University of Groningen Bioinformatics of genomic association mapping Vaez, Ahmad

329 Pages·2017·27.85 MB·English

University of Groningen

Bioinformatics of genomic association mapping
Vaez Barzani, Ahmad
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Vaez Barzani, A. (2015). Bioinformatics of genomic association mapping. [Thesis fully internal (DIV),
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Download date: 17-03-2023
Bioinformatics of
Genomic Association Mapping

Ahmad Vaez

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Ahmad Vaez
Bioinformatics of Genomic Association Mapping

© Ahmad Vaez, 2015. All rights reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted in any form or by any means,
electronic, mechanical, photocopying, recording or otherwise, without written
permission from the author. For articles published or accepted the copyright has been
transferred to the respective publisher.

Cover design: Kowsar: Ahmad Abdollahi
Page layout: Ahmad Vaez
Printed by: Ipskamp Drukkers BV

ISBN: 978-90-367-8202-9 (print)
ISBN: 978-90-367-8201-2 (e-book)

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Bioinformatics of

Genomic Association Mapping

PhD thesis

to obtain the degree of PhD at the
University of Groningen
Ahmad Vaez
on the authority of the
Bioinformatics of Genomic Association Mapping
Rector Magnificus Prof. E. Sterken

and in accordance with
© Ahmad Vaez, 2015. All rights reserved. No part of this publication may be
the decision by the College of Deans.
reproduced, stored in a retrieval system, or transmitted in any form or by any means,

electronic, mechanical, photocopying, recording or otherwise, without written
This thesis will be defended in public on
permission from the author. For articles published or accepted the copyright has been

transferred to the respective publisher.
Wednesday 23 September 2015 at 09:00 hours

Cover design: Kowsar: Ahmad Abdollahi

Page layout: Ahmad Vaez
by
Printed by: Ipskamp Drukkers BV

Ahmad Vaez Barzani
ISBN: 978-90-367-8202-9 (print)

ISBN: 978-90-367-8201-2 (e-book)
born on 11 May 1973

in Isfahan, Iran
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Supervisor
Prof. H. Snieder

Co-supervisors
Dr. B.Z. Alizadeh
Dr. I.M. Nolte

Assessment Committee
Prof. H.M. Boezen
Prof. O. Franco
Prof. D.I. Chasman

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Supervisor CONTENTS

Prof. H. Snieder

Chapter 1 General introduction 7

Co-supervisors Part 1 | How to check if the phenotype is appropriate for
genomic association mapping
Dr. B.Z. Alizadeh
Chapter 2 Genetic and environmental influences on stability and 21
Dr. I.M. Nolte
change in baseline levels of C-reactive protein: a

longitudinal twin study

Assessment Committee
Part 2 | How to perform a GWAS analysis
Prof. H.M. Boezen
Chapter 3 lodGWAS: A software package for genome-wide 41
Prof. O. Franco
association analysis of biomarkers with a limit of detection
Prof. D.I. Chasman
Chapter 4 QCGWAS: A flexible R package for automated quality 63

control of genome-wide association results

Part 3 | How to follow up GWAS results: post-GWAS analyses
Chapter 5 Bivariate genome-wide association study identifies novel 89
pleiotropic loci for lipids and inflammation
Chapter 6 Investigating the causal relationship of C-reactive protein 115
with 32 complex somatic and psychiatric outcomes: A
large scale cross-consortia Mendelian randomization study
Chapter 7 An in silico post-GWAS analysis of C-reactive protein loci 215
suggests an important role for interferons
Part 4 | Bioinformatics of genomic association mapping:
an A to Z walk-through
Chapter 8 General discussion 277
Appendices 303
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Chapter 1

|

General introduction

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Chapter 1

Preface
The successful completion of the Human Genome Project marked a milestone for
genomics research (1). Likewise, new microarray technologies have become widely
available providing simple and elegant ways of rapidly delivering vast amounts of
biological data. Moreover, three major, multi-country projects have been launched
in the past few years to improve the understanding of genomic diversity: The
International HapMap Project (2), the 1000 Genomes Project (3), and the
Encyclopedia of DNA Elements (ENCODE) project (4). All of the data of these
projects have been made publicly available to the scientific community (Box 1, (5)).
However, generating and storing vast amounts of biological data only makes sense
if data are creatively used for further research to produce novel insights into
biological mechanisms (6). Bioinformatics is an interdisciplinary field to address this
growing need. Hence, bioinformatics has been one of the most dynamically
evolving fields of science in the past few years. The Wikipedia encyclopedia
describes bioinformatics as an interdisciplinary field that ‘develops’ and ‘applies’
methods and software tools for understanding biological data. It combines
computer science, statistics, mathematics, and engineering to analyze biological
data. Bioinformatics is an umbrella term for both (i) methodological studies that
‘develop’ novel algorithms, software packages, or analysis pipelines, and (ii)
biological studies that ‘apply’ those tools as part of their methodology (7).
Genetic epidemiology studies the role of genetic factors in health and
disease in families and/or populations (8). It was defined by Morton as “a science
which deals with the etiology, distribution, and control of disease in groups of
relatives and with inherited causes of disease in populations” (9). The vast
availability of biological data as well as handiness of modern bioinformatics-based
methods have yielded rapid advances in discovering genetic variants underlying
different human diseases or traits, so-called genomic association mapping (10, 11).
Unlike rare Mendelian disorders, common complex diseases or traits, such
as ischemic heart disease, hypertension, or serum levels of inflammatory markers,
are controlled by the complex interplay between multiple genetic and
environmental factors (11). For that reason gene mapping of complex traits or
diseases had been largely disappointing in the past. Genomic association mapping is
now a systematic approach aiming to find genetic variants that are associated with
the trait or disease of interest. Thanks to inexpensive availability of microarray data
and also thanks to accessibility of bioinformatics pipelines for data analysis,
8

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Chapter 1 General introduction

Preface Box 1 Timeline of key events of major projects facilitating genomic association
mapping.
The successful completion of the Human Genome Project marked a milestone for
genomics research (1). Likewise, new microarray technologies have become widely
1990 Human Genome Project was launched
available providing simple and elegant ways of rapidly delivering vast amounts of
biological data. Moreover, three major, multi-country projects have been launched 2001 Draft of Human Genome Project was published
in the past few years to improve the understanding of genomic diversity: The
2002 International HapMap Project was launched
International HapMap Project (2), the 1000 Genomes Project (3), and the
2003 Human Genome Project was completed
Encyclopedia of DNA Elements (ENCODE) project (4). All of the data of these
projects have been made publicly available to the scientific community (Box 1, (5)). 2003 International HapMap Project first major public data released
However, generating and storing vast amounts of biological data only makes sense
2003 ENCODE project was launched
if data are creatively used for further research to produce novel insights into
2007 Pilot phase of the ENCODE project was finished
biological mechanisms (6). Bioinformatics is an interdisciplinary field to address this
growing need. Hence, bioinformatics has been one of the most dynamically 2008 1000 Genomes Project was launched
evolving fields of science in the past few years. The Wikipedia encyclopedia
2010 International HapMap Project latest public data released
describes bioinformatics as an interdisciplinary field that ‘develops’ and ‘applies’
2010 Phase 1 of 1000 Genomes Project was completed
methods and software tools for understanding biological data. It combines
computer science, statistics, mathematics, and engineering to analyze biological 2012 ENCODE project initial results were published
data. Bioinformatics is an umbrella term for both (i) methodological studies that
2014 Phase 3 of 1000 Genomes Project data released
‘develop’ novel algorithms, software packages, or analysis pipelines, and (ii)

biological studies that ‘apply’ those tools as part of their methodology (7).

genomic mapping of complex traits or common diseases has become very
Genetic epidemiology studies the role of genetic factors in health and
successful in recent years (11).
disease in families and/or populations (8). It was defined by Morton as “a science
which deals with the etiology, distribution, and control of disease in groups of Genomic association mapping consists of a number of classic steps as
relatives and with inherited causes of disease in populations” (9). The vast described in Figure 1. First we should check if the trait or disease of interest is
availability of biological data as well as handiness of modern bioinformatics-based appropriate for genomic mapping. This can be done by a typical heritability study,
methods have yielded rapid advances in discovering genetic variants underlying that is investigating the proportion of variation in a trait that is due to genetic
different human diseases or traits, so-called genomic association mapping (10, 11). differences between individuals. If there is evidence of heritability, the trait is
(partially) controlled by genetic factors and genomic association mapping is a logical
Unlike rare Mendelian disorders, common complex diseases or traits, such
next step (12).
as ischemic heart disease, hypertension, or serum levels of inflammatory markers,
are controlled by the complex interplay between multiple genetic and Once it has been established that the trait of interest is heritable, we should
environmental factors (11). For that reason gene mapping of complex traits or attempt to identify genetic variants that are significantly associated with the trait.
diseases had been largely disappointing in the past. Genomic association mapping is Although a number of different methodologies can be used, the experimental
now a systematic approach aiming to find genetic variants that are associated with design of the genome-wide association study (GWAS) has been very successful and
the trait or disease of interest. Thanks to inexpensive availability of microarray data played a key role in genomic association mapping during the past few years (11).
and also thanks to accessibility of bioinformatics pipelines for data analysis, GWAS analysis is typically aimed at detecting genetic variants associated with
common complex traits or diseases without prior hypothesis of functionality.
8 9

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