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Breaking the code: Statistical methods and methodological issues in psychiatric genetics
Stringer, S.
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Citation for published version (APA):Stringer, S. (2015). Breaking the code: Statistical methods and methodological issues in psychiatric genetics.
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Download date: 16 Jun 2020
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Nederlandse samenvatting Zoals besproken in Hoofdstuk 1, hebben genoomwijde associatie (GWA)
studies bevestigd dat veel psychiatrische stoornissen, in het bijzonder
schizofrenie, sterk erfelijk zijn. Belangrijker nog, deze studies hebben
laten zien dat de relatie tussen genetische en psychiatrische stoornissen
uiterst complex is. Duizenden genetische varianten, elk met een klein
individueel effect, dragen samen bij aan de relatief grote erfelijkheid van
veel psychiatrische stoornissen. In dit proefschrift ligt de nadruk op een
specifiek type genetische variant, SNPs genaamd, waarbij een enkele
letter in de genetische code varieert.
Waar in GWA studies alle gangbare genetische varianten onderzocht
worden, focussen kandidaat-gen studies op één of enkele genen
(Hoofdstuk 3). De grootte van de benodigde steekproef in een kandidaat
gen studie is veel kleiner dan die in een GWA studie, omdat een veel
minder strikte correctie nodig is voor het aantal statistische testen dat
wordt uitgevoerd. Desondanks laten simulaties van statische power zien
dat in veel gevallen honderden proefpersonen nodig zijn, doordat de
individuele genetische effecten zo klein zijn. In Hoofdstuk 3 zijn ook
twee websites besproken die gebruikt kunnen worden bij het selecteren
van kandidaat genen in een kandidaat gen studie.
Een grote steekproef is echter geen garantie voor significante
resultaten. Een uitgebreide meta-analyse met meer dan 24.000
proefpersonen was niet genoeg om genetische varianten te detecteren
die geassocieerd zijn met cannabisgebruik, hoewel een statistische test
gebaseerd op genen wel twee genen detecteerde. Deze kon echter niet
gerepliceerd worden in een kleine onafhankelijke steekproef (Hoofdstuk
4). Toch blijken meer genetische varianten een p-waarde kleiner dan
230
0.05 te vertonen dan verwacht op basis van kans alleen. Dit duidt er op
dat genetische factoren wel degelijk een rol spelen in het gebruik van
cannabis, maar dat grotere steekproeven nodig zijn om significante
genetische varianten te vinden. Zodra een grote GWA studie
gepubliceerd is, kunnen de resultaten een belangrijke bijdrage leveren
aan toekomstige studies. Zo kunnen bijvoorbeeld in een polygene risk
score analyse ook niet-significante resultaten gebruikt worden om de
genetische overlap tussen ziekten te onderzoeken. In Hoofdstuk 5 werd
op die manier de genetische overlap tussen schizofrenie en drie immuun
ziekten onderzocht: type 1 diabetes, reumatoïde artritis, and de ziekte
van Crohn. Polygene risk score analyse toonde aan dat schizofrenie een
significante genetische overlap vertoont met deze drie immuunziekten.
De genetische overlap tussen schizofrenie en deze drie immuun-
gerelateerde ziekten was significant groter dan de genetische overlap
tussen schizofrenie en type 2 diabetes, dat niet in eerste instantie
gekenmerkt wordt door slecht functioneren van het immuunsysteem.
Statistische modellen zijn altijd gebaseerd op assumpties. Wanneer niet
aan deze assumpties voldaan wordt, kunnen de schattingen van effecten
onzuiver zijn. Over het algemeen wordt in GWA studies één genetische
variant per keer geanalyseerd. Daarbij wordt er impliciet van uitgegaan
dat andere genetische varianten niet bijdragen aan het risico voor
ziekte. Voor genetisch complexe ziekten geldt deze assumptie duidelijk
niet, aangezien zulke ziekten per definitie beïnvloed worden door vele
genetische varianten. Hoofdstuk 6 liet zien dat in lineaire en log-lineaire
modellen, een SNP-per-SNP analyse geen probleem is. Daar staat
tegenover dat in studies waarin zieke en gezonde proefpersonen worden
vergeleken een SNP-per-SNP analyse kan leiden tot onderschatting van
de effectgrootte. Hoofdstuk 7 illustreerde dat een soortgelijk probleem
231
optreedt wanneer een genoomwijde Cox PH survival analyse wordt
uitgevoerd.
In genetische survival analyse wordt vaak ook een andere aanname
geschonden. Over het algemeen zullen niet alle proefpersonen tijdens
hun leven de te onderzoeken ziekte oplopen. Dit feit wordt genegeerd in
een traditionele survival analyse, zoals Cox PH regressie, en kan leiden
tot onzuivere schatters. Zoals Hoofdstuk 7 aantoonde, leidt een cure
survival analyse wel tot zuivere schatters, omdat het expliciet twee
groepen modelleert: proefpersonen die wel de ziekte zullen ontwikkelen
en proefpersonen die dat niet doen.
Model-misspecificatie is soms moeilijk empirisch te toetsen. Het
wetenschappelijk debat over het belang van statische epistase is daar
een voorbeeld van (zie Hoofdstuk 8). Een grote mate van epistase kan
van invloed zijn op de erfelijkheidsschattingen van tweelingstudies.
Hoewel het niet mogelijk is de mate van epistase empirisch te toetsen,
aangezien het betreffende model te complex is, suggereren
simulatiestudies dat de schattingen van tweeling studies alleen onzuiver
zijn als uitgegaan wordt van een groot effect van de gedeelde
omgeving.
Het moge duidelijk zijn dat de wereld van genetisch onderzoek divers is
en vele uitdagingen kent. Door grotere steekproeven en technologische
ontwikkelingen zal langzaam maar zeker steeds meer duidelijk worden
over de complexe relatie tussen genetische code en de ontwikkeling van
psychiatrische stoornissen.
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Acknowledgements First of all, I would like to thank Eske Derks for her supervision,
patience, and advice during the past four years. Eske also encouraged
me to visit Australia when Peter Visscher and Naomi Wray were so kind
to invite me to their lab at QBI as a visiting scholar. I would like to
thank Peter and Naomi and the members of their lab for their
hospitality. René Kahn and Damiaan Denys have also supported my
work in many ways. Not the least by allowing me to visit Australia as a
visiting researcher during my PhD. I am also grateful for the valuable
feedback that my coauthors and reviewers have provided over the years
and of course the time spent by the reading committee. Last, but
certainly not least, I would like to thank my roommates and other
colleagues whose company I have enjoyed at the University Medical
Center Utrecht, Queensland Brain Institute, and Academic Medical
Center Amsterdam for their support, friendship, and many lunches spent
together.
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Curriculum vitae
In 2004 I obtained a master degree in Computer Science at Utrecht
University, with a specialization in Computational Intelligence. Being
interested in methodology as well as cognitive and social psychology, I
also obtained a research master degree in Psychology (2010). My
master thesis on the mathematical modeling of judgment revisions in
response to anonymous advice was a result of my visit as a visiting
student at the department of Cognitive Sciences at the University of
California Irvine.
When I started my PhD project at the Department of Psychiatry at the
University Medical Center, I focused on the effect of genetics on
psychiatric disorders. In 2011 I had the opportunity to do part of my
PhD research at the Complex Trait Genomics Group at the Queensland
Brain Institute. In 2011 I also partly moved to the Department of
Psychiatry at the Academic Medical Center Amsterdam. Currently I am
working as a postdoc in the Complex Trait Genetics Lab at the VU
University. My current interest lies in the use of additional biological
information to identify reliable statistical patterns in genome-wide
association data.
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List of publications Stringer S, Kahn RS, de Witte LD, Ophoff R, & Derks EM (2014). Genetic
liability for schizophrenia predicts risk of immune disorders.
Schizophrenia Research.
Stringer S, Nieman DH, Kahn RS, & Derks EM (in press). Genome-wide
association analysis in schizophrenia. In Genome-Wide
Association Studies: From Polymorphism to Personalized
Medicine.
Wray NR, Byrne EM, Stringer S, & Mowry BJ (2014). Future Directions in
Genetics of Psychiatric Disorders. In Behavior Genetics of
Psychopathology (pp. 311-337). Springer, New York.
Stringer S, Derks EM, Kahn RS, Hill WG, & Wray NR (2013).
Assumptions and properties of limiting pathway models for
analysis of epistasis in complex traits. PLoS One, 8, e68913
Spanagel R, Durstewitz D, Hannsson, … , Stringer S, Smits Y, & Derks
EM (2013). A systems medicine research approach for studying
alcohol addiction. Addiction Biology, 18, 883-896.
Stringer S, Wray NR, Kahn RS, Derks EM (2011). Underestimated effect
sizes in GWAS: fundamental limitations of single SNP analysis for
dichotomous phenotypes. PLoS One, 6, e27964
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Stringer S, Borsboom D, & Wagenmakers, E-J (2011). Bayesian
inference for the information gain model. Behavioral Research
Methods, 43, 297-309.
Stringer S, Ouweneel APE & Le Blanc PM (2009). Emotionele arbeid en
psychologisch welzijn van docenten [Emotional labour and
teacher well-being]. Gedrag en Organisatie [Behavior and
Organisation], 22, 214-231.
