Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction.

Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.

Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients.

BACKGROUND: More than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects. METHODS: We performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study. RESULTS: We identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data. CONCLUSIONS: We provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets.

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits.

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R 2 value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.

The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index.

Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.

Vascular plants occurrences in Dokdo Islands, Korea, based on herbarium collections and legacy botanical literature.

BACKGROUND: The vascular flora of the Dokdo Islands has been reported, based on primary collections made in 2012 and 2013 and legacy botanical literature. The Dokdo Islands are the remotest islands of Korea, located in the East Sea approximately 87 km from Ulleungdo Islands. They comprise two main volcanic islands, Dongdo (east islands) and Seodo (west islands) and minor islets surrounding the two main islands. This research was conducted to document vascular plant species inhabiting Korea's most inaccessible islands. We present a georeferenced dataset of vascular plant species collected during field studies on the Dokdo Islands over the past seven decades. NEW INFORMATION: In the present inventory of the flora of Dokdo, there are listed 108 species belonging to 78 genera and 39 families, including 93 native species and 15 newly human-induced naturalised species for these Islands' flora. The Poaceae and Asteraceae families are the most diverse, with 22 and 15 taxa, respectively. Some of the previously-listed taxa were not found on Dokdo probably because they are rare and the limited time did not allow collectors to find rare species. The spread of introduced species, especially the invasive grass Bromuscatharticus Vahl., affects several native species of Dokdo flora.

Historical collections of vascular plants in the Korean Peninsula by three major collectors in the early 20th century: U. J. Faurie, E. J. Taquet and E. H. Wilson.

BACKGROUND: The digitisation of historical collections aims to increase global access to scientific artifacts, especially those from currently inaccessible areas. Historical collections from North Korea deposited at foreign herbaria play a fundamental role in biodiversity transformation patterns. However, the biodiversity pattern distribution in this region remains poorly understood given the severe gaps in available geographic species distribution records. Access to a dominant proportion of primary biodiversity data remains difficult for the broader scientific and environmental community. The digitisation of foreign collectors' botanical collections of around 60,000 specimens from the Korean Peninsula before World War II is ongoing. In this paper, we aim to fill this gap by developing the first comprehensive, open-access database of biodiversity records for the Korean Peninsula. This paper provides a quantitative and general description of the specimens that Urbain Jean Faurie, Emile Joseph Taquet and Ernest Henry Wilson have collected and are kept in several herbaria. NEW INFORMATION: An open-access database of biodiversity records provides a simple guide to georeferencing historical collections. The first set describes E. H. Wilson's collection of woody plants collected in the Korean Peninsula and preserved at the Harvard University Herbaria (A). This set includes 1,087 records collected from 1917 to 1918. The other collections contain specimens collected by E. J. Taquet (4,727 specimens from Quelpaert (Jeju), 1907-1914) and U. J. Faurie (3,659 specimens from North Korea and Quelpaert, 1901, 1906 and 1907). For each specimen, we recorded the species name, locality indication, collection date, collector, ecology and revision label. This set contains more than 9,400 specimens, with 22% of vascular plants from North Korea and 66% from Quelpaert (Jeju) Island. In these collections, we included some images that correspond to the specimens in this dataset.