8,470 research outputs found
Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability
No full textGenome-wide association studies (GWAS) are conducted with the promise to discover novel genetic variants associated with diverse traits. For most traits, associated markers individually explain just a modest fraction of the phenotypic variation, but their number can well be in the hundreds. We developed a maximum likelihood method that allows us to infer the distribution of associated variants even when many of them were missed by chance. Compared to previous approaches, the novelty of our method is that it (a) does not require having an independent (unbiased) estimate of the effect sizes; (b) makes use of the complete distribution of P-values while allowing for the false discovery rate; (c) takes into account allelic heterogeneity and the SNP pruning strategy. We applied our method to the latest GWAS meta-analysis results of the GIANT consortium. It revealed that while the explained variance of genome-wide (GW) significant SNPs is around 1% for waist-hip ratio (WHR), the observed P-values provide evidence for the existence of variants explaining 10% (CI=[8.5-11.5%]) of the phenotypic variance in total. Similarly, the total explained variance likely to exist for height is estimated to be 29% (CI=[28-30%]), three times higher than what the observed GW significant SNPs give rise to. This methodology also enables us to predict the benefit of future GWA studies that aim to reveal more associated genetic markers via increased sample size
A Bivariate Genome-Wide Approach to Metabolic Syndrome STAMPEED Consortium
No full textOBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants
Across-cohort QC analyses of GWAS summary statistics from complex traits
No full textGenome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics Fst statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy
SHui open data research platform
No full textData collected and revised by individual instutions of the Shui-Consortium. Publication by the EU-China Consortium SHui.For each data-file, the author (institution) of the file is given as “operator”.-- At project end, June 30th, 2022.-- For each data-file, the author/data owner for citation is given as “operator” and “contact”.-- Plot data as .csv; catchment data ad libitum.Spatial situation data: Plot data and catchment data available; country, latitude, and longitude coordinates given.-- Temporal situation data: Long-term and single-season data available. Start and end date for each data file given.CC BY-SA. No embargo. The release on the Shui download site and CSIC repository implies expiration of any embargo delivered by the data owner.Project Co-ordinators: Dr. Jose Alfonso Gómez Calero (Instituto de Agricultura Sostenible (IAS-CISC), Dr. Weifeng Xu (Fujian Agriculture and Forest University, FAFU).This data set contains data from the SHui open-data platform for sharing long-term agricultural experiments aimed to optimizing yield and soil and water. Data and additional material are available under https://shui.boku.ac.at/shui/public/startAlphanumeric data measured at hydrologic and agronomical experiments (e.g., plant development, soil properties, hydrology, erosion, management).Further information on the data, project, partners, and publications under https://www.shui-eu.org/EU-China Consortium SHui: European Union Project 773903 and Chinese MOST.Peer reviewe
Enrichment and characterization of a bacteria consortium capable of heterotrophic nitrification and aerobic denitrification at low temperature
No full textNitrogen removal in wastewater treatment plants is usually severely inhibited under cold temperature. The present study proposes bioaugmentation using psychrotolerant heterotrophic nitrification-aerobic denitrification consortium to enhance nitrogen removal at low temperature. A functional consortium has been successfully enriched by stepped increase in DO concentration. Using this consortium, the specific removal rates of ammonia and nitrate at 10 degrees C reached as high as 3.1 mg N/(g SS h) and 9.6 mg N/ (g SS h), respectively. PCR-DGGE and clone library analysis both indicated a significant reduction in bacterial diversity during enrichment. Phylogenetic analysis based on nearly full-length 16S rRNA genes showed that Alphaproteobacteria. Deltaproteobacteria and particularly Bacteroidetes declined while Gammaproteobacteria (all clustered into Pseudomonas sp.) and Betaproteobacteria (mainly Rhodoferax ferrireducens) became dominant in the enriched consortium. It is likely that Pseudomonas spp. played a major role in nitrification and denitrification, while R. ferrireducens and its relatives utilized nitrate as both electron acceptor and nitrogen source. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.</p
Arabic Treebank : Part 2 v 3.1
No full textArabic Treebank: Part 2 (ATB2) v 3.1 , Linguistic Data Consortium (LDC) catalog number LDC2011T09 and isbn 1-58563-590-1, was developed at LDC. It consists of 501 newswire stories from Ummah Press with part-of-speech (POS), morphology, gloss and syntactic treebank annotation in accordance with the Penn Arabic Treebank (PATB) Guidelines developed in 2008 and 2009
Additional genome-wide significant loci identified by CPASSOC for each trait but not conventional meta-analysis of GIANT consortium.
No full textAdditional genome-wide significant loci identified by CPASSOC for each trait but not conventional meta-analysis of GIANT consortium.</p
Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes (Nature Genetics, (2018), 50, 4, (524-537), 10.1038/s41588-018-0058-3)
No full textIn the HTML version of this article initially published, the author groups ‘AFGen Consortium’, ‘Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’, ‘International Genomics of Blood Pressure (iGEN-BP) Consortium’, ‘INVENT Consortium’, ‘STARNET’, ‘BioBank Japan Cooperative Hospital Group’, ‘COMPASS Consortium’, ‘EPIC-CVD Consortium’, ‘EPIC-InterAct Consortium’, ‘International Stroke Genetics Consortium (ISGC)’, ‘METASTROKE Consortium’, ‘Neurology Working Group of the CHARGE Consortium’, ‘NINDS Stroke Genetics Network (SiGN)’, ‘UK Young Lacunar DNA Study’ and ‘MEGASTROKE Consortium’ appeared at the end of the author list but should have appeared earlier in the list. In addition, the author group ‘MEGASTROKE Consortium’ was duplicated, and its members were not displayed in the ‘Author information’ section. The errors have been corrected in the HTML version of the article
Replication power in the GIANT consortium for BMI and height.
No full textWe tested the novel associations in the UK Biobank discovered by PAT and HIPO for replication in the GIANT consortium. We separately clumped using the lead variant as determined by the m-value. For each variant, we calculate replication power and bin the variants into deciles. The first column lists the trait. The second column is the decile while the third and fourth column are the average power within the set for each respective method. The number of variants tested for replication, the expected number of replications, and the number of variants that replicated are reported in the next six columns. The final two columns contain the number of variants with effect sizes from the GIANT consortium in the same direction seen in the UK Biobank. A binomial test on whether the proportion of effect sizes in the same direction across studies is greater than 50% of all tested variants in the set. A single asterisks means the results are significant at the nominal α = 0.05 and two asterisks indicates significance at .</p
Multivariate Analysis of Anthropometric Traits Using Summary Statistics of Genome-Wide Association Studies from GIANT Consortium.
No full textMeta-analysis of single trait for multiple cohorts has been used for increasing statistical power in genome-wide association studies (GWASs). Although hundreds of variants have been identified by GWAS, these variants only explain a small fraction of phenotypic variation. Cross-phenotype association analysis (CPASSOC) can further improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this study, we performed CPASSOC analysis on the summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium using a novel method recently developed by our group. Sex-specific meta-analysis data for height, body mass index (BMI), and waist-to-hip ratio adjusted for BMI (WHRadjBMI) from discovery phase of the GIANT consortium study were combined using CPASSOC for each trait as well as 3 traits together. The conventional meta-analysis results from the discovery phase data of GIANT consortium studies were used to compare with that from CPASSOC analysis. The CPASSOC analysis was able to identify 17 loci associated with anthropometric traits that were missed by conventional meta-analysis. Among these loci, 16 have been reported in literature by including additional samples and 1 is novel. We also demonstrated that CPASSOC is able to detect pleiotropic effects when analyzing multiple traits
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