WISARD: workbench for integrated superfast association studies for related datasets

The mitochondrial citrate carrier SLC25A1 regulates metabolic reprogramming and morphogenesis in the developing heart

The developing mammalian heart undergoes an important metabolic shift from glycolysis towards mitochondrial oxidation that is critical to support the increasing energetic demands of the maturing heart. Here, we describe a new mechanistic link between mitochondria and cardiac morphogenesis, uncovered by studying mitochondrial citrate carrier (SLC25A1) knockout mice. Slc25a1 null embryos displayed impaired growth, mitochondrial dysfunction and cardiac malformations that recapitulate the congenital heart defects observed in 22q11.2 deletion syndrome, a microdeletion disorder involving the SLC25A1 locus. Importantly, Slc25a1 heterozygous embryos, while overtly indistinguishable from wild type, exhibited an increased frequency of these defects, suggesting Slc25a1 haploinsuffiency and dose-dependent effects. Mechanistically, SLC25A1 may link mitochondria to transcriptional regulation of metabolism through epigenetic control of gene expression to promote metabolic remodeling in the developing heart. Collectively, this work positions SLC25A1 as a novel mitochondrial regulator of cardiac morphogenesis and metabolic maturation, and suggests a role in congenital heart disease.

Mitochondrial citrate carrier SLC25A1 is a dosage-dependent regulator of metabolic reprogramming and morphogenesis in the developing heart

The developing mammalian heart undergoes an important metabolic shift from glycolysis toward mitochondrial oxidation, such that oxidative phosphorylation defects may present with cardiac abnormalities. Here, we describe a new mechanistic link between mitochondria and cardiac morphogenesis, uncovered by studying mice with systemic loss of the mitochondrial citrate carrier SLC25A1. Slc25a1 null embryos displayed impaired growth, cardiac malformations, and aberrant mitochondrial function. Importantly, Slc25a1 heterozygous embryos, which are overtly indistinguishable from wild type, exhibited an increased frequency of these defects, suggesting Slc25a1 haploinsuffiency and dose-dependent effects. Supporting clinical relevance, we found a near-significant association between ultrarare human pathogenic SLC25A1 variants and pediatric congenital heart disease. Mechanistically, SLC25A1 may link mitochondria to transcriptional regulation of metabolism through epigenetic control of gene expression to promote metabolic remodeling in the developing heart. Collectively, this work positions SLC25A1 as a novel mitochondrial regulator of ventricular morphogenesis and cardiac metabolic maturation and suggests a role in congenital heart disease.

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition. Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis. Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before. Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.

Analysis of GWAS-nominated loci for lung cancer and COPD revealed a new asthma locus

BACKGROUND

Asthma, lung cancer (LC) and chronic obstructive pulmonary disease (COPD) are three respiratory diseases characterized by complex mechanisms underlying and genetic predispositions, with asthma having the highest calculated heritability. Despite efforts deployed in the last decades, only a small part of its heritability has been elucidated. It was hypothesized that shared genetic factors by these three diseases could help identify new asthma loci.

METHODS

GWAS-nominated LC and COPD loci were selected among studies performed in Caucasian cohorts using the GWAS Catalog. Genetic analyses were carried out for these loci in the Saguenay-Lac-Saint-Jean (SLSJ) asthma familial cohort and then replicated in two independent cohorts (the Canadian Cohort Obstructive Lung Disease [CanCOLD] and the Epidemiological Study of the Genetics and Environment of Asthma [EGEA]).

RESULTS

Analyses in the SLSJ cohort identified 2851 and 4702 genetic variants to be replicated in the CanCOLD and EGEA cohorts for LC and COPD loci respectively. Replication and meta-analyses allowed the association of one new locus with asthma, 2p24.3, from COPD studies. None was associated from LC studies reported.

CONCLUSIONS

The approach used in this study contributed to better understand the heritability of asthma with shared genetic backgrounds of respiratory diseases.

Integrative Pathway Analysis of SNP and Metabolite Data Using a Hierarchical Structural Component Model

Integrative multi-omics analysis has become a useful tool to understand molecular mechanisms and drug discovery for treatment. Especially, the couplings of genetics to metabolomics have been performed to identify the associations between SNP and metabolite. However, while the importance of integrative pathway analysis is increasing, there are few approaches to utilize pathway information to analyze phenotypes using SNP and metabolite. We propose an integrative pathway analysis of SNP and metabolite data using a hierarchical structural component model considering the structural relationships of SNPs, metabolites, pathways, and phenotypes. The proposed method utilizes genome-wide association studies on metabolites and constructs the genetic risk scores for metabolites referred to as genetic metabolomic scores. It is based on the hierarchical model using the genetic metabolomic scores and pathways. Furthermore, this method adopts a ridge penalty to consider the correlations between genetic metabolomic scores and between pathways. We apply our method to the SNP and metabolite data from the Korean population to identify pathways associated with type 2 diabetes (T2D). Through this application, we identified well-known pathways associated with T2D, demonstrating that this method adds biological insights into disease-related pathways using genetic predispositions of metabolites.

Linkage analysis identifies novel genetic modifiers of microbiome traits in families with inflammatory bowel disease

Dysbiosis of the gut microbiome is a hallmark of inflammatory bowel disease (IBD) and both, IBD risk and microbiome composition, have been found to be associated with genetic variation. Using data from families of IBD patients, we examined the association between genetic and microbiome similarity in a specific IBD context, followed by a genome-wide quantitative trait locus (QTL) linkage analysis of various microbiome traits using the same data. SNP genotypes as well as gut microbiome and phenotype data were obtained from the Kiel IBD family cohort (IBD-KC). The IBD-KC is an ongoing prospective study in Germany currently comprising 256 families with 455 IBD patients and 575 first- and second-degree relatives. Initially focusing upon known IBD risk loci, we noted a statistically significant (FDR<0.05) association between genetic similarity at SNP rs11741861 and overall microbiome dissimilarity among pairs of relatives discordant for IBD. In a genome-wide QTL analysis, 12 chromosomal regions were found to be linked to the abundance of one of seven microbial genera, namely Barnesiella (chromosome 4, region spanning 10.34 cM), Clostridium_XIVa (chr4, 3.86 cM; chr14, two regions spanning 7.05 and 13.02 cM respectively), Pseudoflavonifractor (chr7, 12.80 cM) Parasutterella (chr14, 8.26 cM), Ruminococcus (chr16, two overlapping regions spanning 8.01 and 16.87 cM, respectively), Roseburia (chr19, 7.99 cM), and Odoribacter (chr22, three regions spanning 0.89, 5.57 and 1.71 cM, respectively), as well as the Shannon index of α diversity (chr3, 1.47 cM). Our study thus shows that, in families of IBD patients, pairwise genetic similarity for at least one IBD risk locus is associated with overall microbiome dissimilarity among discordant pairs of relatives, and that hitherto unknown genetic modifiers of microbiome traits are located in at least 12 human genomic regions.

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene-Environment Interactions

Gene-environment interaction (G×E) studies are one of the most important solutions for understanding the "missing heritability" problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene-Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene-alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

Hierarchical structural component model for pathway analysis of common variants

BACKGROUND

Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this 'missing heritability' issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways.

METHODS

We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests.

RESULTS

Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance.

CONCLUSIONS

Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum.

Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis

BACKGROUNDS

Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants.

RESULTS

Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method.

CONCLUSION

In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/ .

Integration of a Large-Scale Genetic Analysis Workbench Increases the Accessibility of a High-Performance Pathway-Based Analysis Method

The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset—namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.

HisCoM-GGI: Software for Hierarchical Structural Component Analysis of Gene-Gene Interactions

Gene-gene interaction (GGI) analysis is known to play an important role in explain missing heritability issue. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in case-control design. In this study, we developed 'Hierarchical structural CoMponent analysis of Gene-Gene Interactions' (HisCoM-GGI) software for gene-gene interaction analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and SNPs, that enables both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various type of genomic data, and supports a data management and multithreading to improve the efficiency of GWAS data analysis. We expect that the HisCoM-GGI software provides advanced accessibility to the researchers on the genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.