The MLH1 c.1852_1853delinsGC (p.K618A) Variant in Colorectal Cancer: Genetic Association Study in 18,723 Individuals

Colorectal cancer is one of the most frequent neoplasms and an important cause of mortality in the developed world. Mendelian syndromes account for about 5% of the total burden of CRC, being Lynch syndrome and familial adenomatous polyposis the most common forms. Lynch syndrome tumors develop mainly as a consequence of defective DNA mismatch repair associated with germline mutations in MLH1, MSH2, MSH6 and PMS2. A significant proportion of variants identified by screening these genes correspond to missense or noncoding changes without a clear pathogenic consequence, and they are designated as "variants of uncertain significance'', being the c.1852_1853delinsGC (p.K618A) variant in the MLH1 gene a clear example. The implication of this variant as a low-penetrance risk variant for CRC was assessed in the present study by performing a case-control study within a large cohort from the COGENT consortium-COST Action BM1206 including 18,723 individuals (8,055 colorectal cancer cases and 10,668 controls) and a case-only genotype-phenotype correlation with several clinical and pathological characteristics restricted to the Epicolon cohort. Our results showed no involvement of this variant as a low-penetrance variant for colorectal cancer genetic susceptibility and no association with any clinical and pathological characteristics including family history for this neoplasm or Lynch syndrome.

Genetic association study of plasma creatine kinase levels in the Montreal Heart Institute Hospital Cohort

Il a déjà été démontré que les statines (ou inhibiteurs de la HMG-CoA réductase) sont efficaces pour réduire le LDL-cholestérol et elles se sont depuis établies comme étant le pilier dans le traitement de la dyslipidémie. Toutefois, environ 10 pourcent des utilisateurs de statines souffrent d'effets indésirables, généralement sous forme de myopathie qui est souvent accompagnée d’un taux élevé de la créatine kinase (CK) plasmatique. Il est fréquent que les patients doivent arrêter les statines à cause d’un taux de CK dépassant un seuil de référence. Nous avons examiné le taux de CK de près de 6000 participants de la biobanque de l’ICM, qui ont récemment été génotypés à l'aide de la micropuce d'ADN ExomChip d'Illumina. Des études antérieures ont démontré une association significative entre le taux de CK plasmatique et des polymorphismes génétiques et nous avons cherché à répliquer ces résultats par association génétique et à l'aide du test SKAT pour les polymorphismes rares. Nous avons répliqué les résultats dans le gène CKM (rs11559024, p=1.59x10-23) et le gène LILRB5 (rs12975366, p=1.44x10-26) dans le chromosome 19. Nous espérons que ces résultats seront éventuellement utilisés en clinique pour la prédiction des taux de référence de CK personnalisés selon le profil génétique des patients utilisateurs de statines.

Genetic association study between Interleukin 10 gene and dental implant loss

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.

STrengthening the REporting of Genetic Association studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

STrengthening the REporting of Genetic Association Studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the strengthening the reporting of observational studies in epidemiology (STROBE) statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence, the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association (STREGA) studies initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

STrengthening the REporting of Genetic Association studies (STREGA): an extension of the STROBE Statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE Statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Genome-wide association study for sight-threatening diabetic retinopathy reveals association with genetic variation near the GRB2 gene

Aims/hypothesis Diabetic retinopathy is a serious complication of diabetes mellitus and can lead to blindness. A genetic component, in addition to traditional risk factors, has been well described although strong genetic factors have not yet been identified. Here, we aimed to identify novel genetic risk factors for sight-threatening diabetic retinopathy using a genome-wide association study. Methods Retinopathy was assessed in white Australians with type 2 diabetes mellitus. Genome-wide association analysis was conducted for comparison of cases of sight-threatening diabetic retinopathy (n = 336) with diabetic controls with no retinopathy (n = 508). Top ranking single nucleotide polymorphisms were typed in a type 2 diabetes replication cohort, a type 1 diabetes cohort and an Indian type 2 cohort. A mouse model of proliferative retinopathy was used to assess differential expression of the nearby candidate gene GRB2 by immunohistochemistry and quantitative western blot. Results The top ranked variant was rs3805931 with p = 2.66 × 10−7, but no association was found in the replication cohort. Only rs9896052 (p = 6.55 × 10−5) was associated with sight-threatening diabetic retinopathy in both the type 2 (p = 0.035) and the type 1 (p = 0.041) replication cohorts, as well as in the Indian cohort (p = 0.016). The study-wide meta-analysis reached genome-wide significance (p = 4.15 × 10−8). The GRB2 gene is located downstream of this variant and a mouse model of retinopathy showed increased GRB2 expression in the retina. Conclusions/interpretation Genetic variation near GRB2 on chromosome 17q25.1 is associated with sight-threatening diabetic retinopathy. Several genes in this region are promising candidates and in particular GRB2 is upregulated during retinal stress and neovascularisation.

Genome-wide association study to identify genetic determinants of severe asthma

Background: The genetic basis for developing asthma has been extensively studied. However, association studies to date have mostly focused on mild to moderate disease and genetic risk factors for severe asthma remain unclear. Objective: To identify common genetic variants affecting susceptibility to severe asthma. Methods: A genome-wide association study was undertaken in 933 European ancestry individuals with severe asthma based on Global Initiative for Asthma (GINA) criteria 3 or above and 3346 clean controls. After standard quality control measures, the association of 480 889 genotyped single nucleotide polymorphisms (SNPs) was tested. To improve the resolution of the association signals identified, non-genotyped SNPs were imputed in these regions using a dense reference panel of SNP genotypes from the 1000 Genomes Project. Then replication of SNPs of interest was undertaken in a further 231 cases and 1345 controls and a meta-analysis was performed to combine the results across studies. Results: An association was confirmed in subjects with severe asthma of loci previously identified for association with mild to moderate asthma. The strongest evidence was seen for the ORMDL3/GSDMB locus on chromosome 17q12-21 (rs4794820, p=1.03×10 (-8)following meta-analysis) meeting genome-wide significance. Strong evidence was also found for the IL1RL1/IL18R1 locus on 2q12 (rs9807989, p=5.59×10 (-8) following meta-analysis) just below this threshold. No novel loci for susceptibility to severe asthma met strict criteria for genome-wide significance. Conclusions: The largest genome-wide association study of severe asthma to date was carried out and strong evidence found for the association of two previously identified asthma susceptibility loci in patients with severe disease. A number of novel regions with suggestive evidence were also identified warranting further study.

Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P<1.09 × 10−9) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the Framingham Study

BACKGROUND: Family studies and heritability estimates provide evidence for a genetic contribution to variation in the human life span. METHODS:We conducted a genome wide association study (Affymetrix 100K SNP GeneChip) for longevity-related traits in a community-based sample. We report on 5 longevity and aging traits in up to 1345 Framingham Study participants from 330 families. Multivariable-adjusted residuals were computed using appropriate models (Cox proportional hazards, logistic, or linear regression) and the residuals from these models were used to test for association with qualifying SNPs (70, 987 autosomal SNPs with genotypic call rate [greater than or equal to]80%, minor allele frequency [greater than or equal to]10%, Hardy-Weinberg test p [greater than or equal to] 0.001).RESULTS:In family-based association test (FBAT) models, 8 SNPs in two regions approximately 500 kb apart on chromosome 1 (physical positions 73,091,610 and 73, 527,652) were associated with age at death (p-value < 10-5). The two sets of SNPs were in high linkage disequilibrium (minimum r2 = 0.58). The top 30 SNPs for generalized estimating equation (GEE) tests of association with age at death included rs10507486 (p = 0.0001) and rs4943794 (p = 0.0002), SNPs intronic to FOXO1A, a gene implicated in lifespan extension in animal models. FBAT models identified 7 SNPs and GEE models identified 9 SNPs associated with both age at death and morbidity-free survival at age 65 including rs2374983 near PON1. In the analysis of selected candidate genes, SNP associations (FBAT or GEE p-value < 0.01) were identified for age at death in or near the following genes: FOXO1A, GAPDH, KL, LEPR, PON1, PSEN1, SOD2, and WRN. Top ranked SNP associations in the GEE model for age at natural menopause included rs6910534 (p = 0.00003) near FOXO3a and rs3751591 (p = 0.00006) in CYP19A1. Results of all longevity phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. CONCLUSION: Longevity and aging traits are associated with SNPs on the Affymetrix 100K GeneChip. None of the associations achieved genome-wide significance. These data generate hypotheses and serve as a resource for replication as more genes and biologic pathways are proposed as contributing to longevity and healthy aging.