Target gene analyses of 39 amelogenesis imperfecta kindreds

<jats:p><jats:italic>Chan H‐C, Estrella NMRP, Milkovich RN, Kim J‐W, Simmer JP, Hu JC‐C. Target gene analyses of 39 amelogenesis imperfecta kindreds. 
Eur J Oral Sci 2011; 119 (Suppl. 1): 311–323. © 2011 Eur J Oral Sci</jats:italic></jats:p><jats:p>Previously, mutational analyses identified six disease‐causing mutations in 24 amelogenesis imperfecta (AI) kindreds. We have since expanded the number of AI kindreds to 39, and performed mutation analyses covering the coding exons and adjoining intron sequences for the six proven AI candidate genes [amelogenin (<jats:italic>AMELX</jats:italic>), enamelin (<jats:italic>ENAM</jats:italic>), family with sequence similarity 83, member H (<jats:italic>FAM83H</jats:italic>), WD repeat containing domain 72 (<jats:italic>WDR72</jats:italic>), enamelysin (<jats:italic>MMP20</jats:italic>), and kallikrein‐related peptidase 4 (<jats:italic>KLK4</jats:italic>)] and for ameloblastin (<jats:italic>AMBN</jats:italic>) (a suspected candidate gene). All four of the X‐linked AI families (100%) had disease‐causing mutations in <jats:italic>AMELX</jats:italic>, suggesting that <jats:italic>AMELX</jats:italic> is the only gene involved in the aetiology of X‐linked AI. Eighteen families showed an autosomal‐dominant pattern of inheritance. Disease‐causing mutations were identified in 12 (67%): eight in <jats:italic>FAM83H</jats:italic>, and four in <jats:italic>ENAM</jats:italic>. No <jats:italic>FAM83H</jats:italic> coding‐region or splice‐junction mutations were identified in three probands with autosomal‐dominant hypocalcification AI (ADHCAI), suggesting that a second gene may contribute to the aetiology of ADHCAI. Six families showed an autosomal‐recessive pattern of inheritance, and disease‐causing mutations were identified in three (50%): two in <jats:italic>MMP20</jats:italic>, and one in <jats:italic>WDR72</jats:italic>. No disease‐causing mutations were found in 11 families with only one affected member. We conclude that mutation analyses of the current candidate genes for AI have about a 50% chance of identifying the disease‐causing mutation in a given kindred.</jats:p>