Quantifying the extent to which index event biases influence large genetic association studies

Hanieh Yaghootkar; Michael P Bancks; Sam E Jones; Aaron McDaid; Robin Beaumont; Louise Donnelly; Andrew R Wood; Archie Campbell; Jessica Tyrrell; Lynne J Hocking; Marcus A Tuke; Katherine S Ruth; Ewan R Pearson; Anna Murray; Rachel M Freathy; Patricia B Munroe; Caroline Hayward; Colin Palmer; Michael N Weedon; James S Pankow; Timothy M Frayling; Zoltán Kutalik

doi:10.1093/hmg/ddw433

Quantifying the extent to which index event biases influence large genetic association studies

Hanieh Yaghootkar, Michael P Bancks, Sam E Jones, Aaron McDaid, Robin Beaumont, Louise Donnelly, Andrew R Wood, Archie Campbell, Jessica Tyrrell, Lynne J Hocking, Marcus A Tuke, Katherine S Ruth, Ewan R Pearson, Anna Murray, Rachel M Freathy, Patricia B Munroe, Caroline Hayward, Colin Palmer, Michael N Weedon, James S PankowTimothy M Frayling (Corresponding Author), Zoltán Kutalik

School of Medicine, Medical Sciences & Nutrition

Research output: Contribution to journal › Article › peer-review

30 Citations (Scopus)

Abstract

As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P < 0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P = 5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P < 5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.

Original language	English
Pages (from-to)	1018-1030
Number of pages	13
Journal	Human Molecular Genetics
Volume	26
Issue number	5
Early online date	30 Dec 2016
DOIs	https://doi.org/10.1093/hmg/ddw433
Publication status	Published - 1 Mar 2017

Bibliographical note

Funding

H.Y., A.R.W. and T.M.F. are supported by the European Research Council grant: 323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). M.A.T., M.N.W. and A.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). R.M.F. is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant: 104150/Z/14/Z). R.B. is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. J.T. is funded by a Diabetes Research and Wellness Foundation Fellowship. Z.K. received financial support from the Leenaards Foundation, the Swiss Institute of Bioinformatics and the Swiss National Science Foundation (31003A-143914) and SystemsX.ch (39). The work of M.P.B was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award no. T32HL007779. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. E.R.P. holds a WT New investigator award 102820/Z/13/Z.

We would like to thank George Davey Smith, Mark McCarthy and Joel Hirschhorn for helpful comments on the manuscript.

Keywords

Journal Article

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Yaghootkar, H., Bancks, M. P., Jones, S. E., McDaid, A., Beaumont, R., Donnelly, L., Wood, A. R., Campbell, A., Tyrrell, J., Hocking, L. J., Tuke, M. A., Ruth, K. S., Pearson, E. R., Murray, A., Freathy, R. M., Munroe, P. B., Hayward, C., Palmer, C., Weedon, M. N., ... Kutalik, Z. (2017). Quantifying the extent to which index event biases influence large genetic association studies. Human Molecular Genetics, 26(5), 1018-1030. https://doi.org/10.1093/hmg/ddw433

Yaghootkar, H, Bancks, MP, Jones, SE, McDaid, A, Beaumont, R, Donnelly, L, Wood, AR, Campbell, A, Tyrrell, J, Hocking, LJ, Tuke, MA, Ruth, KS, Pearson, ER, Murray, A, Freathy, RM, Munroe, PB, Hayward, C, Palmer, C, Weedon, MN, Pankow, JS, Frayling, TM & Kutalik, Z 2017, 'Quantifying the extent to which index event biases influence large genetic association studies', Human Molecular Genetics, vol. 26, no. 5, pp. 1018-1030. https://doi.org/10.1093/hmg/ddw433

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abstract = "As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P < 0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P = 5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P < 5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.",

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note = "Funding H.Y., A.R.W. and T.M.F. are supported by the European Research Council grant: 323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). M.A.T., M.N.W. and A.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). R.M.F. is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant: 104150/Z/14/Z). R.B. is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. J.T. is funded by a Diabetes Research and Wellness Foundation Fellowship. Z.K. received financial support from the Leenaards Foundation, the Swiss Institute of Bioinformatics and the Swiss National Science Foundation (31003A-143914) and SystemsX.ch (39). The work of M.P.B was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award no. T32HL007779. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. E.R.P. holds a WT New investigator award 102820/Z/13/Z. We would like to thank George Davey Smith, Mark McCarthy and Joel Hirschhorn for helpful comments on the manuscript.",

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AU - Yaghootkar, Hanieh

AU - Bancks, Michael P

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AU - Beaumont, Robin

AU - Donnelly, Louise

AU - Wood, Andrew R

AU - Campbell, Archie

AU - Tyrrell, Jessica

AU - Hocking, Lynne J

AU - Tuke, Marcus A

AU - Ruth, Katherine S

AU - Pearson, Ewan R

AU - Murray, Anna

AU - Freathy, Rachel M

AU - Munroe, Patricia B

AU - Hayward, Caroline

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AU - Weedon, Michael N

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AU - Frayling, Timothy M

AU - Kutalik, Zoltán

N1 - Funding H.Y., A.R.W. and T.M.F. are supported by the European Research Council grant: 323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). M.A.T., M.N.W. and A.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). R.M.F. is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant: 104150/Z/14/Z). R.B. is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. J.T. is funded by a Diabetes Research and Wellness Foundation Fellowship. Z.K. received financial support from the Leenaards Foundation, the Swiss Institute of Bioinformatics and the Swiss National Science Foundation (31003A-143914) and SystemsX.ch (39). The work of M.P.B was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award no. T32HL007779. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. E.R.P. holds a WT New investigator award 102820/Z/13/Z. We would like to thank George Davey Smith, Mark McCarthy and Joel Hirschhorn for helpful comments on the manuscript.

PY - 2017/3/1

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N2 - As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P < 0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P = 5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P < 5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.

AB - As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P < 0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P = 5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P < 5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.

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DO - 10.1093/hmg/ddw433

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JF - Human Molecular Genetics

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Quantifying the extent to which index event biases influence large genetic association studies

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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Cite this