Novel genetic matching methods for handling population stratification in genome-wide association studies

BMC Bioinformatics

André Lacour⁴  Vitalia Schüller⁴  Dmitriy Drichel⁴  Christine Herold⁴  Frank Jessen²  Markus Leber³  Wolfgang Maier²  Markus M Noethen¹  Alfredo Ramirez²  Tatsiana Vaitsiakhovich³  Tim Becker³ 

[1] Institut für Humangenetik and Life & Brain Center, Universität Bonn, Sigmund-Freud-Str. 25, Bonn 53127, Germany

[2] Abteilung für Psychiatrie und Psychotherapie, Universitätsklinikum Bonn, Sigmund-Freud-Str. 25, Bonn 53127, Germany

[3] Institut für Medizinische Biometrie, Informatik und Epidemiologie, Universität Bonn, Sigmund-Freud-Str. 25, Bonn 53127, Germany

[4] German Center for Neurodegenerative Diseases (DZNE), Sigmund-Freud-Str. 25, Bonn 53127, Germany

关键词: structured association;    genetic matching;    population stratification;    Genome-wide association studies;   

Others  :  1139036
DOI  :  10.1186/s12859-015-0521-4

 received in 2014-07-11, accepted in 2015-02-27,  发布年份 2015

【 摘 要 】

Background

A usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure.

Results

We assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer’s disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach.

Conclusions

Matching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.

【 授权许可】

   
2015 Lacour et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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