The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Flowchart of the regression approach.

<p>Data balancing was repeated ten times to investigate model robustness. Significant features were identified by the Wilcoxon-rank sum test.</p

PreTIS: A Tool to Predict Non-canonical 5’ UTR Translational Initiation Sites in Human and Mouse

<div><p>Translation of mRNA sequences into proteins typically starts at an AUG triplet. In rare cases, translation may also start at alternative non–AUG codons located in the annotated 5’ UTR which leads to an increased regulatory complexity. Since ribosome profiling detects translational start sites at the nucleotide level, the properties of these start sites can then be used for the statistical evaluation of functional open reading frames. We developed a linear regression approach to predict in–frame and out–of–frame translational start sites within the 5’ UTR from mRNA sequence information together with their translation initiation confidence. Predicted start codons comprise AUG as well as near–cognate codons. The underlying datasets are based on published translational start sites for human HEK293 and mouse embryonic stem cells that were derived by the original authors from ribosome profiling data. The average prediction accuracy of true vs. false start sites for HEK293 cells was 80%. When applied to mouse mRNA sequences, the same model predicted translation initiation sites observed in mouse ES cells with an accuracy of 76%. Moreover, we illustrate the effect of <i>in silico</i> mutations in the flanking sequence context of a start site on the predicted initiation confidence. Our new webservice <i>PreTIS</i> visualizes alternative start sites and their respective ORFs and predicts their ability to initiate translation. Solely, the mRNA sequence is required as input. <i>PreTIS</i> is accessible at <a href="http://service.bioinformatik.uni-saarland.de/pretis" target="_blank">http://service.bioinformatik.uni-saarland.de/pretis</a>.</p></div

Alternative start codons of human gene <i>GIMAP5</i>.

<p>Predicted start sites were subdivided into four confidence groups and highlighted by different colors and dashed lines: very high (hot/best candidates with <i>c</i> ≥ 0.9), high (0.8 ≤ <i>c</i> < 0.9), moderate (0.7 ≤ <i>c</i> < 0.8) and low (<i>t</i> = 0.54 ≤ <i>c</i> < 0.7) initiation confidence <i>c</i>. For this gene, we found one hot candidate with a very high confidence value of 0.92 of being a true start site (AUG at position -203).</p

Evaluation of the regression approach.

<p>Evaluation of the regression approach.</p

Datasets used in this study.

<p>Datasets used in this study.</p

Mean value and standard deviation of the 44 features that were used in the best human model.

<p>Mean value and standard deviation of the 44 features that were used in the best human model.</p

SNP analysis of gene GIMAP5.

<p>Mutation matrix showing the impact of the flanking sequence context of four putative start sites of gene <i>GIMAP5</i> on the predicted initiation confidence. In each case, only one nucleotide is mutated with respect to the reference sequence (top line). Grey means that the start was predicted as true translational start (predicted initiation confidence is greater than 0.54) whereas white means that the start was classified as false start. Mutations at the start sites itself were not considered. The numbers reflect the predicted initiation confidence values. A: CUG at position -36. B: CUG at position -44. C: AUA at position -237. D: CUG at position -160.</p

Performance of the best human HEK293 model applied to the mouse ES and human HEK293–AUG datasets.

<p>Performance of the best human HEK293 model applied to the mouse ES and human HEK293–AUG datasets.</p