A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data

DNA Methylation Changes and Phenotypic Adaptations Induced Repeated Extreme Altitude Exposure at 8848 Meters

Repeated extreme environmental training (RET) enhances adaptability and induces lasting methylation modifications. We recruited 64 participants from a high-altitude region (4700 m), including 32 volunteers with repeated high-altitude exposure, reaching up to 8848 m and as many as 11 exposures. By analyzing 741,489 CpG loci and 39 phenotypes, we identified significant changes in 13 CpG loci (R2 > 0.8, ACC > 0.75) and 15 phenotypes correlated with increasing RET exposures. The phenotypic Bayesian causal network and phenotypic-CpG interaction networks showed greater robustness (node correlation) with more RET exposures, particularly in systolic blood pressure (SP), platelet count (PLT), and neutrophil count (NEUT). Six CpG sites were validated as significantly associated with hypoxia exposure using the GEO public da-taset (AltitudeOmics). Furthermore, dividing the participants into two groups based on the number of RET exposures (n = 9 and 4) revealed six CpG sites significantly corre-lated with PLT and red cell distribution width-standard deviation (RDW.SD). Our findings suggest that increased RET exposures strengthen the interactions between phenotypes and CpG sites, indicating that critical extreme adaptive states may alter methylation patterns, co-evolving with phenotypes such as PLT, RDW.SD, and NEUT.

UGT1A1 genetic variants are associated with increases in bilirubin levels in rheumatoid arthritis patients treated with sarilumab

Sarilumab is a human monoclonal antibody against interleukin (IL)-6Rα that has been approved for the treatment of adult patients with moderately to severely active rheumatoid arthritis (RA) and an inadequate response or intolerance to one or more disease-modifying antirheumatic drugs (DMARDs). Mild liver function test abnormalities have been observed in patients treated with sarilumab. We describe a genome-wide association study of bilirubin elevations in RA patients treated with sarilumab. Array genotyping and exome sequencing were performed on DNA samples from 1075 patients. Variants in the UGT1A1 gene were strongly associated with maximum bilirubin elevations in sarilumab-treated patients (rs4148325; p = 2.88 × 10⁻⁴¹) but were not associated with aminotransferase elevations. No other independent loci showed evidence of association with bilirubin elevations after sarilumab treatment. These findings suggest that most bilirubin increases during sarilumab treatment are related to genetic variation in UGT1A1 rather than underlying liver injury.

Integrative functional linear model for genome-wide association studies with multiple traits

In recent biomedical research, genome-wide association studies (GWAS) have demonstrated great success in investigating the genetic architecture of human diseases. For many complex diseases, multiple correlated traits have been collected. However, most of the existing GWAS are still limited because they analyze each trait separately without considering their correlations and suffer from a lack of sufficient information. Moreover, the high dimensionality of single nucleotide polymorphism (SNP) data still poses tremendous challenges to statistical methods, in both theoretical and practical aspects. In this article, we innovatively propose an integrative functional linear model for GWAS with multiple traits. This study is the first to approximate SNPs as functional objects in a joint model of multiple traits with penalization techniques. It effectively accommodates the high dimensionality of SNPs and correlations among multiple traits to facilitate information borrowing. Our extensive simulation studies demonstrate the satisfactory performance of the proposed method in the identification and estimation of disease-associated genetic variants, compared to four alternatives. The analysis of type 2 diabetes data leads to biologically meaningful findings with good prediction accuracy and selection stability.

Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis

The mainstream of research in genetics, epigenetics, and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex phenotypes remains elusive. Using association analysis as a major analytical platform for the complex data analysis is a key issue that hampers the theoretic development of genomic science and its application in practice. Causal inference is an essential component for the discovery of mechanical relationships among complex phenotypes. Many researchers suggest making the transition from association to causation. Despite its fundamental role in science, engineering, and biomedicine, the traditional methods for causal inference require at least three variables. However, quantitative genetic analysis such as QTL, eQTL, mQTL, and genomic-imaging data analysis requires exploring the causal relationships between two variables. This paper will focus on bivariate causal discovery with continuous variables. We will introduce independence of cause and mechanism (ICM) as a basic principle for causal inference, algorithmic information theory and additive noise model (ANM) as major tools for bivariate causal discovery. Large-scale simulations will be performed to evaluate the feasibility of the ANM for bivariate causal discovery. To further evaluate their performance for causal inference, the ANM will be applied to the construction of gene regulatory networks. Also, the ANM will be applied to trait-imaging data analysis to illustrate three scenarios: presence of both causation and association, presence of association while absence of causation, and presence of causation, while lack of association between two variables. Telling cause from effect between two continuous variables from observational data is one of the fundamental and challenging problems in omics and imaging data analysis. Our preliminary simulations and real data analysis will show that the ANMs will be one of choice for bivariate causal discovery in genomic and imaging data analysis.