Dataset Information

A comprehensive SNP and indel imputability database.

ABSTRACT: Genotype imputation has become an indispensible step in genome-wide association studies (GWAS). Imputation accuracy, directly influencing downstream analysis, has shown to be improved using re-sequencing-based reference panels; however, this comes at the cost of high computational burden due to the huge number of potentially imputable markers (tens of millions) discovered through sequencing a large number of individuals. Therefore, there is an increasing need for access to imputation quality information without actually conducting imputation. To facilitate this process, we have established a publicly available SNP and indel imputability database, aiming to provide direct access to imputation accuracy information for markers identified by the 1000 Genomes Project across four major populations and covering multiple GWAS genotyping platforms.SNP and indel imputability information can be retrieved through a user-friendly interface by providing the ID(s) of the desired variant(s) or by specifying the desired genomic region. The query results can be refined by selecting relevant GWAS genotyping platform(s). This is the first database providing variant imputability information specific to each continental group and to each genotyping platform. In Filipino individuals from the Cebu Longitudinal Health and Nutrition Survey, our database can achieve an area under the receiver-operating characteristic curve of 0.97, 0.91, 0.88 and 0.79 for markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. Specifically, by filtering out 48.6% of markers (corresponding to a reduction of up to 48.6% in computational costs for actual imputation) based on the imputability information in our database, we can remove 77%, 58%, 51% and 42% of the poorly imputed markers at the cost of only 0.3%, 0.8%, 1.5% and 4.6% of the well-imputed markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively.http://www.unc.edu/?yunmli/imputability.html

SUBMITTER: Duan Q

PROVIDER: S-EPMC3570215 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A comprehensive SNP and indel imputability database.

Duan Qing Q Liu Eric Yi EY Croteau-Chonka Damien C DC Mohlke Karen L KL Li Yun Y

Bioinformatics (Oxford, England) 20130103 4

<h4>Motivation</h4>Genotype imputation has become an indispensible step in genome-wide association studies (GWAS). Imputation accuracy, directly influencing downstream analysis, has shown to be improved using re-sequencing-based reference panels; however, this comes at the cost of high computational burden due to the huge number of potentially imputable markers (tens of millions) discovered through sequencing a large number of individuals. Therefore, there is an increasing need for access to imp ...[more]

PMID: 23292738

Similar Datasets

Project description:BackgroundSorghum (Sorghum bicolor) is one of the most important cereal crops globally and a potential energy plant for biofuel production. In order to explore genetic gain for a range of important quantitative traits, such as drought and heat tolerance, grain yield, stem sugar accumulation, and biomass production, via the use of molecular breeding and genomic selection strategies, knowledge of the available genetic variation and the underlying sequence polymorphisms, is required.ResultsBased on the assembled and annotated genome sequences of Sorghum bicolor (v2.1) and the recently published sorghum re-sequencing data, ~62.9 M SNPs were identified among 48 sorghum accessions and included in a newly developed sorghum genome SNP database SorGSD (http://sorgsd.big.ac.cn). The diverse panel of 48 sorghum lines can be classified into four groups, improved varieties, landraces, wild and weedy sorghums, and a wild relative Sorghum propinquum. SorGSD has a web-based query interface to search or browse SNPs from individual accessions, or to compare SNPs among several lines. The query results can be visualized as text format in tables, or rendered as graphics in a genome browser. Users may find useful annotation from query results including type of SNPs such as synonymous or non-synonymous SNPs, start, stop of splice variants, chromosome locations, and links to the annotation on Phytozome (www.phytozome.net) sorghum genome database. In addition, general information related to sorghum research such as online sorghum resources and literature references can also be found on the website. All the SNP data and annotations can be freely download from the website.ConclusionsSorGSD is a comprehensive web-portal providing a database of large-scale genome variation across all racial types of cultivated sorghum and wild relatives. It can serve as a bioinformatics platform for a range of genomics and molecular breeding activities for sorghum and for other C4 grasses.

Project description:BACKGROUND:Leaf mold, one of the major diseases of tomato caused by Cladosporium fulvum (C. fulvum), can dramatically reduce the yield and cause multimillion dollar losses annually worldwide. Mapping the resistance genes (R genes) of C. fulvum and devising MAS based strategies for breeding new cultivars is an effective approach to improve the resistance in tomato. Up to now, many C. fulvum genes or QTLs have been mapped using different genetic materials, but few studies focused on Cf-10 gene positioning. RESULTS:In this study, we investigated the genetic rules for Cf-10 and used a novel combinatorial strategy to rapidly map the Cf-10 gene. Initially, the performance of F1, F2 and BC1F1 individuals after infection, demonstrated that the resistance against C. fulvum was controlled by a single dominant gene. Two pools of resistant and susceptible individuals from F2 population were investigated, using mapping by sequencing approach and Cf-10 was found to be localized to 3.35?Mb and 3.74?Mb on chromosome 1, employing SNP/InDel index methods, respectively. After accounting for overlapping regions, these two algorithms yielded a total length of 3.29?Mb, narrowing down the target region. We further developed five serviceable KASP markers for this region based on sequencing data and conducted local QTL mapping using individuals from the F2 population, except for mapping by sequencing as mentioned above. Finally Cf-10 gene was mapped spanning a region of 790?kb, where only one gene (Solyc01g007130.3) was annotated as probable receptor protein kinase TMK1 with a LRR motif, a common R gene characteristic. The RT-qPCR analysis further confirmed the localization and the relative expression of Solyc01g007130.3 in Ontario 792 and was found to be significantly higher than that in Moneymaker at 9 dpi and 12 dpi, respectively. CONCLUSION:This study proposed a novel combinatorial strategy by combining SNP-index, InDel-index analyses and local QTL mapping using KASP genotyping approach to rapidly map genes responsible for specific traits and provided a robust base for cloning the Cf-10 gene. Furthermore, these analyses suggest that Solyc01g007130.3 is a potential candidate to be regarded as Cf-10 gene.

Dataset Information

A comprehensive SNP and indel imputability database.

Publications

A comprehensive SNP and indel imputability database.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets