Efficient Replication of Over 180 Genetic Associations with Self-Reported Medical Data

While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web-based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in-depth questions to refine self-reported diagnoses. Our data suggests that online collection of self-reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations

Assessment of the Effectiveness and Cost-Effectiveness of Tailored Web- and Text-Based Smoking Cessation Support in Primary Care (iQuit in Practice II): Protocol for a Randomized Controlled Trial.

BACKGROUND: The prevalence of smoking is declining; however, it continues to be a major public health burden. In England, primary care is the health setting that provides smoking cessation support to most smokers. However, this setting has one of the lowest success rates. The iQuit in practice intervention (iQuit) is a tailored web-based and text message intervention developed for use in primary care consultations as an adjunct to routine smoking cessation support with the aim of increasing success rates. iQuit has demonstrated feasibility, acceptability, and potential effectiveness. OBJECTIVE: This definitive trial aims to determine the effectiveness and cost-effectiveness of iQuit when used as an adjunct to the usual support provided to patients who wish to quit smoking, compared with usual care alone. METHODS: The iQuit in Practice II trial is a two-arm, parallel-group, randomized controlled trial (RCT) with a 1:1 individual allocation comparing usual care (ie, pharmacotherapy combined with multisession behavioral support)-the control-with usual care plus iQuit-the intervention. Participants were recruited through primary care clinics and talked to a smoking cessation advisor. Participants were randomized during the initial consultation, and those allocated to the intervention group received a tailored advice report and 90 days of text messaging in addition to the standard support provided to all patients. RESULTS: The primary outcome is self-reported prolonged abstinence biochemically verified using saliva cotinine at 6 months after the quit date. A sample size of 1700 participants, with 850 per arm, would yield 90% power to detect a 4.3% difference in validated quit rates between the groups at the two-sided 5% level of significance. The Cambridge East Research Ethics Committee approved the study in February 2016, and funding for the study was granted from May 2016. In total, 1671 participants were recruited between August 2016 and July 2019. Follow-up for all participants was completed in January 2020. Data analysis will begin in the summer of 2020. CONCLUSIONS: iQuit in Practice II is a definitive, pragmatic RCT assessing whether a digital intervention can augment the impact of routine smoking cessation support in primary care. Previous research has found good acceptability and feasibility for delivering iQuit among smoking cessation advisors working in primary care. If demonstrated to be cost-effective, iQuit could be delivered across primary care and other settings, such as community pharmacies. The potential benefit would likely be highest where less behavioral support is delivered. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 44559004; http://www.isrctn.com /ISRCTN44559004. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17160

An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data

Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data. Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting—‘the gold standard’. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM. Trends over time from 2004 to 2020 were calculated using Poisson regression modelling. Our algorithm outperformed previously published lists identifying 43–72% of cases in primary care with a specificity ≥ 85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9 and 28% of cases with high specificity (> 96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data. Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time. The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed

Web-Based, Participant-Driven Studies Yield Novel Genetic Associations for Common Traits

Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits is the efficient, coordinated collection of genotype and phenotype data. We have developed a novel research framework that facilitates the parallel study of a wide assortment of traits within a single cohort. The approach takes advantage of the interactivity of the Web both to gather data and to present genetic information to research participants, while taking care to correct for the population structure inherent to this study design. Here we report initial results from a participant-driven study of 22 traits. Replications of associations (in the genes OCA2, HERC2, SLC45A2, SLC24A4, IRF4, TYR, TYRP1, ASIP, and MC1R) for hair color, eye color, and freckling validate the Web-based, self-reporting paradigm. The identification of novel associations for hair morphology (rs17646946, near TCHH; rs7349332, near WNT10A; and rs1556547, near OFCC1), freckling (rs2153271, in BNC2), the ability to smell the methanethiol produced after eating asparagus (rs4481887, near OR2M7), and photic sneeze reflex (rs10427255, near ZEB2, and rs11856995, near NR2F2) illustrates the power of the approach

The European Modern Pollen Database (EMPD) project

Modern pollen samples provide an invaluable research tool for helping to interpret the quaternary fossil pollen record, allowing investigation of the relationship between pollen as the proxy and the environmental parameters such as vegetation, land-use, and climate that the pollen proxy represents. The European Modern Pollen Database (EMPD) is a new initiative within the European Pollen Database (EPD) to establish a publicly accessible repository of modern (surface sample) pollen data. This new database will complement the EPD, which at present holds only fossil sedimentary pollen data. The EMPD is freely available online to the scientific community and currently has information on almost 5,000 pollen samples from throughout the Euro-Siberian and Mediterranean regions, contributed by over 40 individuals and research groups. Here we describe how the EMPD was constructed, the various tables and their fields, problems and errors, quality controls, and continuing efforts to improve the available dat

Impact-Induced Muscle Damage: Performance Implications in Response to a Novel Collision Simulator and Associated Timeline of Recovery

The implications of impact-induced muscle damage (IIMD) that results from participation in contact-sport are not well understood. The purpose of the present study was to implement a novel method of generating IIMD and characterise the implications of this on perceptual, biochemical and exercise performance parameters. Eighteen male recreational contact-sport athletes completed a single-group time series with measures assessed at baseline (PRE) and immediately following (POST) an IIMD protocol, with repeat testing 24, 48, and 72 h following the IIMD protocol. Biochemical indices of muscle damage (myoglobin [Mb]) and inflammation (high-sensitivity C-reactive protein [hs-CRP]), 15 m sprint performance, squat jump peak power (SJ-PP), and perceived soreness were compared to PRE using a one-way (time) repeated measures ANOVA with post-hoc t tests. Speed over 5 and 15 m were impaired for 48 h (7.5 ± 4%, p 0.01). IIMD resulted in impaired ability to produce power and speed, whilst negatively influencing perceived soreness. These changes were most pronounced in the 48 h following the IIMD protocol. No change in muscle damage or inflammation indices were observed, primarily due to the highly variable response. Thus, the experimental protocol used in the present study may be used as a model to further investigate other aspects of IIMD

Efficient Replication of Over 180 Genetic Associations with Self‐Reported Medical Data

While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web‐based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We
found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in‐depth questions to refine self‐reported diagnoses. Our data suggests that online collection of self‐reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations