Normalizing a large number of quantitative traits using empirical normal quantile transformation

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Variance-components and regression-based methods are frequently used to map quantitative trait loci. The normality of the trait values is usually assumed and violation of this assumption can have a detrimental effect on the power and type I error of such analyses. Various transformations can be used, but appropriate transformations usually require careful analysis of individual traits, which is not feasible for data sets with a large number of traits like those in Problem 1 of Genetic Analysis Workshop 15 (GAW15). A semiparametric variance-components method can estimate the transformation along with the model parameters, but existing methods are computationally intensive. In this paper, we propose the use of empirical normal quantile transformation to normalize the scaled rank of trait values using an inverse normal transformation. Despite its simplicity and potential loss of information, this transformation is shown, by extensive simulations, to have good control of power and type I error, even when compared with the semiparametric method. To investigate the impact of such a transformation on real data sets, we apply variance-components and variance-regression methods to the expression data of GAW15 and compare the results before and after transformation.

Related collections

Most cited references 5

Record: found
Abstract: found
Article: not found

Robust variance-components approach for assessing genetic linkage in pedigrees.

C I Amos (1994)

To assess evidence for genetic linkage from pedigrees, I developed a limited variance-components approach. In this method, variability among trait observations from individuals within pedigrees is expressed in terms of fixed effects from covariates and effects due to an unobservable trait-affecting major locus, random polygenic effects, and residual nongenetic variance. The effect attributable to a locus linked to a marker is a function of the additive and dominance components of variance of the locus, the recombination fraction, and the proportion of genes identical by descent at the marker locus for each pair of sibs. For unlinked loci, the polygenic variance component depends only on the relationship between the relative pair. Parameters can be estimated by either maximum-likelihood methods or quasi-likelihood methods. The forms of quasi-likelihood estimators are provided. Hypothesis tests derived from the maximum-likelihood approach are constructed by appeal to asymptotic theory. A simulation study showed that the size of likelihood-ratio tests was appropriate but that the monogenic component of variance was generally underestimated by the likelihood approach.

0 comments Cited 57 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Powerful regression-based quantitative-trait linkage analysis of general pedigrees.

Stacey S Cherny, Gonçalo R Abecasis, Shaun Purcell … (2002)

We present a new method of quantitative-trait linkage analysis that combines the simplicity and robustness of regression-based methods and the generality and greater power of variance-components models. The new method is based on a regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method and have performed simulation studies to assess, under a range of conditions, estimation accuracy, type I error rate, and power. For normally distributed traits and in large samples, the method is found to give the correct type I error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus-although, in cases where asymptotic theory is doubtful, significance levels should be checked by simulations. In large sibships, the new method is slightly more powerful than variance-components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.

0 comments Cited 53 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure.

D Allison, M. C. Neale, R Zannolli … (1999)

Detection of linkage to genes for quantitative traits remains a challenging task. Recently, variance components (VC) techniques have emerged as among the more powerful of available methods. As often implemented, such techniques require assumptions about the phenotypic distribution. Usually, multivariate normality is assumed. However, several factors may lead to markedly nonnormal phenotypic data, including (a) the presence of a major gene (not necessarily linked to the markers under study), (b) some types of gene x environment interaction, (c) use of a dichotomous phenotype (i.e., affected vs. unaffected), (d) nonnormality of the population within-genotype (residual) distribution, and (e) selective (extreme) sampling. Using simulation, we have investigated, for sib-pair studies, the robustness of the likelihood-ratio test for a VC quantitative-trait locus-detection procedure to violations of normality that are due to these factors. Results showed (a) that some types of nonnormality, such as leptokurtosis, produced type I error rates in excess of the nominal, or alpha, levels whereas others did not; and (b) that the degree of type I error-rate inflation appears to be directly related to the residual sibling correlation. Potential solutions to this problem are discussed. Investigators contemplating use of this VC procedure are encouraged to provide evidence that their trait data are normally distributed, to employ a procedure that allows for nonnormal data, or to consider implementation of permutation tests.

0 comments Cited 30 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Conference

Journal ID (nlm-ta): BMC Proc

Title: BMC Proceedings

Publisher: BioMed Central

ISSN (Electronic): 1753-6561

Publication date Collection: 2007

Publication date (Electronic): 18 December 2007

Volume: 1

Issue: Suppl 1

Page: S156

Affiliations

[1 ]Department of Epidemiology, The University of Texas, M.D. Anderson Cancer Center, 1155 Pressler Boulevard, Unit 1340, Houston, Texas 77030, USA

Article

Publisher ID: 1753-6561-1-S1-S156

PMC ID: 2367615

PubMed ID: 18466501

SO-VID: 1ee0fe2f-26ec-4999-b608-56e349d36b0f

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Normalizing a large number of quantitative traits using empirical normal quantile transformation

Read this article at

Abstract

Related collections

Karger: Coronavirus / COVID-19

Most cited references 5

Robust variance-components approach for assessing genetic linkage in pedigrees.

Powerful regression-based quantitative-trait linkage analysis of general pedigrees.

Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure.

Author and article information

Conference

Affiliations

Article

History

Categories

Comments

Comment on this article

Similar content 175

Cited by 16

Most referenced authors 19