Genetic analysis of transcription factors in dopaminergic neuronal development in Parkinson’s disease

Background:Genetic variants of dopaminergic transcription factor-encoding genes are suggested to be Parkinson’s disease(PD)risk factors;however,no comprehensive analyses of these genes in patients with PD have been undertaken.Therefore,we aimed to genetically analyze 16 dopaminergic transcription factor genes in Chinese patients with PD.Methods:Whole-exome sequencing(WES)was performed using a Chinese cohort comprising 1917 unrelated patients with familial or sporadic early-onset PD and 1652 controls.Additionally,whole-genome sequencing(WGS)was performed using another Chinese cohort comprising 1962 unrelated patients with sporadic late-onset PD and 1279 controls.Results:We detected 308 rare and 208 rare protein-altering variants in the WES and WGS cohorts,respectively.Gene-based association analyses of rare variants suggested that MSX1 is enriched in sporadic late-onset PD.However,the significance did not pass the Bonferroni correction.Meanwhile,72 and 1730 common variants were found in the WES and WGS cohorts,respectively.Unfortunately,single-variant logistic association analyses did not identify significant associations between common variants and PD.Conclusions:Variants of 16 typical dopaminergic transcription factors might not be major genetic risk factors for PD in Chinese patients.However,we highlight the complexity of PD and the need for extensive research elucidating its etiology.

Modulating the p-band center of carbon nanofibers derived from Co spin state as anode for high-power sodium storage

Carbon nanofibers(CNFs)have received extensive and in-depth studied as anodes for sodium-ion batteries(SIBs),and yet their initial Coulombic efficiency and rate capability remain enormous challenge at practical level.Herein,CNFs anchored with cobalt nanocluster(CNFs-Co)were prepared using chemical vapor deposition and thermal reduction methods.The as-prepared CNFs-Co shows a high initial Coulombic efficiency of 91%and a high specific discharge capacity of 246 mAh/g at 0.1 A/g after 200 cycles as anode for SIBs.Meanwhile,the CNFs-Co anode still delivers a high cycling stability with 108 mAh/g after 1000 cycles at 10 A/g.These excellent electrochemical properties could be attributed to the involved spin state Co,which endows CNFs with large interplanar spacing(0.39 nm)and abundant vacancy defects.Importantly,the spin state Co downshifts the p-band center of carbon and strengthens the Na+adsorption energy from-2.33 eV to-2.64 eV based on density functional theory calculation.This novel strategy of modulating the carbon electronic structure by the spin state of magnetic metals provides a reference for the development of high-performance carbon-based anode materials.

High performance computing of DGDFT for tens of thousands of atoms using millions of cores on Sunway TaihuLight

High performance computing(HPC)is a powerful tool to accelerate the Kohn–Sham density functional theory(KS-DFT)calculations on modern heterogeneous supercomputers.Here,we describe a massively parallel implementation of discontinuous Galerkin density functional theory(DGDFT)method on the Sunway Taihu Light supercomputer.The DGDFT method uses the adaptive local basis(ALB)functions generated on-the-fly during the self-consistent field(SCF)iteration to solve the KS equations with high precision comparable to plane-wave basis set.In particular,the DGDFT method adopts a two-level parallelization strategy that deals with various types of data distribution,task scheduling,and data communication schemes,and combines with the master–slave multi-thread heterogeneous parallelism of SW26010 processor,resulting in large-scale HPC KS-DFT calculations on the Sunway Taihu Light supercomputer.We show that the DGDFT method can scale up to 8,519,680 processing cores(131,072 core groups)on the Sunway Taihu Light supercomputer for studying the electronic structures of twodimensional(2 D)metallic graphene systems that contain tens of thousands of carbon atoms.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects.Hence,an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences.However,it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods.To solve this issue,we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets:(1)rare germline variants from clinical relevant sequence variants(ClinVar),(2)rare somatic variants from Catalogue Of Somatic Mutations In Cancer(COSMIC),(3)common regulatory variants from curated expression quantitative trait locus(eQTL)data,and(4)disease-associated common variants from curated genomewide association studies(GWAS).All 24 tested methods performed differently under various conditions,indicating varying strengths and weaknesses under different scenarios.Importantly,the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve(AUROC)of 0.4481–0.8033 and poor for rare somatic variants from COSMIC(AUROC=0.4984–0.7131),common regulatory variants from curated eQTL data(AUROC=0.4837–0.6472),and disease-associated common variants from curated GWAS(AUROC=0.4766–0.5188).We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder,and found that the combined annotation-dependent depletion(CADD)and context-dependent tolerance score(CDTS)methods showed better performance.Summarily,we assessed the performance of 24 computational methods under diverse scenarios,providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.