Toward 3D facial analysis for recognizing Mendelian causes of autism spectrum disorder.
Mendelian cause
autism
dysmorphology
facial asymmetry
facial dysmorphism
facial signature
Journal
Clinical genetics
ISSN: 1399-0004
Titre abrégé: Clin Genet
Pays: Denmark
ID NLM: 0253664
Informations de publication
Date de publication:
26 Jul 2024
26 Jul 2024
Historique:
revised:
12
07
2024
received:
28
04
2024
accepted:
13
07
2024
medline:
26
7
2024
pubmed:
26
7
2024
entrez:
26
7
2024
Statut:
aheadofprint
Résumé
Recognizing Mendelian causes is crucial in molecular diagnostics and counseling for patients with autism spectrum disorder (ASD). We explored facial dysmorphism and facial asymmetry in relation to genetic causes in ASD patients and studied the potential of objective facial phenotyping in discriminating between Mendelian and multifactorial ASD. In a cohort of 152 ASD patients, 3D facial images were used to calculate three metrics: a computational dysmorphism score, a computational asymmetry score, and an expert dysmorphism score. High scores for each of the three metrics were associated with Mendelian causes of ASD. The computational dysmorphism score showed a significant correlation with the average expert dysmorphism score. However, in some patients, different dysmorphism aspects were captured making the metrics potentially complementary. The computational dysmorphism and asymmetry scores both enhanced the individual expert dysmorphism scores in differentiating Mendelian from non-Mendelian cases. Furthermore, the computational asymmetry score enhanced the average expert opinion in predicting a Mendelian cause. By design, our study does not allow to draw conclusions on the actual point-of-care use of 3D facial analysis. Nevertheless, 3D morphometric analysis is promising for developing clinical dysmorphology applications in diagnostics and training.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Références
Roesser J. Diagnostic yield of genetic testing in children diagnosed with autism spectrum disorders at a regional referral center. Clin Pediatr (Phila). 2011;50:834‐843.
Eriksson MA, Liedén A, Westerlund J, et al. Rare copy number variants are common in young children with autism spectrum disorder. Acta Paediatr. 2015;104:610‐618.
Tammimies K, Marshall CR, Walker S, et al. Molecular diagnostic yield of chromosomal microarray analysis and whole‐exome sequencing in children with autism spectrum disorder. JAMA. 2015;314:595‐903.
Savatt JM, Myers SM. Genetic testing in neurodevelopmental disorders. Front Pediatr. 2021;9:526779.
Bernier R, Golzio C, Xiong B, et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell. 2014;158:263‐276.
Revah‐Politi A, Ganapathi M, Bier L, et al. Loss‐of‐function variants in NFIA provide further support that NFIA is a critical gene in 1p32‐p31 deletion syndrome: a four patient series. Am J Med Genet A. 2017;173:3158‐3164.
Eliyahu A, Barel O, Greenbaum L, et al. Refining the phenotypic spectrum of KMT5B‐associated developmental delay. Front Pediatr. 2022;10:844845.
Marzin P, Rondeau S, Aldinger KA, et al. SETD2 related overgrowth syndrome: presentation of four new patients and review of the literature. Am J Med Genet C Semin Med Genet. 2019;181:509‐518.
O'Donnell‐Luria AH, Pais LS, Faundes V, et al. Heterozygous variants in KMT2E cause a spectrum of neurodevelopmental disorders and epilepsy. Am J Hum Genet. 2019;104:1210‐1222.
Hennekam RC, Biesecker LG, Allanson JE, et al. Elements of morphology: general terms for congenital anomalies. Am J Med Genet A. 2013;161:2726‐2733.
Boutrus M, Maybery MT, Alvares GA, Tan DW, Varcin KJ, Whitehouse AJO. Investigating facial phenotype in autism spectrum conditions: the importance of a hypothesis driven approach. Autism Res. 2017;10:1910‐1918.
Aldridge K, George ID, Cole KK, et al. Facial phenotypes in subgroups of prepubertal boys with autism spectrum disorders are correlated with clinical phenotypes. Mol Autism. 2011;2:15.
Ozgen H, Hellemann GS, Stellato RK, et al. Morphological features in children with autism spectrum disorders: a matched case‐control study. J Autism Dev Disord. 2011;41:23‐31.
Cheung C, McAlonan GM, Fung YY, et al. MRI study of minor physical anomaly in childhood autism implicates aberrant neurodevelopment in infancy. PLoS One. 2011;6:e20246.
Hammond P, Forster‐Gibson C, Chudley A, et al. Face–brain asymmetry in autism spectrum disorders. Mol Psychiatry. 2008;13:614‐623.
Ozgen H, Hellemann GS, De Jonge MV, Beemer FA, Van Engeland H. Predictive value of morphological features in patients with autism versus normal controls. J Autism Dev Disord. 2013;43:147‐155.
Boutrus M, Gilani SZ, Alvares GA, et al. Increased facial asymmetry in autism spectrum conditions is associated with symptom presentation. Autism Res. 2019;12:1774‐1783.
Hammond P, Suttie M. Large‐scale objective phenotyping of 3D facial morphology. Hum Mutat. 2012;33:817‐825.
Matthews HS, Palmer RL, Baynam GS, et al. Large‐scale open‐source three‐dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism. Sci Rep. 2021;11:12175.
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159‐174.
Ekrami O, Claes P, White JD, Zaidi AA, Shriver MD, Van Dongen S. Measuring asymmetry from high‐density 3D surface scans: an application to human faces. PLoS One. 2018;13:e0207895.
Klingenberg CP, Barluenga M, Meyer A. Shape analysis of symmetric structures: quantifying variation among individuals and asymmetry. Evolution (N Y). 2002;56:1909‐1920.
Ruscio J, Mullen T. Confidence intervals for the probability of superiority effect size measure and the area under a receiver operating characteristic curve. Multivar Behav Res. 2012;47:201‐223.
Lumaka A, Cosemans N, Lulebo Mampasi A, et al. Facial dysmorphism is influenced by ethnic background of the patient and of the evaluator. Clin Genet. 2017;92:166‐171.
Shang L, Cho MT, Retterer K, et al. Mutations in ARID2 are associated with intellectual disabilities. Neurogenetics. 2015;16:307‐314.
Helsmoortel C, Vulto‐Van Silfhout AT, Coe BP, et al. A SWI/SNF‐related autism syndrome caused by de novo mutations in ADNP. Nat Genet. 2014;46:380‐384.
Lozano R, Gbekie C, Siper PM, et al. FOXP1 syndrome: a review of the literature and practice parameters for medical assessment and monitoring. J Neurodev Disord. 2021;13:18.
Jansen S, Hoischen A, Coe BP, et al. A genotype‐first approach identifies an intellectual disability‐overweight syndrome caused by PHIP haploinsufficiency. Eur J Hum Genet. 2018;26:54‐63.
Willemsen MH, Nijhof B, Fenckova M, et al. GATAD2B loss‐of‐function mutations cause a recognisable syndrome with intellectual disability and are associated with learning deficits and synaptic undergrowth in Drosophila. J Med Genet. 2013;50:507‐514.
Tan DW, Gilani SZ, Boutrus M, et al. Facial asymmetry in parents of children on the autism spectrum. Autism Res. 2021;14:2260‐2269.
Ferry Q, Steinberg J, Webber C, et al. Diagnostically relevant facial gestalt information from ordinary photos. elife. 2014;3:e02020.
van der Donk R, Jansen S, Schuurs‐Hoeijmakers JHM, et al. Next‐generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders. Genet Med. 2019;21:1719‐1725.
Dudding‐Byth T, Baxter A, Holliday EG, et al. Computer face‐matching technology using two‐dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability. BMC Biotechnol. 2017;17:90.
Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60‐64.
Hsieh TC, Bar‐Haim A, Moosa S, et al. GestaltMatcher facilitates rare disease matching using facial phenotype descriptors. Nat Genet. 2022;54:349‐357.