The mystery of missing heritability: Genetic interactions create phantom heritability

A sample selection strategy for next-generation sequencing

Next-generation sequencing technology provides us with vast amounts of sequence data. It is efficient and cheaper than previous sequencing technologies, but deep resequencing of entire samples is still expensive. Therefore, sensible strategies for choosing subsets of samples to sequence are required. Here we describe an algorithm for selection of a sub-sample of an existing sample if one has either of two possible goals in mind: maximizing the number of new polymorphic sites that are detected, or improving the efficiency with which the remaining unsequenced individuals can have their types imputed at newly discovered polymorphisms. We then describe a variation on our algorithm that is more focused on detecting rarer variants. We demonstrate the performance of our algorithm using simulated data and data from the 1000 Genomes Project.

Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder

Most pathway and gene-set enrichment methods prioritize genes by their main effect and do not account for variation due to interactions in the pathway. A portion of the presumed missing heritability in genome-wide association studies (GWAS) may be accounted for through gene-gene interactions and additive genetic variability. In this study, we prioritize genes for pathway enrichment in GWAS of bipolar disorder (BD) by aggregating gene-gene interaction information with main effect associations through a machine learning (evaporative cooling) feature selection and epistasis network centrality analysis. We validate this approach in a two-stage (discovery/replication) pathway analysis of GWAS of BD. The discovery cohort comes from the Wellcome Trust Case Control Consortium (WTCCC) GWAS of BD, and the replication cohort comes from the National Institute of Mental Health (NIMH) GWAS of BD in European Ancestry individuals. Epistasis network centrality yields replicated enrichment of Cadherin signaling pathway, whose genes have been hypothesized to have an important role in BD pathophysiology but have not demonstrated enrichment in previous analysis. Other enriched pathways include Wnt signaling, circadian rhythm pathway, axon guidance and neuroactive ligand-receptor interaction. In addition to pathway enrichment, the collective network approach elevates the importance of ANK3, DGKH and ODZ4 for BD susceptibility in the WTCCC GWAS, despite their weak single-locus effect in the data. These results provide evidence that numerous small interactions among common alleles may contribute to the diathesis for BD and demonstrate the importance of including information from the network of gene-gene interactions as well as main effects when prioritizing genes for pathway analysis.

Perspectives on systems biology applications in diabetic kidney disease

Diabetic kidney disease (DKD) is a microvascular complication of type 1 and 2 diabetes with a devastating impact on individuals with the disease, their families, and society as a whole. DKD is the single most frequent cause of incident chronic kidney disease cases and accounts for over 40% of the population with end-stage renal disease. Contributing factors for the high prevalence are the increase in obesity and subsequent diabetes combined with an improved long-term survival with diabetes. Environment and genetic variations contribute to DKD susceptibility and progressive loss of kidney function. How the molecular mechanisms of genetic and environmental exposures interact during DKD initiation and progression is the focus of ongoing research efforts. The development of standardized, unbiased high-throughput profiling technologies of human DKD samples opens new avenues in capturing the multiple layers of DKD pathobiology. These techniques routinely interrogate analytes on a genome-wide scale generating comprehensive DKD-associated fingerprints. Linking the molecular fingerprints to deep clinical phenotypes may ultimately elucidate the intricate molecular interplay in a disease stage and subtype-specific manner. This insight will form the basis for accurate prognosis and facilitate targeted therapeutic interventions. In this review, we present ongoing efforts from large-scale data integration translating "-omics" research efforts into improved and individualized health care in DKD.

Network medicine approaches to the genetics of complex diseases

Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.

Osteoporosis genetics: year 2011 in review

Increased rates of osteoporotic fractures represent a worldwide phenomenon, which result from a progressing aging in the population around the world and creating socioeconomic problems. This review will focus mostly on human genetic studies identifying genomic regions, genes and mutations associated with osteoporosis (bone mineral density (BMD) and bone loss) and related fractures, which were published during 2011. Although multiple genome-wide association studies (GWAS) were performed to date, the genetic cause of osteoporosis and fractures has not yet been found, and only a small fraction of high heritability of bone mass was successfully explained. GWAS is a successful tool to initially define and prioritize specific chromosomal regions showing associations with the desired traits or diseases. Following the initial discovery and replication, targeted sequencing is needed in order to detect those rare variants which GWAS does not reveal by design. Recent GWAS findings for BMD included WNT16 and MEF2C. The role of bone morphogenetic proteins in fracture healing has been explored by several groups, and new single-nucleotide polymorphisms present in genes such as NOGGIN and SMAD6 were found to be associated with a greater risk of fracture non-union. Finding new candidate genes, and mutations associated with BMD and fractures, also provided new biological connections. Thus, candidates for molecular link between bone metabolism and lactation (for example, RAP1A gene), as well as possible pleiotropic effects for bone and muscle (ACTN3 gene) were suggested. The focus of contemporary studies seems to move toward whole-genome sequencing, epigenetic and functional genomics strategies to find causal variants for osteoporosis.

Africa: continent of genome contrasts with implications for biomedical research and health

The genomic architecture of African populations is poorly understood and there is considerable variation between ethno-linguistic groups. Genome-wide approaches have been extensively applied to search for genetic associations to complex traits in Europeans, but rarely in Africans. This is largely attributed to lower levels of funding, poor infrastructure and public health systems, and to the small pool of trained scientists. High levels of genetic variation and underlying population structure in Africans present significant challenges, but lower levels of linkage disequilibrium provide an opportunity for more effective localisation of causal variants. High throughput technologies, including dense genotyping arrays, genome sequencing and epigenome studies, together with plummeting costs, are making research more affordable, even for African scientists. Understanding the interactions between genome structure and environmental influences is essential to interpreting their contributions to the increase in infectious diseases and non-communicable diseases, exacerbated by adverse environments and lifestyle choices. The unique genome dynamics in African populations have an important role to play in understanding human health and susceptibility to disease.

The continuing value of twin studies in the omics era

The classical twin study has been a powerful heuristic in biomedical, psychiatric and behavioural research for decades. Twin registries worldwide have collected biological material and longitudinal phenotypic data on tens of thousands of twins, providing a valuable resource for studying complex phenotypes and their underlying biology. In this Review, we consider the continuing value of twin studies in the current era of molecular genetic studies. We conclude that classical twin methods combined with novel technologies represent a powerful approach towards identifying and understanding the molecular pathways that underlie complex traits.

Heritability in the genome-wide association era

Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated from classical heritability methods has been termed the "missing heritability problem". In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phenotypes.

Integration of biological networks and pathways with genetic association studies

Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.

Ultrafast genome-wide scan for SNP-SNP interactions in common complex disease

Long-range gene–gene interactions are biologically compelling models for disease genetics and can provide insights on relevant mechanisms and pathways. Despite considerable effort, rigorous interaction mapping in humans has remained prohibitively difficult due to computational and statistical limitations. We introduce a novel algorithmic approach to find long-range interactions in common diseases using a standard two-locus test that contrasts the linkage disequilibrium between SNPs in cases and controls. Our ultrafast method overcomes the computational burden of a genome × genome scan by using a novel randomization technique that requires 10× to 100× fewer tests than a brute-force approach. By sampling small groups of cases and highlighting combinations of alleles carried by all individuals in the group, this algorithm drastically trims the universe of combinations while simultaneously guaranteeing that all statistically significant pairs are reported. Our implementation can comprehensively scan large data sets (2K cases, 3K controls, 500K SNPs) to find all candidate pairwise interactions (LD-contrast ) in a few hours—a task that typically took days or weeks to complete by methods running on equivalent desktop computers. We applied our method to the Wellcome Trust bipolar disorder data and found a significant interaction between SNPs located within genes encoding two calcium channel subunits: RYR2 on chr1q43 and CACNA2D4 on chr12p13 (LD-contrast test, ). We replicated this pattern of interchromosomal LD between the genes in a separate bipolar data set from the GAIN project, demonstrating an example of gene–gene interaction that plays a role in the largely uncharted genetic landscape of bipolar disorder.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

The aggregate effect of dopamine genes on dependence symptoms among cocaine users: cross-validation of a candidate system scoring approach

Genome-wide studies of psychiatric conditions frequently fail to explain a substantial proportion of variance, and replication of individual SNP effects is rare. We demonstrate a selective scoring approach, in which variants from several genes known to directly affect the dopamine system are considered concurrently to explain individual differences in cocaine dependence symptoms. 273 SNPs from eight dopamine-related genes were tested for association with cocaine dependence symptoms in an initial training sample. We identified a four-SNP score that accounted for 0.55% of the variance in a separate testing sample (p = 0.037). These findings suggest that (1) limiting investigated SNPs to those located in genes of theoretical importance improves the chances of identifying replicable effects by reducing statistical penalties for multiple testing, and (2) considering top-associated SNPs in the aggregate can reveal replicable effects that are too small to be identified at the level of individual SNPs.

The juxtaparanodal proteins CNTNAP2 and TAG1 regulate diet-induced obesity

Despite considerable effort, the identification of genes that regulate complex multigenic traits such as obesity has proven difficult with conventional methodologies. The use of a chromosome substitution strain-based mapping strategy based on deep congenic analysis overcame many of the difficulties associated with gene discovery and led to the finding that the juxtaparanodal proteins CNTNAP2 and TAG1 regulate diet-induced obesity. The effects of a mild Cntnap2 mutation on body weight were highly dependent on genetic background, as both obesity-promoting and obesity-resistant effects of Cntnap2 were observed on different genetic backgrounds. The more severe effect of complete TAG1 deficiency, by decreasing food intake, completely prevented the weight gain normally associated with high-fat-diet feeding. Together, these studies implicate two novel proteins in the regulation of diet-induced obesity. Moreover, as juxtaparanodal proteins have previously been implicated in various neurological disorders, our results suggest a potential genetic and molecular link between obesity and diseases such as autism and epilepsy.

Understanding cardiovascular disease: a journey through the genome (and what we found there)

Cardiovascular disease (CVD) is a major cause of mortality and hospitalization worldwide. Several risk factors have been identified that are strongly associated with the development of CVD. However, these explain only a fraction of cases, and the focus of research into the causes underlying the unexplained risk has shifted first to genetics and more recently to genomics. A genetic contribution to CVD has long been recognized; however, with the exception of certain conditions that show Mendelian inheritance, it has proved more challenging than anticipated to identify the precise genomic components responsible for the development of CVD. Genome-wide association studies (GWAS) have provided information about specific genetic variations associated with disease, but these are only now beginning to reveal the underlying molecular mechanisms. To fully understand the biological implications of these associations, we need to relate them to the exquisite, multilayered regulation of protein expression, which includes chromatin remodeling, regulatory elements, microRNAs and alternative splicing. Understanding how the information contained in the DNA relates to the operation of these regulatory layers will allow us not only to better predict the development of CVD but also to develop more effective therapies.

Effect of genetic predisposition on blood lipid traits using cumulative risk assessment in the korean population

Dyslipidemia, mainly characterized by high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C) levels, is an important etiological factor in the development of cardiovascular disease (CVD). Considering the relationship between childhood obesity and CVD risk, it would be worthwhile to evaluate whether previously identified lipid-related variants in adult subjects are associated with lipid variations in a childhood obesity study (n = 482). In an association analysis for 16 genome-wide association study (GWAS)-based candidate loci, we confirmed significant associations of a genetic predisposition to lipoprotein concentrations in a childhood obesity study. Having two loci (rs10503669 at LPL and rs16940212 at LIPC) that showed the strongest association with blood levels of TG and HDL-C, we calculated a genetic risk score (GRS), representing the sum of the risk alleles. It has been observed that increasing GRS is significantly associated with decreased HDL-C (effect size, -1.13 ± 0.07) compared to single nucleotide polymorphism combinations without two risk variants. In addition, a positive correlation was observed between allelic dosage score and risk allele (rs10503669 at LPL) on high TG levels (effect size, 10.89 ± 0.84). These two loci yielded consistent associations in our previous meta-analysis. Taken together, our findings demonstrate that the genetic architecture of circulating lipid levels (TG and HDL-C) overlap to a large extent in childhood as well as in adulthood. Post-GWAS functional characterization of these variants is further required to elucidate their pathophysiological roles and biological mechanisms.

Genetic susceptibility to lupus: the biological basis of genetic risk found in B cell signaling pathways

Over 50 genetic variants have been statistically associated with the development of SLE (or lupus). Each genetic association is a key component of a pathway to lupus pathogenesis, the majority of which requires further mechanistic studies to understand the functional changes to cellular physiology. Whereas their use in clinical practice has yet to be established, these genes guide efforts to develop more specific therapeutic approaches. The BCR signaling pathways are rich in lupus susceptibility genes and may well provide novel opportunities for the understanding and clinical treatment of this complex disease.

High-resolution association mapping of atherosclerosis loci in mice

OBJECTIVE

The purpose of this study was to fine map previously identified quantitative trait loci affecting atherosclerosis in mice using association analysis.

METHODS AND RESULTS

We recently showed that high-resolution association analysis using common inbred strains of mice is feasible if corrected for population structure. To use this approach for atherosclerosis, which requires a sensitizing mutation, we bred human apolipoprotein B-100 transgenic mice with 22 different inbred strains to produce F1 heterozygotes. Mice carrying the dominant transgene were tested for association with high-density single nucleotide polymorphism maps. Here, we focus on high-resolution mapping of the previously described atherosclerosis 30 locus on chromosome 1. Compared with the previous linkage analysis, association improved the resolution of the atherosclerosis 30 locus by more than an order of magnitude. Using expression quantitative trait locus analysis, we identified one of the genes in the region, desmin, as a strong candidate.

CONCLUSIONS

Our high-resolution mapping approach accurately identifies and fine maps known atherosclerosis quantitative trait loci. These results suggest that high-resolution genome-wide association analysis for atherosclerosis is feasible in mice.

BiForce Toolbox: powerful high-throughput computational analysis of gene-gene interactions in genome-wide association studies

Genome-wide association studies (GWAS) have discovered many loci associated with common disease and quantitative traits. However, most GWAS have not studied the gene–gene interactions (epistasis) that could be important in complex trait genetics. A major challenge in analysing epistasis in GWAS is the enormous computational demands of analysing billions of SNP combinations. Several methods have been developed recently to address this, some using computers equipped with particular graphical processing units, most restricted to binary disease traits and all poorly suited to general usage on the most widely used operating systems. We have developed the BiForce Toolbox to address the demand for high-throughput analysis of pairwise epistasis in GWAS of quantitative and disease traits across all commonly used computer systems. BiForce Toolbox is a stand-alone Java program that integrates bitwise computing with multithreaded parallelization and thus allows rapid full pairwise genome scans via a graphical user interface or the command line. Furthermore, BiForce Toolbox incorporates additional tests of interactions involving SNPs with significant marginal effects, potentially increasing the power of detection of epistasis. BiForce Toolbox is easy to use and has been applied in multiple studies of epistasis in large GWAS data sets, identifying interesting interaction signals and pathways.

Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations

Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene–gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein–protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected P_c = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene–gene interaction in the European American cohorts from the Framingham Heart Study (P_c = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; P_c = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (P_c = 0.004) and in the Hispanic American sample from MESA (P_c = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene–gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations.

Genome-wide association studies (GWAS) have identified many loci associated with complex human traits or diseases. However, the fraction of heritable variation explained by these loci is often relatively low. Gene–gene interactions might play a significant role in complex traits or diseases and are one of the many possible factors contributing to the missing heritability. However, to date only a few interactions have been found and validated in GWAS due to the limited power caused by the need for multiple-testing correction for the very large number of tests conducted. Here, we used three types of prior knowledge, known GWAS hits, protein–protein interactions, and pathway information, to guide our search for gene–gene interactions affecting four lipid levels. We identified an interaction between HMGCR and a locus near LIPC in their effect on high-density lipoprotein cholesterol (HDL-C) and another pair of loci that interact in their effect on low-density lipoprotein cholesterol (LDL-C). We validated the interaction on HDL-C in a number of independent multiple-ethnic populations, while the interaction underlying LDL-C did not validate. The prior knowledge-driven searching approach and a locus-based validation procedure show the potential for dissecting and validating gene–gene interactions in current and future GWAS.

Response to Comments on “The Predictive Capacity of Personal Genome Sequencing”

The power of personal genome sequencing to elucidate disease pathogenesis will ensure that it will become an essential tool in medical practice, but recognizing its limitations for improving public health will minimize false expectations and foster the most fruitful investigations.

High-throughput analysis of epistasis in genome-wide association studies with BiForce

MOTIVATION

Gene-gene interactions (epistasis) are thought to be important in shaping complex traits, but they have been under-explored in genome-wide association studies (GWAS) due to the computational challenge of enumerating billions of single nucleotide polymorphism (SNP) combinations. Fast screening tools are needed to make epistasis analysis routinely available in GWAS.

RESULTS

We present BiForce to support high-throughput analysis of epistasis in GWAS for either quantitative or binary disease (case-control) traits. BiForce achieves great computational efficiency by using memory efficient data structures, Boolean bitwise operations and multithreaded parallelization. It performs a full pair-wise genome scan to detect interactions involving SNPs with or without significant marginal effects using appropriate Bonferroni-corrected significance thresholds. We show that BiForce is more powerful and significantly faster than published tools for both binary and quantitative traits in a series of performance tests on simulated and real datasets. We demonstrate BiForce in analysing eight metabolic traits in a GWAS cohort (323 697 SNPs, >4500 individuals) and two disease traits in another (>340 000 SNPs, >1750 cases and 1500 controls) on a 32-node computing cluster. BiForce completed analyses of the eight metabolic traits within 1 day, identified nine epistatic pairs of SNPs in five metabolic traits and 18 SNP pairs in two disease traits. BiForce can make the analysis of epistasis a routine exercise in GWAS and thus improve our understanding of the role of epistasis in the genetic regulation of complex traits.

AVAILABILITY AND IMPLEMENTATION

The software is free and can be downloaded from http://bioinfo.utu.fi/BiForce/.

CONTACT

wenhua.wei@igmm.ed.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

From genome-wide association studies to disease mechanisms: celiac disease as a model for autoimmune diseases

Celiac disease is characterized by a chronic inflammatory reaction in the intestine and is triggered by gluten, a constituent derived from grains which is present in the common daily diet in the Western world. Despite decades of research, the mechanisms behind celiac disease etiology are still not fully understood, although it is clear that both genetic and environmental factors are involved. To improve the understanding of the disease, the genetic component has been extensively studied by genome-wide association studies. These have uncovered a wealth of information that still needs further investigation to clarify its importance. In this review, we summarize and discuss the results of the genetic studies in celiac disease, focusing on the "non-HLA" genes. We also present novel approaches to identifying the causal variants in complex susceptibility loci and disease mechanisms.

From genome-wide association studies to disease mechanisms: celiac disease as a model for autoimmune diseases

Celiac disease is characterized by a chronic inflammatory reaction in the intestine and is triggered by gluten, a constituent derived from grains which is present in the common daily diet in the Western world. Despite decades of research, the mechanisms behind celiac disease etiology are still not fully understood, although it is clear that both genetic and environmental factors are involved. To improve the understanding of the disease, the genetic component has been extensively studied by genome-wide association studies. These have uncovered a wealth of information that still needs further investigation to clarify its importance. In this review, we summarize and discuss the results of the genetic studies in celiac disease, focusing on the “non-HLA” genes. We also present novel approaches to identifying the causal variants in complex susceptibility loci and disease mechanisms.

Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine

The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of clinical medicine and pharmacology. Principles of systems pharmacology can be applied to drug design and, ultimately, testing in human clinical trials. Rather than focusing exclusively on single drug targets, systems pharmacology examines the holistic response of a phenotype-dependent pathway or pathways to drug perturbation. Knowledge of individual pharmacogenetic profiles further modulates the responses to these drug perturbations, moving the field toward more individualized ('personalized') drug development. The speed with which the information required to assess these system responses and their genomic underpinnings is changing and the importance of identifying the optimal drug or drug combinations for maximal benefit and minimal risk require that clinical trial design strategies be adaptable. In this paper, we review the tenets of adaptive clinical trial design as they may apply to an era of expanding knowledge of systems pharmacology and pharmacogenomics, and clinical trail design in network medicine.

The predictive capacity of personal genome sequencing

New DNA sequencing methods will soon make it possible to identify all germline variants in any individual at a reasonable cost. However, the ability of whole-genome sequencing to predict predisposition to common diseases in the general population is unknown. To estimate this predictive capacity, we use the concept of a "genometype." A specific genometype represents the genomes in the population conferring a specific level of genetic risk for a specified disease. Using this concept, we estimated the maximum capacity of whole-genome sequencing to identify individuals at clinically significant risk for 24 different diseases. Our estimates were derived from the analysis of large numbers of monozygotic twin pairs; twins of a pair share the same genometype and therefore identical genetic risk factors. Our analyses indicate that (i) for 23 of the 24 diseases, most of the individuals will receive negative test results; (ii) these negative test results will, in general, not be very informative, because the risk of developing 19 of the 24 diseases in those who test negative will still be, at minimum, 50 to 80% of that in the general population; and (iii) on the positive side, in the best-case scenario, more than 90% of tested individuals might be alerted to a clinically significant predisposition to at least one disease. These results have important implications for the valuation of genetic testing by industry, health insurance companies, public policy-makers, and consumers.

The genetic architecture of economic and political preferences

Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic-based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.

Elements of 'missing heritability'

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Associations between six classical HLA loci and rheumatoid arthritis: a comprehensive analysis

Although the HLA region contributes to one-third of the genetic factors affecting rheumatoid arthritis (RA), there are few reports on the association of the disease with any of the HLA loci other than the DRB1. In this study we examined the association between RA and the alleles of the six classical HLA loci including DRB1. Six HLA loci (HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1) of 1659 Japanese subjects (622 cases; 488 anti-cyclic citrullinated peptides (CCP) antibody (Ab) positive (82.6%); 103 anti-CCP Ab negative (17.4%); 31 not known and 1037 controls) were genotyped. Disease types and positivity/negativity for CCP autoantibodies were used to stratify the cases. Statistical and genetic assessments were performed by Fisher's exact tests, odds ratio, trend tests and haplotype estimation. None of the HLA loci were significantly associated with CCP sero-negative cases after Bonferroni correction and we therefore limited further analyses to using only the anti CCP-positive RA cases and both anti-CCP positive and anti-CCP negative controls. Some alleles of the non-DRB1 HLA loci showed significant association with RA, which could be explained by linkage disequilibrium with DRB1 alleles. However, DPB1*02:01, DPB1*04:01 and DPB1*09:01 conferred RA risk/protection independently from DRB1. DPB1*02:01 was significantly associated with the highly erosive disease type. The odds ratio of the four HLA-loci haplotypes with DRB1*04:05 and DQB1*04:01, which were the high-risk HLA alleles in Japanese, varied from 1.01 to 5.58. C*07:04, and B*15:18 showed similar P-values and odds ratios to DRB1*04:01, which was located on the same haplotype. This haplotype analysis showed that the DRB1 gene as well as five other HLA loci is required for a more comprehensive understanding of the genetic association between HLA and RA than analyzing DRB1 alone.

Genetics of atherosclerosis

Genome-wide association studies (GWAS) from the past several years have provided the first unbiased evidence of the genes contributing to common cardiovascular disease traits in European and some Asian populations. The results not only confirmed the importance of prior knowledge, such as the central role of lipoproteins, but also revealed that there is still much to learn about the underlying mechanisms of this disease, as most of the associated genes do not appear to be involved in pathways previously connected to atherosclerosis. In this review, I focus on the common forms of the disease and look at both human and animal model studies. I summarize what was known before GWAS, highlight how the field has been changed by GWAS, and discuss future considerations, such as the limitations of GWAS and strategies that may lead to a more complete, mechanistic understanding of atherosclerosis.

Common genetic variation contributes significantly to the risk of childhood B-cell precursor acute lymphoblastic leukemia

Recent genome-wide association studies (GWAS) have provided the first unambiguous evidence that common genetic variation influences the risk of childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL), identifying risk single-nucleotide polymorphisms (SNPs) localizing to 7p12.2, 9p21.3, 10q21.2 and 14q11.2. The testing of SNPs individually for an association in GWA studies necessitates the imposition of a very stringent P-value to address the issue of multiple testing. While this reduces false positives, real associations may be missed and therefore any estimate of the total heritability will be negatively biased. Using GWAS data on 823 BCP-ALL cases by considering all typed SNPs simultaneously, we have calculated that 24% of the total variation in BCP-ALL risk is accounted for common genetic variation (95% confidence interval 6-42%). Our findings provide support for a polygenic basis for susceptibility to BCP-ALL and have wider implications for future searches for novel disease-causing risk variants.

Systems biology approach for new target and biomarker identification

The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.