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      Testing for Hardy–Weinberg equilibrium at biallelic genetic markers on the X chromosome

      1 , * , 2

      Heredity

      Nature Publishing Group

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          Abstract

          Testing genetic markers for Hardy–Weinberg equilibrium (HWE) is an important tool for detecting genotyping errors in large-scale genotyping studies. For markers at the X chromosome, typically the χ 2 or exact test is applied to the females only, and the hemizygous males are considered to be uninformative. In this paper we show that the males are relevant, because a difference in allele frequency between males and females may indicate HWE not to hold. The testing of markers on the X chromosome has received little attention, and in this paper we lay down the foundation for testing biallelic X-chromosomal markers for HWE. We develop four frequentist statistical test procedures for X-linked markers that take both males and females into account: the χ 2 test, likelihood ratio test, exact test and permutation test. Exact tests that include males are shown to have a better Type I error rate. Empirical data from the GENEVA project on venous thromboembolism is used to illustrate the proposed tests. Results obtained with the new tests differ substantially from tests that are based on female genotype counts only. The new tests detect differences in allele frequencies and seem able to uncover additional genotyping error that would have gone unnoticed in HWE tests based on females only.

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          A note on exact tests of Hardy-Weinberg equilibrium.

          Deviations from Hardy-Weinberg equilibrium (HWE) can indicate inbreeding, population stratification, and even problems in genotyping. In samples of affected individuals, these deviations can also provide evidence for association. Tests of HWE are commonly performed using a simple chi2 goodness-of-fit test. We show that this chi2 test can have inflated type I error rates, even in relatively large samples (e.g., samples of 1,000 individuals that include approximately 100 copies of the minor allele). On the basis of previous work, we describe exact tests of HWE together with efficient computational methods for their implementation. Our methods adequately control type I error in large and small samples and are computationally efficient. They have been implemented in freely available code that will be useful for quality assessment of genotype data and for the detection of genetic association or population stratification in very large data sets.
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            Testing for Hardy-Weinberg proportions: have we lost the plot?

            Testing for Hardy-Weinberg proportions (HWP) is routine in almost all genetic studies of natural populations, but many researchers do not demonstrate a full understanding of the purposes of these tests or how to interpret the results. Common problems include a lack of understanding of statistical power and the difference between statistical significance and biological significance, how to interpret results of multiple tests, and how to distinguish between various factors that can cause statistically significant departures. In this perspective, which focuses on analysis of genetic data for nonmodel species, I 1) review factors that can cause departures from HWP at individual loci and linkage disequilibrium (LD) at pairs of loci; 2) discuss commonly used tests for HWP and LD, with an emphasis on multiple-testing issues; 3) show how to distinguish among possible causes of departures from HWP; and 4) outline some simple steps to follow when significant test results are found. Finally, I 5) identify some issues that merit particular attention as we move into an era in which analysis of genomics-scale datasets for nonmodel species is commonplace.
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              GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies.

              GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R's memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation.
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                Author and article information

                Journal
                Heredity (Edinb)
                Heredity (Edinb)
                Heredity
                Nature Publishing Group
                0018-067X
                1365-2540
                June 2016
                13 April 2016
                1 June 2016
                : 116
                : 6
                : 558-568
                Affiliations
                [1 ]Department of Statistics and Operations Research, Universitat Politècnica de Catalunya , Barcelona, Spain
                [2 ]Department of Biostatistics, University of Washington , Seattle, WA, USA
                Author notes
                [* ]Department of Statistics and Operations Research, Universitat Politècnica de Catalunya , Avinguda Diagonal 647, 08028 Barcelona, Spain. E-mail: jan.graffelman@ 123456upc.edu
                Article
                hdy201620
                10.1038/hdy.2016.20
                4868269
                27071844
                Copyright © 2016 The Genetics Society

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

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                Human biology

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