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The impact of newly-identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts: supplement to the paper published in Genetic Epidemiology

October 2008

Juha Karvanen, D.Sc.(Tech.)¹, Kaisa Silander, PhD^2,3, Frank Kee, MD⁴, Laurence Tiret, PhD⁵, Veikko Salomaa, MD, PhD¹, Kari Kuulasmaa, PhD¹, Per-Gunnar Wiklund, MD, PhD⁶, Jarmo Virtamo, MD, PhD¹, Olli Saarela, M.Sc.¹, Claire Perret M.Sc.⁵, Markus Perola MD, PhD^2,3,7, Leena Peltonen, MD, PhD^2,3,7,8,9, Francois Cambien, MD, PhD⁵, Jeanette Erdmann, PhD¹⁰, Nilesh J. Samani, F.Med.Sci.¹¹, Heribert Schunkert, MD¹⁰, Alun Evans, MD⁴ for the MORGAM Project¹²

¹ Department of Health Promotion and Chronic Disease Prevention, National Public Health Institute, Helsinki, Finland,
² Department of Molecular Medicine, National Public Health Institute, Helsinki, Finland,
³ FIMM, Institute for Molecular Medicine Finland, Helsinki, Finland,
⁴ The Queen's University of Belfast, Belfast, United Kingdom,
⁵ INSERM 525, Faculte de Medecine Pitie-Salpetriere, Paris, France,
⁶ Department of Public Health and Clinical Medicine, Umeå University, Sweden,
⁷ Department of Medical Genetics, University of Helsinki, Helsinki, Finland,
⁸ Department of Human Genetics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom,
⁹ The Broad Institute of MIT and Harvard, Boston, MA, USA
¹⁰ Universität zu Lübeck, Lübeck, Germany,
¹¹ University of Leicester, Leicester, United Kingdom
¹² See Annex for the sites and key personnel of contributing MORGAM Centres

© National Institute for Health and Welfare and the MORGAM Project investigators
Last updated: 14 April 2009
For more information, please contact Juha Karvanen (firstname.lastname@thl.fi)

Document identification:

URL:http://www.ktl.fi/publications/morgam/cardiogenics/index.html
URN:NBN:fi-fe200811052059
Recommended citation: Karvanen J, Silander K, Kee F, Tiret L, Salomaa V, Kuulasmaa K, Wiklund P-G, Virtamo J, Saarela O, Perret C, Perola M, Peltonen L, Cambien F, Erdmann J, Samani N. J., Schunkert H, Evans A, for the MORGAM Project. The impact of newly-identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts: supplement to the paper published in Genetic Epidemiology. (2008). Available from URL:http://www.ktl.fi/publications/morgam/cardiogenics/index.html, URN:NBN:fi-fe200811052059.

Introduction
Power calculations
Allele frequencies and Hardy-Weinberg equilibrium tests
Main analyses
Additional analyses
References
Acknowledgements
Annex: Sites and key personnel of contributing MORGAM Centres

Introduction

This document is the web supplement to the article:

Karvanen J, Silander K, Kee F, Tiret L, Salomaa V, Kuulasmaa K, Wiklund P-G, Virtamo J, Saarela O, Perret C, Perola M, Peltonen L, Cambien F, Erdmann J, Samani N. J., Schunkert H, Evans A, for the MORGAM Project. The impact of newly-identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts. Genet Epidemiol. 2009;33:237-246. doi: 10.1002/gepi.20374.

The supplement describes the methods used in power calculations, lists the studied genes and presents the detailed analysis results.

Power calculations

Power calculations are based on the work of Cai and Zeng [1]. Cai and Zeng propose the following formula for the power of a generalisation of the log-rank test

$equation: \Phi\left( Z_\alpha+\sqrt{n_s}\theta\sqrt{\frac{p_1 p_2 p_D}{q+(1-q)p_D}}\right)$

where Φ is the cumulative distribution function of the standard normal distribution, α is the significance level, Z_α is αth quantile of the standard normal distribtion, θ is the log-hazard ratio for two groups, p_j, j=1,2, are the proportion of the two groups in the population (p₁+p₂=1), n_s is the total number of subjects in the subcohort, p_D is the proportion of the failures in the full cohort and q is the sampling fraction of the subcohort. We assume that the formula provides a good approximation for power in our analyses although we do not use the log-rank test. Allele frequency of chromosome 9p21.3 (rs1333049) are used in the calculations leading to p₁=0.41 and p₂=0.59. The size of subcohort is n_s=2381 and the sampling fraction is q=2381/33282=0.07. The proportion of failures is p_D=0.043 for CHD and p_D=0.017 for stroke (cerebral infarction). The risk level is 0.05 and hazard ratios 1.2 and 1.1 corresponding to θ=0.182 and θ=0.095 are used.

Allele frequencies and Hardy-Weinberg equilibrium tests

The table below gives links to the tables reporting missing genotypes, allele and genotype distributions and Hardy-Weinberg equilibrium test p-values for each cohort in the study.

Chromosome	Gene	Gene full name	Possible gene function
1p13.3	PSRC1	proline/serine-rich coiled-coil 1	regulation of microtubule depolymerases (ref: Jang et al. 2008, J Cell Biol 181:255-67)
1p13.3	CELSR2	cadherin, EGF LAG seven-pass G-type receptor 2 (flamingo homolog, Drosophila)	receptor involved in contact-mediated communication (from Entrez Gene)
1q41	MIA3	melanoma inhibitory activity family, member 3	modulation of integrin activity (Arndt et al. 2007, J Leukoc Biol 82:1466-72)
1q43	FMN2	formin 2	cytosekeletal organization and/or establishment of cell polarity (from OMIM)
2q36.3	2Q36_3	no gene (cytogenetic location)
5q21.1	5Q21_1	no gene (cytogenetic location)
6q25.1	MTHFD1L	methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like	enzyme involved in tetrahydrofolate synthesis in mitochondria (from OMIM)
9p21.3	9P21_3	no gene (cytogenetic location)
10q11.21	10Q11_21	no gene (cytogenetic location)
12q12	12Q12	no gene (cytogenetic location)
15q22.33	SMAD3	SMAD family member 3	effector of the TGF-beta response (from OMIM)
19q12	19Q12	no gene (cytogenetic location)
22q12.1	SEZ6L	seizure related 6 homolog (mouse)-like	unknown

Main analyses

Introduction

The main analyses included analysis of disease events (coronary heart disease (CHD), stroke and all deaths) during the follow-up, analysis of disease status at baseline (history myocardial infarction (MI) and history of stroke) and analysis of association between single nucleotide polymorphisms (SNP) and classic risk factors (non-HDL cholesterol, HDL cholesterol, blood pressure and body mass index (BMI)). These analyses were performed for all 42 SNPs and the obtained results are summarised in the tables below. The results are ordered by single test p-values and for each model we report point estimates (odds ratios, hazard ratios or regression coefficients depending on the type of the model) and their confidence intervals, single test p-values and tail area false discovery rate (FDR) [2] calculated over the p-values in the table. The seven lead SNPs are printed in green colour. The link in the point estimate leads to the model summary obtained from R [3].

Definition of variables

The variables in the models are defined in detail in the MORGAM Manual [4]. Links to the variable definitions are given below

The links to the models below are named by the name of the response variable.

Disease status at baseline (logistic regression)

The results presented in Table 3 in the manuscript are based on the following analyses:

Time-to-event analysis (proportional hazards model)

The results presented in Table 4 in the manuscript are based on the following analyses:

Association between SNPs and classic risk factors (linear regression)

The results presented in Table 5 in the manuscript are based on the following analyses:

Additional analyses

The results from these analyses are not presented in the tables of the manuscript but many of them are mentioned in the text.

Disease status at baseline (logistic regression)

Interaction analysis for rs1333049

Time-to-event analysis (proportional hazards model)

Interaction analysis for rs1333049

Disease status at the end of follow-up

Case fatality

Association between SNPs and classic risk factors (linear regression)

References

[1] Cai J, Zeng D. Sample size/power calculation for case-cohort studies. Biometrics. 2004;60(4):1015-1024.

[2] Benjamini Y, Hochberg Y. Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological. 1995;57(1):289-300.

[3] R Development Core Team. R: A language and environment for statistical computing: R Foundation for Statistical Computing, Vienna, Austria; 2008.

Acknowledgements

This research was part funded through the European Community's Seventh Framework Programme (FP7/2007-2013), ENGAGE project, grant agreement HEALTH-F4-2007- 201413 and through the Finnish Heart Association.

Annex

Sites and key personnel of contributing MORGAM Centres:

Finland

FINRISK, National Public Health Institute, Helsinki: V. Salomaa (principal investigator), A. Juolevi, E. Vartiainen, P. Jousilahti;
ATBC, National Public Health Institute, Helsinki: J. Virtamo (principal investigator), H. Kilpeläinen;
MORGAM Data Centre, National Public Health Institute, Helsinki: K. Kuulasmaa (head), Z. Cepaitis, A. Haukijärvi, B. Joseph, J. Karvanen, S. Kulathinal, M. Niemelä, O. Saarela;
MORGAM Central Laboratory, National Public Health Institute, Helsinki: L. Peltonen (responsíble person), M. Perola, K. Silander, M. Alanne, P. Laiho, K. Kristiansson, K. Ahonen;

France

National Coordinating Centre, National Institute of Health and Medical Research (U258), Paris: P. Ducimetière (national coordinator), A. Bingham;
PRIME/Strasbourg, Department of Epidemiology and Public Health, Louis Pasteur University, Faculty of Medicine, Strasbourg: D. Arveiler (principal investigator), B. Haas, A. Wagner;
PRIME/Toulouse, Department of Epidemiology, Faculty of Medicine, Toulouse-Purpan, Toulouse: J. Ferrières (Principal Investigator), J-B. Ruidavets, V. Bongard, D. Deckers, C. Saulet, S. Barrere, M. Soubiraa;
PRIME/Lille, Department of Epidemiology and Public Health, Pasteur Institute of Lille: P. Amouyel (principal investigator), M. Montaye, B. Lemaire, S. Beauchant, D. Cottel, C. Graux, N. Marecaux, C. Steclebout, S. Szeremeta;
MORGAM Laboratory, INSERM U525, Paris: F. Cambien (responsible person), L. Tiret, V. Nicaud, D.A. Tregouet, C. Perret, C. Proust, M. de Suremain

Sweden

Northern Sweden, Umeå University Hospital, Department of Medicine, Umeå: B. Stegmayr (principal investigator), K. Asplund (former principal investigator), M. Eriksson;

United Kingdom

PRIME/Belfast, Queen's University Belfast, Belfast, Northern Ireland: F Kee (principal investigator), A. Evans (former principal investigator) J. Yarnell, E. Gardner;
MORGAM Coordinating Centre, Queen's University Belfast, Belfast, Northern Ireland: A. Evans (MORGAM coordinator), S. Cashman;

MORGAM Management Group:

A. Evans (chair), S. Blankenberg (Mainz, Germany), F. Cambien, M. Ferrario (Varese, Italy) , K. Kuulasmaa, L. Peltonen, M. Perola, V. Salomaa, D. Shields (Dublin, Ireland), P.-G. Wiklund (Sweden), H. Tunstall-Pedoe (Dundee, Scotland), K. Asplund (honorary consultant, Stockholm Sweden), B. Stegmayr (former member).

The impact of newly-identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts: supplement to the paper published in Genetic Epidemiology

Contents

Introduction

Power calculations

Allele frequencies and Hardy-Weinberg equilibrium tests

Main analyses

Introduction

Definition of variables

Disease status at baseline (logistic regression)

Time-to-event analysis (proportional hazards model)

Association between SNPs and classic risk factors (linear regression)

Additional analyses

Disease status at baseline (logistic regression)

Interaction analysis for rs1333049

Time-to-event analysis (proportional hazards model)

Interaction analysis for rs1333049

Disease status at the end of follow-up

Case fatality

Association between SNPs and classic risk factors (linear regression)

References

Acknowledgements

Annex

Finland

France

Sweden

United Kingdom

MORGAM Management Group: