Thursday to Friday we went to the Seminarhaus Freiräume in Osterstedt for our second retreat.
We had 1,5 days full of interes
ting talks from our bachelors, masters, PhD/MD and postdocs. In between there was ample time for leisure, like bosseln, and in the evening we cooked under guidance a delicious Dinner.
Last week we co-authored two papers published in Circulation about better understanding genetic association signals.
The first paper, under the lead of Danesh Saleheen and Muredach Reilly, describes that the protective effect of rs7178051 at ADAMTS7 locus gets lost in smokers.
The second paper, under the lead of Thorsten Kessler, Frank Kaiser and Heri Schunkert, studied rs7692387, an intronic SNP that alters promoter activity of GUCY1A3. ZEB1, a transcription factor, binds preferentially to the non-risk allele leading to an increase in GUCY1A3 expression, higher sGC levels, and higher sGC activity after stimulation. Finally, human and mouse data link augmented sGC expression to lower risk of atherosclerosis.
Genome-wide association studies have so far identified 56 loci associated with risk of coronary artery disease (CAD). Many CAD loci show pleiotropy; that is, they are also associated with other diseases or traits.
This study sought to systematically test if genetic variants identified for non-CAD diseases/traits also associate with CAD and to undertake a comprehensive analysis of the extent of pleiotropy of all CAD loci. In discovery analyses involving 42,335 CAD cases and 78,240 controls we tested the association of 29,383 common (minor allele frequency >5%) SNPs available on the exome array, which included a substantial proportion of known or suspected SNPs associated with common diseases or traits as of 2011.
Suggestive association signals were replicated in an additional 30,533 cases and 42,530 control subjects. To evaluate pleiotropy, we tested CAD loci for association with cardiovascular risk factors (lipid traits, blood pressure phenotypes, body mass index, diabetes, and smoking behavior), as well as with other diseases/traits through interrogation of currently available GWAS catalogs.
We identified 6 new loci associated with CAD at genome-wide significance: on 2q37 (KCNJ13-GIGYF2), 6p21 (C2), 11p15 (MRVI1-CTR9), 12q13 (LRP1), 12q24 (SCARB1), and 16q13 (CETP). Risk allele frequencies ranged from 0.15 to 0.86, and OR per copy of the risk allele ranged from 1.04 to 1.09.
Of 62 new and known CAD loci, 24 (38.7%) showed statistical association with a traditional cardiovascular risk factor, with some showing multiple associations, and 29 (47%) showed associations at p < 1×10-4 with a range of other diseases/traits.
To conclude, several CAD loci show substantial pleiotropy, which may help us understand the mechanisms by which these loci affect CAD risk.
Starting January 1st, 2017, the IIEG will be renamed as “Institute for Cardiogenetics”.
After four years we realized that our Institute is extremely well recognized for its research in the field of cardiovascular genetics and often the very generic name of the IIEG as “Institute for Integrative and Experimental Genomics” was confusing.With the new name of our Institute our research focus is more reflected.
We only changed the name, our aims are still the same: understanding the genetics of cardiovascular diseases to eventually help patients.
Genome wide association studies (GWAS) have greatly advanced our understanding of the genetics of complex diseases such as coronary artery disease, diabetes, and hypertension. In some instances, the identification of SNPs in genomic regions, which point to potential disease-related genes, led to the identification of new therapeutic targets.
However, an extremely large proportion of published GWAS has focused on the analysis of the 22 autosomal chromosomes only. As a consequence, although the X chromosome constitutes 5% of the nuclear genome and underlies almost 10% of Mendelian disorders, it harbors only 15 of the 2,800 (0.5%) significant associations reported by GWAS of nearly 300 traits.
GWAS findings mainly concern autosomal chromosomes and rarely the X chromosome. Indeed, the X chromosome is commonly excluded from GWAS analyses despite being assayed for a limited number of SNPs by all current GWAS microarray platforms.
There are various reasons for not including the X chromosome in GWAS: i) poor coverage of the X chromosome, ii) increased workload owing to gender-specific quality controls, iii) power issues owing to a smaller sample size, and most importantly iv) the requirement for specific tools to analyze X chromosomal data.
Such specific tools are needed because of the characteristics of the X chromosome that make it distinct from autosomal chromosomes. First, we need to consider the unique genetic make-up of the X chromosome; females have two copies, while males have one copy, excluding the pseudo-autosomal regions. Thus, when males are included in analyses, variants on X chromosomal loci require special treatment. For example, the signal intensities obtained from standard array genotyping platforms are lower for males, who carry one allele, than for females, who carry two alleles. This needs to be adequately addressed in the genotype-calling step and has consequences for genotype imputation and association analyses.
Second, we need to consider the process of X chromosomal inactivation. Early in embryonic development, large parts of one of the two female X chromosomes are silenced, which is hypothesized to occur via dosage compensation. This results in the one copy of the X chromosome in males and the two copies of the X chromosome in females having equal effects. This inactivation is incomplete, and it is estimated that about three-quarters of X chromosomal genes are silenced in one female X chromosomes in some individuals. This is important when deciding how to test for associations with X chromosomal variants as described in a recent study by König et al. 2014.
To overcome the hurdles of X chromosomal analyses, Inke König and colleagues have established pipelines for analyzing X chromosomal data within a standard GWAS. By selecting specific algorithms and parameter settings, the analysis of X chromosomal SNPs is manageable and gives new clues as to the genetics of complex diseases.
Here, we now present the very first comprehensive meta-analysis of SNPs located on X chromosome. We expected that inclusion of X chromosomal data might partly explain the so-called missing heritability of complex diseases, especially those with sex-specific features. However, although we analyzed the largest-to-date sample, currently available methods were not able to detect any associations of X-chromosomal variants with CAD.
On October 21th, 2016, we will held our first Lab Retreat at “Die Zimberei” in Lübeck. This event is meant to present the ongoing projects from our senior PIs, PostDocs, PhD and MD students. A full-day of talks and discussions followed by an Evening event with Dinner and Bowling.
Coronary heart disease is a tale of two forms of plasma cholesterol. In contrast to the well-established effects of “bad” cholesterol (LDL-C), the role of “good” cholesterol (HDL-C) is mysterious. Elevated HDL-C correlates with a lower risk of heart disease, yet drugs that raise HDL-C levels do not reduce risk. Zanoni et al. found that some people with exceptionally high levels of HDL-C carry a rare sequence variant in the gene encoding the major HDL-C receptor, scavenger receptor BI. This variant destroys the receptor’s ability to take up HDL-C. Interestingly, people with this variant have a higher risk of heart disease despite having high levels of HDL-C. This remarkable study was published yesterday in Science with contribution of the IIEG.
The interesting background of this project is nicely described here.
Through DNA genotyping using the Exome-Array, we tested 54,003 coding-sequence variants covering 13,715 human genes in up to 72,868 patients with CAD and 120,770 controls who did not have CAD. Furthermore, through DNA sequencing, we studied the effects of loss-of-function mutations in selected
genes.
Here, we confirmed previously observed significant associations between CAD and low-frequency missense variants in the genes LPA and PCSK9. We also found significant associations between CAD and low frequency
missense variants in the genes SVEP1 (p.D2702G; minor-allele frequency, 3.60%; odds ratio for disease, 1.14; P = 4.2×10−10) and ANGPTL4 (p.E40K; minor-allele frequency, 2.01%; odds ratio, 0.86; P = 4.0×10−8), which encodes angiopoietin-like 4.
In a next step, through sequencing of ANGPTL4, we identified 9 loss-of-function mutation carriers among 6924 patients with myocardial infarction, as compared with 19 loss-of-function mutation carriers among 6834 controls (odds ratio, 0.47; P = 0.04).
Interestingly, carriers of ANGPTL4 loss-of-function alleles had triglyceride levels that were 35% lower than the levels among persons who did not carry a loss-of-function allele (P = 0.003).
Functionally, Angiopoietin-like 4 inhibits lipoprotein lipase; we therefore searched for mutations in LPL and identified a loss-of-function variant that was associated with an increased risk of CAD (p.D36N; minor-allele frequency, 1.9%; odds ratio, 1.13; P = 2.0×10−4) and a gain-of-function variant that was associated with protection from CAD (p.S447*; minor-allele frequency, 9.9%; odds ratio,
0.94; P = 2.5×10−7).
This highly collaborative study – involving 129 scientists from 15 countries – was published online on March 2nd, 2016 in the New England Journal of Medicine.
Members of the IIEG together with colleagues from the CARDIoGRAMplusC4D consortium published the most comprehensive genetic study for CAD. Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185,000 CAD cases and controls, interrogating 6.7 million common as well as 2.7 million low frequency variants. In addition to confirmation of most known CAD loci, we identified 10 novel loci, eight additive and two recessive, that contain candidate genes that newly implicate biological processes in vessel walls. Moreover, we observed intra-locus allelic heterogeneity but little evidence of low frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size. Furthermore, we report that only 20% of loci are related to known cardiovascular risk factors. This work has been published in Nature Genetics online ahead of print on September 7th, 2015. A Comprehensive 1000 Genomes-based GWAS meta-analysis of Coronary Artery Disease
A circular Manhattan plot summarizing the 1000 Genomes Project CAD association results.
Genome-wide association studies (GWAS) have to date identified 159 significant and suggestive loci for coronary artery disease (CAD). Almost all of these loci have been identified with major contributions of members of the IIEG.
Here, a study co-led by Ingrid Brnne from the IIEG together with colleagues from the CADgenomics network, funded by the Leducq fondation, now report comprehensive bioinformatics analyses of sequence variation in these loci to predict candidate causal genes. They conclude that the great majority of causal variations affecting CAD risk occur in noncoding regions, with 41% affecting gene expression robustly versus 6% leading to amino acid changes. Interestingly, many of these genes differed from the traditionally annotated genes, which was usually based on proximity to the lead single-nucleotide polymorphism. Indeed, the study obtained evidence that genetic variants at CAD loci affect 98 genes, which had not been linked to CAD previously. However, for a full understanding, each CAD locus will have to be individually investigated using tools, such as experimental organisms and iPS cells. In this study, they have used standard tools to refine the list of candidate genes. Additional approaches that could be useful at present include chromosome conformation analyses, application of novel algorithms for causal SNP analysis, network analyses, and identification of rare variants. Looking forward, new resources and tools, such as noncoding RNA annotation, RNA-binding maps, splicing variants and code annotation, and detailed enhancer and transcription maps in a variety of cell types relevant to atherosclerosis, will greatly assist such efforts. The members of the IIEG are well prepared to be part of these endeavours.
Full reference:
Prediction of Causal Candidate Genes in Coronary Artery Disease Loci.
Ingrid Brænne*, Mete Civelek*, Baiba Vilne*, Antonio Di Narzo, Andrew D. Johnson, Yuqi Zhao, Benedikt Reiz, Veronica Codoni, Thomas R. Webb, Hassan Foroughi Asl, Stephen E. Hamby, Lingyao Zeng, David-Alexandre Trégouët, Ke Hao, Eric J. Topol, Eric E. Schadt, Xia Yang, Nilesh J. Samani, Johan L.M. Björkegren, Jeanette Erdmann, Heribert Schunkert†, Aldons J. Lusis†, on behalf of the Leducq Consortium CAD Genomics‡
Arterioscler Thromb Vasc Biol 2015; first published on August 20 2015 as doi:10.1161/ATVBAHA.115.306108
*denotes equal contribution
Members of the CADgenomics network funded by the Leducq fondation
San Diego, February 2015
