To test the feasibility and acceptability of a randomised controlled trial (RCT) to evaluate a Smartphone-based self-management tool in Early Intervention in Psychosis (EIP) services.A two-arm unblinded feasibility RCT.Six NHS EIP services in England.Adults using EIP services who own an Android Smartphone. Participants were recruited until the recruitment target was met (n=40).Participants were randomised with a 1:1 allocation to one of two conditions: (1) treatment as usual from EIP services (TAU) or (2) TAU plus access to My Journey 3 on their own Smartphone. My Journey 3 features a range of self-management components including access to digital recovery and relapse prevention plans, medication tracking and symptom monitoring. My Journey 3 use was at the users’ discretion and was supported by EIP service clinicians. Participants had access for a median of 38.1 weeks.Feasibility outcomes included recruitment, follow-up rates and intervention engagement. Participant data on mental health outcomes were collected from clinical records and from research assessments at baseline, 4 months and 12 months.83% and 75% of participants were retained in the trial at the 4-month and 12-month assessments. All treatment group participants had access to My Journey 3 during the trial, but technical difficulties caused delays in ensuring timely access to the intervention. The median number of My Journey 3 uses was 16.5 (IQR 8.5 to 23) and median total minutes spent using My Journey 3 was 26.8 (IQR 18.3 to 57.3). No serious adverse events were reported.Recruitment and retention were feasible. Within a trial context, My Journey 3 could be successfully delivered to adults using EIP services, but with relatively low usage rates. Further evaluation of the intervention in a larger trial may be warranted, but should include attention to implementation.ISRCTN10004994. Participant data were collected from a wide range of sources including questionnaires, patient records and from the app.Participants were followed up for a 12-month period; longer than the majority of feasibility trials investigating Smartphone apps for psychosis.We were not able to blind researchers or participants to their treatment allocation.The study recruited users of Early Intervention in Psychosis services that own an Android Smartphone, limiting sample representativeness.This is a feasibility study and therefore does not have the statistical power to conclude the effectiveness of the intervention.Early Intervention in Psychosis (EIP) services have been established across the UK to provide care to adults during the 3 years following an initial episode of psychosis. There is evidence that such services are effective and cost-effective,Illness self-management is an approach designed to support people to manage long-term health conditions by developing their ability to recognise and monitor symptoms and early warning signs of relapse, identify and avoid stressors, make plans for achieving their own recovery and effectively use coping strategies.14Smartphones can run advanced software known as apps that hold promise as an effective tool to assist the monitoring and treatment of mental health problems. Smartphone ownership is rapidly growing worldwide4 5The majority of digital health interventions that have been developed for psychosis have been based on existing psychological therapies such as cognitive-behavioural therapy,To date, only one trial of a self-management app delivered in EIP services has published results regarding the intervention's impact on clinical outcomes.We aimed to address this evidence gap by conducting a feasibility RCT of a supported self-management Smartphone app, ‘My Journey 3’, designed to help EIP service users recognise early warning signs of illness, recognise and monitor symptoms, and create plans for their recovery. My Journey 3 has been designed to be initially set up in EIP services and used with clinician support, but to also be suitable for independent use. The results of the feasibility RCT are a potential step towards a full-scale trial to assess the effectiveness of the intervention.The objectives of this study were as follows:To determine the acceptability of the My Journey 3 self-management app for use in an EIP service context.To determine the feasibility of trial procedures for a definitive trial, including recruitment, intervention enrolment and trial attrition.To test procedures for evaluating intervention engagement and participant outcomes.The App to support Recovery In Early intervention Services (ARIES) study was an unblinded feasibility RCT with a nested qualitative study comparing a supported self-management Smartphone app (My Journey 3) in addition to treatment as usual (TAU), with a control group receiving TAU only. Participants were randomly allocated to one of the two trial arms in a 1:1 ratio. Since this was a feasibility trial, it was not designed to have sufficient statistical power to assess the effectiveness of the My Journey 3 intervention.As the study was a feasibility trial, prospective registration was not required.10.1136/bmjopen-2019-034927.supp1Supplementary dataThe trial was conducted in six EIP services across three NHS Foundation Trusts in England. EIP services are multidisciplinary community mental health services that provide care coordination to people in the first 3 years of a first-episode psychosis, focusing on engagement, achieving social and clinical recovery and delivering a full range of pharmacological, psychological and social interventions.Participants were recruited from the participating EIP services over 7 months. We assumed a conservative 40% attrition rate and accordingly set the target sample size as 40 participants to ensure the trial retained 12 completer participants per group .Clinicians at the participating EIP services were briefed by the research team and were asked to make initial contact with eligible EIP service users. Clinicians explained the trial to service users and enquired whether the service user would be willing to speak to a researcher about participating in the trial. The researcher then made contact with eligible and potentially willing service users and arranged a face-to-face meeting where the trial was explained further. The researcher provided the trial information sheet  and asse10.1136/bmjopen-2019-034927.supp2Supplementary dataFollowing the baseline assessment, participants were randomly allocated in a 1:1 ratio to either the intervention (n=20) or the control group (n=20) by an independent statistician. The treatment group had access to My Journey 3 in addition to TAU, while the control group received TAU only. An independent researcher held the allocation list and did not disclose participants’ allocation to the trial researcher until after completion of the baseline assessments.Due to the nature of the intervention, participants were not blinded to their group allocation. During the recruitment process, participants would have been aware that My Journey 3 was the intervention of interest. As a single researcher carried out the majority of data collection, it was not practical for the allocation of participants to be concealed from the research team. Participants were informed of their allocation by the researcher via a telephone call.My Journey 3 is a Smartphone app developed for adults accessing EIP services. The aim of the intervention is to develop users’ self-management skills to help them to achieve self-determined recovery goals and avoid future relapses. My Journey 3 is suitable for independent use, but also designed to be used with support from EIP service clinicians who will be able to assist with the completion of the self-management components and initial set-up. It is the developers’ aspiration for My Journey 3 to be used initially in collaboration with EIP service clinicians, and for it to support continuing self-management after users have been discharged from EIP services.https://myoxygen.uk) led the technical development of My Journey 3. To limit costs, My Journey 3 is only compatible with Smartphones with Android operating systems at this stage of testing.The development of My Journey 3 has been through several iterations. The first version (My Journey 1) was created by Surrey and Borders Partnership NHS Foundation Trust with leadership from Sarah Amani, for EIP service users to track their symptoms, set reminders for appointments and share their progress with EIP service clinicians. In developing the current version of My Journey 3, we have drawn on existing paper-and-pen self-management intervention componentsMy Journey 3 features four key elements of self-management, an approach with demonstrated efficacy in improving social and clinical outcomes for people with psychosis.32Prior to the feasibility trial reported in this paper, My Journey 3 was tested by EIP service users in laboratory-based usability tests and in a 1-month field study. The final content of My Journey 3 was then refined based on feedback from individual interviews with the participating EIP service users and clinicians. No changes were made to the content of My Journey 3 during the feasibility RCT. A major technical update to My Journey 3 was carried out in January 2018 to fix compatibility issues with older versions of Android operating systems. This did not require any changes to the trial design.Following assignment to the treatment group, participants engaged in individual training sessions with a trial researcher and a supporting EIP service clinician. Training sessions were intended to take place within 6 weeks of the participants’ initial baseline assessment, and lasted for approximately 2 hours. During these sessions, the researcher downloaded My Journey 3 onto the participants’ Smartphone and gave a demonstration of the app and its main functions. Participants were then encouraged to input appropriate information to specific sections of My Journey 3 with the help of the supporting EIP service clinician in attendance. Following this session, it was hoped that all participants had initial personal recovery plans, relapse prevention plans and crisis plans stored on My Journey 3.Participants had access to My Journey 3 on their own Smartphone from the training session until the 12-month time point. Researchers recommended that participants used My Journey 3 at least once a week, but participants had a free choice in how and when they used My Journey 3. Participants did not receive any financial incentives to use My Journey 3, and were free to withdraw from using the app or decline the installation of it on to their Smartphone. At the training session, participants were informed by the researcher that My Journey 3 would be not suitable for seeking urgent medical care while in crisis, and that it is not a substitute for human support.To encourage user engagement with My Journey 3 during the trial, supporting EIP service clinicians were asked to provide regular support and encouragement to service users who had access to My Journey 3. Clinicians were asked to discuss recovery goals and relapse prevention plans in routine appointments with participants, and assist with entering these into the appropriate My Journey 3 sections. Clinicians had an existing understanding of self-management approaches from their clinical training and practice, and would be able to provide appropriate advice with the intervention components of My Journey 3, but they received no formal training on how to implement My Journey 3 into their clinical work. Clinicians’ understanding of operating My Journey 3 was from the training sessions only. Clinician support for My Journey 3 as part of the trial was not manualised or incentivised.Participants were encouraged to contact the trial researcher in the case of technical problems with My Journey 3. The researcher contacted participants a week after the training session to check that My Journey 3 had been functioning without issues and invited any questions about the app. No further prompts were instigated by the researcher during the trial.All participants received TAU regardless of group allocation. TAU for a person under the care of EIP services typically involves regular meetings with a care coordinator, access to a psychiatrist, psychiatric medication and a range of psychological interventions. EIP services are encouraged to deliver self-management programmes that include advice on symptom management, crisis planning and relapse prevention, generally delivered with paper-and-pen tools if at all.The development of My Journey 3 has been guided by the input of people with lived experience of psychosis. Initial development of the design and content involved a collaboration between researchers, experts in digital health and service users. Service users provided further input into the design and functionality of My Journey 3 by providing feedback after taking part in laboratory-based tests and a field study.Participant data were collected from numerous sources including participant assessments, patient records and anonymous My Journey 3 usage reports. There were no pre-specified criteria for assessing trial feasibility and intervention acceptability.Proposed outcome measures for a future trial were assessed at structured face-to-face assessments with a trained researcher at three time points: baseline, 4 months post baseline and 12 months post baseline. At all meetings, participants completed self-report questionnaires that have been previously used with people with first-episode psychosis. Participants were given £20 as a thank you for completing the assessment at each time point.At each assessment, we collected sociodemographic data including age, gender, ethnicity, accommodation and living situation, employment status, educational attainment, Smartphone use and use of other mental health apps. The following self-report measures were also collected: social outcomes ,Clinical structured interviews were also conducted with each participant by the researcher, to assess psychopathology, using the Positive and Negative Syndrome Scale (PANSS).Participants’ engagement with EIP services were measured using the Service Engagement Scale (SES),Clinical data were extracted from patient records at baseline and at the 12-month time point. Clinical measures included most recent diagnosis and use of EIP services, other community mental health teams and acute mental health services in the previous 12 months.The proposed primary outcome for a future RCT (relapse of psychosis) was operationalised as an admission to an acute mental health service  during the 12-month trial period as indicated in patient records. This definition of relapse has been used previously in a recent trial of a self-management intervention.45To assess acceptability of the intervention and user engagement, My Journey 3 usage data were collected for all participants in the treatment group from the training session until the 12-month time point. Whenever users had Wi-Fi internet access on their Smartphone, My Journey 3 automatically uploaded encrypted usage data to a secure server. Data collected included a record of each time the user opened My Journey 3, whether this was in response to a prompt and which components they used. To ensure confidentiality, personal or identifiable data such as text or responses to each sections were not collected.Feedback was obtained through semi-structured interviews as part of a nested qualitative study. Individual interviews were conducted at the 4-month time point with both service user participants that received My Journey 3 and supporting clinical staff.Participant demographic and clinical characteristics, My Journey 3 usage, and rates of participant recruitment and retention were summarised using descriptive statistics. As this was a feasibility RCT, it was not powered to assess the effectiveness of the intervention. Statistical analyses of participant outcome measures were conducted to pilot the methods of analysis for a fully powered effectiveness trial. Logistic regression was used to explore the impact of the My Journey 3 intervention on relapse. Linear regression was used to examine the potential effect of the intervention on continuous outcome measures at 4 months and 12 months separately. We report the effect estimates and corresponding 95% CIs only for unadjusted analyses and for analyses adjusting for the baseline measure of the outcome in question. All analyses were performed using STATA V.14 after completion of the final participant assessment. No interim analyses were conducted.Qualitative data were coded to themes based on the Acceptability of Healthcare Interventions framework.Participant flow is detailed in the CONSORT diagram . A totalAmong those recruited to the trial, attrition rates were generally low: 83% (33/40) and 75% (30/40) of participants successfully attended and completed follow-ups at 4 months and 12 months, respectively. At both time points, the follow-up rate was lower in the control group . Patient record data were available for all participants at baseline and for 95% of the sample (38/40) at the 12-month time point. Completion rates of the SES by clinicians were higher at baseline (90%) than at the 12-month time point (67.5%). Follow-up assessments were conducted from July 2017 to October 2018.All participants in the treatment group attended a training session with a researcher and had access to My Journey 3 during the trial. Issues with Smartphone compatibility initially prevented three participants from downloading My Journey 3. Following an update to the system, two of the participants were able to install and access My Journey 3 on their own Smartphones. Two participants were provided with Smartphones with My Journey 3 pre-installed . The median length of time from trial enrolment to having access to My Journey 3 was 14 weeks (IQR 11 to 17), longer than the planned time of 6 weeks. Participants had access to My Journey 3 for a median of 38.1 weeks (IQR 34.8 to 40.7). There were no reported privacy breaches.My Journey 3 usage data were collected for all participants following the training session, with 500 different data entries available for analysis. Within the 500 data entries, 27 (5.4%) were corrupt and were subsequently removed from the analysis. The unusable data can grouped into two types. The first, duplicates of previous data entries that were subsequently removed. The second, entries where the times were implausible . In addition, a further issue caused errors with accurately recording My Journey 3 usage data of ‘My Recovery Plan’ and ‘My Relapse Plan’ sections. As a result, we were unable to accurately conclude how often participants used these sections.One participant randomised to the control group was wrongly given access to My Journey 3. For the purpose of the statistical analysis, they are classed as a control participant.A summary of demographic and clinical characteristics of the sample is displayed in The level of My Journey 3 use was highly skewed. The median number of times My Journey 3 was used per participant during the trial was 16.5 (IQR 8.5 to 23). Participants accessed My Journey 3 on a median of 3.22% (IQR 1.89 to 6.36) of the days it was available to them, equating to My Journey 3 being used on average once every 31 days (IQR 15.7 to 52.9). Participants spent a median of 26.8 min (IQR 18.3 to 57.3) in total using My Journey 3 over the course of the trial. Eight participants (40%) used My Journey 3 for longer than 30 min in total.Five participants (25%) were still using My Journey 3 six months after downloading it; however, one participant never used the app after the training session . Half ofThe average number of uses by participants for each My Journey 3 component is displayed in Qualitative interviews were conducted with all participants who received My Journey 3 and the majority of clinical staff who supported its delivery. In general, most service user participants found My Journey 3 to be acceptable, and a number of participants reported a clear benefit from using it. Barriers affecting use were identified including a lack of clinician support and concerns around data privacy. A key theme for staff was that they often did not have the time to provide regular support to participants with My Journey 3.No research-related serious adverse events were recorded. Psychotic and general symptoms (measured by the PANSS) were generally low at all times for both groups, suggesting a stable sample. Summary statistics and estimated effect sizes of participant outcomes are displayed in Of the 38 participants whose patient records data were available, only five experienced a relapse during the trial, as indicated by using an acute mental health service. In the treatment group, 15% of participants (3/20) experienced a relapse during the trial period compared with 11% (2/18) in the control group. We found no evidence of a difference in relapse between the two groups , but did not have sufficient power for an informative test.The present study examined the feasibility of conducting an RCT of a supported self-management Smartphone app in EIP services. My Journey 3 aims to facilitate recovery and prevent relapse primarily via the digital delivery of previously developed paper-and-pen self-management tools. The trial indicates that recruitment and retention in an RCT evaluating My Journey 3 is feasible, and that My Journey 3 can be delivered in EIP services. The level of My Journey 3 use was relatively low across the trial period.Building on from extensive preliminary work with NHS staff and service users, adults with lived experience of psychosis and experts in digital health, we were able to successfully develop a self-management Smartphone app that can be used in EIP services. My Journey 3 appeared to be safe with no related serious adverse events reported. My Journey 3 was successfully delivered to all participants in the treatment group; however, technical problems with the intervention caused significant delays in providing access. Prior to any future evaluations, technical problems with My Journey 3 will need to be identified and fixed to ensure the intervention is implemented as intended.My Journey 3 use varied considerably between participants, with only a small proportion of participants frequently engaging with the app after obtaining access to it. This raises questions about whether use was at a level where it is likely that useful self-management activities were taking place: certainly not enough time was spent regularly enough for participants to be engaging in detailed monitoring of symptoms and early warning signs, tracking medication and activities and referring to crisis or recovery plans. Despite that, 40% of participants used My Journey 3 for a minimum of 30 minutes which could be an adequate amount of time for users to effectively monitor relapse signs and follow a crisis plan when needed. We have not found evidence on how regularly EIP service users make use of pen-and-paper self-management interventions delivered in routine settings, and this was not measured in our trial. Long-term engagement with My Journey 3 appears a challenge, but low levels of app use is a common phenomenon with market research showing that 62% of users stop using Smartphone apps after 10 or fewer uses.49Age has been shown to be an important factor linked to engagement with mental health apps and general Smartphone use,Participant retention for research data collection was high, with 75% of the sample attending the 12-month follow-up assessment, and is comparable with other Smartphone app studies.The trial was not powered to detect effectiveness, and, as expected with our small number of participants, we found no significant differences between groups on any outcomes, with CIs generally including substantial effects in either direction. Accordingly, we cannot draw any conclusions regarding the potential impact of My Journey 3 as a mental health intervention. The proposed primary outcome for a full-scale trial, relapse as defined by use of an acute mental health service during the trial period, was marked by low event rates. Only five participants 12.5%) experienced a relapse during the 1-year follow-up period, compared with expected levels of 12% to 47%..5% experMy Journey 3 has been developed with extensive stakeholder input, and the intervention has been tested through laboratory testing and a field study prior to the feasibility RCT. In comparison with previous studies,We recruited until the required number of participants was obtained rather than screening caseloads objectively: as a result, we are not aware of the proportion eligible who were recruited, reasons for non-eligibility and how many EIP service users declined to take part and why. This limits our understanding of how feasible conducting a large-scale trial of this intervention would be. In addition, there were problems with the usage data, which impacts the reliability of our conclusions regarding how often participants engaged with My Journey 3.The trial did not feature an active digital placebo for the control group, meaning that non-specifics of Smartphone use could not be controlled for. Furthermore, data were not collected during the study period from either group regarding frequency of completing recovery work such as relapse prevention plans, recovery plans or crisis plans either in paper-and-pen or digital format, limiting our understanding of whether access to My Journey 3 facilitated increased access to self-management activities.Although clinicians were encouraged to support participants with My Journey 3, support was not manualised and clinicians did not have personal access to the app or associated data, potentially limiting the level and quality of the support offered and therefore user engagement. Future developments of My Journey 3 should focus on effective implementation and delivery within healthcare settings, and there should be considerations on how to facilitate secure data-sharing between My Journey 3 and healthcare records or other secure web-based platforms dependent on user consent, which is likely to increase clinician engagement with the app and its utility.53We did also not define pre-specified criteria for assessing the feasibility of a RCT and the acceptability of My Journey 3. Instead, we will consider all findings from the trial, app usage data and feedback from qualitative interviews yet to be reported in determining whether My Journey 3 will be evaluated in a full-scale trial. This allows all data from the RCT to be thoroughly considered, but may be a less objective approach in determining feasibility than using pre-defined criteria. Although the trial was not designed to assess intervention effectiveness, participants and trial researchers were not blinded to group allocation, and as such could have led to an inflation of any observed effects.Finally, the sample consisted of Android Smartphone users who were generally stable and in an appropriate stage of recovery to consider using a self-management Smartphone app. Participants may therefore not be representative of all EIP service users. Furthermore, contact with a researcher within a trial context could have led to increased intervention engagement that would not occur in a real-world clinical environment.We developed and delivered a self-management Smartphone app for first-episode psychosis in a trial context. Participants were successfully recruited, most engaged at least to some extent with the intervention, and they had high follow-up rates over the 1-year trial period. Based on the data presented, the trial methods appear feasible. My Journey 3 was shown to be safe, but the level of use was lower than anticipated thus potentially limiting its utility.If My Journey 3 is to be further tested in a research setting, attention needs to be given to engagement, a challenge associated with many digital tools in mental health.


We designed synthetic 25-base sequence probes by analyzing previously reported haplotype sequences, and we developed a bioinformatics pipeline to interpret the probes in the context of 16 KIR genes and 16 haplotype structures. We demonstrated its accuracy on a synthetic data set as well as a real whole genome sequences from 748 individuals from The Genome of the Netherlands (GoNL). The GoNL predictions were compared with predictions from SNP-based predictions. Our results show 100% accuracy rate for the synthetic tests and a 99.6% family-consistency rate in the GoNL tests. Agreement with the SNP-based calls on KIR genes ranges from 72%–100% with a mean of 92%; most differences occur in genes KIR2DS2, KIR2DL2, KIR2DS3, and KIR2DL5 where KPI predicts presence and the SNP-based interpretation predicts absence. Overall, the evidence suggests that KPI’s accuracy is 97% or greater for both KIR gene and haplotype-pair predictions, and the presence/absence genotyping leads to ambiguous haplotype-pair predictions with 16 reference KIR haplotype structures. KPI is free, open, and easily executable as a Nextflow workflow supported by a Docker environment at https://github.com/droeatumn/kpi.The killer-cell immunoglobulin-like receptor (KIR) proteins evolve to fight viruses and mediate the body’s reaction to pregnancy. These roles provide selection pressure for variation at both the structural/haplotype and base/allele levels. At the same time, the genes have evolved relatively recently by tandem duplication and therefore exhibit very high sequence similarity over thousands of bases. These variation-homology patterns make it impossible to interpret KIR haplotypes from abundant short-read genome sequencing data at population scale using existing methods. Here, we developed an efficient computational approach for  These genes are ~4–16 kilobases long and evolved r) level , 8. HaplIt is difficult to interpret the KIR region with high-throughput sequencing reads for an individual human genome when the structural arrangements are unknown; indeed, it is difficult even when the structural haplotypes are known, since the read length is too short to map unambiguously to the repetitive and homologous KIR genes. As a consequence, the reads from KIR region are ignored, as to the best of our knowledge, there are currently no algorithms to interpret KIR from whole genome sequencing (WGS). SNP (single nucleotide polymorphism)-based KIR interpretation is more commonly applied. For example, KIR*IMP is a web-application to predict genes and haplotypes from microarray SNP genotypes . As an aSince a general solution for KIR structural interpretation from raw genomic DNA is not currently available, this study implements such an algorithm for the prediction of KIR genes and full structural haplotypes from any type of raw full-region-or-greater genomic sequence at population scale. In particular, we systematically evaluated small markers for KIR genes and then applied those markers to a synthetic KIR probe interpretation (KPI) algorithm for the presence/absence of 16 KIR genes and 16 haplotype structures. Our approach leverages recent bioinformatics innovations for short sequence (‘probe’) genotyping, along recently published KIR reference haplotypes. The KPI algorithm first efficiently counts the occurrence of each kmer probe in the raw sequences, and then uses multiple probes per gene to call its presence/absence. Those 16 genotypes are then used to generate haplotype-pair predictions. In the experiments, we report 100% accuracy on a test set of synthetic haplotypes for comparisons with known truth. We also report that gene and haplotype-pair predictions for the WGS GoNL cohort are family consistent and compare favorably with reference frequencies in comparison to SNP-based predictions using KIR*IMP.The workflow of KPI consists of three steps,Discover the 25mer gene markers based on a multiple sequence alignment analysis of 68 full-length haplotype sequences.Count the 25mer markers in the reads of genomic DNA per individual to generate the individual’s 25mer genotype.Predict presence/absence per gene from the marker genotypes for each individual.Predict haplotype pairs from the gene presence/absence calls for each individual.In the following, we first explain each step and then describe the synthetic data and GoNL data used for the evaluation.KIR2DL1-5, KIR2DS1-5, KIR2DP1, KIR3DL1-3, KIR3DP1, and KIR3DS1. Markers for each gene locus were chosen by selecting all sequences of length 25 (25mers) present in every allele of the gene but not elsewhere in the KIR haplotypes nor the rest of the genome reference GRCh38. More details about the algorithm are in https://github.com/droeatumn/kpi/tree/master/input in text and fasta format.To discover gene marker 25mers, first a multiple sequence alignment (MSA) was created with 68 publicly deposited full-length haplotypes sequences . BrieflyKMC 3, with workflows implemented in Nextflow  and ApacKPI calls presence/absence per gene by aggregating the presence/absence genotypes of many small (25mer) markers, each specific to one gene. 25mers with hit counts less than three are considered sequencing errors and set to zero. If the mean hit count of all the markers per gene is zero, then the gene is predicted absent; otherwise, it is called present. Additional details can be found in KIR2DS4. These 16 haplotypes represent 97% of all haplotypes in the Jiang et al. report.Haplotype-pair predictions were made by fitting the genotype to all possible pairs of the 16 structural reference haplotypes defined in For the GoNL predictions, haplotype ambiguity was reduced by family trio patterns and then further by the EM (Expectation-Maximization)-based methods as described and used in Vierra-Green 2012 . HaplotyKPI was evaluated on a synthetic test set. There are six reference haplotype structures with publicly deposited full-length sequences  was usedKPI was also run on a large real-world example. WGS was obtained from The Genome of the Netherlands (GoNL) , a genomKPI’s GoNL predictions were compared with results from microarray-based interpretation algorithm KIR*IMP. Illumina Immunochip microarray SNP data was obtained from GoNL . The datTo the best of our knowledge, there only exists one method to predict KIR gene content from WGS sequences . HoweverThe predictions were evaluated in the small synthetic test set, where truth is known and a large real-world test set, where truth is unknown except for family relationships. Predictions were evaluated by comparing gene and haplotype-pair predictions to: known truth in the synthetic cohort, and family consistency , reference frequencies from Jiang et al.’s family copy number study , and theKIR2DS2, KIR2DL2, KIR2DL5, and KIR2DS3) compared with 0 genes for KPI , KIR2DL2 (8%), and KIR2DL3 (6%). In the reverse cases, when KIR*IMP calls absent and KPI calls present (KIR2DS2 (20%), KIR2DL2 (20%), and the paralogous (centromeric or telomeric) KIR2DL5 (11%), and KIR2DS3 (19%).Per-individual haplotype-pair predictions for the GoNL cohort are included in KIR*IMP makes one most-likely prediction for all individuals. KPI’s predictions are sometimes ambiguous, with most predictions (mode) having one haplotype-pair but a mean of 2.3, standard deviation of 2.5, and a maximum of 14 haplotype-pair predictions per individual in the context of the 16 reference haplotypes. The KIR*IMP predictions are family consistent 100% of the time compared with 99.6% for KPI. However, the haplotype pair predictions between the two algorithms are concordant only 58% of the predictions.KIR2DS3 and 100% accuracy for the 15 other genes. As they note, it is now possible to interpret HLA binding alleles and the presence/absence of all their KIR receptors from short-read high-throughput sequencing, and this combination is a valuable advancement for research and medicine. Indeed, they compare KPI favorably with respect to clinical accreditation standards.KPI was evaluated by Chen et al. as part of a larger effort . In a coThe findings by Chen et al. are consistent with the results of our synthetic test, whose accuracy was 100% for all genes. One drawback of the design of the synthetic test is that the haplotypes used in the test were also included in the MSA that was used to generate the per-gene probes. However, the main purposes of the synthetic tests were to test the application of the markers to short reads in a variety of genotypes and recover their original geno- and haplo-types; this is value-added compared with simply demonstrating sequences unique to a gene. The almost-perfect results of the Genentech and synthetic experiments, along with a GoNL results that had a 99.6% family-consistency rate and in line with expected frequencies, provide evidence that KPI’s gene predictions are very accurate.The evidence also suggests that KPI’s haplotype results are accurate, although often ambiguous: the accuracy in the synthetic test was 100% and the GoNL family-consistency was 99.6, and the predictions allow EM predictions that align with the expected population frequency from the literature.KIR2DS2, KIR2DL2, and KIR2DS3 by ~20% and KIR2DL5 by ~10% compared with KPI and the historical European frequencies from Allele Frequency Net database  library, we leveraged the information from a multiple sequence alignment of all full-length haplotypes that are available for this study. We believe this is a more accurate approach than using IPD-KIR reference alleles, because the IPD-KIR reference alleles do not require the haplotype location to be known. In addition, fusion alleles are assigned in IPD-KIR to one of the two parent genes, and therefore large sequences of some alleles are not really from the gene in which they are classified. We used 25 for our ‘k’  because BLAST searching indicated this to be a conservative minimum length needed to distinguish a small set of test markers to the KIR region. We did not experiment with any k size other than 25, since the choice gives a reasonable number of significant markers and their lack of off-KIR hits as tested in the GoNL population WGS confirms the effectiveness as gene/intergenic markers. The only fundamental benefit to shorter markers would be in the case when there were no longer markers; however, 25mer markers were found for every gene. The only fundamental benefit to longer markers would be if the markers were not unique to the region; however, all the markers are unique to their region. Many of the 25mers overlap each other, effectively simulating a single longer marker. Similar to the reasoning about probe length, probe mismatches would only need to be relaxed if a locus did not have any markers. Since at least one marker was discovered for each gene, mismatches did not need to be incorporated. Having thus obtained the region markers, we then used the most common (‘peak’) hit count from each gene/intergene’s library of sequences to make the PA genotype calls  and evaluate the genotyping in diverse populations. It is possible that population robustness is a weakness of the method, although the fact that almost half of individuals in the discovery cohort are African or African American provides some optimism.One limitation of the method is that the markers do not mark DNA segments longer than one gene. Perhaps this is primarily due to the frequent recombination between haplotypes. Although recombination has been reported in multiple loci, the hotspot in between the centromeric and telomeric regions is particularly strong, and, in general, any pairing of the two can be expected. We evaluated single markers for haplotypes, but we did not find any. This is particularly relevant in light of the observation that haplotyping from genotypes seems to have limited accuracy under maximum likelihood assumptions Table 5.For the WGS data set, KPI averaged less than 1 h of computing time per individual, with 32 cores of CPU and 32G RAM. The majority of the time is spent using KMC 3 to build the kmer database. kmer counting is an active area of research. Since KPI can easily be altered to use any such application, it has potential for future efficiency improvements.https://github.com/droeatumn/kpi.The markers discovered in this study were enabled by a full-haplotype MSA, as described recently by Roe et al. . That maThis manuscript has been released as a pre-print at bioRxiv with the same title and authors .The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by University of Minnesota. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.DR and RK designed the experiments and wrote the manuscript. DR conducted the experiments. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.