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PURPOSE: Patients undergoing clinical exome sequencing (ES) are routinely offered the option to receive secondary findings (SF). However, little is known about the views of individuals from underrepresented minority pediatric or prenatal populations regarding SF. METHODS: We explored the preferences for receiving hypothetical categories of SF (H-SF) and reasons for accepting or declining actual SF through surveying (n = 149) and/or interviewing (n = 47) 190 families undergoing pediatric or prenatal ES. RESULTS: Underrepresented minorities made up 75% of the probands. In total, 150 families (79%) accepted SF as part of their child/fetus’s ES. Most families (63%) wanted all categories of H-SF. Those who declined SF as part of ES were less likely to want H-SF across all categories. Interview findings indicate that some families did not recall their SF decision. Preparing for the future was a major motivator for accepting SF, and concerns about privacy, discrimination, and psychological effect drove decliners. CONCLUSION: A notable subset of families (37%) did not want at least 1 category of H-SF, suggesting more hesitancy about receiving all available results than previously reported. The lack of recollection of SF decisions suggests a need for alternative communication approaches. Results highlight the importance of the inclusion of diverse populations in genomic research.

This study aimed to understand broad data sharing decisions among predominantly underserved families participating in genomic research.Drawing on clinic observations, semistructured interviews, and survey data from prenatal and pediatric families enrolled in a genomic medicine study focused on historically underserved and underrepresented populations, this paper expands empirical evidence regarding genomic data sharing communication and decision-making.One-third of parents declined to share family data, and pediatric participants were significantly more likely to decline than prenatal participants. The pediatric population was significantly more socioeconomically disadvantaged and more likely to require interpreters. Opt-in was tied to altruism and participants' perception that data sharing was inherent to research participation. Opt-out was associated with privacy concerns and influenced by clinical staff's presentation of data handling procedures. The ability of participants to make informed choices during enrollment about data sharing was weakened by suboptimal circumstances, which was revealed by poor understanding of data sharing in follow-up interviews as well as discrepancies between expressed participant desires and official recorded choices.These empirical data suggest that the context within which informed consent process is conducted in clinical genomics may be inadequate for respecting participants' values and preferences and does not support informed decision-making processes.

Positive autoregulation has been repeatedly proposed as a mechanism for cells to adopt binary fates during embryonic development through bistability. However, without quantitatively determining their parameters, it is unclear whether the plethora of positive autoregulatory modules found within developmental gene regulatory networks are actually bistable. Here, we combinein vivolive imaging with mathematical modeling to dissect the binary cell fate dynamics of the fruit fly pair-rule genefushi tarazu(ftz), which is regulated by two known enhancers: the early (non-autoregulating) element and the autoregulatory element. Live imaging of transcription and protein concentration in the blastoderm revealed that binary Ftz cell states are achieved asftzexpression rapidly transitions from being dictated by the early element to the autoregulatory element. Moreover, we discovered that Ftz concentration alone is insufficient to activate the autoregulatory element, and that this element only becomes responsive to Ftz at a prescribed developmental time. Based on these observations, we developed a dynamical systems model, and quantitated its kinetic parameters directly from experimental measurements. Our model demonstrated that theftzautoregulatory module is indeed bistable and that the early element transiently establishes the content of the binary cell fate decision to which the autoregulatory module then commits. Further analysisin silicorevealed that the autoregulatory element locks the Ftz expression fate quickly, within 35 min of exposure to the transient signal of the early element. Overall, our work confirms the widely held hypothesis that autoregulation can establish developmental fates through bistability and, most importantly, provides a framework for the quantitative dissection of cellular decision-making based on systems dynamics models and real-time measurements of transcriptional and protein dynamics.

Summary Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation., Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequent frequent variants and associated mechanisms. Variants of unknown significance (VUS) in these genes are discovered at a rate that outpaces current ability to classify them using databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns, and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.