2019
DOI: 10.1016/j.ajhg.2019.02.023
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De Novo and Inherited Pathogenic Variants in KDM3B Cause Intellectual Disability, Short Stature, and Facial Dysmorphism

Abstract: By using exome sequencing and a gene matching approach, we identified de novo and inherited pathogenic variants in KDM3B in 14 unrelated individuals and three affected parents with varying degrees of intellectual disability (ID) or developmental delay (DD) and short stature. The individuals share additional phenotypic features that include feeding difficulties in infancy, joint hypermobility, and characteristic facial features such as a wide mouth, a pointed chin, long ears, and a low columella. Notably, two i… Show more

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Cited by 38 publications
(36 citation statements)
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“…All nominated candidate genes were submitted to GeneMatcher. Six individuals were subsequently published within large collaborations connected through GeneMatcher and one individual was published as case report following two previous case descriptions, all together establishing six novel disease‐associated genes for NDDs, namely CYFIP2, KDM3B, IMPDH2, FITM2, RALGAPA1 , and VARS 28‐33 . Those seven individuals were considered as solved and assigned to the overall yield (Supplemental Figure 1A).…”
Section: Resultsmentioning
confidence: 99%
“…All nominated candidate genes were submitted to GeneMatcher. Six individuals were subsequently published within large collaborations connected through GeneMatcher and one individual was published as case report following two previous case descriptions, all together establishing six novel disease‐associated genes for NDDs, namely CYFIP2, KDM3B, IMPDH2, FITM2, RALGAPA1 , and VARS 28‐33 . Those seven individuals were considered as solved and assigned to the overall yield (Supplemental Figure 1A).…”
Section: Resultsmentioning
confidence: 99%
“…Clinical photographs of individuals' faces were analyzed according to the hybrid model reported previously. 26,27 This model combines two algorithms (OpenFace 28 and Clinical Face Phenotype Space 29 ) used for facial recognition to create a 468-dimensional vector of the facial features of a given individual. These vectors are used for calculating the clustering impact factor (CIF) of a patient group.…”
Section: Quantitative Facial Phenotypingmentioning
confidence: 99%
“…Individuals' faces were analyzed from provided frontal photos using the hybrid model reported previously. 45,46 This model combines two algorithms used for facial recognition (OpenFace 47 and Clinical Face Phenotype Space 48 ) to create a 468-dimensional vector of an individuals' facial features. These vectors are used to calculate the clustering impact factor (CIF) of an analyzed group, which is a measurement of how a group of individuals cluster within a group of control subjects.…”
Section: Quantitative Facial Phenotypingmentioning
confidence: 99%