Common Disease-common Variant Research Articles

Global analysis of A-to-I RNA editing reveals association with common disease variants.

RNA editing modifies transcripts and may alter their regulation or function. In humans, the most common modification is adenosine to inosine (A-to-I). We examined the global characteristics of RNA editing in 4,301 human tissue samples. More than 1.6 million A-to-I edits were identified in 62% of all protein-coding transcripts. mRNA recoding was extremely rare; only 11 novel recoding sites were uncovered. Thirty single nucleotide polymorphisms from genome-wide association studies were associated with RNA editing; one that influences type 2 diabetes (rs2028299) was associated with editing in ARPIN. Twenty-five genes, including LRP11 and PLIN5, had editing sites that were associated with plasma lipid levels. Our findings provide new insights into the genetic regulation of RNA editing and establish a rich catalogue for further exploration of this process.

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.

Identification of candidate protective variants for common diseases and evaluation of their protective potential

BackgroundHuman polymorphisms with derived alleles that are protective against disease may provide powerful translational opportunities. Here we report a method to identify such candidate polymorphisms and apply it to common non-synonymous SNPs (nsSNPs) associated with common diseases. Our study also sought to establish which of the identified protective nsSNPs show evidence of positive selection, taking this as indirect evidence that the protective variant has a beneficial effect on phenotype. Further, we performed an analysis to quantify the predicted effect of each protective variant on protein function/structure.ResultsAn initial analysis of eight SNPs previously identified as associated with age-related macular degeneration (AMD), revealed that two of them have a derived allele that is protective against developing the disease. One is in the complement component 2 gene (C2; E318D) and the other is in the complement factor B gene (CFB; R32Q). Then, combining genomewide ancestral allele information with known common disease-associated nsSNPs from the GWAS catalog, we found 32 additional SNPs which have a derived allele that is disease protective. Out of the total 34 identified candidate protective variants (CPVs), we found that 30 show stronger evidence of positive selection than the protective variant in lipoprotein lipase (LPL; S447X), which has already been translated into gene therapy. Furthermore, 11 of these CPVs have a higher probability of affecting protein structure than the lipoprotein lipase protective variant (LPL; S447X).ConclusionsWe identify 34 CPVs from the human genome. Diseases they confer protection against include, but are not limited to, type 2 diabetes, inflammatory bowel disease, age-related macular degeneration, multiple sclerosis and rheumatoid arthritis. We propose that those 30 CPVs with evidence of stronger positive selection than the LPL protective variant, may be considered as priority candidates for therapeutic approaches. The next step towards translation will require testing the hypotheses generated by our analyses, specifically whether the CPV arose from a gain-of-function or a loss-of-function mutation.

DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease.

Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the “common disease-common variant” paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer’s disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.

Less common variants association study and statistical analysis

In the past decade, based on the "common disease-common variant" hypothesis, genomewide association studies (GWAS) have been extensively used to dissect the genetic components of complex diseases and quantitative traits. However, the identified disease-associated common variants by GWASs can only explain small part of the corresponding disease heritability. "Common disease-rare variant" hypothesis has been proposed to explore the missed heritability. With the development of the next generation sequencing technology, various association studies of less common variants are ongoing. This paper provides an overview of the study designs and statistical tests of these variants.

Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression

Genome-wide association studies (GWASs) have identified a multitude of genetic loci involved with traits and diseases. However, it is often unclear which genes are affected in such loci and whether the associated genetic variants lead to increased or decreased gene function. To mitigate this, we integrated associations of common genetic variants in 57 GWASs with 24 studies of expression quantitative trait loci (eQTLs) from a broad range of tissues by using a Mendelian randomization approach. We discovered a total of 3,484 instances of gene-trait-associated changes in expression at a false-discovery rate < 0.05. These genes were often not closest to the genetic variant and were primarily identified in eQTLs derived from pathophysiologically relevant tissues. For instance, genes with expression changes associated with lipid traits were mostly identified in the liver, and those associated with cardiovascular disease were identified in arterial tissue. The affected genes additionally point to biological processes implicated in the interrogated traits, such as the interleukin-27 pathway in rheumatoid arthritis. Further, comparing trait-associated gene expression changes across traits suggests that pleiotropy is a widespread phenomenon and points to specific instances of both agonistic and antagonistic pleiotropy. For instance, expression of SNX19 and ABCB9 is positively correlated with both the risk of schizophrenia and educational attainment. To facilitate interpretation, we provide this lexicon of how common trait-associated genetic variants alter gene expression in various tissues as the online database GWAS2Genes.

Family studies to find rare high risk variants in migraine

IntroductionMigraine has long been known as a common complex disease caused by genetic and environmental factors. The pathophysiology and the specific genetic susceptibility are poorly understood. Common variants only explain a small part of the heritability of migraine. It is thought that rare genetic variants with bigger effect size may be involved in the disease. Since migraine has a tendency to cluster in families, a family approach might be the way to find these variants. This is also indicated by identification of migraine-associated loci in classical linkage-analyses in migraine families. A single migraine study using a candidate-gene approach was performed in 2010 identifying a rare mutation in the TRESK potassium channel segregating in a large family with migraine with aura, but this finding has later become questioned. The technologies of next-generation sequencing (NGS) now provides an affordable tool to investigate the genetic variation in the entire exome or genome. The family-based study design using NGS is described in this paper. We also review family studies using NGS that have been successful in finding rare variants in other common complex diseases in order to argue the promising application of a family approach to migraine.MethodPubMed was searched to find studies that looked for rare genetic variants in common complex diseases through a family-based design using NGS, excluding studies looking for de-novo mutations, or using a candidate-gene approach and studies on cancer. All issues from Nature Genetics and PLOS genetics 2014, 2015 and 2016 (UTAI June) were screened for relevant papers. Reference lists from included and other relevant papers were also searched. For the description of the family-based study design using NGS an in-house protocol was used.ResultsThirty-two successful studies, which covered 16 different common complex diseases, were included in this paper. We also found a single migraine study. Twenty-three studies found one or a few family specific variants (less than five), while other studies found several possible variants. Not all of them were genome wide significant. Four studies performed follow-up analyses in unrelated cases and controls and calculated odds ratios that supported an association between detected variants and risk of disease. Studies of 11 diseases identified rare variants that segregated fully or to a large degree with the disease in the pedigrees.ConclusionIt is possible to find rare high risk variants for common complex diseases through a family-based approach. One study using a family approach and NGS to find rare variants in migraine has already been published but with strong limitations. More studies are under way.

Significance of functional disease-causal/susceptible variants identified by whole-genome analyses for the understanding of human diseases.

Human genome variation may cause differences in traits and disease risks. Disease-causal/susceptible genes and variants for both common and rare diseases can be detected by comprehensive whole-genome analyses, such as whole-genome sequencing (WGS), using next-generation sequencing (NGS) technology and genome-wide association studies (GWAS). Here, in addition to the application of an NGS as a whole-genome analysis method, we summarize approaches for the identification of functional disease-causal/susceptible variants from abundant genetic variants in the human genome and methods for evaluating their functional effects in human diseases, using an NGS and in silico and in vitro functional analyses. We also discuss the clinical applications of the functional disease causal/susceptible variants to personalized medicine.

Extensive Association of Common Disease Variants with Regulatory Sequence.

Overlap between non-coding DNA regulatory sequences and common variant associations can help to identify specific cell and tissue types that are relevant for particular diseases. In a systematic manner, we analyzed variants from 94 genome-wide association studies (reporting at least 12 loci at p<5x10-8) by projecting them onto 466 epigenetic datasets (characterizing DNase I hypersensitive sites; DHSs) derived from various adult and fetal tissue samples and cell lines including many biological replicates. We were able to confirm many expected associations, such as the involvement of specific immune cell types in immune-related diseases and tissue types in diseases that affect specific organs, for example, inflammatory bowel disease and coronary artery disease. Other notable associations include adrenal glands in coronary artery disease, the immune system in Alzheimer’s disease, and the kidney for bone marrow density. The association signals for some GWAS (for example, myopia or age at menarche) did not show a clear pattern with any of the cell or tissue types studied. In general, the identified variants from GWAS tend to be located outside coding regions. Altogether, we have performed an extensive characterization of GWAS signals in relation to cell and tissue-specific DHSs, demonstrating a key role for regulatory mechanisms in common diseases and complex traits.

Genetic and family counselling for schizophrenia: Where do we stand now?

&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; Recent genetic findings have led to profound changes in genetic and family counselling for schizophrenia patients and their families.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives:&lt;/strong&gt; The article gives an overview of the present knowledge regarding the genetic and family counselling for schizophrenia.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method:&lt;/strong&gt; Literature searches were performed on the MEDLINE database (2011–2015) and African Healthline. A current alert service which provides the most recent literature on the topic on a monthly basis was also used in the study. A clinical case example is presented as is experienced in daily psychiatric practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results:&lt;/strong&gt; Genetic risk communication has become the responsibility of the multiprofessional treatment team, moving away from specialists in the field. The treatment team provides information on a daily basis regarding risk predictors in the management of schizophrenia, including risk of relapse, suicide and comorbid substance use. Although genetic information is unique and has implications for blood relatives, genetic risk factors only rarely provide information that is inherently different from that provided by other risk predictors commonly used in healthcare. The common variant common disease and rare variant common disease models as contrasting hypothesis of the genetics of schizophrenia are discussed and debated. An example of a family counselled is given and the place of commercial companies that offer directly to the consumer affordable personal DNA testing for psychiatric illness is discussed. Ethical issues without resolution regarding genetic counselling of schizophrenia are debated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions:&lt;/strong&gt; Recent genetic findings must lead to profound changes in genetic and family counselling in schizophrenia. Exposed attributable risk has immediate effects on genetic counselling of schizophrenia. Psychiatric risk counselling has thus changed from risk estimates based on family history to estimates based on test results in specific individuals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;br /&gt;&lt;/strong&gt;&lt;/p&gt;

Genome-wide pathway analysis identifies oxidative stress related gene MSRA as rheumatoid arthritis susceptibility locus

Objective: Genome-wide association studies (GWASs) carried out in rheumatoid arthritis (RA) have led to the discovery of several genetic associations with this disease. Still, the current associated genetic variations can explain only part of the genetic risk involved in RA, and it is well recognised that these GWASs are likely underpowered to detect all common disease variants. Therefore, we explored the genomic regions showing low-significance associations in previous GWASs of RA. Methods: To reduce the false-positive signal fraction, we exploited pathway analysis to prioritise regions containing genes most likely to be implicated in RA. We hypothesised that true disease genes would be in a similar pathway. Therefore, genes from similar pathways but located in different regions were prioritised for replication using Prioritizer software. 384 genetic variants selected from previous RA GWASs were tested in a Spanish case/control discovery cohort comprising 376 RA patients and 478 healthy controls for replication. Statistically significant associations were further validated in replication cohorts from Spain and Netherlands, up to a total of 1,818 RA patients and 2,498 controls. Results: We detected a novel genetic association between RA and the MSRA gene (rs10903323, P = 2.91×10-5, OR = 1.51) in the Spanish combined population. This association was further tested in our independent Dutch replication cohort. Combined analysis showed an overall association of MSRA with RA (P = 2.19×10-4, OR = 1.28). Conclusion: Novel association in the MSRA gene related to oxidative stress is described herein and support a major role for this process in RA.

The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis

Objective To assess the impact of communicating DNA based disease risk estimates on risk-reducing health behaviours and motivation to engage in such behaviours.Design Systematic review with meta-analysis, using Cochrane methods.Data...

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.

Genetic and family counselling for schizophrenia : where do we stand now? : original research

Background: Recent genetic findings have led to profound changes in genetic and family counselling for schizophrenia patients and their families. Objectives: The article gives an overview of the present knowledge regarding the genetic and family counselling for schizophrenia. Method: Literature searches were performed on the MEDLINE databse (2011-2015) and African Healthline. A current alert service which provides the most recent literature on the topic on a monthly basis was also used in the study. A clinical case example is presented as is experienced in daily psychiatric practice. Results: Genetic risk communication has become the responsibility of the multi-professional treatment team, moving away from specialists in the field. The treatment team provides information on a daily basis regarding risk predictors in the management of schizophrenia, including risk of relapse, suicide and comorbid substance use. Although genetic information is unique and has implications for blood relatives, genetic risk factors only rarely provideinformation that is inherently different from that provided by other risk predictors commonly used in healthcare. The common variant common disease and rare variant common disease models as contrasting hypothesis of the genetics of schizophrenia are discussed and debated. An example of a family counselled is given and the place of commercial companies that offer directly to the consumer affordable personal DNA testing for psychiatric illness is discussed. Ethical issues without resolution regarding genetic counselling of schizophrenia are debated. Conclusions: Recent genetic findings must lead to profound changes in genetic and family counselling in schizophrenia. Exposed attributable risk has immediate effects on genetic counselling of schizophrenia. Psychiatric risk counselling has thus changed from risk estimates based on family history to estimates based on test results in specific individuals.

Rare high-impact disease variants: properties and identifications.

Although many genome-wide association studies have been performed, the identification of disease polymorphisms remains important. It is now suspected that many rare disease variants induce the association signal of common variants in linkage disequilibrium (LD). Based on recent development of genetic models, the current study provides explanations of the existence of rare variants with high impacts and common variants with low impacts. Disease variants are neither necessary nor sufficient due to gene-gene or gene-environment interactions. A new method was developed based on theoretical aspects to identify both rare and common disease variants by their genotypes. Common disease variants were identified with relatively small odds ratios and relatively small sample sizes, except for specific situations in which the disease variants were in strong LD with a variant with a higher frequency. Rare disease variants with small impacts were difficult to identify without increasing sample sizes; however, the method was reasonably accurate for rare disease variants with high impacts. For rare variants, dominant variants generally showed better Type II error rates than recessive variants; however, the trend was reversed for common variants. Type II error rates increased in gene regions containing more than two disease variants because the more common variant, rather than both disease variants, was usually identified. The proposed method would be useful for identifying common disease variants with small impacts and rare disease variants with large impacts when disease variants have the same effects on disease presentation.

Temporal expression profiling identifies pathways mediating effect of causal variant on phenotype.

Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants’ effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biological bases of diseases, it makes differentiating the causative from the correlative effects difficult, as the dynamics of the underlying biological processes are not monitored. Using yeast as a model, we studied genome-wide gene expression dynamics in the presence of a causal variant as the sole genetic determinant, and performed allele-specific functional validation to delineate the causal effects of the genetic variant on the phenotype. Here, we characterized the precise genetic effects of a functional MKT1 allelic variant in sporulation efficiency variation. A mathematical model describing meiotic landmark events and conditional activation of MKT1 expression during sporulation specified an early meiotic role of this variant. By analyzing the early meiotic genome-wide transcriptional response, we demonstrate an MKT1-dependent role of novel modulators, namely, RTG1/3, regulators of mitochondrial retrograde signaling, and DAL82, regulator of nitrogen starvation, in additively effecting sporulation efficiency. In the presence of functional MKT1 allele, better respiration during early sporulation was observed, which was dependent on the mitochondrial retrograde regulator, RTG3. Furthermore, our approach showed that MKT1 contributes to sporulation independent of Puf3, an RNA-binding protein that steady-state transcription profiling studies have suggested to mediate MKT1-pleiotropic effects during mitotic growth. These results uncover interesting regulatory links between meiosis and mitochondrial retrograde signaling. In this study, we highlight the advantage of analyzing allele-specific transcriptional dynamics of mediating genes. Applications in higher eukaryotes can be valuable for inferring causal molecular pathways underlying complex dynamic processes, such as development, physiology and disease progression.

Understanding the Implications of Mitochondrial DNA Variation in the Health of Black Southern African Populations: The 2014 Workshop.

Understanding the Implications of Mitochondrial DNA Variation in the Health of Black Southern African Populations: The 2014 Workshop.

A New Approach to Assessing Affect and the Emotional Implications of Personal Genomic Testing for Common Disease Risk

Aims: Personal genomic testing (PGT) for common disease risk is becoming increasingly frequent, but little is known about people's array of emotional reactions to learning their genomic risk profiles and the psychological harms/benefits of PGT. We conducted a study of post-PGT affect, including positive, neutral, and negative states that may arise after testing. Methods: A total of 228 healthy adults received PGT for common disease variants and completed a semistructured research interview within 2 weeks of disclosure. The study participants reported how the PGT results made them feel in their own words. Using an iterative coding process, the responses were organized into three broad affective categories: negative, neutral, and positive affect. Results: Neutral affect was the most prevalent response (53.9%), followed by positive affect (26.9%) and negative affect (19.2%). We found no differences by gender, race, or education. Conclusions: While <20% of participants reported negative affect in response to learning their genomic risk profile for common diseases, a majority experienced either neutral or positive emotions. These findings contribute to the growing evidence that PGT does not impose significant psychological harms. Moreover, they point to a need to better link theories and assessments in both emotional and cognitive processing to capitalize on PGT information for healthy behavior change.

Highly penetrant alleles in age-related macular degeneration.

Age-related macular degeneration (AMD) is a complex disease caused by a combination of genetic and environmental factors. Genome-wide association studies have identified several common genetic variants associated with AMD, which together account for 15%-65% of the heritability of AMD. Multiple hypotheses to clarify the unexplained portion of genetic variance have been proposed, such as gene-gene interactions, gene-environment interactions, structural variations, epigenetics, and rare variants. Several studies support a role for rare variants with large effect sizes in the pathogenesis of AMD. In this work, we review the methods that can be used to detect rare variants in common diseases, as well as the recent progress that has been made in the identification of rare variants in AMD. In addition, the relevance of these rare variants for diagnosis, prognosis, and treatment of AMD is highlighted.

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.