 
Aim 3: Exploratory Aim: To evaluate healthcare utilization and associated costs of GS. Using novel 
approaches, we will identify healthcare services motivated by the study in the two study arms and examine 
attributable healthcare costs accrued for the infants and other family members. 
This project provides a unique opportunity to gather exploratory trials-based economic data about the 
impact of GS in diverse populations of healthy infants. Data will inform analyses from the health sector and 
societal perspectives.132 We will expand on 
approaches we developed for previous studies and 
use multiple strategies to identify services and costs 
associated with the care of infants, their parents, 
and family members.21; 63; 64 Approaches are 
summarized in Table 3.  
Primary analyses of healthcare utilization and 
costs will expand an “attributable services” approach 
implemented in related work we have conducted.56; 
63 We will use the notes from disclosure sessions to 
identify services that were recommended for infants 
or parents/guardians, and then verify whether the services occurred. By including only services that we can link 
directly to this study, this approach will produce data with the greatest measurement precision. To identify 
efficiencies where genetic tests were avoided by having GS, and to identify instances of cascade genetic 
testing, we will also focus on genetic services that infants and parents/guardians received after disclosure 
sessions. Finally, we will conduct “all costs” analyses where we summarize all health sector costs observed in 
medical records and supplemented by survey items that ask about hospitalizations, health care visits, genetic 
services, and familial out-of-pocket expenses. Due to the expansiveness of this approach, “all costs” analyses 
will focus on costs for the care of the child only.  
Data about health care utilization will be obtained from infants’ medical records, primarily from each 
institution’s corporate data warehouse, and supplemented with participant-reported data from interviews and 
surveys. Costs for services will be assigned to services based on actual costs, where data is available, or by 
assigning cost weights using reimbursement rates per the Centers for Medicare and Medicaid Studies. 
2. Patient Selection and Inclusion/Exclusion Criteria 
Inclusion criteria: 

 
 
HCP participants 
a. Age 18 years of age or older  
b. Pediatric health care provider providing primary care to infants in the clinics from which we will 
enroll (physician, nurse practitioner, physician assistant). 
Infant participants 
a. Age 0-12 months   
b. Seen for pediatric care at participating clinic where local clinic champions have completed the 
GS education program 
c. One parent or guardian meeting the parent/legal guardian inclusion criteria   
Parent/Guardian participants 
c. Biological parent or legal guardian of an infant participating in the study  
d. Age 18 years of age or older  
e. Has unimpaired decisional capacity  
f. English or Spanish speaking 
g. Available to have genetic counseling and provide consent for testing the infant 
h. Available to complete 3 surveys over 9 - 12 months 
Exclusion criteria:  
a. Infant has previously undergone ES or GS 
b. Parent(s)/guardian(s) are unwilling to have genomic reports placed in the infant’s medical record 
or sent to their primary care pediatrician 
c. Any infant in which clinical considerations preclude drawing up to 1.0 ml of blood 
 
3. Description of Study Treatments or Exposures/Predictors  
The intervention in this research study is GS.  
4. Definition of Primary and Secondary Outcomes/Endpoints  
Primary Outcome Measures: 
1. Monogenic disease risks (MDRs): Pathogenic (P) and likely pathogenic (LP) variants identified relevant 
to infant's health (dominant or biallelic recessive disease risks) [Time Frame: 3 months after enrollment] 
2. Carrier status variants: P and LP variants identified as recessive carrier status in infant [Time Frame: 3 
months after enrollment] 
3. MDR-associated phenotype: Signs or symptoms of monogenic disease risk identified by genome 
sequencing [Time Frame: 3 months after enrollment and 1-year post-disclosure (15 months after 
enrollment)] 
4. Parent-Child Relationship: Parenting Stress Index, 4th Edition Short Form; Vulnerable Baby Scale 
[Time Frame: Baseline, post-disclosure (3 months after enrollment), 6 months post-disclosure (9 
months after enrollment)] 
5. Partner Relationship: Kansas Marital Satisfaction Scale; Partner Blame [Time Frame: Baseline, post-
disclosure (3 months after enrollment), 6 months post-disclosure (9 months after enrollment)] 
6. Personal Distress: General Anxiety Disorder-7, Patient Health Questionnaire (PHQ)-9 , Self-Blame 
[Time Frame: Baseline, post-disclosure (3 months after enrollment), 6 months post-disclosure (9 
months after enrollment)]  
Secondary Outcome Measures: 

 
 
7. MDR-associated family history: Signs or symptoms of monogenic disease risk or recessive condition 
present in infant's biological family [Time Frame: 3 months after enrollment and 1-year post-disclosure 
(15 months after enrollment)] 
8. Feelings about genomic testing: Feelings About genomiC Testing Results (FACToR) Questionnaire 
[Time Frame: Baseline, post-disclosure (3 months after enrollment), 6 months post-disclosure (9 
months after enrollment)] 
Other Pre-specified Outcome Measures: 
MDR-associated intervention: Healthcare intervention prompted by monogenic disease risk or 
recessive carrier variant [Time Frame: 1-year post-disclosure (15 months after enrollment)] 
9. Suspected genetic condition: Any phenotype that develops in an infant suspected to have a genetic 
cause, or any genetic testing ordered as part of clinical care [Time Frame: 1-year post-disclosure (15 
months after enrollment)] 
10. Cost of attributable services: Cost of healthcare services that were recommended for infants and 
parents/guardians as part of study disclosure session [Time Frame: 1-year post-disclosure (15 months 
after enrollment)] 
11. Cost of genomic services: Cost of genetic services infants and parents/guardians received after study 
disclosure session [Time Frame: 1-year post-disclosure (15 months after enrollment)] 
12. All healthcare costs: All health sector costs observed in medical records and survey questions 
regarding family out-of-pocket expenses [Time Frame: 1-year post-disclosure (15 months after 
enrollment)] 
5. Data Collection Methods, Assessments, Interventions and Schedule (what assessments 
performed, how often)  
Visit 1 (may occur in multiple parts, remote or in person): Eligibility, consent, demographics, medical 
history (diagnoses, visits, tests, procedures, medications, hospitalizations, surgeries), family history, 
baseline parent survey, sample collection 
Visit 2 (3-4 months after Visit 1- remote or in person): Genome results (for sequencing intervention 
group), updated medical history, updated medical history, physical exam, post-disclosure parent 
surveys. Log clinical disclosure note as PDF.  
Visit 3 (6 months after Visit 2- remote): 6-month post-disclosure parent survey.  
Visit 4 (1 year after Visit 2- remote): Updated participant medical history. Parent(s) medical visits 
and/or testing (due to study information), and parental records relevant to reproductive decision-
making. Document whether each data point is related to genome findings and estimate any associated 
costs.  
Repeat Visit 4 procedures on an annual basis throughout the study period.  
6. Study Timeline (as applicable) 

 
 
 
E. Adverse Event Criteria and Reporting Procedures  
If a participant expresses emotional distress related to study participation or provides survey responses 
suggesting impaired family bonding or emotional distress, they will be referred to a study physician or study 
psychologist for further psychosocial assessment as described above. All serious such cases, including those 
requiring a referral to mental health professional or other therapeutic intervention, will be reported to the IRB as 
per the current IRB reporting guidelines and at the annual continuing review for the protocol  
  
F. Data Management Methods  
Participant and outcomes data collected as part of this research study will be entered into REDCap: a 
secure, HIPAA-compliant software hosted inside the coordinating institution firewall (BWH). An experienced 
data manager will build this database. Each recruitment site will enter identifiable data about participants that 
cannot be viewed by other recruitment sites. Participant surveys will also be administered through REDCap. 
Study team members at the coordinating site will have access to all data entered, as needed for recruitment 
coordination, data monitoring and quality assurance.  
Any identifiable data exports from REDCap will be kept in locked cabinets, or in password protected 
computer files stored securely on institutional HIPAA-compliant platforms. Identifiable data will be kept to the 
minimum necessary and only included if required for specific analyses. Recruitment sites will never have 
access to identifiable data from other sites. Teams involved with analysis of psychosocial survey data will not 
have access to identifiable data unless needed to coordinate interviews. Teams involved with analysis of 
medical and economic data will have access to identifiable data for analysis of medical records and healthcare 
claims.  
The Laboratory for Molecular Medicine will receive biological samples and store data according to their 
secure HIPAA-compliant procedures. 
All reports placed in the medical records of participating infants will be subject to all privacy protections 
afforded clinical information.  

 
 
  
When genomic data are uploaded to dbGaP and/or other databases for sharing with qualified research 
investigators, no PHI information will be uploaded that could lead to the identification of these research 
participants. However, since genome data is a unique dataset (like fingerprints or retinal scans), and since 
advanced computational techniques may allow deductions with other publically available datasets, it is unlikely 
but may be possible in the future for individuals to identify research participants through these datasets. This 
possibility will be discussed in the Pre-enrollment Sessions and in the consent process. We will obtain a 
Certificate of Confidentiality as another layer of confidentiality protection for participants in this study. 
G. Quality Control Method  
Research assistants at each site will be trained on data entry methods prior to beginning recruitment. At 
each recruitment site, one study team member will enter data, and another will cross-check each record until 
they are confident in accuracy and consistency. After this time, records will be cross-checked as needed. The 
coordinating site will have access to all data entry and will review regularly and raise queries as needed. 
Laboratory quality control will be implemented according to current CLIA- approved methods. 
H. Data Analysis Plan  
Aim 2a: To assess the psychosocial impact of infant GS on parents/guardians from varying ethnic and racial 
backgrounds, we will conduct longitudinal surveys and compare validated scales between arms of the RCT.  
We will view the GS arm as non-inferior if upper bounds of confidence intervals for the differences in 
means on these outcomes (FH+GS arm minus FH arm) are less than the differences in scores on each scale 
that are considered clinically meaningful. We will conduct per-protocol analyses in which all families who 
attended disclosure sessions are analyzed. We will use generalized linear models fit with generalized 
estimating equations to conduct repeated measures analyses and use contrasts to compare means in the two 
randomization arms. For analyses of personal distress and parent-child relationships per GAD-7 and PSI-4 
scores, we will use a log link, given the right-skewed distributions, while analyses of marital satisfaction per the 
Kansas Marital Satisfaction scale will use an identity link. Missing data will be imputed using fully conditional 
specification. Models will include terms for time as a categorical variable, interaction between time and 
randomization arm, and the corresponding baseline measure, where applicable.  
Aim 2b: To assess the medical impact of GS on infants and their families, we will review medical records and 
survey parents/guardians to track symptoms, laboratory or diagnostic results, new diagnoses, and medical 
actions attributed to the GS findings. Among infants with an MDR, we will determine whether the MDR: (a) 
reveals an unsuspected phenotype in the infant or family, (b) explains a family history of a condition, and/or (c) 
prompts surveillance in the infant or family.  
The analysis of these data will largely be descriptive, due in part to the nature of the data with 
heterogeneous diagnoses. 
Aim 2c: To assess the impact of GS in infants on clinical care, we will collect feedback from HCPs throughout 
the study by monitoring use of the “Genome Resource Center” and conducting interviews with HCPs towards 
the end of the study. 
Once at least 5 interviews have been transcribed, Drs. Holm and Pereira will work with the project 
coordinator at BWH to develop a coding scheme utilizing thematic content analysis.134 Inductive codes will be 
added to the coding scheme as new themes emerge. Using the coding scheme, we will use standard methods 
for team-based qualitative analysis with consensus coding135 conducting interviews with HCPs towards the end 
of the study.  
Aim 3: Exploratory Aim: To evaluate healthcare utilization and associated costs of GS. Using novel 
approaches, we will identify healthcare services motivated by the study in the two study arms and examine 
attributable healthcare costs accrued for the infants and other family members. 

 
 
Since standards have not been established about what to report from GS, we will collect data to 
conduct one-way scenario analyses that provide insight about reporting strategies that vary the following: 
● Definition of attributable services. To provide a high side estimate of care that may have been 
prompted, we will use genomic references, including GeneReviews, OMIM, and primary 
literature,136; 137 to identify all possible follow-up services that may be used to screen for conditions 
identified on the FHR or NGSR.  
● Classes of findings reported (e.g., reporting only findings from an IBA, or only MDR).  
● Conditions reported (e.g., if we reported findings associated only with pediatric-onset conditions, or 
conditions on the ACMG SF v3.0 list118).  
● Classification criteria (e.g., if we reported only variants classified as pathogenic)  
Intervention costs will include pre-analytics, such as DNA extraction, GS variant classification, and 
disclosure of results. Post-disclosure costs will use actual cost data when available, updated to the year of 
analysis using the medical care component of the Consumer Price Index (CPI).137; 138 Costs will be assigned to 
other downstream healthcare services by multiplying utilization by cost weights derived from the Centers for 
Medicare and Medicaid Studies fee schedules.63 To facilitate analyses from the societal perspective, we will 
collect data about family out-of-pocket expenses using survey items.63; 132 We will use generalized linear 
models with a log link and gamma family error to compare randomization arms on attributable costs for infants 
and their parents/guardians. We will also run regression models that include terms for ethnicity and ethnicity-
randomization arm interactions to determine whether costs overall and/or incremental cost of GS varies by 
ethnicity.  
I. Statistical Power and Sample Considerations  
Aim 2a:  
Based on 1-sided t-tests and non-inferiority bounds of 5 points for the GAD-7, 9 points for the PSI-4 
(0.5 sd), and 1.1 points for the Kansas Marital Satisfaction scale (0.5 sd) and assuming complete data from at 
least 200 (an 80% completion rate) families in each randomization arm, we estimate over 99% power to 
confirm noninferiority of GS on each measure at α = 0.016 (after Bonferroni correction for three outcomes). 
Actual analyses will probably be even more precise due to the use of repeated measures and imputation of 
missing data. Also, for a correlation of about 0.5 among repeated observations in the same subject (as 
observed on multiple outcomes during BabySeq), we find that the sample size needed with 3 observations, 
compared to a single observation, is about 65% for the same power and alpha levels. We will also run 
separate regression models that include terms for ethnicity and ethnicity-randomization arm interactions to 
determine whether outcomes vary by ethnicity and whether any impact of GS varied by ethnicity. Our 
Stakeholder Board will be encouraged to pose additional questions for analysis, review and present results, 
and co-author publications. 
Aim 3: 
We will use generalized linear models with a log link and gamma family error to compare randomization 
arms on attributable costs for infants. Cost analyses will be exploratory, but we anticipate that we will have 
93% power at α=0.05 (two-tailed) to detect a standardized effect size of d=0.31, which is roughly equivalent to 
attributable costs in the GS arm being approximately 61% greater than attributable costs in the control arm.  
J. Study Organization  
Boston Children’s Hospital 
Ingrid Holm, MD, MPH (mPI) 
Alan Beggs, PhD (Co-I) 
Clement Bottino, MD, MPH (Co-I) 
Brigham and Women’s Hospital 

 
 
Robert C. Green, MD, MPH (Contact PI and mPI) 
Harvard Pilgrim Health Care  
Kurt Christensen, PhD (Co-I) 
Icahn School of Medicine at Mount Sinai  
Bruce D. Gelb, MD (Co-I) 
Carol Horowitz, MD, MPH (Co-I) 
University of Alabama at Birmingham 
Joy Dean, MD (Co-I) 
Bruce Korf, MD, PhD (Co-I) 
Laboratory for Molecular Medicine 
Matthew Lebo, PhD (Co-I) 
Heidi Rehm, PhD (Co-I) 
Hana Zouk, PhD (Co-I) 
Baylor College of Medicine  
Amy McGuire, JD, PhD (Co-I) 
Stacey Pereira, PhD (Co-I) 
HudsonAlpha Institute for Biotechnology 
Kelly East, MS (Co-I) 
Neil Lamb, PhD (Co-I) 
 
K. References 
1. Green, E.D., Guyer, M.S., and National Human Genome Research Institute. (2011). Charting a course 
for genomic medicine from base pairs to bedside. Nature 470, 204-213.  
2. Manolio, T.A., Chisholm, R.L., Ozenberger, B., Roden, D.M., Williams, M.S., Wilson, R., Bick, D., 
Bottinger, E.P., Brilliant, M.H., Eng, C., et al. (2013). Implementing genomic medicine in the clinic: the 
future is here. Genet Med 15, 258-267.  
3. Green, R.C., Rehm, H.L., and Kohane, I.S. (2013). Clinical genome sequencing. In Genomic and 
personalized medicine, G.S. Ginsberg and H.F. Willard, eds. (San Diego, CA, Academic Press).  
4. Yang, Y., Muzny, D.M., Xia, F., Niu, Z., Person, R., Ding, Y., Ward, P., Braxton, A., Wang, M., Buhay, 
C., et al. (2014). Molecular findings among patients referred for clinical whole-exome sequencing. 
JAMA 312, 1870-1879.  
5. Yang, Y., Muzny, D.M., Reid, J.G., Bainbridge, M.N., Willis, A., Ward, P.A., Braxton, A., Beuten, J., Xia, 
F., Niu, Z., et al. (2013). Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N 
Engl J Med 369, 1502-1511.  
6. Biesecker, L.G., and Green, R.C. (2014). Diagnostic clinical genome and exome sequencing. N Engl J 
Med 371, 1170.  
7. Sabour, L., Sabour, M., and Ghorbian, S. (2017). Clinical applications of next-generation sequencing in 
cancer diagnosis. Pathol Oncol Res 23, 225-234.  
8. Patel, J.N. (2016). Cancer pharmacogenomics, challenges in implementation, and patient-focused 
perspectives. Pharmgenomics Pers Med 9, 65-77.  
9. Brennan, P., and Wild, C.P. (2015). Genomics of cancer and a new era for cancer prevention. PLOS 
Genet 11, e1005522.  
10. Roychowdhury, S., and Chinnalyan, A.M. (2016). Translating cancer genomes and transcriptomes for 
precision oncology. CA Cancer J Clin 66, 75-88.  
11. Hood, L., and Price, N.D. (2014). Demystifying disease, democratizing health care. Sci Transl Med 6, 
225ed225.  

 
 
12. Hood, L., and Auffray, C. (2013). Participatory medicine: a driving force for revolutionizing healthcare. 
Genome Med 5, 110.  
13. Hood, L., and Friend, S.H. (2011). Predictive, personalized, preventive, participatory (P4) cancer 
medicine. Nat Rev Clin Oncol 8, 184-187.  
14. Rehm, H.L. (2017). Evolving health care through personal genomics. Nat Rev Genet 18, 259-267.  
15. Elroy, M. (2009). Researchers and policymakers point to successes and challenges in personalized 
medicine. In American Association for the Advancement of Science (AAAS).  
16. Vergano, D. (2012). Baby gene map could cut diagnosis to days. In USA Today.  
17. McGowan, M.L., Cho, D., and Sharp, R.R. (2013). The changing landscape of carrier screening: 
expanding technology and options? Health Matrix Clevel 23, 15-33.  
18. Umbarger, M.A., Kennedy, C.J., Saunders, P., Breton, B., Chennagiri, N., Emhoff, J., Greger, V., 
Hallam, S., Maganzini, D., Micale, C., et al. (2014). Next-generation carrier screening. Genet Med 16, 
132-140.  
19. Nazareth, S.B., Lazarin, G.A., and Goldberg, J.D. (2015). Changing trends in carrier screening for 
genetic disease in the United States. Prenat Diagn 35, 931-935.  
20. King, M.C., Levy-Lahad, E., and Lahad, A. (2014). Population-based screening for BRCA1 and BRCA2: 
2014 Lasker Award. JAMA 312, 1091-1092. 
21. Christensen, K.D., Dukhovny, D., Siebert, U., and Green, R.C. (2015). Assessing the costs and 
costeffectiveness of genomic sequencing. J Pers Med 5, 470-486.  
22. Lu, C.Y. (2016). Economic evaluation of whole-genome sequencing in health individuals: what can we 
learn fgrom CEAs of whole-body CT screening? Genet Med 18, 103-104.  
23. Burke, W., Tarini, B., Press, N.A., and Evans, J.P. (2011). Genetic screening. Epidemiol Rev 33, 148-
164.  
24. Nippert, I., Harris, H.J., Julian-Reynier, C., Kristoffersson, U., Ten Kate, L.P., Anionwu, E., Benjamin, 
C., Challen, K., Schmidtke, J., Nippert, R.P., et al. (2011). Confidence of primary care physicians in 
their ability to carry out basic medical genetic tasks-a European survey in five countries-Part 1. J 
Community Genet 2, 1-11.  
25. Najafzadeh, M., Lynd, L.D., Davis, J.C., Bryan, S., Anis, A., Marra, M., and Marra, C.A. (2012). Barriers 
to integrating personalized medicine into clinical practice: a best-worst scaling choice experiment. 
Genet Med 14, 520-526.  
26. Haga, S.B., Carrig, M.M., O'Daniel, J.M., Orlando, L.A., Killeya-Jones, L.A., Ginsburg, G.S., and Cho, 
A. (2011). Genomic risk profiling: attitudes and use in personal and clinical care of primary care 
physicians who offer risk profiling. J Gen Intern Med 26, 834-840.  
27. Korf, B. (2011). Genetics and genomics education: the next generation. Genet Med 13, 201-202.  
28. Selkirk, C.G., Weissman, S.M., Anderson, A., and Hulick, P.J. (2013). Physicians' preparedness for 
integration of genomic and pharmacogenetic testing into practice within a major healthcare system. 
Genet Test Mol Biomarkers 17, 219-225.  
29. Vassy, J.L., Korf, B.R., and Green, R.C. (2015). How to know when physicians are ready for genomic 
medicine. Sci Transl Med 7, 287fs219.  
30. Holm, I., Agrawal, P., Ceyhan- Birsoy, O., Christensen, K.D., Fayer, S., Frankel, L., Genetti, C., Krier, 
J., LaMay, R., Levy, H., et al. (2018). The BabySeq Project: implementing genomic sequencing in 
newborns. BMC pediatrics 18.  
31. Ceyhan-Birsoy, O., Machini, K., Lebo, M., Yu, T., Agrawal, A., Parad, R., Holm, I., McGuire, A., Green, 
R., Beggs, A., et al. (2017). A curated gene list for reporting results in newborn genomic sequencing. 
Gen Med 19, 809-818.  
32. Ceyhan-Birsoy, O., Murry, J.B., Machini, K., Lebo, M.S., Yu, T.W., Fayer, S., Genetti, C.A., Schwartz, 
T.S., Agrawal, P.B., Parad, R.B., et al. (2019). Interpretation of genomic sequencing results in healthy 
and ill newborns: results from the BabySeq Project. Am J Hum Genet 104, 76-93.  
33. Mackay, Z., Dukhovny, D., Phillips, K.A., Beggs, A.H., Green, R.C., Parad, R.B., and Christensen, K.D. 
(2020). Quantifying downstream health care utilization in studies of genomic testing. Value in Health in 
press.  

 
 
34. Pereira, S., Robinson, J.O., Gutierrez, A.M., Petersen, D.K., Hsu, R.L., Lee, C.H., Schwartz, T.S., 
Holm, I.A., Beggs, A.H., Green, R.C., et al. (2019). Perceived benefits, risks, and utility of newborn 
genomic sequencing in the BabySeq Project. Pediatrics 143, S6-S13.  
35. Pereira, S., Petersen, D., Robinson, J., Frankel, L., Christensen, K.D., Waisbren, S.E., Holm, I., Beggs, 
A.H., Green, R., and McGuire, A. (2018). The impact of newborn genomic sequencing on families: 
findings from the BabySeq Project. In American Society of Human Genetics Annual Meeting. (San 
Diego, CA).  
36. Hall, M., and Olopade, O.I. (2005). Confronting genetic testing disparities: Knowledge is power. JAMA 
Cardiol 293, 1783-1785.  
37. Spratt, D.E., Chan, T., Waldron, L., Speers, C., Feng, F.Y., Ogunwobi, O.O., and Osborne, J.R. (2016). 
Racial/ethnic dipsarities in genomic sequencing. JAMA Oncol 2, 1070-1074.  
38. Kaphingst, K.A., and Goodman, M.S. (2016). Importance of race and ethnicity in individuals’ use of and 
responses to genomic information. Pers Med 13, 1-4.  
39. Landry, L.G., Ani, N., Williams, D.R., Rehm, H.L., and Bonham, V.L. (2018). Lack of diversity in 
genomic databases is a barrier to translating precision medicine research into practice. Health Aff 
(Millwood) 37, 780-785.  
40. Halverson, C.M., and Ross, L.F. (2012). Attitudes of African-American parents about biobank 
participation and return of results for themselves and their children. J Med Ethics 38, 561-566.  
41. Drake, B.F., Boyd, D., Carter, K., Gehlert, S., and Thompson, V.S. (2017). Barriers and strategies to 
participation in tissue research among African-American men. J Cancer Educ 32, 51-58.  
42. Streicher, S.A., Sanderson, S.C., Jabs, E.W., Diefenbach, M., Smirnoff, M., Peter, I., Horowitz, C.R., 
Brenner, B., and Richardson, L.D. (2011). Reasons for participating and genetic information needs 
among racially and ethnically diverse biobank participants: a focus group study. J Community Genet 2, 
153-163.  
43. Bussey-Jones, J., Garrett, J., Henderson, G., Moloney, M., Blumenthal, C., and Corbie-Smith, G. 
(2010). The role of race and trust in tissue/blood donation for genetic research. Genet Med 12, 116-
121.  
44. Skloot, R. (2010). The immortal life of Henrietta Lacks.(New York: Broadway Paperbacks).  
45. Rodriguez, E.M., Torres, E.T., and Erwin, D.O. (2013). Awareness and interest in biospecimen 
donation for cancer research: views from gatekeepers and prospective participants in the Latino 
community. J Community Genet 4, 461-468.  
46. Heredia, N.I., Krasny, S., Strong, L.L., Von Hatten, L., Nguyen, L., Reininger, B.M., McNeill, L.H., and 
Fernandez, M.E. (2017). Community perceptions of biobanking participation: a qualitative study among 
Mexican-Americans in three texas cities. Public Health Genomics 20, 46-57.  
47. Rangel, M.L., Heredia, N.I., Reininger, B., McNeill, L., and Fernandez, M.E. (2019). Educating 
hispanics about clinical trials and biobanking. J Cancer Educ 34, 1112-1119.  
48. Loffredo, C.A., Luta, G., Wallington, S., Makgoeng, S.B., Selsky, C., Mandelblatt, J.S., Adams-
Campbell, L.L., and Region 1 Bio-specimen Management of Cancer Health Disparities, P. (2013). 
Knowledge and willingness to provide research biospecimens among foreign-born Latinos using safety-
net clinics. J Community Health 38, 652-659.  
49. Han, E., Hudson Scholle, S., Morton, S., Bechtel, C., and Kessler, R. (2013). Survey shows that fewer 
than a third of patient-centered medical home practice engage patients in quality improvement. Health 
Aff (Millwood) 32, 368-375.  
50. D las Nueces, D., Hacker, K., DiGirolamo, A., and Hicks, L.S. (2012). A systemic review of 
communitybased participatory research to enhance clinical trials in racial and ethnic minority groups. 
Health Serv Res 47, 1363-1386.  
51. Jackson, B.A. (2014). How do we know what information sharing is really worth? exploring 
methodologies to measure the value of information sharing and fusion efforts. Rand corporation  
52. Berg, J., Agrawal, P., Bailey, D., Beggs, A., Brenner, S., Brower, A., Butler, I., Cakici, J., Ceyhan- 
Birsoy, O., Chan, K., et al. (2017). Newborn sequencing in genomic medicine and public health. 
Pediatrics 132.  

 
 
53. Genetti, C.A., Schwartz, T.S., Robinson, J.O., VanNoy, G.E., Petersen, D., Pereira, S., Fayer, S., 
Peoples, H.A., Agrawal, P.B., Betting, W.N., et al. (2019). Parental interest in genomic sequencing of 
newborns: enrollment experience from the BabySeq Project. Genet Med 3, 622-630.  
54. Kalia, S. S., Adelman, K., Bale, S. J., Chung, W. K., Eng, C., Evans, J. P., Herman, G. E., Hufnagel, S. 
B., Klein, T. E., Korf, B. R., McKelvey, K. D., Ormond, K. E., Richards, C. S., Vlangos, C. N., Watson, 
M., Martin, C. L., & Miller, D. T. (2017). 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(2), 249–255. 
55. Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W.W., Hegde, M., Lyon, E., 
Spector, E., et al. (2015). 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.  
56. Vassy, J.L., Christensen, K.D., Schonman, E.F., Bout, C.L., Robinson, J.O., Krier, J.B., Diamond, P.M., 
Lebo, M., and Machini, K. (2017). The impact of whole-genome sequencing on the primary care and 
outcomes of health adult patients: a pilot randomized trial. Ann Intern Medicine 167, 159-169.  
57. Murry, J.B., Machini, K., Ceyhan-Birsoy, O., Kritzer, A., Krier, J.B., Lebo, M.S., Fayer, S., Genetti, C.A., 
Vannoy, G.E., Yu, T.W., et al. (2018). Reconciling newborn screening and a novel splice variant in BTD 
associated with partial biotinidase deficiency: A BabySeq Project case report. Cold Spring Harb Mol 
Case Stud 4.  
58. Hogan, G.J., Vysotskaia, V.S., Beauchamp, K.A., Seisenberger, S., Grauman, P.V., Haas, K.R., Hong, 
S.H., Jeon, D., Kash, S., Lai, H.H., et al. (2018). Validation of an expanded carrier screen that 
optimizes sensitivity via full-exon sequencing and panel-wide copy number variant identification. Clin 
Chem 64, 1063-1073.  
59. VanNoy, G.E., Genetti, C.A., McGuire, A.L., Green, R.C., Beggs, A.H., Holm, I.A., and BabySeq 
Project, G. (2019). Challenging the current recommendations for carrier testing in children. Pediatrics 
143, S27- S32.  
60. Kerruish, N.J., Settle, K., Campbell-Stokes, P., and Taylor, B.J. (2005). Vulnerable Baby Scale: 
development and piloting of a questionnaire to measure maternal perceptions of their baby's 
vulnerability. J Paediatr Child Health 41, 419-423.  
61. Taylor, A., Atkins, R., Kumar, R., Adams, D., and Glover, V. (2005). A new Mother-to-Infant Bonding 
Scale: links with early maternal mood. Arch Womens Ment Health 8, 45-51.  
62. Abidin, R.R. (2012). Parenting stress index, fourth edition. Psychological assessment resources 33, 
698- 702.  
63. Christensen, K.D., Vassy, J.L., Phillips, K.A., Blout, C.B., Azzariti, D.R., Lu, C.Y., Robinson, J.O., Lee, 
K., Douglas, M.P., Yeh, J.M., et al. (2018). Short-term costs of integrating whole-genome sequencing 
into primary care and cardiology settings: a pilot randomized trial. Genet Med 20, 1544-1553.  
64. Christensen, K.D., Phillips, K.A., Green, R.C., and Dukhovny, D. (2018). Cost analyses of genomic 
sequencing: lessons learned from the MedSeq Project. Value Health 21, 1054-1061.  
65. Christensen, K.D., Dukhovny, D., Agrawal, A., Genetti, C., Holm, I., Mackay, Z., Mc, G.A., Pereira, S., 
Schwartz, T., Yu, T., et al. (2019). Screening newborns with genomic sequencing increases short-term 
downstream health care costs: preliminary findings from the BabySeq Project. In American College of 
Medical Genetics. (Seattle, WA).  
66. Green, R.C., Cupples, L.A., Go, R., Benke, K.S., Edeki, T., Griffith, P.A., Williams, M., Hipps, Y., 
GraffRadford, N., Bachman, D., et al. (2002). Risk of dementia among white and African American 
relatives of patients with Alzheimer disease. JAMA 287, 329-336.  
67. Graff-Radford, N., Green, R.C., Go, R., Edeki, T., Bachman, D., Griffith, P., Williams, M., Hipps, Y., 
Haines, J.L., Cupples, L.A., et al. (2002). Association between APOE genotype and Alzheimer disease 
in African Americans. Arch Neurol 59, 594-600.  
68. Roberts, J.S., Connell, C.M., Cisewski, D., Hipps, V., Demissie, S., and Green, R.C. (2003). 
Differences between African Americans and White Americans in perceptions of Alzheimer disease. Alz 
Dis Assoc Dis 17, 19-26.  

 
 
69. Bachman, D.L., Green, R.C., Benke, K.S., Cupples, L.A., Farrer, L.A., and Mirage Study Group. (2003). 
Comparison of Alzheimer's disease risk factors in white and African American families. Neurology 60, 
1372-1374.  
70. Hipps, Y.G., Roberts, J.S., Farrer, L.A., and Green, R.C. (2003). Differences between African 
Americans and whites in their attitudes toward genetic testing for Alzheimer's disease. Genet Test 7, 
39-44.  
71. Christensen, K.D., Roberts, J.S., Royal, C.D.M., Fasaye, G.-A., Obisesan, T., Cupples, L.A., 
Whitehouse, P.J., Relkin, N.R., Farrer, L.A., Cook-Deegan, R., et al. (2008). Incorporating ethnicity into 
genetic risk assessment for Alzheimer's disease: The REVEAL Study experience. Genet Med 10, 207-
214.  
72. Akinleye, I., Roberts, J.S., Royal, C.D., Linnenbringer, E., Obisesan, T.O., Fasaye, G.A., and Green, 
R.C. (2011). Differences between African American and White research volunteers in their attitudes, 
beliefs and knowledge regarding genetic testing for Alzheimer's disease. J Genet Couns 20, 650-659.  
73. Landry, L., Nielsen, D.E., Carere, D.A., Roberts, J.S., Green, R.C., and Group, P.G.S. (2017). Racial 
minority group interest in direct-to-consumer genetic testing: findings from the PGen study. J 
Community Genet 8, 293-301.  
74. Roberts, J.S., Chen, C.A., Uhlmann, W.R., and Green, R.C. (2012). Effectiveness of a condensed 
protocol for disclosing APOE genotype and providing risk education for Alzheimer disease. Genet Med 
14, 742- 748.  
75. Green, R.C., Christensen, K.D., Cupples, L.A., Relkin, N.R., Whitehouse, P.J., Royal, C.D., Obisesan, 
T.O., Cook-Deegan, R., Linnenbringer, E., Butson, M.B., et al. (2015). A randomized noninferiority trial 
of condensed protocols for genetic risk disclosure of Alzheimer's disease. Alzheimers Dement 11, 
1222-1230.  
76. Christensen, K.D., Roberts, J.S., Whitehouse, P.J., Royal, C.D., Obisesan, T.O., Cupples, L.A., 
Vernarelli, J.A., Bhatt, D.L., Linnenbringer, E., Butson, M.B., et al. (2016). Disclosing pleiotropic effects 
during genetic risk assessment for Alzheimer Disease: a randomized trial. Ann Intern Med 164, 155-
163.  
77. Christensen, K.D., Uhlmann, W.R., Roberts, J.S., Linnenbringer, E., Whitehouse, P.J., Royal, C.D.M., 
Obisesan, T.O., Cupples, L.A., Butson, M.B., Fasaye, G.A., et al. (2017). A randomized controlled trial 
of disclosing genetic risk information for Alzheimer disease via telephone. Genet Med 20, 132-141.  
78. Natarajan, P., Gold, N., Bick, A., McLaughlin, H., Kraft, P., Rehm, H., Peloso, G., Wilson, J., Correa, A., 
Seidman, J., et al. (2016). Aggregate penetrance of genomic variants for actionable disorders in 
European and African Americans. Sci Trans Med 9, 364ra151.  
79. Horowitz, C.R., Sabin, T., Ramos, M., Richardson, L.D., Hauser, D., Robinson, M., and Fei, K. (2019). 
Successful recruitment and retention of diverse participants in genomics clinical trial: a good invitation 
to a great party. Genet Med 21, 2364-2370.  
80. National Human Genome Research Institute. (2016). Notice announcing pre-application information 
webinar for clinical sequencing evidence generating research (CSER2) requests for applications 
(RFAs) and frequently asked questions (FAQ) regarding clinical sequencing funding opportunities. In.  
81. Vangeepuram, N., Mayer, V., Fei, K., Hanlen-Rosado, E., Andrade, C., Wright, S., and Horowitz, C. 
(2018). Smartphone ownership and perspectives on health apps among vulnerable population in East 
Harlem, New York. Mhealth, 31.  
82. Makhni, S., Zlatopolsky, R., Fasihuddin, F., Aponte, M., Rogers, J., and Ateja, A. (2017). Usability and 
learning of RxUniverse, an enterprise-wide app prescribing platform used in an academic tertiary care 
hospital. AMIA Annu Symp Proc 2017, 1225-1232.  
83. Horowitz, C.R., Shameer, K., Gabrilove, J., Atreja, A., Shepard, P., Goytia, C.N., Smith, G.W., Dudley, 
J., Manning, R., Bickell, N.A., et al. (2017). Accelerators: sparking innovation and transdisciplinary team 
science in disparities research. Int J Environ Res Public Health 14, E225.  
84. Ramos, M.A., Fox, A., Simon, E.P., and Horowitz, C.R. (2013). A community-academic partnership to 
address racial/ethnic health disparities through grant-making. Public Health Rep 128 Suppl 3, 61-67.  

 
 
85. Orlando, L.A., Volis, C., Horowitz, C.R., Myers, R.A., Arwood, M.J., Cicali, E.J., McDonough, C.W., 
Pollin, T.I., Guan, Y., Levy, K.D., et al. (2019). IGNITE network: response of patients to genomic 
medicine interventions. Mol Genet Genomic Med 7, e636.  
86. Sperber, N.R., Carpenter, J.S., Cavallari, L.H., L, J.D., Cooper-DeHoff, R.M., Denny, J.C., Ginsburg, 
G.S., Guan, Y., Horowitz, C.R., Levy, K.D., et al. (2017). Challenges and strategies for implementing 
genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE 
(IGNITE) network. BMC Med Genomics 10, 35.  
87. Zebrowski, A.M., Ellis, D.E., Barg, F.K., Sperber, N.R., Bernhardt, B.A., Denny, J.C., Dexter, P.R., 
Ginsburg, G.S., Horowitz, C.R., Johnson, J.A., et al. (2019). Qualitative study of system-level factors 
related to genomic implementation. Genet Med 21, 1534-1540.  
88. May, T., Nakano-Okuno, M., Kelley, W.V., East, K., Moss, I.P., Sodeke, S., Korf, B., and Barsh, G. 
(2020). Return of raw data in genomic testing and research: ownership, partnership, and risk-benefit. 
Genet Med 22, 12-14.  
89. Kelly, W., Cannon, A., and East, K. (2020). The Alabama Genomic Health Initiative: early result and 
outcomes of state-funded genomic research screening. In American College of Medical Genomics 2020 
Annual Meeting. (San Antonio, TX).  
90. Holm, I.A., McGuire, A., Pereira, S., Rehm, H., Green, R.C., Beggs, A.H., and and the BabySeq 
Project, T. (2019). Returning a genomic result for an adult-onset condition to the parents of a newborn: 
insights from the BabySeq Project. Pediatrics 143, S37-S43.  
91. Vassy, J.L., Lautenbach, D.M., McLaughlin, H.M., Kong, S.-W., Christensen, K.D., Krier, J., Kohane, 
I.S., Feuerman, L.Z., Blumenthal-Barby, J., Roberts, J.S., et al. (2014). The MedSeq Project: a 
randomized trial of integrating genome sequencing into clinical medicine. Trials 15, 85-97.  
92. Vassy, J.L., McLaughlin, H.L., MacRae, C.A., Seidman, C.E., Lautenbach, D., Krier, J.B., Lane, W.J., 
Kohane, I.S., Murray, M.F., McGuire, A.L., et al. (2015). A one-page summary report of genome 
sequencing for the healthy adult. Public Health Genomics 18, 123-129.  
93. Vassy, J.L., Davis, J.K., Kirby, C., Richardson, I.J., Green, R.C., McGuire, A.L., and Ubel, P.A. (2018). 
How primary care providers talk to patients about genome sequencing results: risk, rationale, and 
recommendation. J Gen Intern Med 33, 877-885.  
94. Pet, D.B., Holm, I.A., Williams, J.L., Myers, M.F., Novak, L.L., Brothers, K.B., Wiesner, G.L., and 
Clayton, E.W. (2019). Physicians' perspectives on reciving unsolicited genomic results. Genet Med 21, 
311-318.  
95. East, K. (2020). Putting it into practice: tracking disclosures of genome sequencing results in the NICU 
by trained non-geneticists. In American College of Medical Genetics Annual Meeting 2020.  
96. Dang, J.H., Rodriguez, E.M., Luque, J.S., Erwin, D.O., Meade, C.D., and Chen, M.S., Jr. (2014). 
Engaging diverse populations about biospecimen donation for cancer research. J Community Genet 5, 
313-327.  
97. Stokols, D., Hall, K.L., Taylor, B.K., and Moser, R.P. (2008). The science of team science: Overview of 
the field and introduction to the supplement. Am J Prev Med 35, S77-89.  
98. Hall, K.L., Feng, A.X., Moser, R.P., Stokols, D., and Taylor, B.K. (2008). Moving the science of team 
science forward: Collaboration and creativity. Am J Prev Med 35, S243-249.  
99. Stokols, D., Harvey, R., Gress, J., Fuqua, J., and Phillips, K. (2008). In vivo studies of transdisciplinary 
scientific collaboration: Lessons learned and implications for active living research. Am J Prev Med 28, 
202-213.  
100. 
Horowitz, C.R., Robinson, M., and Seifer, A. (2009). Community-based participatory research 
from the margin to the mainstream: Are researchers prepared? Circulation 119, 2633-2642.  
101. 
Horowitz, C.R., Brenner, B.L., Lachapelle, S., Amara, D.A., and Arniella, G. (2009). Effective 
recruitment of minority populations through community-led strategies. Am J Prev Med, S195-200.  
102. 
 Horowitz, C.R., Ferryman, K., Negron, R., Sabin, T., Rodriguez, M., Zinberg, R.F., Bottinger, E., 
and Robinson, M. (2017). Race, genomics and chronic disease: What patients with African Ancestry 
have to say. J Health Care Poor Underserved 28, 248-260.  

 
 
103. 
Harris, E.D., Ziniel, S.I., Amatruda, J.G., Clinton, C.M., Savage, S.K., Taylor, P.L., Huntington, 
N.L., Green, R.C., and Holm, I.A. (2012). The beliefs, motivations, and expectations of parents who 
have enrolled their children in a genetic biorepository. Genet Med 14, 330-337.  
104. 
James, S., Arniella, G., Bickell, N.A., Walker, W., Robinson, V., Taylor, B., and Horowitz, C.R. 
(2011). Community ACTION boards: an innovative model for effective community-academic research 
partnerships. Prog Community Health Partnersh 5, 399-404.  
105. 
Kaplan, B., Caddle-Steele, C., Chisholm, G., Esmond, W.A., Ferryman, K., Gertner, M., Goytia, 
C., Hauser, D., Richardson, L.D., Robinson, M., et al. (2017). A culture of understanding: Reflections 
and suggestions from a genomic research community board. Prog Community Health Partnersh 11, 
161- 165.  
106. 
Holmes-Rovner, M., Kroll, J., Schmitt, N., Rovner, D.R., Breer, M.L., Rothert, M.L., Padonu, G., 
and Talarczyk, G. (1996). Patient satisfaction with health care decisions: the satisfaction with decision 
scale. Med Decis Making 16, 58-64.  
107. 
Charlson, M.E., Pompei, P., Ales, K.L., and MacKenzie, C.R. (1987). A new method of 
classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40, 
373-383.  
108. 
Bindman, A.B., Grumbach, K., Osmond, D., Komaromy, M., Vranizan, K., Lurie, N., Billings, J., 
and Stewart, A. (1995). Preventable hospitalizations and access to health care. JAMA 274, 305-311.  
109. 
Sherbourne, C.D., and Stewart, A.L. (1991). The MOS social support survey. Soc Sci Med 32, 
705-714.  
110. 
Wong, M.D., Sarkisian, C.A., Davis, C., Kinsler, J., and Cunngham, W.E. (2007). The 
association between life chaos, health care use, and health status among HIV-infected persons. J Gen 
Intern Med, 1286-1291.  
111. 
Wallace, L.S., Rogers, E.S., Roskos, S.E., Holiday, D.B., and Weiss, B.D. (2006). Brief report: 
screening items to identify patients with limited health literacy skills. J Gen Intern Med, 874-877.  
112. 
Nguyen, T.M., La Caze, A., and Corrtell, N. (2014). What are validated self-report adherence 
scale really missing? a systematic review. Br J Clin Pharmacol, 427-445.  
113. 
LaVeist, T.A., Nickerson, K.J., and Bowie, J.V. (2000). Attitudes about racism, medical mistrust, 
and satisfaction with care among African American and white cardiac patients. Med Care Res Rev, 
146-161.  
114. 
Richards S, Aziz N, Bale S, 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. Genet Med. 2015;17(5):405-424. 
doi:10.1038/gim.2015.30 
115. 
Strande NT, Riggs ER, Buchanan AH, et al. (2017). Evaluating the Clinical Validity of Gene-
Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource. 
Am J Hum Genet, 100(6), 895-906. doi:10.1016/j.ajhg.2017.04.015 
116. 
Abou Tayoun AN, Pesaran T, DiStefano MT, et al. (2018). Recommendations for interpreting 
the loss of function PVS1 ACMG/AMP variant criterion. Hum Mutat, 39(11),1517-1524. 
doi:10.1002/humu.23626 
117. 
Riggs ER, Andersen EF, Cherry AM, Kantarci S, Kearney H, Patel A, Raca G, Ritter DI, South 
ST, Thorland EC, Pineda-Alvarez D, Aradhya S, Martin CL. Technical standards for the interpretation 
and reporting of constitutional copy-number variants: a joint consensus recommendation of the 
American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource 
(ClinGen). Genet Med. 2020 Feb;22(2):245-257. doi: 10.1038/s41436-019-0686-8. 
118. 
Miller, D.T., Lee, K., Chung, W.K. et al. (2021). ACMG SF v3.0 list for reporting of secondary 
findings in clinical exome and genome sequencing: a policy statement of the American College of 
Medical Genetics and Genomics (ACMG). Genet Med 23, 1381–1390. https://doi.org/10.1038/s41436-
021-01172-3 
119. 
PharmGKB. (2021). Clinical Annotation Levels of Evidence 
http://www.pharmgkb.org/page/clinAnnLevels. Accessed October 14, 2021.  

 
 
120. 
Abidin, R.R. (1995). Parenting Stress Index. In Professional Manual, F.L. Odessa, ed. 
(Psychological Assessment Resources, Inc.) 
121. 
Waisbren, S.E., Albers, S., Amato, S., Ampola, M., Brewster, T.G., Demmer, L., Eaton, R.B., 
Greenstein, R., Korson, M., Larson, C., et al. (2003). Effect of expanded newborn screening for 
biochemical genetic disorders on child outcomes and parental stress. JAMA 290, 2564-2572.  
122. 
Gurian, E.A., Kinnamon, D.D., Henry, J.J., and Waisbren, S.E. (2006). Expanded newborn 
screening for biochemical disorders: the effect of a false-positive result. Pediatrics 117, 1915-1921.  
123. 
O’Connor, K., Jukes, T., Goobie, S., DiRaimo, J., Moran, G., Potter, B.K., Chakraborty, P., 
Rupar, C.A., Gannavarapu, S., and Prasad, C. (2018). Psychosocial impact on mothers receiving 
expanded newborn screening results. Eur J Hum Genet 26, 477-484.  
124. 
Forsyth, B.W., Horwitz, S.M., Leventhal, J.M., Burger, J., and Leaf, P.J. (1996). The child 
vulnerability scale: an instrument to measure parental perceptions of child vulnerability. J Pediatr 
Psychol 21, 89- 101. 
125. 
Schumm, W.R., Paff-Bergen, L.A., Hatch, R.C., Obiorah, F.C., Copeland, J.M., Meens, L.D., 
and Bugaighis, M.A. (1986). Concurrent and discriminant validity of the Kansas marital satisfaction 
scale. Journal of marriage and family 48, 381-387.  
126. 
Spitzer, R.L., Kroenke, K., Williams, J.B., and Löwe, B. (2006). A brief measure for assessing 
generalized anxiety disorder. Arch Intern Med 166, 1092-1097.  
127. 
Garcia-Campayo, J., Zamorano, E., Ruiz, M.A., Pardo, A., Perez-Paramo, M., Lopez-Gomez, 
V., Freire, O., and Rejas, J. (2010). Cultural adaption into Spanish of the generalized anxiety disorder-7 
(GAD-7) scale as a screening tool. Health Qual Life Outcomes 20, 8.  
128. 
Checa, I., Perales, J., and Espejo, B. (2019). Measurement invariance of the satisfaction with 
life scale by gender, age, marital status and educational level. Qual Life Res 28, 963-968.  
129. 
Botkin, J.R., Teutsch, S.M., Kaye, C.I., Hayes, M.M., Haddow, J.E., Bradley, L.A., Szegda, K., 
and Dotson, W.D. (2010). Outcomes of interest in evidence-based evaluations of genetic tests. Genet 
Med 12, 228-235.  
130. 
Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., and 
Jinks, C. (2018). Saturation in qualitative research: exploring its conceptualization and 
operationalization. Qual Quant 52, 1893-1907.  
131. 
Malterud, K., Siersma, V.D., and Guassora, A.D. (2016). Sample size in qualitative interview 
studies: guided by information power. Qual Health Res 26, 1753-1760. 
132. 
Sanders, G.D., Neumann, P.J., Basu, A., Brock, D.W., Feeny, D., Krahn, M., Kuntz, K.M., 
Meltzer, D.O., Owens, D.K., Prosser, L.A., et al. (2016). Recommendations for conduct, methodological 
practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health 
and medicine. JAMA 316, 1093-1103.  
133. 
Schiling, J. (2006). On the pragmatics of qualitative assessment: designing the process for 
content analysis. European journal of psychological assessment 22, 28-37. 
134. 
Barbour, R. (2001). Checklists for improving rigour in qualitative research: A case of the tail 
wagging the dog? Bmj 322, 1115-1117. 
135. 
Pagon, R.A., Tarczy-Hornoch, P., Baskin, P.K., Edwards, J.E., Covington, M.L., Espeseth, M., 
Beahler, C., Bird, T.D., Popovich, B., Nesbitt, C., et al. (2002). GeneTests-GeneClinics: genetic testing 
information for a growing audience. Hum Mutat 19, 501-509.  
136. 
Hamosh, A., Scott, A.F., Amberger, J., Valle, D., and McKusick, V.A. (2000). Online Mendelian 
Inheritance in Man (OMIM). Hum Mutat 15, 57-61. 
137. 
Labor, U.B.o. (2018). Consumer Price Index (CPI). In, N.B.o.L. Statistics, ed.  
138. 
Kittle, K., Currier, K., Dyk, L., and Newman, K. (2002). Using a pediatric database to drive 
quality improvement. Semin Pediatr Surg 11, 60-63.  
139. 
Pereira S, Smith HS, Frankel LA, Christensen KD, Islam R, Robinson JO, Genetti CA, Blout 
Zawatsky CL, Zettler B, Parad RB, Waisbren SE, Beggs AH, Green RC, Holm IA, McGuire AL; 
BabySeq Project Team.JAMA Pediatr. 2021 Nov 1;175(11):1132-1141. doi: 
10.1001/jamapediatrics.2021.2829.PMID: 34424265 

 
 
140. 
Miller DT, Lee K, Abul-Husn NS, et al. ACMG SF v3.2 list for reporting of secondary findings in 
clinical exome and genome sequencing: A policy statement of the American College of Medical 
Genetics and Genomics (ACMG). Genet Med. 2023;25(8):100866. doi:10.1016/j.gim.2023.100866 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
Appendix 1: Genomic Reporting Framework  
 
 
 
 
 

 
 
Appendix 2: ACMG SF v3.2 gene and associated phenotypes recommended for return as secondary 
findings from clinical exome and genome sequencing (adapted from Miller et al., 2023, PMID: 
37347242) 
 
 
 
 
 
Genes related to cancer phenotypes 
 
 
 
 
 
Gene 
Inheritance 
Variants to 
report 
Notes 
Familial adenomatous polyposis 
APC 
AD 
All P and LP 
  
Familial medullary thyroid cancer 
RET 
AD 
All P and LP 
RET is also associated 
with multiple endocrine 
neoplasia type 2 
Hereditary breast and/or ovarian 
cancer 
BRCA1 
AD 
All P and LP 
  
BRCA2 
  
PALB2 
  
Hereditary paraganglioma–
pheochromocytoma syndrome 
SDHD 
AD 
All P and LP 
  
SDHAF2 
  
SDHC 
  
SDHB 
  
MAX 
  
TMEM127 
  
Juvenile polyposis syndrome 
BMPR1A 
AD 
All P and LP 
  
SMAD4 
SMAD4 is also 
associated with 
hereditary hemorrhagic 
telangiectasia. 
Li–Fraumeni syndrome 
TP53 
AD 
All P and LP 
  
Lynch syndrome 
MLH1 
AD 
All P and LP 
  
MSH2 
  
MSH6 
  
PMS2 
  
Multiple endocrine neoplasia type 1 
MEN1 
AD 
All P and LP 
  
MUTYH-associated polyposis 
MUTYH 
AR 
P and LP (2 
variants) 
  
Neuroﬁbromatosis type 2 
NF2 
AD 
All P and LP 
  
Peutz–Jeghers syndrome 
STK11 
AD 
All P and LP 
  
PTEN hamartoma tumor syndrome 
PTEN 
AD 
All P and LP 
  
Retinoblastoma 
RB1 
AD 
All P and LP 
  
Tuberous sclerosis complex 
TSC1 
AD 
All P and LP 
  
TSC2 
  
  
  

 
 
von Hippel–Lindau syndrome 
VHL 
AD 
All P and LP 
  
WT1-related Wilms tumor 
WT1 
AD 
All P and LP 
  
 
 
 
 
 
 
 
 
 
 
Genes related to cardiovascular 
phenotypes 
 
 
 
 
 
Gene 
Inheritance 
Variants to 
report 
Notes 
Aortopathies 
FBN1 
AD 
All P and LP 
  
TGFBR1 
  
TGFBR2 
  
SMAD3 
  
ACTA2 
  
MYH11 
  
Arrhythmogenic right ventricular 
cardiomyopathy 
PKP2 
AD 
All P and LP 
  
DSP 
DSP is also associated 
with dilated 
cardiomyopathy (DCM) 
as a primary disease 
DSC2 
  
TMEM43 
  
DSG2 
  
Catecholaminergic polymorphic 
ventricular tachycardia 
RYR2 
AD 
All P and LP 
  
CASQ2 
AR 
P and LP (2 
variants) 
  
TRDN 
TRDN is also associated 
with long QT syndrome 
Dilated cardiomyopathy 
TNNT2 
AD 
All P and LP 
TNNT2 is also associated 
with hypertrophic 
cardiomyopathy (HCM) 
LMNA 
  
FLNC 
  
TTN 
 
 
BAG3 
 
 
 
DES 
 
 
 
RBM20 
 
 
 

 
 
TNNC1 
 
 
 
Ehlers–Danlos syndrome, vascular 
type 
COL3A1 
AD 
All P and LP 
  
Familial hypercholesterolemia 
LDLR 
AD 
All P and LP 
  
APOB 
  
PCSK9 
  
Hypertrophic cardiomyopathy 
MYH7 
AD 
All P and LP 
MYH7 is also associated 
with dilated 
cardiomyopathy (DCM) 
as a primary disease 
MYBPC3 
  
TNNI3 
  
TPM1 
  
MYL3 
  
ACTC1 
  
PRKAG2 
Pathogenic variants in 
PRKAG2 are associated 
with metabolic storage 
disease that mimics a 
HCM, but also can 
involve skeletal muscle. 
MYL2 
  
Long QT syndrome types 1 and 2 
KCNQ1 
AD 
All P and LP 
  
KCNH2 
  
Long QT syndrome 3; Brugada 
syndrome 
SCN5A 
AD 
All P and LP 
SCN5A is also associated 
with dilated 
cardiomyopathy (DCM) 
as a primary disease 
Long QT syndrome types 14-16 
CALM1 
AD 
All P and LP 
 
CALM2 
AD 
 
CALM3 
AD 
 
 
 
 
 
 
 
 
 
 
 
Genes related to inborn errors of 
metabolism phenotype 
 
 
 
 

 
 
 
Gene 
Inheritance 
Variants to 
report 
Notes 
Biotinidase deﬁciency 
BTD 
AR 
P and LP (2 
variants) 
  
Fabry disease 
GLA 
XL 
All hemi, het, 
homozygous P 
and LP 
GLA also applies to the 
cardiovascular category 
Ornithine transcarbamylase deﬁciency 
OTC 
XL 
All hemi, het, 
homozygous P 
and LP 
  
Pompe disease 
GAA 
AR 
P and LP (2 
variants) 
  
 
 
 
 
 
 
 
 
 
 
Genes related to miscellaneous 
phenotypes 
 
 
 
 
 
Gene 
Inheritance 
Variants to 
report 
 
Hereditary hemochromatosis 
HFE 
AR 
HFE 
p.Cys282Tyr 
homozygotes 
only 
(NM_000410.3) 
 
Hereditary hemorrhagic telangiectasia 
ACVRL1 
AD 
All P and LP 
 
ENG 
 
Malignant hyperthermia 
RYR1 
AD 
All P and LP 
 
CACNA1S 
 
Maturity-onset diabetes of the young 
HNF1A 
AD 
All P and LP 
 
RPE65-related retinopathy 
RPE65 
AR 
P and LP (2 
variants) 
 
Wilson disease 
ATP7B 
AR 
P and LP (2 
variants) 
 
Hereditary TTR amyloidosis 
TTR 
AD 
All P and LP