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Am J Hum Genet. 2016 Jul 7;99(1):89-103. doi: 10.1016/j.ajhg.2016.04.013. Epub 2016 Jun 9.

Imputing Phenotypes for Genome-wide Association Studies.

Author information

1
Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
2
Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
3
Department of Nutritional Science and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA.
4
Centre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9AG, UK.
5
Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
6
Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Republic of Korea. Electronic address: buhm.han@amc.seoul.kr.
7
Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: eeskin@cs.ucla.edu.

Abstract

Genome-wide association studies (GWASs) have been successful in detecting variants correlated with phenotypes of clinical interest. However, the power to detect these variants depends on the number of individuals whose phenotypes are collected, and for phenotypes that are difficult to collect, the sample size might be insufficient to achieve the desired statistical power. The phenotype of interest is often difficult to collect, whereas surrogate phenotypes or related phenotypes are easier to collect and have already been collected in very large samples. This paper demonstrates how we take advantage of these additional related phenotypes to impute the phenotype of interest or target phenotype and then perform association analysis. Our approach leverages the correlation structure between phenotypes to perform the imputation. The correlation structure can be estimated from a smaller complete dataset for which both the target and related phenotypes have been collected. Under some assumptions, the statistical power can be computed analytically given the correlation structure of the phenotypes used in imputation. In addition, our method can impute the summary statistic of the target phenotype as a weighted linear combination of the summary statistics of related phenotypes. Thus, our method is applicable to datasets for which we have access only to summary statistics and not to the raw genotypes. We illustrate our approach by analyzing associated loci to triglycerides (TGs), body mass index (BMI), and systolic blood pressure (SBP) in the Northern Finland Birth Cohort dataset.

PMID:
27292110
PMCID:
PMC5005435
DOI:
10.1016/j.ajhg.2016.04.013
[Indexed for MEDLINE]
Free PMC Article

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