Whole exome sequencing combined with integrated variant annotation prediction identifies asymptomatic XXXX with compound heterozygous mutations in @entity21123 gene.

@entity71821 @entity8811 @entity71819 @entity71816 @entity71823 @entity21123 @entity28538 @entity74 @entity71822 @entity71817 @entity71818 @entity71820
OBJECTIVE: Molecular diagnosis for subjects with extremely low HDL-C through candidate-gene approaches requires huge effort. Whole exome-sequencing (WES) has already shown approximately    30% success in the diagnosis of @entity74 . Moreover, novel in silico prediction software for the pathogenicity of novel missense variants named Combined Annotation Dependent Depletion (CADD) has recently been developed, enabling the objective integration of many diverse annotations into a single measure (C-score) for each variant. Here, we investigated whether WES combined with integrated variant annotation prediction could facilitate the molecular diagnosis of this rare condition. METHODS: WES was performed on 8 individuals including 2 individuals exhibiting extremely low HDL-C (2  mg/dl and 6  mg/dl), 2 unaffected family members, and 4 unrelated individuals as controls. We filtered out the following variants: 1) Benign variants predicted by SnpEff; 2) @entity28538 ) Segregation unmatched for the recessive form of inheritance; 4) C-score  <  10. RESULTS: Among 305,202 variants found in those individuals, we found 21,708 nonsense, missense, or splice site variants, of which 5192 were rare (MAF   <=  1% or not reported). Filtering assuming a recessive pattern of inheritance, combined with the use of the C-score, successfully narrowed down the candidates to compound heterozygous mutations in the @entity21123 gene ( @entity71816 or @entity71817 / @entity71818 or @entity71819 , and @entity71820 or @entity71821 / @entity71822 or @entity71823 ). CONCLUSIONS: WES combined with integrated variant annotation prediction successfully identified asymptomatic @entity8811 with novel @entity21123 mutations. This comprehensive approach is useful to determine causative variants, especially in @entity74 .
