Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data.

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

An Analysis of Risk Factors for the Development of Acneiform Eruptions in Patients on Monoclonal Antibody Epidermal Growth Factor Receptor Inhibitors.

Acneiform eruptions occur frequently and early in patients on epidermal growth factor receptor inhibitors (EGFRi). Identification of baseline patient risk factors would prompt earlier referral to dermatology to optimize prevention and management. The primary objective of this retrospective study is to determine the association between clinical and demographic characteristics and the development of acneiform eruptions. A retrospective chart review was conducted on patients diagnosed with colon and head and neck cancers who started EGFRi between January 2017 and December 2021. Patients were followed until death or September 2022. Baseline demographic and clinical parameters were documented and patients were followed from the time of diagnosis to most recent visit for the development and management of an acneiform eruption. Regression analyses were performed to determine the association between baseline characteristics and the development of acneiform eruptions. A total of 66 patients were treated with cetuximab or panitumumab between 2017-2021 were included in the analysis. Forty-seven of the sixty-six patients developed an acneiform eruption while on EGFRi therapy (71.2%). Combination cancer therapy with another chemotherapeutic agent was associated with a lower risk of acneiform eruption (OR 0.03, P = .027). No other baseline features were statistically associated with a lower risk of acneiform eruption. Acneiform eruptions are a common cutaneous adverse event of EGFRi therapy. Ongoing research is required to elucidate risk factors for the development of acneiform eruptions, to improve the quality of life of oncology patients.

Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data.

BACKGROUND: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods. METHODS: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter. RESULTS: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders. CONCLUSIONS: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.

Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals.

High levels of anti-citrullinated protein antibodies (ACPA) are often observed prior to a diagnosis of rheumatoid arthritis (RA). We undertook a replication study to confirm CpG sites showing evidence of differential methylation in subjects positive vs. negative for ACPA, in a new subset of 112 individuals sampled from the population cohort and biobank CARTaGENE in Quebec, Canada. Targeted custom capture bisulfite sequencing was conducted at approximately 5.3 million CpGs located in regulatory or hypomethylated regions from whole blood; library and protocol improvements had been instituted between the original and this replication study, enabling better coverage and additional identification of differentially methylated regions (DMRs). Using binomial regression models, we identified 19,472 ACPA-associated differentially methylated cytosines (DMCs), of which 430 overlapped with the 1909 DMCs reported by the original study; 814 DMRs of relevance were clustered by grouping adjacent DMCs into regions. Furthermore, we performed an additional integrative analysis by looking at the DMRs that overlap with RA related loci published in the GWAS Catalog, and protein-coding genes associated with these DMRs were enriched in the biological process of cell adhesion and involved in immune-related pathways.

Inferring Transmission Histories of Rare Alleles in Population-Scale Genealogies.

Learning the transmission history of alleles through a family or population plays an important role in evolutionary, demographic, and medical genetic studies. Most classical models of population genetics have attempted to do so under the assumption that the genealogy of a population is unavailable and that its idiosyncrasies can be described by a small number of parameters describing population size and mate choice dynamics. Large genetic samples have increased sensitivity to such modeling assumptions, and large-scale genealogical datasets become a useful tool to investigate realistic genealogies. However, analyses in such large datasets are often intractable using conventional methods. We present an efficient method to infer transmission paths of rare alleles through population-scale genealogies. Based on backward-time Monte Carlo simulations of genetic inheritance, we use an importance sampling scheme to dramatically speed up convergence. The approach can take advantage of available genotypes of subsets of individuals in the genealogy including haplotype structure as well as information about the mode of inheritance and general prevalence of a mutation or disease in the population. Using a high-quality genealogical dataset of more than three million married individuals in the Quebec founder population, we apply the method to reconstruct the transmission history of chronic atrial and intestinal dysrhythmia (CAID), a rare recessive disease. We identify the most likely early carriers of the mutation and geographically map the expected carrier rate in the present-day French-Canadian population of Quebec.

A simplified sheathless cell separation approach using combined gravitational-sedimentation-based prefocusing and dielectrophoretic separation.

Prefocusing of the cell mixture is necessary for achieving a high-efficiency and continuous dielectrophoretic (DEP) cell separation. However, prefocusing through sheath flow requires a complex and tedious peripheral system for multi-channel fluid control, hindering the integration of DEP separation systems with other microfluidic functionalities for comprehensive clinical and biological tasks. This paper presented a simplified sheathless cell separation approach that combines gravitational-sedimentation-based sheathless prefocusing and DEP separation methods. Through gravitational sedimentation in a tubing, which was inserted into the inlet of a microfluidic chip with an adjustable steering angle, the cells were focused into a stream at the upstream region of a microchannel prior to separation. Then, a DEP force was applied at the downstream region of the microchannel for the active separation of the cells. Through this combined strategy, the peripheral system for the sheath flow was no longer required, and thus the integration of cell separation system with additional microfluidic functionalities was facilitated. The proposed sheathless scheme focused the mixture of cells with different sizes and dielectric properties into a stream in a wide range of flow rates without changing the design of the microfluidic chip. The DEP method is a label-free approach that can continuously separate cells on the basis of the sizes or dielectric properties of the cells and thus capable of greatly flexible cell separation. The efficiency of the proposed approach was experimentally assessed according to its performance in the separation of human acute monocytic leukemia THP-1 cells from yeast cells with respect to different sizes and THP-1 cells from human acute myelomonocytic leukemia OCI-AML3 cells with respect to different dielectric properties. The experimental results revealed that the separation efficiency of the method can surpass 90% and thus effective in separating cells on the basis of either size or dielectric property.