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. 2012 Aug;49(8):502-12.
doi: 10.1136/jmedgenet-2012-100875. Epub 2012 Jul 7.

Targeted High-Throughput Sequencing for Diagnosis of Genetically Heterogeneous Diseases: Efficient Mutation Detection in Bardet-Biedl and Alström Syndromes

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Free PMC article

Targeted High-Throughput Sequencing for Diagnosis of Genetically Heterogeneous Diseases: Efficient Mutation Detection in Bardet-Biedl and Alström Syndromes

Claire Redin et al. J Med Genet. .
Free PMC article

Abstract

Background: Bardet-Biedl syndrome (BBS) is a pleiotropic recessive disorder that belongs to the rapidly growing family of ciliopathies. It shares phenotypic traits with other ciliopathies, such as Alström syndrome (ALMS), nephronophthisis (NPHP) or Joubert syndrome. BBS mutations have been detected in 16 different genes (BBS1-BBS16) without clear genotype-to-phenotype correlation. This extensive genetic heterogeneity is a major concern for molecular diagnosis and genetic counselling. While various strategies have been recently proposed to optimise mutation detection, they either fail to detect mutations in a majority of patients or are time consuming and costly.

Method: We tested a targeted exon-capture strategy coupled with multiplexing and high-throughput sequencing on 52 patients: 14 with known mutations as proof-of-principle and 38 with no previously detected mutation. Thirty genes were targeted in total including the 16 BBS genes, the 12 known NPHP genes, the single ALMS gene ALMS1 and the proposed modifier CCDC28B.

Results: This strategy allowed the reliable detection of causative mutations (including homozygous/heterozygous exon deletions) in 68% of BBS patients without previous molecular diagnosis and in all proof-of-principle samples. Three probands carried homozygous truncating mutations in ALMS1 confirming the major phenotypic overlap between both disorders. The efficiency of detecting mutations in patients was positively correlated with their compliance with the classical BBS phenotype (mutations were identified in 81% of 'classical' BBS patients) suggesting that only a few true BBS genes remain to be identified. We illustrate some interpretation problems encountered due to the multiplicity of identified variants.

Conclusion: This strategy is highly efficient and cost effective for diseases with high genetic heterogeneity, and guarantees a quality of coverage in coding sequences of target genes suited for diagnosis purposes.

Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Global flowchart of the bioinformatic pipeline implemented for mutation detection. Software acronyms: BWA, Burrows-Wheeler Aligner; SVA, Sequence Variant Analyser; SIFT; Polyphen2; HSF, Human Splicing Finder; MaxEntScan, Maximum Entropy Scanning; NNSplice; Mutation Taster; IGV, Integrative Genome Viewer.
Figure 2
Figure 2
Detection of large deletions in three patients using a depth-of-coverage method. Black peaks: normalized depth of coverage from patients' DNA samples. Empty peaks: normalized mean depth of coverage across samples from the same sequencing lane. Grey squares: bait-covered regions. Black peaks: normalized depth of coverage from patients' DNA samples. Empty peaks: normalized mean depth of coverage across samples from the same sequencing lane. Grey squares: bait-covered regions. Highlighted squares: deleted regions. Gene representation: black squares: exons, dashed lines: introns. Genomic positions are given according to the human reference genome hg19/ GRCh37. (A) Heterozygous deletion of BBS1 (exon #10, 11) in AMV5 patient. Corresponding Log2 ratios between both depths of coverage (normalised mean and AMV5 patient) further highlight the presence of the deletion. (B) Homozygous deletion of BBS3 (exon #1, 2a, 2b, 3) in ALG42 patient. (C) Homozygous deletion of BBS4 (exon #4, 5, 6) in P3 patient. (A and C): targeting intronic sequences allows restricting the deletion breakpoints. (B) and C): Log2 ratios between both depths of coverage (normalised mean and corresponding patients) could also allow detecting both deletions but are not shown (supplementary figure S2).
Figure 3
Figure 3
Compliance with classical BBS phenotype is positively correlated to the efficiency to detect principal mutations in BBS genes. (A) The number of BBS diagnostic major inclusion criteria in patients is correlated to an efficient detection of BBS mutations. (B) Efficiency of detecting mutation in patients fulfilling BBS the phenotypic inclusion criteria or not. BBS inclusion criteria presenting with three major features plus at least two minors, or presenting with four major features and more. Primary criteria include: rod-cone dystrophy, polydactyly, obesity, learning disabilities, hypogonadism and renal anomalies. Secondary features comprise speech delay, other eye anomalies, brachydactyly or syndactyly, ataxia, diabetes, developmental delay, dental anomalies, cardiac anomalies and hepatic fibrosis.

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