The aim of this study was to identify barriers and enablers of diabetic eye screening (DES) attendance amongst young adults with diabetes living in the United Kingdom.Semistructured qualitative interviews with adults aged 18–34 years with diabetes. Participants were purposively sampled to aim for representation across gender, geographical locations, diabetes type, years since diabetes diagnosis and patterns of attendance . Data were collected and analysed using the Theoretical Domains Framework (TDF) to explore potential individual, sociocultural and environmental influences on attendance. Data were analysed using a combined deductive and inductive thematic analysis approach. Barriers/enablers were mapped to behaviour change techniques (BCTs) to identify potential strategies to increase attendance.Knowledge] (e.g. not understanding reasons for attending DES or treatments available if diabetic retinopathy is detected), [Social Influences] (e.g. lack of support following DES results), [Social role and Identity]  and [Environmental Context and Resources] (e.g. lack of appointment flexibility and options for rescheduling). Enablers included: [Social Influences] (e.g. support of family/diabetes team), [Goals] (e.g. DES regarded as ‘high priority’). Many of the reported barriers/enablers were consistent across groups. Potential BCTs to support attendance include Instructions on how to perform the behaviour; Information about health consequences; Social support  and Social comparison.Key barriers to attendance reported by the sample of 29 study participants with type 1 diabetes, fell within the TDF domains: [Attendance to diabetic eye screening in young adults is influenced by a complex set of interacting factors. Identification of potentially modifiable target behaviours provides a basis for designing more effective, tailored interventions to help young adults regularly attend eye screening and prevent avoidable vision loss. An organised screening programme for diabetic eye disease reduces the risk of visual impairment, but attendance is suboptimal, particularly in adults aged 18–34 years.Previous studies have explored modifiable influences on screening attendance, but often do not differentiate between population groups, few studies focus on young adults.We applied the Theoretical Domains Framework to identify modifiable barriers and enablers to screening attendance in young adults with type 1 diabetes living in the United Kingdom.Common barriers included lack of understanding of diabetic eye disease or its treatment; lack of appointment flexibility and the need for information and support following screening results. Social support of family and the diabetes team were identified as a key enabler.These findings provide a basis for developing more targeted interventions. Potential strategies to increase attendance in this group include tailored education, persuasive communication and integration of diabetic eye screening with other diabetes appointments1In the United Kingdom, diabetic eye screening (DES) is managed by the National Screening Committee.This raises questions as to how screening attendance can be increased in young adults to prevent complications and avoidable vision loss. To answer this, we must first understand the reasons why young adults attend or do not attend DES (i.e. the barriers and enablers). There have been a number of studies internationally exploring barriers/enablers to diabetic retinopathy screening, which are summarised in a recent systematic review.Delivery of DES by healthcare professionals (HCPs) and attendance at screening appointments are examples of complex human behaviour. Theories provide explicit statements regarding processes hypothesised to regulate behaviour, and can be used to explain and predict human behaviours.Identify barriers and enablers to DES in young adults aged 18–34 years living in the United Kingdom.Identify potential behaviour change intervention strategies to encourage attendance in this population group.The aim of the current study was to apply the aforementioned behavioural science frameworks to:22.1A behaviour change theory‐informed qualitative study of young adults with diabetes.2.2This study received ethical approval from the NHS Wales Research Ethics Committee 2 (REC reference: 19/WA/0228).2.3Eligible participants were English speaking, adults aged 18–34 years with type 1 or type 2 diabetes, who had attended at least one DES appointment. The choice of the age range 18–34 years was based on previously published audits of the UK diabetic eye screening programme, showing that this group is least likely to attend first and subsequent retinopathy screening and was most likely to be repeat non‐attenders.Purposive sampling was conducted with the aim of achieving variation within the target age group in terms of geographical location, ethnic group, type of diabetes and past history of attendance. For attendance, we retrospectively categorised participants as regular attenders ; unintentional non‐attenders ; and intentional non‐attenders (i.e. participants who have actively chosen to not attend previous DES appointments).The target sample size for the current study was up to 30 interviews. Recruitment continued, with concurrent analysis, until thematic saturation was reached, that is, no new themes were emerging from the data and existing themes were supported by data from several participants.2.4knowledge; skills; social/professional role and identity; beliefs about capabilities; beliefs about consequences; optimism; reinforcement; intention; goals; memory/attention/decision processes; environmental context/resources; behavioural regulation; social influences; and emotion. The interviews were piloted prior to data collection with two young adults with diabetes, and refined accordingly to enhance clarity and flow. The final version of the topic guide is available in Appendix The semistructured interview topic guides aimed to understand reasons why young adults do or do not attend DES attendance. The topic guide was developed by a team of behavioural scientists, health psychologists and clinicians, with input from four young adults with diabetes. The questions in the topic guide were structured around the 14 domains of the TDF: 2.5One‐to‐one interviews were conducted via telephone by the same researcher (LP), a health psychologist with extensive experience in qualitative research. Interviews took place between December 2019 and September 2020. Participants were asked to either complete an informed consent form and send this via email to the researcher ahead of the interview, or provide verbal consent prior to the start of the interview. Interviews were audio‐recorded, transcribed verbatim and fully anonymised, so that no individual could be identified from the data.2.6Developing a coding framework structured around the 14 TDF domains following collaborative coding of three interview transcripts by three researchers (LP/FL/MC). The coding framework is available in Appendix Generating a template summary of each interview transcript following methods for rapid qualitative analysis.Inductive themes, sometimes referred to as ‘Belief Statements’ in the TDF literature,A matrix was used to look for similarities, differences and trends in responses: Following analysis of all 29 interview transcripts, themes were transferred into an Excel table of respondents by domains.The key domains and themes representing barriers/enablers to DES were identified using established criteria: (a) frequency ; (b) spontaneity (did the theme occur spontaneously or was it elicited by a question in the topic guide); (c) elaboration (number of themes per domain).Transcripts were analysed using deductive framework analysis to code text into the broad TDF domains, followed by inductive thematic analysis to further code text within each domain.2.7Using a similar approach to van Allen et al.,33.1We conducted interviews with young adults with type 1 diabetes, lasting an average of 30 min (range 12–50 min). We were unable to recruit participants within our 18–34 age group with type 2 diabetes.Data saturation was deemed achieved after 29 interviews. Fifteen of these participants were regular DES attenders, six were unintentional non‐attenders and eight were intentional non‐attenders. Other participant demographic characteristics are summarised in Table 3.2Optimism. Table Reported barriers and enablers to DES attendance were identified across 13 (of 14) domains, with the exception of Table 3.2.1Knowledge was a mixed influence on DES attendance. Although enablers reported in this domain included understanding both how diabetes affects the eyes and the reasons for attending DES, all but one of the non‐attenders reported that educational courses, for example, DAFNE did not cover diabetic eye disease and DES in detail. A common knowledge barrier was being unaware of the treatments available if diabetic retinopathy were to be detected. Several participants indicated that they would like to know more about the treatments available. In contrast, a small number indicated that they would not want to know more, unless treatment was required.3.2.2Social Role and Identity was a mixed influence on DES attendance. Barriers within this domain were particularly reported by intentional non‐attenders and included participants not knowing other people their age with diabetes, feeling ‘isolated’ and the ‘odd one out’ during school and teenage years and being reluctant to disclose their diabetes. Most intentional non‐attenders described becoming more comfortable disclosing their diabetes as they became older. In support to this, an enabler within this domain included ‘knowing others with diabetes/being part of the online community means you are more engaged with your diabetes management’. A number of intentional non‐attenders described actively making steps to meet other young adults with diabetes. This resulted in them becoming more engaged with their DES appointments around their early to mid‐20s.3.2.3Environmental context and resources was a mixed influence on DES attendance. Common barriers within this domain included lack of flexibility and options for (re‐) scheduling DES appointments  and appointments taking up to half a day or more. A further barrier was transitioning from paediatric to adult care and the impact of university. Transitioning from paediatric to adult diabetes care meant being seen less frequently in diabetes clinics and usually by an unfamiliar team and some participants ‘dropped off’ from attending DES during this ‘difficult period’. Participants experienced difficulties attending DES whilst at university; either due to delays in changing eye screening programme or having to attend DES appointments outside of term time. The lack of co‐ordination between DES and other diabetes appointments was particularly reported as a barrier by unintentional non‐attenders and included issues such as ‘random’ timing of appointments during the year and ineffective communication between the DES and diabetes care teams. The instillation of eye drops to dilate the pupil (mydriasis) is standard practice in the NHS screening programme to improve retinal image quality, although this is not always required in young adults. Advantages of not having mydriatic eye drops was an enabler to DES attendance within this domain. Specific advantages included being able to drive to and from the appointment, and DES taking less time from participants’ working day.3.2.4Social influences barrier was the need for more support and information after receiving DES results. Participants thought the DES results letter provided insufficient ‘blanket’ information. They reported a preference to speak to a diabetes consultant or nurse (either in‐person or over the telephone) to obtain tailored feedback . Impact of HCP communication was a mixed theme within this domain. Impact of HCP communication as an enabler particularly referenced diabetes specialist nurses. Participants reported nurses being ‘really helpful’ with regular contact in‐between appointments (facilitated mainly via text message and email). Communication became a barrier to DES attendance when young adults perceived HCPs displayed a lack of knowledge about diabetes, or they were being ‘judged’ or ‘spoken to harshly’ . Enablers to DES attendance within the Social influences domain include members of the diabetes team checking DES appointment attendance and partners/family members assisting with travel to and from their DES appointments. Although an enabler, participants acknowledged that requiring assistance getting to and from DES appointments was sometimes impractical (e.g. co‐ordinating a time when both they and their family member/partner were available).A common 3.2.5Goals was an enabler to DES attendance. Priorities in diabetes management was a common theme across participants. Attending DES appointments was regarded as a high priority by the majority of participants. Attending DES became more of a priority when participants experienced eye complications. This was described by one interviewee as a ‘jolt’ that said ‘you need to sort yourself out before things get any worse’. This theme also applied to participants with indirect experience of eye complications caused by diabetes (e.g. family members). These two interviewees separately reported feeling genetically more at risk of complications and not wanting ‘to go that way’.3.2.6Intention was an enabler to DES attendance across participants. Almost all participants expressed a strong intention to attend future DES appointments. Pregnancy was a life event which increased one interviewee's likelihood of attending DES, because she knew she was more at risk of complications while pregnant.3.2.7Memory, attention and decision processes was a mixed influence on DES attendance. A common barrier within this domain was forgetting to attend at least one DES appointment. This barrier was especially reported by unintentional non‐attenders, citing issues such as DES appointments being sent to far in advance and not receiving the DES invitation letter. Enablers within this domain included preference to receive their DES appointment via text/e‐mail/phone call, rather than via a letter which can be easily missed and is also less economical. Participants suggested a text message or phone call prior to the appointment would serve as a useful reminder to attend.3.2.8Emotion was a mixed influence of DES attendance. Common enablers reported in Emotion included concern and worry about future diabetic eye complications. Diabetes scare stories were reported as a barrier to DES attendance. This involved young adults either reading or being warned by HCPs, about complications they will experience unless they manage their blood sugars . Another barrier, reported by some intentional non‐attenders, was diabetes distress/burnout. This was caused by the burden of attending multiple appointments, the ‘constant’ demands of blood glucose management and a feeling of being ‘overwhelmed’. Mixed feelings about receiving DES results describe how interviewees felt about finding out the outcome of the screening procedure. Feeling nervous and anxious about receiving the results was especially reported by intentional non‐attenders, some of whom acknowledged that how they feel depends on the result.3.3Table 4Knowledge; Social influences; Social role/identity; Environmental context/resources; Goals and Intention. Many of the same theoretical domains were identified as barriers/enablers to DES in two previous studies using the TDF, including a study of young adults with type 2 diabetes in AustraliaThis study aimed to identify the barriers and enablers to DES attendance using the TDF to code the interviews, with an emphasis on modifiable behaviours. The key TDF domains in terms of frequency, elaboration and importance were Knowledge), strong intentions to attend future DES appointments (Intention). Barriers more specific to non‐attenders included participants not knowing other people their age with diabetes, feeling ‘isolated’ and the ‘odd one out’ during school and teenage years and reluctance to disclose their diabetes , diabetes distress/burnout and feeling nervous and anxious about receiving DES results (Emotion). Barriers more specific to unintentional non‐attenders included the lack of co‐ordinated diabetes care  and lack of coordination between DES and other aspects of diabetes care .Study participants included regular DES attenders, unintentional non‐attenders and those who had intentionally missed one or more screening appointments in the past. Many factors influencing behaviour were consistent across groups, for example, knowledge gaps regarding DES and its treatment  from other aspects of diabetes care. This lack of integration was reflected in the perceived communication difficulties between DES and other members of the diabetes care team. Furthermore, the physical separation between sites providing DES and other processes of care makes it difficult to integrate DES and screening tests for other diabetes complications. Improved communication and collaboration between the screening programme and GP practices4.1We have identified a range of potential strategies to increase DES attendance. Some interventions targeting the person with diabetes are relatively simple, for example, providing age‐appropriate information on the risk of developing retinopathy and its treatment and restructuring the content of results letters. A previous study investigating the effectiveness of a tailored evidence‐based information leaflet to promote uptake of DES in young adults with type 2 diabetes, found that this simple intervention significantly increased knowledge of diabetic retinopathy, an important predictor of DES uptake.Interventions directed at HCPs  could include the development of a nationally approved training programme that includes specific recommendations for actions HCPs could take to support, encourage and enable young adults to attend DES .At a policy level, we have made recommendations to better integrate eye screening with other diabetes services. Although there is no currently high quality evidence from the United Kingdom that integrated ‘one‐stop clinics’ improve DRS uptake specifically in young adults, ‘collaborative case management’, which coordinates the processes of diabetes care, has been shown to improve diabetic retinopathy outcomes in trials of a general population of adults with diabetes.Another policy recommendation is to review the selective use of mydriatic drops in young adults. The National Screening Committee (NSC) currently recommends dilating all people attending for screening on the basis of the ease of organisation and improved image quality; however, there is evidence that targeted mydriasis strategies can be effective for DES.4.2One of the main strengths of the current study is that it addresses an important evidence gap and incorporated the views and experiences of young adults with diabetes in planning and conducting the research. Although there are many studies that have previously reported modifiable barrier/enablers to DES,Our inclusion criteria included young adults with type 1 and type 2 diabetes. Despite recruiting a diverse sample of people with type 1 diabetes in terms of demographic factors and screening behaviour, we were unable to recruit young adults with type 2 diabetes. Recruitment challenges in this population have been previously identified.Whilst the TDF provides a useful and comprehensive theoretical approach to identifying influences on behaviour, if applied too rigidly there is a risk that non TDF‐related factors could be missed.4.3Type 2 diabetes is becoming increasingly prevalent in adolescents and young adults and further research is needed to evaluate strategies to increase their representation in health and medical research.The results from the mapping of TDF domains to BCTs have identified a number of potentially effective target behaviours at multiple levels, many of which have been shown to be effective in a general population of people with diabetes5The current study is the first in‐depth exploration of the factors influencing DES attendance from the perspective of UK young adults with type 1 diabetes. A behavioural approach was used, informed by the TDF, which allowed us to identify a number of barriers to and enablers of DES attendance. Although there were high levels of awareness of the importance of DES, there was a lack of knowledge of treatments available should diabetic retinopathy be detected. Many of the barriers related to the competing time demands and practical issues with scheduling DES appointments, including the lack of coordination with other aspects of diabetes care.Identifying the theory‐informed determinants of DES attendance behaviour provides an opportunity to design interventions that specifically target these behaviours. It is likely that tailored approaches will be needed to facilitate implementation and uptake of DES in young adults. This study has identified a number of potential behavioural targets and programme changes that could be used to inform intervention components to address modifiable barriers and enhance enablers to attendance.None.This report is independent research funded by the National Institute for Health Research , PR‐R20‐0318–22001). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.Supplementary MaterialClick here for additional data file.


Accessible housing is crucial to maintain a good quality of life for older adults with functional limitations, and housing adaptations are instrumental in resolving accessibility problems. It is unclear to what extent older adults, who have a high risk of further functional decline, use housing adaptation grants acquired through the long-term care (LTC) insurance systems. This study aimed to examine the utilization of housing adaptation grants in terms of implementation and costs, for older adults with different types of functional limitations related to accessibility problems.The study sample included individuals from a suburban city in the Tokyo metropolitan area who were certified for care support levels (indicative of the need for preventive care) for the first time between 2010 and 2018 . We followed the study participants over 12 months since the care needs certification. We matched and utilized three datasets containing the same individual’s data: 1) care needs certification for LTC insurance, 2) insurance premium levels, and 3) LTC insurance claims. We conducted a multivariable logistic regression analysis to estimate the likelihood of individuals with different functional limitations of having housing adaptations implemented. Afterward, we conducted a subgroup analysis of only older adults implementing housing adaptation grants to compare costs between groups with different functional limitations using the Mann–Whitney U and Kruskal–Wallis tests.n = 1,622) of the study sample, and the median cost per individual was 1,287 USD. Individuals with lower extremity impairment or poor balance were more likely to implement housing adaptations (adjusted odds ratio (AOR) = 1.290 to AOR = 2.176), while those with visual impairment or lower cognitive function were less likely to implement housing adaptations (AOR = 0.553 to AOR = 0.861). Costs were significantly lower for individuals with visual impairment  compared to others .Housing adaptations were implemented among 15.6% (Older adults with visual or cognitive limitations may not receive appropriate housing adaptations, despite their high risk of accessibility problems. Housing adaptation grants should include various types of services that meet the needs of older people with different disabilities, and the results indicate there may be a need to improve the system.The online version contains supplementary material available at 10.1186/s12877-022-03100-9. It isFor older adults with disabilities, it is important that the housing environment is designed or adapted to compensate for their disabilities to maximize their ability to attain aging in place . PreviouFurthermore, it is important that housing adaptations be focused and carefully planned for older adults with disabilities, who have difficulties in performing daily activities because of accessibility problems. Accessibility is an aspect of person–environment fit and denotes the relationship between an individual’s functional capacity (competence) and the demands or design of the physical environment , 13. It In Japan, a housing adaptation grant using LTC insurance, which is public insurance for people who need preventive care or LTC services, is the typical way for older adults to adapt their housing environment to match their physical functions. Older adults who are certified for care support levels do not require LTC services immediately, but this group is considered at risk of functional deterioration in the near future and is recommended to use preventive care . They arThis housing adaptation grant supports older adults financially with up to 200,000 JPY , and older adults only have to pay 10% or 20% of the housing adaptation costs ; therefoThis study had a retrospective cohort design and utilized data from residents of a Japanese suburban city in the Tokyo metropolitan area. The population of this city was about 490,000 (data from 2018), and older adults aged ≥ 65 years accounted for 27.4%, which was comparable with the national average of 28.1% .We used three separate datasets with data from April 2007–March 2018, including 1) care needs certification for LTC insurance, 2) insurance premium levels, and 3) LTC insurance claims. These datasets are stored in each municipality across Japan to keep a record of the Japanese LTC insurance system. Each residents' data is stored in each dataset according to the type of information as follows.1) Data on care needs certification for LTC insurance, including each individual’s certified levels and assessment results from 74 items on the severity of physical disabilities and cognitive impairments used to assess LTC needs , 24. Eac2) Data on LTC insurance premium levels, which are classified from individual and household-level taxation. The LTC insurance premium levels are used to indicate household income, which we have used as a proxy for economic status.3) Data on LTC reimbursement claims including information on the cost of housing adaptation grants and assistive devices. We utilized data on the implementation and costs of housing adaptation grants based on reimbursement claims.The three datasets comprise:We were granted access to these datasets in accordance with an agreement from a collaborative research project between this municipality and the Dia Foundation for Research on Ageing Societies, Tokyo. All data were linked using the pre-assigned anonymous identifying numbers.n = 11,229), we excluded those younger than 65 years old (n = 46), those lacking data on LTC insurance premium levels (n = 62), and those who were excluded from certification because they moved out of the municipality within 12 months of the certification or because of their death (n = 749). With these exclusions, 10,372 individuals were included in the final study sample.The study sample comprised residents aged ≥ 65 years old who had been certified for care support levels in LTC insurance for the first time by the local government between April 2010 and March 2018. In Japanese LTC insurance system, the eligibility criteria are for 65 years or older, or those who are 40–64 years of age with any of 16 specific diseases. These people can apply for the certification, and are classified into one of seven levels ; we focused only on older adults with care support level 1 or 2. We regarded certification as being the first for an individual if we found no existing certification data between April 2007 and March 2010, because care needs certification is generally reviewed every two years. From the initial sample , treatment of pressure ulcers, and catheterization. Medical procedures were categorized as “received” if at least one of these types of medical procedures had been provided. Specific medical procedures were used because medical backgrounds might be important to decide the implementation of housing adaptation grants to recognize effects on their disabilities and prognosis, as with other LTC services , 31. HowDisability levels were also selected, referring to the previous research , 30, 32 Functional limitations related to housing accessibility are referred to in the previous reports , 38. TheCognitive function was assessed based on the nationally standardized methods designated by the Japanese Ministry of Health, Labour and Welfare, and was measured as the “degree of independent daily living for older adults with dementia” . CognitiVisual impairment was ascertained from four ranked answers and categorized into “intact” or “visually impaired” . Hearing impairment was also ascertained from four ranked answers and categorized into “intact” or “hearing impaired” (“can hear as much as the regular volume of sound” to “hardly hear anything”). Poor balance was assessed as to whether the individuals can keep standing and divided into three categories: “can keep standing without some support,” “can keep standing with some support,” or “cannot keep standing.” Upper extremity impairment was assessed dichotomously based on whether the patient had paralysis in the upper extremities on the right or left side. Upper extremity impairment was categorized as “upper extremity impaired” if at least one item was applicable. Lower extremity impairment was assessed in the same way and was categorized as “lower extremity impaired” if at least one item was applicable.Implementation of housing adaptation was categorized into implemented or not implemented based on the existence of reimbursement claims data. We selected the study sample at the date of care needs certification, and we decided to implement or not implement housing adaptations during the 12-month follow-up. We defined the index year as the one containing the month with the date of care needs certification. The reason we focused on the 12 months after certification was that the maximum valid duration of the first certification was 12 months . The cos2 test.To examine the associations between functional limitations related to housing accessibility with the implementation of the housing adaptation grants, we determined the number and percentage of individuals for each characteristic. These functional limitations were compared to implement or not implemented housing adaptations using the χSubsequently, we used a multivariable logistic regression analysis to estimate the likelihood of implementation of housing adaptations, illustrated by the odds ratio (OR) with a 95% confidence interval (95% CI). The dependent variables were the implementation of housing adaptation grants; independent variables were functional limitations related to housing accessibility. As covariates, we included predisposing characteristics (age and sex), enabling resources (household income level), and need factors . In addition, there was concern that the results would change over time during these nine years, although the basic system of housing adaptation grants has not changed. We conducted a sensitivity analysis to check the difference in the first and last three years (2010–2012 and 2015–2018).p = 0.05 (two-tailed).Later, we conducted a subgroup analysis and extracted only the sample who implemented housing adaptation grants to examine whether the costs were different by their functional limitations. The costs were compared using the Mann–Whitney U test and Kruskal–Wallis test. Multiple comparisons with the statistically significant independent variables were conducted using Dunn’s test with a Bonferroni adjustment . SPSS ven = 1,622) had housing adaptations implemented in the year after the care needs certification. The housing adaptation usage the second time comprised 6.2% of individuals in this study’s data. The distribution of implementation by month is shown in Supplementary Information Of the total study sample, 15.6% n = 1,62 had houA comparison of predisposing, enabling, and need factors, except for functional limitations, between individuals who implemented housing adaptations and those who did not is provided in Table The association of functional limitations with the implementation of housing adaptations is shown in Table The association of functional limitations related to housing accessibility with the implementation of housing adaptation after adjusting for covariates is shown in Table Regarding other factors except for functional limitations, females , and those who had lower disability levels at Level A1 , or Level B1 or worse  were more likely to implement housing adaptations. Individuals receiving public assistance  or receiving specific medical procedures  were less likely to implement housing adaptations.In a sensitivity analysis comparing the first and last three years (2010–2012 and 2015–2018) with the results of the whole period (2010–2018), the associations of sex, medical procedures, disability levels (only Level A1), visual impairments, and lower limb functions with the use of housing adaptations did not reach the threshold for statistical significance. However, the estimates were similar across all periods.The differences in housing adaptation costs among individuals who implemented housing adaptations are shown in Table This study was undertaken to address whether the current utilization of housing adaptations among older adults in Japan differed depending on the types of functional limitations. To the best of our knowledge, this is the first study to examine the associations of various functional limitations with the implementation and costs of housing adaptations in Japan.At first, poor balance and lower extremity impairment substantially increased the likelihood of having housing adaptations implemented. Poor balance and lower extremity impairment affect mobility significantly . As the Notably, older adults with lower cognitive function or visual impairment were less likely to implement housing adaptations, and especially older adults with visual impairment paid significantly less for housing adaptations. There are few studies on the effect of housing accessibility problems on people with cognitive impairments . With reHowever, these results could also imply that the existing types of housing adaptations in Japan do not meet the needs of older adults with lower cognitive functioning or visual impairments. For instance, older adults with cognitive impairment frequently need housing adaptations to lower the hot water temperature to avoid burns . SimilarOur aim in this study is to examine whether housing adaptation grants are appropriately used by older adults with different types and levels of need; we found that older adults with public assistance were less likely to implement housing adaptation grants compared to older adults with an average level of household income. People receiving public assistance can apply for housing adaptations as long as they are certified by LTC insurance. Moreover, the 10% out-of-pocket payments for housing adaptations are paid as housing assistance for people who receive public assistance ; therefoA notable strength of this study is that we had access to comprehensive data covering all individuals certified for care support levels in one medium-large city in Japan, which supports the validity of inferences drawn from the results.Our findings should be interpreted with three limitations in consideration. First, we cannot determine if some people did not require housing adaptations because their family had already implemented them owing to their functional decline, or they might have conducted housing adaptations before 2006. However, we used care needs certification for LTC insurance data from 2007 to 2011 to increase the appropriateness of the study sample and thus limit the inclusion of such individuals. Second, this study only uses LTC data; therefore, we cannot adjust for associated medical conditions. While we used specific medical procedures to address this, we could not adjust for the severity of underlying diseases that do not receive medical procedures. Third, individuals admitted to LTC facilities or residences for older adults with LTC services were included; therefore, residual confounding may occur in these cases. These facilities and residences have building standards that eliminate building hazards and ensure safety , 56, andThe results showed that older adults with cognitive or visual limitations implemented housing adaptations to a lesser extent, despite the high risk for accessibility problems in this population. Further research is warranted, as these results might suggest that housing adaptations are not used appropriately for all older adults with disabilities. Although the current study was conducted in a Japanese context, visual and cognitive limitations are strongly associated with age. With populations aging worldwide, the concern that these issues are not appropriately addressed may be similar in many countries. Housing adaptation grants should include the types of services that meet the needs of older people with all kinds of disabilities. The results may therefore indicate a need to improve the system that other countries should also look into.Additional file 1: Supplementary Information 1. The application of Japanese long-term care certification items to functional limitations related to housing accessibility. Supplementary Information 2. Timing of the implementation of housing adaptations during one year from care needs certification. Supplementary Information 3. The distribution of cost for housing adaptation during one year from care needs certification.


This approach represents a novel biologic‐based treatment for inflammasomes‐initiated inflammatory diseases.Inflammasomes are multiprotein complexes that signal by oligomerizing the apoptosis speck‐like protein with caspase recruitment and activator domain (ASC) and are involved in multiple inflammatory, metabolic and degenerative diseases. Pharmacological targeting of specific inflammasomes with small molecules is leading to the development of novel drugs for most common diseases. The targeting of ASC oligomers will result in a pan‐inflammasome treatment. In their study, Bertheloot  P. Pelegrin & A. Sahil discuss the study by Bertheloot et al (in this issue of EMBO Mol Med) that reports specific anti‐ASC nanobodies able to disaggregate ASC oligomers and treat inflammatory diseases in animal models.  Nanobodies, represent a promising novel class of biologic derived from single chain only camelid‐antibodies. The antigen‐binding domain derived from those camelid‐antibodies can hence be used alone to produce a single‐domain binding module, named VHH or nanobody, which represents the smallest known type of antibody. Here, the authors have generated and selected an anti‐ASC nanobody based on its ability to bind and inhibit ASC oligomerization. Interestingly, the selected nanobody was also able to disaggregate already formed ASC oligomers inside the cells, provided that the nanobody gain access to the intracellular compartment, as well as ASC oligomers present in the extracellular milieu , it mediates a specific type of necrotic‐like cell death called pyroptosis. The resulting N‐terminal fragment of GSDMD inserts into the plasma membrane and self‐oligomerizes, leading to the formation of pores of around 10–15 nm in inner diameter that allows the release of pro‐inflammatory cytokines and other intracellular content able to initiate and amplify the inflammatory response (Broz PP is the founder and consultant of Viva in vitro diagnostics SL, a company dedicated to use inflammasome for clinical diagnostics, and is also a consultant of Glenmark Pharmaceuticals and Monte Rosa Tex companies interested to develop inflammasome blockers. SA declare no conflict of interest.


To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models.Risk models were identified from a recent systematic review and the Cancer‐PRS web directory. A narrative synthesis of the models, previous validation studies and related genome‐wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort .A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic‐only models (seven of 25) and most of the mixed genetic‐phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers.Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined. Recent developments in genetic research have led to the identification of 100s of genetic variants associated with the development of different cancers ). TherefThere are several features of genetic risk models that will appeal to both clinicians and researchers. Firstly, germline genetic risk factors, including SNPs, do not change over the lifetime of an individual. This facilitates lifetime risk prediction rather than fixed‐time risk predictions  and may help identify younger individuals at higher risk before the development of other risk factors. Secondly, genetic risk models do not rely on self‐reporting and so are not at risk of recall or response bias. In the future, routine collection of genetic risk factors via a cheek swab or a pin‐prick blood sample may be easier than the collection of other data. Thirdly, genetic factors are largely independent of, and hence complimentary to, other risk factors . ConsequThe potential for genetic risk models to enhance disease risk prediction is appealing in the context of kidney cancer. A lack of symptoms, even at late stages of the disease, makes the detection of kidney cancer a challenge: 60% of kidney cancers in the UK are currently diagnosed incidentally and ~20% of those are late stage (III–IV) at diagnosis with associated poor 5‐year cancer‐specific survival rates (6% for Stage IV) . TogetheIn this review, we identify and evaluate existing models that both predict the development of kidney cancer and include genetic risk factors  to provide an overview of the current state of research in this area. We also assess the performance of the identified risk models in a large UK population (the UK Biobank [UKB] cohort) to enable a comparison between the included models and with genetic risk models for other cancers. A glossary of terms is provided in Boxes Box 1SNP (single nucleotide polymorphism) – the most common type of genetic variation, SNPs refer to the difference of a nucleotide in a specific location in DNA .GWAS (genome‐wide association studies) – a genome‐wide association study is an approach that involves scanning markers across complete sets of DNA of many individuals to find SNPs associated with a particular disease.Discrimination (of a risk model) – a measure of how well a prediction model distinguishes between individuals with and without the outcome of interest. A model with discriminative ability will, on average, assign higher risk to the cases than the controls.Calibration (of a risk model) – a measure of the agreement between the predicted and observed outcomes, the risk predicted by a model and observed risk.AUROC (area under the receiver operating characteristic) curve – A ROC curve plots the sensitivity against 1‐specificity for a range of cut‐off points. The area under the curve is equal to the probability that an individual with the outcome is assigned a higher risk than a randomly chosen control. An AUROC value of 1.0 indicates a model with perfect discriminative ability, a value of 0.5 indicates discrimination no better than random assignment. Harrell’s concordance index (c‐index) is an equivalent measure used in open cohort  analysis.Population attributable fraction (PAF) – a widely used epidemiological measure of the fraction of all cases of a particular disease or other adverse condition in a population that is attributable to a specific exposure. This can be interpreted as the proportion of cases that would not have occurred if the exposure was not present.Phenotypic – the observable characteristics of an individual resulting from the interaction of their genome with the environment. In this review, we refer to phenotypic models that may include demographic, lifestyle, and clinical risk factors.PRS (polygenic risk score) – also referred to as genome‐wide score or genetic risk score summarise the estimated effect of many genetic variants (SNPs) on an individual. Here, we specifically use the term PRS to refer to models constructed from weights derived from a GWAS.https://prsweb.sph.umich.edu:8443/Cancer PRS web directory – an on‐line repository for polygenic risk scores for major cancer traits Germline mutations – mutations or variation association that are present in germ cells and can be passed on to offspring (as opposed to somatic mutations that occur outside of germline cells and cannot be passed on to offspring).Truncating variants – a genetic variation that results in a shorter version of the associated protein being expressed, which can cause loss of function for the gene in which they are present.Minor allele fraction (MAF) – the proportion at which the second most common allele occurs in a given population. Common variants are considered to be those with a MAF of >5% . Rare variants, while they can confer a high risk, will only be present in a small number of the cases and therefore will have little effect on the overall predictive accuracy of the model.We identified risk models from a recent systematic review  and the n = 452) were excluded from the analysis.The performance of the models was then assessed in the UKB cohort, a large population based cohort of ~500 000 individuals aged 40–69 years enrolled between 2006 and 2010 . All parTwo of the models included in this review, Fritsche et al. , uses SNThe performance, both discrimination and calibration, was measured for all of the models included in the validation. Discrimination was measured using the AUROC curve and the mean standardised score (MSS). Calibration was assessed graphically in deciles . For modA complete case approach was used for the primary analysis; each model was only computed for individuals with data for all of the risk factors used in that model. As this was done on a model‐by‐model basis, the cohort size varies slightly for each validation. Any phenotypic variables with >5% missing data were multiply imputed using a predictive mean matching approach . SeveralA total of 22 studies describing 39 models that predict the risk of kidney cancer using genetic risk factors were identified and included in the narrative synthesis , 37, 38.n = 63) are only used in a single study; however, the remainder of SNPs (n = 27) are implemented in models developed in more than one study. The most commonly used SNPs  were used in models from six different studies. Further details of the SNPs used  and imputation score in the UKB) are given in Table In all, 90 genetic variants (SNPs) are used in the 39 models. The number of SNPs included in each of the models ranges from one Most of the SNPs included in the models were relatively common variants within the UKB cohort, with only seven rare alleles (MAF of <5%) identified. In particular, we note the models developed by Lin et al. , Fritschn = 14), including all of those published before 2017, selected small numbers of SNPs of interest to include in models, based on known causal pathways for RCC  and NIHR Cambridge BRC. The University of Cambridge has received salary support in respect of Simon J. Griffin from the NHS in the East of England through the Clinical Academic Reserve. Juliet A. Usher‐Smith was funded by a Cancer Research UK Prevention Fellowship (C55650/A21464) and is now supported by a National Institute of Health Research Advanced Fellowship (NIHR300861). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.The UKB study was approved by the North West Multi‐Centre Research Ethics Committee (reference number 06/MRE09/65), and at recruitment all participants gave informed consent.Informed written consent to participate in UKB and be followed up, using a signature capture device.AUROCarea under receiver operating characteristic (curve)BMIbody mass indexGWASgenome‐wide association studiesMAFminor allele fractionmiRNAmicroRNA(N)(P)PV(negative) (positive) predictive valueORodds ratioPAFpopulation attributable fractionPRSpolygenic risk scoreSNPsingle nucleotide polymorphismUKBUK BiobankFig. S1. (a) Sensitivity analysis comparing the model discrimination (AUROC curve) in men and women. (b) Sensitivity analysis comparing the model discrimination (AUROC curve) in the whole cohort and the White‐only cohort. (c) Sensitivity analysis comparing the model discrimination (AUROC curve) in the whole cohort and cohorts excluding individuals with multiple close relatives and individuals with any third degree relatives.Click here for additional data file.Fig. S2. (a) Calibration plots for GWAS models. (b) Calibration plots for causal gene models. (c) Calibration plots for mixed genetic and phenotypic models.Click here for additional data file.Fig. S3. Selection process of UKB cohort for primary analysis.Click here for additional data file.Table S1. Details of included models.Click here for additional data file.Table S2. Included studies and their development populations.Click here for additional data file.Table S3. Previously published external validations.Click here for additional data file.Table S4. (a and b) Use of UKB phenotypic variables.Click here for additional data file.Table S5. Primary analyses : model accuracy in deciles.Click here for additional data file.Table S6. Primary analyses : alternative measures of discrimination.Click here for additional data file.Table S7. Details of the single nucleotide polymorphisms (SNPs) used in the analysis.Click here for additional data file.Table S8. Model discrimination (AUROC) in sensitivity analyses.Click here for additional data file.Appendix S1. Supplementary Methods Section.Click here for additional data file.


The success of many clinical, association, or population genetics studies critically relies on properly performed variant calling step. The variety of modern genomics protocols, techniques, and platforms makes our choices of methods and algorithms difficult and there is no “one size fits all” solution for study design and data analysis. In this review, we discuss considerations that need to be taken into account while designing the study and preparing for the experiments. We outline the variety of variant types that can be detected using sequencing approaches and highlight some specific requirements and basic principles of their detection. Finally, we cover interesting developments that enable variant calling for a broad range of applications in the genomics field. We conclude by discussing technological and algorithmic advances that have the potential to change the ways of calling DNA variants in the nearest future.  A key advantage of new technologies is their ability to give a nearly complete view on the content of our genomes with digital precision. With recent improvements in scalability and constantly decreasing prices, genome sequencing has all potential for being a method of choice for a broad range of genomics studies. However, as a relatively new invention that also has multiple technological peculiarities, the analysis of genome variants using sequencing data is less standardized compared to genotyping arrays. In this review, we will discuss current approaches to variant calling using sequencing data and specifically focus on considerations while planning genomics experiments, practices while executing them, and promising developments to consider for future projects.2DNA sequencing is popular among researchers and companies, one cannot complain about the limited choice of platforms and kits for preparing and sequencing the samples. Before engaging in a genomics project using sequencing, it is a good idea to consider different options and choose the one that matches your research question and provides the most cost‐efficient solution.2.1The basic experimental procedures such as DNA isolation can have systematic effects on the representation of different genomic regions  or sequencing adaptors directly to DNA . Even after the PCR amplification of such a library, UMIs will help to discard PCR duplicates. When neither PCR‐free prep nor UMIs were used, it is a common practice to mark additional reads with shared mapping coordinates as potential duplicates. However, this computational method might overcorrect (duplicated regions) or miss (repetitive regions) the real duplicates.2.3Choice of sequencing strategy has an important effect on the average depth of the genome coverage. Short‐read whole‐genome sequencing offers the most complete approach and typically yields 30×, while long reads with lower per‐base quality are routinely sequenced to 60× coverage. Targeted resequencing, that involves selection of genomic regions, exhibits a rather nonuniform coverage of target regions . As sufficient coverage is a key parameter in variant calling, exome sequencing employs an even higher 90×–100× average coverage to compensate for uneven coverage among selected regions. Interestingly, some projects deliberately turn to a lower sequencing depth, when sequencing related individuals or looking for common association signals in a large population  is currently the most popular and cost‐effective strategy for variant detection. As price is typically proportional to the number of bases read, the preference should be given to the longest possible reads (available for this platform) and paired‐end over single‐end mode Figure . This wiTo conclude, we tried to outline some of the important choices that need to be made at the beginning and might affect the efficiency of calling and completeness of the resulting variant set. When your experience with variant calling is rather limited it is recommended to involve a data analysis specialist at the stage of project conception rather than after data is produced. This can help to avoid issues of lacking power or poor ability to call certain types of variants from one side and wasting the resources from another.33.1A poor choice of the reference assembly version can affect the results and thus should be carefully considered beforehand based on the aim of the study. Inclusion of alternative haplotypes for hypervariable regions such as major histocompatibility locus (MHC), pseudoautosomal regions of chromosome Y may result in loss of unique mapping for some of the genes and, hence, reduced sensitivity of variant calling. On the other hand, the inclusion of unplaced and unlocalized contigs will prevent the mismapping of reads originating from these sequences and prevent many false‐positive calls. It is a general recommendation to use the so‐called primary assembly version  of the reference genome unless a specific aim of the study calls for the use of an extended version.3.2To avoid the multiple sources of errors in data, such as amplification biases, errors introduced during sequencing and base calling, and mapping artifacts that arise during reads alignment, the data must be appropriately pre‐processed. At this stage, it is preferable to prioritize sensitivity over specificity, to ensure the abundance of potential variants, rather than missing any. A good example of balanced preprocessing steps is described in Genome Analysis ToolKit (GATK) best practices tutorial , a substitution of a single base at a certain position in the genome. A typical genome sequencing project detects several million SNVs per individual human sample. When reads are properly aligned to the genome reference, calling a standalone single‐base “typo” is rather straightforward. Apart from the GATK program suite mentioned above, several others such as BCFtools and FreeBayes are popular tools that can generate a list of small variants in a computation‐efficient way Table  Danecek. InteresOther small variants that lead to small insertion or deletion between 1 and 20 DNA bases are commonly known as indels. These changes are shorter than a sequencing read and can be found by realigning partially mapped or unmapped reads using split‐read aligners , are difficult to locate from individual short reads. Alterations that encompass multiple base pairs typically interfere with the mapping of reads that overlap this type of DNA variants Figure . These a(i) Change in read density. Large deletions and duplications result in segmental increase or decrease of DNA copy number and, as a result, contribute to a local change of read density, when compared to unaffected regions. These two types of SVs, collectively known as copy‐number variants (CNVs) can be identified by segmentation of read coverage profiles using algorithms involving hidden Markov models (HMM) or circular binary segmentation (CBS).(ii) Altered distance between paired reads. When the distance between mapping positions of paired reads significantly deviates from the expected size of the DNA fragment, it may indicate local DNA content difference between reference and genome of the patient. A longer distance between mapped paired reads indicates more DNA bases in reference than in the patient sample and hence deletion, while a shorter distance can be a hallmark of an insertion.(iii) Discordant orientation of paired reads. When aligned, paired reads typically map to opposite DNA strands. In rare cases when one read from a pair overlaps the breakpoint of an SV, the relative mapping of reads in a pair can exhibit unexpected directionality. Thus, if one of the reads ends in an inverted segment, both reads would map to the same DNA strand. Similarly, tandem duplications frequently show inverse read order  since, in the patient DNA, these reads belong to two sequential copies of the same reference DNA segment.(iv) Unmapped reads. When an alignment of a read to the reference genome fails, it might indicate that the read overlaps a breakpoint of a structural variant. In this case, split‐mapping of this read may reveal the large variants. With many possibilities of arbitrary splitting of an unmapped short read, it is difficult to avoid mis‐mapping. However, the genomic coordinate of the mapped “mate” read from the same pair can be used as a proxy to find the start of the SV breakpoint.The state‐of‐the‐art SV discovery combines these aforementioned SV signatures in a clever way. Thus, a relative position of read pairs allows discrimination between tandem and dispersed CNVs, which is impossible from the read density analysis alone. The robust and specific SV calls, therefore, rely on multiple independent read pairs and are supported by multiple SV signatures Figure .In some of the applications, the assumptions underlying SV signatures might be violated. For example, read density in exome data is very nonuniform and CNV detection methods need to discriminate between effects of change in copy number and technical variability in fragmentation and target selection efficiency. This is especially challenging for small sample sets and single‐exon CNVs —“parasitic” DNA sequences with the ability to copy and/or propagate themselves across DNA. Autonomous elements, such as long interspersed nuclear element 1 (LINE‐1) encode for enzymes that can convert its RNA to DNA and insert an extra copy of itself into the genome. Nonautonomous elements are typically short  and use enzymes produced by their autonomous “cousins” for their propagation. Nearly half of our genome may in fact be the result of the past activity of mobile elements. The arms race between these “egoistic” DNA elements and cellular processes suppressing their replication limits the number of functional full‐size copies around a hundred LINEs per human genome  functionality opens a possibility for other RNA molecules to “enter” our genomes. In rare cases, a regular processed human mRNA can be recognized by enzymes encoded by LINE‐1, resulting in the introduction of retrocopy of a gene. Such gene retrotransposition insertion polymorphisms (GRIPs) typically represent intronless, partial, and nonfunctional copies of actual genes  inserts of mtDNA. These nuclear inserts of mitochondrial DNA (termed NUMTs) can be detected using discordantly mapped read pairs or alignment‐free methods  copies of genes, and mtDNA segments that are not represented in the genome reference introduces additional challenges in variant calling. The alignment of paralogous sequences to the same segment of genome reference leads to the addition of paralogous sequence variants to the list of true polymorphisms. This problem can be alleviated by extending the reference to incorporating new inserts (see section “Completeness of genome reference” below).Another genetic mechanism that can result in large‐scale changes in our DNA is non‐allelic gene conversion (NAGC)—a consequence of improper DNA repair event when a highly similar, but not identical copy of segment is used as a template during homologous recombination repair  and methods  for the identification of complex and structural variants  and then using them in the second mapping stage to avoid misalignments and false SNPs  samples. The latter show multiple signs of DNA deterioration including degradation, DNA cross‐linking, fragmentation, strand breaks, base deamination, and abasic sites. To boost the specificity of variant calling for FFPE samples, the processing of multiple biopsies per patient can be a good strategy. For example, paired tumor and control  experiment design improves the interpretation of tumor sequencing results and aids in differentiating between somatic and germline mutations.The ability to call somatic changes holds great prospects for early diagnostics of human diseases. The recent advances in sample preparation and sequencing technologies enable the profiling of cell‐free DNA circulating in the blood, allowing for real‐time, noninvasive profiling of cancer or monitoring disease treatment  represent genomic segments detected in sequencing data, which are currently not represented in the human genome reference. These sequences are often polymorphic in the population and thus can be considered as a type of structural variation  offer numerous advantages over short‐read sequencing. Their main strength lies in the ability to routinely produce reads that are tens and hundreds of thousands of kilobases long. Such long reads simplify de novo assembly, improve the unambiguity of read mapping, and the detection of structural variants. Long‐read platforms are also able to provide insight into the nature and frequency of nucleotide modifications, such as methylation. Nevertheless, long‐read sequencing is still more costly than short‐reads technology and can be expensive for large sequencing projects. Further, their relatively high base error rate usually requires consensus polishing of high coverage data or combining of long‐read and short‐read data to achieve maximum base call accuracy. The recent and remarkable advances in long‐read technologies, such as high fidelity (HiFi) reads, provide proof of principle that these technologies are instrumental to detect the most challenging variants, such as mid‐sized SVs on repetitive sequence background, and decode end‐to‐end representation of human chromosomes (Miga et al., 5In this paper we reviewed the common practices of variant calling, focusing on preparatory steps, decisions to be made, diversity of variant types, and basic principles of their identification. As the pace of technological developments in genome sequencing does not seem to slow down, we can expect that many obstacles we encounter today will disappear. As a result, researchers would be able to obtain a comprehensive set of variants more easily and dedicate more of their time to clinical and biological questions.


Oncorhynchus nerka) that uses Redfish Lake in central Idaho as a spawning and nursery lake. Recovery efforts include the release of maturing adults into the lake for volitional spawning. The lake is also inhabited by a population of resident O. nerka that is genetically indistinguishable, but phenotypically smaller, to the maturing adults released into the lake. The resident population is difficult to sample and the reproductive patterns between the two groups are unknown. We used results of paired‐ and single‐parentage assignments to specifically examine the reproductive patterns of male fish released into the lake under an equal sex ratio and a male‐biased sex ratio. Assignment results of offspring leaving the lake indicated a reproductive shift by males under the two scenarios. Males displayed an assortative mating pattern under an equal sex ratio and spawned almost exclusively with the released females. Under a male‐biased sex ratio most males shifted to a negative‐assortative mating pattern and spawned with smaller females from the resident population. These males were younger and smaller than males that spawned with released females suggesting they were unable to compete with larger males for spawning opportunities with the larger, released females. The results provided insights into the reproductive behavior of this endangered population and has implications for recovery efforts.Understanding reproductive patterns in endangered species is critical for supporting their recovery efforts. In this study we use a combination of paired‐parent and single‐parent assignments to examine the reproductive patterns in an endangered population of sockeye salmon ( Parentage assignments indicated a reproductive shift by male sockeye salmon under two different sex ratios. Males displayed an assortative mating pattern under an equal sex ratio and spawned almost exclusively with similarly sized females but shifted to a negative‐assortative mating pattern under a male‐biased sex ratio and spawned with smaller females in the resident population. Recovery efforts for this evolutionary significant unit are undertaken collaboratively through the multi‐agency Stanley Basin Sockeye Technical Oversight Committee  are unknown but predictions can be made based on theoretical expectations and tested using parentage analyses. Released fish are expected to exhibit size‐assortative mating in which females preferentially spawn with similarly sized males . grandma uses allele frequencies from the observed parental genotypes to simulate candidate parent–offspring pairs that have varying degrees of relatedness in order to explore the expected frequency of error rates when close relatives are present in the dataset. This approach allows one to assess whether the molecular dataset can accurately identify single‐parent assignments and allows the user to select an appropriate assignment threshold to minimize the false positive and false negative rates for their study‐specific analysis.A common method for evaluating reproductive patterns is the use of genetic parentage analysis in which genotypes of offspring are matched to those of a potential pair of parents. Parentage analysis has provided numerous insights into the ecology, evolution, and behavior of organisms  under an equal sex ratio the males released into the lake will display an assortative mating pattern and spawn with larger females also released into the lake (b) under a male‐biased sex ratio some released males will display a negative‐assortative mating pattern and spawn with smaller resident females, (c) males with a negative‐assortative mating pattern will be smaller than males that spawned assortatively. Results from this study provide insights into the reproductive patterns of this endangered population and have implications for recovery efforts.22.1Releases of adult fish into Redfish Lake for volitional spawning occur annually but the number of individuals varies based on the abundance of anadromous returns and availability of captive‐reared fish. We examined reproductive patterns in two consecutive spawn years (SY2013 and SY2014), each with differing sex ratios of adults released into the lake. Captive‐reared fish released during these years were propagated following practices outlined for ESA‐listed stocks  and female adults (n = 108) were released into the lake resulting in a male:female sex ratio over 2:1. Progeny from these parents hatched the following spring, reared for either 1 or 2 years in the lake and then emigrated as smolts the following spring. Emigrating offspring were sampled at the outlet of Redfish Lake in the spring of 2015 and 2016 and analyzed with paired‐parent and single‐parentage analyses to identify offspring originating from the 2013 spawn year.Adults released in the fall of 2013 had a sex ratio skewed towards males. A combination of male (n = 977) and female adults (n = 1094) were released for volitional spawning resulting in a male:female sex ratio approaching 1:1. Potential progeny from these parents were sampled as emigrating smolts in 2016 and 2017 and analyzed to identify individuals originating from the 2014 spawn year.Adults released in the subsequent spawn year of 2014 had a nearly equal sex ratio. A combination of male . If an offspring received a single‐parent assignment then that parent must have mated disassortatively . If a parent was detected in both a paired‐parent and a single‐parent assignment then it was determined that the parent participated in both spawning strategies (assortative and disassortative). The proportion of assortatively and disassortatively mating males under the two sex ratios was compared. While the null hypothesis is that the proportion of males in each category will be equal under the two different sex ratios, we expect the proportion of males that mate disassortatively  will be higher when the sex ratio of the larger spawners is male‐biased than when the sex ratio is equal.2.3www.progenygenetics.com) housed by the Eagle Fish Genetics Laboratory.Genomic DNA from parents and offspring was extracted from fin clips following the methods for the Nexttec Genomic DNA Isolation Kit from XpressBio . We genotyped samples at 382 SNPs using target sequences described by Hasselman et al. . SNPs we2.4snppit  and a log of odds ratio (LOD). We only accepted assignments with a stringent FDR threshold of <0.05% and an LOD of >19. Offspring that did not receive a paired‐parent assignment were then evaluated with single‐parentage assignments.Analyses for paired‐parent assignments were conducted with the program https://github.com/delomast/gRandma). All functions use calculations that assume loci are in Hardy–Weinberg equilibrium and are unlinked. Population allele frequencies, genotyping error rates, and rates of missing genotypes for each locus were estimated and then treated as known. Population allele frequencies were estimated as the posterior means with Beta  priors and the observed allele frequencies of the potential parents. Rates of missing genotypes were calculated as the posterior means with Beta  priors and the observed frequencies of missing genotypes of both potential parents and potential offspring. The genotyping error rate was also assumed to be 0.5% per allele for all loci.Functions to perform single‐parent analyses using the method described here are available in an R package we provide . Pairs with missing data have a lower probability of exclusion because missing data reduces the number of observed MIs. This threshold was calculated following the method described by Anderson  and assuming genotypes at all loci were observed.Potential parent–offspring pairs were evaluated using a two‐step process. The first step eliminates potential parent–offspring pairs with Mendelian incompatibilities (MIs) above a calculated threshold value. The maximum number of allowable MIs was calculated for each parental population such that the probability of excluding a true parent–offspring pair was <10Anderson . Brieflyc) the assignment was accepted, otherwise the assignment was rejected. Values of c were selected by evaluating the estimated false positive and false negative error rates associated with a range of potential values (0–20) for c in each parental dataset.The second step calculated a log‐likelihood ratio (LLR) comparing the likelihood of observing the genotypes of a potential parent–offspring pair given a true parent–offspring relationship and the likelihood of observing their genotypes if the pair was unrelated  c will minimize the false positive rate at the cost of increasing the false negative rate and a value of c that is too lenient  will minimize the false negative rate while increasing the false positive rate. The value of c should be determined on a case‐by‐case basis for datasets because it will be dependent upon project‐specific factors including number and variability of markers, the number of sampled adults, the number of sampled offspring, and the user's willingness to accept each type of error.In order to determine the level of confidence placed in the single‐parent assignments, it is important to estimate two types of error: the false positive rate  and the false negative rate . A trade‐off exists between these two forms of error and the selection of an appropriate threshold for the LLR, α, is dependent upon the true relationship between the pair of individuals being assessed  is the probability that a pair of samples with the given true relationship  for which α is being calculated has the corresponding number of observed MIs. Anderson  is known and that observation of missing genotypes is independent across loci and individuals, the probabilities of each possibility for ai can be calculated according to standard probability arguments. The probability of being in state x after a given locus can then be calculated asThe size of a stratum . Because we assume that missing genotypes are independent between individuals, the probability of both samples having a valid number of missing genotypes is then straightforward to calculate and only the probabilities of states with m or fewer MIs and with both individuals having an allowable number of missing genotypes need to be saved.This can be evaluated recursively to obtain the probabilities of each final state. The size of each stratum can then be calculated given a maximum number of missing genotypes. However, computer memory constraints can make saving all the probabilities impractical even with moderate numbers of loci. The probability of observing an individual with more than the allowed number of missing genotypes can be obtained through a similar process , then the pair of genotypes are either sampled conditional upon an MI being present or not.Once 2.6N is the total number of comparisons, Punrelated is the probability that a given comparison contains unrelated individuals, Eu is the false positive rate for unrelated individuals, Paunt is the probability that a given comparison contains an aunt/uncle and niece/nephew, Ea is the false positive rate for aunt/uncle and niece/ nephew comparisons, and similarly for half‐aunt and cousin of true parent. This assumes that no relationships other than the four described are present at meaningful levels. However, as we show later, the only category of relatedness that is meaningful for our study is Ea, the aunt/uncle error rate . All other error rates are too small to be of concern  that a single‐parentage comparison in the assignment analysis would contain the full‐sibling of a true parent and its offspring  using the formulaTo fully estimate experiment‐wide false positive rates within a dataset requires the calculation of the per‐comparison error rate under each level of relatedness as described above. It also requires estimating the probability that a potential parent in a comparison is the full‐sib, half‐sib, or cousin of the true parent. The most comprehensive estimate of experiment‐wide error rates can be estimated asn Figure . Becausen Figure , we wereFp) for each application of the single‐parentage methodology:Np is the number of sampled parents, No is the number of sampled offspring, and αLLR is the false positive rate for the selected value of c (LLR assignment threshold).This is the expected number of aunts/uncles divided by the total number of parents, assuming all potential offspring are offspring of the potential parents and that all matings were between unrelated individuals. If some potential offspring were produced by an unsampled population , then this estimate is biased high and leads to an overestimate of the error rate. We then incorporated this probability into an estimate of the experiment‐wide false positive error rate  using a similar forward–backward algorithm to the one described above but with the state only representing the number of missing genotypes in an individual.We do not expect false negative rates to drive the selection of an LLR threshold for analyses and generally expect false negative rates to be low in most cases  given the power of modern genetic panels. As long as false‐negative rates are not excessively high we recommend that users minimize the false positive rate and simply take their false negative rate into account during interpretation. We adopted the approach of Anderson and Garza  used for33.1Over 99.5% of sampled offspring genotyped successfully Table . Three SAll adults released in 2013  were successfully genotyped ensuring that any offspring produced by released parents could be identified as such and that all single‐parentage results can be interpreted as a mating between a released fish and a resident fish. One SNP (One_1a.43386‐37) failed to genotype in all parental samples and the average genotyping success rate for parents across the remaining SNPs was 97.3%. Parentage analyses detected 27 (25.0%) females from the 108 released that successfully reproduced. Of these, 22 (81.4%) were part of a paired‐parent assignment, three (11.1%) were part of a single‐parent assignment, and two (7.4%) occurred in both single‐ and paired‐parent assignments. Of the 238 released males, parentage analyses detected 51 (21.4%) that successfully reproduced. Of these, 21 (41.2%) were part of a paired‐parent assignment, 28 (54.9%) were part of a single‐parent assignment, and two (3.9%) occurred in both assignments , 13 failed to genotype successfully , thus 2058 (99.4%) of the parents were used in the parentage analysis for this spawn year. This results in an estimate of 98.8% of offspring  produced by the released fish being detectable in a paired‐parent assignment. The remaining proportion of offspring (1.2%) would not be detectable in a paired‐parent analysis and might instead receive a single‐parent assignment or fail to receive any kind of parentage assignment. One SNP (One_1a.43386‐37) failed to genotype in all parental samples and the average genotyping success rate for parents across the remaining SNPs was 96.3%. Of the 1094 released females, parentage analyses detected 387 (35.4%) that successfully reproduced. Of these, 304 (78.6%) were part of a paired‐parent assignment, 22 (5.7%) were part of a single‐parent assignment, and 61 (15.8%) occurred in both single‐ and paired‐parent assignments. Of the 977 released males, parentage analyses detected 306 (31.2%) that successfully reproduced. Of these, 303 (99.0%) were part of a paired‐parent assignment, no males were detected exclusively from a single‐parent assignment, but three males (1.0%) occurred in paired‐ and single‐parent assignments . Males identified through the single‐parentage analysis (disassortative spawners) were significantly  smaller  than assortative males identified through the paired‐parentage assignment . Disassortative males were also significantly  younger  than assortative males . Most (93%) of disassortative males were of the youngest age category (3 years old) while just 29% of the assortative males were 3 years old. None of the disassortative males were of the oldest age class (5 years old) while 19% of the assortative males were of this age group. Disassortative males were also smaller at a given age than the assortative males. Disassortative 3‐year‐old males were on average 48 mm smaller than 3‐year‐old assortative males and disassortative 4‐year‐old males were on average 25 mm smaller than 4‐year‐old assortative males  can be calculated by multiplying together the error rate, the probability of an evaluation containing the full‐sibling of the true parent (Pfs), the number of parents sampled, and the number of offspring evaluated. For example, our LLR threshold of 20 is estimated to produce 5.3 false positives for the SY2013 dataset and the LLR threshold of 19 is estimated to produce 6.4 false positives for the SY2014 dataset . Second, disassortative mating patterns by some males supports predictions that intrasexual competition under a male‐biased sex ratio excludes those individuals from spawning with the females released into the lake  in this scenario were identified only through the single‐parentage analysis, indicating a tendency to mate disassortatively with unsampled resident females. In contrast, most of the released females that were reproductively successful (81.4%) were identified through paired‐parent analysis, indicating that they exhibited assortative mating and spawned with released males. These patterns occurred despite similar proportions of reproductively successful males and females Table . Under t Morbey,  which li Morbey, . Males t Morbey, .n = 1094) released into the lake in 2014 resulted in 15.8% of females being detected in both single‐ and paired‐parent assignments. Alternatively, the small number of females (n = 107) released into the lake in 2013 resulted in only a small fraction of females (3.8%) being detected in both types of parentage analyses . The single‐parentage methodology, however, is not without limitations and quantifying error rates is an important step in the process. This study system was particularly well positioned for evaluating the experiment‐wide false positive error rate (Fp) because the population is well pedigreed which allows for direct measurement of the number of full‐sibling families within each parental dataset (Pfs). Other researchers that apply our methodology may have to estimate the relatedness among the potential parents using a sibship analysis with programs such as colony . Researchers might find that implementation of single‐parentage may be limited to study systems where the number of samples collected from the parental population or offspring is limited. The ability to estimate and control error rates should give researchers the confidence to apply this technique if these rates are found to be acceptably low.The findings of this study were possible because of the application of the single‐parentage assignment methodology. Our method has the advantage of helping researchers to estimate project‐specific assignment errors which we show can be controlled through selection of an appropriate assignment threshold ; Formal analysis (lead); Investigation (lead); Writing – original draft (lead); Writing – review & editing (lead). Thomas A. Delomas: Methodology (lead); Software (lead); Writing – review & editing (supporting). Matthew R. Campbell: Writing – review & editing (supporting). John H. Powell: Formal analysis (supporting); Supervision (lead); Writing – review & editing (supporting).