Disease-common Variant Hypothesis Research Articles

Epigenetics and the exposomes: Obesity and beyond

ABSTRACT: The 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.

Association score testing for rare variants and binary traits in family data with shared controls.

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2,WQLS,andSKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

Genome-Wide Association Studies for life-style related diseases

After the completion of human genome project, development of single nucleotide polymorphism (SNP) typing technology and collation of information regarding linkage disequilibrium in the human genome have facilitated genome-wide association studies (GWAS) for investigating genes associated with disease susceptibility across the entire human genome. In case of type 2 diabetes, approximately 100 genetic loci have been identified and confirmed as susceptibility to the disease through GWAS in different ethnic groups, including Japanese, European, East Asian and South Asian populations. However, integration of these information accounts for less than 20% of the disease heritability, and thus most of the heritability of type 2 diabetes remain to be identified. Since the rationale of GWAS is based on the hypothesis that common variants contribute to the susceptibility to common diseases, common disease-common variant hypothesis, GWAS have selectively identified common susceptibility variants (allele frequency>=0.05) with lower effect size (odds ratio<1.5), that is a limitation of the GWAS approach. Although GWAS have brought a significant breakthrough in the field of genetic study for life-style related diseases, new approaches other than GWAS, such as whole genome sequencing to identify rare variants with greater effect size or integration of genetic and environmental information, will be required to elucidate a heritability of life-style related diseases completely.

Association of copy number polymorphisms at the promoter and translated region of COMT with Japanese patients with schizophrenia

Chromosome 22q11.2 deletion syndrome and genetic variations including single-nucleotide polymorphism (SNP) and copy number variation (CNV) in catechol-O-methyltransferase (COMT) situated at 22q11.2 remains controversial. Here, the genetic relationship between COMT and Japanese patients with schizophrenia was investigated by examining whether the SNPs correlated with schizophrenia based on a common disease-common variant hypothesis. Additionally, 22q11.2DS were screened based on a common disease-rare variant hypothesis; low-frequency CNVs situated at two COMT promoters and exons were investigated based on the low-frequency variants with an intermediate effect; and positive findings from the first stage were reconfirmed using a second-stage replication study including a larger sample size. Eight SNPs and 10 CNVs were investigated using Taqman SNP and CNV quantitative real-time polymerase chain reaction method. For the first-stage analysis, 513 unrelated Japanese patients with schizophrenia and 705 healthy controls were examined. For the second-stage replication study, positive findings from the first stage were further investigated using a larger sample size, namely 1,854 patients with schizophrenia and 2,137 controls. The first-stage analysis showed significant associations among schizophrenia, intronic SNP rs165774, CNV6 situated at promoter 1, CNV8 at exon 6, and CNV9 at exon 7. The second-stage study showed that intronic SNP rs165774 (χ(2) = 8.327, P = 0.0039), CNV6 (χ(2) = 19.66, P = 0.00005), and CNV8 (χ(2) = 16.57, P = 0.00025) were significantly associated with schizophrenia. Large and rare CNVs as well as low-frequency CNVs and relatively small CNVs, namely <30 kb in COMT, may be genetic risk factors for schizophrenia.

A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required.

The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.

Genomic insights into the etiology of Alzheimer&amp;#39;s disease: a review

Over the past decade, studies capitalizing on high-throughput genome technologies have significantly advanced the knowledge on the genetic underpinnings of Alzheimer's disease (AD) by identifying a wide set of pathophysiological mechanisms implicated in the disease in addition to amyloid precursor protein (APP) metabolism. These include: innate immune response and inflammation, lipid metabolism, endocytosis, cell migration, tau pathol - ogy, hippocampal synaptic function and axonal transport, regulation of gene expression and posttranslational modification of proteins, and microglial and myeloid cell function. The cumu - lative population attributable fraction associated with known genetic variants amounts now to ∼80%. High-throughput sequencing studies have started to map specific causative variants in these genes and have provided invaluable evidence for an involvement of rare variants in AD, overturning the common disease-common variant hypothesis. The ongoing and future large-scale translational studies and next generation whole genome or whole exome sequencing efforts hold the promise of mapping the specific causative variants in these genes; of identifying additional risk variants, including rare and structural variants; and of identifying novel targets for genetic testing, prevention, and treatment.

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.

Resequencing the susceptibility gene, ITGAM, identifies two functionally deleterious rare variants in systemic lupus erythematosus cases.

IntroductionThe majority of the genetic variance of systemic lupus erythematosus (SLE) remains unexplained by the common disease-common variant hypothesis. Rare variants, which are not detectable by genome-wide association studies because of their low frequencies, are predicted to explain part of this ”missing heritability.” However, recent studies identifying rare variants within known disease-susceptibility loci have failed to show genetic associations because of their extremely low frequencies, leading to the questioning of the contribution of rare variants to disease susceptibility. A common (minor allele frequency = 17.4% in cases) nonsynonymous coding variant rs1143679 (R77H) in ITGAM (CD11b), which forms half of the heterodimeric integrin receptor, complement receptor 3 (CR3), is robustly associated with SLE and has been shown to impair CR3-mediated phagocytosis.MethodsWe resequenced ITGAM in 73 SLE cases and identified two previously unidentified, case-specific nonsynonymous variants, F941V and G1145S. Both variants were genotyped in 2,107 and 949 additional SLE cases, respectively, to estimate their frequencies in a disease population. An in vitro model was used to assess the impact of F941V and G1145S, together with two nonsynonymous ITGAM polymorphisms, A858V (rs1143683) and M441T (rs11861251), on CR3-mediated phagocytosis. A paired two-tailed t test was used to compare the phagocytic capabilities of each variant with that of wild-type CR3.ResultsBoth rare variants, F941V and G1145S, significantly impair CR3-mediated phagocytosis in an in vitro model (61% reduction, P = 0.006; 26% reduction, P = 0.0232). However, neither of the common variants, M441T and A858V, had an effect on phagocytosis. Neither rare variant was observed again in the genotyping of additional SLE cases, suggesting that there frequencies are extremely low.ConclusionsOur results add further evidence to the functional importance of ITGAM in SLE pathogenesis through impaired phagocytosis. Additionally, this study provides a new example of the identification of rare variants in common-allele-associated loci, which, because of their extremely low frequencies, are not statistically associated. However, the demonstration of their functional effects adds support to their contribution to disease risk, and questions the current notion of dismissing the contribution of very rare variants on purely statistical analyses.

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

The role of common and rare variants in risk for Alzheimer's disease across populations

Until recently, APOE was the only well-established risk gene for late-onset Alzheimer disease (LOAD). Efforts to identify additional LOAD loci have concentrated on testing the common disease-common variant hypothesis by conducting genome-wide association studies (GWAS) using large case/control datasets. Thus far, these studies, conducted primarily in non-Hispanic white populations, have identified robust associations of AD with non-coding common variants (CVs) in 21 genes. The effect sizes for these variants are small and account for only a portion of the heritability of LOAD. An alternative hypothesis suggests that multiple rare variants (RVs) in these and other genes contribute to AD risk, each with stronger individual effects than CVs. Moreover, although nearly all of the known functional RVs linked to AD are deleterious, the observation that some individuals with high genetic liability (e.g., APOE ε4/ε4 genotype) remain cognitively intact after age 85 indicates that some AD-related CVs and RVs may be protective. This presentation will summarize the evidence emerging from GWAS, candidate gene and next generation sequencing (NGS) studies in diverse ethnic populations supporting roles for CVs and RVs in LOAD. The effect of some loci are age and sex dependent, whereas others are modulated by epistatic influences of other loci or environmental factors Trans-ethnic studies have yielded examples of variants influencing LOAD risk in multiple populations (albeit with variable effect sizes), allelic heterogeneity and locus heterogeneity. Interpretations are complicated by variable risk allele frequencies and linkage disequilibrium patterns across populations. Recent studies using NGS approaches have identified highly penetrant risk and protective RVs, some of which appear to be population-specific. Ambitious studies such as the United States AD Sequencing Project will likely uncover many additional genes and RVs for AD. Very large samples will be needed to verify association with RVs and small-effect CVs, as well as demonstrate gene-gene and gene-environment interaction. Trans-ethnic studies may be very useful to localize complex association signals and add power because of increased effect sizes (CVs) and frequencies (RVs) in some groups. These studies will likely suggest new pathways and novel targets that may lead to new therapeutic and preventive strategies.

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.

Pooled-DNA sequencing identifies novel causative variants in PSEN1, GRN and MAPT in a clinical early-onset and familial Alzheimer's disease Ibero-American cohort

IntroductionSome familial Alzheimer's disease (AD) cases are caused by rare and highly-penetrant mutations in APP, PSEN1, and PSEN2. Mutations in GRN and MAPT, two genes associated with frontotemporal dementia (FTD), have been found in clinically diagnosed AD cases. Due to the dramatic developments in next-generation sequencing (NGS), high-throughput sequencing of targeted genomic regions of the human genome in many individuals in a single run is now cheap and feasible. Recent findings favor the rare variant-common disease hypothesis by which the combination effects of rare variants could explain a large proportion of the heritability. We utilized NGS to identify rare and pathogenic variants in APP, PSEN1, PSEN2, GRN, and MAPT in an Ibero-American cohort.MethodsWe performed pooled-DNA sequencing of each exon and flanking sequences in APP, PSEN1, PSEN2, MAPT and GRN in 167 clinical and 5 autopsy-confirmed AD cases (15 familial early-onset, 136 sporadic early-onset and 16 familial late-onset) from Spain and Uruguay using NGS. Follow-up genotyping was used to validate variants. After genotyping additional controls, we performed segregation and functional analyses to determine the pathogenicity of validated variants.ResultsWe identified a novel G to T transition (g.38816G>T) in exon 6 of PSEN1 in a sporadic early-onset AD case, resulting in a previously described pathogenic p.L173F mutation. A pathogenic p.L392V mutation in exon 11 was found in one familial early-onset AD case. We also identified a novel CC insertion (g.10974_10975insCC) in exon 8 of GRN, which introduced a premature stop codon, resulting in nonsense-mediated mRNA decay. This GRN mutation was associated with lower GRN plasma levels, as previously reported for other GRN pathogenic mutations. We found two variants in MAPT (p.A152T, p.S318L) present only in three AD cases but not controls, suggesting that these variants could be risk factors for the disease.ConclusionsWe found pathogenic mutations in PSEN1, GRN and MAPT in 2.33% of the screened cases. This study suggests that pathogenic mutations or risk variants in MAPT and in GRN are as frequent in clinical AD cases as mutations in APP, PSEN1 and PSEN2, highlighting that pleiotropy of MAPT or GRN mutations can influence both FTD and AD phenotypic traits.

Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data

Genome-wide association studies for complex traits are based on the common disease/common variant (CDCV) and common disease/rare variant (CDRV) assumptions. Under the CDCV hypothesis, classical genome-wide association studies using single-marker tests are powerful in detecting common susceptibility variants, but under the CDRV hypothesis they are not as powerful. Several methods have been recently proposed to detect association with multiple rare variants collectively in a functional unit such as a gene. In this paper, we compare the relative performance of several of these methods on the Genetic Analysis Workshop 17 data. We evaluate these methods using the unrelated individual and family data sets. Association was tested using 200 replicates for the quantitative trait Q1. Although in these data the power to detect association is often low, our results show that collapsing methods are promising tools. However, we faced the challenge of assessing the proper type I error to validate our power comparisons. We observed that the type I error rate was not well controlled; however, we did not find a general trend specific to each method. Each method can be conservative or nonconservative depending on the studied gene. Our results also suggest that collapsing and the single-locus association approaches may not be affected to the same extent by population stratification. This deserves further investigation.

Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies

Variants identified in recent genome-wide association studies based on the common-disease common-variant hypothesis are far from fully explaining the hereditability of complex traits. Rare variants may, in part, explain some of the missing hereditability. Here, we explored the advantage of the extreme phenotype sampling in rare-variant analysis and refined this design framework for future large-scale association studies on quantitative traits. We first proposed a power calculation approach for a likelihood-based analysis method. We then used this approach to demonstrate the potential advantages of extreme phenotype sampling for rare variants. Next, we discussed how this design can influence future sequencing-based association studies from a cost-efficiency (with the phenotyping cost included) perspective. Moreover, we discussed the potential of a two-stage design with the extreme sample as the first stage and the remaining nonextreme subjects as the second stage. We demonstrated that this two-stage design is a cost-efficient alternative to the one-stage cross-sectional design or traditional two-stage design. We then discussed the analysis strategies for this extreme two-stage design and proposed a corresponding design optimization procedure. To address many practical concerns, for example measurement error or phenotypic heterogeneity at the very extremes, we examined an approach in which individuals with very extreme phenotypes are discarded. We demonstrated that even with a substantial proportion of these extreme individuals discarded, an extreme-based sampling can still be more efficient. Finally, we expanded the current analysis and design framework to accommodate the CMC approach where multiple rare-variants in the same gene region are analyzed jointly.

Strategic approaches to unraveling genetic causes of cardiovascular diseases.

DNA sequence variants are major components of the "causal field" for virtually all medical phenotypes, whether single gene familial disorders or complex traits without a clear familial aggregation. The causal variants in single gene disorders are necessary and sufficient to impart large effects. In contrast, complex traits are attributable to a much more complicated network of contributory components that in aggregate increase the probability of disease. The conventional approach to identification of the causal variants for single gene disorders is genetic linkage. However, it does not offer sufficient resolution to map the causal genes in small families or sporadic cases. The approach to genetic studies of complex traits entails candidate gene or genome-wide association studies. Genome-wide association studies provide an unbiased survey of the effects of common genetic variants (common disease-common variant hypothesis). Genome-wide association studies have led to identification of a large number of alleles for various cardiovascular diseases. However, common alleles account for a relatively small fraction of the total heritability of the traits. Accordingly, the focus has shifted toward identification of rare variants that might impart larger effect sizes (rare variant-common disease hypothesis). This shift is made feasible by recent advances in massively parallel DNA sequencing platforms, which afford the opportunity to identify virtually all common as well as rare alleles in individuals. In this review, we discuss various strategies that are used to delineate the genetic contribution to medically important cardiovascular phenotypes, emphasizing the utility of the new deep sequencing approaches.

The relative importance of common and rare genetic variants in the development of hypertriglyceridemia

Plasma lipid levels are a complex trait with a genetic and an environmental component. There are two models for the genetic basis of complex traits: the common-disease common-variant hypothesis, in which susceptibility is due to variants occurring at relatively high frequency but low effect size; and the common-disease rare-variant hypothesis, where disease is due to multiple rare variants each occurring at low frequency but with high effect size. Genome-wide association studies have identified a number of common variants associated with plasma lipid levels. However, they account for only a proportion of the genetic variance. Resequencing studies are revealing the importance of rare variants in accounting for the missing variance. Next-generation sequencing will allow the relative importance of the two hypotheses to be assessed.

Common Disease: Are Causative Alleles Common or Rare?

Common Disease: Are Causative Alleles Common or Rare?

Comparison of Six Statistics of Genetic Association Regarding Their Ability to Discriminate between Causal Variants and Genetically Linked Markers

Objectives: Genome-wide association (GWA) studies still rely on the common-disease common-variant hypothesis since the assumption is associated with increased power. In GWA studies, polymorphisms are genotyped and their association with disease is investigated. Most of the identified associations are indirect and reflect a shared inheritance of the genotyped markers and genetically linked causal variants. We have compared six statistics of genetic association regarding their ability to discriminate between markers and causal susceptibility variants, including a probability value (Pval) and a Bayes Factor (BF) based on logistic regression, and the attributable familial relative risk (FRR). Methods: We carried out a simulation-based sensitivity analysis to explore several conceivable scenarios. Theoretical results were illustrated by established causal associations with age-related macular degeneration and by using imputed data based on HapMap for a case-control study of breast cancer. Results: Our data indicate that a representation of genetic association by FRRs and BFs generally facilitates the distinction of causal variants. The FRR showed the best discriminative power under most investigated scenarios, but no single statistic outperformed the others in all situations. For example, rare moderate- to low-penetrance variants (allele frequency: 1%, dominant odds ratio: ≤2.0) seem to be best discriminated by BFs. Conclusions: Present results may help to fully utilize the data generated in association studies that take advantage of next generation sequencing and/or multiple imputation based on the 1000 Genomes Project.

Common Variation in ISL1 Confers Genetic Susceptibility for Human Congenital Heart Disease

Congenital heart disease (CHD) is the most common birth abnormality and the etiology is unknown in the overwhelming majority of cases. ISLET1 (ISL1) is a transcription factor that marks cardiac progenitor cells and generates diverse multipotent cardiovascular cell lineages. The fundamental role of ISL1 in cardiac morphogenesis makes this an exceptional candidate gene to consider as a cause of complex congenital heart disease. We evaluated whether genetic variation in ISL1 fits the common variant–common disease hypothesis. A 2-stage case-control study examined 27 polymorphisms mapping to the ISL1 locus in 300 patients with complex congenital heart disease and 2,201 healthy pediatric controls. Eight genic and flanking ISL1 SNPs were significantly associated with complex congenital heart disease. A replication study analyzed these candidate SNPs in 1,044 new cases and 3,934 independent controls and confirmed that genetic variation in ISL1 is associated with risk of non-syndromic congenital heart disease. Our results demonstrate that two different ISL1 haplotypes contribute to risk of CHD in white and black/African American populations.

Genetics of neurodegenerative diseases: insights from high-throughput resequencing

During the past three decades, we have witnessed remarkable advances in our understanding of the molecular etiologies of hereditary neurodegenerative diseases, which have been accomplished by ‘positional cloning’ strategies. The discoveries of the causative genes for hereditary neurodegenerative diseases accelerated not only the studies on the pathophysiologic mechanisms of diseases, but also the studies for the development of disease-modifying therapies. Genome-wide association studies (GWAS) based on the ‘common disease–common variants hypothesis’ are currently undertaken to elucidate disease-relevant alleles. Although GWAS have successfully revealed numerous susceptibility genes for neurodegenerative diseases, odds ratios associated with risk alleles are generally low and account for only a small proportion of estimated heritability. Recent studies have revealed that the effect sizes of the disease-relevant alleles that are identified based on comprehensive resequencing of large data sets of Parkinson disease are substantially larger than those identified by GWAS. These findings strongly argue for the role of the ‘common disease–multiple rare variants hypothesis’ in sporadic neurodegenerative diseases. Given the rapidly improving technologies of next-generation sequencing next-generation sequencing (NGS), we expect that NGS will eventually enable us to identify all the variants in an individual's personal genome, in particular, clinically relevant alleles. Beyond this, whole genome resequencing is expected to bring a paradigm shift in clinical practice, where clinical practice including diagnosis and decision-making for appropriate therapeutic procedures is based on the ‘personal genome’. The personal genome era is expected to be realized in the near future, and society needs to prepare for this new era.