Impact of Brain-Derived Neurotrophic Factor Val66Met Polymorphism on Cortical Thickness and Voxel-Based Morphometry in Healthy Chinese Young Adults

BACKGROUND: Following voxel-based morphometry (VBM), brain-derived neurotrophic factor (BDNF) Val66Met polymorphism (rs6265) has been shown to affect human brain morphology in Caucasians. However, little is known about the specific role of the Met/Met genotype on brain structure. Moreover, the relationship between BDNF Val66Met polymorphism and Chinese brain morphology has not been studied. METHODOLOGY/PRINCIPAL FINDINGS: The present study investigated brain structural differences among three genotypes of BDNF (rs6265) for the first time in healthy young Chinese adults via cortical thickness analysis and VBM. Brain differences in Met carriers using another grouping method (combining Val/Met and Met/Met genotypes into a group of Met carriers as in most previous studies) were also investigated using VBM. Dual-approach analysis revealed less gray matter (GM) in the frontal, temporal, cingulate and insular cortices in the Met/Met group compared with the Val/Val group (corrected, P<0.05). Areas with less GM in the Val/Met group were included in the Met/Met group. VBM differences in Met carriers were only found in the middle cingulate cortex. CONCLUSIONS/SIGNIFICANCE: The current results indicated a unique pattern of brain morphologic differences caused by BDNF (rs6265) in young Chinese adults, in which the Met/Met genotype markedly affected the frontal, temporal, cingulate, and insular regions. The grouping method with Met carriers was not suitable to detect the genetic effect of BDNF Val66Met polymorphism on brain morphology, at least in the Chinese population, because it may hide some specific roles of Met/Met and Val/Met genotypes on brain structure

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude