Inferring Parametric Energy Consumption Functions at Different Software Levels:ISA vs. LLVM IR

The static estimation of the energy consumed by program executions is an important challenge, which has applications in program optimization and verification, and is instrumental in energy-aware software development. Our objective is to estimate such energy consumption in the form of functions on the input data sizes of programs. We have developed a tool for experimentation with static analysis which infers such energy functions at two levels, the instruction set architecture (ISA) and the intermediate code (LLVM IR) levels, and re ects it upwards to the higher source code level. This required the development of a translation from LLVM IR to an intermediate representation and its integration with existing components, a translation from ISA to the same representation, a resource analyzer, an ISA-level energy model, and a mapping from this model to LLVM IR. The approach has been applied to programs written in the XC language running on XCore architectures, but is general enough to be applied to other languages. Experimental results show that our LLVM IR level analysis is reasonably accurate (less than 6:4% average error vs. hardware measurements) and more powerful than analysis at the ISA level. This paper provides insights into the trade-off of precision versus analyzability at these levels

Additional file 1: Table S1. of Association of a rare NOTCH4 coding variant with systemic sclerosis: a family-based whole exome sequencing study

Table of rare coding variants segregating with the SSc/scleroderma phenotype. (XLS 27 kb

Gene Network Analysis in a Pediatric Cohort Identifies Novel Lung Function Genes

<div><p>Lung function is a heritable trait and serves as an important clinical predictor of morbidity and mortality for pulmonary conditions in adults, however, despite its importance, no studies have focused on uncovering pediatric-specific loci influencing lung function. To identify novel genetic determinants of pediatric lung function, we conducted a genome-wide association study (GWAS) of four pulmonary function traits, including FVC, FEV₁, FEV₁/FVC and FEF_25–75% in 1556 children. Further, we carried out gene network analyses for each trait including all SNPs with a P-value of <1.0×10⁻³ from the individual GWAS. The GWAS identified SNPs with notable trends towards association with the pulmonary function measures, including the previously described <i>INTS12</i> locus association with FEV1 (p_meta = 1.41<b>×</b>10⁻⁷). The gene network analyses identified 34 networks of genes associated with pulmonary function variables in Caucasians. Of those, the glycoprotein gene network reached genome-wide significance for all four variables. P-value range p_meta = 6.29×10⁻⁴ - 2.80×10⁻⁸ on meta-analysis. In this study, we report on specific pathways that are significantly associated with pediatric lung function at genome-wide significance. In addition, we report the first loci associated with lung function in both pediatric Caucasian and African American populations<b>.</b></p></div

Study participant characteristics.

<p>Study participant characteristics.</p

Top functional annotation networks associated with lung function variables in Caucasian cohort.

<p>Top functional annotation networks associated with lung function variables in Caucasian cohort.</p

Functional analysis results: Benjamini-Hochberg corrected p-values for glycoprotein term association with lung function variables in pediatric and adult cohorts.

<p>Functional analysis results: Benjamini-Hochberg corrected p-values for glycoprotein term association with lung function variables in pediatric and adult cohorts.</p

Additional file 7: of Genomic copy number variation association study in Caucasian patients with nonsyndromic cryptorchidism

CNV calls for Group 2 cases passed sample QC. Each column in Additional file 7 represents CNV location, SNPs numbers contained within the CNV, the length of the CNV, copy number (cn) of the CNV call, sample id, the starting marker identifier and the ending marker identifier in the CNV, and confidence score in PennCNV calling. (XLSX 534 kb

Additional file 5: of Genomic copy number variation association study in Caucasian patients with nonsyndromic cryptorchidism

CNV calls for Group 1 cases passed sample QC. Each column in Additional file 5 represents CNV location, SNPs numbers contained within the CNV, the length of the CNV, copy number (cn) of the CNV call, sample id, the starting marker identifier and the ending marker identifier in the CNV, and confidence score in PennCNV calling. (XLSX 653 kb

Additional file 1: of Variants in CXCR4 associate with juvenile idiopathic arthritis susceptibility

Supplemental Data: Table S1. The clinical characteristics of samples in each JIA cohort. Table S2. Genome-wide significant associations at the HLA locus (p < 5×10-8 in the discovery cohort).Table S3. Association results for top SNPs in known JIA associated genes PTPN22, IL2RA, ANTXR2. TableS4. The most significantly associated SNPs at CXCR4 locus on chromosome 2q22.1. Table S5 . Genomewideassociation results for imputed SNPs (p < 1×10-4 in combined analysis) in the vicinity of CXCR4 in our JIA cohort. Table S6. Primers used in Sanger sequencing validation of rare variants at CXCR4 locus. Figure S1. Genome-wide association results for JIA. Figure S2. Regional association plot for the 2q22.1 region. Figure S3. CXCR4 tissue-specific gene expression levels. Figure S4. CXCR4 expression levels stratified by SNP genotype. (DOC 466 kb