A review and performance evaluation of clustering frameworks for single-cell Hi-C data.

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes-Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.

Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation.

Quantitative trait locus (QTL) analyses of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), have been widely used to infer the functional effects of genome variants. However, the QTL discovery is largely restricted by the limited study sample size, which demands higher threshold of minor allele frequency and then causes heavy missing molecular trait-variant associations. This happens prominently in single-cell level molecular QTL studies because of sample availability and cost. It is urgent to propose a method to solve this problem in order to enhance discoveries of current molecular QTL studies with small sample size. In this study, we presented an efficient computational framework called xQTLImp to impute missing molecular QTL associations. In the local-region imputation, xQTLImp uses multivariate Gaussian model to impute the missing associations by leveraging known association statistics of variants and the linkage disequilibrium (LD) around. In the genome-wide imputation, novel procedures are implemented to improve efficiency, including dynamically constructing a reused LD buffer, adopting multiple heuristic strategies and parallel computing. Experiments on various multiomic bulk and single-cell sequencing-based QTL datasets have demonstrated high imputation accuracy and novel QTL discovery ability of xQTLImp. Finally, a C++ software package is freely available at https://github.com/stormlovetao/QTLIMP.

Exploring causal effects of sarcopenia on risk and progression of Parkinson disease by Mendelian randomization.

Previous observational studies suggested that sarcopenia is associated with Parkinson disease (PD), but it is unclear whether this association is causal. The objective of this study was to examine causal associations between sarcopenia-related traits and the risk or progression of PD using a Mendelian randomization (MR) approach. Two-sample bidirectional MR analyses were conducted to evaluate causal relationships. Genome-wide association study (GWAS) summary statistics for sarcopenia-related traits, including right handgrip strength (n = 461,089), left handgrip strength (n = 461,026), and appendicular lean mass (n = 450,243), were retrieved from the IEU OpenGWAS database. GWAS data for the risk of PD were derived from the FinnGen database (4235 cases; 373,042 controls). Summary-level data for progression of PD, including progression to Hoehn and Yahr stage 3, progression to dementia, and development of levodopa-induced dyskinesia, were obtained from a recent GWAS publication on progression of PD in 4093 patients from 12 longitudinal cohorts. Significant causal associations identified in MR analysis were verified through a polygenic score (PGS)-based approach and pathway enrichment analysis using genotype data from the Parkinson's Progression Markers Initiative. MR results supported a significant causal influence of right handgrip strength (odds ratio [OR] = 0.152, 95% confidence interval [CI] = 0.055-0.423, adjusted P = 0.0036) and appendicular lean mass (OR = 0.597, 95% CI = 0.440-0.810, adjusted P = 0.0111) on development of levodopa-induced dyskinesia. In Cox proportional hazard analysis, higher PGSs for right handgrip strength (hazard ratio [HR] = 0.225, 95% CI = 0.095-0.530, adjusted P = 0.0019) and left handgrip strength (HR = 0.303, 95% CI = 0.121-0.59, adjusted P = 0.0323) were significantly associated with a lower risk of developing levodopa-induced dyskinesia, after adjusting for covariates. Pathway enrichment analysis revealed that genome-wide significant single-nucleotide polymorphisms for right handgrip strength were substantially enriched in biological pathways involved in the control of synaptic plasticity. This study provides genetic evidence of the protective role of handgrip strength or appendicular lean mass on the development of levodopa-induced dyskinesia in PD. Sarcopenia-related traits can be promising prognostic markers for levodopa-induced dyskinesia and potential therapeutic targets for preventing levodopa-induced dyskinesia in patients with PD.