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PLoS One. 2013 Dec 31;8(12):e83316. doi: 10.1371/journal.pone.0083316. eCollection 2013.

Statistical inference for Hardy-Weinberg proportions in the presence of missing genotype information.

Author information

1
Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Spain.
2
Biomedical Research Unit, Vall d'Hebron Research Institute, Barcelona, Spain.
3
Financial Network Analytics, Barcelona, Spain.
4
Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Biomedical Research Institute of Bellvitge (IDIBELL), Consorcio de Investigación Biomédica de Epidemiología y Salud Pública (CIBERESP), and School of Medicine, University of Barcelona (UB), Barcelona, Spain.

Abstract

In genetic association studies, tests for Hardy-Weinberg proportions are often employed as a quality control checking procedure. Missing genotypes are typically discarded prior to testing. In this paper we show that inference for Hardy-Weinberg proportions can be biased when missing values are discarded. We propose to use multiple imputation of missing values in order to improve inference for Hardy-Weinberg proportions. For imputation we employ a multinomial logit model that uses information from allele intensities and/or neighbouring markers. Analysis of an empirical data set of single nucleotide polymorphisms possibly related to colon cancer reveals that missing genotypes are not missing completely at random. Deviation from Hardy-Weinberg proportions is mostly due to a lack of heterozygotes. Inbreeding coefficients estimated by multiple imputation of the missings are typically lowered with respect to inbreeding coefficients estimated by discarding the missings. Accounting for missings by multiple imputation qualitatively changed the results of 10 to 17% of the statistical tests performed. Estimates of inbreeding coefficients obtained by multiple imputation showed high correlation with estimates obtained by single imputation using an external reference panel. Our conclusion is that imputation of missing data leads to improved statistical inference for Hardy-Weinberg proportions.

PMID:
24391752
PMCID:
PMC3877411
DOI:
10.1371/journal.pone.0083316
[Indexed for MEDLINE]
Free PMC Article

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