The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Applications of a new power normal family

The main purpose of this paper is to give an algorithm to attain joint normality of non-normal multivariate observations through a new power normal family introduced by the author (Isogai, 1999). The algorithm tries to transform each marginal variable simultaneously to joint normality, but due to a large number of parameters it repeats a maximization process with respect to the conditional normal density of one transformed variable given the other transformed variables. A non-normal data set is used to examine performance of the algorithm, and the degree of achievement of joint normality is evaluated by measures of multivariate skewness and kurtosis. Besides the above topic, making use of properties of our power normal family, we discuss not only a normal approximation formula of non-central F distributions in the frame of regression analysis but also some decomposition formulas of a power parameter, which appear in a Wilson-Hilferty power transformation setting.Power normal family, non-normality, joint normality, measures of multivariate skewness and kurtosis,

On using influence functions for testing multivariate normality

Influence function, multivariate normality, measure of dependence, measures of multivariate skewness and kurtosis, score function, linear regression,

Asymptotic expansions for the distributions of latent roots ofS h S e −1 and of certain test statistics in MANOVA

asymptotic expansion, distribution of latent roots, Wishart, MANOVA, perturbation, test for dimensionality,

Power transformation of the F distribution and a power normal family

To transform the F distribution to a normal distribution, two types of formula for power transformation of the F variable are introduced. One formula is an extension of the Wilson-Hilferty transformation for the chi 2 variable, and the other type is based on the median of the F distribution. Combining those two formulas, a simple formula for the median of the F distribution is derived, and its numerical accuracy is evaluated. Simplification of the formula of the Wilson-Hilferty transformation, through the median formula, leads us to construct a power normal family from the generalized F distribution. Unlike the Box-Cox power normal family, our family has a property that the covariance structure of the maximum-likelihood estimates of the parameters is invariant under a scale transformation of the response variable. Numerical examples are given to show the diff erence between two power normal families.

Analysis of factorial experiments for survival data with long-tailed distributions

Two left-truncated survival data sets are collected in one-way factorial designs to examine the quality of products. We cannot specify our survival function completely, and only know that the tail has a power functional form of its argument. Thus, our problem is a left-truncated one with incomplete survivor functions. One of our data sets is the case where the usual analysis of variance (ANOVA) may be adapted. The other is a repeated measurement case. We note that the likelihood function is expressed as a product of conditional and marginal likelihood functions. Estimates of power parameters are always obtained by the conditional likelihood. Location parameters describing treatment eff ects are included in the marginal likelihood only, and their estimates are undetermined, because of missing values resulting from left truncation. However, in the ANOVA case, we show that a common structure of power parameters and some simple assumptions about the missing values enable us to construct an approximate F test for treatment effects through the marginal likelihood. This result is extended to a regression case. With the data in repeated measurements, a systematic variation of the power parameters and an apparent deviation from our presupposed model make an application of the ANOVA mentioned impossible, and compel us to generalize our model. By using the ratio of those generalized models, we show that a descriptive model for evaluating treatment effects can be constructed.