Common Disease-common Variant Research Articles

Top associated SNPs in prostate cancer are significantly enriched incis-expression quantitative trait loci and at transcription factor binding sites

While genome-wide association studies (GWAS) have revealed thousands of disease risk single nucleotide polymorphisms (SNPs), their functions remain largely unknown. Recent studies have suggested the regulatory roles of GWAS risk variants in several common diseases; however, the complex regulatory structure in prostate cancer is unclear. We investigated the potential regulatory roles of risk variants in two prostate cancer GWAS datasets by their interactions with expression quantitative trait loci (eQTL) and/or transcription factor binding sites (TFBSs) in three populations. Our results indicated that the moderately associated GWAS SNPs were significantly enriched with cis-eQTLs and TFBSs in Caucasians (CEU), but not in African Americans (AA) or Japanese (JPT); this was also observed in an independent pan-cancer related SNPs from the GWAS Catalog. We found that the eQTL enrichment in the CEU population was tissue-specific to eQTLs from CEU lymphoblastoid cell lines. Importantly, we pinpointed two SNPs, rs2861405 and rs4766642, by overlapping results from cis-eQTL and TFBS as applied to the CEU data. These results suggested that prostate cancer associated SNPs and pan-cancer associated SNPs are likely to play regulatory roles in CEU. However, the negative enrichment results in AA or JPT and the potential mechanisms remain to be elucidated in additional samples.

How important are rare variants in common disease?

Genome-wide association studies have uncovered hundreds of common genetic variants involved in complex diseases. However, for most complex diseases, these common genetic variants only marginally contribute to disease susceptibility. It is now argued that rare variants located in different genes could in fact play a more important role in disease susceptibility than common variants. These rare genetic variants were not captured by genome-wide association studies using single nucleotide polymorphism-chips but with the advent of next-generation sequencing technologies, they have become detectable. It is now possible to study their contribution to common disease by resequencing samples of cases and controls or by using new genotyping exome arrays that cover rare alleles. In this review, we address the question of the contribution of rare variants in common disease by taking the examples of different diseases for which some resequencing studies have already been performed, and by summarizing the results of simulation studies conducted so far to investigate the genetic architecture of complex traits in human. So far, empirical data have not allowed the exclusion of many models except the most extreme ones involving only a small number of rare variants with large effects contributing to complex disease. To unravel the genetic architecture of complex disease, case-control data will not be sufficient, and alternative study designs need to be proposed together with methodological developments.

Systematic Prioritization and Integrative Analysis of Copy Number Variations in Schizophrenia Reveal Key Schizophrenia Susceptibility Genes

Schizophrenia is a common mental disorder with high heritability and strong genetic heterogeneity. Common disease-common variants hypothesis predicts that schizophrenia is attributable in part to common genetic variants. However, recent studies have clearly demonstrated that copy number variations (CNVs) also play pivotal roles in schizophrenia susceptibility and explain a proportion of missing heritability. Though numerous CNVs have been identified, many of the regions affected by CNVs show poor overlapping among different studies, and it is not known whether the genes disrupted by CNVs contribute to the risk of schizophrenia. By using cumulative scoring, we systematically prioritized the genes affected by CNVs in schizophrenia. We identified 8 top genes that are frequently disrupted by CNVs, including NRXN1, CHRNA7, BCL9, CYFIP1, GJA8, NDE1, SNAP29, and GJA5. Integration of genes affected by CNVs with known schizophrenia susceptibility genes (from previous genetic linkage and association studies) reveals that many genes disrupted by CNVs are also associated with schizophrenia. Further protein-protein interaction (PPI) analysis indicates that protein products of genes affected by CNVs frequently interact with known schizophrenia-associated proteins. Finally, systematic integration of CNVs prioritization data with genetic association and PPI data identifies key schizophrenia candidate genes. Our results provide a global overview of genes impacted by CNVs in schizophrenia and reveal a densely interconnected molecular network of de novo CNVs in schizophrenia. Though the prioritized top genes represent promising schizophrenia risk genes, further work with different prioritization methods and independent samples is needed to confirm these findings. Nevertheless, the identified key candidate genes may have important roles in the pathogenesis of schizophrenia, and further functional characterization of these genes may provide pivotal targets for future therapeutics and diagnostics.

A commentary on the promise of whole-exome sequencing in medical genetics

The dawn of next-generation sequencers (NGSs) and innovative sequencing technologies have brought a paradigm shift in medical research and clinical practice. Furthermore, the cost reduction of NGSs enables personalized medicine to come to fruition. However, whole-genome sequencing (WGS) remains expensive when applied to personal genome analysis. WGS generates a large amount of data that requires highperformance computer processing. Targeted whole-exon capture and sequencing [wholeexome sequencing (WES)] is more costeffective when compared with WGS because exons represent only B1–2% of the genome and also higher sequence coverage can be achieved by NGSs. In addition, most Mendelian disorders are caused by exonic mutations or splice-junction mutations, and protein-coding genes harbor B85% of the mutations that have large effects on diseaserelated traits.1 Thus, WES will provide many advantages and lower costs than WGS when analyzing personal genomes. WES was first successfully used in 2010 to discover the gene responsible for Miller syndrome, a Mendelian disorder.2 Since then, WES has been increasingly used as a fast and accurate genomic discovery approach to investigate both rare genetic disorders and common diseases. WES is widely applied across different areas of medicine, because it has the added advantage of reduced cost and requires analysis of a much smaller but essential dataset when compared with WGS. In addition, recent clinical molecular diagnostics have used WES to detect heterogeneous Mendelian diseases.3,4 A recent review of WES approaches in medical genetics describes the usefulness of WES in medicine and medical research and the impact of WES on clinical diagnoses.5 WES approaches have greatly facilitated the discovery of candidate genes or gene variants in Mendelian disorders and rare variants in common diseases and genomic characterization in cancer. Currently, WES is increasingly being applied to disease gene discovery, cancer typing and molecular diagnosis.5 Presently, WES is an essential tool in medical genetics, especially in the research of Mendelian disorders. WES or multigene tests using NGSs are widely applied to heterogeneous disorders including deafness or ciliopathy.5,6 WES is also being increasingly applied to genetic testing for undiagnosed patients.4,5 Yang et al.4 performed WES in undiagnosed patients whose phenotypes were suggestive of potential genetic disorders and achieved a molecular diagnosis for 62 of 250 (25%) patients. Because WES detects individual genetic variation, it can be used to construct a variation database of anthropic and ethnic populations. At the same time, because WES can detect groups of genetic variations that are unrelated to the indication for the first diagnostic purpose but are of medical value for individual patient care, such ‘incidental findings’ pose potential ethical problems that should be strongly considered and discussed in clinical practice.5,7 WES is a widely applied technique in medical genetics that is capable of detecting variations in whole exons. However, in practical use, understanding WES methodology and limitations are important. Current WES techniques are not capable of detecting all of the variations surrounding exons. Detecting variation by WES is limited by the experimental methods, probe coverage and/or platforms used.8–10 Hence, WES may not always detect pathogenic or causative variations in a genetic disease. In addition, because WES is a method to detect genomic sequence variations, when a candidate of causative variation in the disease is detected, it requires verification or support by secondary analyses. In particular, further functional analyses are important to confirm whether the variant is pathogenic or benign. Nevertheless, WES enables the unprecedented low cost and highly efficient analysis of whole exons. WES can be easily used to comprehensively detect individual variations in exons. It is without doubt that WES is a powerful tool in genome analysis, and it greatly progresses medical genetics. Although WESs’ limitations need to be overcome, we anticipate that WES will be used not only in medical research but also in clinical practice for example, molecular diagnosis (whole-gene test) and personal genomics before WGS becomes a common place in medical genetics. Thus, a paradigm shift in medicine by advancement in both WES and WGS is expected to continue.

Epigenetics and the exposomes: Obesity and beyond

ABSTRACTThe specific genetic alterations that result in diseases and complex syndromes have been and continue to be identified. Search for the origins of disease have led to investigations into the roles of dietary and environmental factors as potential triggers or modifiers of risk. Genome-wide association studies have identified concepts such as the rare variant-common disease hypothesis and the common variant-common disease hypothesis.1 Through association studies, unique gene-environment interactions, which may occur with or without specific periods of permissiveness or vulnerabilities, have also been identified. Major conditions where the role of exposomes and epigenetics are rapidly evolving are obesity, neurological disorders, immune disorders and cancers. These concepts are particularly intriguing in the context of obesity. BACKGROUND: Epigenetics can be defined as heritable traits resulting from changes in DNA or chromatin structure without alterations in the DNA sequence.2 Nutritional epigenetics is seen as a means for the prevention of developmental diseases and cancer, and to delay processes associated with aging.3,4 Diseases in which epigenetic factors are considered significant include type 2 diabetes mellitus, obesity, inflammation, cardiovascular diseases, neurocognitive disorders, and immune diseases, with neural function influenced by environmental factors including early experience.5 Studies with rodent models suggest that during both early development and in adult life, environmental signals can activate intracellular pathways that directly remodel the epigenome, leading to changes in gene expression and function. These studies define a biological basis for the interplay between environmental signals and the genome in the regulation of individual differences in behavior, cognition, and physiology.6 In reproduction, certain genes are turned on while others are turned off in the process of imprinting. In the case of imprinting, even though there are two copies of the gene, only one copy is expressed and there is no substitute functional allele. For this reason, imprinting makes the imprinted genes more vulnerable to the negative effects of mutations.7

In search of low-frequency and rare variants affecting complex traits

The allelic architecture of complex traits is likely to be underpinned by a combination of multiple common frequency and rare variants. Targeted genotyping arrays and next-generation sequencing technologies at the whole-genome sequencing (WGS) and whole-exome scales (WES) are increasingly employed to access sequence variation across the full minor allele frequency (MAF) spectrum. Different study design strategies that make use of diverse technologies, imputation and sample selection approaches are an active target of development and evaluation efforts. Initial insights into the contribution of rare variants in common diseases and medically relevant quantitative traits point to low-frequency and rare alleles acting either independently or in aggregate and in several cases alongside common variants. Studies conducted in population isolates have been successful in detecting rare variant associations with complex phenotypes. Statistical methodologies that enable the joint analysis of rare variants across regions of the genome continue to evolve with current efforts focusing on incorporating information such as functional annotation, and on the meta-analysis of these burden tests. In addition, population stratification, defining genome-wide statistical significance thresholds and the design of appropriate replication experiments constitute important considerations for the powerful analysis and interpretation of rare variant association studies. Progress in addressing these emerging challenges and the accrual of sufficiently large data sets are poised to help the field of complex trait genetics enter a promising era of discovery.

SP0108 Next Generation Sequencing and Related Technologies, A Way Forward for Arthritis?

The publication of the human genome sequence in 2001 represented one of the major milestones in the history of biology and much effort has since been devoted to describing and...

Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants

Recent developments in sequencing technologies have made it possible to uncover both rare and common genetic variants. Genome-wide association studies (GWASs) can test for the effect of common variants, whereas sequence-based association studies can evaluate the cumulative effect of both rare and common variants on disease risk. Many groupwise association tests, including burden tests and variance-component tests, have been proposed for this purpose. Although such tests do not exclude common variants from their evaluation, they focus mostly on testing the effect of rare variants by upweighting rare-variant effects and downweighting common-variant effects and can therefore lose substantial power when both rare and common genetic variants in a region influence trait susceptibility. There is increasing evidence that the allelic spectrum of risk variants at a given locus might include novel, rare, low-frequency, and common genetic variants. Here, we introduce several sequence kernel association tests to evaluate the cumulative effect of rare and common variants. The proposed tests are computationally efficient and are applicable to both binary and continuous traits. Furthermore, they can readily combine GWAS and whole-exome-sequencing data on the same individuals, when available, and are also applicable to deep-resequencing data of GWAS loci. We evaluate these tests on data simulated under comprehensive scenarios and show that compared with the most commonly used tests, including the burden and variance-component tests, they can achieve substantial increases in power. We next show applications to sequencing studies for Crohn disease and autism spectrum disorders. The proposed tests have been incorporated into the software package SKAT.

Physiology and Redundancy

This year's Walter B. Cannon lecture is titled “Is Physiology Redundant?” I chose this title to reflect two divergent definitions of the word redundant. In the first definition, redundant can mean extra, nonessential, or no longer needed. In the 1980s and 1990s, the view that physiology as a

Knowledge-constrained K-medoids Clustering of Regulatory Rare Alleles for Burden Tests.

Rarely occurring genetic variants are hypothesized to influence human diseases, but statistically associating these rare variants to disease is challenging due to a lack of statistical power in most feasibly sized datasets. Several statistical tests have been developed to either collapse multiple rare variants from a genomic region into a single variable (presence/absence) or to tally the number of rare alleles within a region, relating the burden of rare alleles to disease risk. Both these approaches, however, rely on user-specification of a genomic region to generate these collapsed or burden variables, usually an entire gene. Recent studies indicate that most risk variants for common diseases are found within regulatory regions, not genes. To capture the effect of rare alleles within non-genic regulatory regions for burden tests, we contrast a simple sliding window approach with a knowledge-guided k-medoids clustering method to group rare variants into statistically powerful, biologically meaningful windows. We apply these methods to detect genomic regions that alter expression of nearby genes.

Heuristic Methods for Finding Pathogenic Variants in Gene Coding Sequences

These are exciting times, with a plethora of new technologies that are expediting discovery of the genetic underpinnings of human disease. Comprehensive resequencing of the human genome is now feasible and affordable, allowing each person's entire genetic makeup to be revealed. The major focus of

The more subjects the better in research?

In child and adolescent psychiatry, perhaps more so than in other medical specialties, there can sometimes be an uncomfortable gap between research and clinic. While scientific research is typically aimed at findings obtained from groups of children and adolescents, clinicians are committed to provide optimal assessment and treatment for individual patients and families. And it is true that it is not always straightforward to translate findings from research into clinical practice. For example, when a randomized controlled trial has demonstrated efficacy of a certain treatment for children with a particular disorder, this unfortunately does not guarantee that the treatment benefits each individual child with that disorder. Similarly, research findings related to causes of disorders may not always have relevance for the individual child. Some clinicians may even feel that each patient and family is unique and that group-based research findings offer them little for their clinical practice. Intriguingly, in the research field of genetics a currently ongoing debate of the pros and cons of an approach directed at finding common genetic polymorphisms associated with a disorder in large groups of patients versus an approach directed at individual pedigrees has similarities with the situation described above. Most of the research efforts aimed at finding genes associated with disorders have so far been performed under the ‘‘common variant common disease’’ assumption. The most straightforward avenue to gene discovery under this assumption is association methodologies, aimed at demonstrating that a particular common allele is overrepresented in the population of affected individuals. Here, the primary objective is to identify genetic polymorphisms that account for common genetic risks for a disorder shared by the population. So far, this approach has failed to reveal definitive findings for the common child and adolescent psychiatry disorders. It has become clear that it is too simplistic to imply that a few genetic loci of moderate effect and intermediate frequency would explain major parts of the disease risk in a population. Rather, two contrasting assumptions have now been put forward, to account for the observed heritability of mental disorders, that is, a very large number of very small-effect common variants (the infinitesimal model) versus a large number of large-effect rare variants (the rare allele model). There are valid arguments for either assumption [1]. The first assumption requires much larger sample sizes than have so far been used in gene-wide association studies in the field of child and adolescent psychiatry. Indeed genome-wide association studies, for example in the field of body mass index have been performed in sample sizes approaching 2,500,000 subjects [3], which is a far step from the sample sizes so far collected in the field of child and adolescent psychiatry. On the other hand, studies designed under the rare allele model have been able to identify disease-related genes with surprisingly small sample sizes; a gene associated with Tourette’s disorder has been identified in a twogeneration pedigree, with ten individuals [2].The latter finding illustrates that there is not only value in looking for common characteristics shared by many individuals with a disorder but that also important knowledge can be gained from a more individual approach, much as is the case with regard to clinical research, where in depth study of individual patients can provide much new information. P. J. Hoekstra (&) Department of Psychiatry, University Medical Center Groningen, University of Groningen, P.O. Box 660, 9700 AR Groningen, The Netherlands e-mail: p.hoekstra@accare.nl

Genetics of Pediatric Obesity

Onset of obesity has been anticipated at earlier ages, and prevalence has dramatically increased worldwide over the past decades. Epidemic obesity is mainly attributable to modern lifestyle, but family studies prove the significant role of genes in the individual's predisposition to obesity. Advances in genotyping technologies have raised great hope and expectations that genetic testing will pave the way to personalized medicine and that complex traits such as obesity will be prevented even before birth. In the presence of the pressing offer of direct-to-consumer genetic testing services from private companies to estimate the individual's risk for complex phenotypes including obesity, the present review offers pediatricians an update of the state of the art on genomics obesity in childhood. Discrepancies with respect to genomics of adult obesity are discussed. After an appraisal of findings from genome-wide association studies in pediatric populations, the rare variant-common disease hypothesis, the theoretical soil for next-generation sequencing techniques, is discussed as opposite to the common disease-common variant hypothesis. Next-generation sequencing techniques are expected to fill the gap of "missing heritability" of obesity, identifying rare variants associated with the trait and clarifying the role of epigenetics in its heritability. Pediatric obesity emerges as a complex phenotype, modulated by unique gene-environment interactions that occur in periods of life and are "permissive" for the programming of adult obesity. With the advent of next-generation sequencing techniques and advances in the field of exposomics, sensitive and specific tools to predict the obesity risk as early as possible are the challenge for the next decade.

Is It Rare or Common?

Many genome-wide association studies (GWAS) have signals with unknown etiology. This paper addresses the question-is such an association signal caused by rare or common variants that lead to increased disease risk? For a genomic region implicated by a GWAS, we use single nucleotide polymorphism (SNP) data in a case-control setting to predict how many common or rare variants there are, using a Bayesian analysis. Our objective is to compute posterior probabilities for configurations of rare and/or common variants. We use an extension of coalescent trees--the ancestral recombination graphs--to model the genealogical history of the samples based on marker data. As we expect SNPs to be in linkage disequilibrium with common disease variants, we can expect the trees to reflect the type of variants. To demonstrate the application, we apply our method to candidate gene sequencing data from a German case-control study on nonsyndromic cleft lip with or without cleft palate.

Rare and Common Variants in CARD14, Encoding an Epidermal Regulator of NF-kappaB, in Psoriasis

Psoriasis is a common inflammatory disorder of the skin and other organs. We have determined that mutations in CARD14, encoding a nuclear factor of kappa light chain enhancer in B cells (NF-kB) activator within skin epidermis, account for PSORS2. Here, we describe fifteen additional rare missense variants in CARD14, their distribution in seven psoriasis cohorts (>6,000 cases and >4,000 controls), and their effects on NF-kB activation and the transcriptome of keratinocytes. There were more CARD14 rare variants in cases than in controls (burden test p value = 0.0015). Some variants were only seen in a single case, and these included putative pathogenic mutations (c.424G>A [p.Glu142Lys] and c.425A>G [p.Glu142Gly]) and the generalized-pustular-psoriasis mutation, c.413A>C (p.Glu138Ala); these three mutations lie within the coiled-coil domain of CARD14. The c.349G>A (p.Gly117Ser) familial-psoriasis mutation was present at a frequency of 0.0005 in cases of European ancestry. CARD14 variants led to a range of NF-kB activities; in particular, putative pathogenic variants led to levels >2.5× higher than did wild-type CARD14. Two variants (c.511C>A [p.His171Asn] and c.536G>A [p.Arg179His]) required stimulation with tumor necrosis factor alpha (TNF-α) to achieve significant increases in NF-kB levels. Transcriptome profiling of wild-type and variant CARD14 transfectants in keratinocytes differentiated probably pathogenic mutations from neutral variants such as polymorphisms. Over 20 CARD14 polymorphisms were also genotyped, and meta-analysis revealed an association between psoriasis and rs11652075 (c.2458C>T [p.Arg820Trp]; p value = 2.1 × 10(-6)). In the two largest psoriasis cohorts, evidence for association increased when rs11652075 was conditioned on HLA-Cw*0602 (PSORS1). These studies contribute to our understanding of the genetic basis of psoriasis and illustrate the challenges faced in identifying pathogenic variants in common disease.

Resolving the variable genome and epigenome in human disease

The individual human genome and epigenome are being defined at unprecedented resolution by current advances in sequencing technologies with important implications for human disease. This review uses examples relevant to clinical practice to illustrate the functional consequences of genetic and epigenetic variation. The insights gained from genome-wide association studies are described together with current efforts to understand the role of rare variants in common disease, set in the context of recent successes in Mendelian traits through the application of whole exome sequencing. The application of functional genomics to interrogate the genome and epigenome, build up an integrated picture of the regulatory genomic landscape and inform disease association studies is discussed, together with the role of expression quantitative trait mapping and analysis of allele-specific gene expression.

Currents in Contemporary Bioethics

From the earliest days of the Human Genome Project, the holy grail of genomics was the ability to perform whole-genome sequencing quickly, accurately, and relatively inexpensively so that the benefits of genomics would be widely available in clinical settings. Although the mythical $1,000 genome sequence seemed elusive for many years, next-generation sequencing technologies and other recent advances clearly indicate that inexpensive whole-genome sequencing is at hand.Whole-genome sequencing has demonstrable value in elucidating the genetic etiology of rare disorders, in identifying atypical variants in common diseases, in determining pharmacogenomically appropriate drugs and dosages, in performing tumor genome sequencing, and in aiding other clinical applications for the diagnosis and treatment of individuals who are symptomatic or whose family health history places them at substantial risk. Undoubtedly, the clinical applications of wholegenome sequencing will increase in the future.

Editorial Room for the candidate gene approach in the genome-wide association era: an example from GSTP1 gene polymorphism

Editorial Room for the candidate gene approach in the genome-wide association era: an example from GSTP1 gene polymorphism

Identification of disease-related nsSNPs via the integration of protein sequence features and domain-domain interaction data

Recent studies have suggested the common disease-rare variant (CD-RV) hypothesis in the mapping of disease-related genetic variants and have proposed a number of statistical methods to detect associations between rare variants and human inherited diseases. However, most of these methods take the selection of functional variants as a preliminary step in order to maximise the power of statistical tests. To meet this end, we put forward a filtration approach to identify genetic variants that are potentially associated with a query disease of interest from the perspective of one-class novelty learning. We propose to prioritise candidate non-synonymous single nucleotide polymorphisms (nsSNPs) relying on the integrated use of two sequence conservation properties of amino acids calculated from multiple sequence alignment of protein sequences and one functional similarity measure derived from domain-domain interaction data. We show the power of this approach in the detection of disease-related nsSNP via large-scale leave-one-out cross-validation experiments.

Copy-Number Variations Observed in a Japanese Population by BAC Array CGH: Summary of Relatively Rare CNVs

Copy-number variations (CNVs) may contribute to genetic variation in humans. Reports regarding existence and characteristics of CNVs in a large apparently healthy Japanese cohort are quite limited. We report the data from a screening of 213 unrelated Japanese individuals using comparative genomic hybridization based on a bacterial artificial chromosome microarray (BAC aCGH). In a previous paper, we summarized the data by focusing on highly polymorphic CNVs (in ≥5.0 % of the individuals). However, rare variations have recently received attention from scientists who espouse a hypothesis called “common disease and rare variants.” Here, we report CNVs identified in fewer than 10 individuals in our study population. We found a total of 126 CNVs at 52 different BAC regions in the genome. The CNVs observed at 27 of the 52 BAC-regions were found in only one unrelated individual. The majority of CNVs found in this study were not identified in the Japanese who were examined in the other studies. Family studies were conducted, and the results demonstrated that the CNVs were inherited from one parent in the families.