A statistically robust variance‐components approach for quantitative trait linkage analysis

Wang, J.; Guerra, Rudy; Cohen, Jonathan C.

doi:10.1046/j.1469-1809.1999.6330249.x

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…(60) This could be the likeliest reason underlying detection and replication of quantitative trait loci (QTL) in severely under powered studies by the same study group (also see Are replicated findings always true?). In addition, robust approaches that can accommodate outliers (68)(69)(70) are valuable alternatives.…”

Section: Quality Controlmentioning

confidence: 99%

Nonreplication in Genetic Studies of Complex Diseases—Lessons Learned From Studies of Osteoporosis and Tentative Remedies

Shen

Liu

et al. 2005

Journal of Bone and Mineral Research

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Inconsistent results have accumulated in genetic studies of complex diseases/traits over the past decade. Using osteoporosis as an example, we address major potential factors for the nonreplication results and propose some potential remedies.Over the past decade, numerous linkage and association studies have been performed to search for genes predisposing to complex human diseases. However, relatively little success has been achieved, and inconsistent results have accumulated. We argue that those nonreplication results are not unexpected, given the complicated nature of complex diseases and a number of confounding factors. In this article, based on our experience in genetic studies of osteoporosis, we discuss major potential factors for the inconsistent results and propose some potential remedies. We believe that one of the main reasons for this lack of reproducibility is overinterpretation of nominally significant results from studies with insufficient statistical power. We indicate that the power of a study is not only influenced by the sample size, but also by genetic heterogeneity, the extent and degree of linkage disequilibrium (LD) between the markers tested and the causal variants, and the allele frequency differences between them. We also discuss the effects of other confounding factors, including population stratification, phenotype difference, genotype and phenotype quality control, multiple testing, and genuine biological differences. In addition, we note that with low statistical power, even a "replicated" finding is still likely to be a false positive. We believe that with rigorous control of study design and interpretation of different outcomes, inconsistency will be largely reduced, and the chances of successfully revealing genetic components of complex diseases will be greatly improved.

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Section: Quality Controlmentioning

confidence: 99%

Nonreplication in Genetic Studies of Complex Diseases—Lessons Learned From Studies of Osteoporosis and Tentative Remedies

Shen

Liu

et al. 2005

Journal of Bone and Mineral Research

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“…Issues on robustness have been popularly addressed in statistical inference when there exist nuisance parameters that interfere with the powers of the studied tests. In genetic studies, this issue has also been investigated widely by researchers (Wang et al, 1999; Gastwirth & Freidlin, 2000; Kraft, 2001; Zheng et al, 2002; Xu et al, 2003; Diao & Lin, 2005; Tai & Hou, 2006; Yan et al, 2008; Wang et al, 2008). To obtain robust statistics over all plausible genetic models, we have referred to the studies of Davies (1977) and Zheng & Chen (2005).…”

Section: Discussionmentioning

confidence: 99%

Robust Quantitative Trait Association Tests in the Parent‐Offspring Triad Design: Conditional Likelihood‐Based Approaches

Wang

Tai

2009

Annals of Human Genetics

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SummaryAssociation studies, based on either population data or familial data, have been widely applied to mapping of genes underlying complex diseases. In family-based association studies, using case-parent triad families, the popularly used transmission/disequilibrium test (TDT) was proposed for avoidance of spurious association results caused by other confounders such as population stratification. Originally, the TDT was developed for analysis of binary disease data. Extending it to allow for quantitative trait analysis of complex diseases and for robust analysis of binary diseases against the uncertainty of mode of inheritance has been thoroughly discussed. Nevertheless, studies on robust analysis of quantitative traits for complex diseases received relatively less attention. In this paper, we use parent-offspring triad families to demonstrate the feasibility of establishment of the robust candidate-gene association tests for quantitative traits. We first introduce the score statistics from the conditional likelihoods based on parent-offspring triad data under various genetic models. By applying two existing robust procedures we then construct the robust association tests for analysis of quantitative traits. Simulations are conducted to evaluate empirical type I error rates and powers of the proposed robust tests. The results show that these robust association tests do exhibit robustness against the effect of misspecification of the underlying genetic model on testing powers.

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“…Inferences using the likelihood ratio test and inappropriately assuming multivariate normality can be inaccurate when the underlying trait data display either skewness or kurtosis. Wang et al 70 proposed a data trimming approach in which extreme observations are replaced by a specified quantile. For instance, if the data are trimmed to the 99th percentile, then any observations more extreme than the 99th percentile are replaced by the 99th quantile of the data.…”

Section: Variance-components Approachesmentioning

confidence: 99%

“…We also detected some excess of false positive findings when there was a large genetic background, consistent with findings from Allison et al 68 who found that marked non-normality affects the nominal type I error rate of likelihood ratio tests for the VC method . These results indicate the need to implement robust, 70 semiparametric 51,71 or Monte-Carlo 72 tests when studying ascertained data, when the total heritability of the trait is high. When multivariate phenotypes are considered or when multiplex sampling is used, equation ( 4) is modified to be based upon the multivariate normal distribution.…”

Section: Selectionmentioning

confidence: 99%

Genetic linkage methods for quantitative traits

Amos

Andrade

2001

Stat Methods Med Res

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We discuss methods for detecting genetic linkage for quantitative data. The usual LOD score method uses a pseudolikelihood formulation and has optimal power provided all parameters are correctly specified, but can lead to erroneous estimates of the location for the locus influencing a trait under misspecification of parameters describing the variance of the trait. Alternative methods, in which attention focuses upon modelling covariation among relatives as a function of genetic marker, similarity lead to unbiased estimates of the location and major gene heritability of the trait influencing locus. The Haseman-Elston approach uses a regression method to perform linkage analysis and its properties have been widely studied. This method is generally less powerful than variance components procedures, but the maximum likelihood-based variance components procedures require normality of the trait to ensure robustness of the genetic linkage tests (i.e. a correct false positive rate). When samples are non-randomly selected an ascertainment correction is generally required in order to obtain unbiased parameter estimates when applying variance components methods. For quantitative traits, ascertainment corrections usually condition either on the proband exceeding a threshold, or on the trait value of the proband. We summarize simulations that show that both approaches lead to similar efficiencies for estimating genetic effects. Finally, we discuss methods for analysing diseases that include time-to-onset information. A variety of methods are available for the linkage analysis of quantitative traits. Here, we have reviewed the most commonly used methods.

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A statistically robust variance‐components approach for quantitative trait linkage analysis

Cited by 9 publications

References 12 publications

Nonreplication in Genetic Studies of Complex Diseases—Lessons Learned From Studies of Osteoporosis and Tentative Remedies

Nonreplication in Genetic Studies of Complex Diseases—Lessons Learned From Studies of Osteoporosis and Tentative Remedies

Robust Quantitative Trait Association Tests in the Parent‐Offspring Triad Design: Conditional Likelihood‐Based Approaches

Genetic linkage methods for quantitative traits

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