Once diagnosed with Fanconi anemia (FA), identification of the causative gene and underlying mutations is an arduous task. The current screening process is a sequential, multi-step approach and, thus, is time consuming and expensive to perform. FA genes are large, with multiple exons, and harbor a wide spectrum of compound heterozygous mutations spread throughout the gene including large genomic deletions. Therefore, there is a need for an efficient approach that scans the entire length of all the FA genes, and detects wide spectrum of changes. The massively parallel sequencing technologies allow for rapid sequencing of large (megabase) regions of the genome. Recent and ongoing advances in DNA sequencing and genomic capture have allowed us to target all known FA genes in a single sequencing reaction for 27 individuals with no known mutations. We evaluated three different capture technologies: MIP (Molecular Inversion Probes), WES (Whole Exome Sequencing), and TruSeq (targeted TruSeq capture). We have identified mutations in FA genes in 27/27 individuals: FANCA (7), FANCB (3), FANCC (3), FANCD1 (1), FANCD2 (3), FANCF (2), FANCG (2), FANCI (1), FANCJ (2) and FANCL (3). To achieve complete identification, we also applied RNA sequencing technologies (identifying a number of alternate splicing events associated with variants) as well as array CGH (identifying large-size copy number variants). The former helped identify splicing defects associated with variants deep in introns in one FANCI and two FANCL families and lack of allele specific expression in a FANCC family with deletion of a non-coding exon, whereas the latter methodology identified deletions in three FANCA, one FANCC, one FANCD2, and a duplication in a FANCB family. The use of complementary technologies allowed for the high degree of success, and suggests these technologies will prove valuable for a priori mutation discovery. The capture technologies have improved over time, resulting in lower costs, higher sensitivity, and increased throughput. Additionally, these technological improvements have allowed better genotype coverage across the FANCD2 gene, which has proved difficult to sequence due to the presence of pseudogenes. Massively-parallel sequencing technologies have resulted in the identification of underlying disease mutations in an ever increasing number of genetic disorders, and we have shown here that it is an effective approach to identify variations underlying a highly genetically heterogeneous disorder. Though FA patients can carry mutations in any of the 15 known genes, about two-thirds are affected by mutations in FANCA gene. Thus, for all FA individuals with no assigned complementation group, checking for FANCA mutations by Sanger sequencing method will serve as an efficient initial step. We sequenced DNA from 195 patients that included 88 with prior assignment to FANCA complementation group. We also did array CGH to identify large deletions. We were able identify both FANCA mutations in 153 patients, only one mutation in 16, and none for 26. The mutations we identified included 28 novel missense variations. Pathogenic consequences of these variants were evaluated by mutagenesis studies, and all but five were found to be pathogenic. Array CGH identified 62 deletions accounting for 20% of the mutations. Further analysis of the nonFANCA (26) group by nextgen methodologies is ongoing. We have analyzed a larger number of Patient DNA samples (198) using array CGH methodologies designed to scan all FA genes for deletions and duplications. We found 90 deletions that included FANCA (80), FANCB (1), FANCC (7), FANCD2 (2), and one duplication in a FANCB patient. For even larger deletions, we employ high-density SNP chips, and these chips scan the entire genomic region and reveal any other chromosomal variations, in addition to deletions and duplications. For all deletion type mutations we are interested in the existence of common breakpoints and haplotypes associated with those mutations. Hence, an analysis of to determine the type and frequency of such is ongoing in collaboration with the Ostrander lab. In summary, our sequencing and arrayCGH efforts have resulted in identifying spectrum of FA genes and mutations: FANCA (169), FANCB (4), FANCC (9), FANCD1 (1), FANCD2 (5), FANCF (2), FANCG (2) FACNCI (3), FANCJ (2) and FANCL (3). The next generation sequencing, CGH array and SNP array technologies, along with the Sanger sequencing, will take us towards our goal of determining both the disease-causing mutations in all the FA samples in our collection (300). In addition to FA individuals, these technologies can be employed to explore the role of FA genes in susceptibility to pancreatic and other cancers.
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