Epigenetic Clocks: In Aging-Related and Complex Diseases

BACKGROUND

There is evidence that complex diseases and mortality are associated with DNA methylation and age acceleration. Numerous epigenetic clocks, including Horvath, Hannum, DNA PhenoAge, DNA GrimAge, and Dunedin Pace of Aging Methylation, continue to be developed in this young scientific field. The most well-known epigenetic clocks are presented here, along with information about how they relate to chronic disease.

SUMMARY

We examined all the literature until January 2023, investigating associations between measures of age acceleration and complex and age-related diseases. We focused on the scientific literature and research that are most strongly associated with epigenetic clocks and that have shown promise as biomarkers for obesity, cardiovascular illness, type 2 diabetes, and neurodegenerative disease.

KEY MESSAGES

Understanding the complex interactions between accelerated epigenetic clocks and chronic diseases may have significant effects on both the early diagnosis of disease and health promotion. Additionally, there is a lot of interest in developing treatment plans that can delay the onset of illnesses or, at the very least, alter the underlying causes of such disorders.

SAGES masters program: the top 10 seminal articles for the laparoscopic left and sigmoid colectomy pathway for complex disease

BACKGROUND

The SAGES University Colorectal Masters Program is a structured educational curriculum that is designed to aid practicing surgeons develop and maintain knowledge and technical skills for laparoscopic colorectal surgery. The Colorectal Pathway is based on three anchoring procedures (laparoscopic right colectomy, laparoscopic left and sigmoid colectomy for uncomplicated and complex disease, and intracorporeal anastomosis for minimally invasive right colectomy) corresponding to three levels of performance (competency, proficiency and mastery). This manuscript presents focused summaries of the top 10 seminal articles selected for laparoscopic left and sigmoid colectomy for complex benign and malignant disease.

METHODS

A systematic literature search of Web of Science for the most cited articles on the topic of laparoscopic complex left/sigmoid colectomy yielded 30 citations. These articles were reviewed and ranked by the SAGES Colorectal Task Force and invited subject experts according to their citation index. The top 10 ranked articles were then reviewed and summarized, with emphasis on relevance and impact in the field, study findings, strength and limitations and conclusions.

RESULTS

The top 10 seminal articles selected for the laparoscopic left/sigmoid colectomy for complex disease anchoring procedure include advanced procedures such as minimally invasive splenic flexure mobilization techniques, laparoscopic surgery for complicated and/or diverticulitis, splenic flexure tumors, complete mesocolic excision, and other techniques (e.g., Deloyers or colonic transposition in cases with limited colonic reach after extended left-sided resection).

CONCLUSIONS

The SAGES Colorectal Masters Program top 10 seminal articles selected for laparoscopic left and sigmoid colectomy for complex benign and malignant disease anchoring procedure are presented. These procedures were the most essential in the armamentarium of practicing surgeons that perform minimally invasive surgery for complex left and sigmoid colon pathology.

Ancestry, diversity, and genetics of health-related traits in African-derived communities (quilombos) from Brazil

Brazilian quilombos are communities formed by enslaved Africans and their descendants all over the country during slavery and shortly after its abolition. Quilombos harbor a great fraction of the largely unknown genetic diversity of the African diaspora in Brazil. Thus, genetic studies in quilombos have the potential to provide important insights not only into the African roots of the Brazilian population but also into the genetic bases of complex traits and human adaptation to diverse environments. This review summarizes the main results of genetic studies performed on quilombos so far. Here, we analyzed the patterns of African, Amerindian, European, and subcontinental ancestry (within Africa) of quilombos from the five different geographic regions of Brazil. In addition, uniparental markers (from the mtDNA and the Y chromosome) studies are analyzed together to reveal demographic processes and sex-biased admixture that occurred during the formation of these unique populations. Lastly, the prevalence of known malaria-adaptive African mutations and other African-specific variants discovered in quilombos, as well as the genetic bases of health-related traits, are discussed here, together with their implication for the health of populations of African descent.

Neurologists' views on patient reported outcomes in multiple sclerosis care

Background

The value that patient reported outcomes (PROs) can bring to the clinical encounter is increasingly being recognized. Within the field of Multiple Sclerosis (MS), a number of activities have been initiated internationally with the aim of integrating PROs in MS care. Integration of PROs in MS care will, among other things, require MS neurologists' acceptance of PROs. This qualitative study aimed to explore MS specialized neurologists’ view on the potentials and barriers for the use of PROs in the clinical setting.

Methods

Eight neurologists specialized in MS participated in a series of individual in-depth semi-structured interviews. Interviews were audio-recorded and transcribed. A thematic analysis was conducted using a reflexive thematic approach to identify meaning units and themes emerging from the interviews. The analysis focused on barriers and potentials for PROs that relate to the specific characteristics of MS.

Results

Three main themes emerged from the data. “The role of PROs in describing the patient's situation” describes how informants perceived MS as a complex disease, and in this context, PROs can bring forth new or otherwise hidden information, but they may also blur the picture. The theme “The validity of PROs reported by MS patients may be questionable” describes how impairments caused by MS, such as cognitive and physical disabilities, were identified as risk factors that could cause unreliable self-reported outcome measures within this patient group. Finally, the theme “Involving the patient” regards how the clinical conversation was viewed as the most important source of patient reported information, but at the same time PROs carry the potential to enhance shared decision making.

Conclusion

This study indicates that, according to MS neurologists, integration of PROs in MS clinical practice, though possibly valuable, is not without challenges. Possible benefits of PROs include the ability to bring forth otherwise hidden information on the patient's health status and the enhancement of shared decision making. Barriers include difficulties in capturing the full situation of the patient via PROs due to the complexity of MS as well as the issue of various MS-related impairments compromising the validity of PROs reported by MS patients.

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The complexity of MS affects neurologists' attitudes towards PROs.

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PROs hold the potential to bring forth otherwise hidden information.

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Neurologists are sceptical regarding the quality of information from PROs.

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PROs may not provide sufficiently detailed information about patients' situations.

Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer

INTRODUCTION

Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer.

AREAS COVERED

The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.

EXPERT OPINION

In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

Polygenic risk prediction based on singular value decomposition with applications to alcohol use disorder

BACKGROUND/AIM

The polygenic risk score (PRS) shows promise as a potentially effective approach to summarize genetic risk for complex diseases such as alcohol use disorder that is influenced by a combination of multiple variants, each of which has a very small effect. Yet, conventional PRS methods tend to over-adjust confounding factors in the discovery sample and thus have low power to predict the phenotype in the target sample. This study aims to address this important methodological issue.

METHODS

This study proposed a new method to construct PRS by (1) approximating the polygenic model using a few principal components selected based on eigen-correlation in the discovery data; and (2) conducting principal component projection on the target data. Secondary data analysis was conducted on two large scale databases: the Study of Addiction: Genetics and Environment (SAGE; discovery data) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; target data) to compare performance of the conventional and proposed methods.

RESULT AND CONCLUSION

The results show that the proposed method has higher prediction power and can handle participants from different ancestry backgrounds. We also provide practical recommendations for setting the linkage disequilibrium (LD) and p value thresholds.

A trans locus causes a ribosomopathy in hypertrophic hearts that affects mRNA translation in a protein length-dependent fashion

BACKGROUND

Little is known about the impact of trans-acting genetic variation on the rates with which proteins are synthesized by ribosomes. Here, we investigate the influence of such distant genetic loci on the efficiency of mRNA translation and define their contribution to the development of complex disease phenotypes within a panel of rat recombinant inbred lines.

RESULTS

We identify several tissue-specific master regulatory hotspots that each control the translation rates of multiple proteins. One of these loci is restricted to hypertrophic hearts, where it drives a translatome-wide and protein length-dependent change in translational efficiency, altering the stoichiometric translation rates of sarcomere proteins. Mechanistic dissection of this locus across multiple congenic lines points to a translation machinery defect, characterized by marked differences in polysome profiles and misregulation of the small nucleolar RNA SNORA48. Strikingly, from yeast to humans, we observe reproducible protein length-dependent shifts in translational efficiency as a conserved hallmark of translation machinery mutants, including those that cause ribosomopathies. Depending on the factor mutated, a pre-existing negative correlation between protein length and translation rates could either be enhanced or reduced, which we propose to result from mRNA-specific imbalances in canonical translation initiation and reinitiation rates.

CONCLUSIONS

We show that distant genetic control of mRNA translation is abundant in mammalian tissues, exemplified by a single genomic locus that triggers a translation-driven molecular mechanism. Our work illustrates the complexity through which genetic variation can drive phenotypic variability between individuals and thereby contribute to complex disease.

SNPs in Sites for DNA Methylation, Transcription Factor Binding, and miRNA Targets Leading to Allele-Specific Gene Expression and Contributing to Complex Disease Risk: A Systematic Review

INTRODUCTION

The complex genetic diversity among human populations results from an assortment of factors acting at various sequential levels, including mutations, population migrations, genetic drift, and selection. Although there are a plethora of DNA sequence variations identified through genome-wide association studies (GWAS), the challenge remains to explain the mechanisms underlying interindividual phenotypic disparity accounting for disease susceptibility. Single nucleotide polymorphisms (SNPs) present in the sites for DNA methylation, transcription factor (TF) binding, or miRNA targets can alter the gene expression. The systematic review aimed to evaluate the complex crosstalk among SNPs, miRNAs, DNA methylation, and TFs for complex multifactorial disease risk.

METHODS

PubMed and Scopus databases were used from inception until May 15, 2019. Initially, screening of articles involved studies assessing the interaction of SNPs with TFs, DNA methylation, or miRNAs resulting in allele-specific gene expression in complex multifactorial diseases. We also included the studies which provided experimental validation of the interaction of SNPs with each of these factors. The results from various studies on multifactorial diseases were assessed.

RESULTS

A total of 11 articles for SNPs interacting with DNA methylation, 30 articles for SNPs interacting with TFs, and 11 articles for SNPs in miRNA binding sites were selected. The interactions of SNPs with epigenetic factors were found to be implicated in different types of cancers, autoimmune diseases, cardiovascular diseases, diabetes, and asthma.

CONCLUSION

The systematic review provides evidence for the interplay between genetic and epigenetic risk factors through allele-specific gene expression in various complex multifactorial diseases.

Non-additive (dominance) effects of genetic variants associated with refractive error and myopia

Genome-wide association studies (GWAS) have revealed that the genetic contribution to certain complex diseases is well-described by Fisher's infinitesimal model in which a vast number of polymorphisms each confer a small effect. Under Fisher's model, variants have additive effects both across loci and within loci. However, the latter assumption is at odds with the common observation of dominant or recessive rare alleles responsible for monogenic disorders. Here, we searched for evidence of non-additive (dominant or recessive) effects for GWAS variants known to confer susceptibility to the highly heritable quantitative trait, refractive error. Of 146 GWAS variants examined in a discovery sample of 228,423 individuals whose refractive error phenotype was inferred from their age-of-onset of spectacle wear, only 8 had even nominal evidence (p < 0.05) of non-additive effects. In a replication sample of 73,577 individuals who underwent direct assessment of refractive error, 1 of these 8 variants had robust independent evidence of non-additive effects (rs7829127 within ZMAT4, p = 4.76E-05) while a further 2 had suggestive evidence (rs35337422 in RD3L, p = 7.21E-03 and rs12193446 in LAMA2, p = 2.57E-02). Accounting for non-additive effects had minimal impact on the accuracy of a polygenic risk score for refractive error (R2 = 6.04% vs. 6.01%). Our findings demonstrate that very few GWAS variants for refractive error show evidence of a departure from an additive mode of action and that accounting for non-additive risk variants offers little scope to improve the accuracy of polygenic risk scores for myopia.

Thinking outside the nucleus: Mitochondrial DNA copy number in health and disease

Mitochondrial DNA copy number (mtDNA-CN) is a biomarker of mitochondrial function and levels of mtDNA-CN have been reproducibly associated with overall mortality and a number of age-related diseases, including cardiovascular disease, chronic kidney disease, and cancer. Recent advancements in techniques for estimating mtDNA-CN, in particular the use of DNA microarrays and next-generation sequencing data, have led to the comprehensive assessment of mtDNA-CN across these and other diseases and traits. The importance of mtDNA-CN measures to disease and these advancing technologies suggest the potential for mtDNA-CN to be a useful biomarker in the clinic. While the exact mechanism(s) underlying the association of mtDNA-CN with disease remain to be elucidated, we review the existing literature which supports roles for inflammatory dynamics, immune function and alterations to cell signaling as consequences of variation in mtDNA-CN. We propose that future studies should focus on characterizing longitudinal, cell-type and cross-tissue profiles of mtDNA-CN as well as improving methods for measuring mtDNA-CN which will expand the potential for its use as a clinical biomarker.

Systems Biology of Selenium and Complex Disease

Selenium is an essential trace element for maintenance of overall health, whose deficiency and dyshomeostasis have been linked to a variety of diseases and disorders. The majority of previous researches focused on characterization of genes encoding selenoproteins or proteins involved in selenium metabolism as well as their functions. Many studies in humans also investigated the relationship between selenium and complex diseases, but their results have been inconsistent. In recent years, systems biology and "-omics" approaches have been widely used to study complex and global variations of selenium metabolism and function in physiological and different pathological conditions. The present paper reviews recent progress in large-scale and systematic analyses of the relationship between selenium status or selenoproteins and several complex diseases, mainly including population-based cohort studies and meta-analyses, genetic association studies, and some other omics-based studies. Advances in ionomics and its application in studying the interaction between selenium and other trace elements in human health and diseases are also discussed.

Resolving complexity in mitochondrial disease: Towards precision medicine

Mitochondrial diseases, caused by mutations in either the nuclear or mitochondrial genomes (mtDNA), are the most common form of inherited neurometabolic disorders. They are remarkably heterogeneous, both in their clinical presentation and genetic etiology, presenting challenges for diagnosis, clinical management and elucidation of molecular mechanism. The multifaceted nature of these diseases, compounded by the unique characteristics of mitochondrial genetics, cement their space in the field of complex disease. In this review we examine the m.3243A>G variant, one of the most prevalent mitochondrial DNA mutations, using it as an exemplar to demonstrate the challenges presented by these complex disorders. Disease caused by m.3243A>G is one of the most phenotypically diverse of all mitochondrial diseases; we outline known causes of this heterogeneity including mtDNA heteroplasmy, mtDNA copy number and nuclear genetic factors. We consider the impact that this has in the clinic, discussing the personalized management of common manifestations attributed to this pathogenic mtDNA variant, including hearing impairment, diabetes mellitus, myopathy, cardiac disease, stroke-like episodes and gastrointestinal disturbances. Future research into this complex disorder must account for this heterogeneity, benefitting from the use of large patient cohorts to build upon current clinical expertise. Through multi-disciplinary collaboration, the complexities of this mitochondrial disease can be addressed with the variety of diagnostic, prognostic, and treatment approaches that are moulded to best fit the needs of each individual patient.

Type 2 diabetes: a multifaceted disease

Type 2 diabetes is a complex disease usually diagnosed with little regard to aetiology. In the broader sense, it is a mix of different clearly defined aetiologies, such as monogenic diabetes, that we need to be better at identifying as this has major implications for treatment and patient management. Beyond this, however, type 2 diabetes is a highly heterogeneous polygenic disease. This review outlines the recent developments that recognise this heterogeneity by deconvoluting the aetiology of type 2 diabetes into pathophysiological processes, either by measuring physiological variables (such as beta cell function or insulin resistance) or using partitioned polygenic scores, and addresses recent work that clusters type 2 diabetes into distinct subgroups. Increasing evidence suggests that considering the aetiological components of type 2 diabetes matters, in terms of progression rates, treatment response and complications. In other words, clinicians need to recognise that type 2 diabetes is multifaceted and that its characteristics are important for how patients are managed.

Rat Models of Exercise for the Study of Complex Disease

Variation in exercise capacity is a translationally powerful indicator for overall health and disease. Here we review the basic methods used for development of theoretically based and hypothesis-driven rat models that divide for both exercise capacity and numerous complex disease risks This rat model system was made by selectively breeding genetically heterogeneous rat populations for low and high performance on a speed ramped treadmill running test.

integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

BACKGROUND

The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.

METHODS

To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).

RESULTS

We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub ( https://github.com/haleyeidem/integRATE ).

CONCLUSIONS

Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

Prioritization and functional assessment of noncoding variants associated with complex diseases

Unraveling functional noncoding variants associated with complex diseases is still a great challenge. We present a novel algorithm, Prioritization And Functional Assessment (PAFA), that prioritizes and assesses the functionality of genetic variants by introducing population differentiation measures and recalibrating training variants. Comprehensive evaluations demonstrate that PAFA exhibits much higher sensitivity and specificity in prioritizing noncoding risk variants than existing methods. PAFA achieves improved performance in distinguishing both common and rare recurrent variants from non-recurrent variants by integrating multiple annotations and metrics. An integrated platform was developed, providing comprehensive functional annotations for noncoding variants by integrating functional genomic data, which can be accessed at http://159.226.67.237:8080/pafa .

Success stories of natural product-based hybrid molecules for multi-factorial diseases

Complex diseases comprises of highly complicated etiology resulting in limited applicability of conventional targeted therapies. Consequently, conventional medicinal compounds suffer major failure when used for such disease conditions. Additionally, development of multidrug resistance (MDR), adverse drug reactions and clinical specificity of single targeted drug therapy has increased thrust for novel drug therapy. In this rapidly evolving era, natural product-based discovery of hybrid molecules or multi-targeted drug therapies have shown promising results and are trending now a days. Historically, nature has blessed human with different sources viz. plant, animal, microbial, marine and ethnopharmaceutical sources which has given a wide variety of medicinally active compounds. These compounds from natural origin are always choice of interest of medicinal chemists because of their minimum side effects. Hybrid molecules synthesized by fusing or conjugating different active molecules obtained from these sources are reported to synergistically block different pathways which contribute in the pathogenesis of complex diseases. This review strives to encompass all natural product-derived hybrid molecules which act as multi-targeting agents striking various targets involved in different pathways of complex diseased conditions reported in literature.

A Summary of the Biological Processes, Disease-Associated Changes, and Clinical Applications of DNA Methylation

DNA methylation at cytosines followed by guanines, CpGs, forms one of the multiple layers of epigenetic mechanisms controlling and modulating gene expression through chromatin structure. It closely interacts with histone modifications and chromatin remodeling complexes to form the local genomic and higher-order chromatin landscape. DNA methylation is essential for proper mammalian development, crucial for imprinting and plays a role in maintaining genomic stability. DNA methylation patterns are susceptible to change in response to environmental stimuli such as diet or toxins, whereby the epigenome seems to be most vulnerable during early life. Changes of DNA methylation levels and patterns have been widely studied in several diseases, especially cancer, where interest has focused on biomarkers for early detection of cancer development, accurate diagnosis, and response to treatment, but have also been shown to occur in many other complex diseases. Recent advances in epigenome engineering technologies allow now for the large-scale assessment of the functional relevance of DNA methylation. As a stable nucleic acid-based modification that is technically easy to handle and which can be analyzed with great reproducibility and accuracy by different laboratories, DNA methylation is a promising biomarker for many applications.

Mapping a shared genetic basis for neurodevelopmental disorders

Distinct neurodevelopmental disorders have a common genetic etiology that explains the high degree of comorbidity among these disorders. A recent study sought to identify copy number variants across five neurodevelopmental disorders, and detected an enrichment for chromosome 9p24.3 duplication encompassing DOCK8 and KANK1 in affected individuals. Such large-scale studies will help uncover additional causative and modifier loci within common pathways, which will enable the development of therapeutic targets for the treatment of multiple disorders.

See related research 10.1186/s13073-017-0494-1

Genetic determinants of cholangiopathies: Molecular and systems genetics

Familial cholangiopathies are rare but potentially severe diseases. Their spectrum ranges from fairly benign conditions as, for example, benign recurrent intrahepatic cholestasis to low-phospholipid associated cholelithiasis and progressive familial intrahepatic cholestasis (PFIC). Many cholangiopathies such as primary biliary cholangitis (PBC) or primary sclerosing cholangitis (PSC) affect first the bile ducts ("ascending pathophysiology") but others, such as PFIC, start upstream in hepatocytes and cause progressive damage "descending" down the biliary tree and leading to end-stage liver disease. In recent years our understanding of cholestatic diseases has improved, since we have been able to pinpoint numerous disease-causing mutations that cause familial cholangiopathies. Accordingly, six PFIC subtypes (PFIC type 1-6) have now been defined. Given the availability of genotyping resources, these findings can be introduced in the diagnostic work-up of patients with peculiar cholestasis. In addition, functional studies have defined the pathophysiological consequences of some of the detected variants. Furthermore, ABCB4 variants do not only cause PFIC type 3 but confer an increased risk for chronic liver disease in general. In the near future these findings will serve to develop new therapeutic strategies for patients with liver diseases. Here we present the latest data on the genetic background of familial cholangiopathies and discuss their application in clinical practice for the differential diagnosis of cholestasis of unknown aetiology. As look in the future we present "system genetics" as a novel experimental tool for the study of cholangiopathies and disease-modifying genes. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.

Painting a new picture of personalised medicine for diabetes

The current focus on delivery of personalised (or precision) medicine reflects the expectation that developments in genomics, imaging and other domains will extend our diagnostic and prognostic capabilities, and enable more effective targeting of current and future preventative and therapeutic options. The clinical benefits of this approach are already being realised in rare diseases and cancer but the impact on management of complex diseases, such as type 2 diabetes, remains limited. This may reflect reliance on inappropriate models of disease architecture, based around rare, high-impact genetic and environmental exposures that are poorly suited to our emerging understanding of type 2 diabetes. This review proposes an alternative 'palette' model, centred on a molecular taxonomy that focuses on positioning an individual with respect to the major pathophysiological processes that contribute to diabetes risk and progression. This model anticipates that many individuals with diabetes will have multiple parallel defects that affect several of these processes. One corollary of this model is that research efforts should, at least initially, be targeted towards identifying and characterising individuals whose adverse metabolic trajectory is dominated by perturbation in a restricted set of processes.

Outcomes of a Randomized Controlled Trial of Genomic Counseling for Patients Receiving Personalized and Actionable Complex Disease Reports

There has been very limited study of patients with chronic disease receiving potentially actionable genomic based results or the utilization of genetic counselors in the online result delivery process. We conducted a randomized controlled trial on 199 patients with chronic disease each receiving eight personalized and actionable complex disease reports online. Primary study aims were to assess the impact of in-person genomic counseling on 1) causal attribution of disease risk, 2) personal awareness of disease risk, and 3) perceived risk of developing a particular disease. Of 98 intervention arm participants (mean age = 57.8; 39% female) randomized for in-person genomic counseling, 76 (78%) were seen. In contrast, control arm participants (n = 101; mean age = 58.5; 54% female) were initially not offered genomic counseling as part of the study protocol but were able to access in-person genomic counseling, if they requested it, 3-months post viewing of at least one test report and post-completion of the study-specific follow-up survey. A total of 64 intervention arm and 59 control arm participants completed follow-up survey measures. We found that participants receiving in-person genomic counseling had enhanced objective understanding of the genetic variant risk contribution for multiple complex diseases. Genomic counseling was associated with lowered participant causal beliefs in genetic influence across all eight diseases, compared to control participants. Our findings also illustrate that for the majority of diseases under study, intervention arm participants believed they knew their genetic risk status better than control arm subjects. Disease risk was modified for the majority during genomic counseling, due to the assessment of more comprehensive family history. In conclusion, for patients receiving personalized and actionable genomic results through a web portal, genomic counseling enhanced their objective understanding of the genetic variant risk contribution to multiple common diseases. These results support the development of additional genomic counseling interventions to ensure a high level of patient comprehension and improve patient-centered health outcomes.

Comparative network stratification analysis for identifying functional interpretable network biomarkers

Background

A major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions.

Results

In this paper, we present a new computational framework based on an integer programming model, named as Comparative Network Stratification (CNS), to extract functional or interpretable network biomarkers, which are of strongly discriminative power on disease states and also readable interpretation on biological functions. In addition, CNS can not only recognize the pathogen biological functions disregarded by traditional Expression-based/Network-based methods, but also uncover the active network-structures underlying such dysregulated functions underestimated by traditional Function-based methods. To validate the effectiveness, we have compared CNS with five state-of-the-art methods, i.e. GSVA, Pathifier, stSVM, frSVM and AEP on four datasets of different complex diseases. The results show that CNS can enhance the discriminative power of network biomarkers, and further provide biologically interpretable information or disease pathogenic mechanism of these biomarkers. A case study on type 1 diabetes (T1D) demonstrates that CNS can identify many dysfunctional genes and networks previously disregarded by conventional approaches.

Conclusion

Therefore, CNS is actually a powerful bioinformatics tool, which can identify functional or interpretable network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. CNS was implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/CNSpackage_0.1.rar.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1462-x) contains supplementary material, which is available to authorized users.

Gene-centric analysis implicates nuclear encoded mitochondrial protein gene variants in migraine susceptibility

Background

Migraine is a common neurological disorder which affects a large proportion of the population. The Norfolk Island population is a genetically isolated population and is an ideal discovery cohort for genetic variants involved in complex disease susceptibility given the reduced genetic and environmental heterogeneity. Given that the majority of proteins responsible for mitochondrial function are nuclear encoded, this study aimed to investigate the role of Nuclear Encoded Mitochondrial Protein (NEMP) genes in relation to migraine susceptibility.

Methods

A gene‐centric association analysis of NEMP genes was undertaken in the most related individuals (n = 315) within the genetically isolated Norfolk Island population. The discovery phase included genes with three or more SNP associations (P < 0.005), which were investigated further in a replication phase using an unrelated migraine case–control cohort (544 patients and 584 controls).

Results

The discovery phase of the study implicated SNPs in 5 NEMP genes to be associated with migraine susceptibility (P < 0.005). Replication analysis validated some of these implicated genes with SNPs in three NEMP genes shown to be associated with migraine in the replication cohort. These were CSNK1G3 (P = 0.00037), ELOVL6 (P = 0.00035) and SARDH (P = 0.00081), which are involved in phosphorylation, fatty acid metabolism, and oxidative demethylation, respectively.

Conclusion

Here we provide evidence that variation in NEMP genes is associated with migraine susceptibility. This study provides evidence for a link between mitochondrial function and migraine susceptibility.

Genomic strategies to understand causes of keratoconus

Keratoconus (KTCN) is a degenerative disorder of the eye characterized by the conical shape and thinning of the cornea. The abnormal structure of KTCN-affected cornea results in loss of visual acuity. While many studies examine how environmental factors influence disease development, finding the genetic triggers has been a major emphasis of KTCN research. This paper focuses on genomic strategies that were implemented for finding candidate genes, including linkage and association studies, and presents different approaches of mutation screening. The advantages and limitations of particular tools are discussed based on literature and personal experience. Since etiology underlying KTCN is complex, numerous findings indicating heterogeneity of genetic factors involved KTCN etiology are presented.

Counselees' Perspectives of Genomic Counseling Following Online Receipt of Multiple Actionable Complex Disease and Pharmacogenomic Results: a Qualitative Research Study

Genomic applications raise multiple challenges including the optimization of genomic counseling (GC) services as part of the results delivery process. More information on patients' motivations, preferences, and informational needs are essential to guide the development of new, more efficient practice delivery models that capitalize on the existing strengths of a limited genetic counseling workforce. Semi-structured telephone interviews were conducted with a subset of counselees from the Coriell Personalized Medicine Collaborative following online receipt of multiple personalized genomic test reports. Participants previously had either in-person GC (chronic disease cohort, n = 20; mean age 60 years) or telephone GC (community cohort, n = 31; mean age 46.8 years). Transcripts were analyzed using a Grounded Theory framework. Major themes that emerged from the interviews include 1) primary reasons for seeking GC were to clarify results, put results into perspective relative to other health-related concerns, and to receive personalized recommendations; 2) there is need for a more participant driven approach in terms of mode of GC communication (in-person, phone, video), and refining the counseling agenda pre-session; and 3) there was strong interest in the option of follow up GC. By clarifying counselees' expectations, views and desired outcomes, we have uncovered a need for a more participant-driven GC model when potentially actionable genomic results are received online.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Genetics in Keratoconus: where are we?

Keratoconus (KC) is a non-inflammatory thinning and protrusion of the cornea in which the cornea assumes a conical shape. Complex etiology of this condition at present remains an enigma. Although environmental factors have been involved in KC pathogenesis, strong underlining genetic susceptibility has been proven. The lack of consistent findings among early genetic studies suggested a heterogeneity and complex nature of the genetic contribution to the development of KC. Recently, genome-wide linkage studies (GWLS) and genome-wide association studies (GWAS) were undertaken. Next-generation sequencing (NGS)-based genomic screens are also currently being carried out. Application of these recently developed comprehensive genetic tools led to a much greater success and increased reproducibility of genetic findings in KC. Involvement of the LOX gene identified through GWLS has been confirmed in multiple cohorts of KC patients around the world. KC susceptibility region located at the 2q21.3 chromosomal region near the RAB3GAP1 gene identified through GWAS was independently replicated. Rare variants in the ZNF469 gene (mutated in corneal dystrophy Brittle Cornea Syndrome) and in the TGFBI gene (mutated in multiple corneal epithelial–stromal TGFBI dystrophies) have been repeatedly identified in familial and sporadic KC patients of different ethnicities. Additional comprehensive strategies using quantitative endophenotypes have been successfully employed to bring further understanding to the genetics of KC. Additional genetic determinants including the COL5A1 gene have been identified in the GWAS of KC-related trait central corneal thickness. These recent discoveries confirmed the importance of the endophenotype approach for studying complex genetic diseases such as KC and showed that different connective tissue disorders may have the same genetic determinants.

The nature of genetic susceptibility to multiple sclerosis: constraining the possibilities

BACKGROUND

Epidemiological observations regarding certain population-wide parameters (e.g., disease-prevalence, recurrence-risk in relatives, gender predilections, and the distribution of common genetic-variants) place important constraints on the possibilities for the genetic-basis underlying susceptibility to multiple sclerosis (MS).

METHODS

Using very broad range-estimates for the different population-wide epidemiological parameters, a mathematical model can help elucidate the nature and the magnitude of these constraints.

RESULTS

For MS no more than 8.5 % of the population can possibly be in the "genetically-susceptible" subset (defined as having a life-time MS-probability at least as high as the overall population average). Indeed, the expected MS-probability for this subset is more than 12 times that for every other person of the population who is not in this subset. Moreover, provided that those genetically susceptible persons (genotypes), who carry the well-established MS susceptibility allele (DRB1*1501), are equally or more likely to get MS than those susceptible persons, who don't carry this allele, then at least 84 % of MS-cases must come from this "genetically susceptible" subset. Finally, because men, compared to women, are at least as likely (and possibly more likely) to be susceptible, it can be demonstrated that women are more responsive to the environmental factors that are involved in MS-pathogenesis (whatever these are) and, thus, susceptible women are more likely actually to develop MS than susceptible men. Finally, in contrast to genetic susceptibility, more than 70 % of men (and likely also women) must have an environmental experience (including all of the necessary factors), which is sufficient to produce MS in a susceptible individual.

CONCLUSIONS

As a result, because of these constraints, it is possible to distinguish two classes of persons, indicating either that MS can be caused by two fundamentally different pathophysiological mechanisms or that the large majority of the population is at no risk of the developing this disease regardless of their environmental experience. Moreover, although environmental-factors would play a critical role in both mechanisms (if both exist), there is no reason to expect that these factors are the same (or even similar) between the two.

Enacting the molecular imperative: How gene-environment interaction research links bodies and environments in the post-genomic age

Despite a proclaimed shift from 'nature versus nurture' to 'genes and environment' paradigms within biomedical and genomic science, capturing the environment and identifying gene-environment interactions (GEIs) has remained a challenge. What does 'the environment' mean in the post-genomic age? In this paper, we present qualitative data from a study of 33 principal investigators funded by the U.S. National Institutes of Health to conduct etiological research on three complex diseases (cancer, cardiovascular disease and diabetes). We examine their research practices and perspectives on the environment through the concept of molecularization: the social processes and transformations through which phenomena (diseases, identities, pollution, food, racial/ethnic classifications) are re-defined in terms of their molecular components and described in the language of molecular biology. We show how GEI researchers' expansive conceptualizations of the environment ultimately yield to the imperative to molecularize and personalize the environment. They seek to 'go into the body' and re-work the boundaries between bodies and environments. In the process, they create epistemic hinges to facilitate a turn from efforts to understand social and environmental exposures outside the body, to quantifying their effects inside the body. GEI researchers respond to these emergent imperatives with a mixture of excitement, ambivalence and frustration. We reflect on how GEI researchers struggle to make meaning of molecules in their work, and how they grapple with molecularization as a methodological and rhetorical imperative as well as a process transforming biomedical research practices.

Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine

Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.

"Touching Triton": Building Student Understanding of Complex Disease Risk

Life science classrooms often emphasize the exception to the rule when it comes to teaching genetics, focusing heavily on rare single-gene and Mendelian traits. By contrast, the vast majority of human traits and diseases are caused by more complicated interactions between genetic and environmental factors. Research indicates that students have a deterministic view of genetics, generalize Mendelian inheritance patterns to all traits, and have unrealistic expectations of genetic technologies. The challenge lies in how to help students analyze complex disease risk with a lack of curriculum materials. Providing open access to both content resources and an engaging storyline can be achieved using a "serious game" model. "Touching Triton" was developed as a serious game in which students are asked to analyze data from a medical record, family history, and genomic report in order to develop an overall lifetime risk estimate of six common, complex diseases. Evaluation of student performance shows significant learning gains in key content areas along with a high level of engagement.

Functional polymorphisms in Nrf2: implications for human disease

Nuclear factor (erythroid derived)-2 like 2 (NFE2L2), also known as nuclear factor erythroid 2 (NF-E2)-related factor 2 (Nrf2), is a ubiquitous transcription factor essential for protecting cells and tissues from oxidative stress-induced injury. Positional cloning and studies with Nrf2 knockout mice have identified important roles for this transcription factor in disease phenotypes for many organ systems. Studies have also characterized the means through which human Nrf2 is regulated and the mechanisms of interaction with antioxidant response elements (ARE) in promoters of effector genes. Moreover, single nucleotide polymorphisms (SNPs) in Nrf2 have been identified and evaluated for effects on gene expression and function, and translational investigations have sought to determine whether loss of function SNPs associate with disease progression. In this review, we present 1) an overview of the human Nrf2 gene and protein domain, 2) identification of genetic mutations in Nrf2 and associations of the mutations with multiple diseases, and 3) the role of somatic mutations in Nrf2 in diseases, primarily various cancers.

eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs

The causal and interplay mechanisms of Single Nucleotide Polymorphisms (SNPs) associated with complex diseases (complex disease SNPs) investigated in genome-wide association studies (GWAS) at the transcriptional level (mRNA) are poorly understood despite recent advancements such as discoveries reported in the Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTex). Protein interaction network analyses have successfully improved our understanding of both single gene diseases (Mendelian diseases) and complex diseases. Whether the mRNAs downstream of complex disease genes are central or peripheral in the genetic information flow relating DNA to mRNA remains unclear and may be disease-specific. Using expression Quantitative Trait Loci (eQTL) that provide DNA to mRNA associations and network centrality metrics, we hypothesize that we can unveil the systems properties of information flow between SNPs and the transcriptomes of complex diseases. We compare different conditions such as naïve SNP assignments and stringent linkage disequilibrium (LD) free assignments for transcripts to remove confounders from LD. Additionally, we compare the results from eQTL networks between lymphoblastoid cell lines and liver tissue. Empirical permutation resampling (p<0.001) and theoretic Mann-Whitney U test (p<10(-30)) statistics indicate that mRNAs corresponding to complex disease SNPs via eQTL associations are likely to be regulated by a larger number of SNPs than expected. We name this novel property mRNA hubness in eQTL networks, and further term mRNAs with high hubness as master integrators. mRNA master integrators receive and coordinate the perturbation signals from large numbers of polymorphisms and respond to the personal genetic architecture integratively. This genetic signal integration contrasts with the mechanism underlying some Mendelian diseases, where a genetic polymorphism affecting a single protein hub produces a divergent signal that affects a large number of downstream proteins. Indeed, we verify that this property is independent of the hubness in protein networks for which these mRNAs are transcribed. Our findings provide novel insights into the pleiotropy of mRNAs targeted by complex disease polymorphisms and the architecture of the information flow between the genetic polymorphisms and transcriptomes of complex diseases.

Puzzling role of genetic risk factors in human longevity: "risk alleles" as pro-longevity variants

Complex diseases are major contributors to human mortality in old age. Paradoxically, many genetic variants that have been associated with increased risks of such diseases are found in genomes of long-lived people, and do not seem to compromise longevity. Here we argue that trade-off-like and conditional effects of genes can play central role in this phenomenon and in determining longevity. Such effects may occur as result of: (i) antagonistic influence of gene on the development of different health disorders; (ii) change in the effect of gene on vulnerability to death with age (especially, from “bad” to “good”); (iii) gene–gene interaction; and (iv) gene–environment interaction, among other factors. A review of current knowledge provides many examples of genetic factors that may increase the risk of one disease but reduce chances of developing another serious health condition, or improve survival from it. Factors that may increase risk of a major disease but attenuate manifestation of physical senescence are also discussed. Overall, available evidence suggests that the influence of a genetic variant on longevity may be negative, neutral or positive, depending on a delicate balance of the detrimental and beneficial effects of such variant on multiple health and aging related traits. This balance may change with age, internal and external environments, and depend on genetic surrounding. We conclude that trade-off-like and conditional genetic effects are very common and may result in situations when a disease “risk allele” can also be a pro-longevity variant, depending on context. We emphasize importance of considering such effects in both aging research and disease prevention.

A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq

BACKGROUND

In complex diseases, alterations of multiple molecular and cellular components in response to perturbations are indicative of disease physiology. While expression level of genes from high-throughput analysis can vary among patients, the common path among disease progression suggests that the underlying cellular sub-processes involving associated genes follow similar fates. Motivated by the interconnected nature of sub-processes, we have developed an automated methodology that combines ideas from biological networks, statistical models, and game theory, to probe connected cellular processes. The core concept in our approach uses probability of change (POC) to indicate the probability that a gene's expression level has changed between two conditions. POC facilitates the definition of change at the neighborhood, pathway, and network levels and enables evaluation of the influence of diseases on the expression. The 'connected' disease-related genes (DRG) identified display coherent and concomitant differential expression levels along paths.

RESULTS

RNA-Seq and microarray breast cancer subtyping expression data sets were used to identify DRG between subtypes. A machine-learning algorithm was trained for subtype discrimination using the DRG, and the training yielded a set of biomarkers. The discriminative power of the biomarkers was tested using an unseen data set. Biomarkers identified overlaps with disease-specific identified genes, and we were able to classify disease subtypes with 100% and 80% agreement with PAM50, for microarray and RNA-Seq data set respectively.

CONCLUSIONS

We present an automated probabilistic approach that offers unbiased and reproducible results, thus complementing existing methods in DRG and biomarker discovery for complex diseases.

What is the Contribution of Genetics to Periodontal Risk?

This review addresses the multicausal etiology of periodontitis, in which genetic factors play a role. The various proposed causes for periodontitis always work simultaneously, but the relative contribution of each of these varies from case to case. We are still at an early stage to identify the genes involved, in comparison with other chronic diseases. To date, the genetic variations firmly and repeatedly associated with periodontitis in some populations are found within the following genes: ANRIL, COX2, IL1, IL10, DEFB1, whereas many other proposed periodontitis candidate genes have not been firmly proven or replicated.

Genomic imprinting: A missing piece of the Multiple Sclerosis puzzle?

Evidence for parent-of-origin effects in complex diseases such as Multiple Sclerosis (MS) strongly suggests a role for epigenetic mechanisms in their pathogenesis. In this review, we describe the importance of accounting for parent-of-origin when identifying new risk variants for complex diseases and discuss how genomic imprinting, one of the best-characterized epigenetic mechanisms causing parent-of-origin effects, may impact etiology of complex diseases. While the role of imprinted genes in growth and development is well established, the contribution and molecular mechanisms underlying the impact of genomic imprinting in immune functions and inflammatory diseases are still largely unknown. Here we discuss emerging roles of imprinted genes in the regulation of inflammatory responses with a particular focus on the Dlk1 cluster that has been implicated in etiology of experimental MS-like disease and Type 1 Diabetes. Moreover, we speculate on the potential wider impact of imprinting via the action of imprinted microRNAs, which are abundantly present in the Dlk1 locus and predicted to fine-tune important immune functions. Finally, we reflect on how unrelated imprinted genes or imprinted genes together with non-imprinted genes can interact in so-called imprinted gene networks (IGN) and suggest that IGNs could partly explain observed parent-of-origin effects in complex diseases. Unveiling the mechanisms of parent-of-origin effects is therefore likely to teach us not only about the etiology of complex diseases but also about the unknown roles of this fascinating phenomenon underlying uneven genetic contribution from our parents. This article is part of a Directed Issue entitled: Epigenetics dynamics in development and disease.

Is uncertainty in complex disease epidemiology resolvable?

The imposed limitations on what we can know about nature have been long recognized. Yet in the field of epidemiology a futile search for lifestyle-related risk factors for common chronic diseases continues unabated. This has led to the production of a growing body of evidence about potential lifestyle risk factors that tend to be marginal, contradictory, irreproducible, or hard to interpret. While epidemiologists are calling for a more refined methodology, I argue that our limitation in studying complex diseases is insurmountable. This is because the study of lifestyle-related small risks requires accurate measurement of multiple behaviors-exposures over a long period of time. It is also because in complex systems such as population's health, the effect of rich interactions between its parts cannot be predicted based on traditional causal models of epidemiology. Within complex systems, understanding the interactions between system components can be more important than the contribution of each to disease risk.

The variation game: Cracking complex genetic disorders with NGS and omics data

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

Genetic determinants of risk and progression in multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease that represents a primary cause of neurological disability in the young adult population. Converging evidence supports the importance of genetic determinants for MS etiology. However, with the exception of the major histocompatibility complex, their nature has been elusive for more than 20 years. In the last decade, the advent of large genome-wide association studies has significantly improved our understanding of the disease, leading to the golden era of MS genetic research. To date more than 110 genetic variants have been firmly associated to an increased risk of developing MS. A large part of these variants tag genes involved in the regulation of immune response and several of them are shared with other autoimmune diseases, suggesting a common etiological root for this class of disorders. Despite the impressive body of data obtained in the last years, we are still far from fully decoding MS genetic complexity. For example, we ignore how these genetic factors interact with each other and with the environment. Thus, the biggest challenge for the next era of MS research will consist in identifying and characterizing the molecular mechanisms and the cellular pathways in which these risk variants play a role.

Epistasis amongst PTPN2 and genes of the vitamin D pathway contributes to risk of juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is a leading cause of childhood-onset disability. Although epistasis (gene-gene interaction) is frequently cited as an important component of heritability in complex diseases such as JIA, there is little compelling evidence that demonstrates such interaction. PTPN2, a vitamin D responsive gene, is a confirmed susceptibility gene in JIA, and PTPN2 has been suggested to interact with vitamin D pathway genes in type 1 diabetes. We therefore, tested for evidence of epistasis amongst PTPN2 and the vitamin D pathway genes GC, VDR, CYP24A1, CYP2R1, and DHCR7 in two independent JIA case-control samples (discovery and replication). In the discovery sample (318 cases, 556 controls), we identified evidence in support of epistasis across six gene-gene combinations (e.g., GC rs1155563 and PTPN2 rs2542151, ORint=0.45, p=0.00085). Replication was obtained for three of these combinations. That is, for GC and PTPN2, CYP2R1 and VDR, and VDR and PTPN2, similar epistasis was observed using the same SNPs or correlated proxies in an independent JIA case-control sample (1008 cases, 9287 controls). Using SNP data imputed across a 4 MB region spanning each gene, we obtained highly significant evidence for epistasis amongst all 6 gene-gene combinations identified in the discovery sample (p-values ranging from 5.6×10(-9) to 7.5×10(-7)). This is the first report of epistasis in JIA risk. Epistasis amongst PTPN2 and vitamin D pathway genes was both demonstrated and replicated.

Pathway-based analysis tools for complex diseases: a review

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehensive understanding of the molecular mechanisms underlying complex diseases. Extensive studies utilizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods—the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available pathway-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are discussed. This review will provide a useful guide to dissect complex diseases.

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies

Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics.

Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions.

An omnibus permutation test on ensembles of two-locus analyses can detect pure epistasis and genetic heterogeneity in genome-wide association studies

This article presents the ability of an omnibus permutation test on ensembles of two-locus analyses (2LOmb) to detect pure epistasis in the presence of genetic heterogeneity. The performance of 2LOmb is evaluated in various simulation scenarios covering two independent causes of complex disease where each cause is governed by a purely epistatic interaction. Different scenarios are set up by varying the number of available single nucleotide polymorphisms (SNPs) in data, number of causative SNPs and ratio of case samples from two affected groups. The simulation results indicate that 2LOmb outperforms multifactor dimensionality reduction (MDR) and random forest (RF) techniques in terms of a low number of output SNPs and a high number of correctly-identified causative SNPs. Moreover, 2LOmb is capable of identifying the number of independent interactions in tractable computational time and can be used in genome-wide association studies. 2LOmb is subsequently applied to a type 1 diabetes mellitus (T1D) data set, which is collected from a UK population by the Wellcome Trust Case Control Consortium (WTCCC). After screening for SNPs that locate within or near genes and exhibit no marginal single-locus effects, the T1D data set is reduced to 95,991 SNPs from 12,146 genes. The 2LOmb search in the reduced T1D data set reveals that 12 SNPs, which can be divided into two independent sets, are associated with the disease. The first SNP set consists of three SNPs from MUC21 (mucin 21, cell surface associated), three SNPs from MUC22 (mucin 22), two SNPs from PSORS1C1 (psoriasis susceptibility 1 candidate 1) and one SNP from TCF19 (transcription factor 19). A four-locus interaction between these four genes is also detected. The second SNP set consists of three SNPs from ATAD1 (ATPase family, AAA domain containing 1). Overall, the findings indicate the detection of pure epistasis in the presence of genetic heterogeneity and provide an alternative explanation for the aetiology of T1D in the UK population.

Next generation sequencing in cardiovascular diseases

In the last few years, the advent of next generation sequencing (NGS) has revolutionized the approach to genetic studies, making whole-genome sequencing a possible way of obtaining global genomic information. NGS has very recently been shown to be successful in identifying novel causative mutations of rare or common Mendelian disorders. At the present time, it is expected that NGS will be increasingly important in the study of inherited and complex cardiovascular diseases (CVDs). However, the NGS approach to the genetics of CVDs represents a territory which has not been widely investigated. The identification of rare and frequent genetic variants can be very important in clinical practice to detect pathogenic mutations or to establish a profile of risk for the development of pathology. The purpose of this paper is to discuss the recent application of NGS in the study of several CVDs such as inherited cardiomyopathies, channelopathies, coronary artery disease and aortic aneurysm. We also discuss the future utility and challenges related to NGS in studying the genetic basis of CVDs in order to improve diagnosis, prevention, and treatment.

Genetic mechanisms underlying the pathogenesis of tropical calcific pancreatitis

Chronic pancreatitis is known to be a heterogeneous disease with varied etiologies. Tropical calcific pancreatitis (TCP) is a severe form of chronic pancreatitis unique to developing countries. With growing evidence of genetic factors contributing to the pathogenesis of TCP, this review is aimed at compiling the available information in this field. We also propose a two hit model to explain the sequence of events in the pathogenesis of TCP.