Rare Variants in Ischemic Stroke: An Exome Pilot Study

The genetic architecture of ischemic stroke is complex and is likely to include rare or low frequency variants with high penetrance and large effect sizes. Such variants are likely to provide important insights into disease pathogenesis compared to common variants with small effect sizes. Because a significant portion of human functional variation may derive from the protein-coding portion of genes we undertook a pilot study to identify variation across the human exome (i.e., the coding exons across the entire human genome) in 10 ischemic stroke cases. Our efforts focused on evaluating the feasibility and identifying the difficulties in this type of research as it applies to ischemic stroke. The cases included 8 African-Americans and 2 Caucasians selected on the basis of similar stroke subtypes and by implementing a case selection algorithm that emphasized the genetic contribution of stroke risk. Following construction of paired-end sequencing libraries, all predicted human exons in each sample were captured and sequenced. Sequencing generated an average of 25.5 million read pairs (75 bp×2) and 3.8 Gbp per sample. After passing quality filters, screening the exomes against dbSNP demonstrated an average of 2839 novel SNPs among African-Americans and 1105 among Caucasians. In an aggregate analysis, 48 genes were identified to have at least one rare variant across all stroke cases. One gene, CSN3, identified by screening our prior GWAS results in conjunction with our exome results, was found to contain an interesting coding polymorphism as well as containing excess rare variation as compared with the other genes evaluated. In conclusion, while rare coding variants may predispose to the risk of ischemic stroke, this fact has yet to be definitively proven. Our study demonstrates the complexities of such research and highlights that while exome data can be obtained, the optimal analytical methods have yet to be determined

Surrogate-Based Physics-Informed Neural Networks for Elliptic Partial Differential Equations

The purpose of this study is to investigate the role that a deep learning approach could play in computational mechanics. In this paper, a convolutional neural network technique based on modified loss function is proposed as a surrogate of the finite element method (FEM). Several surrogate-based physics-informed neural networks (PINNs) are developed to solve representative boundary value problems based on elliptic partial differential equations (PDEs). According to the authors’ knowledge, the proposed method has been applied for the first time to solve boundary value problems with elliptic partial differential equations as the governing equations. The results of the proposed surrogate-based approach are in good agreement with those of the conventional FEM. It is found that modification of the loss function could improve the prediction accuracy of the neural network. It is demonstrated that to some extent, the deep learning approach could replace the conventional numerical method as a significant surrogate model

Summary of the pipeline steps.

<p>Summary of the pipeline steps.</p

Select Gene Analyses: 10 Exome Cases vs. 3 Control Populations.

<p>Select Gene Analyses: 10 Exome Cases vs. 3 Control Populations.</p

SNPs in the 10 cases exomes vs. 3 control populations.

<p>SNPs in the 10 cases exomes vs. 3 control populations.</p

SNP Summary for SNPs in target exome regions only.

<p>SNP Summary for SNPs in target exome regions only.</p

GEOS population characteristics by case-control status.

<p>GEOS population characteristics by case-control status.</p

Compound heterozygotes in exome data for genes in which every case had a least two novel variants in the same gene isoform, only 6 genes (9 total isoforms) satisfied this criterion.

<p>The figure illustrates the 9 isoforms (left y-axis) per sample (x-axis) by variant type (left y-axis). Notably, two of the <i>CTPB2</i> isoforms (NM_001329 and NM_ 001083914) were seen to have a non-sense codon occurring in all 10 samples.</p

Characteristics of the 10 male stroke cases implemented in the pilot study.

<p>Characteristics of the 10 male stroke cases implemented in the pilot study.</p

Sequencing and Alignment Statistics for Ten Stroke Exomes.

<p>Sequencing and Alignment Statistics for Ten Stroke Exomes.</p