OBJECTIVE: The causes and triggering factors of epilepsy are still unknown. The results of genome-wide association studies can be utilized for a phenome-wide association study using Mendelian randomization (MR) to identify potential risk factors for epilepsy.
METHODS: This study utilizes two-sample MR analysis to investigate whether 316 phenotypes, including lifestyle, environmental factors, blood biomarker, and more, are causally associated with the occurrence of epilepsy. The primary analysis employed the inverse variance weighted (IVW) model, while complementary MR analysis methods (MR Egger, Wald ratio) were also employed. Sensitivity analyses were also conducted to evaluate heterogeneity and pleiotropy.
RESULTS: There was no evidence of a statistically significant causal association between the examined phenotypes and epilepsy following Bonferroni correction (p < 1.58 × 10-4) or false discovery rate correction. The results of the MR analysis indicate that the frequency of tiredness or lethargy in the last 2 weeks (p = 0.042), blood uridine (p = 0.003), blood propionylcarnitine (p = 0.041), and free cholesterol (p = 0.044) are suggestive causal risks for epilepsy. Lifestyle choices, such as sleep duration and alcohol consumption, as well as biomarkers including steroid hormone levels, hippocampal volume, and amygdala volume were not identified as causal factors for developing epilepsy (p > 0.05).
CONCLUSIONS: Our study provides additional insights into the underlying causes of epilepsy, which will serve as evidence for the prevention and control of epilepsy. The associations observed in epidemiological studies may be partially attributed to shared biological factors or lifestyle confounders.

BACKGROUND: Genetic variants in the coding region could directly affect the structure and expression levels of genes and proteins. However, the importance of variants in the non-coding region, such as microRNAs (miRNAs), remain to be elucidated. Genetic variants in miRNA-related sequences could affect their biogenesis or functionality and ultimately affect disease risk. Yet, their implications and pleiotropic effects on many clinical conditions remain unknown.
METHODS: Here, we utilised genotyping and hospital records data in the UK Biobank (N = 423,419) to investigate associations between 346 genetic variants in miRNA-related sequences and a wide range of clinical diagnoses through phenome-wide association studies. Further, we tested whether changes in blood miRNA expression levels could affect disease risk through colocalisation and Mendelian randomisation analysis.
RESULTS: We identified 122 associations for six variants in the seed region of miRNAs, nine variants in the mature region of miRNAs, and 27 variants in the precursor miRNAs. These included associations with hypertension, dyslipidaemia, immune-related disorders, and others. Nineteen miRNAs were associated with multiple diagnoses, with six of them associated with multiple disease categories. The strongest association was reported between rs4285314 in the precursor of miR-3135b and celiac disease risk (odds ratio (OR) per effect allele increase = 0.37, P = 1.8 × 10-162). Colocalisation and Mendelian randomisation analysis highlighted potential causal role of miR-6891-3p in dyslipidaemia.
CONCLUSIONS: Our study demonstrates the pleiotropic effect of miRNAs and offers insights to their possible clinical importance.

BACKGROUND: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race.
METHODS: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center.
RESULTS: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks.
CONCLUSIONS: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

The Fusarium graminearum virus 1 (FgV1) causes noticeable phenotypic changes such as reduced mycelial growth, increase pigmentation, and reduced pathogenicity in its host fungi, Fusarium graminearum. Previous study showed that the numerous F. graminearum genes including regulatory factors were differentially expressed upon FgV1 infection, however, we have limited knowledge on the effect(s) of specific transcription factor (TF) during FgV1 infection in host fungus. Using gene-deletion mutant library of 657 putative TFs in F. graminearum, we transferred FgV1 by hyphal anastomosis to screen transcription factors that might be associated with viral replication or symptom induction. FgV1-infected TF deletion mutants were divided into three groups according to the mycelial growth phenotype compare to the FgV1-infected wild-type strain (WT-VI). The FgV1-infected TF deletion mutants in Group 1 exhibited slow or weak mycelial growth compare to that of WT-VI on complete medium at 5 dpi. In contrast, Group 3 consists of virus-infected TF deletion mutants showing faster mycelial growth and mild symptom compared to that of WT-VI. The hyphal growth of FgV1-infected TF deletion mutants in Group 2 was not significantly different from that of WT-VI. We speculated that differences of mycelial growth among the FgV1-infected TF deletion mutant groups might be related with the level of FgV1 RNA accumulations in infected host fungi. By conducting real-time quantitative reverse transcription polymerase chain reaction, we observed close association between FgV1 RNA accumulation and phenotypic differences of FgV1-infected TF deletion mutants in each group, i.e., increased and decreased dsRNA accumulation in Group 1 and Group 3, respectively. Taken together, our analysis provides an opportunity to identify host\'s regulator(s) of FgV1-triggered signaling and antiviral responses and helps to understand complex regulatory networks between FgV1 and F. graminearum interaction.

BACKGROUND: We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race.
METHODS: The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center.
RESULTS: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks.
CONCLUSIONS: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

Leaf senescence is influenced by its life history, comprising a series of developmental and physiological experiences. Exploration of the biological principles underlying leaf lifespan and senescence requires a schema to trace leaf phenotypes, based on the interaction of genetic and environmental factors. We developed a new approach and concept that will facilitate systemic biological understanding of leaf lifespan and senescence, utilizing the phenome high-throughput investigator (PHI) with a single-leaf-basis phenotyping platform. Our pilot tests showed empirical evidence for the feasibility of PHI for quantitative measurement of leaf senescence responses and improved performance in order to dissect the progression of senescence triggered by different senescence-inducing factors as well as genetic mutations. Such an establishment enables new perspectives to be proposed, which will be challenged for enhancing our fundamental understanding on the complex process of leaf senescence. We further envision that integration of phenomic data with other multi-omics data obtained from transcriptomic, proteomic, and metabolic studies will enable us to address the underlying principles of senescence, passing through different layers of information from molecule to organism.

Omics is a form of high-throughput systems science. However, taxonomies for omics studies are limited, inviting us to rethink new ways in which we classify, prioritize, and rank various omics systems science studies. In this overarching context, the genome-wide study approaches have proliferated in number and popularity over the past decade. However, their hierarchy is not well organized and the development of attendant terminology is not controlled. In the present study, we searched the literature in PubMed and the Web of Science databases published from March 1999 to September 2016 using the keywords, including genome-wide, association, whole genome, transcriptome-wide, metabolome, epigenome, and phenome. We identified the whole genome study approaches and sorted them according to the omics technology types (genomics, proteomics, and so on) and hierarchy. Thirty-four studies from over 90 publications were sorted into 10 omics groups: DNA level, transcriptomics, proteomics, interactomics, metabolomics, epigenomics, miRNomics/ncRNomics, phenomics, environmental omics, and pharmacogenomics. We suggest here modifications of terminology for study approaches, which share the same acronyms such as EWAS for epigenome-wide association and environment-wide association studies, and MWAS for methylome-wide association and metabolome-wide association studies. Taken together, our study presented here provides the first systematic review and analyses of whole genome approaches and presents a baseline for further controlled terminology development, with a view to a new taxonomy for omics and multi-omics studies in the future. Finally, we call for greater dialogue and collaboration across diverse omics knowledge domains and applications, for example, across plants, animals, clinical medicine, and ecology.