Molecular genetic studies of complex phenotypes

Quality control metrics improve repeatability and reproducibility of single-nucleotide variants derived from whole-genome sequencing

Although many quality control (QC) methods have been developed to improve the quality of single-nucleotide variants (SNVs) in SNV-calling, QC methods for use subsequent to single-nucleotide polymorphism-calling have not been reported. We developed five QC metrics to improve the quality of SNVs using the whole-genome-sequencing data of a monozygotic twin pair from the Korean Personal Genome Project. The QC metrics improved both repeatability between the monozygotic twin pair and reproducibility between SNV-calling pipelines. We demonstrated the QC metrics improve reproducibility of SNVs derived from not only whole-genome-sequencing data but also whole-exome-sequencing data. The QC metrics are calculated based on the reference genome used in the alignment without accessing the raw and intermediate data or knowing the SNV-calling details. Therefore, the QC metrics can be easily adopted in downstream association analysis.

Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

Genome-wide association analysis of radiation resistance in Drosophila melanogaster

Background

Ionizing radiation is genotoxic to cells. Healthy tissue toxicity in patients and radiation resistance in tumors present common clinical challenges in delivering effective radiation therapies. Radiation response is a complex, polygenic trait with unknown genetic determinants. The Drosophila Genetic Reference Panel (DGRP) provides a model to investigate the genetics of natural variation for sensitivity to radiation.

Methods and Findings

Radiation response was quantified in 154 inbred DGRP lines, among which 92 radiosensitive lines and 62 radioresistant lines were classified as controls and cases, respectively. A case-control genome-wide association screen for radioresistance was performed. There are 32 single nucleotide polymorphisms (SNPs) associated with radio resistance at a nominal p<10⁻⁵; all had modest effect sizes and were common variants with the minor allele frequency >5%. All the genes implicated by those SNP hits were novel, many without a known role in radiation resistance and some with unknown function. Variants in known DNA damage and repair genes associated with radiation response were below the significance threshold of p<10⁻⁵ and were not present among the significant hits. No SNP met the genome-wide significance threshold (p = 1.49×10⁻⁷), indicating a necessity for a larger sample size.

Conclusions

Several genes not previously associated with variation in radiation resistance were identified. These genes, especially the ones with human homologs, form the basis for exploring new pathways involved in radiation resistance in novel functional studies. An improved DGRP model with a sample size of at least 265 lines and ideally up to 793 lines is recommended for future studies of complex traits.

Single nucleotide polymorphism-specific regulation of matrix metalloproteinase-9 by multiple miRNAs targeting the coding exon

Microribonucleic acids (miRNAs) work with exquisite specificity and are able to distinguish a target from a non-target based on a single nucleotide mismatch in the core nucleotide domain. We questioned whether miRNA regulation of gene expression could occur in a single nucleotide polymorphism (SNP)-specific manner, manifesting as a post-transcriptional control of expression of genetic polymorphisms. In our recent study of the functional consequences of matrix metalloproteinase (MMP)-9 SNPs, we discovered that expression of a coding exon SNP in the pro-domain of the protein resulted in a profound decrease in the secreted protein. This missense SNP results in the N38S amino acid change and a loss of an N-glycosylation site. A systematic study demonstrated that the loss of secreted protein was due not to the loss of an N-glycosylation site, but rather an SNP-specific targeting by miR-671-3p and miR-657. Bioinformatics analysis identified 41 SNP-specific miRNA targeting MMP-9 SNPs, mostly in the coding exon and an extension of the analysis to chromosome 20, where the MMP-9 gene is located, suggesting that SNP-specific miRNAs targeting the coding exon are prevalent. This selective post-transcriptional regulation of a target messenger RNA harboring genetic polymorphisms by miRNAs offers an SNP-dependent post-transcriptional regulatory mechanism, allowing for polymorphic-specific differential gene regulation.

Characterizing genetic variants for clinical action

Genome-wide association studies, DNA sequencing studies, and other genomic studies are finding an increasing number of genetic variants associated with clinical phenotypes that may be useful in developing diagnostic, preventive, and treatment strategies for individual patients. However, few variants have been integrated into routine clinical practice. The reasons for this are several, but two of the most significant are limited evidence about the clinical implications of the variants and a lack of a comprehensive knowledge base that captures genetic variants, their phenotypic associations, and other pertinent phenotypic information that is openly accessible to clinical groups attempting to interpret sequencing data. As the field of medicine begins to incorporate genome-scale analysis into clinical care, approaches need to be developed for collecting and characterizing data on the clinical implications of variants, developing consensus on their actionability, and making this information available for clinical use. The National Human Genome Research Institute (NHGRI) and the Wellcome Trust thus convened a workshop to consider the processes and resources needed to: (1) identify clinically valid genetic variants; (2) decide whether they are actionable and what the action should be; and (3) provide this information for clinical use. This commentary outlines the key discussion points and recommendations from the workshop.

Quantification of population benefit in evaluation of biomarkers: practical implications for disease detection and prevention

BACKGROUND

With the rapid development of "-omic" technologies, an increasing number of purported biomarkers have been identified for cancer and other diseases. The process of identifying those that are most promising and validating them for use at the population level for prevention and early detection is a critical next step in achieving significant health benefits.

METHODS

In this paper, we propose that in order to effectively translate biomarkers for practical clinical use, it is important to distinguish and quantify the differences between the use of biomarkers and other risk factors to identify preventive interventions versus their use in disease risk prediction and early detection. We developed mathematical models for quantitatively evaluating risk and benefit in use of biomarkers for disease prevention or early detection. Simple numerical examples were used to demonstrate the potential applications of the models for various types of data.

RESULTS

We propose an index which takes into account potential adverse consequences of biomarker-driven interventions - the 'naïve' ratio of population benefit (RPB) - to facilitate evaluating the potential impact of biomarkers on cancer prevention and personalized medicine. The index RPB is developed for both binary and continuous biomarkers/risk factors. Examples with computational analyses are presented in the paper to contrast the differences in using biomarkers/risk factors for prevention and early detection.

CONCLUSIONS

Integrating epidemiologic knowledge into clinical decision making is a key step to translate new biomarkers/risk factors into practical use to achieve health benefits. The RPB proposed in this paper considers the absolute risk of a disease in intervention, and takes into account the risk-benefit effects simultaneously for a marker/exposure at the population level. The RPB illustrates a unique approach to quantitatively assess the risk and potential benefits of using a biomarker/risk factor for intervention in both early detection and prevention.

Human pharmacogenomic variation of antihypertensive drugs: from population genetics to personalized medicine

Aim: To investigate the human pharmacogenetic variation related to antihypertensive drugs, providing a survey of functional interpopulation differences in hypertension pharmacogenes. Materials & methods: The study was divided into two stages. In the first stage, we analyzed 1249 variants located in 57 hypertension pharmacogenes. This first-stage analysis confirmed that geographic origin strongly affects hypertension pharmacogenomic variation and that 31 pharmacogenes are geographically differentiated. In the second stage, we focused our attention on the ethnic-differentiated pharmacogenes, investigating 55,521 genetic variants. In silico analyses were performed to predict the effect of genetic variation. Results: Our analyses indicated functional interpopulation differences, suggesting insight into the mechanisms of antihypertensive drug response. Moreover, our data suggested that rare variants mainly determine the functionality of genes related to antihypertensive drugs. Conclusion: Our study provided important knowledge about the genetics of the antihypertensive drug response, suggesting that next-generation sequencing technologies may develop reliable pharmacogenetic tests for antihypertensive drugs.

Original submitted 19 September 2013; Revision submitted 14 November 2013

Reduced representation approaches to interrogate genome diversity in large repetitive plant genomes

Technology and software improvements in the last decade now provide methodologies to access the genome sequence of not only a single accession, but also multiple accessions of plant species. This provides a means to interrogate species diversity at the genome level. Ample diversity among accessions in a collection of species can be found, including single-nucleotide polymorphisms, insertions and deletions, copy number variation and presence/absence variation. For species with small, non-repetitive rich genomes, re-sequencing of query accessions is robust, highly informative, and economically feasible. However, for species with moderate to large sized repetitive-rich genomes, technical and economic barriers prevent en masse genome re-sequencing of accessions. Multiple approaches to access a focused subset of loci in species with larger genomes have been developed, including reduced representation sequencing, exome capture and transcriptome sequencing. Collectively, these approaches have enabled interrogation of diversity on a genome scale for large plant genomes, including crop species important to worldwide food security.

Whole-exome sequencing: opportunities in pediatric endocrinology

Pediatric endocrinology services see a wide variety of patients with diverse clinical symptoms, including disorders of growth, metabolism, bone and sexual development. Molecular diagnosis plays an important role in this branch of medicine. Traditional PCR-based Sanger sequencing is a mainstay format for molecular testing in pediatric cases despite its relatively high cost, but the large number of gene defects associated with the various endocrine disorders renders gene-by-gene testing increasingly unattractive. Using new high-throughput sequencing technologies, whole genomes, whole exomes or candidate-gene panels (targeted gene sequencing) can now be cost-effectively sequenced for endocrine patients. Based on our own recent experiences with exome sequencing in a research context, we describe the general clinical ascertainment of relevant pediatric endocrine patients, compare different formats for next-generation sequencing and provide examples. Our view is that protocols involving next-generation sequencing should now be considered as an appropriate component of routine clinical diagnosis for relevant patients.

The future for genetic studies in reproduction

Genetic factors contribute to risk of many common diseases affecting reproduction and fertility. In recent years, methods for genome-wide association studies (GWAS) have revolutionized gene discovery for common traits and diseases. Results of GWAS are documented in the Catalog of Published Genome-Wide Association Studies at the National Human Genome Research Institute and report over 70 publications for 32 traits and diseases associated with reproduction. These include endometriosis, uterine fibroids, age at menarche and age at menopause. Results that pass appropriate stringent levels of significance are generally well replicated in independent studies. Examples of genetic variation affecting twinning rate, infertility, endometriosis and age at menarche demonstrate that the spectrum of disease-related variants for reproductive traits is similar to most other common diseases. GWAS 'hits' provide novel insights into biological pathways and the translational value of these studies lies in discovery of novel gene targets for biomarkers, drug development and greater understanding of environmental factors contributing to disease risk. Results also show that genetic data can help define sub-types of disease and co-morbidity with other traits and diseases. To date, many studies on reproductive traits have used relatively small samples. Future genetic marker studies in large samples with detailed phenotypic and clinical information will yield new insights into disease risk, disease classification and co-morbidity for many diseases associated with reproduction and infertility.

Pro-inflammatory genetic markers of atherosclerosis

Atherosclerosis (AS) is a chronic, progressive, multifactorial disease mostly affecting large and medium-sized elastic and muscular arteries. It has formerly been considered a bland lipid storage disease. Currently, multiple independent pathways of evidence suggest this pathological condition is a peculiar form of inflammation, triggered by cholesterol-rich lipoproteins and influenced both by environmental and genetic factors. The Human Genome Project opened up the opportunity to dissect complex human traits and to understand basic pathways of multifactorial diseases such as AS. Population-based association studies have emerged as powerful tools for examining genes with a role in common multifactorial diseases that have a strong environmental component. These association studies often estimate the risk of developing a certain disease in carriers and non-carriers of a particular genetic polymorphism. Dissecting out the influence of pro-inflammatory genes within the complex pathophysiology of AS and its complications will help to provide a more complete risk assessment and complement known classical cardiovascular risk factors. The detection of a risk profile will potentially allow both the early identification of individuals susceptible to disease and the possible discovery of potential targets for drug or lifestyle modification; i.e. it will open the door to personalized medicine.

QTL mapping of leafy heads by genome resequencing in the RIL population of Brassica rapa

Leaf heads of cabbage (Brassica oleracea), Chinese cabbage (B. rapa), and lettuce (Lactuca sativa) are important vegetables that supply mineral nutrients, crude fiber and vitamins in the human diet. Head size, head shape, head weight, and heading time contribute to yield and quality. In an attempt to investigate genetic basis of leafy head in Chinese cabbage (B. rapa), we took advantage of recent technical advances of genome resequencing to perform quantitative trait locus (QTL) mapping using 150 recombinant inbred lines (RILs) derived from the cross between heading and non-heading Chinese cabbage. The resequenced genomes of the parents uncovered more than 1 million SNPs. Genotyping of RILs using the high-quality SNPs assisted by Hidden Markov Model (HMM) generated a recombination map. The raw genetic map revealed some physical assembly error and missing fragments in the reference genome that reduced the quality of SNP genotyping. By deletion of the genetic markers in which recombination rates higher than 20%, we have obtained a high-quality genetic map with 2209 markers and detected 18 QTLs for 6 head traits, from which 3 candidate genes were selected. These QTLs provide the foundation for study of genetic basis of leafy heads and the other complex traits.

Genetic variants in transforming growth factor-β gene (TGFB1) affect susceptibility to schizophrenia

Immense body of evidence indicates that dysfunction of immune system is implicated in the etiology of schizophrenia. The immune theory of schizophrenia is supported by alterations in cytokine profile in the brain and peripheral blood. Given the strong genetic background of schizophrenia, it might be assumed that aberrant production of cytokines might be the consequence of genetic factors. This study aimed at investigating the association between schizophrenia susceptibility and selected functional polymorphisms in genes encoding cytokines including: interleukin-2 (IL2 −330T>G, rs2069756), interleukin-6 (IL-6 −174G>C, rs1800795), interferon-γ (IFNG +874T>A, rs2430561) as well as for the first time transforming growth factor-β1 (TGFB1 +869T>C, rs1800470 and +916G>C, rs1800471). We recruited 151 subjects with schizophrenia and 279 controls. There was a significant difference in the genotype distribution and allelic frequency of the TGFB1 +869T>C between patients with schizophrenia and healthy controls (p < 0.05). The risk of schizophrenia was more than two-fold higher in carriers of T allele (CT+TT genotypes) than individuals with CC genotype. Given documented gender differences in incidence of schizophrenia, we conducted separate analyses of male and female participants. We have shown that the association was significant in females, while in males it reached a trend toward statistical significance. To the best of our knowledge, it is the first report showing the association between TGFB1 +869T>C polymorphism and schizophrenia.

The role and challenges of exome sequencing in studies of human diseases

Recent advances in next-generation sequencing technologies have transformed the genetics study of human diseases; this is an era of unprecedented productivity. Exome sequencing, the targeted sequencing of the protein-coding portion of the human genome, has been shown to be a powerful and cost-effective method for detection of disease variants underlying Mendelian disorders. Increasing effort has been made in the interest of the identification of rare variants associated with complex traits in sequencing studies. Here we provided an overview of the application fields for exome sequencing in human diseases. We describe a general framework of computation and bioinformatics for handling sequencing data. We then demonstrate data quality and agreement between exome sequencing and exome microarray (chip) genotypes using data collected on the same set of subjects in a genetic study of panic disorder. Our results show that, in sequencing data, the data quality was generally higher for variants within the exonic target regions, compared to that outside the target regions, due to the target enrichment. We also compared genotype concordance for variant calls obtained by exome sequencing vs. exome genotyping microarrays. The overall consistency rate was >99.83% and the heterozygous consistency rate was >97.55%. The two platforms share a large amount of agreement over low frequency variants in the exonic regions, while exome sequencing provides much more information on variants not included on exome genotyping microarrays. The results demonstrate that exome sequencing data are of high quality and can be used to investigate the role of rare coding variants in human diseases.

Building a genome analysis pipeline to predict disease risk and prevent disease

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.

Will SNPs be useful predictors of normal tissue radiosensitivity in the future?

The ability to predict individual risk of radiation-induced normal tissue complications is a long sought goal in radiobiology. The last decade saw increasing interest in identifying associations between single nucleotide polymorphisms (SNPs) and normal tissue complication risk. Nevertheless, it remains controversial whether SNPs will be useful predictors of normal tissue radiosensitivity. This paper provides a summary of a scientific debate held at the 31st ESTRO conference in which four scientists argued in favor or against the motion that SNPs will be useful predictors of normal tissue radiosensitivity in the future.

Functional diversity of the glutathione peroxidase gene family among human populations: implications for genetic predisposition to disease and drug response

Aim: To analyze the human genetic variation of glutathione peroxidases (GPX), estimating the functional differences among human populations and suggesting interethnic differences in predisposition to disease and drug response. Materials & methods: Using 1000 Genomes Project data, we analyzed 723 GPX variants in 1092 individuals belonging to 14 populations. Combining functional prediction analyses of coding and noncoding variants, we developed a method to estimate haplotype functionality. Results: GPX rare variants have a higher functional impact than common variants. The frequency among Asian patients of haplotypes associated with normal functionality is significantly higher for GPX1 and lower for GPX3 than for non-Asian patients; no adaptation signals in GPX1 and GPX3 were found in Asian patients. Conclusion: GPX1 and GPX3 differences may be associated with alterations in antioxidant capacity and redox regulation, which suggests diverse susceptibility to complex disease and diverse response to relevant drugs in Asians compared with individuals with other ethnic origins.

Original submitted 7 February 2013; Revision submitted 16 May 2013

Risk scores of common genetic variants for lipid levels influence atherosclerosis and incident coronary heart disease

OBJECTIVE

Circulating levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides are recognized risk factors for cardiovascular disease. We tested the hypothesis that the cumulative effects of common genetic variants for lipids are collectively associated with subclinical atherosclerosis and incident coronary heart disease.

APPROACH AND RESULTS

Participants were drawn from the Erasmus Rucphen Family Study (n=2269) and the Rotterdam Study (n=8130). Linear regression and Cox proportional hazards models were applied to assess the influence of 4 risk scores derived from common genetic variants for lipids (total cholesterol, LDL-C, high-density lipoprotein cholesterol, and triglycerides) on carotid plaque, intima-media thickness, incident myocardial infarction, and coronary heart disease. Adjusted for age and sex, all 4 risk scores were associated with carotid plaque. This relationship was the strongest for the LDL-C score, which increased plaque score by 0.102 per SD increase in genetic risk score (P=3.2 × 10(-8)). The LDL-C score was also nominally associated with intima-media thickness, which increased 0.006 mm per SD increase in score (P=0.05). Both the total cholesterol and LDL-C scores were associated with incident myocardial infarction and coronary heart disease with hazard ratios between 1.10 and 1.13 per SD increase in score. Inclusion of additional risk factors as covariates minimally affected these results.

CONCLUSIONS

Common genetic variants with small effects on lipid levels are, in combination, significantly associated with subclinical and clinical cardiovascular outcomes. As knowledge of genetic variation increases, preclinical genetic screening tools might enhance the prediction and prevention of clinical events.

Use of whole exome and genome sequencing in the identification of genetic causes of primary immunodeficiencies

PURPOSE OF REVIEW

This review discusses the strengths and challenges of using whole genome sequencing (WGS)/whole exome sequencing (WES) for identifying novel genetic causes of primary immunodeficiencies.

RECENT FINDINGS

WGS permits comprehensive sequencing of introns and exons, whereas WES allows deeper sequencing of exonic regions at a lower cost. Due to the large number of genetic variants found in each genome, it is necessary to use filtering approaches to distinguish deleterious from benign variants. WES has been used successfully to identify novel genetic causes of primary immunodeficiency. Complex structural variations and non-Mendelian disorders remain challenges for WGS/WES.

SUMMARY

WGS/WES is a powerful screening tool with great potential to identify genetic causes of primary immunodeficiencies for research and clinical applications. To use WGS/WES effectively, it is necessary to understand how to filter the sequencing data and to realize its limitations as well as its strengths.

Genetic variants in the NOTCH4 gene influence the clinical features of migraine

BACKGROUND

Recent studies suggested an important role for vascular factors in migraine etiopathogenesis. Notch4 belongs to a family of transmembrane receptors that play an important role in vascular development and maintenance. The aim of this study was to test the hypothesis that polymorphisms of the NOTCH4 gene would modify the occurrence and the clinical features of migraine.

FINDINGS

Using a case-control strategy, we genotyped 239 migraine patients and 264 controls for three different non-synonymous polymorphisms (T320A, G835V, R1346P) of the NOTCH4 gene and for the (CTG) n-encoding polyleucine polymorphism in exon 1. Although the analyzed polymorphisms resulted not associated with migraine, the clinical characteristics of our patients were significantly influenced by the different NOTCH4 genotypes. Longer duration of disease and severity of neurovegetative symptoms during headache attacks were associated with the R1346P and G835V polymorphisms, respectively. In female patients, worsening of migraine symptoms at menarche was significantly correlated with T320A polymorphism.

CONCLUSIONS

Our study shows that genetic variations within the NOTCH4 gene significantly modify the clinical characteristics of migraine and may have a role in disease pathogenesis.

A survey of tools for variant analysis of next-generation genome sequencing data

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

Integrating optogenetic and pharmacological approaches to study neural circuit function: current applications and future directions

Optogenetic strategies to control genetically distinct populations of neurons with light have been rapidly evolving and widely adopted by the neuroscience community as one of the most important tool sets to study neural circuit function. Although optogenetics have already reshaped neuroscience by allowing for more precise control of circuit function compared with traditional techniques, current limitations of these approaches should be considered. Here, we discuss several strategies that combine optogenetic and contemporary pharmacological techniques to further increase the specificity of neural circuit manipulation. We also discuss recent advances that allow for the selective modulation of cellular function and gene expression with light. In addition, we outline a novel application of optogenetic circuit analysis for causally addressing the role of pathway-specific neural activity in mediating alterations in postsynaptic transcriptional processing in genetically defined neurons. By determining how optogenetic activation of specific neural circuits causally contributes to alterations in gene expression in a high-throughput fashion, novel biologic targets for future pharmacological intervention may be uncovered. Lastly, extending this experimental pipeline to selectively target pharmacotherapies to genetically defined neuronal populations or circuits will not only provide more selective control of neural circuits, but also may lead to the development of neural circuit specific pharmacological therapeutics.

Current and emerging technology approaches in genomics

PURPOSE

To introduce current and emerging approaches that are being utilized in the field of genomics so the reader can conceptually evaluate the literature and appreciate how these approaches are advancing our understanding of health-related issues.

ORGANIZING CONSTRUCT

Each approach is described and includes information related to how it is advancing research, its potential clinical utility, exemplars of current uses, challenges related to technologies used for these approaches, and when appropriate information related to understanding the evidence base for clinical utilization of each approach is provided. Web-based resources are included for the reader who would like more in-depth information and to provide opportunity to stay up to date with these approaches and their utility.

CONCLUSIONS

The chosen approaches-genome sequencing, genome-wide association studies, epigenomics, and gene expression-are extremely valuable approaches for collecting research data to help us better understand the pathophysiology of a variety of health-related conditions, but they are also gaining in utility for clinical assessment and testing purposes.

CLINICAL RELEVANCE

Our increased understanding of the molecular underpinnings of disease will assist with better development of screening tests, diagnostic tests, tests that allow us to prognosticate, tests that allow for individualized treatments, and tests to facilitate post-treatment surveillance.

Systems biology approach reveals genome to phenome correlation in type 2 diabetes

Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes.

The enigma of genetics etiology of atherosclerosis in the post-GWAS era

Coronary atherosclerosis is a complex heritable trait with an enigmatic genetic etiology. Genome-wide association studies (GWAS) have successfully led to identification of over 100 different loci for susceptibility to coronary atherosclerosis. Most identified single nucleotide polymorphisms (SNP)s and genes have not been previously implicated in the pathogenesis of atherosclerosis and hence, have modest biological plausibility. The novel discoveries, however, might provide the opportunity for identification of new pathways and consequently novel preventive and therapeutic targets. A notable outcome of GWAS is relatively modest effect sizes of the associated SNPs. Collectively, the identified SNPs account for a relatively small fraction of heritability of coronary atherosclerosis, which raises the question of "missing heritability". Because GWAS test the common disease-comment variant hypothesis, a plausible explanation might be the presence of uncommon and rare variants in the genome that are untested in GWAS but that might exert large effect sizes on the risk of atherosclerosis. The latter, however, remains an empiric question pending validation through experimentation. Alternative mechanisms, such as transgenerational epigenetics including microRNAs, might in part account for the heritability of coronary atherosclerosis. Collectively, the recent findings are indicative of the etiological complexity of coronary atherosclerosis. Hence, it is expected that genetic etiology of coronary atherosclerosis will remain enigmatic in the foreseeable future.

A systematic approach to multifactorial cardiovascular disease: causal analysis

The combination of systems biology and large data sets offers new approaches to the study of cardiovascular diseases. These new approaches are especially important for the common cardiovascular diseases that have long been described as multifactorial. This promise is undermined by biologists' skepticism of the spider web-like network diagrams required to analyze these large data sets. Although these spider webs resemble composites of the familiar biochemical pathway diagrams, the complexity of the webs is overwhelming. As a result, biologists collaborate with data analysts whose mathematical methods seem much like those of experts using Ouija boards. To make matters worse, it is not evident how to design experiments when the network implies that many molecules must be part of the disease process. Our goal is to remove some of this mystery and suggest a simple experimental approach to the design of experiments appropriate for such analysis. We will attempt to explain how combinations of data sets that include all possible variables, graphical diagrams, complementation of different data sets, and Bayesian analyses now make it possible to determine the causes of multifactorial cardiovascular disease. We will describe this approach using the term causal analysis. Finally, we will describe how causal analysis is already being used to decipher the interactions among cytokines as causes of cardiovascular disease.

Systems biology approaches to finding novel pain mediators

Chronic pain represents a major health burden; this maladaptive pain state occurs as a consequence of hypersensitivity within the peripheral and central components of the somatosensory system. High throughput technologies (genomics, transciptomics, lipidomics, and proteomics) are now being applied to tissue derived from pain patients as well as experimental pain models to discover novel pain mediators. The use of clustering, meta-analysis and other techniques can help refine potential candidates. Of particular importance are systems biology methods, such as co-expression network generating algorithms, which infer potential associations/interactions between molecules and build networks based on these interactions. Protein-protein interaction networks allow the lists of potential targets generated by these different platforms to be analyzed in their biological context. Outputs from these different methods must also be related to the clinical pain phenotype. The improved and standardized phenotyping of pain symptoms and sensory signs enables much better subject stratification. Our hope is that, in the future, the use of computational approaches to integrate datasets including sensory phenotype as well as the outputs of high throughput technologies will help define novel pain mediators and provide insights into the pathogenesis of chronic pain.

Interpreting genetic effects through models of cardiac electromechanics

Multiscale models of cardiac electromechanics are being increasingly focused on understanding how genetic variation and environment underpin multiple disease states. In this paper we review the current state of the art in both the development of specific models and the physiological insights they have produced. This growing research body includes the development of models for capturing the effects of changes in function in both single and multiple proteins in both specific expression systems and in vivo contexts. Finally, the potential for using this approach for ultimately predicting phenotypes from genetic sequence information is discussed.

New insights into behaviour using mouse ENU mutagenesis

Identifying genes involved in behavioural disorders in man is a challenge as the cause is often multigenic and the phenotype is modulated by environmental cues. Mouse mutants are a valuable tool for identifying novel pathways underlying specific neurological phenotypes and exploring the influence both genetic and non-genetic factors. Many human variants causing behavioural disorders are not gene deletions but changes in levels of expression or activity of a gene product; consequently, large-scale mouse ENU mutagenesis has the advantage over the study of null mutants in that it generates a range of point mutations that frequently mirror the subtlety and heterogeneity of human genetic lesions. ENU mutants have provided novel and clinically relevant functional information on genes that influence many aspects of mammalian behaviour, from neuropsychiatric endophenotypes to circadian rhythms. This review will highlight some of the most important findings that have been made using this method in several key areas of neurological disease research.

Genetic Influences on Physiological and Subjective Responses to an Aerobic Exercise Session among Sedentary Adults

Objective. To determine whether genetic variants suggested by the literature to be associated with physiology and fitness phenotypes predicted differential physiological and subjective responses to a bout of aerobic exercise among inactive but otherwise healthy adults. Method. Participants completed a 30-minute submaximal aerobic exercise session. Measures of physiological and subjective responding were taken before, during, and after exercise. 14 single nucleotide polymorphisms (SNPs) that have been previously associated with various exercise phenotypes were tested for associations with physiological and subjective response to exercise phenotypes. Results. We found that two SNPs in the FTO gene (rs8044769 and rs3751812) were related to positive affect change during exercise. Two SNPs in the CREB1 gene (rs2253206 and 2360969) were related to change in temperature during exercise and with maximal oxygen capacity (VO(2) max). The SLIT2 SNP rs1379659 and the FAM5C SNP rs1935881 were associated with norepinephrine change during exercise. Finally, the OPRM1 SNP rs1799971 was related to changes in norepinephrine, lactate, and rate of perceived exertion (RPE) during exercise. Conclusion. Genetic factors influence both physiological and subjective responses to exercise. A better understanding of genetic factors underlying physiological and subjective responses to aerobic exercise has implications for development and potential tailoring of exercise interventions.

The need for mouse models in osteoporosis genetics research

Osteoporosis, the progressive loss of bone mass resulting in fragility fractures, affects ∼75 million people in the United States, Europe and Japan. Bone mineral density (BMD) correlates with fracture risk and is widely used in clinical settings to predict fracture. Numerous studies have demonstrated that peak bone mass is highly heritable and consequently a number of genome-wide association studies (GWASs) have been conducted to identify the genes that regulate BMD. Traditional intercross mapping in the mouse has met with limited successes in the field of skeletal biology. With the advent of human GWAS, questions have arisen about the continued need for mouse models in genetics research. However, significant advances have been made in the field of mouse genetics, including new genetics resource populations and loci mapping techniques, which enable gene-level mapping resolution. In this review, we discuss the need for mouse models to help understand the skeletal biology underlying novel human GWAS findings, how loci discovered in the mouse can be used to complement GWAS analysis and highlight the recent advances made in the field of skeletal biology from the use of these new and developing resources. We conclude this paper with a discussion of the need for systems-level approaches in the skeletal biology field, with an emphasis on the need for pathway and network analyses.

Elements of 'missing heritability'

PURPOSE OF REVIEW

To discuss the basis of 'missing heritability', which has emerged as an enigma in the post-genome-wide association studies (GWAS) era.

RECENT FINDINGS

Alleles identified through GWAS account for a relatively small fraction of heritability of the complex phenotypes. Accordingly, a significant part of heritability of the complex traits remains unaccounted for ('missing heritability'). Recent findings offer several explanations, including overestimation of heritability of the complex traits and underestimation of the effects of alleles identified through GWAS. In addition, yet-to-be identified common as well as rare alleles might in part explain the 'missing heritability'. Moreover, gene-gene (epistasis) and gene-environmental interactions might explain another fraction of heritability of complex traits. Moreover, transgenerational epigenetic changes, regulated in part by microRNAs, might also contribute to the 'missing heritability'.

SUMMARY

The new findings suggest a multifarious nature of the 'missing heritability'. The findings de-emphasize the focus on delineating the basis of 'missing heritability' and shift the focus to elucidation of the molecular mechanisms by which genomic and genetic factors govern the pathogenesis of the complex phenotypes.