Hereditary hemochromatosis type 1 phenotype modifiers in Italian patients. The controversial role of variants in HAMP, BMP2, FTL and SLC40A1 genes

Hereditary hemochromatosis (HH) is a heterogeneous disorder of iron metabolism. The most common form of the disease is Classic or type 1 HH, mainly caused by a biallelic missense p.Cys282Tyr (c.845G>A) mutation in the HFE gene. However, the penetrance of p.Cys282Tyr/p.Cys282Tyr genotype is incomplete in terms of both biochemical and clinical expressivity. Lack of penetrance is thought to be caused by several genetic and environmental factors. Recently, a lot of evidences on HH genetic modifiers were produced, often without conclusive results. We investigated 6 polymorphisms (rs10421768 in HAMP gene, rs235756 in BMP2 gene, rs2230267 in FTL gene, rs1439816 in SLC40A1 gene, rs41295942 in TFR2 gene and rs2111833 in TMPRSS6 gene) with uncertain function in order to further evaluate their role in an independent cohort of 109 HH type 1 patients. Our results make it likely the role of rs10421768, rs235756, rs2230267 and rs1439816 polymorphisms, respectively in HAMP, BMP2, FTL and SLC40A1 genes in HH expressivity. In addition, previous and our findings support a hypothetical multifactorial model of HH, characterized by a principal gene (HFE in HH type 1) and minor genetic and environmental factors that still have to be fully elucidated.Hereditary hemochromatosis (HH) is a heterogeneous disorder of iron metabolism. The most common form of the disease is Classic or type 1 HH, mainly caused by a biallelic missense p.Cys282Tyr (c.845G N A) mutation in the HFE gene. However, the penetrance of p.Cys282Tyr/p.Cys282Tyr genotype is incomplete in terms of both biochemical and clinical expressivity. Lack of penetrance is thought to be caused by several genetic and environmental factors. Recently, a lot of evidences on HH genetic modifiers were produced, oftenwithout conclusive results. We investigated 6 polymorphisms (rs10421768 in HAMP gene, rs235756 in BMP2 gene, rs2230267 in FTL gene, rs1439816 in SLC40A1 gene, rs41295942 in TFR2 gene and rs2111833 in TMPRSS6 gene) with uncertain function in order to further evaluate their role in an independent cohort of 109HHtype 1 patients.Our resultsmake it likely the role of rs10421768, rs235756, rs2230267 and rs1439816 polymorphisms, respectively in HAMP, BMP2, FTL and SLC40A1 genes in HH expressivity. In addition, previous and our findings support a hypothetical multifactorial model of HH, characterized by a principal gene (HFE in HH type 1) andminor genetic and environmental factors that still have to be fully elucidated

A Systematic Assessment of Accuracy in Detecting Somatic Mosaic Variants by Deep Amplicon Sequencing: Application to <i>NF2</i> Gene

<div><p>The accurate detection of low-allelic variants is still challenging, particularly for the identification of somatic mosaicism, where matched control sample is not available. High throughput sequencing, by the simultaneous and independent analysis of thousands of different DNA fragments, might overcome many of the limits of traditional methods, greatly increasing the sensitivity. However, it is necessary to take into account the high number of false positives that may arise due to the lack of matched control samples. Here, we applied deep amplicon sequencing to the analysis of samples with known genotype and variant allele fraction (VAF) followed by a tailored statistical analysis. This method allowed to define a minimum value of VAF for detecting mosaic variants with high accuracy. Then, we exploited the estimated VAF to select candidate alterations in <i>NF2</i> gene in 34 samples with unknown genotype (30 blood and 4 tumor DNAs), demonstrating the suitability of our method. The strategy we propose optimizes the use of deep amplicon sequencing for the identification of low abundance variants. Moreover, our method can be applied to different high throughput sequencing approaches to estimate the background noise and define the accuracy of the experimental design.</p></div

ROC curve analysis of variants found in calibration samples.

<p>ROC Curve analysis for SNVs (a) and InDels (b) events. Data are obtained by averaging results of calibration samples with the same dilution. Cross, triangle and circle points are relative to 1%, 5% and 10% dilution degree. Data do not contain recurrent events.</p

Results of ROC curve analysis on calibration samples.

<p>^a TPR and FPR are calculated from ROC curves at the best VAF cut-off. This data do not contain recurrent events.</p><p>^b The number of FPs at each cut-off value was estimated by the product of FPR and the number of detected variants per sample</p><p>Results of ROC curve analysis on calibration samples.</p

Known variants present in calibration samples.

<p>^<i>a</i>: <i>NF2</i>: NM_181832.2; <i>SMARCB1</i>:NM_003073.3</p><p>^<i>b</i>: The DNA variant numbering is based on cDNA sequences for both genes, with the A of the ATG translation-initiation codon numbered as +1.</p><p>Known variants present in calibration samples.</p

Sample results obtained by filtering on the basis of VAF cut-off and functional criteria.

<p>^a: B: blood; T: tumor.</p><p>^b:The DNA variant numbering is based on the <i>NF2</i> cDNA sequences (GenBank accession number NM_181832.2) with the A of the ATG translation-initiation codon numbered as +1.</p><p>^c: Variants already characterized.</p><p>^d: COLD-PCR protocol.</p><p>Sample results obtained by filtering on the basis of VAF cut-off and functional criteria.</p

Validation of <i>NF2</i> variant in an unknown NF2 mosaic patient.

<p>The c.459C>T variant in exon 5 of the <i>NF2</i> gene was identified by deep sequencing. A) 410 DNA sample differed appreciably from wild-type melting curves at HRMA analysis; but the alteration was not detectable by Sanger sequencing after standard PCR (B). C) COLD-PCR allowed to enrich the variant allele as much as necessary to make the alteration clearly visible by Sanger sequencing.</p

Features of false positives events detected in calibration samples.

<p>(a) Boxplot of VAFs of events detected by MuTect (SNVs) and IndelGenotyperV2 (InDels). n is the total number of detected events, n* is the mean number of events (per sample) with the corresponding standard deviation. (b) Histogram of events recurrence among calibration samples (n = 30) for SNVs (black) and InDels (grey). Corresponding cumulative percentages are reported in dashed black (SNVs) and grey (InDels) lines.</p