The landscape of hereditary haemochromatosis risk and diagnosis across the British Isles and Ireland

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: minor_revision

Abstract
Hereditary haemochromatosis is caused by pathogenic variants in the homeostatic iron regulator gene HFE. Outcomes include liver cancer, cirrhosis and arthropathy, but penetrance is incomplete. Here, we use genetic data from >400,000 subjects to determine the genetic risk across 29 regions of the British Isles and Ireland. Northwest Irish and Outer Hebrideans are at the highest risk (1/54–1/62 carry the major risk genotype), Mainland Scots are also at increased risk (1/117), declining to 1/212 in Southern England. We also assessed the prevalence of clinically diagnosed haemochromatosis in >63 million people in NHS England and identified 70,365 cases. White Irish individuals have the highest prevalence (3.7x white British). Among white British, prevalence varied 11-fold from 1/1972 in parts of Kent to 1/177 in Liverpool. Discrepancies between genetic risks and prevalences of clinical diagnoses for Birmingham, Cumbria, Northumberland and Durham suggest under-diagnosis in these regions. We show heightened genetic risk of haemochromatosis in people of Northwest Irish and Hebridean ancestry and suggest health-economic modelling of community screening should be targeted to these priority areas.

Introduction
Hereditary haemochromatosis (HH) is an iron overload disorder that is one of the most common genetic conditions in people of European ancestry1. Homozygosity for a pathogenic variant in the HFE gene, resulting in the amino-acid substitution p.Cys282Tyr, is the major risk genotype for HH in European populations. The highest European frequencies of this allele are observed in the formerly Celtic-speaking populations in Ireland and in the United Kingdom2,3, hence haemochromatosis is sometimes called the Celtic Curse. It occurs at particularly high rates in people of Irish ancestry4,5 and analysis of ancient DNA6 showed that an Early Bronze Age individual from Rathlin, an island off the coast of Northern Ireland, was a p.Cys282Tyr carrier. This means the allele existed at least 4000 years ago, and in a geographic region where it is found to be common today. It has been speculated that this may relate to an evolutionary advantage when a low iron diet predominated3 or during epidemics7, although given such a diet and epidemics were widespread, it may be more likely that the high frequency is simply due to genetic drift over thousands of years. The symptoms of haemochromatosis range from chronic fatigue, joint and bone conditions to liver fibrosis, cirrhosis, hyperpigmentation and hepatocellular carcinoma. The risk of ill health and morbidity arising from haemochromatosis is higher in males than females, due at least in part to recurrent physiological blood loss in pre-menopausal women8.

Early studies reported low penetrance of pathogenic HFE variants when two copies are present9. This has changed recently, as detailed phenotypic data from very large cohorts with long-term follow-up in electronic health records, such as the UK Biobank (UKB), have become available. The UKB is a cosmopolitan biomedical database containing genetic, lifestyle and health information from half a million UK participants10. Analysis of clinical outcomes to age 8011 revealed that male and female p.Cys282Tyr homozygotes experienced greater excess morbidity than previously documented12. Particularly striking results in male homozygotes include a Hazard Ratio of 7.9 (5.5–11.4) for liver cancer and 2.6 (2.1–3.1) for any liver disease11. Much of this excess morbidity occurred after the age of 60 years; therefore, the life-course evidence on HFE penetrance has been expanded. Furthermore, analysis of outcomes is complicated by the fact that those participants with a diagnosis of haemochromatosis should have less excess morbidity if the condition were identified and treated early enough13. Such robust population cohort-based evidence (reviewed in ref. 14) prompted the addition in 2021 of homozygous p.Cys282Tyr variants in HFE to the ACMG v3.0 list of recommendations for reporting secondary findings in clinical exome and genome sequencing15. This was an important step in recognising the value of notification of this risk genotype when data is available. There is considerable benefit in identifying the overall genetic risk for HH, since the symptoms evolve over decades, and early diagnosis and treatment have been shown to mostly prevent the adverse consequences of iron overload16. Moreover, the opportunity to intervene and prevent disease is both simple and effective, primarily through regular venesection, which in turn can increase the supply of blood through donation.

The p.Cys282Tyr variant is the second most common pathogenic variant in HFE, while the most common is a variant that results in a different missense change in the protein, p.His63Asp. The main contribution to haemochromatosis risk of the p.His63Asp allele is through compound heterozygosity with p.Cys282Tyr, but with a penetrance about ten-fold lower than p.Cys282Tyr homozygotes11. A wider range of allele frequencies has been reported for p.Cys282Tyr in different populations worldwide, while p.His63Asp is more consistent in frequency, particularly across European populations. Approximately 1 in 156 people of European ancestry in the UK (as represented by the UK Biobank) are homozygous for p.Cys282Tyr12, while around 1 in 40 are compound heterozygous for p.Cys282Tyr/p.His63Asp11. The frequency of these HFE risk genotypes is lower in southern Europeans and lower still in those with ancestry from outside Europe17,18, but the extent of variation across the British Isles and Ireland has not been systematically assessed until now.

The International Classification of Diseases (ICD-10) code for haemochromatosis is E83.1 (Disorders of iron metabolism—haemochromatosis excluding anaemia). Lucas et al.11 assessed the number of UKB participants of European ancestry who have this code, i.e., a diagnosis of haemochromatosis by the UK NHS, recorded in their electronic health record (EHR). Of the 451,270 UKB participants of European ancestry, at baseline (age range in the UKB at baseline was 40-69 years), 238 out of 267 men (89%) and 79 out of 87 women (91%) who had been diagnosed with haemochromatosis with code E83.1 carried one or two copies of the HFE pathogenic variants p.His63Asp and p.Cys282Tyr. When incident hospital diagnoses during a follow-up period of a mean of 13.3 years in the UKB participants were analysed11, the additional numbers of people with both the code and at least one of the two most common pathogenic variants were 505 out of 590 men (86%) and 432 out of 476 women (91%). In total, 1254 out of the 1420 UKB participants (88%) with the E83.1 code had one or more copies of the p.Cys282Tyr or p.His63Asp alleles. These high percentages show that the code E83.1 provides a practical method of measuring hereditary haemochromatosis diagnoses in the NHS, but we note it is possible that in some cases the code only indicates the presence of the major risk genotype, rather than of iron overload.

It seems clear that targeting any population genetics screening to areas with greater numbers of p.Cys282Tyr homozygotes should improve cost-effectiveness. We therefore describe the detailed landscape of pathogenic HFE variation across the British Isles and Ireland, along with the geography of haemochromatosis prevalence across England, to allow any work to build the evidence base for future screening programmes to prioritise areas of particularly high frequency, or discrepancies between overall genetic risk and prevalence.

Results
Pathogenic variants in HFE affecting risk of haemochromatosis in the UK Biobank
We undertook a detailed regional analysis of hereditary haemochromatosis risk across the British Isles and Ireland. The ClinVar database19 contains a total of 27 variants in HFE that are recorded as pathogenic, short variants (<50 bps), with a rating of at least 1* according to their system of classification (Supplementary Data 1). The Allele Frequency Browser (afb.ukbiobank.ac.uk) of whole genome sequenced data from nearly 500,000 people in the UK Biobank allows assessment of allele frequency for any variant passing QC and with an ID reference, grouped by ancestry. 25 of the 27 pathogenic variants listed in ClinVar are either not detected or are ultra-rare in the UKB. Of those 25, the total number of alleles for the 13 variants with a minor allele count ≥1 is only 201, a combined allele frequency of 2.0×10-4 (Supplementary Data 1). They will therefore only be seen vanishingly rarely as compound heterozygotes with the actionable variant p.Cys282Tyr (approximately 1/33,000 people in the UK will be compound heterozygotes of an ultra-rare pathogenic variant and p.Cys282Tyr). Interestingly, rare HFE variants as compound heterozygotes with p.Cys282Tyr were reported to be the most frequent cause of haemochromatosis in non-C282Y homozygous patients with the condition20. Nine private HFE variants, some of which are also found in UKB (Supplementary Data 1), were identified in 13 of 206 unrelated patients20. However, even when all are considered together, the collective allele frequency of these ultra-rare variants does not affect the landscape of pathogenic HFE allele variation in the British Isles and Ireland, beyond that shown by p.Cys282Tyr and p.His63Asp, and they were therefore not further analysed here.

The HFE variant c.845G>A (chr6-26092913-G-A), which results in p.Cys282Tyr, has a total allele count of 71,949 out of 980,944 alleles in the UKB. Almost all (99%) of these were found in participants with genetic similarity to Non-Finnish Europeans, the ancestry group with by far the highest minor allele frequency of 0.078 (not shown). Researchers (e.g., ref. 11.) have therefore confined their studies of clinical outcomes of HFE risk genotypes in the UK Biobank to the European-heritage subset, and here we focus on the genomically British, in whom it is predicted that 1/164 will carry the major risk genotype (p.Cys282Tyr homozygote; Table 1).

The frequencies of p.Cys282Tyr are much lower in other population groups who live in the UK, particularly for non-European-heritage groups. Even for Continental Europeans, Italians are an order of magnitude less likely to carry the major risk genotype, whereas South and East Asian and African-heritage populations have predicted genotype frequencies three or more orders of magnitude lower. While still low risk, Afro-Caribbeans are noticeably more at risk (1/16,000) than other sub-Saharan African-heritage populations (<1/1M). In contrast, the more common pathogenic variant HFE c.187C>G (chr6-26090951-C-G), which results in p.His63Asp, varies little across European-heritage populations, but is also much rarer particularly in African- and East Asian-heritage populations (Table 1).

Non-HFE haemochromatosis variants
Variants in five further genes have been reported to cause other forms of hereditary haemochromatosis: SLC40A1 [MIM #604653], TFR2 [604720], HAMP [606464], HJV [608374] and FTH1 [134770]. In total, 1256 minor alleles are observed (including only one homozygote), for a combined allele frequency of only 0.0014 (Supplementary Data 2). Variants in all genes but SLC40A1 (for which there are 96 minor alleles observed) act recessively. Thus, similarly to the ultra-rare HFE variants, the non-HFE variants will not have a significant effect on the geography of hereditary haemochromatosis risk across the British Isles and Ireland.

Population frequencies of the HFE p.Cys282Tyr and p.His63Asp variants in regions of the British Isles and Ireland
We divided the British Isles and Ireland into 29 geographic regions, 24 in Britain and its associated islands, and 5 in Ireland (the latter derive from the 7574 people in UKB who report they were born in the Republic of Ireland or Northern Ireland and that their ethnicity is Irish). The British samples were assigned to a region according to their birthplace (13 in England, 2 in Wales and 9 in Scotland). In the absence of birthplace information for the Irish samples, they were divided into five geographic groups on the basis of genetic clustering with Irish samples of known grandparental ancestry (“Methods”).

We focussed first on predicted p.Cys282Tyr homozygote genotype frequencies, which are more accurate than depending only on the low counts of observed homozygotes, as it takes the >20-fold more numerous heterozygotes into account (both variants are in Hardy-Weinberg equilibrium in the white British sample used here, p>10-4, χ2=11.3). The predicted genotype frequency and therefore proportion of people at risk varies by nearly 6-fold across the regions of Britain and Ireland (Fig. 1 and Supplementary Data 3).

In England, there is a decreasing cline of predicted p.Cys282Tyr homozygotes from north to south, with North, Northwest, Merseyside and Manchester all being higher than the rest of England (overall 1/142), with London, Southwest, Southeast and East England having the lowest frequencies (overall 1/212, significantly lower than the northern regions, p<0.00001, χ2=360). Yorkshire, the Midlands and Birmingham have intermediate frequencies (overall 1/175). There is a weaker west to east cline, from Wales (1/156) through the Midlands to East Anglia (p<0.00001, χ2=26.0 for Wales-East Anglia difference).

Mainland Scotland’s frequencies of predicted p.Cys282Tyr homozygotes increase beyond north English levels (overall 1/117), with a maximum in Southwest Scotland (including Glasgow), which is also the highest in Mainland Britain (1/108, significantly higher than northern England, p<0.00001, χ2=58.0). Frequencies in Ireland are higher still (overall 1/75), however there is considerable variation across communities. An ancestry group found in Northern Ireland and also Lowland Scotland has the lowest frequency (1/123) while the highest is in the Northwest Irish group (1/54, significantly higher than SW Scotland, p<0.00001, χ2=55.0), found across Northern Ireland, including also a broad sweep of the northern counties of the Republic of Ireland, from north Mayo to Meath, up to Donegal. The Outer Hebrides have the highest frequency in the UK (1/62, significantly higher than SW England, p<0.00001, χ2=58.8), and higher than Northern Ireland (overall 1/71). The Northern Isles of Scotland have low frequencies, Orkney (1/209) being similar to southern England, while Shetland (1/309) is the lowest of all the British Isles and Ireland.

The p.His63Asp allele varies only 1.4-fold across Britain and Ireland, from a maximum in north and northeast Scotland (17.5%) to a minimum in the Outer Hebrides (12.7%). All other Mainland Scottish, Irish, Welsh and English regions have frequencies between 13.4%-15.9%.

The majority of the risk of haemochromatosis arising from the p.His63Asp variant is conferred through compound heterozygosity with p.Cys282Tyr, hence we have mapped out the variation in the predicted genotype frequency of the compound heterozygote (Supplementary Fig. 1). The more even distribution of p.His63Asp means there is less geographic variation predicted for the compound heterozygote, and it is correlated with that for the p.Cys282Tyr homozygote to give a very similar landscape: the highest frequency is also in the Northwest Irish group and the lowest in Shetland.

We can use the cumulative incidence for haemochromatosis to age 80 years for each genotype (calculated in UK Biobank11), that is 0.56 for p.Cys282Tyr homozygotes and 0.06 for p.Cys282Tyr/p.His63Asp compound heterozygotes (in European-heritage males), to calculate a combined genetic risk of haemochromatosis in each region (Supplementary Fig. 2). This is very strongly correlated with the predicted p.Cys282Tyr homozygote frequency (r=0.999), and thus shows the same clines across England and Wales, and heightened risk in Scotland and Ireland. The highest risk in the UK is found in the Outer Hebrides, where 1/94 males are predicted to develop iron overload by the age of 80 years, while in the Northwest Irish group, 1/80 males are predicted to develop the condition. In Northern Ireland overall, 1/133 men are predicted to develop iron overload, with 1/103 in the Republic of Ireland, 1/161 in Mainland Scotland, 1/208 in Wales and 1/226 in England.

Prevalence of a diagnosis of haemochromatosis in England using NHS DigiTrials
The total number of people in the UK with a current diagnosis of haemochromatosis has been difficult for researchers to ascertain. Symptoms are dependent on age and sex, and there is also considerable evidence from the UKB11,12 and elsewhere21 that many individuals with homozygous p.Cys282Tyr genotypes who are clinically affected have not been identified and diagnosed. The charity Haemochromatosis UK reported that NHS acute trusts and health boards were aware of a total of 20,698 people in the UK who were receiving care for genetic haemochromatosis in 2018/19 (https://www.haemochromatosis.org.uk/state-of-the-nation-2020). However, this figure is subject to considerable potential error, as 37% of NHS Trusts were unable to provide figures of people diagnosed, and some of the data provided relate to patient episodes, rather than individual patients. Accurate knowledge of the number of people with hereditary haemochromatosis in each area of the UK is clearly important for commissioning effective clinical care and, in conjunction with genetic data, may help identify regions particularly affected by under-diagnosis.

We therefore made use of the recently available NHS Digitrials resource (“Methods”). Symptoms due to type 1 haemochromatosis rarely develop before 30 years of age, instead usually becoming apparent between 40-60 years11. Consistent with that, only 3.1% of the White British individuals with haemochromatosis in the Digitrials dataset were aged 30 years or younger at their most recent diagnosis. We note that the data in DigiTrials provides only the age group range at the time of the most recent recording of the condition. Counts of E83.1 case numbers as a population percentage in the NHS DigiTrials White British dataset were analysed for males and females separately. As expected from the epidemiology of the condition, we determined the prevalence to be higher in males (59.2% of diagnoses) than in females. We found that on 31/10/2024, a total of 70,365 people of all ages, ethnicities and genders had the EHR code E83.1 for haemochromatosis across all 42 NHS England Integrated Care Boards (Table 2). These are among the 63,664,825 people in the DigiTrials dataset (Table 2). There is little variation in prevalence across the ten deciles of deprivation—among white British, the lowest prevalence decile (second most deprived) has 0.154% and the highest prevalence decile (third least deprived) 0.173%, a factor of only 12% (Supplementary Data 4).

Among the self-declared ethnicities, the highest prevalence of haemochromatosis is among the White Irish (Table 2), of whom there are 225,320 in the dataset. The prevalence of 1 in 166 is 3.7-fold higher than in the second-highest ethnic group, which is White British. In contrast, the prevalence for a different ICD-10 code, G35 (multiple sclerosis, n=88,015 in White British) is virtually identical between the two groups (data not shown). Similarly, little variation in prevalence between the White British and White Irish groups in the DigiTrials dataset was reported for seven common complex conditions22. The prevalence of E83.1 in other ethnic groups are much lower, ranging from 1/778 for the Chinese to 1/2934 for all Mixed backgrounds (Table 2).

We further analysed the 53,575 White British individuals with a diagnosis of haemochromatosis by Integrated Care Board (ICB). The prevalence by population shows a variation of nearly 4-fold across these 42 areas of England, with the lowest in Gloucestershire, Somerset, Norfolk & Waveney and Kent & Medway, in which 1/1001–1/1137 people are diagnosed, and the highest in Cheshire & Merseyside, where 1/290 people are diagnosed (Supplementary Data 5). There is a considerable drop in prevalence in the next three ICBs, North Central London, North West London and Greater Manchester, all with 1/454-1/468 being diagnosed.

Data are also available at the level of 134 English NHS Trusts, which clearly show the peak of diagnoses in Merseyside: among White British, the top seven trusts are all in Liverpool/Merseyside, varying from 1/177 to 1/278 prevalence (Supplementary Data 6). At the opposite end, trusts in Kent, Norfolk and Gloucestershire all have <1/1100 diagnoses, with Medway NHS Foundation Trust having only 1/1972 diagnoses of E83.1, 11-fold fewer than the Liverpool Heart and Chest Hospital NHS Foundation Trust. To make comparisons more straightforward, we have further grouped the data into the same 13 larger regions of England as used for genetic analysis (Table 3). Manchester and London follow Merseyside at the high prevalence end of the table, while Birmingham is at the low end, along with the southeast and southwest of England.

Discrepancies between the combined genetic risk and prevalence
Unfortunately, haemochromatosis prevalence data from the NHS are not available for Scotland, Wales and Northern Ireland. Within England, we find a general agreement between the predicted frequency of those at genetic risk (among genomically British in UK Biobank) and the raw prevalence of diagnoses among white British in DigiTrials (r=0.65 between raw prevalence and combined genetic risk from p.Cys282Tyr and p.His63Asp; Table 4). However, comparison of the ratio of combined genetic risk to raw prevalence reveals Birmingham and North England (North Cumbria, Northumberland, Durham) to have the highest ratios (3.36 and 3.24, respectively), that is, a much lower prevalence than would be expected from their combined genetic risks, suggesting under-diagnosis in these areas. At the other end of the scale, Cheshire & Merseyside and London have the lowest ratios (1.57 and 1.93, respectively), suggesting diagnostic rates in these regions are up to twice as good as in the worst regions.

Methods
Study populations
HFE allele frequencies were measured in two population cohorts10,34 using a total of ~440,000 research volunteers with ancestry covering all regions of the British Isles and Ireland, together with all substantial minorities within the UK.

The UK Biobank contains genetic, lifestyle and health information from half a million UK participants10, recruited in urban centres. Viking Genes comprises three island-ancestry cohort studies—the Orkney Complex Disease Study (ORCADES), VIKING I and VIKING II/III. ORCADES contains more than 2000 deeply phenotyped and exome-sequenced research subjects with three or four self-reported grandparents from Orkney35, whereas VIKING I is a similar-sized cohort of participants from Shetland36. VIKING II/III is a worldwide cohort of 6000 people, recruited online, with ancestry (two or more grandparents) from the Northern or the Western Isles of Scotland34.

Genotyping and DNA sequencing
Details of genotyping, sample and variant quality control of UK Biobank genotyping data are described in Bycroft et al.37. The exome sequenced dataset of 2113 VIKING I38,39 and 5106 VIKING II/III sequences passed all genotype quality control thresholds (including removal of samples with disagreement between genetically determined and reported sex, high rates of heterozygosity or low sequence coverage, and removal of sites read depth <7 or allele balance <15%), as described for the 2097 exomes in the ORCADES cohort40. The 5106 VIKING II/III sequences are reported for the first time in this manuscript. All Viking Genes sequence data were prepared at the Regeneron Genetics Center, following the process detailed for UK Biobank41. Imputed genotypes were used for UK Biobank (data field F.22828); p.Cys282Tyr and p.His63Asp had impute scores of 0.997 and 1.00, respectively.

Pathogenic HFE genotypes for all 48 individuals from the Viking II/III exome dataset were verified by a different sequence analysis method. The HFE c.845G>A (chr6-26092913-G-A) variant was confirmed in Viking II/III p.Cys282Tyr homozygotes by a Taqman assay (Applied Biosystems by Life Technologies predesigned assay ID C_1085595_10, results analysed using QuantStudio Design & Analysis Software). Twelve further p.Cys282Tyr homozygotes from ORCADES and VIKING I were validated using Sanger sequencing23.

Identification of ethnic groups and minority populations in UK Biobank
Data analysis used R version 4.4.2. We quantified HFE variant frequencies in the 13 largest non-white-British or -Irish population groups in the UK by first selecting individuals from the UKB matching country of birth (UKB data field F.20115) or ethnic background (field F.21000). For each group, we then selected the core genetic cluster of individuals from that group using the dbscan algorithm implemented in the R package dbscan (version 1.2–0). We applied this to the co-ordinates from the top two principal components (field F.22009), selecting the DBSCAN cluster consistent with that population group. We did not assess any group with under 500 volunteers. The Irish-born individuals in the UK Biobank who identified as Irish were assigned to five genetic clusters on the basis of identity-by-descent (IBD) segment sharing. UK Biobank genotypes (UK Biobank data field F.22418) were first filtered using PLINK v242 for variants and individuals with missingness <5%, variants with a minor allele frequency >1% and a Hardy-Weinberg Equilibrium p-value >1×10-9. The last used the PLINK flag --hwe with the additional flags midp and keep-fewhet. Individuals with a heterozygosity >3 standard deviations from the mean were subsequently removed. Relatives closer than 3rd degree were identified with KING43 version 2.3.2, and one from each pair was removed. This cleaned genotype dataset was subsequently phased with Eagle version 2.4.144 using human genome build GRCh37, and IBD segments with a length >3 cM were detected with hap-ibd45 version June 14, 2023. Using IBD segments >3 cM and <30 cM, we calculated the total sum length of IBD segments between all individuals and performed Leiden network community detection46 using the python implementation in the leidenalg package (version 0.10.2). We annotated the five detected clusters using samples with known grandparental birthplace, from the Irish DNA Atlas47, People of the British Isles25 and the Trinity Student dataset48 based on Irish Provincial identity of these references and shared principal component analysis (PCA) co-ordinates. These PCA co-ordinates were estimated in PLINK v2 from a merged genotypes dataset of the UKB Irish and Irish-British references following the same quality control thresholds as above. PCA was carried out using a pruned set of variants filtered for linkage disequilibrium identified with the PLINK --indep-pairwise 1000 50 0.2 command. PCA was subsequently performed using the PLINK --pca command. All first and second degree relatives in Viking Genes were removed using PLINK v1.904b and PRIMUS v1.9.0.

Non-HFE haemochromatosis variants
Using ClinVar, we created a catalogue of 174 pathogenic or likely pathogenic variants that were associated with some form of haemochromatosis with at least 1* evidence (no such variants were observed in FTH1). We assessed the frequencies of 56 of these variants present in the UK Biobank using the allele frequency browser.

NHS DigiTrials
The NHS DigiTrials Feasibility Self-Service allows researchers to search routinely collected healthcare records from over 63 million patients across NHS England national datasets in a secure environment, to count and locate relevant individuals without identifying them (https://digital.nhs.uk/services/nhs-digitrials/feasibility-service). Data are searchable for people who are alive, have a valid NHS number, are registered with a GP in England, and whose demographic record is not flagged as sensitive or invalid. Individuals are recorded once without duplicates, and only the latest episode is available from the database. Hospital Episode Statistics (HES) data were available for people diagnosed between 01/04/1989 and 31/10/2024. The NHS DigiTrials data can be divided geographically, and can also be queried by gender, ethnicity, age and socio-economic status. Ethnicity is self-reported using the 2001 UK census classification. Data from queries was downloaded through the Safe Output Service, rounded to the nearest increments of 5. We note that the genetic data can be studied according to birthplace, whereas the DigiTrials regional data is recorded by place of residence.

Data handling and statistical testing
We visualised the variant frequencies across Great Britain and Ireland using shapefile data implemented in R, processed using the packages sf, and graphically visualised using the ggplot2 package version 3.5.1. We sourced Irish county boundary data from OpenStreetMap (https://openstreetmap.org) and British International Territorial Level 3 Boundaries from the UK Office for National Statistics (https://www.data.gov.uk/dataset/9f55fc2c-f90e-49b3-9c17-f96d8240b4f1/international-territorial-level-3-january-2021-boundaries-uk-bfc-v31). Differences in genotype frequencies and Hardy-Weinberg equilibrium were tested for significance using one-sided χ2 tests with one degree of freedom.

Ethical approval
Eligible participants were recruited to ORCADES, the Viking Health Study Shetland (VIKING I), and to VIKING II/III, collectively Viking Genes34, South East Scotland Research Ethics Committee, reference 19/SS/0104. This research has been conducted using data from the UK Biobank (approved by North West Centre for Research Ethics Committee, reference 11/NW/0382), as part of project numbers 103770 and 19655. All cohort participants gave written informed consent for research procedures that included DNA sequencing. Some frequencies for HFE variants reported in Table 1, Supplementary Data 1 and Supplementary Data 2 were derived from the UK Biobank Whole Genome Sequencing (WGS) project and were obtained from the UK Biobank Allele Frequency Browser (afb.ukbiobank.ac.uk), which was generated by the WGS consortium under the UK Biobank Resource (project ID 52293).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Kerr and colleagues present data on the prevalence of HFE p.Cys282Tyr in the British Isles and Ireland. They look at diagnostic rates and morbidity and propose areas of the region in which to focus screening. The study is very well conducted and provides important data for understanding the epidemiology of this disorder in the part of the world with the highest prevalence. Some issues that should be addressed are:\n1. The correct nomenclature is p.Cys282Tyr. p.C282Y should no longer be used.\n2. Line 47- “Symptoms range from hyperpigmentation…” Hyperpigmentation is a symptom of very severe iron overload and therefore should come in the latter part of the sentence with liver fibrosis and cirrhosis rather than in list of symptoms that are non-specific and generally related to lower iron overload.\n3. Line 64- change “This was an important step change..” to “This was an important step…”\n4. How representative of the UK population is the UK Biobank across the many different regions assessed in this study?\n5. Line 192- p.C282T should be p.C282Y which in turn should be p.Cys282Tyr!\n6. Line 220- p.H63A should be p.H63D which in turn should be p.His63Asp.\n7. Line 223- variants in HJV and HAMP are associated with juvenile haemochromatosis. The other genes are not generally associated with that phenotype.\n8. Line 370- p.Cys282Tyr homozygotes should be referred to as such rather than as “carriers” which is a term that should be used for heterozygotes.\n9. The suggestion that different segments of a region as small as the British Isles and Ireland should have different approaches to screening for haemochromatosis is unwise, and in fact unethical, in my view. A person with the average risk of a white person from England who lives in a low prevalence area should have the same access to screening as a person of Irish ancestry living in Ireland. In addition, it is impractical to offer screening programs to subsets of a population based on geographical location. I suggest the discussion be revised to at least note these issues.", "reviewer_2": "This manuscript describes rates of genetic risk for hereditary hemochromatosis and clinical diagnoses of hereditary hemochromatosis across geographic regions in the British Isles and Ireland. It also summarizes rates of genetic risk across ancestral groups. The manuscript, which is generally clearly written, highlights high, but variable, prevalence of genetic risk across regions and potential under-ascertainment of at-risk individuals in certain regions. The methods are thoughtful, clearly described, and appropriate for the study’s goals. Findings could have implications for genomic screening for hereditary hemochromatosis, though additional clarity on the points below could inform how significant these findings are for the design and anticipated outcomes of population genomic screening programs.\n1. Given that the rate of individuals with the major risk HFE genotype is relatively high across all regions and that, as the authors point out, genomic screening that includes the major risk HFE genotype is believed to be cost-effective, one could argue that findings provide further support for genomic screening for hereditary hemochromatosis across all regions in the study, rather than for targeting screening to certain regions. Yet, given the exceedingly low rates of individuals with the major risk genotype in some ancestral groups, this “screen everyone” approach might not be optimal. Beyond suggesting that genomic screening be targeted to regions that appear to under-ascertain at-risk individuals, what do study findings suggest about how genomic screening programs in ancestrally diverse populations should consider inclusion of the major HFE risk genotype?\n2. Consider shortening the Discussion in ways that minimize restatement of results and focus on interpretation of findings.\n3. It is at times difficult to follow exactly what genetic risk is being discussed. I would suggest clearly defining each related term (genetic risk, major risk genotype, combined genetic risk) early in the manuscript and using them consistently throughout. It should be clear when, for example, p.H63D/p.C282Y compound heterozygotes are included with p.C282Y homozygotes in calculations and when genetic risk is multiplied by the cumulative incidence of hemochromatosis.\n4. If possible, it would be helpful to report the sensitivity and specificity of the E83.1 ICD-10 code for hemochromatosis. While the code appears to be commonly applied in individuals with HFE variants, it is not clear that it indicates iron overload (and may just indicate the presence of genetic risk in individuals with known HFE variants).\n5. In the Introduction, consider expounding upon what is known regarding the association between early detection and treatment of hemochromatosis and prevention or reversal of end organ damage.\nA few minor suggestions:\n6. Please include citation(s) for the statement in line 51 regarding low penetrance of pathogenic HFE variants.\n7. Please include citation(s) for the statement in lines 93-94 that targeting population genomic screening to areas with greater numbers of p.C282Y homozygotes would improve cost-effectiveness.\n8. Consider moving the description of methods for assessing frequencies of non-HFE variants (lines 223-228) to the Methods section.\n9. Consider explaining why hemochromatosis prevalence data from the NHS are not available for Scotland, Wales and Northern Ireland (lines 346-347).\n10. My understanding is that ACMG prefers the phrasing ‘ACMG secondary findings v3.0’ list (e.g., in line 365).\n11. Our Future Health is first mentioned in line 371 – I suggest mentioning earlier if relevant or explaining here."}, "author_response": "Response to review Kerr et al. The landscape of hereditary haemochromatosis risk and diagnosis\nacross the British Isles and Ireland\nReviewer #1:\nKerr and colleagues present data on the prevalence of HFE p.Cys282Tyr in the British Isles and\nIreland. They look at diagnostic rates and morbidity and propose areas of the region in which to\nfocus screening. The study is very well conducted and provides important data for understanding the\nepidemiology of this disorder in the part of the world with the highest prevalence.\nWe thank the reviewer for these kind words.\nSome issues that should be addressed are:\n1. The correct nomenclature is p.Cys282Tyr. p.C282Y should no longer be used.\nWe have now altered all instances of p.C282Y to p.Cys282Tyr.\n2. Line 47- “Symptoms range from hyperpigmentation…” Hyperpigmentation is a symptom of very\nsevere iron overload and therefore should come in the latter part of the sentence with liver fibrosis\nand cirrhosis rather than in list of symptoms that are non-specific and generally related to lower iron\noverload.\nWe thank the reviewer for this information and have now re-arranged the sentence such that\nhyperpigmentation is adjacent to the other symptoms related to more severe iron overload.\n3. Line 64- change “This was an important step change..” to “This was an important step…”\nWe have deleted the word “change.”\n4. How representative of the UK population is the UK Biobank across the many different regions\nassessed in this study?\nWhile it is well recognised that UK Biobank, along with all cohorts which involve volunteering,\ndoes not accurately represent the UK population socioeconomically, we do not believe that these\nbiases are likely to have operated differentially across different parts of the UK. We make this\npoint around lines 404-407:\n“We recognise that no volunteer cohort can be a perfect representation of the population from which\nit is drawn. There is evidence of a “healthy volunteer” selection bias in the UKB38, but we consider it\nunlikely that these biases operated to different degrees in different parts of the UK, such that they\nwould substantively affect our conclusions.”\n5. Line 192- p.C282T should be p.C282Y which in turn should be p.Cys282Tyr!\nWe thank the reviewer for catching this typo, which we have now fixed to the correct notation.\n6. Line 220- p.H63A should be p.H63D which in turn should be p.His63Asp.\nWe thank the reviewer for picking up on this mistake, which we have now fixed, along with all\ninstances of p.H63D.\n7. Line 223- variants in HJV and HAMP are associated with juvenile haemochromatosis. The other\ngenes are not generally associated with that phenotype.\nmentioned are involved in the risk of this form of haemochromatosis.\n8. Line 370- p.Cys282Tyr homozygotes should be referred to as such rather than as “carriers” which\nis a term that should be used for heterozygotes.\nWe thank the reviewer for this advice and have now explicitly named p.Cys282Tyr homozygotes as\nsuch. There are no other places in the paper where carrier has been used to refer to a\nhomozygote.\n9. The suggestion that different segments of a region as small as the British Isles and Ireland should\nhave different approaches to screening for haemochromatosis is unwise, and in fact unethical, in my\nview. A person with the average risk of a white person from England who lives in a low prevalence\narea should have the same access to screening as a person of Irish ancestry living in Ireland. In\naddition, it is impractical to offer screening programs to subsets of a population based on\ngeographical location. I suggest the discussion be revised to at least note these issues.\nWe thank the reviewer for sharing their opinion on this matter. We believe that screening should\nbe available to all, but we doubt that this is going to be possible at least in the beginning, given\nthe resource implications. We have, however, noted this more clearly by modifying a sentence in\nthe discussion (around line 4810 to read as follows:\n“While we advocate that screening should be available to all, given the resource implications, the\ndata presented here suggest prioritisation of populations for screening in a variety of ways.”\nIn this connection, we note that screening is already available to subsets of the UK population, for\ninstance, breast, ovarian and prostate cancer risk screening of the BRCA1 and BRCA2 genes is\navailable for individuals with one or more Jewish grandparents, and who are under the care of\nNHS England (but not NHS Scotland and not for people without Jewish grandparents):\nhttps://nhsjewishbrcaprogramme.org.uk/\nMoreover, Jnetics provides screening for 47 recessive Mendelian disorders in the Jewish\npopulation in the UK:\nhttps://www.jnetics.org/\nReviewer #2:\nThis manuscript describes rates of genetic risk for hereditary hemochromatosis and clinical\ndiagnoses of hereditary hemochromatosis across geographic regions in the British Isles and Ireland.\nIt also summarizes rates of genetic risk across ancestral groups. The manuscript, which is generally\nclearly written, highlights high, but variable, prevalence of genetic risk across regions and potential\nunder-ascertainment of at-risk individuals in certain regions. The methods are thoughtful, clearly\ndescribed, and appropriate for the study’s goals. Findings could have implications for genomic\nscreening for hereditary hemochromatosis, though additional clarity on the points below could\ninform how significant these findings are for the design and anticipated outcomes of population\ngenomic screening programs.\nWe thank the reviewer for this summary.\n1. Given that the rate of individuals with the major risk HFE genotype is relatively high across all\nregions and that, as the authors point out, genomic screening that includes the major risk HFE\n2\ngenotype is believed to be cost-effective, one could argue that findings provide further support for\ngenomic screening for hereditary hemochromatosis across all regions in the study, rather than for\ntargeting screening to certain regions. Yet, given the exceedingly low rates of individuals with the\nmajor risk genotype in some ancestral groups, this “screen everyone” approach might not be\noptimal. Beyond suggesting that genomic screening be targeted to regions that appear to under-\nascertain at-risk individuals, what do study findings suggest about how genomic screening programs\nin ancestrally diverse populations should consider inclusion of the major HFE risk genotype?\nWe agree that the findings provide further support for screening across all regions, and we have\nnow stated that in the discussion (lines 481-483):\n“While we advocate that screening should be available to all, given the resource implications, the\ndata presented here suggest prioritisation of populations for screening in a variety of ways.”\nWe go on to suggest a number of options for prioritisation.\n2. Consider shortening the Discussion in ways that minimize restatement of results and focus on\ninterpretation of findings.\nWe have now reduced the restatement of results in the discussion by deleting various sentences\nor parts of sentences around lines 395-398.\n3. It is at times difficult to follow exactly what genetic risk is being discussed. I would suggest clearly\ndefining each related term (genetic risk, major risk genotype, combined genetic risk) early in the\nmanuscript and using them consistently throughout. It should be clear when, for example,\np.H63D/p.C282Y compound heterozygotes are included with p.C282Y homozygotes in calculations\nand when genetic risk is multiplied by the cumulative incidence of hemochromatosis.\nWe thank the reviewer for this comment. We have now defined “major risk genotype” in the third\nline of the introduction. Combined genetic risk is defined at lines 290-293. Throughout the\nmanuscript we have now been very careful to distinguish overall or combined genetic risk and\nmajor risk genotype.\n4. If possible, it would be helpful to report the sensitivity and specificity of the E83.1 ICD-10 code for\nhemochromatosis. While the code appears to be commonly applied in individuals with HFE variants,\nit is not clear that it indicates iron overload (and may just indicate the presence of genetic risk in\nindividuals with known HFE variants).\nWe thank the reviewer for this comment. Lucas et al., 2024 have investigated this and we report\ntheir figures in the manuscript (lines 86-92) – approximately 90% of people with the E83.1 code\nhave a genetic risk for haemochromatosis arising from p.Cys282Tyr or p.His63Asp. However, it is\ntrue that in some cases this may only be an indication of the presence of risk, rather than of\nsymptoms related to iron overload, and we now make this explicit in lines 96-97:\n“but we note it is possible that in some cases the code only indicates the presence of the major risk\ngenotype, rather than of iron overload.”\n5. In the Introduction, consider expounding upon what is known regarding the association between\nearly detection and treatment of hemochromatosis and prevention or reversal of end organ\ndamage.\nWe thank the reviewer for this suggestion and have now cited Niederau C. et al., (1996)\nGastroenterology 110: 1107-19 on the fact that early diagnosis and treatment largely prevent the\n3\nadverse consequences of iron overload.\nA few minor suggestions:\n6. Please include citation(s) for the statement in line 51 regarding low penetrance of pathogenic HFE\nvariants.\nWe thank the reviewer and have now added a citation for this statement.\n7. Please include citation(s) for the statement in lines 93-94 that targeting population genomic\nscreening to areas with greater numbers of p.C282Y homozygotes would improve cost-effectiveness.\nThis statement is made because it’s logically impossible not to be the case. If cost effectiveness is\nmeasured by how many homozygotes one finds for a given amount of money spent, then it\nfollows that the places with the highest frequency must be the most cost-effective places to\nscreen. There is identical reasoning behind any screening programme that is targeted - e.g. to a\nparticular age-group (such as bowel or breast cancer screening in the UK), ethnicity (e.g. Jewish\nBRCA screening) or cases of a disease (e.g. retinopathy screening for diabetics), indeed any\nsubgroup that is at higher risk than the general population. Hence, cost-effectiveness must\nimprove if screening, which has a fixed cost per person screened, is targeted to places with higher\nfrequencies of the causal risk factor, in this case the p.Cys282Tyr genotype. Nevertheless, at lines\n98-99 we have modified the sentence (altered text underlined) to read, “It seems clear that\ntargeting …. to areas with greater numbers … should improve cost-effectiveness.”\n8. Consider moving the description of methods for assessing frequencies of non-HFE variants (lines\n223-228) to the Methods section.\nWe have moved two sentences from the section highlighted by the reviewer into the Methods.\n9. Consider explaining why hemochromatosis prevalence data from the NHS are not available for\nScotland, Wales and Northern Ireland (lines 346-347).\nWe wish that aggregated haemochromatosis (and other) prevalence data were available from\nScotland, Wales and Northern Ireland, but no joined-up data service equivalent to DigiTrials exists\nin these nations. For example, in NHS Scotland, similar data would involve applications to 14\ndifferent health boards, which was not possible with the funding (and therefore time) available.\nHowever, we believe that discussion of the data limitations outside NHS England DigiTrials is\nbeyond the scope of our paper.\n10. My understanding is that ACMG prefers the phrasing ‘ACMG secondary findings v3.0’ list (e.g., in\nline 365).\nWe thank the reviewer for noticing this error, which we have now fixed by using the preferred\nphrasing.\n11. Our Future Health is first mentioned in line 371 – I suggest mentioning earlier if relevant or\nexplaining here.\nWe thank the reviewer for pointing this out. We have now deleted the mention of Our Future\nHealth, as it was extraneous.\n4"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am happy with the responses to the reviews except for the response to the issue of differential screening based on geographic location. The example given where a Jewish person in England can have screening for BRCA but a person in Scotland with the same risk not having access is not a justification for differential screening for haemochromatosis. It is clearly unethical that in the same country that different rules apply based on geographic location. I believe the authors should be making this point rather than suggesting that a person with high risk ancestry living in a lower prevalence region should not have access to screening available to a person with the same ancestry living in a higher prevalence region.", "reviewer_2": "This is a thoughtful revision that improves clarity and context of the methods and findings. I have no further comments or suggestions."}, "author_response": "Response to second round of review\nReviewer #1:\nI am happy with the responses to the reviews except for the response to the issue of differential\nscreening based on geographic location. The example given where a Jewish person in England can\nhave screening for BRCA but a person in Scotland with the same risk not having access is not a\njustification for differential screening for haemochromatosis. It is clearly unethical that in the same\ncountry that different rules apply based on geographic location. I believe the authors should be\nmaking this point rather than suggesting that a person with high risk ancestry living in a lower\nprevalence region should not have access to screening available to a person with the same ancestry\nliving in a higher prevalence region.\nOf course, we believe that the Northern Irish and Hebridean diasporas (in England or Mainland\nScotland, for example) should be screened, but in most cases, the screening tends to begin where\nthe at-risk population is more concentrated, which is where the cost-benefit is going to be most\npronounced. This may not be ideal, but it is likely to be the reality, particularly at the start of a\nscreening programme, i.e., before its effectiveness for a full rollout can be fully assessed. It is likely\nthat this approach would also be a powerful approach to raise awareness in the descendant\ncommunities.\nTo temper our proposals, we have modified the wording around targeted screening, e.g. in the\nabstract, instead of proposing “priority areas in which to offer population screening”, we now\n“suggest health economic modelling of community screening should be targeted to these priority\nareas.”\nIn the introduction, we say: “to allow any work to build the evidence base for future screening\nprogrammes to prioritise areas of particularly high frequency, or discrepancies between overall\ngenetic risk and prevalence.”\nIn the discussion, we have modified our sentence, beginning “While we advocate that screening\nshould be available to all, given the resource implications, the data presented here suggest\nprioritisation of” to end “consideration of communities for screening, for example, through health-\neconomic analyses, in a variety of ways.”\nReviewer #2:\nThis is a thoughtful revision that improves clarity and context of the methods and findings. I have no\nfurther comments or suggestions.\nWe thank the reviewer for their comments."}]

Decision letter