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Bioinformatics. 2017 Feb 15;33(4):561-563. doi: 10.1093/bioinformatics/btw696.

seXY: a tool for sex inference from genotype arrays.

Author information

1
Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, NH 03756, USA.
2
Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA.
3
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
4
Division of Genetics and Epidemiology, Institute of Cancer Research, London SW7 3RP, UK.
5
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA.
6
Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA.

Abstract

Motivation:

Checking concordance between reported sex and genotype-inferred sex is a crucial quality control measure in genome-wide association studies (GWAS). However, limited insights exist regarding the true accuracy of software that infer sex from genotype array data.

Results:

We present seXY, a logistic regression model trained on both X chromosome heterozygosity and Y chromosome missingness, that consistently demonstrated >99.5% sex inference accuracy in cross-validation for 889 males and 5,361 females enrolled in prostate cancer and ovarian cancer GWAS. Compared to PLINK, one of the most popular tools for sex inference in GWAS that assesses only X chromosome heterozygosity, seXY achieved marginally better male classification and 3% more accurate female classification.

Availability and Implementation:

https://github.com/Christopher-Amos-Lab/seXY.

Contact:

Christopher.I.Amos@dartmouth.edu.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28035028
PMCID:
PMC6041889
DOI:
10.1093/bioinformatics/btw696
[Indexed for MEDLINE]
Free PMC Article

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