Single Collection Et King Rare
Part of the following topical collections:.AbstractAs child mortality rates overall are decreasing, non-communicable conditions, such as genetic disorders, constitute an increasing proportion of child mortality, morbidity and disability. To date, policy and public health programmes have focused on common genetic disorders. Rare single gene disorders are an important source of morbidity and premature mortality for affected families. When considered collectively, they account for an important public health burden, which is frequently under-recognised. To document the collective frequency and health burden of rare single gene disorders, it is necessary to aggregate them into large manageable groupings and take account of their family implications, effective interventions and service needs. Here, we present an approach to estimate the burden of these conditions up to 5 years of age in settings without empirical data. This approaches uses population-level demographic data, combined with assumptions based on empirical data from settings with data available, to provide population-level estimates which programmes and policy-makers when planning services can use.
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Overall child mortality rates have shown large decreases over the past decades, in particular from reductions in deaths from infections, diarrhoea and vaccine-preventable diseases. Consequently, child mortality levels are now very low in many settings and policy attention is shifting to focus on non-communicable conditions, which now make up a larger relative proportion of all under-five deaths (Liu et al. In addition, in the Sustainable Development Goal era, strategies are increasingly seeking to move beyond survival to consider morbidity and disability outcomes, as highlighted in the Global Strategy for Women’s, Children’s and Adolescent’s Health (2016–2030) themes—Survive, Thrive, Transform (Every Woman Every Child ).
In settings with very low levels of communicable disease mortality, genetically determined disorders make up an important proportion of both stillbirths and child mortality, and ongoing disability. Genetically determined disorders can be divided into two broad groups: ‘single gene disorders’ caused by gene variants with strong effect and ‘genetic risk factors’—gene variants with weaker effect causing disease only when combined with other genetic and/or environmental factors.Single gene disorders arise in the first place from gene mutation. Since this can occur in any gene, single gene disorders can affect any aspect of structure or function and they are extraordinarily diverse (McKusick-Nathans Institute of Genetic Medicine ).
Despite their clinical diversity, single gene disorders have a common biological basis, all have the potential to be passed on to offspring and all require the same basic genetic and management services. These include accurate diagnosis, risk assessment and information for the affected individual and their family, and access to options for managing risk and services for affected children.The Modell Global Database of Congenital Disorders MGDb uses a set of defined methods to relate demographic data to the known birth prevalence of selected groups of congenital disorders, in order to generate estimates relevant to public health, policy-making and clinical practice (Modell et al. For the purpose of MGDb, single gene disorders are divided into two groups: firstly, ‘rare single gene disorders’, where the birth prevalence can be predicted from the balance between the rate at which disease gene variants arise by new mutation, and the rate at which they are lost because affected individuals die or fail to reproduce (Haldane; Harris; Cavalli-Sforza and Bodmer ); secondly, ‘common single gene disorders’ when the frequency in the population is increased as the causative gene variant confers a selective advantage in the local environment (e.g. The sickle cell gene providing protection against malaria) and country-specific information is necessary to define their birth prevalence. In addition, selected ‘genetic risk factors’ that can have an impact on early life mortality and morbidity are included in MGDb (Bhutani et al.; Smits-Wintjens et al.
).This paper is the sixth in this special issue on methods for estimating the global burden of congenital disorders. Here, we describe the methods used in the MGDb to estimate the collective baseline birth prevalence of rare single gene disorders, and the effect of available interventions on affected birth prevalence and outcomes. Methods to estimate ‘genetic risk factors’ or ‘common single gene disorders’ are not discussed in this paper.
Further details of genetic risk factors included in MGDb (rhesus negativity, G6PD deficiency and alpha plus thalassaemia) and common single gene disorders (e.g. Haemoglobin disorders, cystic fibrosis, oculo-cutaneous albinism) can be accessed online (Modell et al. The inheritance of rare single gene disorders generally follows Mendelian inheritance patterns. They include autosomal dominant conditions, autosomal recessive and X-linked disorders. In MGDb since the overall aim is to support policy-making in maternal and child health, only early-onset rare single gene disorders are considered. For the purposes of MGDb, these disorders are grouped as ‘early-onset dominant disorders’, ‘recessive disorders’, ‘X-linked disorders’ and ‘genetic type unknown’ (Table ).
Later-onset single gene disorders such as family cancer syndromes, adult polycystic disease of the kidney or familial hypercholesterolaemia are not included. InheritanceTerminology of groupings of disorders used in MGDbAutosomal dominantEarly-onset dominant disorders manifest at or soon after birth, are often due to new mutation, and usually lead to early death or seriously impaired reproductive fitness. Most occur without a family history, and are likely to be eliminated by natural selection in the first generation in the absence of diagnosis and care. Examples include osteogenesis imperfecta, achondroplasia and Apert’s syndrome.Autosomal RecessiveRecessive disorders occur in one in four of the offspring of couples who both carry a potentially lethal variant of the same gene. Most people are asymptomatic carriers of at least one potentially lethal gene variant, but as most gene variants are individually rare in the general population, the chance of an at-risk union is low. Therefore, most recessive disorders are uncommon, and most affected infants are born to parents without an antecedent family history. Examples include cystic fibrosis and glycogen storage disorders.Parental consanguinity increases the chances that a couple will both carry the same recessive gene variant and hence is associated with an increased birth prevalence of recessive single gene disorders.
In MGDb the term ‘consanguinity-associated disorders’ is used to refer to this increment, and they are grouped separately because their birth prevalence depends on the population prevalence of parental consanguinity (Bittles and Black; Sheridan et al. ).X-linkedX-linked disorders are typically transmitted by unaffected female carriers and expressed in 50% of their male offspring. Female carriers may be affected to varying degrees; female homozygotes are rare. They tend to present during childhood and there is often a family history.
Examples include haemophilia, Duchenne muscular dystrophy and X-linked mental retardation.Genetic type unknownGenetic type unknown disorders for which the type of inheritance (e.g. Dominant, X-linked, recessive) is not known.
As with other conditions modelled in MGDb, the first step is to estimate the baseline birth prevalence of single gene disorders, in the absence of any interventions (Fig. Baseline or potential birth prevalence includes stillbirths and livebirths, but excludes miscarriages. MGDb follows the European Congenital Anomalies Registry ( ) convention and uses ‘fetal death’ (death in utero after 20 weeks’ gestation) as a proxy for stillbirth and all losses before 20 weeks’ gestation are viewed as miscarriages (European Surveillance of Congenital Anomalies ( )). In keeping with ICD-10, regardless of gestation, all births with any signs of life following separation from the mother are counted as livebirths (World Health Organization ). 1Overview of estimation of rare single gene disorders in MGDbThe global prevalence of rare single gene disorders is estimated in MGDb using data on the birth prevalence of these disorders from populations with available data and applying these estimates to populations currently lacking such data, with adjustments where necessary.
Step 1—Estimation of baseline birth prevalenceSince birth prevalence of rare single gene disorders with no selective advantage reflects a balance between new mutation and loss due to natural selection, we considered factors that could affect this balance. These include the effect of advanced paternal age on mutation rate and of parental consanguinity on affected birth prevalence and thus on selection pressure.We estimated baseline birth prevalence separately for non-consanguinity- and consanguinity-associated disorders. The overall baseline birth prevalence of rare single gene disorders is estimated as the baseline prevalence of non-consanguinity-associated disorders plus the baseline prevalence of consanguinity-associated disorders. 1a—Estimation of non-consanguinity-related baseline rare single gene disorders prevalence. Population-based congenital abnormality registers form an important data source for the birth prevalence of many congenital disorders (Moorthie et al. However, as only a minority of single gene disorders are clinically recognisable at birth, and diagnosis in the remainder usually requires specialist diagnostic facilities, only those conditions that cause physical abnormalities are captured in these registers.
The published literature was therefore reviewed for alternative prevalence data sources. Three ‘classical’ population-based studies and one review of the collective prevalence of single gene disorders were identified (Ash et al.; Baird et al.; Stevenson; Trimble and Doughty ) (see p2).
These studies were set in Northern European or North American populations and reported broadly similar results. They all date prior to 1990, as recent research has tended to focus on basic science only.
In the absence of more recent data on birth prevalence, MGDb uses the rates of Baird et al. ( ), based on the British Colombia Health Registry, to represent global baseline birth prevalence of early-onset single gene disorders as these provide the most recent and complete data. However, they apply only for live births and do not include fetal deaths.
Single Collection Et King Rare Set
As no readily accessible data on fetal death associated with early-onset autosomal dominant and X-linked disorders could be found, the livebirth rates reported by Baird et al. Are assumed to be equal to the total births affected and no further adjustment is undertaken. A similar approach is taken for the ‘genetic type unknown’ category.
However, reliable data exists on prevalence of stillbirths related to consanguinity-associated recessive disorders (Bittles and Black; Bundey and Alam ). These data were used to adjust the rates reported by Baird et al. (Baird et al.
The same stillbirth rate is assumed for non-consanguinity-associated recessive disorders (Table ). Investigation of effects of paternal age on gene mutation rateMutations arise because of uncorrected errors in DNA replication during cell division. In females, relatively few cell divisions occur in the formation of mature oocytes, but as adult males generate sperm life-long, spermatogonial stem cells may have undergone more than 1000 divisions by 60 years of age.
An association between paternal age and prevalence of new mutations is therefore to be expected and has long been recognised (Ramasamy et al.; Tuente ), although it has proved hard to quantify. This is because new mutations occur on one chromosome only—i.e. They are heterozygous; therefore, only early-onset severe dominant disorders will show a clinical effect in terms of fetal death, early death or disability in the first generation. The birth prevalence of X-linked and recessive disorders reflects the average mutation rate over previous generations. This would only be affected by medium- to long-term changes in parental age distribution.Previous studies have demonstrated an exponential effect of paternal age on mutation rate, with a more than four-fold increase at paternal age 45–49 years when compared to a baseline of 30–34 years (Modell and Kuliev ) (see p3).
There are large inter-country variations in paternal age (United Nations ), and hence, substantial differences would be expected in mutation rates, and the baseline prevalence of single gene disorders. Trends in paternal age distribution for countries with available data have shown a reduction in paternal age from the earliest records in 1940 to the mid-1970s, associated with increased availability of family planning services and the reduction in overall family size in these countries (United Nations ). However, since that time, average paternal age has increased and estimated mutation rates based on paternal age distributions have rebounded to similar levels estimated in the1940s and 1950s.
This suggests the overall long-term effects of the observed oscillations are quite small. No adjustment for the effects of parental age was therefore included (see p4 for details). In human genetics, the definition of a consanguineous union is one in which the partners are related as second cousins or closer. That is, they have one or more common ancestors within the preceding three generations. Consanguineous partnership increases the chance that a couple will both carry the same recessive disease variant and be at risk for having affected children; the effect is particularly marked for rare disorders. Therefore, where consanguineous marriage is common, there is an increased birth prevalence of a wide spectrum of rare recessive conditions (Corry ).
The genetic implications for offspring are expressed as a coefficient of consanguinity ( F), which describes the proportion of the children’s gene pairs that are identical because they are inherited from a common ancestor. Table shows the commonest types of parental consanguinity and associated coefficients of consanguinity. Global data on consanguinity levelsEstimated values of the mean coefficient of consanguinity ( F) are available for 288 countries and vary from 0.0001 in many developed countries to 0.0332 in Pakistan (Bittles and Black ). In MGDb, these estimated values are used to calculate the total percentage of all parent couples who are consanguineous, assuming that around two thirds of all consanguineous parents are first cousins and one-third are more distant relatives (see p5). To enable comparisons between populations, MGDb uses a coefficient of consanguinity of 0.01 (equivalent to 1% of genes identical by recent descent) as a unit of parental consanguinity.
Limited or no data on excess mortality with early-onset dominant, X-linked rare single gene disorders or for the group classified as ‘genetic type unknown’ are available. Available evidence from dominant haemoglobin disorders supports the assumption that early-onset dominant disorders have a higher mortality than recessive disorders. X-linked disorders are typically less severe, and hence are assumed to have a lower mortality. No data on survival are available for the group classified as ‘genetic type unknown’. In order to generate a conservative estimate, this group is assumed to have the same mortality risk as X-linked disorders. A summary of case fatality rates used in MGDb is provided in Table. Further details are available online (Modell et al.
1Reliable collective figures for neonatal death are only available for consanguinity-associated and recessive disorders. Rates for other groups are estimated at 60% of the infant mortality rate Child disability outcomesWhilst allogeneic haematopoietic stem cell transplantation has been used as a curative treatment for beta-thalassaemia and severe sickle cell disorders and there is some promising research on the effect of gene editing, there is currently no definitive cure for any rare single gene disorders (King and Shenoy; Wang and Gao ). Therefore, all survivors affected with rare single gene disorders are assumed to have some degree of disability. This ranges from conditions such as phenylketonuria and thalassaemia where affected individuals with continuing access to appropriate medical intervention can be ‘well on treatment’, to severe physical and mental disability for those unable to access care, or with conditions where no treatment currently exists. Longer-term outcomesThe steps shown above provide details of requirements to estimate child outcomes. However, available survival data enables the construction of life-time survival curves, which can be used in MGDb to calculate mean life expectancy and other longer term outcomes (Modell et al. Regional estimates of baseline birth prevalence of rare single gene disorders.
Any genetic diagnosis (whether in an affected or an unaffected person) involves the family as well as the presenting individual. Relatives need information on the mode of inheritance and possible health and reproductive risks for themselves, access to definitive diagnosis when this is available, and supportive genetic counselling. This requirement, which is specific for single gene disorders, introduces new concepts, and new educational and work force requirements into health services (Samavat and Modell ).
It is therefore necessary to view these disorders as a coherent group.Three important points are raised in this article. Firstly, the potential of using existing data to estimate the birth prevalence and outcomes of rare single gene disorders. Secondly, the importance of the contribution of consanguinity-associated disorders to total single gene and overall congenital disorders (Fig. Finally, the potential effect of prospective carrier detection by new methods of genomic analysis, for increasing the currently very limited effect of genetic counselling on the birth prevalence of rare single gene disorders. However, as stated above, this requires consideration of the evidence base in relation to the pathogenicity of particular variants, how they could be incorporated into prospective screening and the ethical, legal and social implications of such a venture.To date, public health approaches to congenital disorders have tended to focus on congenital anomalies (Black et al.; Liu et al.; Lopez et al. ), whilst single gene disorders are seen as too rare, too diverse and too difficult to handle. However, they are an important category of congenital disorder as although individually rare, collectively, they contribute significantly to infant mortality and morbidity (Baird et al.; Emery and Rimoin; Lacaze et al.
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One barrier to assessing the disease burden of single gene disorders is that most initiatives for their treatment and/or prevention have been devised by treating clinicians or lay support groups and so tend to be specific to particular disorders. The inevitable focus on individual diagnoses means that the need for patient care obscures their common mode of inheritance and common genetic service needs and tends to favour competition rather than co-operation. The development of the Rare Diseases initiative should help to overcome this difficulty (Dharssi et al. Though the definition of a rare disease is based on frequency rather than cause, around 80% of recognised rare diseases are in fact single gene disorders. Another barrier is that single gene disorders can affect any aspect of structure or functioning and so are scattered through many categories of the International Classification of Diseases (ICD) (World Health Organization ).
Therefore, reliance on ICD10 classification can make analysis for this group of disorders very cumbersome. An alternative approach is to apply the basic principles of population genetics to single gene disorders rather than dealing with individual diagnoses, and sufficient information is available to apply this method for assessing their global birth prevalence.In this paper, we present this non-ICD-based approach to assess the collective epidemiology of rare single gene disorders. A notable limitation is the reliance on few data sources to inform the estimation of their birth prevalence. In recent years there has been a proliferation in epidemiological data regarding single gene disorders in high income settings, with some countries establishing national rare disease registries (www.orpha.net ). However, whilst low levels of consistency between studies, poor documentation of methods, confusion between incidence and prevalence and over birth prevalence currently limit the use of these sources for accurate prevalence data. In the future, an adequately funded multi-country umbrella registry organisation could overcome some of these barriers and may provide useful comparable prevalence data for policy-making.In the absence of reliable data, several assumptions are required, including regarding access to care, and women’s behaviour concerning TOP for an affected pregnancy. These could affect the accuracy of the estimates of actual birth prevalence, e.g.
Over-estimation of access to pre-natal diagnosis and uptake of TOP would lead to falsely low livebirth prevalence, which may lead to under-provision of care for affected children. Under-estimation of access to optimal care for affected children could result in an over-estimation of single-gene-associated deaths, and an under-estimate of the requirements for ongoing care for those affected living with disability.In addition, apart from consanguinity-related recessive disorders, data to inform mortality outcomes is limited, often relying on historical data from high-income settings. In the future, data from cohort follow-up studies building on the rare disease registries platforms could provide improved data for high-income settings.Despite these limitations, this work demonstrates the important role of consanguinity in the prevalence of rare single gene disorders, with around half of all rare single gene disorders globally estimated as being consanguinity-associated (Fig. This shows the need to develop appropriate genetic services to reach those most at risk. Genetic counselling has been shown to have a very limited impact, with around 5% reduction in birth prevalence, when relying on the current retrospective approach (Blencowe et al.; Modell et al.
This compares with an observed 85% reduction in birth prevalence of thalassaemia and 15% reduction for sickle cell disorders in Western Europe, where risk is usually identified prospectively. If prospective risk identification becomes available for the majority of recessive disorders using new techniques such as novel sequencing technologies, and 50% are perceived as severe and 50% as less severe, the experience of haemoglobin disorders suggests that their collective birth prevalence, and associated early mortality and disability, could fall by 50% or more. General deployment of the new diagnostic methods could therefore cause a reduction of around 10% in under-5 deaths, with an even more marked effect on numbers living with disability. Rare single gene disorders are an important source of morbidity and premature mortality for affected families. When considered collectively, they account for an important public health burden, which is frequently under-recognised.
Single Collection Et King Rare Pokemon
MGDb provides a method to estimate the burden of these conditions in settings without empirical data, providing population-level estimates that can be used now by programmes and policy makers when planning services. Estimates using this approach will be strengthened in the future as more data become available from a variety of settings to improve the model parameters. AcknowledgementsCongenital Disorders Expert Group: AH Bittles, H Blencowe, A Christianson, S Cousens, M Darlison, S Gibbons, H Hamamy, B Khoshnood, CP Howson, J E Lawn, P Mastroiacovo, B Modell, S Moorthie, JK Morris, PA Mossey, AJ Neville, M Petrou, S Povey, J Rankin, L Schuler-Faccini, C Wren, KA Yunis FundingThis work was supported by grants from the WHO Regional Office for the Eastern Mediterranean. The time of SM and HB was funded in part through a grant from the Bill and Melinda Gates Foundation to the Child Health Epidemiology Reference Group (CHERG). Compliance with ethical standardsThis article does not contain any studies with human or animal subjects performed by any of the authors.