A comparison of genotyping arrays

Joost A.M. Verlouw; Eva Clemens; Jard H. de Vries; Oliver Zolk; Annemieke J.M.H. Verkerk; Antoinette am Zehnhoff-Dinnesen; Carolina Medina-Gomez; Claudia Lanvers-Kaminsky; Fernando Rivadeneira; Thorsten Langer; Joyce B.J. van Meurs; Marry M. van den Heuvel-Eibrink; André G. Uitterlinden; Linda Broer

doi:10.1038/s41431-021-00917-7

A comparison of genotyping arrays

Joost A.M. Verlouw, Eva Clemens, Jard H. de Vries, Oliver Zolk, Annemieke J.M.H. Verkerk, Antoinette am Zehnhoff-Dinnesen, Carolina Medina-Gomez, Claudia Lanvers-Kaminsky, Fernando Rivadeneira, Thorsten Langer, Joyce B.J. van Meurs, Marry M. van den Heuvel-Eibrink, André G. Uitterlinden, Linda Broer^*

^*Corresponding author for this work

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Abstract

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study’s requirements.

Original language	English
Pages (from-to)	1611-1624
Number of pages	14
Journal	European Journal of Human Genetics
Volume	29
Issue number	11
Early online date	18 Jun 2021
DOIs	https://doi.org/10.1038/s41431-021-00917-7
Publication status	Published - Nov 2021

Bibliographical note

Funding Information:
Acknowledgements First of all, we would like to acknowledge both Affymetrix and Illumina for providing us with the HapMap samples genotyped on a number of their arrays, making this comparison possible. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters, MSc, and CM-G, MSc, for their help in creating the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and CM-G, MSc, for the creation and analysis of imputed data. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalog number NTR6831. All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians. The Pancare LIFE work was supported by EU-FP7. This work was supported by the PanCare LIFE project that has received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under Grant Agreement No. 602030.

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Verlouw, J. A. M., Clemens, E., de Vries, J. H., Zolk, O., Verkerk, A. J. M. H., am Zehnhoff-Dinnesen, A., Medina-Gomez, C., Lanvers-Kaminsky, C., Rivadeneira, F., Langer, T., van Meurs, J. B. J., van den Heuvel-Eibrink, M. M., Uitterlinden, A. G., & Broer, L. (2021). A comparison of genotyping arrays. European Journal of Human Genetics, 29(11), 1611-1624. https://doi.org/10.1038/s41431-021-00917-7

@article{324c55a7280741c89ffbb203303f9c03,

title = "A comparison of genotyping arrays",

abstract = "Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study{\textquoteright}s requirements.",

author = "Verlouw, {Joost A.M.} and Eva Clemens and {de Vries}, {Jard H.} and Oliver Zolk and Verkerk, {Annemieke J.M.H.} and {am Zehnhoff-Dinnesen}, Antoinette and Carolina Medina-Gomez and Claudia Lanvers-Kaminsky and Fernando Rivadeneira and Thorsten Langer and {van Meurs}, {Joyce B.J.} and {van den Heuvel-Eibrink}, {Marry M.} and Uitterlinden, {Andr{\'e} G.} and Linda Broer",

note = "Funding Information: Acknowledgements First of all, we would like to acknowledge both Affymetrix and Illumina for providing us with the HapMap samples genotyped on a number of their arrays, making this comparison possible. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters, MSc, and CM-G, MSc, for their help in creating the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and CM-G, MSc, for the creation and analysis of imputed data. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalog number NTR6831. All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians. The Pancare LIFE work was supported by EU-FP7. This work was supported by the PanCare LIFE project that has received funding from the European Union{\textquoteright}s Seventh Framework Programme for research, technological development, and demonstration under Grant Agreement No. 602030. Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = nov,

doi = "10.1038/s41431-021-00917-7",

language = "English",

volume = "29",

pages = "1611--1624",

journal = "European Journal of Human Genetics",

issn = "1018-4813",

publisher = "Springer Nature",

number = "11",

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Verlouw, JAM, Clemens, E, de Vries, JH, Zolk, O, Verkerk, AJMH, am Zehnhoff-Dinnesen, A, Medina-Gomez, C, Lanvers-Kaminsky, C, Rivadeneira, F, Langer, T, van Meurs, JBJ , van den Heuvel-Eibrink, MM , Uitterlinden, AG & Broer, L 2021, 'A comparison of genotyping arrays', European Journal of Human Genetics, vol. 29, no. 11, pp. 1611-1624. https://doi.org/10.1038/s41431-021-00917-7

TY - JOUR

T1 - A comparison of genotyping arrays

AU - Verlouw, Joost A.M.

AU - Clemens, Eva

AU - de Vries, Jard H.

AU - Zolk, Oliver

AU - Verkerk, Annemieke J.M.H.

AU - am Zehnhoff-Dinnesen, Antoinette

AU - Medina-Gomez, Carolina

AU - Lanvers-Kaminsky, Claudia

AU - Rivadeneira, Fernando

AU - Langer, Thorsten

AU - van Meurs, Joyce B.J.

AU - van den Heuvel-Eibrink, Marry M.

AU - Uitterlinden, André G.

AU - Broer, Linda

N1 - Funding Information: Acknowledgements First of all, we would like to acknowledge both Affymetrix and Illumina for providing us with the HapMap samples genotyped on a number of their arrays, making this comparison possible. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters, MSc, and CM-G, MSc, for their help in creating the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and CM-G, MSc, for the creation and analysis of imputed data. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalog number NTR6831. All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians. The Pancare LIFE work was supported by EU-FP7. This work was supported by the PanCare LIFE project that has received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under Grant Agreement No. 602030. Publisher Copyright: © 2021, The Author(s).

PY - 2021/11

Y1 - 2021/11

N2 - Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study’s requirements.

AB - Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study’s requirements.

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DO - 10.1038/s41431-021-00917-7

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EP - 1624

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A comparison of genotyping arrays

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