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Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies. Author summary: Linkage disequilibrium (LD) is a crucial concept in genetics, but current methods of measuring LD are not reliable for rare variants. In our study, we introduce a new statistic called cumulative LD (cLD) that captures the linkage disequilibrium between genetic regions containing rare variants, which are often unanalyzed. We demonstrate that cLD is a stable statistic and show through simulations using real data that it is more reliable than traditional LD for rare variants. Our findings show that cLD is a valuable tool for studying genetic associations and molecular evolution. We use cLD to reveal increased genetic association between genes in 3D chromatin interactions, which standard LD failed to detect. Additionally, we show that cLD identifies unreported protein-protein interactions and distinguishes case/control samples in association studies. Our work is significant because it provides a stable and reliable method for analyzing rare variants, which were previously overlooked due to unreliable LD measurements. Our findings have implications for future genetic association studies and molecular evolution research, particularly in understanding rare variant interactions and their biological significance. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 virus outbreak continues worldwide, with many variants emerging, some of which are considered variants of concern (VOCs). The WHO designated Omicron as a VOC and assigned it under variant B.1.1.529. Here, we used computational studies to examine the VOCs, including Omicron subvariants, and one variant of interest. Here we found that the binding affinity of human receptor angiotensin‐converting enzyme 2 (hACE2) and receptor‐binding domain (RBDs) increased in the order of wild type (Wuhan‐strain) < Beta < Alpha < OmicronBA.5 < Gamma < Delta < Omicron BA.2.75 < BA.1 < BA.3 < BA.2. Interactions between docked complexes revealed that the RBD residue positions like 452, 478, 493, 498, 501, and 505 are crucial in creating strong interactions with hACE2. Omicron BA.2 shows the highest binding capacity to the hACE2 receptor among all the mutant complexes. The BA.5's L452R, F486V, and T478K mutation significantly impact the interaction network in the BA.5 RBD‐hACE2 interface. Here for the first time, we report the His505, an active residue on the RBD forming a salt bridge in the BA.2, leading to increased mutation stability. When the active RBD residues are mutated, binding affinity and intermolecular interactions increase across all mutant complexes. By examining the differences in different variants, this study may provide a solid foundation for structure‐based drug design for newly emerging variants. [ABSTRACT FROM AUTHOR]

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-variants to enhance model building for TF downstream target genes. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these prediction models to large GWAS datasets for breast, prostate, lung cancers and other diseases. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene expression prediction models and identifying disease-associated genes, as shown by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study shed new light on several genetically driven key TF regulators and their associated TF-gene regulatory networks underlying disease susceptibility., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from an HIV cohort derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.