FANCONI ANEMIA:GENOTYPE-PHENOTYPE CORRELATIONS

一旦被诊断患有范可尼贫血（FA），鉴定致病基因和突变是一项艰巨的任务。传统的筛选过程是顺序的多步骤方法，因此效率低且执行起来昂贵。FA基因是大的，具有多个外显子，并且具有广泛的复合杂合突变，其分布在整个基因中，包括大的基因组缺失。因此，在国际范可尼贫血登记处（IFAR）登记的800个家庭中，近一半的家庭的分子诊断仍然未知。 我们采用大规模平行测序技术对代表所有FA和相关DNA修复途径基因的基因组大区域（2Mb）进行测序。我们设计了比较基因组杂交阵列（aCGH）阵列，以探索同一组基因中的大尺寸拷贝数变异。 我们还采用RNAseq技术来确定导致异常剪接的不可疑变体的致病性。使用互补技术成功鉴定了43个个体的FA基因突变：FANCA（17），FANCB（4），FANCC（5），FANCD 1（1），FANCE（1），FANCD 2（3），FANCF（2），FANCG（2），FANCI（1），FANCJ（4）和FANCL（3）。 我们采用的策略是一种有效的方法，以确定潜在的高度遗传异质性疾病，如FA的变化，并确保及时和有效的分子诊断未来的登记。 虽然FA患者可以携带16种已知基因中的任何一种突变，但约三分之二的患者受到FANCA基因突变的影响。因此，对于所有FA个体，检查FANCA突变可以作为有效的初始步骤。在此之前，我们已经使用桑格测序方法对195例FA患者的DNA中FANCA编码区和剪接点进行了测序。今年，我们探索了下一代测序方法，Truseq定制扩增子，用于筛选来自58名患者的DNA中所有43个FANCA外显子沿着100 bp的相邻区域。设计了67个定制扩增子（长度为200 bp），它们靶向了总共14，642 bp，几乎覆盖了RefSeq FANCA转录物的整个长度（6191 bp中的6090个）。在捕获、测序和与参考基因组比对后，序列深度范围广泛地从204 - 6215（中值2762），除了四个外显子（1、6 - 7、15），其中深度小得多并且范围从20 - 78。在测序的58个DNA中，我们发现24个有两个FANCA突变，24个中有一个，10个中没有。Truseq定制扩增子允许一次有效评估大量DNA样品中的序列变异，并且读取深度（100 - 1000倍）应允许检测小比例患者DNA中存在的变异。 缺失导致FANCA中相当大比例的突变。作为鉴定入组IFAR的患者的所有致病突变的全面努力的一部分，我们使用包括比较基因组杂交阵列（aCGH）在内的高通量方法分析了202个FA家族的缺失和插入突变。该阵列包含135，000个50 mer探针，平均间隔37bp，跨越15个已知FA基因和12个其他功能相关基因的上游和下游200kb。 我们在98个家族中发现了缺失，包括88个FANCA，7个FANCC，2个FANCD 2和1个FANCB家族。通过aCGH确定的精确边界使得能够设计跨缺失区域的PCR测定，然后跨断点进行克隆和测序。发现52个FANCA缺失末端和1个FANCC缺失末端延伸超出基因边界，可能影响邻近基因。80%的FANCA缺失断裂点是α-Alu介导的，主要由α-Alu Y元件介导。在内含子21、17和5中鉴定了单独的Alu热点。定义多个家庭共享的四个FANCA缺失的单倍型显示，三个共享一个共同的祖先，并且都是最近的起源。我们现在使用MLPA检测FANCA外显子缺失的患者DNA样本量较小，因此不足以进行CGH分析。缺失的详细表征对于更好地理解FA表型至关重要。 总之，我们的测序和arrayCGH工作已经确定了超过230例患者的FA基因突变谱。我们的目标是全面编目IFAR中招募的所有患者的突变。

Once diagnosed with Fanconi anemia (FA), identification of the causative gene and the mutations is an arduous task. The conventional screening process is a sequential, multi-step approach and, thus, is inefficient 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. Thus, molecular diagnosis of nearly half of the 800 families enrolled in the International Fanconi Anemia Registry (IFAR) remained unknown. We employed the massively parallel sequencing technologies to sequence large (2Mb) regions of the genome representing all FA and related DNA-repair pathway genes. We designed Comparative Genome Hybridization arrays (aCGH) arrays to explore large-size copy number variants in the same set of genes. We also employed RNAseq technologies for determining the pathogenicity of unsuspecting variants resulting in aberrant splicing. The use of complementary technologies allowed for successful identification of mutations in FA genes in 43 individuals: FANCA (17), FANCB (4), FANCC (5), FANCD1 (1), FANCE (1), FANCD2 (3), FANCF (2), FANCG (2), FANCI (1), FANCJ (4) and FANCL (3). The strategy we employed was an effective approach to identify variations underlying a highly genetically heterogeneous disorder such as FA, and ensures a timely and efficient molecular diagnosis of future enrollees. Though FA patients can carry mutations in any of the 16 known genes, about two-thirds are affected by mutations in FANCA gene. Thus, for all FA individuals checking for FANCA mutations may serve as an efficient initial step. Earlier, we had used Sanger sequencing method to sequence FANCA coding region and splice junctions in DNA from 195 FA patients. This year, we explored a next-generation sequencing methodology, Truseq custom amplicon, for screening all the 43 FANCA exons along with 100bp of the adjacent regions in DNA from 58 patients. Sixty seven custom amplicons (200 bp in length) were designed, and they targeted a total of 14,642 bp that covered nearly the entire length of the RefSeq FANCA transcript (6090 of the 6191bp). Upon capturing, sequencing and aligning to the reference genome, the sequence depth ranged widely from 204 - 6215 (median 2762) except for four exons (1, 6-7, 15) where the depth was much less and ranged from 20 - 78. Of the 58 DNA sequenced, we found two FANCA mutations for 24, one in 24, and none in ten. The Truseq custom amplicon allows for an efficient evaluation of sequence variations in a large number of DNA samples at once, and the read depth (100s-1000s fold) should allow for detection of variants present in a small proportion of patient DNA. Deletions contribute to a substantial proportion of mutations in FANCA. As part of a comprehensive effort to identify all the disease-causing mutations for patients enrolled IFAR, we analyzed 202 FA families for deletion and insertion mutations using high throughput methods including Comparative Genome Hybridization arrays (aCGH). The arrays contained 135,000 50mer probes, spaced an average interval of 37bp, spanning up to 200kb upstream and downstream of the 15 known FA genes and 12 other functionally relevant genes. We found deletions in 98 families consisting of 88 FANCA, seven FANCC, two FANCD2, and one FANCB families. The precise boundaries identified by aCGH enabled design of PCR assays across the deleted regions, followed by cloning and sequencing across the breakpoints. Fifty-two FANCA deletion ends, and one FANCC deletion end were found to extend beyond the gene boundaries, potentially affecting neighboring genes. Eighty percent of the FANCA deletion breakpoints are Alu-Alu mediated, predominantly by AluY elements. Individual Alu hotspots were identified in introns 21, 17 and 5. Defining the haplotypes of four FANCA deletions shared by multiple families revealed that three share a common ancestry, and all are of recent origin. We are now employing MLPA for detection of deletions in FANCA exons for patient DNA samples in smaller quantities and thus insufficient for CGH analysis. Detailed characterization of deletions is critical for a better understanding of the FA phenotypes. In summary, our sequencing and arrayCGH efforts have resulted in identifying a spectrum of FA gene mutations for over 230 patients. Our goal is to comprehensively catalog mutations in all patients enrolled in the IFAR.