Search for: All records

VO2 is renowned for its electric transition from an insulating monoclinic (M1) phase, characterized by V–V dimerized structures, to a metallic rutile (R) phase above 340 K. This transition is accompanied by a magnetic change: the M1 phase exhibits a non-magnetic spin-singlet state, while the R phase exhibits a state with local magnetic moments. Simultaneous simulation of the structural, electric, and magnetic properties of this compound is of fundamental importance, but the M1 phase alone has posed a significant challenge to the density functional theory (DFT). In this study, we show none of the commonly used DFT functionals, including those combined with on-site Hubbard U to treat 3d electrons better, can accurately predict the V–V dimer length. The spin-restricted method tends to overestimate the strength of the V–V bonds, resulting in a small V–V bond length. Conversely, the spin-symmetry-breaking method exhibits the opposite trends. Each of these two bond-calculation methods underscores one of the two contentious mechanisms, i.e., Peierls lattice distortion or Mott localization due to electron–electron repulsion, involved in the metal–insulator transition in VO2. To elucidate the challenges encountered in DFT, we also employ an effective Hamiltonian that integrates one-dimensional magnetic sites, thereby revealing the inherent difficulties linked with the DFT computations.

 

In density-functional theory, the exchange–correlation (XC) energy can be defined exactly through the coupling-constant (λ) averaged XC hole n̄xc(r,r′), representing the probability depletion of finding an electron at r′ due to an electron at r. Accurate knowledge of n̄xc(r,r′) has been crucial for developing XC energy density-functional approximations and understanding their performance for molecules and materials. However, there are very few systems for which accurate XC holes have been calculated since this requires evaluating the one- and two-particle reduced density matrices for a reference wave function over a range of λ while the electron density remains fixed at the physical (λ = 1) density. Although the coupled-cluster singles and doubles (CCSD) method can yield exact results for a two-electron system in the complete basis set limit, it cannot capture the electron–electron cusp using finite basis sets. Focusing on Hooke’s atom as a two-electron model system for which certain analytic solutions are known, we examine the effect of this cusp error on the XC hole calculated using CCSD. The Lieb functional is calculated at a range of coupling constants to determine the λ-integrated XC hole. Our results indicate that, for Hooke’s atoms, the error introduced by the description of the electron–electron cusp using Gaussian basis sets at the CCSD level is negligible compared to the basis set incompleteness error. The system-, angle-, and coupling-constant-averaged XC holes are also calculated and provide a benchmark against which the Perdew–Burke–Ernzerhof and local density approximation XC hole models are assessed.

 

Polygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying genetic architectures and improves overall prediction accuracy for complex diseases by allowing for a wide class of prior choices. To take full advantage of the framework, we propose a summary-statistics-based cross-validation strategy to automatically select suitable chromosome-level priors, which demonstrates a striking variability of the prior preference of each chromosome, for the same complex disease, and further significantly improves the prediction accuracy.

Simulation studies and real data applications with seven disease datasets from the Wellcome Trust Case Control Consortium cohort and eight groups of large-scale genome-wide association studies demonstrate that NeuPred achieves substantial and consistent improvements in terms of predictive r2 over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods.

The R package implementing NeuPred is available at https://github.com/shuangsong0110/NeuPred.

Supplementary data are available at Bioinformatics online.

 

Abstract Motivation Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. Results We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. Availability and implementation The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. Supplementary information Supplementary data are available at Bioinformatics online. 

Genome‐wide association studies (GWAS) have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade, however, they are still hampered by limited statistical power and difficulties in biological interpretation. With the recent progress in expression quantitative trait loci (eQTL) studies, transcriptome‐wide association studies (TWAS) provide a framework to test for gene‐trait associations by integrating information from GWAS and eQTL studies.

In this review, we will introduce the general framework of TWAS, the relevant resources, and the computational tools. Extensions of the original TWAS methods will also be discussed. Furthermore, we will briefly introduce methods that are closely related to TWAS, including MR‐based methods and colocalization approaches. Connection and difference between these approaches will be discussed.

Finally, we will summarize strengths, limitations, and potential directions for TWAS.