Domesticated apple (Malus x domestica Borkh.) is a major global crop and the genetic diversity held within the pool of cultivated varieties is important for the development of future cultivars. The aim of this study was to investigate the diversity held within the domesticated form, through the analysis of a major international germplasm collection of cultivated varieties, the UK National Fruit Collection, consisting of over 2,000 selections of named cultivars and seedling varieties. We utilised Diversity Array Technology (DArT) markers to assess the genetic diversity within the collection. Clustering attempts, using the software STRUCTURE revealed that the accessions formed a complex and historically admixed group for which clear clustering was challenging. Comparison of accessions using the Jaccard similarity coefficient allowed us to identify clonal and duplicate material as well as revealing pairs and groups that appeared more closely related than a standard parent-offspring or full-sibling relations. From further investigation, we were able to propose a number of new pedigrees, which revealed that some historically important cultivars were more closely related than previously documented and that some of them were partially inbred. We were also able to elucidate a number of parent-offspring relationships that had resulted in a number of important polyploid cultivars. This included reuniting polyploid cultivars that in some cases dated as far back as the 18^th century, with diploid parents that potentially date back as far as the 13^th century.

This study presents a novel statistical and computational approach using nonparametric regression, which capitalizes on correlation structure to deal with the high-dimensional data often found in pharmacogenomics, for instance, in Crohn’s inflammatory bowel disease. The empirical correlation between the test statistics, investigated via simulation, can be used as an estimate of noise. The theoretical distribution of −log₁₀(p-value) is used to support the estimation of that optimal bandwidth for the model, which adequately controls type I error rates while maintaining reasonable power. Two proposed approaches, involving normal and Laplace-LD kernels, were evaluated by conducting a case-control study using real data from a genome-wide association study on Crohn’s disease. The study successfully identified single nucleotide polymorphisms on the NOD2 gene associated with the disease. The proposed method reduces the computational burden by approximately 33% with reasonable power, allowing for a more efficient and accurate analysis of genetic variants influencing drug responses. The study contributes to the advancement of statistical methodology for analyzing complex genetic data and is of practical advantage for the development of personalized medicine.

This study investigates influenza trends in Thailand from 2003 to 2023, employing statistical models for robust analysis. Yearly cumulative influenza patient data, categorized by age groups, are examined using a Poisson Generalised Linear Model (GLM) with a log-link function. The influence of age subgroups, particularly 5-14 and 0-4 years, on disease spread is identified. Additionally, a cubic B-spline regression model is applied to predict cumulative yearly influenza cases, with optimal tuning parameters determined. Results reveal that the year 2019, coinciding with the COVID-19 pandemic, experienced the highest influenza patient count, suggesting a potential relationship between the two diseases. The study utilises the R programming language for thorough statistical analysis, contributing valuable insights into influenza dynamics in Thailand.

In the past decades, drought has become an escalating issue in many parts of Thailand and around the world as a result of the impact of climate change increasing the temperature. Droughts do not only affect agriculture and industry but also human health and the environment. The efficient operation and management of water resources are crucial. Reservoir is one of the key factors of water resources operation and management. Therefore, it requires the accurate forecasting of the reservoir inflows. The monthly reservoir inflow data of Bhumibol and Sirikit Dams, Thailand collected by Water Analysis and Assessment Division, the Royal Irrigation Department were analysed to obtain the best-fitting models for the reservoir inflows. The time series models employed in this paper are Winters’ exponential smoothing, autoregressive integrated moving average, time series regression, and combined forecasting models. The findings indicate that the simple average-based combination and time series regression models are the most accurate models for forecasting the reservoir inflows of Bhumibol and Sirikit Dams respectively.