Rates Of Missing Data Research Articles

A Cross-Sectional Feasibility Study of Nutrient Intake Patterns in People With Parkinson’s Compared to Government Nutrition Guidelines

Background: Diet could have implications for disease progression and management in people with Parkinson’s disease (PwP). However, the knowledge of diet intake patterns in PwP is limited.Objectives: We set out to assess the feasibility of collecting diet data in PwP to determine food and nutrient intake, in order to compare to national nutrition guidelines and thus understand the habits in this population.Methods: In this cross-sectional feasibility study, PwP were approached through local support groups throughout the Thames Valley and were asked to complete a Food Frequency Questionnaire. Eligibility criteria included a self-reported neurologist confirmed diagnosis of Parkinson’s disease. Completeness of questionnaires was reported and 80% was considered appropriate for each measure including demographic information.Results: Response rate was 61% and missing data rate for the 121 returned questionnaires was 74%; however, of the 90 used for analysis there was 100% completion of the questionnaires. Compared to the UK government guidelines, protein was significantly higher for both males and females and fluid intake was lower for both genders (p < 0.001). There were several other differences in nutrient intake compared to guidelines.Conclusion: We observed high levels of engagement from PwP and found that assessing food and nutrient patterns in PwP was feasible. Importantly, the diet was generally healthy overall, yet there were specific nutrients that may affect medication metabolism in PwP that were found to be high. Therefore further research into this emerging and important area is warranted.

Traffic Estimation for Large Urban Road Network with High Missing Data Ratio.

Intelligent transportation systems require the knowledge of current and forecasted traffic states for effective control of road networks. The actual traffic state has to be estimated as the existing sensors does not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data poses challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle based approach with Kriging interpolation is proposed. The performance of the particle based Kriging interpolation for different missing data ratios was investigated for a large road network comprising 1000 segments. Results indicate that the effect of missing data in a large road network can be mitigated by the Kriging interpolation within the particle filter framework.

A model for predicting missing items on the Health of the Nation Outcome Scale (HoNOS)

HoNOS is one of the most widely used clinician rated outcome measures in mental health services. A commonly encountered problem is that one or more of the 12 individual HoNOS items is left uncompleted (missing data rates of up to 25% have been reported), which affects the degree to which organisations can rely on the accuracy of historical HoNOS data. In this brief paper we outline a simple statistical method of predicting missing item scores for HoNOS, both in general adult and 65+ populations. The method accounts for the average pattern of responding being non-uniform across items: i.e., some HoNOS items consistently elicit higher scores than others. By calculating individual item weights based on a very large sample of fully completed HoNOS assessments, we were able to accurately predict the value of missing items in a new sample. We contrast the accuracy of this approach with two other simple statistical procedures, and show that the weighted means model returns a much lower error rate. Although this is not the only method of predicting missing items, it carries the advantages of being: (i) free of charge, (ii) easily applicable to large datasets using a spreadsheet and (iii) unreliant on the availability of previous assessment data for the same patients. We hope this method will be of use to other organisations that are processing large volumes of HoNOS data.

A physical activity intervention to improve the quality of life of patients with a stoma: a feasibility study protocol

BackgroundPhysical activity (PA) is positively associated with quality of life. People with a stoma are less likely to engage in PA than those without a stoma.MethodsIn this feasibility intervention study, we will perform the following: (1) Develop a PA intervention for people with a stoma. An Expert Working Group of behavioural scientists, exercise scientists, clinicians and a Patient Advisory Group of people with a bowel stoma will meet with the research team to inform the development of a PA intervention for people with a stoma. A manual of the intervention will be the main output. (2) Explore PA instructors’ experiences of delivering the PA intervention. PA instructors will record on paper the number of PA consultations with each patient and a researcher will interview the PA instructors about their experiences of delivering the intervention. (3) Assess the level of patient (bowel cancer or inflammatory bowel disease (IBD) patients with a stoma between 6 weeks and 24 months post-surgery) engagement with the PA intervention and their views on intervention acceptability and usefulness. Patients will keep a PA diary to record daily pedometer recorded step count and type and duration of activities. A researcher will interview patients about their experiences of the PA intervention. (4) Assess screening, eligibility, consent, data completion, loss to follow up, and missing data rates, representativeness of participants and potential treatment effects. A researcher will record on paper all study procedure parameters. Quality of life (stoma-quality of life; Functional Assessment of Cancer Therapy, Short IBD questionnaire), fatigue (FACIT fatigue scale) and PA (accelerometer) will be measured pre- and post-intervention in patients. For IBD patients only, blood will be taken to measure systemic inflammation.DiscussionWe hypothesise that a PA intervention will be an effective means of improving the quality of life of people with a stoma. Before embarking on a full randomised controlled trial to test this hypothesis, a PA intervention needs to be developed and a feasibility study of the proposed PA intervention conducted.Trial registrationISRCTN58613962, Protocol version: 0.1. 14 September 2017.

Kayıp Veri Varlığında DINA Model Madde-Özellik İlişkisinin Parametre Kestirimine Etkisi

The objective of this study is to investigate the relation between the number of items and attributes and to analyze the manner in which the different rates of missing data affect the model estimations based on the simulation data. A Q-matrix contains 24 items, and data are generated using four attributes. A dataset of n = 3000 is generated by associating the first, middle, and final eight items in the Q-matrix with one, two, and three attributes, respectively, and 5%, 10%, and 15% of the data have been randomly deleted from the first, middle, and final eight-item blocks in the Q-matrix, respectively. Subsequently, imputation was performed using the multiple imputation (MI) method with these datasets, 100 replication was performed for each condition. The values obtained from these datasets were compared with the values obtained from the full dataset. Thus, it can be observed that an increase in the amount of missing data negatively affects the consistency of the DINA parameters and the latent class estimations. Further, the latent class consistency becomes less affected by the missing data as the number of attributes associated with the items increase. With an increase in the number of attributes associated with the items, the missing data in these items affect the consistency level of the g parameter (guessing) less and the s parameter (slip) more. Furthermore, it can be observed from the results that the test developers using the cognitive diagnosis models should specifically consider the item–attribute relation in items with missing data.

Clinical Validation of the BREAST-Q Breast-Conserving Therapy Module.

The BREAST-Q is a patient-reported outcome measure to evaluate satisfaction and health-related quality of life (HRQOL) after breast surgery. The aim of this study is to test the acceptability, reliability, and validity of the most recently developed BREAST-Q module for breast-conserving therapy (BCT) in a prospective clinical cohort. The BREAST-Q BCT module was translated into German according to international guidelines. A total of 253 women with primary breast cancer undergoing BCT were recruited preoperatively. This study evaluated the BREAST-Q BCT subscales by using psychometric methods including acceptability, reliability, and validity. To examine construct validity, convergent and discriminant validity were determined by testing the instrument's scales against the EORTC C30 and BR23 as reference questionnaires. Acceptability was supported by a high follow-up rate (90%) and low frequency of missing data (< 10%) in all but three scales. Scale reliability was supported by high Cronbach's alpha coefficients (> 0.86) and item-total correlations (range of means, 0.33-0.89). Validity was shown by convergent and divergent correlations. The hypotheses of relationships between the scales of the BREAST-Q and the EORTC QLQ C30 BR23 revealed moderate to high correlations. The BREAST-Q BCT module proved to be an accepted, reliable, and valid questionnaire for the assessment of HRQOL and patient satisfaction after BCT in breast cancer patients. It can be recommended as a possible standard PROM for individual clinical analysis, quality assessment, and future trials.

Trauma Surveillance and Registry Development in Mozambique: Results of a 1-Year Study and the First Phase of National Implementation.

Mozambique has had no policy-driven trauma system and no hospital-based trauma registries, and injury was not a public health priority. In other low-income countries, trauma system implementation and trauma registries have helped to reduce mortality from injury by up to 35%. In 2014, we introduced a trauma registry in four hospitals in Maputo serving 18,000 patients yearly. The project has since expanded nationally. This study summarizes the challenges, results, and lessons learned from this large national undertaking. Between October 2014-September 2015, we implemented a trauma registry at four hospitals in Maputo. In October 2015, the project began to be expanded nationally. Physicians and allied health professionals at each hospital were trained to implement the registry, and each identified and trained data collectors. We conducted semi-structured interviews with the key stakeholders of this project to identify the challenges, results, and creative solutions implemented for the success of this project. Most participants identified the importance of having a trauma registry and its usefulness in identifying gaps in trauma care. The registry identified that less than 5% of injured patients arrived by ambulance, which served as evidence for the need for a prehospital system, which the Ministry of Health had already begun implementing. Participants also highlighted how the registry has allowed for a structured clinical approach to patients, ensuring that severely injured patients are identified early. Challenges reported included the high rates of missing data, the difficulty in establishing a streamlined flow of trauma patients within each hospital, and the bureaucratic challenges faced when attempting to improve capacity for trauma care at each hospital by introducing a trauma bay and new technologies. Participants identified the need to improve data completeness, to disseminate the results of the project nationally and internationally, to improve inter-divisional cooperation, and to continue educating health providers on the importance of registries. Participants also identified political instabilities in the region as a potential source of challenge in expanding the project nationally; they also identified the lack of uniform resource allocation and low personnel in many areas, especially rural, as a major burden that would need to be overcome. Introduction of a trauma registry system in Mozambique is feasible and necessary. Initial findings provide insight into the nature of traumas seen in Maputo hospitals, but also underscore future challenges, especially in minimizing missing data, utilizing data to develop evidence-based trauma prevention policies, and ensuring the sustainability of these efforts by ensuring continued governmental support, education, and resource allocation. Many of these measures are being undertaken.

미계측 유역에서의 수문 분석을 위한 글로벌 격자형 기후 자료의 활용성 평가

We assessed the usability of satellite and reanalysis data for estimating streamflow amount in an ungauged watershed in consideration of the spatial distribution of precipitation. We used CHIRPS, PERSIANN-CDR, CFSR, ERA-Interim, and MERRA2 to obtain grid-based global data and daily GHCN as measured data for comparison. Evaluation results of daily GHCN data through comparison to the ASOS data from Gangneung station adjacent to Chungju Dam basin showed a greater than 60% missing data rate. The GHCN data underestimated extreme rainfall events caused by typhoons and overestimated rainfall events in certain years compared to the nromal range. In the comparison of grid-based global data, MERRA2 data showed the best reproducibility, with a 0.86 correlation coefficient, on the daily time scale. The SWAT model was applied to Chungju Dam basin for estimating streamflow and showed -16.1% total error when the MERRA2 data was used as model input without bias-correction. However, both total error and RMSE were decreased when the monthly biases of MERRA2 data at the weather stations were corrected by comparing to the 30-year average values. In addition, the biases were spatially interpolated for areas in the basin without a station. Total error was slightly improved to -13.1% when the bias was corrected using the GHCN data from Gangneung station, while the total error showed a satisfactory -6.3% when using ASOS data from Daegwallyeong station for streamflow estimation after bias-correction. In the absence of measured weather data, it is desirable to use the most appropriate grid-based data after bias-correction through comparison with neighboring weather stations to reflect the characteristics of the area.

Development and validation of the caregiver roles and responsibilities scale in cancer caregivers

PurposeThe caregiver roles and responsibilities scale (CRRS) was developed to facilitate formal assessment of broad life impacts for informal (i.e. unpaid) caregivers to people with cancer. Here we report the development and initial validation.MethodsThe CRRS was developed from the thematic analysis of two interview studies with cancer patients (stage III-IV breast, gynaecological, lung or melanoma) and caregivers. In the evaluation studies, participants completed the CRRS alongside the Caregiver Quality of Life—Cancer, the main criterion measure for concurrent validity, and the WHOQOL-BREF for additional convergent validity data. Questionnaires were completed at baseline, 7-days and 2-months. Demographic data and patient characteristics were collected at baseline.ResultsTwo-hundred and forty-five caregivers to people with stage I-IV breast, colorectal, gynaecological, head and neck, lung or renal cancer or melanoma completed the CRRS at least once. The final 41 core items selected comprised five subscales: Support and Impact, Lifestyle, Emotional Health and Wellbeing, Self-care and Financial Wellbeing as well as three standalone items. Missing data rate was low (0.6%); there were no ceiling or floor effects for total scores. Cronbach’s alpha was 0.92 for the CRRS-41; 0.75–0.87 for the subscales. CRRS showed good test–retest reliability (ICC = 0.91), sensitivity to change and the predicted pattern of correlation with validation measures r = 0.75–0.89. The standalone 7-item jobs and careers subscale requires further validation.ConclusionsInitial evaluation shows the CRRS has good validity and reliability and is a promising tool for the assessment of the effects of cancer and cancer treatment on the lives and wellbeing of informal caregivers.

A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data.

MotivationRecent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets. However, very limited choices of software tools are currently available for practical implementation of functional QTL mapping and variable selection.ResultsWe propose a Bayesian Gaussian process (GP) approach for functional QTL mapping. We use GPs to model the continuously varying coefficients which describe how the effects of molecular markers on the quantitative trait are changing over time. We use an efficient gradient based algorithm to estimate the tuning parameters of GPs. Notably, the GP approach is directly applicable to the incomplete datasets having even larger than 50% missing data rate (among phenotypes). We further develop a stepwise algorithm to search through the model space in terms of genetic variants, and use a minimal increase of Bayesian posterior probability as a stopping rule to focus on only a small set of putative QTL. We also discuss the connection between GP and penalized B-splines and wavelets. On two simulated and three real datasets, our GP approach demonstrates great flexibility for modeling different types of phenotypic trajectories with low computational cost. The proposed model selection approach finds the most likely QTL reliably in tested datasets.Availability and implementationSoftware and simulated data are available as a MATLAB package ‘GPQTLmapping’, and they can be downloaded from GitHub (https://github.com/jpvanhat/GPQTLmapping). Real datasets used in case studies are publicly available at QTL Archive.Supplementary information Supplementary data are available at Bioinformatics online.

PolyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids.

Low or uneven read depth is a common limitation of genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), resulting in high missing data rates, heterozygotes miscalled as homozygotes, and uncertainty of allele copy number in heterozygous polyploids. Bayesian genotype calling can mitigate these issues, but previously has only been implemented in software that requires a reference genome or uses priors that may be inappropriate for the population. Here we present several novel Bayesian algorithms that estimate genotype posterior probabilities, all of which are implemented in a new R package, polyRAD. Appropriate priors can be specified for mapping populations, populations in Hardy-Weinberg equilibrium, or structured populations, and in each case can be informed by genotypes at linked markers. The polyRAD software imports read depth from several existing pipelines, and outputs continuous or discrete numerical genotypes suitable for analyses such as genome-wide association and genomic prediction.

Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are often missing a large number of values. Reconstructing the missing values is essential for improving the usability of the geostationary satellite LST data. However, current reconstruction methods mainly aim to fill the values of a small number of invalid pixels with many valid pixels, which can provide useful land surface temperature values. When the missing data extent becomes large, the reconstruction effect will worsen because the relationship between different spatiotemporal geostationary satellite LSTs is complex and highly nonlinear. Inspired by the superiority of the deep convolutional neural network (CNN) in solving highly nonlinear and dynamic problems, a multiscale feature connection CNN model is proposed to fill missing LSTs with large missing regions. The proposed method has been tested on both FengYun-2G and Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager geostationary satellite LST datasets. The results of simulated and actual experiments show that the proposed method is accurate to within about 1 °C, with 70% missing data rates. This is feasible and effective for large regions of LST reconstruction tasks.

Item nonresponse on the Myeloproliferative Neoplasms Symptom Assessment Form (MPN-SAF): a comparison of missing data strategies

Administering questionnaires to patients is an efficient and effective method for assessing patients’ symptoms. However, item nonresponse (skipped questions) potentially compromises the utility of these questionnaires. Using an international sample of 2,067 patients with myeloproliferative neoplasms, we evaluated the impact of item nonresponse on scoring of the Myeloproliferative Neoplasms Symptom Assessment Form Total Symptom Score (MPN-SAF TSS or MPN-10). We characterized item nonresponse on the MPN-10 and compared strategies for addressing item nonresponse (available-case analysis, proration, and multiple imputation) on the MPN-10 (multi-symptom assessment) and Brief Fatigue Inventory (BFI; single-symptom assessment). Characteristics of multi-symptom assessments would be expected to adversely affect proration, yet proration and multiple imputation provided very similar results for both the MPN-10 and BFI. This is likely because the MPN-10 item missing data rates were low, consistent with prior clinic- and internet-based studies. These results support the published scoring method for the MPN-10 (proration).

A randomized controlled feasibility trial of a home-based walking behavior–change intervention for people with intermittent claudication

Walking treatment is recommended for improving intermittent claudication (IC), a debilitating symptom of leg pain caused by peripheral arterial disease. However, center-based exercise programs offered in a community or hospital setting are often not implemented or adhered to. We developed a home-delivered behavior-change intervention, MOtivating Structured walking Activity in Intermittent Claudication (MOSAIC), to increase walking in people with IC. A feasibility randomized controlled trial with nested qualitative interviews involving a subsample of trial participants was conducted. Feasibility criteria evaluated participant recruitment and retention; suitability of proposed outcome measures; and acceptability and adherence to the intervention and trial. Participants (adults aged ≥18years diagnosed with IC identified from vascular outpatient clinics) were randomized 1:1 to receive MOSAIC treatment (two 60-minute home-based sessions and two 20-minute booster telephone calls incorporating behavior-change techniques) or an attention-control comparison. Outcomes (baseline and 16-week follow-up) included the 6-minute walking distance (meters), pedometer-assessed daily walking activity (steps/d), health-related quality of life, physical functioning, and beliefs about walking treatment, peripheral arterial disease, and self-regulatory processes. Twenty-four participants (mean age: 66.8±9.4years, 79% male) were included. Feasibility criteria achieved were recruitment rate (25%), participant retention (92%), and adherence to assigned treatment or attention-control sessions (71%). Missing data rates were <10% for all outcomes except for baseline daily walking activity (36%). The trial protocol and interventions were acceptable to participants and the clinician. In conclusion, the MOSAIC trial was feasible to conduct, with the exception of high missing pedometer data. The intervention is an acceptable approach to facilitate walking among people with IC.

Model for End-Stage Liver Disease Score Independently Predicts Mortality in Cardiac Surgery

Although liver disease increases surgical risk, it is not considered in The Society for Thoracic Surgeons (STS) risk calculator. This study assessed the impact of Model for End-Stage Liver Disease (MELD) on outcomes after cardiac surgical procedures and the additional predictive value of MELD in the STS risk model. Deidentified records of 21,272 patients were extracted from a regional STS database. Inclusion criteria were any cardiac operation with a risk score available (2011-2016). Exclusion criteria included missing MELD (n= 2,895) or preoperative anticoagulation (n= 144). Patients were stratified into three categories, MELD < 9 (low), MELD 9 to 15 (moderate), and MELD > 15 (high). Univariate and multivariate logistic regression assessed risk-adjusted associations between MELD and operative outcomes. Increasing MELD scores were associated with greater comorbid disease, mitral operation, prior cardiac operation, and higher STS-predicted risk of mortality (1.1%, 2.3%, and 6.0% by MELD category; p < 0.0001). The operative mortality rate increased with increasing MELD score (1.6%, 3.9%, and 8.4%; p < 0.0001). By logistic regression MELD score was an independent predictor of operative mortality (odds ratio, 1.03 per MELD score point; p < 0.0001) as were the components total bilirubin (odds ratio, 1.22 per mg/dL; p= 0.002) and international normalized ratio (odds ratio, 1.40 per unit; p < 0.0001). Finally, MELD score was independently associated with STS major morbidity and the component complications renal failure and stroke. Increasing MELD score, international normalized ratio, and bilirubin all independently increase risk of operative mortality. Because high rates of missing data currently limit utilization of MELD, efforts to simplify and improve data collection would help improve future risk models.

Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting

Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to ~16% from 2015–2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the k-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model’s performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method.

Imputation accuracy of wheat genotyping-by-sequencing (GBS) data using barley and wheat genome references.

Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat (Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references (Chinese Spring survey sequence, W7984, and IWGSC RefSeq v1.0) and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.

SU89 - TRANSETHNIC ANALYSIS OF HIGH-RESOLUTION HLA ALLELES AND COMPLEMENT 4 STRUCTURAL POLYMORPHISMS IN SCHIZOPHRENIA

Background Genome-Wide Association Studies (GWAS) have produced strong evidence for association of Schizophrenia (SCZ) in the extended Major Histocompatibility Complex (chromosome 6q), including the Human Leukocyte Antigen (HLA) region. It has been reported that structural polymorphisms in the C4 gene can explain the signal in this region, but it has not been determined by direct high-resolution genotyping whether HLA alleles have independent effects. We have used a novel next-generation sequencing method to achieve HLA typing with 8-digit (4-field) resolution over classical HLA genes (A, B, C, DRB1, DRB3-5, DQA1, DQB1, DPA1 and DPB1) in cohorts of three ethnicities; and assayed C4 structural polymorphisms in a subset of the sample to permit imputation in the entire cohorts. These data permit analysis of the association of SCZ with HLA alleles while accounting for C4 variants. Methods Subjects included 21,592 individuals: SCZ cases/controls from the MGS and GPC cohorts (European-ancestry: 5,762 cases, 5,558 controls; African-American: 2,671 cases, 2,373 controls), and 1,670 Taiwan families (3,340 parents, 1,728 affected offspring). HLA genotyping was carried out using the Mia Fora NGS Flex HLA Typing Kit (Immucor), which uses long-range PCR to capture 11 HLA loci (the entire gene in most cases) followed by high-depth, multiplexed Illumina sequencing. C4 polymorphisms were assayed using ddPCR using published primers and calling algorithms, in 940 individuals representing all ancestries and genotyping platforms. Analyses are almost completed, including imputation of C4 structural alleles into all samples (HIBAG), followed by frequentist association tests of HLA alleles with and without conditioning on C4 alleles, taking ancestry covariates into account (PLINK). It is expected that imputation of HLA genotypes can also be improved for each ancestry based on the high-resolution genotyping of large cohorts. Results All analyses are in progress and will be completed prior to the Congress. Missing data rates across loci are ~1%, and Mendelian error rate in trios is low. C4 alleles can be imputed with high accuracy in all combinations of ancestry and genotyping platform. Imputation accuracy was over 90% for all HLA genes. Discussion The results of this study will provide important new information about whether HLA alleles have effects on SCZ risk, independent of their linkage disequilibrium with C4 structural polymorphisms. The study will also provide improved resolution of imputation of both C4 polymorphisms and of HLA alleles.

Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

Accurate estimation of traffic counts [(i.e., annual average daily traffic (AADT)] is essential to transportation agencies for traffic demand forecasting, emission evaluation, pavement design, and project prioritization. Traditional AADT estimation methods rely on either temporal data imputation techniques based on historical records or kriging-based spatial interpolation approaches. However, Kriging method utilizes the correlation function as the sole descriptor of spatial dependency, posing limitations to yield accurate interpolation results for unstable AADTs under complex traffic patterns due to diverse road functions or land uses. This study proposed a copula-based model that combines spatial dependency and marginal distribution for missing AADT interpolation to weaken the limitation of Kriging method. Thus, the proposed model not only can describe the spatial dependency but also is robust to outliers. AADT data collected from the California state highway network were used to evaluate the effectiveness of spatial copula models with varying missing data rates. Four road segments with regular and recreational traffic patterns were selected to compare with existing kriging-based approaches. Results suggested that the spatial copulas yielded significantly higher accuracy rates than kriging did for irregular travel patterns with high missing data rates. Spatial copula models hold a great potential to improve the performance of large-scale transportation network-wide data imputation for planning and operational usages.

Combining RNA-seq and ddRAD-seq to SNP discovery of a highly migratory and apex predator

Combining RNA-seq and ddRAD-seq to SNP discovery of a highly migratory and apex predator