PLoS ONE (Jan 2014)

Association of CHRDL1 mutations and variants with X-linked megalocornea, Neuhäuser syndrome and central corneal thickness.

  • Alice E Davidson,
  • Sek-Shir Cheong,
  • Pirro G Hysi,
  • Cristina Venturini,
  • Vincent Plagnol,
  • Jonathan B Ruddle,
  • Hala Ali,
  • Nicole Carnt,
  • Jessica C Gardner,
  • Hala Hassan,
  • Else Gade,
  • Lisa Kearns,
  • Anne Marie Jelsig,
  • Marie Restori,
  • Tom R Webb,
  • David Laws,
  • Michael Cosgrove,
  • Jens M Hertz,
  • Isabelle Russell-Eggitt,
  • Daniela T Pilz,
  • Christopher J Hammond,
  • Stephen J Tuft,
  • Alison J Hardcastle

DOI
https://doi.org/10.1371/journal.pone.0104163
Journal volume & issue
Vol. 9, no. 8
p. e104163

Abstract

Read online

We describe novel CHRDL1 mutations in ten families with X-linked megalocornea (MGC1). Our mutation-positive cohort enabled us to establish ultrasonography as a reliable clinical diagnostic tool to distinguish between MGC1 and primary congenital glaucoma (PCG). Megalocornea is also a feature of Neuhäuser or megalocornea-mental retardation (MMR) syndrome, a rare condition of unknown etiology. In a male patient diagnosed with MMR, we performed targeted and whole exome sequencing (WES) and identified a novel missense mutation in CHRDL1 that accounts for his MGC1 phenotype but not his non-ocular features. This finding suggests that MMR syndrome, in some cases, may be di- or multigenic. MGC1 patients have reduced central corneal thickness (CCT); however no X-linked loci have been associated with CCT, possibly because the majority of genome-wide association studies (GWAS) overlook the X-chromosome. We therefore explored whether variants on the X-chromosome are associated with CCT. We found rs149956316, in intron 6 of CHRDL1, to be the most significantly associated single nucleotide polymorphism (SNP) (p = 6.81×10(-6)) on the X-chromosome. However, this association was not replicated in a smaller subset of whole genome sequenced samples. This study highlights the importance of including X-chromosome SNP data in GWAS to identify potential loci associated with quantitative traits or disease risk.