Integrative genomics in cardiovascular medicine

Genome-to-phenome research in rats: progress and perspectives

Because of their relatively short lifespan (<4 years), rats have become the second most used model organism to study health and diseases in humans who may live for up to 120 years. First-, second- and third-generation sequencing technologies and platforms have produced increasingly greater sequencing depth and accurate reads, leading to significant advancements in the rat genome assembly during the last 20 years. In fact, whole genome sequencing (WGS) of 47 strains have been completed. This has led to the discovery of genome variants in rats, which have been widely used to detect quantitative trait loci underlying complex phenotypes based on gene, haplotype, and sweep association analyses. DNA variants can also reveal strain, chromosome and gene functional evolutions. In parallel, phenome programs have advanced significantly in rats during the last 15 years and more than 10 databases host genome and/or phenome information. In order to discover the bridges between genome and phenome, systems genetics and integrative genomics approaches have been developed. On the other hand, multiple level information transfers from genome to phenome are executed by differential usage of alternative transcriptional start (ATS) and polyadenylation (APA) sites per gene. We used our own experiments to demonstrate how alternative transcriptome analysis can lead to enrichment of phenome-related causal pathways in rats. Development of advanced genome-to-phenome assays will certainly enhance rats as models for human biomedical research.

Indirect effect inference and application to GAW20 data

BACKGROUND

Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed.

RESULTS

We propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied.

CONCLUSIONS

The proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses.

Sparse simultaneous signal detection for identifying genetically controlled disease genes

Genome-wide association studies (GWAS) and differential expression analyses have had limited success in finding genes that cause complex diseases such as heart failure (HF), a leading cause of death in the United States. This paper proposes a new statistical approach that integrates GWAS and expression quantitative trait loci (eQTL) data to identify important HF genes. For such genes, genetic variations that perturb its expression are also likely to influence disease risk. The proposed method thus tests for the presence of simultaneous signals: SNPs that are associated with the gene's expression as well as with disease. An analytic expression for the p-value is obtained, and the method is shown to be asymptotically adaptively optimal under certain conditions. It also allows the GWAS and eQTL data to be collected from different groups of subjects, enabling investigators to integrate public resources with their own data. Simulation experiments show that it can be more powerful than standard approaches and also robust to linkage disequilibrium between variants. The method is applied to an extensive analysis of HF genomics and identifies several genes with biological evidence for being functionally relevant in the etiology of HF. It is implemented in the R package ssa.

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.

The heritable path of human physical performance: from single polymorphisms to the "next generation"

Human physical performance is a complex multifactorial trait. Historically, environmental factors (e.g., diet, training) alone have been unable to explain the basis of all prominent phenotypes for physical performance. Therefore, there has been an interest in the study of the contribution of genetic factors to the development of these phenotypes. Support for a genetic component is found with studies that shown that monozygotic twins were more similar than were dizygotic twins for many physiological traits. The evolution of molecular techniques and the ability to scan the entire human genome enabled association of several genetic polymorphisms with performance. However, some biases related to the selection of cohorts and inadequate definition of the study variables have complicated the already difficult task of studying such a large and polymorphic genome, often resulting in inconsistent results about the influence of candidate genes. This review aims to provide a critical overview of heritable genetic aspects. Novel molecular technologies, such as next-generation sequencing, are discussed and how they can contribute to improving understanding of the molecular basis for athletic performance. It is important to ensure that the large amount of data that can be generated using these tools will be used effectively by ensuring well-designed studies.

Whole-Genome Sequencing and Integrative Genomic Analysis Approach on Two 22q11.2 Deletion Syndrome Family Trios for Genotype to Phenotype Correlations

The 22q11.2 deletion syndrome (22q11DS) affects 1:4,000 live births and presents with highly variable phenotype expressivity. In this study, we developed an analytical approach utilizing whole-genome sequencing (WGS) and integrative analysis to discover genetic modifiers. Our pipeline combined available tools in order to prioritize rare, predicted deleterious, coding and noncoding single-nucleotide variants (SNVs), and insertion/deletions from WGS. We sequenced two unrelated probands with 22q11DS, with contrasting clinical findings, and their unaffected parents. Proband P1 had cognitive impairment, psychotic episodes, anxiety, and tetralogy of Fallot (TOF), whereas proband P2 had juvenile rheumatoid arthritis but no other major clinical findings. In P1, we identified common variants in COMT and PRODH on 22q11.2 as well as rare potentially deleterious DNA variants in other behavioral/neurocognitive genes. We also identified a de novo SNV in ADNP2 (NM_014913.3:c.2243G>C), encoding a neuroprotective protein that may be involved in behavioral disorders. In P2, we identified a novel nonsynonymous SNV in ZFPM2 (NM_012082.3:c.1576C>T), a known causative gene for TOF, which may act as a protective variant downstream of TBX1, haploinsufficiency of which is responsible for congenital heart disease in individuals with 22q11DS.

Genetics of diabetes complications

Chronic hyperglycemia and duration of diabetes are the major risk factors associated with development of micro- and macrovascular complications of diabetes. Although it is believed that hyperglycemia induces damage to the particular cell subtypes, e.g., mesangial cells in the renal glomerulus, capillary endothelial cells in the retina, and neurons and Schwann cells in peripheral nerves, the exact mechanisms underlying these damaging defects are not yet well understood. Clustering of micro- and macrovascular complications in families of patients with diabetes suggests a strong genetic susceptibility. However, until now only a handful number of genetic variants were reported to be associated with either nephropathy (ACE, ELMO1, FRMD3, and AKR1B1) or retinopathy (VEGF, AKR1B1, and EPO), and only a few studies were carried out for genetic susceptibility to cardiovascular diseases (ADIPOQ, GLUL) in patients with diabetes. It is, therefore, obvious that the accumulation of more data from larger studies and better phenotypically characterized cohorts is needed to facilitate genetic discoveries and unravel novel insights into the pathogenesis of diabetic complications.

More powerful genetic association testing via a new statistical framework for integrative genomics

Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high-dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.