Article

# Conditional likelihood score functions for mixed models in linkage analysis.

Department of Mathematics, Stockholm University, S-106 91 Stockholm, Sweden.
Biostatistics (Impact Factor: 2.43). 05/2005; 6(2):313-32. DOI:10.1093/biostatistics/kxi012
Source: PubMed

ABSTRACT In this paper, we develop a general strategy for linkage analysis, applicable for arbitrary pedigree structures and genetic models with one major gene, polygenes and shared environmental effects. Extending work of Whittemore (1996), McPeek (1999) and Hossjer (2003d), the efficient score statistic is computed from a conditional likelihood of marker data given phenotypes. The resulting semiparametric linkage analysis is very similar to nonparametric linkage based on affected individuals. The efficient score S depends not only on identical-by-descent sharing and phenotypes, but also on a few parameters chosen by the user. We focus on (1) weak penetrance models, where the major gene has a small effect and (2) rare disease models, where the major gene has a possibly strong effect but the disease causing allele is rare. We illustrate our results for a large class of genetic models with a multivariate Gaussian liability. This class incorporates one major gene, polygenes and shared environmental effects in the liability, and allows e.g. binary, Gaussian, Poisson distributed and life-length phenotypes. A detailed simulation study is conducted for Gaussian phenotypes. The performance of the two optimal score functions S(wpairs) and S(normdom) are investigated. The conclusion is that (i) inclusion of polygenic effects into the score function increases overall performance for a wide range of genetic models and (ii) score functions based on the rare disease assumption are slightly more powerful.

0 0
·
0 Bookmarks
·
60 Views
• Source
##### Article: Effects of misspecifying genetic parameters in lod score analysis.
[hide abstract]
ABSTRACT: The lod score method is widely used to test linkage and to estimate the recombination fraction between a disease locus and a marker locus. The parameters (gene frequency, penetrance, and degree of dominance) are assumed to be known at each locus. This condition may not be fulfilled at the disease locus. In this paper, we evaluate the errors due to the use of wrong parameters. The power of the linkage test is sensitive to the degree of dominance, and slightly to the penetrance, but not to the gene frequency. In contrast, the estimation of the recombination fraction may be strongly affected by an error on any genetic parameter.
Biometrics 07/1986; 42(2):393-9. · 1.41 Impact Factor
• Source
##### Article: Asymptotic estimation theory of multipoint linkage analysis under perfect marker information
[hide abstract]
ABSTRACT: We consider estimation of a disease susceptibility locus $\tau$ at a chromosome. With perfect marker data available, the estimator $\htau_N$ of $\tau$ based on $N$-pedigrees has a rate of convergence $N^{-1}$ under mild regularity conditions. The limiting distribution is the arg max of a certain compound Poisson process. Our approach is conditional on observed phenotypes, and therefore treats parametric and nonparametric linkage, as well as quantitative trait loci methods within a unified framework. A constant appearing in the asymptotics, the so-called asymptotic slope-to-noise ratio, is introduced as a performance measure for a given genetic model, score function and weighting scheme. This enables us to define asymptotically optimal score functions and weighting schemes. Interestingly, traditional $N^{-1/2}$ theory breaks down, in that, for instance, the ML-estimator is not asymptotically optimal. Further, the asymptotic estimation theory automatically takes uncertainty of $\tau$ into account, which is otherwise handled by means of multiple testing and Bonferroni-type corrections. ¶ Other potential applications of our approach that we discuss are general sampling criteria for planning of linkage studies, appropriate grid size of marker maps, robustness w.r.t. choice of map function (dropping assumption of no interference) and quantification of information loss due to heterogeneity (with linked or unlinked trait loci). ¶ We also discuss relations to pointwise performance criteria and pay special attention to weak genetic models, so-called local specificity models.
The Annals of Statistics 01/2003; · 2.53 Impact Factor
• Source
##### Article: Gaussian models for genetic linkage analysis using complete high-resolution maps of identity by descent.
[hide abstract]
ABSTRACT: Gaussian-process models are developed to detect genetic linkage using complete high-resolution maps of identity by descent between affected relative pairs. Approximations are given for the significance level and power of the likelihood-ratio test of no linkage and for likelihood-ratio confidence regions for trait loci. The sample sizes required to detect linkage by using different classes of affected relative pairs are compared, and the problem of combining data from different classes of relatives is discussed.
The American Journal of Human Genetics 08/1993; 53(1):234-51. · 11.20 Impact Factor