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Record W2273251241 · doi:10.1038/gim.2015.92

Shift happens: family background influences clinical variability in genetic neurodevelopmental disorders

2015· letter· en· W2273251241 on OpenAlexaboutno aff
Brenda Finucane, Thomas D. Challman, Christa Lese Martin, David H. Ledbetter

Bibliographic record

VenueGenetics in Medicine · 2015
Typeletter
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsMedicinePsychologyGeneticsBiology

Abstract

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There was a time in medical genetics when intellectual disability (ID), autism, and other neurodevelopmental symptoms were predominantly viewed within the context of distinct clinical syndromes. By definition, a syndrome has a circumscribed pattern of phenotypic features that can be quantified into objective prevalence estimates, such as a 44% risk for a cardiac defect in a newborn with Down syndrome. From a practical standpoint, knowing the prevalence of associated clinical features in genetic syndromes allows targeted assessment and anticipatory medical management of these conditions. Binary categorizations (i.e., “present” versus “absent”) work well for symptoms such as congenital structural defects and most other physical aspects of a clinical phenotype. Traditionally, cognitive and behavioral features of genetic syndromes have been described in the same categorical way as physical findings, based on prevalence statistics that imply an all-or-none chance for symptoms such as ID and psychiatric disorders. Recent trends toward more fine-grained research on cognitive and behavioral phenotypes, along with genomic evidence that blurs the lines among formerly distinct psychiatric diagnoses, have revealed the inadequacy of using a categorical model to describe neurodevelopmental outcomes in genetic disorders.1.Insel T. Cuthbert B. Garvey M. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.10.1176/appi.ajp.2010.09091379Am J Psychiatry. 2010; 167: 748-751Google Scholar For example, parents of an infant newly diagnosed with 22q11.2 deletion syndrome worry whether their child will be among the 30% with ID or the estimated 70% without this diagnosis, not appreciating that the syndrome’s intellectual deficits occur along a continuum, where the threshold demarcating ID is set, by convention, at a particular point. In addition, the diagnosis of well-described, discrete genetic syndromes has been overtaken by newly identified copy-number variants (CNVs) and single-gene variants about which little is yet known, apart from a vague risk for a broad array of cognitive and behavioral symptoms. These are challenging times for geneticists and genetic counselors, but even more so for families who leave the medical genetics clinic with many worries and few focused answers about neurodevelopmental risk. Emerging new perspectives in psychiatry,1.Insel T. Cuthbert B. Garvey M. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.10.1176/appi.ajp.2010.09091379Am J Psychiatry. 2010; 167: 748-751Google Scholar based partly on genomic findings, provide an opportunity to reevaluate our approach to the assessment and discussion of intellectual and behavioral prognoses in the medical genetics setting. Whole-genome copy-number and sequencing studies have revealed that identical genetic causes are common among apparently distinct developmental and psychiatric conditions.2.Sebat J. Levy D.L. McCarthy S.E. Rare structural variants in schizophrenia: one disorder, multiple mutations; one mutation, multiple disorders.1:CAS:528:DC%2BD1MXhsV2gsLzP10.1016/j.tig.2009.10.004Trends Genet. 2009; 25: 528-535Google Scholar,3.Girirajan S. Brkanac Z. Coe B.P. Relative burden of large CNVs on a range of neurodevelopmental phenotypes.1:CAS:528:DC%2BC3MXhsFyntLbF10.1371/journal.pgen.1002334PLoS Genet. 2011; 7: e1002334Google Scholar,4.Moreno-De-Luca A. Myers S.M. Challman T.D. Moreno-De-Luca D. Evans D.W. Ledbetter D.H. Developmental brain dysfunction: revival and expansion of old concepts based on new genetic evidence.10.1016/S1474-4422(13)70011-5Lancet Neurol. 2013; 12: 406-414Google Scholar Shared etiological underpinnings now directly connect a host of seemingly unrelated disorders, including autism and schizophrenia. From a genomics perspective, the reconceptualization and merging of childhood developmental and adult-onset psychiatric disorders have profound implications for pedigree construction, risk modeling, and anticipatory guidance. The traditional pedigree that separates autism, ID, bipolar disorder, and schizophrenia as distinct and unrelated conditions in a family can no longer be considered valid (Figure 1). In many cases these multiple diagnostic subtypes of brain dysfunction represent variable expressivity of a single underlying genetic cause. Developmental brain disorders reflect varying degrees of dysfunction along a continuum of heritable human traits, including intelligence, social abilities, and motor skills.4.Moreno-De-Luca A. Myers S.M. Challman T.D. Moreno-De-Luca D. Evans D.W. Ledbetter D.H. Developmental brain dysfunction: revival and expansion of old concepts based on new genetic evidence.10.1016/S1474-4422(13)70011-5Lancet Neurol. 2013; 12: 406-414Google Scholar All humans fall somewhere along the functional continuum for these quantitative traits, with categorical diagnoses such as autism and ID defined by an artificial threshold at one end of a spectrum. Family background has long been known to play an important role in influencing patterns of phenotypic expression in genetic syndromes and common diseases. Parental height, for example, is a significant predictor of adult stature in girls with Turner syndrome,5.Cohen A. Kauli R. Pertzelan A. Final height of girls with Turner’s syndrome: correlation with karyotype and parental height.1:STN:280:DyaK2MzlvFKruw%3D%3D10.1111/j.1651-2227.1995.tb13693.xActa Paediatr. 1995; 84: 550-554Google Scholar whereas family history modulates lifetime risk for cardiovascular disease. Phenotypic variability in women with BRCA1/2 mutations has been linked to genomic modifying factors that may ultimately be used to further refine their lifetime cancer risk.6.KConFab Investigators; Ontario Cancer Genetics Network; HEBON; EMBRACE; GEMO Study Collaborators; GENICA Network Identification of a BRCA2-specific modifier locus at 6p24 related to breast cancer risk.1:CAS:528:DC%2BC3sXlvValtLs%3D10.1371/journal.pgen.1003173PLoS Genet. 2013; 9: e1003173Google Scholar Relatively few studies have directly examined the relationship between parental functioning and neurodevelopmental outcomes in children with genetic syndromes. Significant intelligence quotient correlations between probands and their first-degree relatives have been documented in Down, Klinefelter, Prader-Willi, fragile X, and 22q11.2 deletion syndromes.7.Olszewski P. Radoeva D. Fremont W. Kates W.R. Antshel K.M. Is child intelligence associated with parent and sibling intelligence in individuals with developmental disorders? An investigation in youth with 22q11.2 deletion (velo-cardio-facial) syndrome.10.1016/j.ridd.2014.08.034Res Dev Dis. 2014; 35: 3582-3590Google Scholar Our recent family study of de novo 16p11.2 deletions8.Moreno-De-Luca A. Evans D.W. Ledbetter D.H. The role of parental cognitive, behavioral, and motor profiles in clinical variability in individuals with chromosome 16p11.2 deletions.10.1001/jamapsychiatry.2014.2147JAMA Psychiatr. 2015; 72: 119-126Google Scholar demonstrated that neurodevelopmental outcomes in children with this CNV represent a predictable “shift” from expected functioning, based in part on parental background across multiple domains. Such studies have important implications for genetic counseling and medical genomics practice. Depending on the parental starting point for quantitative traits such as intelligence and social abilities, the shift in functioning due to a specific CNV may or may not cause a child to reach the defined threshold for a diagnosable clinical disorder (Figure 2). For example, the empiric risk for autism in children with a de novo 16p11.2 deletion is about 15%.8.Moreno-De-Luca A. Evans D.W. Ledbetter D.H. The role of parental cognitive, behavioral, and motor profiles in clinical variability in individuals with chromosome 16p11.2 deletions.10.1001/jamapsychiatry.2014.2147JAMA Psychiatr. 2015; 72: 119-126Google Scholar The deletion confers a 2.2 standard deviation deleterious effect on social behavior, a highly heritable and continuously distributed trait that can be reliably measured using the Social Responsiveness Scale.9.Constantino J.N. Davis S.A. Todd R.D. Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised.10.1023/A:1025014929212J Autism Dev Disord. 2003; 33: 427-433Google Scholar An infant with a de novo 16p11.2 deletion whose parents have higher than average social abilities, as measured on the Social Responsiveness Scale, actually has a lower risk for autism than one born to parents whose personalities are naturally skewed toward the less sociable end of the scale.8.Moreno-De-Luca A. Evans D.W. Ledbetter D.H. The role of parental cognitive, behavioral, and motor profiles in clinical variability in individuals with chromosome 16p11.2 deletions.10.1001/jamapsychiatry.2014.2147JAMA Psychiatr. 2015; 72: 119-126Google Scholar Although research in this area is still evolving, it is likely that different CNVs and single-gene disorders have distinct profiles of deleterious impact on various functional domains. The well-established 25% risk for schizophrenia among individuals with a 22q11.2 deletion does not hold true for those with fragile X syndrome, for example, although the likelihood of autism in fragile X syndrome is much higher. In addition, family studies are unlikely to refine prognosis for some disorders, including those with severe cognitive effects and a narrow range of phenotypic variability. Parental studies may prove particularly helpful in focusing the prognosis for conditions with milder and more variable constellations of cognitive and behavioral features, including the growing number of pathogenic but poorly characterized CNVs involving psychiatric symptoms. A potential future application of family studies might be the development of neurodevelopmental risk algorithms, similar to those in use for cancer and cardiovascular genetic counseling. Such algorithms could take into account variables that compound developmental risk, including the results of parental assessments on relevant functional domains, such as cognition, social behavior, and motor skills; the known deleterious effect on those domains for a specific genetic condition; other diagnosis-specific variables (e.g., gender, the presence of congenital anomalies); and environmental factors, such as prematurity. The result could be a customized profile that would ideally guide intervention by identifying a child’s main area(s) of neurodevelopmental vulnerability. A child with a 15q11.2–13.1 duplication, for example, might have a particularly high risk for developing autism, warranting early and intensive behavioral therapy. Another child with the same genetic diagnosis might have a relatively low risk for autism but a heightened chance for significant ID. By better quantifying the type and magnitude of neurodevelopmental risks in children with various CNVs and sequence variations, clinicians could more effectively pinpoint areas for proactive intervention, as opposed to the current approach of “watchful waiting.” The ability to preemptively identify subgroups of children with distinct neurodevelopmental vulnerabilities will become increasingly important as pharmaceuticals that target dysfunctional molecular pathways in the brain enter clinical practice in future decades. In light of genomic evidence and the rapidly evolving psychiatric landscape, traditional models of pedigree interpretation and categorical description are no longer adequate to explain the neurodevelopmental aspects of genetic disorders. Gone are the days when an empiric prevalence figure for ID or autism can simply be listed in the same breath as a cleft lip or a club foot. Counseling about neurodevelopmental prognosis for genetic disorders needs to more accurately convey the continuously distributed nature of intellectual and behavioral traits, as well as their cross-connections with clinical psychiatric diagnoses. Those discussions could potentially be enhanced by family studies that identify vulnerabilities and target interventions. Such an approach is wholly consistent with efforts to expand “precision medicine” beyond cancer treatment to other areas of medical practice.10.Insel T. Cuthbert B.N. Brain disorders? Precisely. Precision medicine comes to psychiatry.1:CAS:528:DC%2BC2MXptlWis7Y%3D10.1126/science.aab2358Science. 2015; 348: 499-500Google Scholar Could the future of medical genetics management include assessment of measures of social responsiveness, motor skills, language abilities, and cognitive functioning in a genetically diagnosed child, as well as his parents? This notion is intriguing, but one that must be carefully researched and approached with great caution. Genetics has a well-documented and checkered past with regard to family studies that resulted in errant social policies, including involuntary sterilization, based on naive notions about the heritability of intelligence, criminality, and antisocial behavior. The future goal of family studies in the context of neurodevelopmental disorders should be to enhance prognostic focus and maximize the appropriateness of interventions to improve outcomes. Regarding family studies for intellectual and behavioral traits, medical genetics now has a rare opportunity for a “do-over,” with the benefit of hindsight in a (hopefully) more socially enlightened era. Meticulous research on clinical applications of neurodevelopmental “shift,” in close collaboration with families and with careful consideration of social consequences, may allow us to get it right the second time around. D.H.L. is a consultant to Natera, Inc. The other authors declared no conflict of interest. This work was supported by the National Institute of Mental Health of the National Institutes of Health under award RO1MH074090. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.135
GPT teacher head0.397
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations43
Published2015
Admission routes1
Has abstractyes

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