Background Fanconi anaemia (FA) is a rare inherited bone marrow failure disease caused by germline pathogenic variants in any of the 22 genes involved in the FA-DNA interstrand crosslink (ICL) repair pathway. Accurate laboratory investigations are required for FA diagnosis for the clinical management of the patients. We performed chromosome breakage analysis (CBA), FANCD2 ubiquitination (FANCD2-Ub) analysis and exome sequencing of 142 Indian patients with FA and evaluated the efficiencies of these methods in FA diagnosis.
Methods We performed CBA and FANCD2-Ub analysis in the blood cells and fibroblasts of patients with FA. Exome sequencing with improved bioinformatics to detect the single number variants and CNV was carried out for all the patients. Functional validation of the variants with unknown significance was done by lentiviral complementation assay.
Results Our study showed that FANCD2-Ub analysis and CBA on peripheral blood cells could diagnose 97% and 91.5% of FA cases, respectively. Exome sequencing identified the FA genotypes consisting of 45 novel variants in 95.7% of the patients with FA. FANCA (60.2%), FANCL (19.8%) and FANCG (11.7%) were the most frequently mutated genes in the Indian population. A FANCL founder mutation c.1092G>A; p.K364=was identified at a very high frequency (~19%) in our patients.
Conclusion We performed a comprehensive analysis of the cellular and molecular tests for the accurate diagnosis of FA. A new algorithm for rapid and cost-effective molecular diagnosis for~90% of FA cases has been established.
- molecular diagnostic techniques
- bone marrow diseases
- founder effect
- genotyping techniques
Data availability statement
Data are available upon reasonable request.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
WHAT IS ALREADY KNOWN ON THIS TOPIC
Diagnosis of Fanconi anaemia (FA) is challenging as the disease-associated pathogenic variants are present in a large number of genes.
Patients with FA present with progressive bone marrow failure and are at risk of developing malignancies in the later stages of life.
Therefore, early diagnosis is extremely important for proper clinical management of the disease.
WHAT THIS STUDY ADDS
We performed comprehensive cellular and molecular analysis of peripheral blood and fibroblasts of a large number of patients with FA to establish a novel robust algorithm for laboratory diagnosis.
FANCD2 ubiquitination analysis, compared with chromosome breakage analysis, will provide a more accurate FA diagnosis.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The designed algorithm will aid in the early diagnosis of FA, which may improve treatment outcomes.
Fanconi anaemia (FA) is a rare inherited bone marrow failure (BMF) disease, with an estimated incidence of 1 per 360 000 live births.1 This disease is caused by germline pathogenic variants in any of the 22 genes of the FA DNA repair pathway,2 which consists of core complex proteins encoded by the FA upstream pathway genes (FANCA, FANCB, FANCC, FANCE, FANCF, FANCG, FANCL and FANCM) that monoubiquitinate FANCD2/I complex, which recruits the proteins encoded by the downstream pathway genes (BRCA2, BRIP1, PALB2, RAD51C, SLX4, ERCC4, RAD51, BRCA1, XRCC2, REV7 and RFWD3) for DNA interstrand crosslink (ICL) repair. FA is predominantly an autosomal recessive disorder, with the exceptions of FANCB and FANCR, which show X linked and autosomal dominant inheritance, respectively.3 4
BMF leading to pancytopenia, with variable latency, is the most common phenotype of FA and is observed in about 90% of the patients.5 The characteristic congenital physical abnormalities associated with FA are observed in 75% of the patients.6 Acute myeloid leukaemia (AML) and solid tumours develop in approximately 20% and 30% of the patients, respectively, and the incidence of myelodysplastic syndrome (MDS) is about 40% by the age of 50 years.7 Some patients present with malignancies before the underlying FA is diagnosed.8 More importantly, patients with FA do not tolerate the standard doses of DNA damaging chemotherapy drugs used for treating other haematological diseases and as part of the conditioning regimen for a curative allogeneic haematopoietic cell transplantation (allo-HCT) in aplastic anaemia (AA). It is also challenging to differentiate idiopathic AA from FA without physical abnormalities. For these reasons, an accurate laboratory diagnosis of FA is essential.5 9
Chromosome breakage analysis (CBA) is the most widely used diagnostic test for FA.10 However, some rare haematological diseases can also cause chromosomal breakage and pose false positives.10–12 Although increased G2/M cell-cycle arrest in FA cells treated with ICL agents can be used as a marker of FA,13 it is also observed in AML cells.14 Complementation analysis, which uses viral vectors to express wild type cDNAs of FA proteins in the FA cells and corrects the cellular phenotype,15 is laborious and can take 4–5 weeks to establish a diagnosis. Pathogenic variants resulting in defects in the FA core complex proteins, which occur in >90% of patients with FA,1 3 fail to monoubiquitinate the short form of FANCD2 to the active long form.3 The analysis of defective FANCD2 ubiquitination (FANCD2-Ub) is a robust test;16 however, this method is not routinely used for FA diagnosis. Due to somatic mosaicism17 observed in 25% of patients with FA, which can yield false-negative results by CBA and FANCD2-Ub analysis in the blood cells,18 non-haematopoietic cells (fibroblasts) are preferred to perform these tests.19
Identifying defective genes and pathogenic variants is crucial for carrier detection and prenatal diagnosis of FA in the affected families and genotype-phenotype correlation in the patients. A few whole exome sequencing (WES) studies have been carried out to determine the frequencies of defective genes and the spectrum of mutations in populations.2 20–22 Strong associations between malignancies and biallelic pathogenic variants in FANCD1/BRCA2 and FANCN/PALB27 23–26 and monoallelic pathogenic variants in FANCS/BRCA1, FANCJ/BRIP1 and FANCO/RAD51C have been established.3 27 Genotyping a large number of patients from different populations, especially those with high consanguinity rates, helps better comprehend the genotype-phenotype correlation.7 20 28–30 We performed CBA, FANCD2-Ub analysis and exome sequencing of 142 patients with FA from the Indian population and evaluated the efficacy of these methods in diagnosis. Based on our findings, we could establish an efficient algorithm for a faster and more cost-effective laboratory diagnosis of FA.
Patients and methods
Patients with FA in this study included those with pancytopenia, with or without FA-like physical abnormalities and higher CBA scores in the peripheral blood cells than normal controls or those with normal scores but had FA-like physical abnormalities. The patients were recruited from June 2009 to 2021 after their clinical evaluation at the Department of Haematology, Christian Medical College, Vellore (India). The idiopathic AA group included patients with pancytopenia with low CBA scores and who did not have FA physical abnormalities. Written informed consent was obtained before sample collection.
Chromosome breakage analysis
CBA was performed using a previously described protocol31 as detailed in the online supplemental methods. A total of 40 well-spread metaphases of the cells with normal ploidy were analysed.
Culture of human dermal fibroblasts
Human dermal fibroblasts were isolated from skin biopsies and cultured using a previously described protocol.32
FANCD2-Ub analysis in the peripheral blood cells and dermal fibroblasts
Peripheral blood mononuclear cells (PBMNCs) isolated from 5 mL of blood were stimulated with phytohemagglutinin-A to culture T cells for the whole cell lysate preparation. Fibroblasts treated with MMC (mitomycin C) for 16 hours were harvested for lysate preparation. Western blot was carried out using standard protocols using a FANCD2 antibody (Santa Cruz Biotechnology; sc-20022) to detect the presence or absence of FANCD2 monoubiquitination.
Next-generation sequencing to identify the FA genotypes
Exon capture for 35 samples was performed using SureSelect Human All Exon V5 (Agilent), 35 samples with xGen Exome Research Panel v1 (Integrated DNA Technologies)), 42 samples with SureSelect Human All Exon V5+UTR (untranslated region) (Agilent), 25 samples with SureSelect Human All Exon V6+UTR (Agilent) and 5 samples with a focused exome sequencing panel that covers all the genes described in OMIM. Sequencing was performed on an Illumina HiSeq X system to generate 2×150 bp sequence reads with 8–10 GB of data per sample.
Detection of deletions using multiplex ligation-dependent probe amplification and gene dosage analysis
Deletions in FANCA were identified by multiplex ligation-dependent probe amplification (MLPA) using SALSA (Selective Adaptor Ligation, Selective Amplification) MLPA P031 and P032 Probe Mixes and SALSA MLPA EK1 reagent kit (MRC Holland) and Coffalyser software. FANCT deletion was determined by gene dosage analysis by capillary electrophoresis of fluorescently labelled PCR products.
Long-amplicon next-generation sequencing
Long-amplicon next-generation sequencing (LA-NGS) was performed for FANCA and FANCG genes, and the data were analysed using publicly available bioinformatic analysis tools using Galaxy (https://usegalaxy.org/) (refer to the online supplemental section).
Generation of lentiviral plasmids and complementation analysis
pLX301-FANCA, pLX301-FANCG and pLX301-FANCC plasmids were generated by Gateway cloning, and pCW-FANCL and pCW-FANCF plasmids were generated by cloning the cDNAs in pCW lentiviral plasmid.
The detailed methods are provided in the online supplemental methods.
Clinical phenotypes of patients with FA
As per the inclusion criteria described in the methods section, 142 patients (55 female and 87 male) were selected for the molecular diagnosis of FA. The majority of patients (78.9%) were from the southern states of India (Andhra Pradesh: 28.1%, Tamil Nadu: 24.6% and Kerala: 21.1%) (online supplemental table S1, online supplemental figure S1). The median age at diagnosis at our centre was 10 years (range: 1–45 years), with the predominant phenotype being cytopenia, estimated based on the peripheral blood cell counts, observed in 140 (98.6%) patients and hypocellular bone marrow observed in 126 (88.8%) patients. FA characteristic physical abnormalities were observed in 136 (95.7%) patients, with skin pigmentation in 119/142 (83.8%) patients, radial ray abnormalities in 62 (43.6%), short stature in 41 (28.8%) and microcephaly in 41 (28.8%) patients (online supplemental table S1). Haematological malignancies were observed in 21 (14.7%) patients (median: 27; range: 6–45). Of these 21 patients, 3 had AML and 16 (11.2%) had MDS at presentation, while 2 patients with MDS transformed to AML during the follow-up. Eighty-eight (61.9%) patients were treated with androgen therapy (danazol and stanozolol), while 42 (29.6%) received an allo-HCT (online supplemental table S1). Eighteen (12.6%) patients had family members diagnosed with FA.
Chromosome breakage and FANCD2-Ub analysis
We performed CBA (figure 1A) and FANCD2-Ub analysis (figure 1B) to compare the sensitivities of these two tests to distinguish FA cases from non-FA cases. CBA was performed in the peripheral blood of 137 of the 142 patients with FA. As expected, the patients with FA had overall higher CBA scores (median: 54.7; range: 0–192.2) compared with the 72 patients with idiopathic AA (median: 4.1; range: 4.1–98.9) who did not have FA characteristic physical abnormalities and 83 normal individuals (median: 0; range: 0–8.2) (figure 1C). There were 16/137 (11.6%) patients with FA with very low CBA scores (median: 0; range: 0–8.2) overlapping with those of normal controls and patients with AA (figure 1C and online supplemental table S2). As these patients had FA-related physical abnormalities in addition to pancytopenia, they were further analysed as described below. FANCD2-Ub analysis was performed for 134 patients with FA. Depending on the type of samples available, this test was carried out in peripheral blood cells of 53 patients, fibroblasts of 26 patients, and both cell types of 55 patients (figure 1B and online supplemental figure S7). Lack of FANCD2-Ub (FANCD2-Ub−) was observed in 51/53, 25/26 and 52/55 patients in each group. To identify patients with mosaicism, we compared the results of FANCD2-Ub analysis in both T cells and fibroblasts of 55 patients. Only 3/55 patients were FANCD2-Ub+ in the peripheral blood cells and FANCD2-Ub− in fibroblasts (online supplemental figure S7), suggestive of mosaicism in these patients. Of these patients, one was lost to the follow-up, and two presented with MDS and were evaluated for FA due to their marginally elevated CBA scores and FA physical abnormalities. Overall, 128/134 (95.5%) were FANCD2-Ub− (online supplemental figure S7), which suggested that FA in the Indian population is predominantly caused by pathogenic variants in the FA upstream pathway genes.1 33 NGS confirmed the FA pathway upstream genes, FANCA, FANCC, FANCE, FANCF, FANCG, FANCI, FANCL and FANCT, in these patients who were FANCD2-Ub−.
We could compare the sensitivities of CBA and FANCD2-Ub analysis in the peripheral blood cells of 106 patients for whom both the tests were performed. CBA scores were >15 in 94 (88.6%) patients and FANCD2-Ub− in 98 (92.5%) patients. There were three patients with CBA >15 and FANCD2-Ub+, caused by downstream pathogenic variants. NGS confirmed pathogenic variants in FANCJ in two patients and BRCA2 in one patient. Of 16 patients with low CBA scores (<15), 7 were FANCD2-Ub−. In the five patients with low CBA scores and FANCD2-Ub+ in the peripheral blood, two were FANCD2-Ub− in the fibroblasts suggesting mosaicism in these cases (online supplemental table S2). The overall sensitivity of FANCD2-Ub analysis in the non-mosaic FA cases was 97% and that of CBA was 91.5% when peripheral blood cells were analysed (online supplemental table S8).
Detection of pathogenic variants by exome sequencing
We performed NGS for all the 142 patients with FA recruited in this study using the DNA extracted from the PBMNCs of 63 patients and the fibroblasts of 79 patients. The bioinformatics pipeline for identifying the pathogenic variants is shown in online supplemental figure S2. Single nucleotide variant (SNV) and short insertions/deletions were identified either in homozygous or compound heterozygous states in the FA pathway genes of 114 (80.3%) patients (online supplemental table S3). Of the remaining 28 patients, 16 without any SNVs and 12 heterozygous for SNVs were analysed for CNVs using ExomeDepth,34 which compares the test exome reads to a reference set data from the same batch to normalise the read depths to detect the CNVs. As CNVs are less frequent in FA, we compared the reads of each patient’s exome data with those of other patients that we analysed in the same batch (online supplemental figures S3A,C). The CNVs were predicted in 22 patients by ExomeDepth (online supplemental figure S3B, table 1), 5 with heterozygous FANCA deletions, 1 compound heterozygous with two different FANCA deletions and 15 with homozygous FANCA and 1 with homozygous FANCT deletions. MLPA analysis confirmed the presence and zygosity of the FANCA deletions in the 19 patients who were predicted to have deletions by ExomeDepth (online supplemental figures S3A and S4). The predicted homozygous FANCT exon 7 deletion was confirmed by a quantitative PCR using fluorescently labelled primers (online supplemental table S6) and capillary electrophoresis (online supplemental figure S3D,E).
By combining SNVs and CNVs, the disease-associated genotypes were identified in 136 out of 142 (95.7%) patients by exome sequencing (online supplemental table S3). As reported earlier, FA in the Indian population was caused by pathogenic variants in the upstream genes, FANCA, FANCC, FANCG, FANCL and FANCT. Pathogenic variants in the downstream genes were found in FANCJ and BRCA2. A total of 93 unique variants were identified in nine genes of the FA-pathway, and 45 (48.4%) were novel variants (online supplemental table S3). Of these, 119 patients were homozygous (104 with SNVs and 15 with deletions) and 17 were compound heterozygous (10 with two different SNVs, 6 with SNVs and large deletions and 1 with two different large deletions). In six patients whose FA genotypes could not be established, a heterozygous pathogenic variant was identified in one patient, likely benign in four patients and was a variant of uncertain significance (VUS) in one patient as per the ACMG classification (online supplemental table S4. These six samples were also analysed by Golden Helix VarSeq 2.2.0 (Golden Helix, Bozeman, Montana, USA), the clinical genomics interpretation and reporting platform, to detect the SNVs and CNVs that could probably be missed due to the low read counts and by the filtration strategies in our pipeline. We could not detect any additional variants in these samples. Excluding the variants detected in more than one family, there were 93 unique variants: 20 (21.5%) splicing, 24 (25.8%) missense, 23 (24.7%) frameshift, 11 (11.8%) nonsense, 1 (1%) stop loss and 14 (15 %) large deletion (figure 2). Contrary to the previous studies that showed compound heterozygous pathogenic variants in the majority of patients with FA,2 35 83.8% of our patients were homozygous (figure 2) due to the high rate of consanguinity in the population.
The FA physical abnormalities in the patients with pathogenic variants in the three predominant defective genes, FANCA, FANCL and FANCG, were evaluated (online supplemental table S1). Kidney abnormalities were observed in 7.1% (6/83) patients with FANCA, 7.4% (2/27) with FANCL and 37.5% (6/16) with FANCG pathogenic variants. The major FA physical abnormalities observed in patients were radial ray abnormalities (56.3% patients with FANCG, 33.3% FANCL and 42.2% FANCA), short stature (37.5% in FANCG, 29.6% in FANCL and 26.5% in FANCA), microcephaly (25% in FANCG, 44.4% in FANCL and 22.9% in FANCA) and facial abnormalities (56.3% in FANCG, 37.7% in FANCL and 40.9% in FANCA). There were 21 patients who developed AML and MDS. Of these 21 patients, 16 had MDS and 3 had AML at presentation, while 2 patients with MDS transformed to AML during the follow-up. Of the 21 patients who developed MDS/AML, 12/21 (57.1 %) had FANCA pathogenic variants, and FANCA exon 29 pathogenic variants were found in 4/21 (8%) patients. Other defective FA pathway genes in those with MDS/AML were FANCT (2/21; 9.5%), FANCG (2/21; 9.5%), FANCJ (2/21; 9.5%), BRCA2 (1/21: 4.8%), FANCF (1/21; 4.8%) and FANCL (1/21; 4.8%).
Highly frequent pathogenic variants in the Indian population
FANCA was the most frequently mutated gene (60.2%) in our study as reported in other populations (60%–80%)2 20 36(online supplemental table S3, figure 2). In the 82 patients with FANCA pathogenic variants, 96 pathogenic variants were identified, out of which 32 were novel. Homozygous pathogenic variants were observed in 68 patients, while 14 had compound heterozygous mutations. We found that 14.7% of the FANCA pathogenic variants were deletions. There were 14 different deletions in 20 patients. As reported in a previous Indian population study,22 we also observed a high frequency of FANCA exon 27 deletion (3.6%) in our patients (online supplemental figure S3B, table 1). FANCC, the second frequently mutated gene with a frequency of 10%–15% in other populations,20 22 was rare (1.5%) in our patients. The frequency of FANCG pathogenic variants was comparable with other populations (11.7% in this study vs 9%–12% in other populations).2 36 Pathogenic variants in rare FA genes include those in FANCT/UBE2T in three patients, FANCI in one patient, FANCJ/BRIP1 in two patients, FANCF in two patients and FANCD1/BRCA2 in one patient (online supplemental table S3, figure 2).
Although pathogenic variants in FANCL are rare, WES revealed 27 (19.8%) patients with FANCL pathogenic variants in our patients (online supplemental table S3). A synonymous splicing variant c.1092G>A;p.K364=in the FANCL gene was found in a homozygous state in 26 (19.1%) patients. Sanger sequence analysis of the PCR-amplified FANCL cDNA from a patient with this variant confirmed skipping of exon 13, as reported previously21(online supplemental figure S5A–C). Lentiviral transduction of wild type FANCL cDNA restored FANCD2-Ub in the fibroblasts of a patient with this pathogenic variant (online supplemental figure S5D). All the patients with this pathogenic variant were from South Indian states (12 from Andhra Pradesh, 9 from Kerala, 5 from Tamil Nadu and 1 from Karnataka) (online supplemental figure S1). Although this pathogenic variant was reported previously in 12 Indian patients with FA,21 our study, with representative samples from all over the country, revealed its frequency among the Indian patients with FA with better accuracy. We identified another FANCL pathogenic variant: a nonsense variant c.997C>T; p.Gln333* found in the compound heterozygous state with FANCL c.1092G>A;p.K364=in another patient (online supplemental table S3). Other highly frequent pathogenic variants included c.2786A>C (n=5), c.1761–2A>C (n=5) in FANCG and c.3066+1G>T (n=4), c.319delG (n=4) and c.826+2T>C (n=4) in FANCA (online supplemental table S3).
Determination of pathogenicity of missense variants
We identified 24 missense variants in the 142 patients with FA that we genotyped. The pathogenic effect of these variants was assessed using ACMG guidelines,37 ClinVar database37 38 and VarSome variant discovery tool,39 which use several pathogenicity prediction methods to classify the variants as pathogenic, likely pathogenic or VUS. We identified seven pathogenic variants by ACMG guidelines, eight by ClinVar and six by Varsome (table 2). We also analysed the missense variants using the evolutionary model of variant effect (EVE)40 tool (https://evemodel.org/) for the pathogenicity prediction, which showed that out of the 24 missense variants in our patients, 20 were pathogenic and 2 were VUS and 2 of them did not have any EVE scores (table 2).
Complementation analysis by lentiviral-mediated gene transfer of wild type cDNA into FA cells and correction of the cellular phenotypes is a feasible method for confirming the pathogenicity of the variants.15 After antibiotic selection of the fibroblasts transduced with lentiviral vectors encoding wild type cDNAs, the cells were treated with MMC and were analysed for their FANCD2-Ub status. We first validated complementation analysis in the fibroblasts of 13 patients with pathogenic null variants in the FA upstream pathway genes (online supplemental table S7) and observed restoration of FANCD2-Ub in all of them (figure 3, online supplemental table S7). Subsequently, we performed complementation analysis in seven patients with VUS and likely pathogenic variants as determined by the ACMG classification in homozygous or compound heterozygous states for whom fibroblasts were available. These included four FANCA, 1 FANCG, 1 FANCC and 1 FANCF variants (online supplemental table S7). All these patients showed restoration of FANCD2-Ub after complementation.
A robust molecular diagnosis strategy designed for FA
Our study showed that 57.4% of the patients had SNVs in FANCA and FANCG genes. Therefore, we developed a LA-NGS method to detect pathogenic variants in these genes. We amplified the FANCA gene as six LAs and FANCG as one LA by LA-PCR (online supplemental Figure S6A,B), and the PCR products were pooled in a single tube, and NGS and subsequent bioinformatics analysis were performed. The robustness of this method for detecting SNVs was confirmed using DNA samples from 24 patients with known SNVs in FANCA and FANCG (online supplemental table S5). This method is cost-effective and faster than the current molecular diagnostic strategies and involves less bioinformatics analysis than exome sequencing.
As 19.1% of the patients with FA have FANCL c.1092G>A;p.K364=pathogenic variant, Sanger sequencing to detect this variant can be performed as the first test for genotyping the Indian patients with FA. MLPA can detect FANCA deletions, which constitute 14.7% of the overall pathogenic variants. The results from these two tests can be obtained in 48 hours. For those who are negative for the pathogenic variants by these two methods, LA-NGS can detect SNVs in the FANCA and FANCG genes, which constitute ~57% of the FA pathogenic variants. Thus, this algorithm can help in the molecular diagnosis of ~90% of the patients with FA in the Indian population (figure 4A). This diagnostic algorithm was tested in 27 new patients with FA with a median CBA score of 66.8 (0–115) and confirmed that it provides a faster and more cost-effective molecular diagnosis of FA in the Indian population (figure 4B).
An accurate laboratory diagnosis of FA is mandatory for the clinical management of this disease. Although CBA is considered a ‘gold standard’ test for FA, this test has several issues, including laborious standardisation and user variability in the scores. The comparison of CBA and FANCD2-Ub analysis performed in a large number of patients with FA in this study confirmed that FANCD2-Ub analysis, which is currently not being used for diagnosis, is also suitable for FA diagnosis (online supplemental table S8). We found increased sensitivity of CBA scores in FA diagnosis when a cut-off of 15 (arrived at using the receiver operating characteristic curve (ROC) curve and Youden’s Index) was used. However, a randomised comparative analysis is required to confirm this cut-off. Among 16 patients with low CBA scores, 12 were analysed for FANCD2-Ub analysis. Seven of these 12 patients showed defective FANCD2-Ub (58.3%), and subsequent pathogenic variant analysis confirmed them to be FA cases. Defects in the downstream FA pathway genes, which do not affect FANCD2-Ub, are very rare (2%–6 %)4 20 in patients with FA, and we also found the downstream pathogenic variants in only ~2.2% of the Indian patients. Therefore, FANCD2-Ub analysis may be used as a reliable test for the diagnosis of FA. Spontaneous reversal of pathogenic variants occurs in the haematopoietic cells of 15%–25% of the patients with FA.41 FANCD2-Ub analysis performed in both T cells and fibroblasts in 55 patients in this study identified only 3 (5.4%) mosaics, with FANCD2-Ub+ in T cells and FANCD2-Ub− in fibroblasts. The reduced incidence of mosaicism (<15%) observed in our cohort may be because the patients were referred from a haematology clinic after evaluating pancytopenia and other haematological and physical abnormalities.
Detection of defective genes and pathogenic variants is important for genetic counselling and the development of targeted prenatal genetic testing. Early molecular diagnosis is also essential for participation in gene therapy for FA.42 Although targeted gene panels have been developed for FA,20 43 44 WES allows the discovery of new genes associated with the diseases. There were very few reports on WES analysis of a limited number of patients with FA, with 15–25 patients.45 Recently, a comprehensive WES study in 68 European patients with FA identified pathogenic variants in 93.3% of patients.2 We performed a WES analysis of the largest number of patients with FA and identified pathogenic variants with 95.7% genotyping efficiency. In the six patients for whom only heterozygous variants were identified, gene expression and protein analysis may identify the probably missed pathogenic variants.
NGS has limitations in detecting CNVs. Therefore, robust bioinformatics methods are required to detect deletions. A recent study has applied a bioinformatics tool using custom scripts to identify the deletions in FA genes efficiently.2 We used ExomeDepth34 for CNV analysis and applied filters to discard the false positives to obtain 100% accuracy in detecting deletions in our patients. Our results confirmed that the improved bioinformatics could efficiently detect CNVs. As reported earlier in other populations,46–48 we also found that FANCA deletions are very common (14.7%) in Indian patients with FA. The combined analysis of SNVs and CNVs identified the disease-associated genotypes in ~95% of the patients. Such a high pathogenic variant detection rate in FA was reported previously by Bogliolo et al,2 which also analysed both SNVs and CNVs.
FANCA has high genetic heterogeneity and is the most often mutated FA gene, with frequencies ranging from 60% to 80% in different populations.2 20 36 However, we found that FANCA (60.2%), FANCL (19.8%) and FANCG (11.7%) are the most common mutated genes in our cohort of patients with FA. Even though we identified a large number of patients (~20%) with homozygous FANCL c.1092G>A;p.K364=, they presented diverse phenotypes. More than 83.2% of the patients were homozygous for pathogenic variants in the FA genes due to this population’s high consanguinity rate. A large number of patients, 65 (45%), were born from consanguineous marriages. We found only 93 variants in the 142 cases analysed by NGS due to high homozygosity and a few recurrent pathogenic variants. There were 19 recurrent variants found in more than one patient; their frequencies ranged from 1.3% to 17.5%.
We found pathogenic variants in only nine FA genes, FANCA, FANCG, FANCC, FANCL, FANCF, FANCT, FANCI, FANCD1 and FANCJ. These genes could be prioritised for designing the FA genotyping panel and the bioinformatics analysis of Indian patients. We found that Sanger sequencing to detect the FANCL pathogenic variant and MLPA to detect FANCA deletions could diagnose 33.8% of FA cases. We also developed a faster and cost-effective LA-NGS strategy to detect point pathogenic variants in FANCA and FANCG genes, constituting 57.4% of the genotypes in Indian patients with FA. The presence of a FANCL founder variant and the high frequency of FANCA and FANCG pathogenic variants helped establish a new, faster, cost-effective molecular diagnosis strategy for Indian patients with FA that could diagnose ~90% of the patients with FA. Altogether, the algorithm established would expedite the FA diagnosis and be a cost-effective alternative compared with WES for FA diagnosis.
Data availability statement
Data are available upon reasonable request.
Patient consent for publication
This study involves human participants and was approved by the Institutional Research and Ethics Committee, Christian Medical College, Vellore (R.C.Min.No.6561). Participants gave informed consent to participate in the study before taking part.
The authors thank MedGenome Labs Ltd, Bengaluru, India, for the exome sequencing service.
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Twitter @joshigaurav1410, @NancyArthurPhD
Contributors GJ performed research, data analysis and wrote the manuscript. NBJA performed research. TSG performed data analysis. PVRD collected data and wrote the manuscript. KM, DR, ADC, PS and SP performed standardisation of methods and provided the laboratory data. FNA analysed the clinical data. VR performed whole exome sequencing for a part of the patients and critically reviewed the manuscript. AA provided clinical data. VMS provided the chromosome breakage scores for a part of the samples. AS provided clinical data and critical inputs for the manuscript. UPK performed clinical data analysis and provided inputs for the manuscript. BG performed clinical data analysis and supervised the clinical part of the study. SRV designed the research, analysed and interpreted the data, supervised the study and revised and finalised the manuscript. SRV is guarantor for this study and accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.
Funding The Wellcome Trust DBT India Alliance Senior Research Fellowship to SRV (Grant number IA/S/17/1/503118) and MedGenome Labs Ltd.
Map disclaimer The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.