ArticleAmerican journal of human genetics2003
Recent advances in human quantitative-trait-locus mapping: comparison of methods for selected sibling pairs.
Article in American journal of human genetics, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Computationally efficient multipoint linkage analysis on extended pedigrees for trait models with two contributing major Loci.Genetic epidemiology · 2012Article
- Family-based analysis of a myocardial infarction endophenotype: comparison of sampling designs.BMC proceedings · 2009Article
- Influence of genotyping error in linkage mapping for complex traits--an analytic study.BMC genetics · 2008Article
- Robust score statistics for QTL linkage analysis.American journal of human genetics · 2008Article
- Selection of eating-disorder phenotypes for linkage analysis.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2005Article
- No "bias" toward the null hypothesis in most conventional multipoint nonparametric linkage analyses.American journal of human genetics · 2004Article
- Theoretical and empirical power of regression and maximum-likelihood methods to map quantitative trait loci in general pedigrees.American journal of human genetics · 2004Article
- Mapping quantitative traits with random and with ascertained sibships.Proceedings of the National Academy of Sciences of the United States of America · 2004Article
- Recent advances in human quantitative-trait-locus mapping: comparison of methods for discordant sibling pairs.American journal of human genetics · 2003Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
During the past few years, there has been a great deal of new work on methods for mapping quantitative-trait loci by use of sibling pairs and sibships. There are several new methods based on linear regression, as well as several more that are based on score statistics. In theory, most of the new methods should be relatively robust to violations of distributional assumptions and to selected sampling, but, in practice, there has been little evaluation of how the methods perform on selected samples. We survey most of the new regression-based statistics and score statistics and propose a few minor variations on the score statistics. We use simulation to evaluate the type I error and the power of all of the statistics, considering (a) population samples of sibling pairs and (b) sibling pairs ascertained on the basis of at least one sibling with a trait value in the top 10% of the distribution. Most of the statistics have correct type I error for selected samples. The statistics proposed by Xu et al. and by Sham and Purcell are generally the most powerful, along with one of our score statistic variants. Even among the methods that are most powerful for "nice" data, some are more robust than others to non-Gaussian trait models and/or misspecified trait parameters.
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