Skip to main content
NCBI home page
JAMA Network logoLink to JAMA Network
. 2023 Mar 6;177(5):472–478. doi: 10.1001/jamapediatrics.2023.0008

Diagnostic Yield of Exome Sequencing in Cerebral Palsy and Implications for Genetic Testing Guidelines

A Systematic Review and Meta-analysis

Pedro J Gonzalez-Mantilla 1, Yirui Hu 2, Scott M Myers 1, Brenda M Finucane 1, David H Ledbetter 3, Christa L Martin 1, Andres Moreno-De-Luca 1,4,5,✉
PMCID: PMC9989956  PMID: 36877506

This study attempts to evaluate if the diagnostic yield of exome or genome sequencing in cerebral palsy is similar to that of other neurodevelopmental disorders.

Key Points

Question

Is the diagnostic yield of exome or genome sequencing in cerebral palsy similar to that of other neurodevelopmental disorders for which exome sequencing is recommended as a first-tier clinical diagnostic test?

Findings

In this systematic review and meta-analysis that included 13 studies and 2612 individuals with cerebral palsy, the diagnostic yield of exome or genome sequencing was 31.1%, which is similar to that of other neurodevelopmental disorders, regardless of comorbid intellectual disability/developmental delay.

Meaning

This study provides evidence to support the inclusion of cerebral palsy in the current recommendation of exome sequencing in the diagnostic evaluation of individuals with neurodevelopmental disorders.

Abstract

Importance

Exome sequencing is a first-tier diagnostic test for individuals with neurodevelopmental disorders, including intellectual disability/developmental delay and autism spectrum disorder; however, this recommendation does not include cerebral palsy.

Objective

To evaluate if the diagnostic yield of exome or genome sequencing in cerebral palsy is similar to that of other neurodevelopmental disorders.

Data Sources

The study team searched PubMed for studies published between 2013 and 2022 using cerebral palsy and genetic testing terms. Data were analyzed during March 2022.

Study Selection

Studies performing exome or genome sequencing in at least 10 participants with cerebral palsy were included. Studies with fewer than 10 individuals and studies reporting variants detected by other genetic tests were excluded. Consensus review was performed. The initial search identified 148 studies, of which 13 met inclusion criteria.

Data Extraction and Synthesis

Data were extracted by 2 investigators and pooled using a random-effects meta-analysis. Incidence rates with corresponding 95% CIs and prediction intervals were calculated. Publication bias was evaluated by the Egger test. Variability between included studies was assessed via heterogeneity tests using the I2 statistic.

Main Outcomes and Measures

The primary outcome was the pooled diagnostic yield (rate of pathogenic/likely pathogenic variants) across studies. Subgroup analyses were performed based on population age and on the use of exclusion criteria for patient selection.

Results

Thirteen studies were included consisting of 2612 individuals with cerebral palsy. The overall diagnostic yield was 31.1% (95% CI, 24.2%-38.6%; I2 = 91%). The yield was higher in pediatric populations (34.8%; 95% CI, 28.3%-41.5%) than adult populations (26.9%; 95% CI, 1.2%-68.8%) and higher among studies that used exclusion criteria for patient selection (42.1%; 95% CI, 36.0%-48.2%) than those that did not (20.7%; 95% CI, 12.3%-30.5%).

Conclusions and Relevance

In this systematic review and meta-analysis, the genetic diagnostic yield in cerebral palsy was similar to that of other neurodevelopmental disorders for which exome sequencing is recommended as standard of care. Data from this meta-analysis provide evidence to support the inclusion of cerebral palsy in the current recommendation of exome sequencing in the diagnostic evaluation of individuals with neurodevelopmental disorders.

Introduction

Neurodevelopmental disorders (NDDs) are a heterogeneous group of conditions presenting with impairments in cognition, behavior, language, and/or motor skills arising from altered neural development. The disorders covered by this definition include diagnoses encompassed by the neurodevelopmental disorders category of the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5), such as intellectual disability (ID), global developmental delay (DD), autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder, and communication disorders, as well as non–DSM-5 disorders, such as cerebral palsy (CP) and epilepsy.1

CP is a clinical diagnosis defined as “a group of permanent disorders of the development of movement and posture, causing activity limitations that are attributed to nonprogressive disturbances that occurred in the developing fetal or infant brain.”2,3 CP is the most common major disabling motor disorder of childhood with an estimated prevalence of 2 to 3 cases per 1000 births.4,5 Alterations in muscle tone, posture, and/or movement are noted in the first years of life as a consequence of altered brain development, following a permanent and nonprogressive clinical course that often coexists with other NDDs, such as ID in 27% to 45%, epilepsy in 38%, speech disorders in 33% to 82%, and ASD in 3% to 9% of cases.6,7,8,9

CP has been attributed to multiple risk factors, including prematurity, low birth weight, and birth complications10; however, the underlying cause of CP is uncertain in most cases.11 Birth asphyxia secondary to intrapartum complications has been long considered as the leading cause of CP; however, numerous large population-based studies have established that birth asphyxia accounts for less than 10% of CP cases.12

There is increasing evidence that rare genomic variants of large effect size, including gene-level copy number variants (CNVs) and single-nucleotide variants (SNVs), contribute significantly to the etiology of CP.13 Prior studies using chromosomal microarray analysis (CMA) have identified rare CNVs in 10% to 31% of cases14,15,16,17,18 and studies using exome sequencing (ES) or genome sequencing (GS) have revealed pathogenic or likely pathogenic (P/LP) SNVs in 7% to 55% of cases.19,20,21,22,23,24,25,26,27,28,29,30,31

A recent meta-analysis and multidisciplinary consensus statement recommended ES as a first-tier clinical diagnostic test for individuals with NDDs, including DD, ID, and ASD.32 Similarly, the American College of Medical Genetics and Genomics recommended that ES/GS be considered as a first- or second-tier test for patients with ID/DD or congenital anomalies.33 However, these recommendations did not include CP in the study design. We conducted a meta-analysis of studies that performed ES or GS in individuals with CP to test the hypothesis that the diagnostic yield of these tests in CP is similar to that of other NDDs for which ES is recommended as a standard of care.

Methods

Search Strategy

We searched PubMed using a combination of medical subject headings (MeSH) terms and keywords related to CP and genetic testing (eTable 1 in Supplement 1). Search terms included “cerebral palsy,” “genetic testing,” “exome sequencing,” and “genome sequencing.” The search was limited to studies published in English between January 1, 2013 and March 31, 2022. Authors of the studies were not contacted.

Research ethics committees or other entities overseeing the use of patients’ data approved the studies included in this meta-analysis. Only published, publicly available data were included in this study, so neither individual consent nor specific approval for this meta-analysis were required. This study used the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines.

Inclusion and Exclusion Criteria

Studies were eligible if they included individuals with CP undergoing either ES or GS and analyzed at least 10 probands. For studies reporting GS data, we only included gene-level SNVs and CNVs and no other type of changes. Exclusion criteria included case reports or studies with fewer than 10 individuals and studies reporting variants detected by genetic tests other than ES or GS (eg, CMA, gene panel sequencing, epigenetic analyses, transcriptomic studies).

Statistical Analysis

Meta-analyses were conducted for each included outcome using a random-effects model. The primary outcome was the diagnostic yield, which was annotated for each included study by 2 independent investigators/reviewers (P.J.G.M. and A.M.D.). Individuals with P/LP variants, including SNVs and CNVs detected by ES or GS, were included in the primary outcome, whereas those with variants of uncertain significance (VUS), likely benign, or benign variants were excluded. Incidence rates with corresponding 95% CIs and prediction intervals were estimated for the diagnostic yield from included studies. Arcsine transformation was used as a variance-stabilizing transformation for proportions. The secondary outcome was to compare the diagnostic performance in individuals with CP with and without comorbid ID/DD. Subgroup analyses were performed based on population age (pediatric, adult, and mixed cohorts) and on the presence or absence of exclusion criteria for patient selection. Studies with exclusion criteria were defined as those that excluded individuals with risk factors for CP (eg, prematurity, low birth weight, perinatal complications) or certain neuroimaging findings (eg, periventricular leukomalacia, hypoxic-ischemic encephalopathy, infarcts, intracranial hemorrhage, brain malformations). Each included study’s pooled estimates and measures of variability were used to generate forest plots. Publication bias was evaluated by the Egger test. Variability between included studies was assessed via heterogeneity tests using the I2 statistic. All analyses were conducted in RStudio, version 1.0.13634 (R Institute) using the Meta and Metafor package.35,36

Results

The initial literature search, based on MeSH terms and keywords related to CP and genetic testing, identified 148 articles. Commentaries, published errata, and duplicates were removed and the titles and abstracts of 142 studies were reviewed. One ES study was excluded because it only reported CNVs,37 another study was excluded because it conducted multiple sequencing methods,38 and a third study was excluded as it reported probands from another study.23 The final number of studies meeting the inclusion criteria was 13 (Figure 1). Two of the 13 studies reported both pediatric and adult cohorts separately.25,29 The Egger test showed no publication bias (eTable 2 in Supplement 1).

Figure 1. Flowchart of Study Selection.

Figure 1.

Open in a new tab

CMA indicates chromosomal microarray analysis; CP, cerebral palsy; ES, exome sequencing; GPS, gene panel sequencing; GS, genome sequencing; NDD, neurodevelopmental disorders; SNV, single-nucleotide variants.

The included studies differed in the target population, cohort size, availability of CNV calling, and implementation of standards and guidelines for the interpretation of sequence variants from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.39 Other differences among studies were the use of exclusion criteria for patient selection, such as presence of risk factors (eg, prematurity, low birth weight, birth asphyxia) or neuroimaging findings (eg, periventricular leukomalacia, hypoxic-ischemic encephalopathy, brain malformations) and the inclusion of VUS and novel variants in the diagnostic yield (Table 1).

Table 1. Characteristics of Studies Included in the Meta-analysis.

Source Population No. of probands Type of genetic variants ACMG/AMP criteria Exclusion criteriaa Diagnostic yield, No./total No. (%)b Excluded probands
McMichael et al,19 2015 Pediatric 98 SNV No No 14/98 (14.3) 0
Takezawa et al,20 2018 Mixed 17 SNV Yes Yes 9/17 (52.9) 2 VUS
Matthews et al,21 2019 Mixed 49 SNV Yes Yes 20/49 (40.8) 12 VUS
Rosello et al,22 2021 Pediatric 20 SNV Yes Yes 11/20 (55.0) 0
van Eyk et al,24 2021 Mixed 150 CNV/SNV Yes No 37/150 (24.7) 52 VUS
Moreno-De-Luca et al,25 2021 Pediatric 1345 CNV/SNV Yes No 440/1345 (32.7) 0
Adult 181 19/181 (10.5) 0
May et al,26 2021 Mixed 151 SNV Yes No 11/151 (7.3) 3 VUS
Yechieli et al,18 2022 Pediatric 45 SNV Yes Yes 18/45 (40.0) 8 CMA, 3 novel
Li et al,27 2022 Pediatric 122 SNV Yes Yes 47/122 (38.5) 4 CMA, 1 MS, 3 novel
Al Zahrani et al,29 2022 Pediatric 64 CNV/SNV No Yes 18/64 (28.1) 3 VUS
Adult 37 18/37 (48.6) 10 VUS
Nejabat et al,28 2021 Pediatric 66 SNV Yes Yes 33/66 (50.0) 29 VUS
Mei et al,31 2022 Pediatric 217 CNV/SNV Yes No 78/217 (35.9) 0
Chopra et al,30 2022 Mixed 50 SNV Yes No 13/50 (26.0) 4 VUS, 2 novel
Open in a new tab

Abbreviations: ACMG/AMP, American College of Medical Genetics and Genomics/Association for Molecular Pathology; CMA, chromosomal microarray analysis; CNV, copy number variant; MS, mitochondrial sequencing; SNV, single-nucleotide variant; VUS, variant of uncertain significance.

a

Use of exclusion criteria for patient selection based on cerebral palsy risk factors (prematurity, low birth weight, perinatal complications) or neuroimaging findings (periventricular leukomalacia, hypoxic-ischemic encephalopathy, infarcts, intracranial hemorrhage, brain malformations).

b

Rate of pathogenic or likely pathogenic variants.

A random-effects meta-analysis, including 13 studies and 2612 individuals with CP, showed a genetic diagnostic yield of ES/GS in CP of 31.1% (95% CI, 24.2%-38.6%; I2 = 91%) (Figure 2). Subgroup analyses comparing the diagnostic yield among cohorts with different age groups (pediatric, adult, and mixed) revealed the highest yield in pediatric cohorts (34.8%; 95% CI, 28.3%-41.5%; I2 = 80%), followed by mixed cohorts (27.4%; 95% CI, 13.7%-43.7%; I2 = 91%), and adult cohorts (26.9%; 95% CI, 1.2%-68.8%; I2 = 96%) (eFigure 1 in Supplement 1). Studies that applied exclusion criteria for patient selection had a higher diagnostic yield (42.1%; 95% CI, 36.0%-48.2%; I2 = 34%) compared with those that did not (20.7%; 95% CI, 12.3%-30.5%; I2 = 95%) (eFigure 2 in Supplement 1). As indicated by the I2 values, there was high heterogeneity between the included studies (I2 = 91%), which decreased after performing subgroup analyses for studies that applied exclusion criteria for patient selection (I2 = 34%).

Figure 2. Forest Plot of Diagnostic Yield of Exome or Genome Sequencing in Cerebral Palsy Studies.

Figure 2.

Open in a new tab

Dx indicates diagnositic; P/LP, pathogenic/likely pathogenic.

Nine studies reported individual-level data on the presence or absence of ID/DD from 2095 probands with CP, including 1604 with comorbid ID/DD and 491 without ID/DD. The secondary analysis of the diagnostic yield of ES or GS given this comorbidity showed a higher diagnostic yield (37.8%; 95% CI, 29.4%-46.6%; I2 = 80%) in individuals with CP and ID/DD compared with those without comorbid ID/DD (17.6%; 95% CI, 9.2%-28.0%; I2 = 82%) (eFigure 3 in Supplement 1). The genes with the largest number of P/LP variants across all studies were CTNNB1 (21 cases), SPAST (18 cases), GNAO1 (15 cases), ATL1, CACNA1A, and TUBB4A (14 cases each), and COL4A1 and KCNQ2 (13 cases each) (eTable 3 in Supplement 1).

Five studies reported the frequency of changes in clinical care secondary to the identification of P/LP variants. Across studies, 75 of 204 individuals with P/LP variants (36.8%) benefited from genomic-informed changes in their clinical management, including initiation of specific medications, replacement therapy, avoidance of certain medications, dietary modifications, deep brain stimulation, and medical surveillance for potential comorbidities (Table 2; eTable 4 in Supplement 1).

Table 2. Studies Reporting Clinical Changes After the Diagnosis of a Pathogenic/Likely Pathogenic (P/LP) Variant.

Source Individuals with P/LP variants Individuals with clinical change, No. (%)
Matthews et al,21 2019 20 4 (20.0)
van Eyk et al,24 2021 37 20 (54.1)
Nejabat et al,28 2021 33 6 (18.2)
Al Zahrani et al,29 2022 (adult) 18 8 (44.4)
Al Zahrani et al,29 2022 (pediatric) 18 11 (61.1)
Mei et al,31 2022 78 26 (33.3)
Total 204 75 (36.8)
Open in a new tab

Discussion

This meta-analysis, including 13 studies, 15 cohorts, and 2612 individuals with CP, revealed a diagnostic yield of ES or GS of 31.1%, which is similar to the yield identified in a recent meta-analysis of individuals with other NDDs (36%).32 As expected, the yield was higher in studies that applied exclusion criteria for patient selection compared with studies that included all individuals with CP, regardless of the presence or absence of risk factors or neuroimaging findings. Similarly, the diagnostic yield was higher among individuals with CP and comorbid ID/DD; however, even in individuals without ID/DD (17.6%) the yield was similar to the yield of ES in ASD (15%).40 Therefore, these results provide evidence to support the inclusion of CP in the current recommendation of ES as a first-tier test for individuals with NDDs.

Both ES and GS are appropriate tests in the diagnostic evaluation of individuals with CP, with variable genetic diagnostic yields according to the characteristics of the population studied. Although ES provides a narrower genetic analysis compared with GS, approximately 85% of known disease-related variants are within the exons, making ES the most cost-effective option currently available.41 However, GS will likely become the test of choice when GS costs become similar to the current cost of ES. Trio analysis allows the detection of de novo and compound heterozygous variants, a 10-fold reduction in the number of candidate variants, faster return of results, and an increment of approximately 50% in the diagnostic yield compared to singleton analysis.42,43 Unfortunately, this approach is not always possible, especially in adult populations where parental samples may not be available; however, a genetic diagnosis can still be established in a large proportion of singleton cases (ie, proband only).

The detection of CNVs from ES data lead to higher diagnostic performance than restricting the analysis to sequence variants in single genes and it is increasingly being implemented by clinical laboratories. If ES-derived CNV calling is not available, CMA can be performed to assess for CNVs if a genetic diagnosis is not achieved by ES. For those unresolved cases by ES with CNV analysis or ES plus CMA, reanalyzing ES data at 1- or 2-year intervals increases the diagnostic yield by approximately 12%.40,42 If ES/GS and CNV calling are nondiagnostic, additional tests can be performed on a case-by-case basis based on the clinical presentation, such as metabolic testing, fragile X analysis, and mitochondrial DNA sequencing (unless already performed with ES).40

The identification of causative genomic variants in individuals with CP has multiple short- and long-term benefits.43 It provides closure to families who often undergo daunting diagnostic tests, procedures, and consultations, thus ending the diagnostic odyssey. Knowing a genetic etiology can influence family planning with accurate recurrence risks and can provide the opportunity to access condition-specific resources and support groups where families often support each other, share their experiences and learn from the experiences of others, and may contribute to gene-specific research, including development of therapeutics tailored toward the molecular abnormality. Finding a genetic etiology may also have direct implications in medical management (precision health) as demonstrated by the 36.8% of individuals with P/LP variants across studies who benefited from genomic-informed changes in their clinical management, including surveillance of gene-related conditions (eg, acute myeloid leukemia associated with DNMT3A44) and genomics-informed interventions (eg, deep brain stimulation in cases with GNAO1 variants45 and high-dose riboflavin in those with SLC52A2 variants46).

In current clinical practice, individuals with CP may not be offered genetic testing unless they have a comorbid NDD, such as ID/DD, for which ES is recommended as standard of care. Waiting for an additional NDD diagnosis to consider genetic testing in CP is a missed opportunity to improve clinical outcomes, as CP can be diagnosed earlier than ID or ASD and testing sooner could result in a more timely genetic diagnosis allowing early interventions with the potential to have a positive impact on health outcomes. Furthermore, our findings showed a diagnostic yield of 17.6% among individuals with CP who do not have ID/DD and this subgroup of patients could be precluded from a genetic diagnosis. Similarly, individuals with CP risk factors, such as prematurity, low birth weight, or perinatal asphyxia, are also less likely to have genetic testing. Two of the studies included in the meta-analysis addressed the variation in diagnostic yield according to the presence of CP risk factors, which ranged from 8% to 16% in individuals with risk factors, and 14% to 48% in those without risk factors.26,31 These results demonstrate that restricting genetic testing to individuals without risk factors is another missed opportunity to identify a genetic diagnosis in a significant proportion of individuals with CP.

Limitations

Due to the nature of meta-analyses, our study has some limitations. First, the difference in ascertainment methods and cohort characteristics among the included studies resulted in high heterogeneity, which decreased in the subgroup analyses of studies performing exclusion criteria for patient selection; however, it remained high for studies of unselected cohorts, likely because of variable rates of comorbid NDDs and other CP risk factors. Second, 5 studies did not follow international consensus criteria for CP diagnosis and their diagnostic yields may have included individuals that do not fit the current CP definition. Third, we did not include variants identified by genetic platforms other than ES or GS, such as gene panels, resulting in a smaller sample size and a more conservative diagnostic yield. Fourth, the diagnostic yield was likely underestimated for the following reasons: 9 studies did not perform CNV calling, trio analysis was not available for all cohorts, and our analyses were restricted to P/LP variants and excluded VUS or novel variants, some of which will likely be reclassified as P/LP once additional studies and evidence of pathogenicity becomes available. Therefore, the genetic contribution to the etiology of CP is likely greater than what we report in this study. Fifth, we could not evaluate the effect of imaging findings on the diagnostic yield.

Conclusions

Our finding of a high genetic diagnostic yield in CP supports the inclusion of CP in the current recommendation of ES as a first-tier test for individuals with NDDs, regardless of their comorbidities or risk factors. We expect that continued identification of monogenic forms of CP will drive the understanding of its pathophysiology and that ongoing validation studies of rare genomic variants will further increase the current diagnostic yield.

Supplement 1.

eFigure 1. Subgroup analysis forest plot of diagnostic yield based on population age

eFigure 2. Subgroup analysis forest plot of diagnostic yield based on exclusion

criteria

eFigure 3. Subgroup analysis forest plot of diagnostic yield based on comorbid

ID/DD

eTable 1. Search strategy

eTable 2. Publication bias for proportion rate

eTable 3: Most frequently mutated genes across studies

eTable 4: Genomic-driven interventions reported in individuals with P/LP variants

Click here for additional data file. (2.3MB, pdf)
Supplement 2.

Data sharing statement

Click here for additional data file. (11.4KB, pdf)

References

  • 1.Moreno-De-Luca A, Myers SM, Challman TD, Moreno-De-Luca D, Evans DW, Ledbetter DH. Developmental brain dysfunction: revival and expansion of old concepts based on new genetic evidence. Lancet Neurol. 2013;12(4):406-414. doi: 10.1016/S1474-4422(13)70011-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Rosenbaum P, Paneth N, Leviton A, et al. A report: the definition and classification of cerebral palsy April 2006. Dev Med Child Neurol Suppl. 2007;109(s109):8-14. doi: 10.1111/j.1469-8749.2007.tb12610.x [DOI] [PubMed] [Google Scholar]
  • 3.Smithers-Sheedy H, Badawi N, Blair E, et al. What constitutes cerebral palsy in the twenty-first century? Dev Med Child Neurol. 2014;56(4):323-328. doi: 10.1111/dmcn.12262 [DOI] [PubMed] [Google Scholar]
  • 4.Oskoui M, Coutinho F, Dykeman J, Jetté N, Pringsheim T. An update on the prevalence of cerebral palsy: a systematic review and meta-analysis. Dev Med Child Neurol. 2013;55(6):509-519. doi: 10.1111/dmcn.12080 [DOI] [PubMed] [Google Scholar]
  • 5.McGuire DO, Tian LH, Yeargin-Allsopp M, Dowling NF, Christensen DL. Prevalence of cerebral palsy, intellectual disability, hearing loss, and blindness, National Health Interview Survey, 2009-2016. Disabil Health J. 2019;12(3):443-451. doi: 10.1016/j.dhjo.2019.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Reid SM, Meehan EM, Arnup SJ, Reddihough DS. Intellectual disability in cerebral palsy: a population-based retrospective study. Dev Med Child Neurol. 2018;60(7):687-694. doi: 10.1111/dmcn.13773 [DOI] [PubMed] [Google Scholar]
  • 7.Hollung SJ, Bakken IJ, Vik T, et al. Comorbidities in cerebral palsy: a patient registry study. Dev Med Child Neurol. 2020;62(1):97-103. doi: 10.1111/dmcn.14307 [DOI] [PubMed] [Google Scholar]
  • 8.Delobel-Ayoub M, Klapouszczak D, van Bakel MME, et al. Prevalence and characteristics of autism spectrum disorders in children with cerebral palsy. Dev Med Child Neurol. 2017;59(7):738-742. doi: 10.1111/dmcn.13436 [DOI] [PubMed] [Google Scholar]
  • 9.Mei C, Reilly S, Bickerton M, et al. Speech in children with cerebral palsy. Dev Med Child Neurol. 2020;62(12):1374-1382. doi: 10.1111/dmcn.14592 [DOI] [PubMed] [Google Scholar]
  • 10.Korzeniewski SJ, Slaughter J, Lenski M, Haak P, Paneth N. The complex aetiology of cerebral palsy. Nat Rev Neurol. 2018;14(9):528-543. doi: 10.1038/s41582-018-0043-6 [DOI] [PubMed] [Google Scholar]
  • 11.Nelson KB. Causative factors in cerebral palsy. Clin Obstet Gynecol. 2008;51(4):749-762. doi: 10.1097/GRF.0b013e318187087c [DOI] [PubMed] [Google Scholar]
  • 12.Ellenberg JH, Nelson KB. The association of cerebral palsy with birth asphyxia: a definitional quagmire. Dev Med Child Neurol. 2013;55(3):210-216. doi: 10.1111/dmcn.12016 [DOI] [PubMed] [Google Scholar]
  • 13.Moreno-De-Luca A, Ledbetter DH, Martin CL. Genetic insights into the causes and classification of the cerebral palsies. Lancet Neurol. 2012;11(3):283-292. doi: 10.1016/S1474-4422(11)70287-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.McMichael G, Girirajan S, Moreno-De-Luca A, et al. Rare copy number variation in cerebral palsy. Eur J Hum Genet. 2014;22(1):40-45. doi: 10.1038/ejhg.2013.93 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Segel R, Ben-Pazi H, Zeligson S, et al. Copy number variations in cryptogenic cerebral palsy. Neurology. 2015;84(16):1660-1668. doi: 10.1212/WNL.0000000000001494 [DOI] [PubMed] [Google Scholar]
  • 16.Oskoui M, Gazzellone MJ, Thiruvahindrapuram B, et al. Clinically relevant copy number variations detected in cerebral palsy. Nat Commun. 2015;6:7949. doi: 10.1038/ncomms8949 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Zarrei M, Fehlings DL, Mawjee K, et al. De novo and rare inherited copy-number variations in the hemiplegic form of cerebral palsy. Genet Med. 2018;20(2):172-180. doi: 10.1038/gim.2017.83 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Yechieli M, Gulsuner S, Ben-Pazi H, et al. Diagnostic yield of chromosomal microarray and trio whole exome sequencing in cryptogenic cerebral palsy. J Med Genet. 2022;59(8):759-767. doi: 10.1136/jmedgenet-2021-107884 [DOI] [PubMed] [Google Scholar]
  • 19.McMichael G, Bainbridge MN, Haan E, et al. Whole-exome sequencing points to considerable genetic heterogeneity of cerebral palsy. Mol Psychiatry. 2015;20(2):176-182. doi: 10.1038/mp.2014.189 [DOI] [PubMed] [Google Scholar]
  • 20.Takezawa Y, Kikuchi A, Haginoya K, et al. Genomic analysis identifies masqueraders of full-term cerebral palsy. Ann Clin Transl Neurol. 2018;5(5):538-551. doi: 10.1002/acn3.551 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Matthews AM, Blydt-Hansen I, Al-Jabri B, et al. ; TIDE BC, United for Metabolic Diseases and the CAUSES Study . Atypical cerebral palsy: genomics analysis enables precision medicine. Genet Med. 2019;21(7):1621-1628. doi: 10.1038/s41436-018-0376-y [DOI] [PubMed] [Google Scholar]
  • 22.Rosello M, Caro-Llopis A, Orellana C, et al. Hidden etiology of cerebral palsy: genetic and clinical heterogeneity and efficient diagnosis by next-generation sequencing. Pediatr Res. 2021;90(2):284-288. doi: 10.1038/s41390-020-01250-3 [DOI] [PubMed] [Google Scholar]
  • 23.Jin SC, Lewis SA, Bakhtiari S, et al. Mutations disrupting neuritogenesis genes confer risk for cerebral palsy. Nat Genet. 2020;52(10):1046-1056. doi: 10.1038/s41588-020-0695-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.van Eyk CL, Webber DL, Minoche AE, et al. Yield of clinically reportable genetic variants in unselected cerebral palsy by whole genome sequencing. NPJ Genom Med. 2021;6(1):74. doi: 10.1038/s41525-021-00238-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Moreno-De-Luca A, Millan F, Pesacreta DR, et al. Molecular diagnostic yield of exome sequencing in patients with cerebral palsy. JAMA. 2021;325(5):467-475. doi: 10.1001/jama.2020.26148 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.May HJ, Fasheun JA, Bain JM, et al. ; New York Presbyterian Hospital/Columbia University Irving Medical Center Genomics Team . Genetic testing in individuals with cerebral palsy. Dev Med Child Neurol. 2021;63(12):1448-1455. doi: 10.1111/dmcn.14948 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Li N, Zhou P, Tang H, et al. In-depth analysis reveals complex molecular aetiology in a cohort of idiopathic cerebral palsy. Brain. 2022;145(1):119-141. doi: 10.1093/brain/awab209 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nejabat M, Inaloo S, Sheshdeh AT, et al. Genetic testing in various neurodevelopmental disorders which manifest as cerebral palsy: a case study from Iran. Front Pediatr. 2021;9:734946. doi: 10.3389/fped.2021.734946 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Al Zahrani H, Siriwardena K, Young D, Lehman A, Horvath GA, Goez H. Genomics in cerebral palsy phenotype across the lifespan: comparison of diagnostic yield between children and adult population. Mol Genet Metab. 2022;137(4):420-427. doi: 10.1016/j.ymgme.2021.07.007 [DOI] [PubMed] [Google Scholar]
  • 30.Chopra M, Gable DL, Love-Nichols J, et al. Mendelian etiologies identified with whole exome sequencing in cerebral palsy. Ann Clin Transl Neurol. 2022;9(2):193-205. doi: 10.1002/acn3.51506 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Mei H, Yang L, Xiao T, et al. Genetic spectrum identified by exome sequencing in a Chinese pediatric cerebral palsy cohort. J Pediatr. 2022;242:206-212.e6. doi: 10.1016/j.jpeds.2021.11.019 [DOI] [PubMed] [Google Scholar]
  • 32.Srivastava S, Love-Nichols JA, Dies KA, et al. ; NDD Exome Scoping Review Work Group . Correction: Meta-analysis and multidisciplinary consensus statement: exome sequencing is a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders. Genet Med. 2020;22(10):1731-1732. doi: 10.1038/s41436-020-0913-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Manickam K, McClain MR, Demmer LA, et al. ; ACMG Board of Directors . Exome and genome sequencing for pediatric patients with congenital anomalies or intellectual disability: an evidence-based clinical guideline of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2021;23(11):2029-2037. doi: 10.1038/s41436-021-01242-6 [DOI] [PubMed] [Google Scholar]
  • 34.RStudio Team . RStudio: integrated development for R. Accessed January 30, 2023. https://www.rstudio.com/
  • 35.Balduzzi S, Rücker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health. 2019;22(4):153-160. doi: 10.1136/ebmental-2019-300117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Viechtbauer W. Conducting meta-analyses in R with the metafor package. JSS Journal of Statistical Software. 2010;36(3):1-48. doi: 10.18637/jss.v036.i03 [DOI] [Google Scholar]
  • 37.Corbett MA, van Eyk CL, Webber DL, et al. Pathogenic copy number variants that affect gene expression contribute to genomic burden in cerebral palsy. NPJ Genom Med. 2018;3:33. doi: 10.1038/s41525-018-0073-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Zouvelou V, Yubero D, Apostolakopoulou L, et al. The genetic etiology in cerebral palsy mimics: The results from a Greek tertiary care center. Eur J Paediatr Neurol. 2019;23(3):427-437. doi: 10.1016/j.ejpn.2019.02.001 [DOI] [PubMed] [Google Scholar]
  • 39.Richards S, Aziz N, Bale S, et al. ; ACMG Laboratory Quality Assurance Committee . 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 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Savatt JM, Myers SM. Genetic testing in neurodevelopmental disorders. Front Pediatr. 2021;9:526779. doi: 10.3389/fped.2021.526779 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet. 2014;30(9):418-426. doi: 10.1016/j.tig.2014.07.001 [DOI] [PubMed] [Google Scholar]
  • 42.Ji J, Leung ML, Baker S, Deignan JL, Santani A. Clinical exome reanalysis: current practice and beyond. Mol Diagn Ther. 2021;25(5):529-536. doi: 10.1007/s40291-021-00541-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Elliott AM, Guimond C. Genetic counseling considerations in cerebral palsy. Mol Genet Metab. 2022;137(4):428-435. doi: 10.1016/j.ymgme.2021.07.004 [DOI] [PubMed] [Google Scholar]
  • 44.DiNardo CD, Beird HC, Estecio M, et al. Germline DNMT3A mutation in familial acute myeloid leukaemia. Epigenetics. 2021;16(5):567-576. doi: 10.1080/15592294.2020.1809871 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Waak M, Mohammad SS, Coman D, et al. GNAO1-related movement disorder with life-threatening exacerbations: movement phenomenology and response to DBS. J Neurol Neurosurg Psychiatry. 2018;89(2):221-222. doi: 10.1136/jnnp-2017-315653 [DOI] [PubMed] [Google Scholar]
  • 46.Foley AR, Menezes MP, Pandraud A, et al. Treatable childhood neuronopathy caused by mutations in riboflavin transporter RFVT2. Brain. 2014;137(Pt 1):44-56. doi: 10.1093/brain/awt315 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eFigure 1. Subgroup analysis forest plot of diagnostic yield based on population age

eFigure 2. Subgroup analysis forest plot of diagnostic yield based on exclusion

criteria

eFigure 3. Subgroup analysis forest plot of diagnostic yield based on comorbid

ID/DD

eTable 1. Search strategy

eTable 2. Publication bias for proportion rate

eTable 3: Most frequently mutated genes across studies

eTable 4: Genomic-driven interventions reported in individuals with P/LP variants

Click here for additional data file. (2.3MB, pdf)
Supplement 2.

Data sharing statement

Click here for additional data file. (11.4KB, pdf)

Articles from JAMA Pediatrics are provided here courtesy of American Medical Association

RESOURCES