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Imputation and quality control steps for combining multiple genome-wide datasets.

Verma SS, de Andrade M, Tromp G, Kuivaniemi H, Pugh E, Namjou-Khales B, Mukherjee S, Jarvik GP, Kottyan LC, Burt A, Bradford Y, Armstrong GD, Derr K, Crawford DC, Haines JL, Li R, Crosslin D, Ritchie MD - Front Genet (2014)

Bottom Line: A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms.We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets.The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University Pennsylvania, PA, USA.

ABSTRACT
The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 51,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R (2) (estimated correlation between the imputed and true genotypes), and the relationship between allelic R (2) and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.

No MeSH data available.


Related in: MedlinePlus

Best practices for analyzing imputed data.
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Figure 5: Best practices for analyzing imputed data.

Mentions: In Figure 5 we summarize all of the “Best Practices” steps and measures for imputed data prior to using the data in any further analyses. We provided a final quality control (QC) dataset filtered at info score = 0.7 and marker call rate = 99%. We did not apply any sample call rate, and MAF filter as that depends on the type of analysis being performed. Tables 6, 7 show total counts of SNPs at each threshold we used during quality control for both the adults and pediatric datasets.


Imputation and quality control steps for combining multiple genome-wide datasets.

Verma SS, de Andrade M, Tromp G, Kuivaniemi H, Pugh E, Namjou-Khales B, Mukherjee S, Jarvik GP, Kottyan LC, Burt A, Bradford Y, Armstrong GD, Derr K, Crawford DC, Haines JL, Li R, Crosslin D, Ritchie MD - Front Genet (2014)

Best practices for analyzing imputed data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263197&req=5

Figure 5: Best practices for analyzing imputed data.
Mentions: In Figure 5 we summarize all of the “Best Practices” steps and measures for imputed data prior to using the data in any further analyses. We provided a final quality control (QC) dataset filtered at info score = 0.7 and marker call rate = 99%. We did not apply any sample call rate, and MAF filter as that depends on the type of analysis being performed. Tables 6, 7 show total counts of SNPs at each threshold we used during quality control for both the adults and pediatric datasets.

Bottom Line: A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms.We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets.The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University Pennsylvania, PA, USA.

ABSTRACT
The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 51,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R (2) (estimated correlation between the imputed and true genotypes), and the relationship between allelic R (2) and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.

No MeSH data available.


Related in: MedlinePlus