Mapping rare and common causal alleles for complex human diseases

Advances in genotyping and sequencing technologies have revolutionized the genetics of complex disease by locating rare and common variants that influence an individual's risk for diseases, such as diabetes, cancers, and psychiatric disorders. However, to capitalize on these data for prevention and therapies requires the identification of causal alleles and a mechanistic understanding for how these variants contribute to the disease. After discussing the strategies currently used to map variants for complex diseases, this Primer explores how variants may be prioritized for follow-up functional studies and the challenges and approaches for assessing the contributions of rare and common variants to disease phenotypes.     

Population genetics models of common diseases

The number and frequency of susceptibility alleles for common diseases are important factors to consider in the efficient design of disease association studies. These quantities are the results of the joint effects of mutation, genetic drift and selection. Hence, population genetics models, informed by empirical knowledge about patterns of disease variation, can be used to make predictions about the allelic architecture of common disease susceptibility and to gain an overall understanding about the evolutionary origins of such diseases. Equilibrium models and empirical studies suggest a role for both rare and common variants. In addition, increasing evidence points to changes in selective pressures on susceptibility genes for common diseases; these findings are likely to form the basis for further modeling studies.     

Epistasis and Its Implications for Personal Genetics

The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.     

Psoriasis patients are enriched for genetic variants that protect against HIV-1 disease

An important paradigm in evolutionary genetics is that of a delicate balance between genetic variants that favorably boost host control of infection but which may unfavorably increase susceptibility to autoimmune disease. Here, we investigated whether patients with psoriasis, a common immune-mediated disease of the skin, are enriched for genetic variants that limit the ability of HIV-1 virus to replicate after infection. We analyzed the HLA class I and class II alleles of 1,727 Caucasian psoriasis cases and 3,581 controls and found that psoriasis patients are significantly more likely than controls to have gene variants that are protective against HIV-1 disease. This includes several HLA class I alleles associated with HIV-1 control; amino acid residues at HLA-B positions 67, 70, and 97 that mediate HIV-1 peptide binding; and the deletion polymorphism rs67384697 associated with high surface expression of HLA-C. We also found that the compound genotype KIR3DS1 plus HLA-B Bw4-80I, which respectively encode a natural killer cell activating receptor and its putative ligand, significantly increased psoriasis susceptibility. This compound genotype has also been associated with delay of progression to AIDS. Together, our results suggest that genetic variants that contribute to anti-viral immunity may predispose to the development of psoriasis.     

In search of genes involved in neurodegenerative disorders

Dissecting the genetics of Alzheimer's disease (AD) and Parkinson's disease (PD) has contributed significantly to our understanding of the pathogenesis of neurodegeneration in these two complex disorders. For AD, three highly penetrant genes (amyloid precursor protein (APP, PSEN1 and PSEN2) and one susceptibility gene (APOE) have been identified. For PD, seven genes (SNCA, Parkin, UCHL1, NR4A2, DJ1, PINK1 and LRRK2) have been found. These genes explain only a small proportion of AD and PD patients and are mostly associated with an early onset presentation of the disease. APOE remains the only common gene, which increases the risk of both rare early and late onset AD. The ongoing challenge is to unravel the genetics of the most frequent forms of these complex disorders. In the present paper, we briefly review the state of the art in the genetics of AD and PD. We also discuss the prospects of finding new genes associated with common forms of these diseases in light of two hypotheses concerning the genetic variation of complex diseases: common disease/common variants and common disease/rare variants.     

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.     

Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic Tests

Summary Current ongoing genome‐wide association (GWA) studies represent a powerful approach to uncover common unknown genetic variants causing common complex diseases. The discovery of these genetic variants offers an important opportunity for early disease prediction, prevention, and individualized treatment. We describe here a method of combining multiple genetic variants for early disease prediction, based on the optimality theory of the likelihood ratio (LR). Such theory simply shows that the receiver operating characteristic (ROC) curve based on the LR has maximum performance at each cutoff point and that the area under the ROC curve so obtained is highest among that of all approaches. Through simulations and a real data application, we compared it with the commonly used logistic regression and classification tree approaches. The three approaches show similar performance if we know the underlying disease model. However, for most common diseases we have little prior knowledge of the disease model and in this situation the new method has an advantage over logistic regression and classification tree approaches. We applied the new method to the type 1 diabetes GWA data from the Wellcome Trust Case Control Consortium. Based on five single nucleotide polymorphisms, the test reaches medium level classification accuracy. With more genetic findings to be discovered in the future, we believe a predictive genetic test for type 1 diabetes can be successfully constructed and eventually implemented for clinical use.     

Leprosy as a genetic model for susceptibility to common infectious diseases

Leprosy (Hansen’s disease) is a human infectious disease that can be effectively treated with long-term administration of multi-drug therapy. In 2006, over 250,000 new cases were reported to the World Health Organization. In the nineteenth century, disagreement among leprologists regarding the hereditary or infectious nature of leprosy was resolved with the identification of the etiological agent, Mycobacterium leprae. However, epidemiological studies maintain the importance of host genetics in leprosy susceptibility. A model free genome-wide linkage scan in multi-case families from Vietnam led to the positional cloning of global genetic risk factors in the PARK2/PACRG and LTA genes. The process of identifying the susceptibility variants provided invaluable insight into the replication of genetic effects, particularly the importance of considering population-specific linkage-disequilibrium structure. As such, these studies serve to improve our understanding of leprosy pathogenesis by implicating novel biological pathways while simultaneously providing a genetic model for common infectious diseases.     

Identifying host targets for drug development with knowledge from genome-wide studies: lessons from HIV-AIDS

Advances in human genomics are now being effectively applied to the search for host factors underlying susceptibility to common diseases. From the steady stream of studies showing association of host genetic factors with viral diseases, it has become clear that host factors contribute substantially to the variability of viral infections in humans. Candidate gene studies that seek to show associations between single-nucleotide polymorphisms (SNPs) with a disease outcome have predominated, but whole-genome association studies (GWAS) have recently appeared. A major goal of these studies is to understand how human genetic variation contributes to individual differences in susceptibility and to exploit this knowledge for targeted drug development.     

From genomic advances to public health benefits: the unbearable lightness of being stuck

Genetic determinants of common human diseases are still poorly understood. Due to large investments, many small successes have been made and the research field is rapidly expanding. However, genetic susceptibility variants showing repeatable associations with common diseases are usually of small effect. They are therefore unlikely to individually explain substantial share of disease burden in any community or provide new insights into disease pathogenesis that could lead to development of new drugs effective in considerable portion of the disease cases in a population. Genetic architecture of common diseases is beginning to reveal an incredible diversity of potential genetic causes that act through somewhat limited number of mechanisms with important contribution of environmental interactions. In light of these findings, we present current understanding of genetic architecture of a spectrum of human diseases. We address the encountered problems in susceptibility gene identification, review the success of leading gene identification strategies and discuss current prospects for translating genomic advances into measurable public health benefits.     

Family study designs in the age of genome-wide association studies: experience from the Framingham Heart Study

PURPOSE OF REVIEW: The past year has seen the publication of many genome-wide association studies, most of which are case-control studies. These publications are at the forefront of current research into the examination of genetic effects for numerous diseases, including diabetes, heart disease and cancer. Over the past 25 years the tour de force of genetics research has been in family studies, using segregation, linkage and association analyses. Are these approaches now passé? Here we discuss the role of family studies in modern genetics research, using results from the Framingham Heart Study as examples. RECENT FINDINGS: Family studies permit both linkage and association analyses. Importantly, family-based association tests that consider transmission of genetic variants within a family provide important information on the genetic etiology of disease traits and avoid the potential of false-positive findings due to population substructure. SUMMARY: Family-based study designs continue to contribute much to the modern era of genome-wide association studies.     

Genetic Approaches to the Study of Gene Variants and Their Impact on the Pathophysiology of Type 2 Diabetes

Diabetes mellitus is an incurable progressive disease, characterized by elevated blood glucose levels, which lead to the development of micro- and macrovascular complications. Although the etiopathology of the disease remains unclear, it seems to be multifactorial, with an important interaction between genetics and environmental causes. Currently, the genetics of type 2 diabetes (T2D) is poorly understood. The recent advance of the genetic technologies and with a better understanding of genetics, more than 120 distinct genetic loci, with more than 150 variants, have been identified that may be involved in the pathogenesis of T2D. However, as these variants can account for only approximately 20% of the heritability of T2D, there is an obvious need for additional approaches to identify susceptibility genes or genetic mechanisms involved in the development of this disease. There is a growing number of genes found to be related to T2D; however, their individual impact on the pathogenesis of the disease appears to be low, while silencing of protective genes may also contribute to the development of this disease. The present review attempts to summarize our current knowledge in the field of genetics of T2D, highlighting the possible practical applications for each approach.     

The genetics of lupus: a functional perspective

Systemic lupus erythematosus (SLE) is an autoimmune disease with a strong genetic component and is characterized by chronic inflammation and the production of anti-nuclear auto-antibodies. In the era of genome-wide association studies (GWASs), elucidating the genetic factors present in SLE has been a very successful endeavor; 28 confirmed disease susceptibility loci have been mapped. In this review, we summarize the current understanding of the genetics of lupus and focus on the strongest associated risk loci found to date (P <1.0 × 10−8). Although these loci account for less than 10% of the genetic heritability and therefore do not account for the bulk of the disease heritability, they do implicate important pathways, which contribute to SLE pathogenesis. Consequently, the main focus of the review is to outline the genetic variants in the known associated loci and then to explore the potential functional consequences of the associated variants. We also highlight the genetic overlap of these loci with other autoimmune diseases, which indicates common pathogenic mechanisms. The importance of developing functional assays will be discussed and each of them will be instrumental in furthering our understanding of these associated variants and loci. Finally, we indicate that performing a larger SLE GWAS and applying a more targeted set of methods, such as the ImmunoChip and next generation sequencing methodology, are important for identifying additional loci and enhancing our understanding of the pathogenesis of SLE.     

The value of animal models in predicting genetic susceptibility to complex diseases such as rheumatoid arthritis

For a long time, genetic studies of complex diseases were most successfully conducted in animal models. However, the field of genetics is now rapidly evolving, and human genetics has also started to produce strong candidate genes for complex diseases. This raises the question of how to continue gene-finding attempts in animals and how to use animal models to enhance our understanding of gene function. In this review we summarize the uses and advantages of animal studies in identification of disease susceptibility genes, focusing on rheumatoid arthritis. We are convinced that animal genetics will remain a valuable tool for the identification and investigation of pathways that lead to disease, well into the future.     

Pathway analysis of seven common diseases assessed by genome-wide association

Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identifying multiple low-penetrance variations (or "polygenes") contributing to disease susceptibility will be difficult. We present a pathway analysis approach to characterizing the likely polygenic basis of seven common diseases using the Wellcome Trust Case Control Consortium (WTCCC) GWAS results. We identify numerous pathways implicated in disease predisposition that would have not been revealed using standard single-locus GWAS statistical analysis criteria. Many of these pathways have long been assumed to contain polymorphic genes that lead to disease predisposition. Additionally, we analyze the genetic relationships between the seven diseases, and based upon similarities with respect to the associated genes and pathways affected in each, propose a new way of categorizing the diseases.     

iPSC-derived organ-on-a-chip models for personalized human genetics and pharmacogenomics studies

Genome-wide association studies (GWAS) have now correlated hundreds of genetic variants with complex genetic diseases and drug efficacy. Functional characterization of these factors remains challenging, particularly because of the lack of human model systems. Molecular and nanotechnological advances, in particular the ability to generate patient-specific PSC lines, differentiate them into diverse cell types, and seed and combine them on microfluidic chips, have led to the establishment of organ-on-a-chip (OoC) platforms that recapitulate organ biology. OoC technology thus provides unique personalized platforms for studying the effects of host genetics and environmental factors on organ physiology. In this review we describe the technology and provide examples of how OoCs may be used for disease modeling and pharmacogenetic research.     

The power comparison of the haplotype-based collapsing tests and the variant-based collapsing tests for detecting rare variants in pedigrees

Background

Both common and rare genetic variants have been shown to contribute to the etiology of complex diseases. Recent genome-wide association studies (GWAS) have successfully investigated how common variants contribute to the genetic factors associated with common human diseases. However, understanding the impact of rare variants, which are abundant in the human population (one in every 17 bases), remains challenging. A number of statistical tests have been developed to analyze collapsed rare variants identified by association tests. Here, we propose a haplotype-based approach. This work inspired by an existing statistical framework of the pedigree disequilibrium test (PDT), which uses genetic data to assess the effects of variants in general pedigrees. We aim to compare the performance between the haplotype-based approach and the rare variant-based approach for detecting rare causal variants in pedigrees.

Results

Extensive simulations in the sequencing setting were carried out to evaluate and compare the haplotype-based approach with the rare variant methods that drew on a more conventional collapsing strategy. As assessed through a variety of scenarios, the haplotype-based pedigree tests had enhanced statistical power compared with the rare variants based pedigree tests when the disease of interest was mainly caused by rare haplotypes (with multiple rare alleles), and vice versa when disease was caused by rare variants acting independently. For most of other situations when disease was caused both by haplotypes with multiple rare alleles and by rare variants with similar effects, these two approaches provided similar power in testing for association.

Conclusions

The haplotype-based approach was designed to assess the role of rare and potentially causal haplotypes. The proposed rare variants-based pedigree tests were designed to assess the role of rare and potentially causal variants. This study clearly documented the situations under which either method performs better than the other. All tests have been implemented in a software, which was submitted to the Comprehensive R Archive Network (CRAN) for general use as a computer program named rvHPDT.     

Proteomics in immunity and herpes simplex encephalitis

The genetic theory of infectious diseases has proposed that susceptibility to life-threatening infectious diseases in childhood, occurring in the course of primary infection, results mostly from individually rare but collectively diverse single-gene variants. Recent evidence of an ever-expanding spectrum of genes involved in susceptibility to infectious disease indicates that the paradigm has important implications for diagnosis and treatment. One such pathology is childhood herpes simplex encephalitis, which shows a pattern of rare but diverse disease-disposing genetic variants. The present report shows how proteomics can help to understand susceptibility to childhood herpes simplex encephalitis and other viral infections, suggests that proteomics may have a particularly important role to play, emphasizes that variation over the population is a critical issue for proteomics and notes some new challenges for proteomics and related bioinformatics tools in the context of rare but diverse genetic defects.     

Detection and characterization of interleukin-6 gene variants in Canis familiaris: association studies with periodontal disease

Periodontal disease (PD) is the most common inflammatory disease of the oral cavity of domestic carnivores. In Human Medicine molecular genetics research showed that several genes play a role in the predisposition and progression of this complex disease, primarily through the regulation of inflammatory mediators, but the exactly mechanisms are poorly understood. This study aims to contribute to the characterization of the genetic basis of PD in the dog, a classically accepted model in Periodontology. We searched for genetic variations in the interleukin-6 (IL6) gene, in order to verify its association with PD in a case-control study including 25 dogs in the PD case group and 45 dogs in the control group. We indentified and characterized three new genetic variations in IL6 gene. No statistically significant differences were detected between the control and PD cases groups. Our results do not support an evidence for a major role contribution of these variants in the susceptibility to PD in the analyzed population. Nevertheless, the sequence variant I/5_g.105 G > A leads to an amino acid change (arginine to glutamine) and was predicted to be possibly damaging to the IL6 protein. A larger cohort and functional studies would be of extreme importance in a near future to understand the possible role of IL6 variants in this disease.     

Common Variants for Heart Failure

Heart failure (HF) is a common disease with high morbidity and mortality; however, none of the drugs available are now entirely optimal for the treatment of HF. In addition to various clinical diseases and environment influences, genetic factors also contribute to the development and progression of HF. Identifying the common variants for HF by genome-wide association studies will facilitate the understanding of pathophysiological mechanisms underlying HF. This review summarizes the recently identified common variants for HF risk and outcome and discusses their implications for the clinic therapy.