Background Over the past 5 years, exome sequencing and whole-genome sequencing have been extensively used
to identify genes underlying rare mendelian disorders. These techniques have accelerated not only discovery but also
false-positive reports of causality. To address this issue, we developed a statistical inference framework that evaluates
the strength of fi ndings from such studies. This method was applied to exome sequencing data from individuals with
a specifi c retinal dystrophy, aiming to elucidate the genetic basis of their visual impairment.
Methods 28 unrelated patients and 1917 controls with no retinal disease underwent exome sequencing. Patients had
a progressive retinal dystrophy phenotype with early cone photoreceptor involvement, absence of retinal fl ecks on
fundus autofl uorescence imaging, and an unknown molecular diagnosis. Genetic data from cases and controls were
analysed with the same bioinformatics pipeline. A gene-based case-control association study was then performed and
gene-based p values were derived.
Findings The initial analysis focused on rare, presumed loss-of-function variants; the most signifi cant binomial p value
(p=2 × 10–
⁵) was obtained for TTLL5. Closer inspection highlighted biallelic loss-of-function variants in this gene as
a probable cause of the studied retinal dystrophy. A second analysis using a recessive model (presence of ≥2 rare,
potentially functional variants) was then performed. The most signifi cant binomial p value (p=1 × 10–
⁴) was obtained for
DRAM2, highlighting mutations in this gene as another likely cause of this retinal dystrophy. Validation studies
identifi ed additional mutation-positive individuals (four with TTLL5-retinopathy and fi ve with DRAM2-retinopathy in
total). Overall, a molecular diagnosis was identifi ed in 15 of the 28 patients.
Interpretation Using a phenotype-driven cluster analysis, we have identifi ed two previously unreported diseaseassociated
genes. The success of our method highlights the key role of precise phenotyping in enhancing the utility
of genomic investigations. Additionally, we have described a robust genome-wide statistical framework for objectively
assigning probability of causation to new candidate genes and variants. This approach is broadly applicable to the
study of rare mendelian disorders. |
