Quark-gluon vertex in general kinematics

The original publication can be found at www.springerlink.com Submitted to Cornell University’s online archive www.arXiv.org in 2007 by Jon-Ivar Skullerud. Post-print sourced from www.arxiv.org.We compute the quark–gluon vertex in quenched lattice QCD in the Landau gauge, using an off-shell mean-field O(a)-improved fermion action. The Dirac-vector part of the vertex is computed for arbitrary kinematics. We find a substantial infrared enhancement of the interaction strength regardless of the kinematics.Ayse Kizilersu, Derek B. Leinweber, Jon-Ivar Skullerud and Anthony G. William

Systematic Cell-Based Phenotyping of Missense Alleles Empowers Rare Variant Association Studies: A Case for <i>LDLR</i> and Myocardial Infarction

<div><p>A fundamental challenge to contemporary genetics is to distinguish rare missense alleles that disrupt protein functions from the majority of alleles neutral on protein activities. High-throughput experimental tools to securely discriminate between disruptive and non-disruptive missense alleles are currently missing. Here we establish a scalable cell-based strategy to profile the biological effects and likely disease relevance of rare missense variants <i>in vitro</i>. We apply this strategy to systematically characterize missense alleles in the low-density lipoprotein receptor (<i>LDLR</i>) gene identified through exome sequencing of 3,235 individuals and exome-chip profiling of 39,186 individuals. Our strategy reliably identifies disruptive missense alleles, and disruptive-allele carriers have higher plasma LDL-cholesterol (LDL-C). Importantly, considering experimental data refined the risk of rare <i>LDLR</i> allele carriers from 4.5- to 25.3-fold for high LDL-C, and from 2.1- to 20-fold for early-onset myocardial infarction. Our study generates proof-of-concept that systematic functional variant profiling may empower rare variant-association studies by orders of magnitude.</p></div

Functions and distribution of <i>LDLR</i> rare missense alleles identified through exome sequencing of 3,235 individuals.

<p><b>(A)</b> Plasma LDL-C (in mg/dl) in <i>LDLR</i> missense allele carriers (dots) from the ATVB cohort according to functional category (for classification, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>). LoF, loss-of-function. Means are indicated by horizontal bars. p-value was determined by 2-sided, 2-tailed Student’s t-test. <b>(B, C)</b> Individual <i>LDLR</i> missense variants identified through exome sequencing of indicated number of individuals are depicted according to genomic position starting at the 5’end (top). The numbers next to each variant represent the number of times the respective variant was observed in cases and controls, respectively, with regard to plasma LDL-C levels (b) and early-onset myocardial infarction (MI; c). Colors in circles represent indicated functional classes as determined either by an overlap of four bioinformatic prediction tools (PolyPhen-2, SIFT, MutationAssessor and MutationTaster; see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>) (“prediction”) or cell-based experimental studies of LDL-uptake. Variants in bold have been observed in both, cases and controls. <b>(D)</b> Power calculations for the number of sequenced individuals needed to reach exome-wide significance (p<2.5×10^-6, reflected by power = 1) for association with MI-risk when the indicated classes of rare <i>LDLR</i> alleles are taken into account. For details, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>.</p

Systematic functional profiling of low-density lipoprotein receptor (<i>LDLR</i>) alleles.

<p><b>(A)</b><i>LDLR</i> missense variants were functionally characterized by monitoring cellular uptake of fluorescently-labeled LDL (DiI-LDL; <i>red</i>) into cells (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>). Shown are automatically acquired images of HeLa-Kyoto cells transiently expressing siRNA-resistant full-length human wildtype LDLR linked to EGFP (LDLR’-GFP), empty GFP-control plasmid, or two FH mutants known to inhibit transport (p.G549D; FH class-2) or endocytosis (p.Y828C; FH class-4) of the LDLR protein. Arrows denote GFP-positive cells. Note the localization of FH mutants to different subcellular compartments. Bars = 15μm. <b>(B)</b> Graphs depict relative signal intensities of total DiI-LDL in endosome-like subcellular compartments (total LDL signal; y-axis, in arbitrary units) plotted against total cellular GFP expression (x-axis, in arbitrary units) for wildtype LDLR (LDLR’-GFP, upper panel) and indicated FH mutants. Each graph depicts results from a single experimental replica upon either overexpression of the respective cDNA-construct (left graphs) or complementation settings (i.e., siRNA knockdown of endogenous LDLR followed by reconstitution with indicated LDLR-GFP constructs; right graphs). Each dot represents one individual cell. Dashed vertical bars separate cells classified as GFP-negative (left from bar) from cells defined as GFP-positive. Dashed horizontal lines in complementation setting indicate mean total LDL signal in control siRNA-treated cells expressing endogenous LDLR. Cells where total LDL signal fell above this threshold (indicating over-compensation by LDLR’-GFP expression) were not respected for quantifications (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>). <b>(C)</b> LDLR activity was measured with five phenotypic parameters: total LDL signal in endosome-like compartments, LDL concentration, number (seg. number) and area (seg. area) of subcellular DiI-positive endosome-like structures, and cellular GFP-expression. The heatmap represents means from all experimental replicas per variant under the overexpression setting. Red reflects reduced, blue increased signal relative to wildtype LDLR’-GFP. Phenotypes meeting statistical criteria as described in Methods are framed in orange. Bar graph depicts total LDL signal ±SD normalized to wildtype LDLR’-GFP.</p

Association of a burden of rare variants in the low-density lipoprotein receptor (<i>LDLR</i>) gene with plasma low-density lipoprotein cholesterol (LDL-C) levels and the risk for early-onset myocardial infarction.

<p>Association of a burden of rare variants in the low-density lipoprotein receptor (<i>LDLR</i>) gene with plasma low-density lipoprotein cholesterol (LDL-C) levels and the risk for early-onset myocardial infarction.</p

Workflow of this study to determine the functional impact of 70 rare missense variants on LDLR protein activities and improve rare variant association testing for plasma LDL-C and the risk for early-onset MI.

<p>Variants were identified through whole-exome sequencing of 3,235 individuals from the Italian Study of Early-onset Myocardial Infarction (ATVB) cohort.</p

List of significantly associated haplotypes.

<p>List of significantly associated haplotypes.</p

Differentially expressed genes by the risk alleles at 29 Mb and 33 Mb play important role in T-cell immunity.

<p>A. The risk allele at the 29 Mb at homozygous state has a clear cis-regulation effect on the expression levels of <i>TRPC6</i>, <i>KIAA1377</i>, and <i>ANGPTL5</i>, three of the most proximal genes. <i>BIRC3</i>, which is also proximal to the 29 Mb risk locus, had a significant p-value, however the FDR value was slightly above the threshold of 0.05. The risk allele at 29 Mb was also associated with a regulatory effect on genes near the 33 Mb locus and a change in the expression of <i>PIK3R6</i> significantly. B. A large network of molecules that play a major role in activation of T-lymphocyte and other immune cells (IPA category: cell-to-cell signaling and interaction, hematological system development and function). This network includes 15 molecules of which expressions are significantly altered in individuals carrying at least one copy of the shared risk allele at the 33 Mb locus. The outcomes of such expression changes are significantly linked to decrease in T-cell activation.</p

Comparison of canFam2.0 and canFam3.1.

<p>Comparison of canFam2.0 and canFam3.1.</p

Two neighboring loci on chromosome 5 are independently associated with disease risk.

<p>A. The top SNP of the first peak (29 Mb) is in high LD with nearby variants and shows no evidence of linkage to the top SNPs in the second peak (33 Mb). B. The 29 Mb peak is comprised of two haplotype blocks, and C. the risk haplotypes for the 29 Mb peak are rather common in the population. Similarly, D. the second peak also shows no linkage with the first peak in the combined analysis, whereas E. analysis of only B-cell lymphoma shows SNPs in strong LD within the second peak and in moderate LD with SNPs in the first peak. The top SNPs in the combined analysis and B-cell-lymphoma-only analysis are independent, and F. make up separate haplotypes at the second locus. G. Both risk haplotypes at the second locus are rare. Color-coding of SNPs in A, D, E, reflects their r² value relative the top SNP of that region, ranging from grey (not in LD) to red (strong LD).</p