Daniel F Schwarz

New susceptibility locus for coronary artery disease on chromosome 3q22.3

We present a three-stage analysis of genome-wide SNP data in 1,222 German individuals with myocardial infarction and 1,298 controls, in silico replication in three additional genome-wide datasets of coronary artery disease (CAD) and subsequent replication in ∼25,000 subjects. We identified one new CAD risk locus on 3q22.3 in MRAS (P = 7.44 × 10−13; OR = 1.15, 95% CI = 1.11–1.19), and suggestive association with a locus on 12q24.31 near HNF1A-C12orf43 (P = 4.81 × 10−7; OR = 1.08, 95% CI = 1.05–1.11).

Repeated Replication and a Prospective Meta-Analysis of the Association Between Chromosome 9p21.3 and Coronary Artery Disease

Background— Recently, genome-wide association studies identified variants on chromosome 9p21.3 as affecting the risk of coronary artery disease (CAD). We investigated the association of this locus with CAD in 7 case-control studies and undertook a meta-analysis. Methods and Results— A single-nucleotide polymorphism (SNP), rs1333049, representing the 9p21.3 locus, was genotyped in 7 case-control studies involving a total of 4645 patients with myocardial infarction or CAD and 5177 controls. The mode of inheritance was determined. In addition, in 5 of the 7 studies, we genotyped 3 additional SNPs to assess a risk-associated haplotype (ACAC). Finally, a meta-analysis of the present data and previously published samples was conducted. A limited fine mapping of the locus was performed. The risk allele (C) of the lead SNP, rs1333049, was uniformly associated with CAD in each study (P<0.05). In a pooled analysis, the odds ratio per copy of the risk allele was 1.29 (95% confidence interval, 1.22 to 1.37; P=0.0001). Haplotype analysis further suggested that this effect was not homogeneous across the haplotypic background (test for interaction, P=0.0079). An autosomal-additive mode of inheritance best explained the underlying association. The meta-analysis of the rs1333049 SNP in 12 004 cases and 28 949 controls increased the overall level of evidence for association with CAD to P=6.04×10−10 (odds ratio, 1.24; 95% confidence interval, 1.20 to 1.29). Genotyping of 31 additional SNPs in the region identified several with a highly significant association with CAD, but none had predictive information beyond that of the rs1333049 SNP. Conclusion— This broad replication provides unprecedented evidence for association between genetic variants at chromosome 9p21.3 and risk of CAD.

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data

MOTIVATION Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohns disease. This may partly be due to the low power of standard statistical approaches to detect gene-gene and gene-environment interactions when small marginal effects are present. A promising alternative is Random Forests, which have already been successfully applied in candidate gene analyses. Important single nucleotide polymorphisms are detected by permutation importance measures. To this day, the application to GWA data was highly cumbersome with existing implementations because of the high computational burden. RESULTS Here, we present the new freely available software package Random Jungle (RJ), which facilitates the rapid analysis of GWA data. The program yields valid results and computes up to 159 times faster than the fastest alternative implementation, while still maintaining all options of other programs. Specifically, it offers the different permutation importance measures available. It includes new options such as the backward elimination method. We illustrate the application of RJ to a GWA of Crohns disease. The most important single nucleotide polymorphisms (SNPs) validate recent findings in the literature and reveal potential interactions. AVAILABILITY The RJ software package is freely available at http://www.randomjungle.org

Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A

Although physiological data on microcircuits involving a few inhibitory neurons in the mammalian cerebral cortex are available, data on the quantitative relation between inhibition and excitation in cortical circuits involving thousands of neurons are largely missing. Because the distribution of neurons is very inhomogeneous in the cerebral cortex, it is critical to map all neurons in a given volume rather than to rely on sparse sampling methods. Here, we report the comprehensive mapping of interneurons (INs) in cortical columns of rat somatosensory cortex, immunolabeled for neuron-specific nuclear protein and glutamate decarboxylase. We found that a column contains ∼2,200 INs (11.5% of ∼19,000 neurons), almost a factor of 2 less than previously estimated. The density of GABAergic neurons was inhomogeneous between layers, with peaks in the upper third of L2/3 and in L5A. IN density therefore defines a distinct layer 2 in the sensory neocortex. In addition, immunohistochemical markers of IN subtypes were layer-specific. The “hot zones” of inhibition in L2 and L5A match the reported low stimulus-evoked spiking rates of excitatory neurons in these layers, suggesting that these inhibitory hot zones substantially suppress activity in the neocortex.

Novel type of Ras effector interaction established between tumour suppressor NORE1A and Ras switch II.

A class of putative Ras effectors called Ras association domain family (RASSF) represents non‐enzymatic adaptors that were shown to be important in tumour suppression. RASSF5, a member of this family, exists in two splice variants known as NORE1A and RAPL. Both of them are involved in distinct cellular pathways triggered by Ras and Rap, respectively. Here we describe the crystal structure of Ras in complex with the Ras binding domain (RBD) of NORE1A/RAPL. All Ras effectors share a common topology in their RBD creating an interface with the switch I region of Ras, whereas NORE1A/RAPL RBD reveals additional structural elements forming a unique Ras switch II binding site. Consequently, the contact area of NORE1A is extended as compared with other Ras effectors. We demonstrate that the enlarged interface provides a rationale for an exceptionally long lifetime of the complex. This is a specific attribute characterizing the effector function of NORE1A/RAPL as adaptors, in contrast to classical enzymatic effectors such as Raf, RalGDS or PI3K, which are known to form highly dynamic short‐lived complexes with Ras.

Picking single-nucleotide polymorphisms in forests

With the development of high-throughput single-nucleotide polymorphism (SNP) technologies, the vast number of SNPs in smaller samples poses a challenge to the application of classical statistical procedures. A possible solution is to use a two-stage approach for case-control data in which, in the first stage, a screening test selects a small number of SNPs for further analysis. The second stage then estimates the effects of the selected variables using logistic regression (logReg). Here, we introduce a novel approach in which the selection of SNPs is based on the permutation importance estimated by random forests (RFs). For this, we used the simulated data provided for the Genetic Analysis Workshop 15 without knowledge of the true model.The data set was randomly split into a first and a second data set. In the first stage, RFs were grown to pre-select the 37 most important variables, and these were reduced to 32 variables by haplotype tagging. In the second stage, we estimated parameters using logReg.The highest effect estimates were obtained for five simulated loci. We detected smoking, gender, and the parental DR alleles as covariates. After correction for multiple testing, we identified two out of four genes simulated with a direct effect on rheumatoid arthritis risk and all covariates without any false positive.We showed that a two-staged approach with a screening of SNPs by RFs is suitable to detect candidate SNPs in genome-wide association studies for complex diseases.

Structural and Thermodynamic Characterization of Nore1-SARAH: A Small, Helical Module Important in Signal Transduction Networks

Tumor suppressor Nore1, its acronym coming from novel Ras effector, is one of the 10 members of the Rassf (Ras association domain family) protein family that have been identified. It is expressed as two mRNA splice variants, Nore1A and a shorter isoform, Nore1B. It forms homo- and heterocomplexes through its C-terminal SARAH (Sav/Rassf/Hpo) domain. The oligomeric state of Nore1 and other SARAH domain-containing proteins is important for their cellular activities. However, there are few experimental data addressing the structural and biophysical characterization of these domains. In this study, we show that the recombinant SARAH domain of Nore1 crystallizes as an antiparallel homodimer with representative characteristics of coiled coils. As is typical for coiled coils, the SARAH domain shows a heptad register, yet the heptad register is interrupted by two stutters. The comparisons of the heptad register of Nore1-SARAH with the primary structure of Rassf1-4, Rassf6, MST1, MST2, and WW45 indicate that these proteins have a heptad register interrupted by two stutters, too. Moreover, on the basis of the structure of Nore1-SARAH, we also generate structural models for Rassf1 and Rassf3. These models indicate that Rassf1- and Rassf3-SARAH form structures very similar to that of Nore1-SARAH. In addition, we show that, as we have previously found for MST1, the SARAH domain of Nore1 undergoes association-dependent folding. Nevertheless, the Nore1 homodimer has a lower affinity and thermodynamic stability than the MST1 homodimer, while the monomer is slightly more stable. Our experimental results along with our theoretical considerations indicate that the SARAH domain is merely a dimerization domain and that the differences between the individual sequences lead to different stabilities and affinities that might have an important functional role.

SNPtoGO: characterizing SNPs by enriched GO terms.

UNLABELLED For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data. AVAILABILITY A web interface and source code are offered at https://webtools.imbs.uni-luebeck.de/snptogo

On safari to Random Jungle

Motivation: Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohn’s disease. This may partly be due to the low power of standard statistical approaches to detect gene–gene and gene– environment interactions when small marginal effects are present. A promising alternative is Random Forests, which have already been successfully applied in candidate gene analyses. Important single nucleotide polymorphisms are detected by permutation importance measures. To this day, the application to GWA data was highly cumbersome with existing implementations because of the high computational burden. Results: Here, we present the new freely available software package Random Jungle (RJ), which facilitates the rapid analysis of GWA data. The program yields valid results and computes up to 159 times faster than the fastest alternative implementation, while still maintaining all options of other programs. Specifically, it offers the different permutation importance measures available. It includes new options such as the backward elimination method. We illustrate the application of RJ to a GWA of Crohn’s disease. The most important single nucleotide polymorphisms (SNPs) validate recent findings in the literature and reveal potential interactions. Availability: The RJ software package is freely available at http://www.randomjungle.org Contact: inke.koenig@imbs.uni-luebeck.de; ziegler@imbs.uni-

Evaluation of single-nucleotide polymorphism imputation using random forests

Genome-wide association studies (GWAS) have helped to reveal genetic mechanisms of complex diseases. Although commonly used genotyping technology enables us to determine up to a million single-nucleotide polymorphisms (SNPs), causative variants are typically not genotyped directly. A favored approach to increase the power of genome-wide association studies is to impute the untyped SNPs using more complete genotype data of a reference population.Random forests (RF) provides an internal method for replacing missing genotypes. A forest of classification trees is used to determine similarities of probands regarding their genotypes. These proximities are then used to impute genotypes of untyped SNPs.We evaluated this approach using genotype data of the Framingham Heart Study provided as Problem 2 for Genetic Analysis Workshop 16 and the Caucasian HapMap samples as reference population. Our results indicate that RFs are faster but less accurate than alternative approaches for imputing untyped SNPs.