In chapter 4, interim effect size estimators were used for sample size reassessment in a candidate gene association study. Furthermore, the usefulness of interval estimators, derived in chapter 3 (for details see Scherag et al., 2002), to indicate the necessity of sample size reassessments was explored. Initial simulations at least warranty a need for further investigation.
With regard to the role of interim estimators, it was argued that the estimation of genetic effect sizes provides more information than the mere result of a statistical test such as the TDT (Spielman et al., 1993). Yet, one should also note that using the estimates proposed in chapter 3 implies that marker and trait locus coincide. Otherwise, LD between the marker SNP and the disease locus will lead to effect size estimates biased towards the null hypothesis (Franke et al., 2005; Zondervan and Cardon, 2004). If the same data set was used to identify the variant of interest (Garner, 2007; Lohmueller et al., 2003; G¨oring et al., 2001), an upward bias called the “winners curse” is more likely to be present. This is due to at least two reasons: First, samples that are used for the initial association mapping are often collected to oversample affected individuals relative to their frequency in the population. As a consequence, genetic risk estimates may not be transferable to the general population. Second, for the situation of a genomewide association study selecting the most extreme test statistics such as the smallest p-values is often equivalent to selecting the most extreme genetic effect size estimates. Hence, the estimates must also be adjusted with respect to this multiplicity.
To circumvent this problem, estimation of genetic effect sizes may be done in indepen-dent, appropriately powered confirmatory studies (Garner, 2007). Alternatively, these problems may be addressed by developing bias-corrected estimators which is an active area of research (Yu et al., 2007; Z¨ollner and Pritchard, 2007; Huang and Lin, 2007).
Note that unbiased estimation of genetic effects is also of crucial importance for sample
size planning of confirmatory studies. Moreover, reliable gene characterization is a first step towards clinical applications - the implementation of genetic marker information for prognostic, diagnostic or interventional purposes. A vision for such aspects of ge-nomics research can be found in Collins et al. (2003). Zeggini and McCarthy (2007) and Janssens et al. (2006c,a,b), however, discuss the merits of predictive genetic testing for non-insulin dependent diabetes mellitus given the real data example of the transcrip tion factor 7 -lik e 2 (T C F7 L 2 ) haplotypes (e.g., Grant et al., 2006; Helgason et al., 2007) and within a wider scope.
More than 10 years after the proposal of Risch and Merikangas (1996) to initiate
genomewide association studies, the “harvest” seems to begin as indicated by weekly
reports of such studies in high-ranking journals (e.g., Wellcome Trust Case Control
Consortium, 2007; Frayling et al., 2007; Frayling, 2007; McPherson et al., 2007; Hampe
et al., 2007; Sladek et al., 2007; Herbert et al., 2006; Arking et al., 2006; K lein et al.,
2005). Exploiting these findings in order to understand pathological processes (e.g.,
Bourgain et al., 2007) and to address clinical goals, however, is a far more difficult task
that will vary substantially between the complex clinical phenotypes investigated. While
the identification of associated markers with smaller, consistent effects will continue,
cooperations with experts in clinical, cellular, animal and molecular biology will be
needed in order to work out the mechanisms behind these associations and to bridge the
gap between a statistical finding and its clinical implication.
7 Summary
Genetic association studies have become the most widely used gene mapping tool for the identification of disease susceptibility loci of complex common traits or diseases. In order to obtain sufficient power at a certain significance level (type I error risk), these analyses require a complete pre-specification of the total number of individuals to be sampled and genotyped. However, in most of these studies little information about the genetic effect size is available beforehand and thus it is difficult to calculate a reasonable sample size.
By addressing these problems, this thesis aims at introducing, extending, and evaluating statistical methodology on design adaptations for genetic association studies.
In particular, it is shown how a confirmatory candidate gene association study can be planned and analyzed given the mentioned uncertainties. For this purpose, an adaptive group sequential procedure is developed. If no rejection of the null hypothesis is pos-sible at the interim analysis, the design of the subsequent study part can be modified depending on interim data. As an example, sample size reassessment may be done using interim effect size estimates developed by the author of this thesis, as well. Finally, a new flexible two-stage design for genomewide association studies is presented. While providing strong control of the genomewide, family-wise type I error rate, the new design might also be more cost-efficient due to greater flexibility in comparison to all previously suggested designs. Examples of which are the possibility to base marker selection upon biological criteria instead of statistical criteria or the option to modify the sample size at any time during the course of the project.
Both the candidate and the genomewide association designs are evaluated using
simu-lated and real data sets. Finally, prospects and limits of design adaptation methods and
estimators of genetic effects in genetic association studies of complex traits are discussed.
8 Zusammenfassung
Genetische Assoziationsstudien stellen eine h¨aufig verwendete Methode zur Identifikation von Suszeptibilit¨atsgenen komplexer Erkrankungen (z.B. Asthma, Adipositas, Brustkrebs) dar. Um bei diesen Studien eine Aussage bez¨ uglich der statistischen Power bei vorgegebenem Signifikanzniveau (Risiko eines Fehlers 1. Art) machen zu k¨onnen, ist die Angabe der zu genotypisierenden Personen notwendig. Valide Fallzahlplanungen h¨angen stark von den erwarteten genetischen Effekten ab und ¨ uber letztere sind oft keine oder nur sehr wenige Informationen verf¨ ugbar. Ziel dieser Dissertation ist es, Methoden zur daten-adaptiven Anpassung des Studienplans f¨ ur den Bereich genetischer Assoziationsstudien einzuf¨ uhren, zu entwickeln und zu evaluieren.
Nach einer Einleitung in die oben skizzierte Problematik wird zun¨achst die
Pla-nung und Auswertung einer konfirmatorischen Kandidatengenstudie unter Einbeziehung
der gegebenen Unsicherheiten dargestellt. Zu diesem Zweck wird eine adaptive,
grup-pensequentielle Prozedur entwickelt. Ist bei einer Zwischenauswertung kein Verwerfen
der Nullhypothese m¨oglich, kann mit Hilfe des vorgeschlagenen Verfahrens eine
daten-abh¨angige Anpassung des Studiendesigns erfolgen. Ein Beispiel ist die Fallzahl¨anderung
in Abh¨angigkeit von Sch¨atzern genetischer Effekte, die ebenfalls im Rahmen dieser
Ar-beit erarAr-beitet werden. Anschließ end erfolgt die ¨ Ubertragung dieser Idee auf genomweite
Assoziationsstudien. Neben der genomweiten Kontrolle des Fehlers 1. Art (family-wise
type I error rate in a strong sense) kann das neu entwickelte zweistufige Verfahren,
be-dingt durch seine Flexibilit¨at, zu einer gr¨osseren Kosteneffizienz im Vergleich zu allen
bisher propagierten Verfahren beitragen. So ist nun beispielsweise auch die Auswahl
genetischer Marker f¨ ur eine zweite Genotypisierungsstufe anhand biologischer Kriterien
m¨oglich.
Die entwickelten Verfahren f¨ ur Kandidatengen- und genomweite Assoziationsstudien
werden sowohl theoretisch als auch an Hand von Simulationsstudien evaluiert. Zus¨atzlich
werden reale Datens¨atze komplexer Ph¨anotypen zur Demonstration der Anwendbarkeit
der Verfahren verwendet. Den Abschluss der Dissertation bildet eine Diskussion zu
Perspektiven und Grenzen adaptiver Verfahren und genetischer Sch¨atzer in genetischen
Assoziationsstudien komplexer Erkrankungen.
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Pers¨ onliche Daten
Andr´e Scherag Rellinghauserstr. 266 45136 Essen
Tel.: (0201) 266 7405
E-Mail: andre scherag@hotmail.de geb. am 07. 12. 1974 in Bad Hersfeld ledig, deutsch
Berufserfahrung
seit 2007 Wissenschaftliche Mitarbeiter am Institut f¨ur Medizinische Infor-matik, Biometrie und Epidemiologie (Prof. Dr. J¨ockel), Universit¨at Duisburg-Essen
2002–2007 Wissenschaftliche Mitarbeiter am Institut f¨ur Medizinische Biome-trie und Epidemiologie (Prof. Dr. Sch¨afer), Philipps-Universit¨at Mar-burg
1998–2001 Wissenschaftliche Hilfskraft im DFG-Projekt
” Dynamik mentaler Re-pr¨asentationen“ (Prof. Dr. R¨osler), Philipps-Universit¨at Marburg
Studium und Weiterbildung
seit 2006 Masterstudiengang
” Medical Biometry/Biostatistics“, Ruprecht-Karls-Universit¨at Heidelberg
2003–2007 Promotionsstudium Erg¨anzungsstudiengang
” Humanbiologie“, Philipps-Universit¨at Marburg
2002–2007 Postgraduelle Ausbildung
” Medizinische Biometrie“, Ruprecht-Karls-Universit¨at Heidelberg,
(Abschlussnote: summa cum laude)
1996–2002 Studium der Psychologie, Philipps-Universit¨at Marburg, Diplomarbeit
” Gibt es r¨aumlich getrennte semantische Repr¨asen-tationen von Nomen und Verben im Mentalen Lexikon ?“ (Prof.
R¨osler), Diplom in Psychologie (Note: sehr gut)
(Exmatrikulation auf eigenen Wunsch)
Auslandsaufenthalt
2001–2002 Forschungsaufenthalt am
” Brain Development Lab“
(Prof. Dr. Neville), University of Oregon Eugene, USA
Schulbildung und Wehrersatzdienst
1994–1995 Wehrersatzdienst im Bereich Altenpflege, Diakonisches Werk, Bad Hersfeld
1981–1994 Grundschule, Gymnasialzweig und gymnasiale Oberstufe
(Allgemei-ne Hochschulreife), Bad Hersfeld
Br¨onner G, Hinney A, Reichwald K, Wermter AK, Scherag A, Friedel S, Hebebrand J (2006). Gene variants and obesity., pp. 266–299. Weinheim: Wiley-VCH.
Dempfle A, Wudy SA, Saar K, Hagemann S, Friedel S, Scherag A, Berthold LD, et al (2006). Evidence for involvement of the vitamin D receptor gene in idiopathic short stature via a genome-wide linkage study and subsequent association studies. Hum Mol Genet 15(18):2772–2783.
Eberhart LH, Frank S, Lange H, Morin AM, Scherag A, Wulf H, Kranke P (2006).
Systematic review on the recurrence of postoperative nausea and vomiting after a first episode in the recovery room - implications for the treatment of PONV and related clinical trials. BMC Anesthesiol 6:14.
Friedel S, Reichwald K, Scherag A, Brumm H, Wermter AK, Fries HR, Koberwitz K, et al (2007). Mutation screen and association studies in the Diacylglycerol O-acyltransferase homolog 2 gene (DGAT2), a positional candidate gene for early onset obesity on chromosome 11q13. BMC Genet 8:17.
Hinney A, Bettecken T, Tarnow P, Brumm H, Reichwald K, Lichtner P, Scherag A, et al (2006). Prevalence, spectrum, and functional characterization of melanocortin-4 receptor gene mutations in a representative population-based sample and obese adults from Germany. J Clin Endocrinol Metab 91(5):1761–1769.
Hinney A, Nguyen TT, Scherag A, Friedel S, Br¨onner G, M¨uller TD, Grallert H, et al (2007). Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS ONE 2(12):e1361.
H¨olter K, Wermter AK, Scherag A, Siegfried W, Goldschmidt H, Hebebrand J, Hinney A (2007). Analysis of sequence variations in the suppressor of cytokine signaling (SOCS)-3 gene in extremely obese children and adolescents. BMC Med Genet 8:21.
Khader P, Scherag A, Streb J, R¨osler F (2003). Differences between noun and verb processing in a minimal phrase context: a semantic priming study using event-related brain potentials. Brain Res Cogn Brain Res 17(2):293–313.
Lyon HN, Emilsson V, Hinney A, Heid IM, Lasky-Su J, Zhu X, Thorleifsson G, et al
(2007). The association of a SNP upstream of INSIG2 with body mass index is
reproduced in several but not all cohorts. PLoS Genet 3(4):e61.
Im Dokument
Design adaptation methods for genetic association studies
(Seite 92-144)