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Methodisch-statistische Herausforderungen an die genombasierte Vorhersage von Erkrankungen

Methodological challenges for genome-based prediction of diseases

  • Leitthema
  • Published:
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz Aims and scope

Zusammenfassung

Mittels der sich schnell entwickelnden Genotypisierungstechnologie wurden in den letzten Jahren viele genetische Faktoren entdeckt, die zur Pathogenese komplexer Krankheiten beitragen. Daraus hat sich das Ziel abgeleitet, diese Erkenntnisse zu nutzen, um auf Basis des individuellen genetischen Profils z. B. maßgeschneiderte Präventionsmaßnahmen oder Therapien anzubieten. Zu diesem Zweck werden genetische Tests entwickelt, die es erlauben sollen, Personen zu identifizieren, die aufgrund ihrer genetischen Prädisposition in Bezug auf eine bestimmte Krankheit zu einer Hochrisikogruppe gehören. Solche Tests basieren auf bekannten genetischen Risikofaktoren, die häufig in genomweiten Assoziationsstudien identifiziert wurden. Oft werden die Effektschätzer aus diesen Studien weiterverwendet, um ein genetisches Risikomaß zur Prognose eines Phänotyps zu entwickeln. Der vorliegende Beitrag beschreibt verschiedene statistisch-methodische Herausforderungen, die bei der Entwicklung eines genetischen Prädiktionsmodells berücksichtigt werden müssen: Ausgehend von dem Ziel, unverzerrte Effektschätzer zu erhalten, um geeignete genetische Risikoprädiktoren zu identifizieren, müssen genetische Risikomaße entwickelt und der prädiktive Wert eines neuen genetischen Tests etabliert werden. Diese zentralen Anforderungen bei der statistischen Risikoprädiktion in der Genetik werden in drei Abschnitten behandelt und abschließend unter Public-Health-Perspektive diskutiert.

Abstract

The rapidly develo** genoty** technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective.

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Authors and Affiliations

  1. Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Achterstr. 30, 28359, Bremen, Deutschland

    Ronja Foraita, M. Jäger & I. Pigeot

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  1. Ronja Foraita
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  2. M. Jäger
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  3. I. Pigeot
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Correspondence to Ronja Foraita.

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R. Foraita, M. Jäger und I. Pigeot geben an, dass kein Interessenkonflikt besteht.

Dieser Beitrag beinhaltet keine Studien an Menschen oder Tieren.

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Foraita, R., Jäger, M. & Pigeot, I. Methodisch-statistische Herausforderungen an die genombasierte Vorhersage von Erkrankungen. Bundesgesundheitsbl. 58, 131–138 (2015). https://doi.org/10.1007/s00103-014-2091-4

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