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G‐STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies
Author(s) -
Wang Miaoyan,
Jakobsdottir Johanna,
Smith Albert V.,
McPeek Mary Sara
Publication year - 2016
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21982
Subject(s) - pedigree chart , genetic association , statistical power , selection (genetic algorithm) , sample size determination , dna sequencing , trait , computational biology , biology , genetics , sequence (biology) , computer science , statistics , mathematics , genotype , machine learning , gene , single nucleotide polymorphism , programming language
In a large‐scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed‐size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes related individuals, power for association can be gained by including partial information, such as phenotype data of ungenotyped relatives, in the analysis, and this should be taken into account when assessing whom to sequence. We propose G‐STRATEGY, which uses simulated annealing to choose a subset of individuals for sequencing that maximizes the expected power for association. In simulations, G‐STRATEGY performs extremely well for a range of complex disease models and outperforms other strategies with, in many cases, relative power increases of 20–40% over the next best strategy, while maintaining correct type 1 error. G‐STRATEGY is computationally feasible even for large datasets and complex pedigrees. We apply G‐STRATEGY to data on high‐density lipoprotein and low‐density lipoprotein from the AGES‐Reykjavik and REFINE‐Reykjavik studies, in which G‐STRATEGY is able to closely approximate the power of sequencing the full sample by selecting for sequencing a only small subset of the individuals.