Benefits of accurate imputations in GWAS

Shefali S. Verma; Peggy Peissig; Deanna Sue Cross; Carol Waudby; Murray Brilliant; Catherine A. McCarty; Marylyn D. Ritchie

doi:10.1007/978-3-662-45523-4_71

Benefits of accurate imputations in GWAS

Shefali S. Verma, Peggy Peissig, Deanna Sue Cross, Carol Waudby, Murray Brilliant, Catherine A. McCarty, Marylyn D. Ritchie

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Imputation methods have been suggested as an efficient way to increase both utility and coverage in genome-wide association studies, especially when combining data generated from different genotyping arrays. We aim to demonstrate that imputation results are extremely accurate and the association analysis from imputed data does not over-inflate the results. Instead imputation leads to an increase in the power of the dataset without introducing any systematic biases. The majority of common variants can be imputed with very high accuracy (r²>0.9) and we validated the accuracy of imputations by comparing actual genotypes from low-throughput genotyping assays against imputed genotypes. Imputation was performed using IMPUTE2 and the 1000 Genomes cosmopolitan reference panel, which results in about 38 million SNPs. After quality control and filtering we performed case-control associations with 3,159,556 markers. We show a comparison of results from genotyped and imputed data and also determine how accurate ancestry is determined by imputations.

Original language	English
Title of host publication	Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Revised Selected Papers
Editors	Anna I. Esparcia-Alcázar
Publisher	Springer Verlag
Pages	877-889
Number of pages	13
ISBN (Electronic)	9783662455227
DOIs	https://doi.org/10.1007/978-3-662-45523-4_71
State	Published - 1 Jan 2014
Event	17th European Conference on Applications of Evolutionary Computation, EvoApplications 2014 - Granada, Spain Duration: 23 Apr 2014 → 25 Apr 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8602
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	17th European Conference on Applications of Evolutionary Computation, EvoApplications 2014
Country/Territory	Spain
City	Granada
Period	23/04/14 → 25/04/14

Keywords

1000 Genomes
Cataract
Genome wide association studies
Imputations
PhenX
Type 2 diabetes

Access to Document

10.1007/978-3-662-45523-4_71

Cite this

Verma, S. S., Peissig, P., Cross, D. S., Waudby, C., Brilliant, M., McCarty, C. A., & Ritchie, M. D. (2014). Benefits of accurate imputations in GWAS. In A. I. Esparcia-Alcázar (Ed.), Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Revised Selected Papers (pp. 877-889). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8602). Springer Verlag. https://doi.org/10.1007/978-3-662-45523-4_71

Verma, Shefali S. ; Peissig, Peggy ; Cross, Deanna Sue et al. / Benefits of accurate imputations in GWAS. Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Revised Selected Papers. editor / Anna I. Esparcia-Alcázar. Springer Verlag, 2014. pp. 877-889 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Imputation methods have been suggested as an efficient way to increase both utility and coverage in genome-wide association studies, especially when combining data generated from different genotyping arrays. We aim to demonstrate that imputation results are extremely accurate and the association analysis from imputed data does not over-inflate the results. Instead imputation leads to an increase in the power of the dataset without introducing any systematic biases. The majority of common variants can be imputed with very high accuracy (r2>0.9) and we validated the accuracy of imputations by comparing actual genotypes from low-throughput genotyping assays against imputed genotypes. Imputation was performed using IMPUTE2 and the 1000 Genomes cosmopolitan reference panel, which results in about 38 million SNPs. After quality control and filtering we performed case-control associations with 3,159,556 markers. We show a comparison of results from genotyped and imputed data and also determine how accurate ancestry is determined by imputations.",

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Verma, SS, Peissig, P, Cross, DS, Waudby, C, Brilliant, M, McCarty, CA & Ritchie, MD 2014, Benefits of accurate imputations in GWAS. in AI Esparcia-Alcázar (ed.), Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8602, Springer Verlag, pp. 877-889, 17th European Conference on Applications of Evolutionary Computation, EvoApplications 2014, Granada, Spain, 23/04/14. https://doi.org/10.1007/978-3-662-45523-4_71

Benefits of accurate imputations in GWAS. / Verma, Shefali S.; Peissig, Peggy; Cross, Deanna Sue et al.
Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Revised Selected Papers. ed. / Anna I. Esparcia-Alcázar. Springer Verlag, 2014. p. 877-889 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8602).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Peissig, Peggy

AU - Cross, Deanna Sue

AU - Waudby, Carol

AU - Brilliant, Murray

AU - McCarty, Catherine A.

AU - Ritchie, Marylyn D.

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N2 - Imputation methods have been suggested as an efficient way to increase both utility and coverage in genome-wide association studies, especially when combining data generated from different genotyping arrays. We aim to demonstrate that imputation results are extremely accurate and the association analysis from imputed data does not over-inflate the results. Instead imputation leads to an increase in the power of the dataset without introducing any systematic biases. The majority of common variants can be imputed with very high accuracy (r2>0.9) and we validated the accuracy of imputations by comparing actual genotypes from low-throughput genotyping assays against imputed genotypes. Imputation was performed using IMPUTE2 and the 1000 Genomes cosmopolitan reference panel, which results in about 38 million SNPs. After quality control and filtering we performed case-control associations with 3,159,556 markers. We show a comparison of results from genotyped and imputed data and also determine how accurate ancestry is determined by imputations.

AB - Imputation methods have been suggested as an efficient way to increase both utility and coverage in genome-wide association studies, especially when combining data generated from different genotyping arrays. We aim to demonstrate that imputation results are extremely accurate and the association analysis from imputed data does not over-inflate the results. Instead imputation leads to an increase in the power of the dataset without introducing any systematic biases. The majority of common variants can be imputed with very high accuracy (r2>0.9) and we validated the accuracy of imputations by comparing actual genotypes from low-throughput genotyping assays against imputed genotypes. Imputation was performed using IMPUTE2 and the 1000 Genomes cosmopolitan reference panel, which results in about 38 million SNPs. After quality control and filtering we performed case-control associations with 3,159,556 markers. We show a comparison of results from genotyped and imputed data and also determine how accurate ancestry is determined by imputations.

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ER -

Verma SS, Peissig P, Cross DS, Waudby C, Brilliant M, McCarty CA et al. Benefits of accurate imputations in GWAS. In Esparcia-Alcázar AI, editor, Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Revised Selected Papers. Springer Verlag. 2014. p. 877-889. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-662-45523-4_71

Benefits of accurate imputations in GWAS

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