Abstract: Cis-regulatory elements (CREs) are noncoding DNA segments that regulate transcription of genes residing on the same chromosome. Connecting millions of candidate CREs (cCREs) in the human genome with their target genes is critical for decoding the cis-regulatory mechanisms of gene expression and disease risk, but this remains a major and open challenge. We present Linkreg, a Bayesian variable selection framework that identifies cCRE-gene linkages from genome-wide transcriptomic and epigenomic data across diverse biosamples. Extensive benchmarking analyses show that Linkreg consistently outperforms state-of-the-art methods in simulations based on real epigenomic data from 304 human biosamples, as well as in both CRISPR perturbation and chromatin conformation experiments on human cell lines. Applying Linkreg to matched transcriptomic and epigenomic data of 31 blood and immune-related biosamples yields a high-quality genome-wide atlas of cCRE-gene linkages. Integrating this atlas with expression quantitative trait loci in whole blood and genome-wide association studies of 22 blood and immune-related traits not only demonstrates significantly stronger enrichments of biosample-relevant genetic signals than those obtained by existing methods from the same data, but also highlights putative mechanisms at GWAS loci of white blood cell trait and autoimmune disease. Overall, Linkreg provides an interpretable and efficient solution for the genome-wide identification of biosample-specific cCRE-gene linkages.

Abstract: Recent Hi-C technology enables more comprehensive chromosomal conformation research, including the detection of structural variations, especially translocation. In this paper, we formulate the interchromosomal translocation detection as a problem of scan clustering in a spatial point process. We then develop TranScan, a new translocation detection method through scan statistics with the control of false discovery. Evaluation of TranScan against current translocation detection methods on realistic breakpoint simulations generated from real data suggests better discriminative power. Both the simulation and real data analysis indicate that TranScan has great potentials in interchromosomal translocation detection using Hi-C data.

Abstract: Circulating tumor DNA (ctDNA) is becoming central to oncology R&D—enabling patient selection, minimal residual disease (MRD) detection, on-treatment response monitoring, and resistance profiling. A key principle is fit-for-purpose assay selection: different clinical questions demand different ctDNA characteristics (sensitivity, breadth, turnaround time, logistics, and cost). Broadly, assays fall into tumor-naïve and tumor-informed categories. Tumor-naïve panels offer simpler workflows and faster turnaround but lower sensitivity, whereas tumor-informed assays achieve higher sensitivity at the expense of more complex tissue requirements, longer timelines, and higher costs. For mutation-based assays, clonal hematopoiesis (CHIP) can confound results unless white-blood-cell sequencing or robust bioinformatic filters are employed. Clinical signals are emerging across settings. In neoadjuvant immunotherapy for NSCLC, early ctDNA clearance correlates with pathologic complete response (pCR) and improved event-free survival (EFS), supporting MRD as a pharmacodynamic and prognostic readout. Post-surgery MRD status can guide escalation/de-escalation in resectable disease, and in metastatic NSCLC, a ≥50% ctDNA drop (“molecular response”) associates with better outcomes. To translate these insights into routine decision-making, the field needs harmonization: standardized pre-analytics, CHIP handling, common definitions for MRD and molecular response, cross-platform QC, and transparent statistical plans aligned with regulatory expectations.

Abstract: Hidden confounding biases hinder identifying causal protein biomarkers for Alzheimer’s disease in non-randomized studies. While Mendelian randomization (MR) can mitigate these biases using protein quantitative trait loci (pQTLs) as instrumental variables, some pQTLs violate core assumptions, leading to biased conclusions. To address this, we propose MR-SPI, a novel MR method that selects valid pQTL instruments using Leo Tolstoy’s Anna Karenina principle and performs robust post-selection inference. Integrating MR-SPI with AlphaFold3, we developed a computational pipeline to identify causal protein biomarkers and predict 3D structural changes. Applied to genome-wide proteomics data from 54,306 UK Biobank participants and 455,258 subjects (71,880 cases and 383,378 controls) for a genome-wide association study of Alzheimer’s disease, we identified seven proteins (TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55) with structural alterations due to missense mutations. These findings offer insights into the etiology and potential drug targets for Alzheimer’s disease.