Despite intensive efforts using linkage and candidate gene approaches, the genetic etiology for the majority of families with a multi-generational breast cancer predisposition is unknown. In this study, we used whole-exome sequencing of thirty-three individuals from 15 breast cancer families to identify potential predisposing genes. Our analysis identified families with heterozygous, deleterious mutations in the DNA repair genes FANCC and BLM, which are responsible for the autosomal recessive disorders Fanconi Anemia and Bloom syndrome. In total, screening of all exons in these genes in 438 breast cancer families identified three with truncating mutations in FANCC and two with truncating mutations in BLM. Additional screening of FANCC mutation hotspot exons identified one pathogenic mutation among an additional 957 breast cancer families. Importantly, none of the deleterious mutations were identified among 464 healthy controls and are not reported in the 1,000 Genomes data. Given the rarity of Fanconi Anemia and Bloom syndrome disorders among Caucasian populations, the finding of multiple deleterious mutations in these critical DNA repair genes among high-risk breast cancer families is intriguing and suggestive of a predisposing role. Our data demonstrate the utility of intra-family exome-sequencing approaches to uncover cancer predisposition genes, but highlight the major challenge of definitively validating candidates where the incidence of sporadic disease is high, germline mutations are not fully penetrant, and individual predisposition genes may only account for a tiny proportion of breast cancer families.
Currently, we know that a woman who inherits a fault in one of two genes, BRCA1 or BRCA2, has a high risk of developing both breast and ovarian cancer. However, such faults account for only half of all families with a strong family history of breast cancer. In this study, we planned to identify new genes that may be associated with an increased risk of developing breast cancer by looking for faults in every gene in the blood DNA of multiple women with breast cancer from large families with a strong family history of the condition over multiple generations. We can then track which gene fault is present in all the women with breast cancer in that family and in other families, but is not found in the women who did not develop breast cancer or have no family history. Using this approach, we identified faults in two genes, Fanconi C and Bloom helicase, in six families. Faults in these genes appear to increase the risk of developing breast cancer. Both these genes work in a similar way as BRCA1 and BRCA2, and this highlights the importance of these functions in preventing breast cancer. Further studies need to be done to confirm our results.
Citation: Thompson ER, Doyle MA, Ryland GL, Rowley SM, Choong DYH, et al. (2012) Exome Sequencing Identifies Rare Deleterious Mutations in DNA Repair Genes FANCC and BLM as Potential Breast Cancer Susceptibility Alleles. PLoS Genet 8(9): e1002894. doi:10.1371/journal.pgen.1002894
Editor: Marshall S. Horwitz, University of Washington, United States of America
Received: February 9, 2012; Accepted: June 28, 2012; Published: September 27, 2012
Copyright: © Thompson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by grants from the Victorian Government, through Victorian Cancer Agency (http://www.victoriancanceragency.org.au/) funding and a Priority-driven Collaborative Cancer Research Scheme grant from Cancer Australia (http://canceraustralia.gov.au/) and the National Breast Cancer Foundation (www.nbcf.org.au/) (#628610 and #628333). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Around one in six women who develop breast cancer has a first degree relative with the condition . In the mid 1990s, a classical linkage approach identified germline mutations in two genes, BRCA1 and BRCA2, which are associated with a high risk of developing both breast and ovarian cancer , . Although BRCA1 and BRCA2-specific genetic testing is rapidly evolving in the clinical setting, mutations in these genes are successful at explaining only around half of the dominant multi-case breast cancer only families , and their contribution to the heritable risk of breast cancer has been estimated to be no more than around 20% of the total , . Importantly, the identification and management of individuals with high-risk breast cancer predisposition gene mutations is now well accepted in clinical practice. Although evidence-based risk management is only possible in a relatively small group of families, as it is limited by the identification of an underlying genetic mutation, the benefits for those individuals are well established .
Through a candidate gene approach, mutations in other high and moderate penetrance cancer-susceptibility genes have been identified in a further small proportion of families but the underlying etiology of the increased susceptibility to breast cancer in the majority of multi-case breast cancer families remains unknown. Recent advances in massively parallel sequencing technology have provided an agnostic means by which to efficiently identify germline mutations in individuals with inherited cancer syndromes at the individual family or cancer-specific level , . The aim of this study is to identify through a whole exome sequencing approach, the underlying familial predisposition to breast cancer in multiple multi-generational breast cancer families in whom no BRCA1 or BRCA2 mutation was identified (BRCA1/2 negative families), and to assess the candidate genes identified by this means in a cohort of familial BRCA1/2 negative breast and ovarian cancer patients.
We performed intra-family exome sequence analysis of multiple affected relatives from 15 high-risk, trans-generational breast cancer families in whom full BRCA1 and BRCA2 mutation analysis had been performed and was uninformative in at least one breast cancer-affected family member (Table 1). Sequencing was performed on GAIIx or HiSeq instruments (Illumina). The average read depth achieved for target regions was 83.19 and at least 80% (average 89.12%) of the capture target regions were covered by 10 or more sequence reads for all samples (Table S1). Following data filtering, an average of 35 overtly deleterious and 284 non-synonymous mutations were identified per individual (Table S1).
To identify candidate predisposition genes we only considered those with overtly deleterious mutations that were shared by multiple affected relatives and/or were targeted in more than one family and further priority was given to genes with a role in mechanistically well-established breast cancer–associated DNA repair. A list of all overtly deleterious mutations identified in among the 33 individuals sequenced is provided in Table S2. Two of the fifteen families were found to carry independent heterozygous truncating mutations in the Fanconi Anemia (FA) gene, FANCC. Neither family was reported to be of Ashkenazi Jewish ancestry and the mutations are different to those commonly reported among this ethnic group. Family 1 carried a novel nonsense mutation (FANCC c.535C>T, p.Arg179*) that was present in the youngest affected individual (breast cancer at age 37) and in her mother who had ovarian cancer at age 66, but not in her breast cancer-affected sister who was diagnosed at age 46 (Figure 1). Family 2 was found to harbor a known pathogenic FA mutation (FANCC c.553C>T, p.Arg185*)  which was present in two sisters who developed breast cancer aged 36, and bilateral breast cancer aged 46 and 53, respectively. A third family analyzed by exome sequencing was found to carry a heterozygous c.1993C>T mutation in the BLM gene which is predicted to truncate the protein at codon 645 (p.Gln645*). This known pathogenic Bloom syndrome mutation  co-segregated with cancer in the family (Figure 1), being present in all three sisters diagnosed with breast cancer aged 39, 39 and 41 years respectively and absent in the two unaffected sisters. Although retrospective likelihood segregation analysis of these limited pedigrees did not reach significance (see Text S1), overall, co-segregation of FANCC and BLM mutations in these families appears consistent with that expected for moderately penetrant breast cancer alleles.
Males and females are represented by squares and circles, respectively. Arrows indicate individuals who underwent whole exome sequencing (families 1–3) or were the index case in subsequent mutation analysis (FANCC p.Asp23fs, p.Leu554Pro and p.Arg185Gln and BLM p.Arg899* families). Cancer-affected individuals are represented with the following symbols: breast cancer, top right quadrant filled in; bilateral breast cancer, top half; ovarian cancer, bottom left quadrant; or other cancers as indicated, centre circle. Mutation status is indicated with either the family specific mutation or wildtype (wt) under each tested individual. Age at cancer diagnosis or year of birth (b.) where known is shown for all mutation carriers. Breast cancer (BC), ovarian cancer (OC), acute leukaemia (AL), colorectal cancer (CRC), haematological malignancy (type unspecified) (Haem.), kidney cancer (KC), liver cancer (LivC), melanoma (Mel.), pancreatic cancer (PaC), prostate cancer (PrC), skin non-melanoma (Non-mel.) stomach cancer (SC), testicular cancer (TestC). Mutations indicated in parentheses indicate untested obligate carriers. Family 2 contains an individual (indicated by #) for whom mutation status is inferred assuming that non-paternity or gonadal mosaicism have not occurred.
Mutation analysis of all coding exons of FANCC and BLM was extended to the index cases from a further 438 BRCA1/2 negative breast cancer families (from kConFab). This approach identified one further family with a heterozygous, known pathogenic FANCC mutation, (c.67delG, p.Asp23Ilefs*23, rs104886459)  and one with a heterozygous pathogenic BLM mutation (c.2695C>T, p.Arg899*) . For FANCC, mutation hotspot exons 2, 5, 7, 14 and 15 were screened in the index cases from an additional 957 BRCA1/2 uninformative breast cancer families attending familial cancer services (including 561 obtained from the Peter MacCallum Cancer Centre Familial Cancer Centre and a further 396 from kConFab). One further family with a heterozygous FANCC c.1661T>C (p.Leu554Pro, rs104886458) missense variant, which is a functionally validated pathogenic FA mutation, was identified .
The index case in the FANCC c.67delG family developed breast cancer at age 60 but independent clinical testing subsequently identified a deleterious mutation in BRCA2 (c.8297delC, p.Thr2766Asnfs*11) in other breast cancer-affected family members (Figure 1). Genotyping of both mutations within this family suggests that different individuals may carry risk conferred by one or both of these family mutations.
The index case of the FANCC c.1661T>C family developed bilateral breast cancer at age 44 and 55, but DNA from other family members was not available for segregation analysis. All FANCC variants detected in index cases or controls are summarized in Table S3.
The index case of the BLM c.2695C>T family developed breast cancer at age 33 but segregation analysis showed the mutation was inherited from her father rather than her mother whose reported family history of breast cancer had initiated their recruitment into kConFab (Figure 1). Interestingly, breast cancer was diagnosed much earlier in the index case compared to her maternal relatives (33 years versus 58 to 73 years) possibly indicating a different genetic etiology. Unfortunately data regarding family history on the paternal side are limited. Neither the father nor the paternal grandparents were reported to have developed cancer but no further information regarding number or cancer status of other relatives is available. All BLM variants detected in index cases or controls are summarized in Table S4.
No pathogenic BLM mutations were detected in 464 healthy controls and none have been reported in the 1000 Genomes data (20100804 release, n = 1,092)  compared to 2/438 breast cancer families with BLM mutations. Likewise, no known pathogenic or overtly deleterious FANCC mutations were identified among the 464 controls or the 1000 Genomes data or among 654 healthy controls examined in an independent study . The Exome Variant Server (EVS), NHLBI Exome Sequencing Project, Seattle, WA, does report deleterious mutations in FANCC and BLM in 3/3,510 and 4/3,510 individuals of European decent, respectively. However, this cohort includes extreme tail sampling of traits relating to heart, lung and blood disorders. The latter group in particular may be expected to show enrichment for mutations in DNA repair machinery including FA genes. Excluding the Exome Variant Server frequency data, a total of 4/1,395 breast cancer families screened for all or at least the mutation hot spot exons carried overtly deleterious FANCC mutations compared to none among the combined control population (n = 2,210). While this is indicative that overtly deleterious mutation in FANCC and BLM are likely to be very rare in the population this must be considered a crude measure as the controls were drawn from diverse populations the majority of which were not matched to the index cases. However, it is possible that more families in our breast cancer family cohort may be explained by FANCC and BLM mutations since, for both genes, private non-synonymous variants were identified that are predicted to be damaging by in silico algorithms. One such variant, for which there was DNA available for segregation analysis, was FANCC p.Arg185Gln. This variant closely segregated with disease in this family, which included four female blood relatives with breast cancers diagnosed at ages 34, 51, 47 and 62 (Figure 1). The p.Arg185Gln variant was identified in 1/1,395 breast cancer families but not in any of 464 controls and has not been reported in the 1000 Genomes project or EVS database.
Homozygous mutations in FANCC and BLM are responsible for FA (complementation group C) and Bloom syndrome, respectively, and individuals diagnosed with these syndromes have a high risk of cancer. Functionally, the FA and Bloom syndrome pathways play important roles in homologous recombination (HR) based repair of double-stranded DNA breaks , . Constitutional inactivating mutations in genes integral to error-free HR and responsible for FA have been clearly associated with an increased susceptibility to both breast and ovarian cancer , and include the genes BRCA1, BRCA2 (FANCD1), FANCN (PALB2), FANCJ (BRIP1), RAD51C (FANCO) and RAD51D. Thus, in addition to the direct genetic evidence that we have described here, FANCC and BLM are strong candidates for breast cancer susceptibility genes due to their role in the precise regulation of HR and some of its associated functions. Although there is limited data, heterozygous FANCC mutations have previously been linked to an increased incidence of breast and early onset pancreatic cancer , , , however, no excess breast and ovarian cancer was observed among Ashkenazi Jews carrying the FANCC c.711+4A>T mutation . While another previous study failed to identify overtly pathogenic FANCC mutations in breast cancer, the study cohort size was small (n = 88) . In keeping with our data, two recurrent truncating mutations in the BLM gene were shown in a case control study to be associated with increased breast cancer risk in Russia . Gruber et al reported an elevated risk of colorectal cancer in Ashkenazi Jews carrying the common BLMASH mutation and a non-significant excess of breast cancer  although a later study failed to confirm these findings .
Further to the germline mutations in FANCC and BLM, exome sequencing identified mutations in the breast cancer predisposition genes, PTEN and BRCA2 in an additional three of the original 15 families (Figure S1). The truncating PTEN mutation (c.217G>T, p.Glu73*) was identified in only one branch of the family suggesting another susceptibility gene may explain the extended family history. Prior to this finding, the treating familial cancer centre reported no PTEN-associated clinical features within the family. In family 5, exome sequencing identified a deleterious BRCA2 mutation (c.5722_5723delCT, p.Leu1908Argfs*2, rs80359530) in two of the three family members tested (Figure S1). The mutation is present in a male diagnosed with breast cancer but not in the youngest affected female relative in the family, who had been offered the original clinical BRCA1 and BRCA2 mutation test in the clinic setting. Similarly in family 6, exome sequencing identified a deleterious BRCA2 mutation (c.26delC, p.Pro9Glnfs*16, rs80359343) in a female diagnosed with breast cancer at age 30, but not in her cousin who was diagnosed at age 36 and was the only family member to have undergone full diagnostic BRCA1 and BRCA2 gene sequencing (Figure S1). These families are interesting in a clinical context since they were designated as unresolved on the basis of best clinical practice and demonstrate the need for targeted sequencing of all proven breast and ovarian cancer susceptibility genes to obtain maximum information in the clinical setting (as previously demonstrated ). Our data also highlights the major challenge confounding genetic studies of common adult onset familial disease; the presence of ‘phenocopies’ in families with an inherited genetic predisposition and/or the convergence of pedigrees with different genetic causes (e.g. PTEN family 4). Among the remaining nine breast cancer families there were numerous genes that were recurrently targeted that warrant further investigation. It is noteworthy that in one family, one individual harbored a known FA pathogenic truncating mutation in FANCL. Mutation of this gene is responsible for a very small fraction of FA families and only three pathogenic mutations in FANCL are recorded in the Fanconi Anemia Mutation Database.
In conclusion, we describe two potential breast cancer susceptibility genes FANCC and BLM both of which have functional roles in the regulation of HR. The heterozygous mutation carrier rate in Caucasians for these genes is extremely low (for FANCC it is estimated at 1/3,000 , whilst the carrier frequency of BLM mutations is unknown since the syndrome is exceedingly rare) and notwithstanding the possibility of the “winners curse” , the exome sequencing data is strongly suggestive that FANCC and BLM represent breast cancer predisposing genes. Together with the recently identified association of RAD51 paralogues with cancer predisposition , , our findings suggest that the number of unidentified moderate to high-risk susceptibility genes is very much larger than previously expected and the number of families explained by each gene is likely to be much less than 1% (cf. RAD51C , ). Consequently, providing definitive evidence for a causative role for novel breast cancer genes will be challenging and will require validation of rare mutations in thousands rather than hundreds of families. We predict that this will be a generic problem associated with identifying causative mutations in common diseases such as breast cancer and that validation rather than the technical exercise of exome sequencing is where the real challenge lies.
Materials and Methods
This study was approved by the Peter Mac Ethics Committee (project numbers 09/62 and 11/50). Informed consent was obtained from all participants. Fifteen high-risk breast cancer families with at least four cases of multi-generational breast cancer including at least one additional high-risk feature (such as bilateral, early onset or male breast cancer, or ovarian cancer) and at least two available blood specimens from breast cancer-affected individuals, were selected for whole exome sequencing from among approximately 800 BRCA1 and BRCA2 mutation negative families from the Kathleen Cunningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), which has been collecting biospecimens and clinical and epidemiological information from families recruited through Familial Cancer Centres in Australia and New Zealand since 1997 . DNA from two or three breast cancer-affected individuals were obtained from each family for analysis (as shown in Table 1), at least one of whom had previously been screened for BRCA1 and BRCA2 mutations (by sequencing of all coding exons and Multiplex Ligation-dependent Probe Amplification). Blood DNA from index cases from a further 834 mutation negative kConFab families and 561 mutation negative families obtained from the Peter MacCallum Cancer Centre Familial Cancer Centre were obtained for mutation analysis of candidate genes. Of those index cases obtained through the Familial Cancer Centre, individuals were breast cancer-affected, had a strong family history and been assessed for the probability of harboring a BRCA1 or BRCA2 mutations using BRCAPRO  and had been found on the basis of a verified family and personal history of having a 10% or greater probability. The index cases had undergone full diagnostic BRCA1/2 mutation search and no mutation was identified. However, it should be noted that the majority of these families did not fulfill the very stringent family history criteria that was required for recruitment to kConFab, the research cohort from which the families for the initial exome sequencing were taken . Non-cancer control DNA samples were obtained from kConFab (226 age- and ethnicity-matched best friend controls) and from the Princess Anne Hospital, UK (238 Caucasian female volunteers, as described previously ). DNA for candidate gene mutation analysis underwent whole genome amplification (WGA) using Repli-G Phi-mediated amplification system (Qiagen) prior to mutation analysis.
2–3 µg of DNA was fragmented to approximately 200 bp by sonication (Covaris) and used to prepare single- or paired-end libraries using the SPRIworks Fragment Library System I for Illumina Genome Analyzer on the SPRI-TE Nucleic Acid Extractor (Beckman Coulter). Exome enrichment was performed using the NimbleGen Sequence Capture 2.1 M Exome Array, EZ Exome Library (Roche NimbleGen) or SureSelect Human All Exon version 2 or 50 Mb libraries (Agilent Technologies) according to the recommended protocols. Sequencing was performed on GAIIx or HiSeq instruments (Illumina). Library preparation and sequencing details for each sample are provided in Table S1. We did not observe any significant differences in performance of the different exome capture platforms.
Sequencing Alignment and Variant Calling
Paired-end sequence reads were aligned to the human genome (hg19 assembly) using the Burrows–Wheeler Aligner (BWA) program . Local realignment around indels was performed using the Genome Analysis Tool Kit (GATK) software . Subsequently, duplicate reads were removed using Picard and base quality score recalibration performed using GATK software. Single nucleotide variants (SNVs) and indels were identified using the GATK Unified Genotyper and variant quality score recalibration. Variants were annotated with information from Ensembl release 62 using Ensembl Perl Application Program Interface (API) including SNP Effect Predictor , . Single-end sequence reads were aligned as above except duplicate reads were flagged prior to base quality score recalibration and included in variant calling.
Candidate Variant Identification
Variants were first filtered for confident calls originating from bidirectional sequence reads using a quality threshold of ≥30, read depth of ≥10 and allele frequency ≥0.15. Prior to further filtering, variants were assessed for overtly deleterious mutation in known breast cancer associated genes . Then, all variants present in the dbSNP database v132, except those also reported in the public version of the Human Gene Mutation Database (HGMD)  were removed, as were all common variants detected in >10 out of 33 exomes. Next, variants with functionally deleterious consequences (nonsense SNVs, frameshift indels, essential splice variants and complex indels) were identified for evaluation . Functionally deleterious variants were evaluated in each individual as well as pairwise between relatives.
Variant Validation Using Sanger Sequencing
Primers flanking the BRCA2, PTEN, FANCC and BLM mutations identified by whole exome sequence analysis were used to amplify germline DNA from affected index cases and all available relatives. The purified products were directly sequenced using BigDye terminator v3.1 chemistry on a 3130 Genetic Analyzer (Applied Biosystems).
Mutation Analysis of FANCC and BLM
High resolution melt (HRM) analysis was performed on duplicate PCR products amplified from 15 ng WGA DNA. Primer sequences and PCR conditions are provided in Table S5. Melt analyses were performed on a LightCycler 480 Instrument using Gene Scanning Software (Roche). Duplicate PCR products exhibiting variant DNA melt curves were Sanger sequenced to identify sequence variations. All novel sequence variants were confirmed by Sanger sequencing an independent PCR amplified from non-WGA DNA. The functional effect of missense variants were evaluated using in silico prediction tools SIFT and PolyPhen-2 , .
The following GenBank reference sequences were used for variant annotation: FANCC, NM_000136 BLM, NM_000057; PTEN, NM_000314 and BRCA2, NM_000059.
1000 Genomes Browser, http://browser.1000genomes.org/; Ensembl, http://www.ensembl.org/index.html; The Genome Analysis Toolkit, http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit; HGMD, http://www.hgmd.org/; Picard, http://picard.sourceforge.net; HGVS nomenclature for the description of sequence variants, http://www.hgvs.org/mutnomen/; NCBI SNP database, http://www.ncbi.nlm.nih.gov/projects/SNP/; The Fanconi Anemia Mutation Database, http://www.rockefeller.edu/fanconi/; BLMbase mutation registry, http://bioinf.uta.fi/BLMbase/; SIFT, http://sift.jcvi.org/; PolyPhen-2, http://genetics.bwh.harvard.edu/pph2/. Exome Variant Server, http://evs.gs.washington.edu/EVS/.
PTEN and BRCA2 mutations identified in familial breast cancer pedigrees. Males and females are represented by squares and circles, respectively. The arrows indicate individuals who underwent whole exome sequencing (families 4–6). Cancer-affected individuals are represented with the following symbols: breast cancer, top right quadrant filled in; bilateral breast cancer, top half; ovarian cancer, bottom left quadrant; or other cancers as indicated, centre circle. Mutation status is indicated with either the family specific mutation or wildtype (wt) under each tested individual. Age at cancer diagnosis or year of birth (b.) where known is shown for all mutation carriers. Breast cancer (BC), ovarian cancer (OC), cervical cancer (CervC), colorectal cancer (CRC), cancer of unknown primary (CUP), endocrine cancer (EndoC), endometrial cancer (EndomC), haematological malignancy (type unspecified) (Haem.), lymphoma (Lymph), melanoma (Mel.), pancreatic cancer (PaC), prostate cancer (PrC), stomach cancer (SC), thyroid cancer (ThyrC). Mutations indicated in parentheses indicate untested obligate carriers.
Whole exome sequencing performance and variant count. aIndicates adaptor type (and protocol) used for library preparation. Paired-end (PE), single-end (SE), multiplex paired-end (in PE). bLibraries were prepared by hand (manual) or using the SPRIworks Fragment Library System (SPRIworks) incorporating size selection as indicated in parentheses. cExome enrichment was performed using either the NimbleGen Sequence Capture 2.1 M Exome Array (NG exome array), EZ Exome Library version 1 or 2 (NG exome array v1 or v2) or Agilent SureSelect Human All Exon version 2 (Ag Exome v2) or 50 Mb (Ag Exome 50 Mb) libraries. dPercentage of reads that map and align to the reference genome and overlap with the targeted bases by at least one base. The target regions differ according to the capture method used. eNumber of variants shared by all exome-sequenced family members (i.e. either 2 or 3).
Overtly deleterious variants identified by exome sequencing. aGenomic position (chromosome_nucleotide) of the reference nucleotide for each variant provided relative to human geneome reference assembly GRCh37 (hg19). bReference nucleotide sequence. cAlternate (or variant) nucleotide sequence detected by exome sequencing. This variant list has not been extensively validated by Sanger sequencing and may include sequencing artefacts. dPredicted consequence of variant relative to ensembl transcript provided. Only variants with an overtly deleterious predicted consequence (i.e. stop_gained, frameshift_coding, complex_indel or essential_splice_site) are included in this list. eNumber of times variant detected among exome data from 33 individuals. Only those variants present in fewer than 10/33 individuals are included in this list. fNumber of families in whom variant was detected. gNumber of times variant was detected in an individual who also carried a FANCC or BLM mutation. hPredicted position of alteration relative to protein associated with ensembl transcript. iPredicted amino acid change (provided for SNVs only). jdbSNP IDs for previously identified variants co-ocurring at the same variant position (not matched for nucleotide change). Variants with dbSNP references were filtered from this list with the exception of variants in known breast cancer genes (i.e. BRCA1, BRCA2, ATM, CHEK2, BRIP, PALB2 etc.) or variants reported in the HGMD public database. kOther existing variation IDs, primarily from the HGMD public database.
Variants identified by HRM-based mutation analysis of FANCC. aExon (Ex), intervening sequence (IVS). bVariant positions are reported in reference to GenBank reference sequence NM_000136.2 (mRNA). cIn addition to the non-coding variants listed, dbSNP rs4647534 (c.1155-38T>C) were detected at high frequency in both cases and controls. dAll variants were queried against 1000 Genomes (1000 G) data using the 1000 Genomes Browser (http://browser.1000genomes.org/index.html), which integrates SNP and indel calls from 1,092 individuals (data release 20110521 v3). The minor allele frequency (MAF) is provided here. eAll variants were queried against Exome Variant Server (EVS), NHLBI Exome Sequencing Project (ESP) (http://evs.gs.washington.edu/EVS/) [May 2012]. EVS contains SNP information from 5,379 individuals (data release ESP5400). The minor allele frequency (MAF) is provided here. fAll variants were queried against the public version of the Human Gene Mutation Database (HGMD, http://www.hgmd.cf.ac.uk/). gAll variants were queried against the Fanconi Anemia Mutation Database (http://www.rockefeller.edu/fanconi/). The number in parentheses indicates the number of times this variant has been reported in FA cases. hThe numbers of samples examined varied by exon, as indicated for each variant.
Variants identified by HRM-based mutation analysis of BLM. aExon (Ex), intervening sequence (IVS). bVariant positions are reported in reference to GenBank reference sequence NM_000057.2 (mRNA). cIn addition to the variants listed, dbSNPs rs11852361 (c.2603C>T), rs7167216 (c.3961G>A), rs2227933 (c.3102G>A), rs2227934 (c.3531C>A), rs1063147 (c.3945C>T), rs28385029 (c.2193+61G>C), rs17181698 (c.2193+84C>T), rs17273206 (c.2308-50G>A), rs3815003 (c.2555+7T>C), rs17273842 (c.3358+32T>G) were detected at high frequency in cases or 1000 Genomes data. dAll variants were queried against 1000 Genomes (1000 G) data using the 1000 Genomes Browser (http://browser.1000genomes.org/index.html), which integrates SNP and indel calls from 1,092 individuals (data release 20110521 v3). The minor allele frequency (MAF) is provided here. eAll variants were queried against Exome Variant Server (EVS), NHLBI Exome Sequencing Project (ESP) (http://evs.gs.washington.edu/EVS/) [May 2012]. EVS contains SNP information from 5,379 individuals (data release ESP5400). The minor allele frequency (MAF) is provided here. fAll variants were queried against the public version of the Human Gene Mutation Database (HGMD, http://www.hgmd.cf.ac.uk/). gAll variants were queried against the BLMbase Mutation Registry (http://bioinf.uta.fi/BLMbase/). The number in parentheses indicates the number of times this variant has been reported in FA cases.
Primers used for mutation analyses of FANCC and BLM. The list of forward and reverse primers used for mutation analyses of FANCC and BLM.
Retrospective likelihood segregation analysis methods and data.
We wish to thank Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up Study for their contributions to this resource, as well as the many families who contribute to kConFab.
Conceived and designed the experiments: IGC GM ERT. Performed the experiments: ERT GLR RWT SMR DYHC YCA. Analyzed the data: ERT MAD JL JE GKP DRB. Contributed reagents/materials/analysis tools: kConFab HT GM PAJ AHT. Wrote the paper: ERT IGC AHT.
- 1. Pharoah PD, Lipscombe JM, Redman KL, Day NE, Easton DF, et al. (2000) Familial predisposition to breast cancer in a British population: implications for prevention. Eur J Cancer 36: 773–779. doi: 10.1016/s0959-8049(00)00023-x
- 2. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, et al. (1994) A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266: 66–71. doi: 10.1126/science.7545954
- 3. Wooster R, Bignell G, Lancaster J, Swift S, Seal S, et al. (1995) Identification of the breast cancer susceptibility gene BRCA2. Nature 378: 789–792. doi: 10.1038/378789a0
- 4. Ford D, Easton DF, Stratton M, Narod S, Goldgar D, et al. (1998) Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium. Am J Hum Genet 62: 676–689. doi: 10.1086/301749
- 5. Mavaddat N, Pharoah PD, Blows F, Driver KE, Provenzano E, et al. (2010) Familial relative risks for breast cancer by pathological subtype: a population-based cohort study. Breast Cancer Res 12: R10. doi: 10.1186/bcr2476
- 6. Thompson D, Easton D (2004) The genetic epidemiology of breast cancer genes. J Mammary Gland Biol Neoplasia 9: 221–236. doi: 10.1023/b:jomg.0000048770.90334.3b
- 7. Domchek SM, Friebel TM, Singer CF, Evans DG, Lynch HT, et al. (2010) Association of risk-reducing surgery in BRCA1 or BRCA2 mutation carriers with cancer risk and mortality. JAMA 304: 967–975. doi: 10.1001/jama.2010.1237
- 8. Comino-Mendez I, Gracia-Aznarez FJ, Schiavi F, Landa I, Leandro-Garcia LJ, et al. (2011) Exome sequencing identifies MAX mutations as a cause of hereditary pheochromocytoma. Nat Genet 43: 663–667. doi: 10.1038/ng.861
- 9. Jones S, Hruban RH, Kamiyama M, Borges M, Zhang X, et al. (2009) Exomic sequencing identifies PALB2 as a pancreatic cancer susceptibility gene. Science 324: 217. doi: 10.1126/science.1171202
- 10. Gibson RA, Hajianpour A, Murer-Orlando M, Buchwald M, Mathew CG (1993) A nonsense mutation and exon skipping in the Fanconi anaemia group C gene. Human Molecular Genetics 2: 797–799. doi: 10.1093/hmg/2.6.797
- 11. German J, Sanz MM, Ciocci S, Ye TZ, Ellis NA (2007) Syndrome-causing mutations of the BLM gene in persons in the Bloom's Syndrome Registry. Hum Mutat 28: 743–753. doi: 10.1002/humu.20501
- 12. Strathdee CA, Gavish H, Shannon WR, Buchwald M (1992) Cloning of cDNAs for Fanconi's anaemia by functional complementation. Nature 356: 763–767. doi: 10.1038/356763a0
- 13. Gavish H, dos Santos CC, Buchwald M (1993) A Leu554-to-Pro substitution completely abolishes the functional complementing activity of the Fanconi anemia (FACC) protein. Human Molecular Genetics 2: 123–126. doi: 10.1093/hmg/2.2.123
- 14. The 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073.
- 15. Couch FJ, Johnson MR, Rabe K, Boardman L, McWilliams R, et al. (2005) Germ line Fanconi anemia complementation group C mutations and pancreatic cancer. Cancer Research 65: 383–386.
- 16. Wang W (2007) Emergence of a DNA-damage response network consisting of Fanconi anaemia and BRCA proteins. Nat Rev Genet 8: 735–748. doi: 10.1038/nrg2159
- 17. Heyer WD, Ehmsen KT, Liu J (2010) Regulation of homologous recombination in eukaryotes. Annu Rev Genet 44: 113–139. doi: 10.1146/annurev-genet-051710-150955
- 18. van der Heijden MS, Yeo CJ, Hruban RH, Kern SE (2003) Fanconi anemia gene mutations in young-onset pancreatic cancer. Cancer Research 63: 2585–2588.
- 19. Berwick M, Satagopan JM, Ben-Porat L, Carlson A, Mah K, et al. (2007) Genetic heterogeneity among Fanconi anemia heterozygotes and risk of cancer. Cancer Research 67: 9591–9596. doi: 10.1158/0008-5472.can-07-1501
- 20. Baris HN, Kedar I, Halpern GJ, Shohat T, Magal N, et al. (2007) Prevalence of breast and colorectal cancer in Ashkenazi Jewish carriers of Fanconi anemia and Bloom syndrome. Isr Med Assoc J 9: 847–850.
- 21. Seal S, Thompson D, Renwick A, Elliott A, Kelly P, et al. (2006) Truncating mutations in the Fanconi anemia J gene BRIP1 are low-penetrance breast cancer susceptibility alleles. Nature Genetics 38: 1239–1241. doi: 10.1038/ng1902
- 22. Sokolenko AP, Iyevleva AG, Preobrazhenskaya EV, Mitiushkina NV, Abysheva SN, et al. (2011) High prevalence and breast cancer predisposing role of the BLMc.1642 C>T (Q548X) mutation in Russia. International Journal of Cancer doi: 10.1002/ijc.26342.
- 23. Gruber SB, Ellis NA, Scott KK, Almog R, Kolachana P, et al. (2002) BLM heterozygosity and the risk of colorectal cancer. Science 297: 2013. doi: 10.1126/science.1074399
- 24. Cleary SP, Zhang W, Di Nicola N, Aronson M, Aube J, et al. (2003) Heterozygosity for the BLM(Ash) mutation and cancer risk. Cancer Res 63: 1769–1771.
- 25. Walsh T, Lee MK, Casadei S, Thornton AM, Stray SM, et al. (2010) Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing. Proceedings of the National Academy of Sciences of the United States of America 107: 12629–12633. doi: 10.1073/pnas.1007983107
- 26. Zollner S, Pritchard JK (2007) Overcoming the winner's curse: estimating penetrance parameters from case-control data. Am J Hum Genet 80: 605–615. doi: 10.1086/512821
- 27. Meindl A, Hellebrand H, Wiek C, Erven V, Wappenschmidt B, et al. (2010) Germline mutations in breast and ovarian cancer pedigrees establish RAD51C as a human cancer susceptibility gene. Nature Genetics 42: 410–416. doi: 10.1038/ng.569
- 28. Loveday C, Turnbull C, Ramsay E, Hughes D, Ruark E, et al. (2011) Germline mutations in RAD51D confer susceptibility to ovarian cancer. Nature Genetics 43: 879–882. doi: 10.1038/ng.893
- 29. Thompson ER, Boyle SE, Johnson J, Ryland GL, Sawyer S, et al. (2012) Analysis of RAD51C germline mutations in high-risk breast and ovarian cancer families and ovarian cancer patients. Human Mutation 33: 95–99. doi: 10.1002/humu.21625
- 30. Osborne RH, Hopper JL, Kirk JA, Chenevix-Trench G, Thorne HJ, et al. (2000) kConFab: a research resource of Australasian breast cancer families. Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer [letter]. med j aust 172: 463–464.
- 31. Berry DA, Iversen ES Jr, Gudbjartsson DF, Hiller EH, Garber JE, et al. (2002) BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 20: 2701–2712. doi: 10.1200/jco.2002.05.121
- 32. Baxter SW, Choong DY, Eccles DM, Campbell IG (2001) Polymorphic variation in CYP19 and the risk of breast cancer. Carcinogenesis 22: 347–349. doi: 10.1093/carcin/22.2.347
- 33. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760. doi: 10.1093/bioinformatics/btp324
- 34. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, et al. (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20: 1297–1303. doi: 10.1101/gr.107524.110
- 35. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, et al. (2010) Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26: 2069–2070. doi: 10.1093/bioinformatics/btq330
- 36. Rios D, McLaren WM, Chen Y, Birney E, Stabenau A, et al. (2010) A database and API for variation, dense genotyping and resequencing data. BMC Bioinformatics 11: 238. doi: 10.1186/1471-2105-11-238
- 37. Stenson PD, Ball EV, Howells K, Phillips AD, Mort M, et al. (2009) The Human Gene Mutation Database: providing a comprehensive central mutation database for molecular diagnostics and personalized genomics. Hum Genomics 4: 69–72. doi: 10.1186/1479-7364-4-2-69
- 38. Ng PC, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome Research 11: 863–874. doi: 10.1101/gr.176601
- 39. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7: 248–249. doi: 10.1038/nmeth0410-248