106 research outputs found
GWAtoolbox: an R package for fast quality control and handling of genome-wide association studies meta-analysis data
No full textAbstract
Summary: The GWAtoolbox is an R package that standardizes and accelerates the handling of data from genome-wide association studies (GWAS), particularly in the context of large-scale GWAS meta-analyses. A key feature of GWAtoolbox is its ability to perform quality control (QC) of any number of files in a matter of minutes. The implemented workflow has been structured to check three particular data quality aspects: (i) data formatting, (ii) quality of the GWAS results and (iii) data consistency across studies. Output consists of an extensive list of quality statistics and plots which allow inspection of individual files and between-study comparison to identify systematic bias.
Availability: http://www.eurac.edu/GWAtoolbox
Contact: [email protected]; [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p
Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function
No full textReduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways
emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets
No full textAbstractSummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.Availability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/[email protected] informationSupplementary data are available at Bioinformatics online.</jats:sec
LASER server: ancestry tracing with genotypes or sequence reads
No full textAbstract
Summary
To enable direct comparison of ancestry background in different studies, we developed LASER to estimate individual ancestry by placing either sezquenced or genotyped samples in a common ancestry space, regardless of the sequencing strategy or genotyping array used to characterize each sample. Here we describe the LASER server to facilitate application of the method to a wide range of genetic studies. The server provides genetic ancestry estimation for different geographic regions and user-friendly interactive visualization of the results.
Availability and Implementation
The LASER server is freely accessible at http://laser.sph.umich.edu/
Supplementary information
Supplementary data are available at Bioinformatics online.
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Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program
No full textFull author list omitted for brevity. For the full list of authors, see article.The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)(1). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%
SNP Prioritization Using a B ayesian Probability of Association
No full textPrioritization is the process whereby a set of possible candidate genes or SNP s is ranked so that the most promising can be taken forward into further studies. In a genome‐wide association study, prioritization is usually based on the P ‐values alone, but researchers sometimes take account of external annotation information about the SNP s such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate B ayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome‐wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers’ subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P ‐value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta‐analysis of kidney function genome‐wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P ‐values alone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96317/1/gepi21704.pd
Importance of Different Types of Prior Knowledge in Selecting Genome‐Wide Findings for Follow‐Up
No full textBiological plausibility and other prior information could help select genome‐wide association ( GWA ) findings for further follow‐up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts’ opinions and empirical evidence to estimate the relative importance of 15 types of information at the single‐nucleotide polymorphism ( SNP ) and gene levels. Opinions were elicited from 10 experts using a two‐round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNP s established as being associated with seven disease traits through GWA meta‐analysis and independent replication, with the corresponding frequency in a randomly selected set of SNP s. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta‐analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96262/1/gepi21705.pd
GWAS of Hematuria
No full textBackground and objectives
Glomerular hematuria has varied causes but can have a genetic basis, including Alport syndrome and IgA nephropathy.
Design, setting, participants, & measurements
We used summary statistics to identify genetic variants associated with hematuria in White British UK Biobank participants. Individuals with glomerular hematuria were enriched by excluding participants with genitourinary conditions. A strongly associated locus on chromosome 2 (COL4A4-COL4A3) was identified. The region was reimputed using the Trans-Omics for Precision Medicine Program followed by sequential rounds of regional conditional analysis, conditioning on previous genetic signals. Similarly, we applied conditional analysis to identify independent variants in the MHC region on chromosome 6 using imputed HLA haplotypes.
Results
In total, 16,866 hematuria cases and 391,420 controls were included. Cases had higher urinary albumin-creatinine compared with controls (women: 13.01 mg/g [8.05–21.33] versus 12.12 mg/g [7.61–19.29]; P<0.001; men: 8.85 mg/g [5.66–16.19] versus 7.52 mg/g [5.04–12.39]; P<0.001) and lower eGFR (women: 88±14 versus 90±13 ml/min per 1.72 m2; P<0.001; men: 87±15 versus 90±13 ml/min per 1.72 m2; P<0.001), supporting enrichment of glomerular hematuria. Variants at six loci (PDPN, COL4A4-COL4A3, HLA-B, SORL1, PLLP, and TGFB1) met genome-wide significance (P<5E-8). At chromosome 2, COL4A4 p.Ser969X (rs35138315; minor allele frequency=0.00035; P<7.95E-35; odds ratio, 87.3; 95% confidence interval, 47.9 to 159.0) had the most significant association, and two variants in the locus remained associated with hematuria after conditioning for this variant: COL4A3 p.Gly695Arg (rs200287952; minor allele frequency=0.00021; P<2.16E-7; odds ratio, 45.5; 95% confidence interval, 11.8 to 168.0) and a common COL4A4 intron 25 variant (not previously reported; rs58261427; minor allele frequency=0.214; P<2.00E-9; odds ratio, 1.09; 95% confidence interval, 1.06 to 1.12). Of the HLA haplotypes, HLA-B (*0801; minor allele frequency=0.14; P<4.41E-24; odds ratio, 0.84; 95% confidence interval, 0.82 to 0.88) displayed the most statistically significant association. For remaining loci, we identified three novel associations, which were replicated in the deCODE dataset for dipstick hematuria (nearest genes: PDPN, SORL1, and PLLP).
Conclusions
Our study identifies six loci associated with hematuria, including independent variants in COL4A4-COL4A3 and HLA-B. Additionally, three novel loci are reported, including an association with an intronic variant in PDPN expressed in the podocyte
WEGS: a cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome
No full textWhole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a novel, cost-effective method, which we call “Whole Exome Genome Sequencing” (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7-2.0 times cheaper than standard WES (no-plexing), 1.8-2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus
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