Predicting signatures of "synthetic associations" and "natural associations" from empirical patterns of human genetic variation

Down-regulated RGS5 by genetic variants impairs endothelial cell function and contributes to coronary artery disease

AIMS

Genetic contribution to coronary artery disease (CAD) remains largely unillustrated. Although transcriptomic profiles have identified dozens of genes that are differentially expressed in normal and atherosclerotic vessels, whether those genes are genetically associated with CAD remains to be determined. Here, we combined genetic association studies, transcriptome profiles and in vitro and in vivo functional experiments to identify novel susceptibility genes for CAD.

METHODS AND RESULTS

Through an integrative analysis of transcriptome profiles with genome-wide association studies for CAD, we obtained 18 candidate genes and selected one representative single nucleotide polymorphism (SNP) for each gene for multi-centred validations. We identified an intragenic SNP, rs1056515 in RGS5 gene (odds ratio = 1.17, 95% confidence interval =1.10-1.24, P = 3.72 × 10-8) associated with CAD at genome-wide significance. Rare genetic variants in linkage disequilibrium with rs1056515 were identified in CAD patients leading to a decreased expression of RGS5. The decreased expression was also observed in atherosclerotic vessels and endothelial cells treated by various cardiovascular risk factors. Through siRNA knockdown and adenoviral overexpression, we further showed that RGS5 regulated endothelial inflammation, vascular remodelling, as well as canonical NF-κB signalling activation. Moreover, CXCL12, a specific downstream target of the non-canonical NF-κB pathway, was strongly affected by RGS5. However, the p100 processing, a well-documented marker for non-canonical NF-κB pathway activation, was not altered, suggesting an existence of a novel mechanism by which RGS5 regulates CXCL12.

CONCLUSIONS

We identified RGS5 as a novel susceptibility gene for CAD and showed that the decreased expression of RGS5 impaired endothelial cell function and functionally contributed to atherosclerosis through a variety of molecular mechanisms. How RGS5 regulates the expression of CXCL12 needs further studies.

Crop genome-wide association study: a harvest of biological relevance

With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.

Association Analysis of the Cubilin (CUBN) and Megalin (LRP2) Genes with ESRD in African Americans

BACKGROUND AND OBJECTIVES

Genetic variation in the cubilin (CUBN) gene is associated with albuminuria and CKD. Common and rare coding variants in CUBN and the gene encoding its transport partner megalin (LRP2) were assessed for association with ESRD in blacks.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Sixty-six CUBN and LRP2 single-nucleotide polymorphisms (SNPs) were selected and analyzed in this multistage study. Exome sequencing data from 529 blacks with type 2 diabetes (T2D) -associated ESRD and 535 controls lacking T2D or nephropathy (the Type 2 Diabetes Genes [T2D-GENES] Consortium) were first evaluated, focusing on coding variants in CUBN and LRP2; 15 potentially associated SNPs identified from the T2D-GENES Consortium as well as 51 other selected SNPs were then assessed in an independent T2D-ESRD sample set of blacks (the Affymetrix Axiom Biobank Genotyping Array [AXIOM]; 2041 patients with T2D-ESRD, 627 patients with T2D without nephropathy, and 1140 nondiabetic, non-nephropathy controls). A meta-analysis combining the T2D-GENES Consortium and the AXIOM data was performed for 18 overlapping SNPs. Additionally, all 66 SNPs were genotyped in the Wake Forest School of Medicine samples of blacks with nondiabetic ESRD (885 patients with nondiabetic ESRD and 721 controls). Association testing with ESRD was performed in models including age, sex, African ancestry proportion, and apolipoprotein L1 gene renal-risk variants.

RESULTS

CUBN SNP rs1801239 (I2984V), previously associated with albuminuria, was significantly associated with T2D-ESRD in blacks (the T2D-GENES Consortium and the AXIOM meta-analysis, P=0.03; odds ratio, 1.31; 95% confidence interval, 1.03 to 1.67; minor allele frequency =0.028). A novel LRP2 missense variant, rs17848169 (N2632D), was also significantly protective from T2D-ESRD (the T2D-GENES Consortium and the AXIOM, P<0.002; odds ratio, 0.47; 95% confidence interval, 0.29 to 0.75; meta-analysis minor allele frequency =0.007). Neither SNP was associated with T2D when contrasting patients with T2D with controls lacking diabetes. CUBN and LRP2 SNPs were not associated with nondiabetic etiologies of ESRD.

CONCLUSIONS

Evidence for genetic association exists between a cubilin and a rare megalin variant with diabetes-associated ESRD in populations with recent African ancestry.

Haplotype synthesis analysis reveals functional variants underlying known genome-wide associated susceptibility loci

MOTIVATION

The functional mechanisms underlying disease association remain unknown for Genome-wide Association Studies (GWAS) susceptibility variants located outside coding regions. Synthesis of effects from multiple surrounding functional variants has been suggested as an explanation of hard-to-interpret findings. We define filter criteria based on linkage disequilibrium measures and allele frequencies which reflect expected properties of synthesizing variant sets. For eligible candidate sets, we search for haplotype markers that are highly correlated with associated variants.

RESULTS

Via simulations we assess the performance of our approach and suggest parameter settings which guarantee 95% sensitivity at 20-fold reduced computational cost. We apply our method to 1000 Genomes data and confirmed Crohn's Disease (CD) and Type 2 Diabetes (T2D) variants. A proportion of 36.9% allowed explanation by three-variant-haplotypes carrying at least two functional variants, as compared to 16.4% for random variants ([Formula: see text]). Association could be explained by missense variants for MUC19, PER3 (CD) and HMG20A (T2D). In a CD GWAS-imputed using haplotype reference consortium data (64 976 haplotypes)-we could confirm the syntheses of MUC19 and PER3 and identified synthesis by missense variants for 6 further genes (ZGPAZ, GPR65, CLN3/NPIPB8, LOC102723878, rs2872507, GCKR). In all instances, the odds ratios of the synthesizing haplotypes were virtually identical to that of the index SNP. In summary, we demonstrate the potential of synthesis analysis to guide functional follow-up of GWAS findings.

AVAILABILITY AND IMPLEMENTATION

All methods are implemented in the C/C ++ toolkit GetSynth, available at http://sourceforge.net/projects/getsynth/

CONTACT

tim.becker@uni-greifswald.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetics of juvenile idiopathic arthritis: new tools bring new approaches

PURPOSE OF REVIEW

In juvenile idiopathic arthritis (JIA), there are now more than 25 regions represented by single nucleotide polymorphisms that show strong genetic associations. The causal variants and corresponding functions have not yet been defined for the majority of these regions. Here, we review current JIA association findings and the recent progress in the annotation of noncoding portion of the human genome as well as the new technologies necessary to apply this knowledge to JIA association findings.

RECENT FINDINGS

An international collaboration was able to amass sufficient numbers of JIA and control samples to identify significantly robust genetic associations for JIA. The Encyclopedia of DNA Elements project and the National Institutes of Health (NIH) Roadmap Epigenetics Program have now annotated more than 80% of the noncoding genome, important in understanding the impact of risk loci, the majority of which fall outside of protein coding regions. Recent technological advances in high throughput sequencing, chromatin structure determination, transcription factor and enhancer binding site mapping and genome editing will likely provide a basis for understanding JIA genetic risk.

SUMMARY

Understanding the role of genetic variation in the cause of JIA will provide insight for disease mechanism and may explain disease heterogeneity between JIA subtypes and between autoimmune diseases in general.

Principal component analysis characterizes shared pathogenetics from genome-wide association studies

Genome-wide association studies (GWASs) have recently revealed many genetic associations that are shared between different diseases. We propose a method, disPCA, for genome-wide characterization of shared and distinct risk factors between and within disease classes. It flips the conventional GWAS paradigm by analyzing the diseases themselves, across GWAS datasets, to explore their "shared pathogenetics". The method applies principal component analysis (PCA) to gene-level significance scores across all genes and across GWASs, thereby revealing shared pathogenetics between diseases in an unsupervised fashion. Importantly, it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology. We applied disPCA to 31 GWASs, including autoimmune diseases, cancers, psychiatric disorders, and neurological disorders. The leading principal components separate these disease classes, as well as inflammatory bowel diseases from other autoimmune diseases. Generally, distinct diseases from the same class tend to be less separated, which is in line with their increased shared etiology. Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system, while also pointing to pathways that have yet to be explored before in this context. Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases, to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases.

High burden of private mutations due to explosive human population growth and purifying selection

Background

Recent studies have shown that human populations have experienced a complex demographic history, including a recent epoch of rapid population growth that led to an excess in the proportion of rare genetic variants in humans today. This excess can impact the burden of private mutations for each individual, defined here as the proportion of heterozygous variants in each newly sequenced individual that are novel compared to another large sample of sequenced individuals.

Results

We calculated the burden of private mutations predicted by different demographic models, and compared with empirical estimates based on data from the NHLBI Exome Sequencing Project and data from the Neutral Regions (NR) dataset. We observed a significant excess in the proportion of private mutations in the empirical data compared with models of demographic history without a recent epoch of population growth. Incorporating recent growth into the model provides a much improved fit to empirical observations. This phenomenon becomes more marked for larger sample sizes, e.g. extrapolating to a scenario in which 10,000 individuals from the same population have been sequenced with perfect accuracy, still about 1 in 400 heterozygous sites (or about 6,000 variants) at the 10,001^st individual are predicted to be novel, 18-times as predicted in the absence of recent population growth. The proportion of private mutations is additionally increased by purifying selection, which differentially affect mutations of different functional annotations.

Conclusions

The burden of private mutations for each individual, which are singletons (i.e. appearing in a single copy) in a larger sample that includes this individual, is predicted to be greatly increased by recent population growth, as well as by purifying selection. Comparison with empirical data supports that European populations have experienced recent rapid population growth, consistent with previous studies. These results have important implications to the design and analysis of sequencing-based association studies of complex human disease as they pertain to private and very rare variants. They also imply that personalized genomics will indeed have to be very personal in accounting for the large number of private mutations.

Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis

Introduction

Our objective was to utilise network analysis to identify protein clusters of greatest potential functional relevance in the pathogenesis of oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile idiopathic arthritis (JIA).

Methods

JIA genetic association data were used to build an interactome network model in BioGRID 3.2.99. The top 10% of this protein:protein JIA Interactome was used to generate a minimal essential network (MEN). Reactome FI Cytoscape 2.83 Plugin and the Disease Association Protein-Protein Link Evaluator (Dapple) algorithm were used to assess the functionality of the biological pathways within the MEN and to statistically rank the proteins. JIA gene expression data were integrated with the MEN and clusters of functionally important proteins derived using MCODE.

Results

A JIA interactome of 2,479 proteins was built from 348 JIA associated genes. The MEN, representing the most functionally related components of the network, comprised of seven clusters, with distinct functional characteristics. Four gene expression datasets from peripheral blood mononuclear cells (PBMC), neutrophils and synovial fluid monocytes, were mapped onto the MEN and a list of genes enriched for functional significance identified. This analysis revealed the genes of greatest potential functional importance to be PTPN2 and STAT1 for oligoarticular JIA and KSR1 for RF-ve polyarticular JIA. Clusters of 23 and 14 related proteins were derived for oligoarticular and RF-ve polyarticular JIA respectively.

Conclusions

This first report of the application of network biology to JIA, integrating genetic association findings and gene expression data, has prioritised protein clusters for functional validation and identified new pathways for targeted pharmacological intervention.

Fine-mapping the HOXB region detects common variants tagging a rare coding allele: evidence for synthetic association in prostate cancer

The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10(-14)). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.

Deciphering the genetic architecture of low-penetrance susceptibility to colorectal cancer

Recent genome-wide association studies (GWASs) have identified common variants at 16 autosomal regions influencing the risk of developing colorectal cancer (CRC). To decipher the genetic basis of the association signals at these loci, we performed a meta-analysis of data from five GWASs, totalling 5626 cases and 7817 controls, using imputation to recover un-typed genotypes. To enhance our ability to discover low-frequency risk variants, in addition to using 1000 Genomes Project data as a reference panel, we made use of high-coverage sequencing data on 253 individuals, 199 with early-onset familial CRC. For 13 of the regions, it was possible to refine the association signal identifying a smaller region of interest likely to harbour the functional variant. Our analysis did not provide evidence that any of the associations at the 16 loci being a consequence of synthetic associations rather than linkage disequilibrium with a common risk variant.

Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.